update code

0_FONCIER/TdB_FEDE/site_TdB_2024_complete.py

@@ -4,8 +4,8 @@
 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'
+dico_data = '/media/cgeier/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'
+bilan_2023 = '/media/cgeier/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]

0_FONCIER/TdB_FEDE/sites_TdB_fede.py

@@ -18,7 +18,7 @@ from pycen import con_bdcen as con, con_fon, wfs
 
 annee = 2023
 term_parcelle_conv = ['CONVENTION',"CONVENTION D'USAGE",'ACCORD VERBAL']
-path_agri = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/TRAVAUX/Couches de référence/'
+path_agri = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/TRAVAUX/Couches de référence/'
 agri_partenaire_surf = gpd.read_file(path_agri+'AGRIS_partenaires_surface exploitées.shp')
 agri_partenaire = gpd.read_file(path_agri+'AGRIS_partenaires.shp')
 id_rnx = ['RNGL','RNIP']

0_FONCIER/add_c_anim_foncier_v2.py

@@ -6,7 +6,7 @@ from datetime import datetime as dt
 sql = "SELECT * FROM sites.c_sites_zonages WHERE code_site = 'LEZE'"
 sql = "SELECT * FROM foncier.c_anim_foncier_v2"
 parc_site = gpd.read_postgis(sql,con_bdcen)
-parc_site[(~parc_site.classif_prop.str.contains('cen',case=False))&(parc_site.maitrise.str.contains('acqui',case=False))].to_csv('/media/colas/SRV/FICHIERS/TRANSFERTS-EQUIPE/AB/verif_anim_foncier_v2.csv',index=False)
+parc_site[(~parc_site.classif_prop.str.contains('cen',case=False))&(parc_site.maitrise.str.contains('acqui',case=False))].to_csv('/media/cgeier/SRV/FICHIERS/TRANSFERTS-EQUIPE/AB/verif_anim_foncier_v2.csv',index=False)
 # parc_site.loc[parc_site.maitrise=='ACQUI PAR LE CEN ISERE','maitrise'] = 'ACQUIS PAR LE CEN ISERE'
 # update_to_sql(
 #     parc_site[['par_id','maitrise']],con_bdcen,'c_anim_foncier_v2','foncier','par_id'

0_FONCIER/cutfantoir_bydep.py

@@ -2,7 +2,7 @@
 import pandas as pd
 import csv
 
-path = '/media/colas/SRV/FICHIERS/TRANSFERTS-EQUIPE/CG/FONCIER/CADASTRE/2023/Fichier national FANTOIR (situation avril 2023)/'
+path = '/media/cgeier/SRV/FICHIERS/TRANSFERTS-EQUIPE/CG/FONCIER/CADASTRE/2023/Fichier national FANTOIR (situation avril 2023)/'
 fant = path+'FANTOIR0423'
 lst_dep = ['07','26','38','42']
 

0_FONCIER/grant_table.py

@@ -46,7 +46,7 @@ def revoke_all_table(con,sch):
     
 
 if __name__ == "__main__":
-    sch_cad = '38_202501'
+    sch_cad = '26_202504'
-    sch_old = '42_202207'
+    sch_old = '38_202501'
     grant_all_table(con_cad,sch_cad)
     # revoke_all_table(con_cad,sch_old)

1_SICEN/SERENA_siege/recovery_geo.py

@@ -19,7 +19,7 @@ def get_site_serena():
 if __name__ == "__main__":
 
     # Lecture des sites SIG
-    PATH = '/media/colas/SRV/FICHIERS/OUTILS/BASES DE DONNEES/RHOMEO/2018/RhoMéO_Données'
+    PATH = '/media/cgeier/SRV/FICHIERS/OUTILS/BASES DE DONNEES/RHOMEO/2018/RhoMéO_Données'
     FILE = '38_sig_points_de_suivis_point.shp'
     sig = gpd.read_file(path.join(PATH,FILE))
 

1_SICEN/SERENA_siege/recovery_relv.py

@@ -26,7 +26,7 @@ DICT_OBS_COLS = {
 
 def list_access_datatable():
 
-    PATH_IN = '/media/colas/SRV/FICHIERS/OUTILS/BASES DE DONNEES/ESPECES/backup'
+    PATH_IN = '/media/cgeier/SRV/FICHIERS/OUTILS/BASES DE DONNEES/ESPECES/backup'
     BDD_IN  = 'AVENIR.serena_V2.mdb'
 
     LST_SCHEMA = mdb.read_schema(path.join(PATH_IN,BDD_IN))

1_SICEN/SERENA_siege/recovery_rhomeo.py

@@ -8,7 +8,7 @@ from os import path
 
 
 postgis_sch = 'serenabase'
-ACCESS_PATH = '/media/colas/SRV/FICHIERS/OUTILS/BASES DE DONNEES/RHOMEO/2012/CEN-38_RHOMEO_BDD_2012'
+ACCESS_PATH = '/media/cgeier/SRV/FICHIERS/OUTILS/BASES DE DONNEES/RHOMEO/2012/CEN-38_RHOMEO_BDD_2012'
 ACCESS_FILE = 'BD_Rhomeo_data.mdb'
 DICT_COLS = {
     'obse_id':'id_origine',

3_AZALEE/create_tab_zh_ponctuel.py

@@ -7,7 +7,7 @@ from os import path,listdir
 from re import findall
 
 v_zh = zh().v_zoneshumides()
-PATH = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/Zones humides'
+PATH = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/Zones humides'
 lst_layer = [x for x in listdir(PATH) if (x.endswith('.shp') or x.endswith('.gpkg')) and x.startswith('ZHP')]
 
 def upper_first_letter(s):

3_AZALEE/insert_ps.py

@@ -106,7 +106,7 @@ def format_date(df):
     df.date = pd.to_datetime(df.date,format='%d%b%Y')
 
   if table == '"PS_BIEVRE_CEN38_2016"':
-    df.date = df.date.astype(str).replace('\.','',regex=True)
+    df.date = df.date.astype(str).replace(r'\.','',regex=True)
     df.date = df.date.str.rsplit(' ',1).str[0]\
       .replace(['avr','mai','juin','juil'],['apr','may','jun','jul'],regex=True)
     df.date = pd.to_datetime(df.date,format='%d %b %Y')
@@ -541,7 +541,7 @@ def normalize_formatIdsite(id_site):
   import re
   df = pd.DataFrame({'id_site':id_site})
   df['dept'] = '38'
-  df[['org','num','other']] = [re.split('(\d+)',s) for s in [*df.id_site]]
+  df[['org','num','other']] = [re.split(r'(\d+)',s) for s in [*df.id_site]]
   del df['other']
   df.loc[df.num.str.len() == 1,'num'] = '000'+ df.loc[df.num.str.len() == 1,'num']
   df.loc[df.num.str.len() == 2,'num'] = '00' + df.loc[df.num.str.len() == 2,'num']
@@ -601,7 +601,7 @@ def normalize_pratiques(prat):
   p = normalize_paturage(p)
   p = p.replace(dic)
   p.fillna('N.D', inplace=True)
-  p[p.str.contains('\?| ou ',na=False)] = 'incertain'
+  p[p.str.contains(r'\?| ou ',na=False)] = 'incertain'
   pp = p.str.split(',',expand=True)
   pp.columns = 'p' + pp.columns.astype(str)
   for c in pp.columns:
@@ -903,7 +903,7 @@ def insert_attrsPS(df,ps_statut='actif'):
     del df['pratiques']
 
   for col in params:
-    df[col] = df[col].astype(str).replace(['\.0'],[''],regex=True)
+    df[col] = df[col].astype(str).replace([r'\.0'],[''],regex=True)
 
   if 'niv_embrous' in df.columns:
     df.niv_embrous.replace(['nan'],['N.D'],inplace=True)
@@ -971,9 +971,9 @@ def insert_attrsPS(df,ps_statut='actif'):
 
 def split_codehab(lst_codehab):
   lst = lst_codehab.copy()
-  lst = lst.str.split('x|X|,|&|/|\(|et',expand=True) \
+  lst = lst.str.split(r'x|X|,|&|/|\(|et',expand=True) \
     .stack().str.strip() \
-    .str.replace('\)','',regex=True)\
+    .str.replace(r'\)','',regex=True)\
     .droplevel(-1).reset_index(drop=False)
   return lst
 
@@ -994,7 +994,7 @@ def format_codehab2insert(lst_codehab):
   suspect = lst.code_hab.str.split('(',expand=True)
   if suspect.shape[1] > 1:
     suspect[0] = suspect[0].str.strip()
-    suspect[1] = suspect[1].str.replace('\)','',regex=True)
+    suspect[1] = suspect[1].str.replace(r'\)','',regex=True)
     suspect.columns = ['code_hab','suspect']
     del lst['code_hab']
     # raise Exception('habitat suspecté en développement')
@@ -1011,7 +1011,7 @@ def insert_cbPS(df):
   drop_col = ['auteur','structure']
   df = df.copy()
   df.date = df.date.astype(str)
-  df.code_hab = df.code_hab.replace(['\*'],[''],regex=True).str.strip()
+  df.code_hab = df.code_hab.replace([r'\*'],[''],regex=True).str.strip()
   df.code_hab = df.code_hab.replace(['None','nan','0'],[None,None,None])
   df.dropna(subset=['code_hab'],inplace=True)
   ids = select_ID(df[df.columns.drop(['code_hab',*drop_col])],sch=pycen.ps.schema,tab='r_site_habitat')
@@ -1061,7 +1061,7 @@ def insert_habPS(df,ps_statut='actif'):
   #   df[t] = df[t].astype(str)
   df = df.stack().reset_index(-1)
   df.columns = ['param','value']
-  df[['param','index','other']] = [re.split('(\d+)',s) for s in [*df.param]]
+  df[['param','index','other']] = [re.split(r'(\d+)',s) for s in [*df.param]]
   del df['other']
   df['index'] = df['index'].astype(int)
   df['value'] = df['value'].replace(['-'],[None])
@@ -1137,7 +1137,7 @@ def filter_saisierror(df):
   tmp.set_index([*cc], inplace=True)
   tmp = tmp.stack().reset_index(-1)
   tmp.columns = ['param','value']
-  tmp[['param','index','other']] = [re.split('(\d+)',s) for s in [*tmp.param]]
+  tmp[['param','index','other']] = [re.split(r'(\d+)',s) for s in [*tmp.param]]
   del tmp['other']
   tmp['index'] = tmp['index'].astype(int)
   tmp['value'] = tmp['value'].replace(['-'],[None])
@@ -1180,7 +1180,7 @@ def filter_saisierror(df):
 
 
   cb = pycen.ref_hab().get_CB()
-  tmp.code_hab = tmp.code_hab.replace(['\*'],[''],regex=True).str.strip()
+  tmp.code_hab = tmp.code_hab.replace([r'\*'],[''],regex=True).str.strip()
   tmp.code_hab = tmp.code_hab.replace(['0'],[None])
   tmp.dropna(subset=['code_hab'], inplace=True)
   tmpp = tmp[['id_site','index','code_hab']].copy()
@@ -1303,8 +1303,9 @@ if __name__ == "__main__":
   ]
   # from_table = '"cr_ECRIN_habitats_CBNA_2014"'
   from_table = None
-  from_file  = 'PS38_modifs_AG_2024.gpkg'
+  from_file  = 'PS prioritaire.geojson'
-  path0 = '/home/colas/Documents/9_PROJETS/2_PS/TO IMPORT/2024/'
+  from_tab = 'Tableau_saisie_PS_2026.xlsx'
+  path0 = '/home/colas/Documents/9_PROJETS/2_PS/TO IMPORT/2026/'
 
   # org = from_file.split('/')[1]
   tutu = pd.DataFrame()

3_AZALEE/refHydro_to_db.py

@@ -17,7 +17,7 @@ con = create_engine('postgresql+psycopg2://{0}:{1}@{2}:{3}/{4}'.format(user_cad,
 
 
 
-file = '/media/colas/Disk2/5_BDD/RPG/1_DONNEES_LIVRAISON_2021/RPG_2-0_SHP_LAMB93_R84_2021-01-01/ILOTS_ANONYMES.shp'
+file = '/media/cgeier/Disk2/5_BDD/RPG/1_DONNEES_LIVRAISON_2021/RPG_2-0_SHP_LAMB93_R84_2021-01-01/ILOTS_ANONYMES.shp'
 dic = {
   'CD_SSBV': 'cdssbv',
   'LIB_SSBV': 'nom',

3_AZALEE/tmp/compare_inv2021_actu2025.py

@@ -0,0 +1,36 @@
+import pandas as pd
+import geopandas as gpd
+from pycen import con_gn
+
+old = gpd.read_file('/home/cgeier/Téléchargements/Telechargement_1772622390_2696/data/n_zone_humide_s_038/n_zone_humide_s_038.json')
+new = gpd.read_postgis('SELECT * FROM gn_exports.v_synthese_zones_humides_isere',con_gn).to_crs(2154)
+tab = pd.read_csv('/home/cgeier/Documents/9_PROJETS/1_ZH/2024/RENDU SIG/pv_evolution_dossier.csv',sep=',')
+
+
+
+for etude in tab.nom_etude.unique():
+    if tab[(tab.nom_etude==etude)].organisme_transmission.any():
+        continue
+    print('\n',etude)
+    news = tab[(tab.nom_etude==etude) & (tab.status_inventaire == 'Nouvelle')]
+    modif = tab[(tab.nom_etude==etude) & (tab.status_inventaire== 'Modification')]
+    supp = tab[(tab.nom_etude==etude) & (tab.status_inventaire == 'Supression')]
+
+    new_add = new[new.code.isin(news.code_zh.tolist())].copy()
+    del_zh  = old[old.id_local.isin(supp.code_zh.tolist())]
+    print('\tNouvelles surfaces ajoutées : %f ha'%(round(new_add.area.sum()/10000,2)),' ; %i zh'%new_add.shape[0],' ; \n\tcode : ',', '.join(list(new_add.code)))
+    print('\tSurfaces supprimées : %f ha'%(round(del_zh.area.sum()/10000,2)),' ; %i zh'%del_zh.shape[0],' ; \n\tcode : ',', '.join(list(del_zh.id_local)))
+
+    code_modif = modif.code_zh.tolist()
+    diff = (new[new.code.isin(code_modif)]
+        .sort_values('code').set_index('code').area - old[old.id_local.isin(code_modif)]
+        .sort_values('id_local').set_index('id_local').area
+    )
+    add = diff[diff > 0]
+    red = diff[diff < 0]
+
+    print('\tModification surfaces ajoutées : %f ha'%(round(add.sum()/10000,2)),' ; %i zh'%add.shape[0],' ; \n\tcode : ',', '.join(list(add.index)))
+    print('\tModification surfaces perdues : %f ha'%(round(red.sum()/10000,2)),' ; %i zh'%red.shape[0],' ; \n\tcode : ',', '.join(list(red.index)))
+
+
+

3_AZALEE/tmp/hab_aggreg_cbna_2024.py

@@ -38,7 +38,7 @@ r_geom.sort_values(['id_site','date'],inplace=True)
 r_geom.drop_duplicates(['id_site'],keep='last',inplace=True)
 
 
-PATH = '/home/colas/Documents/9_PROJETS/1_ZH/MAJ/Actu 2024/CBNA/zh38'
+PATH = '/home/colas/Documents/9_PROJETS/1_ZH/MAJ/Actu 2024/FAIT/CBNA/zh38'
 file = 'hab_agreg.gpkg'
 df = gpd.read_file(path.join(PATH,file))
 df.rename_geometry('geom', inplace=True)
@@ -91,6 +91,7 @@ inters_foret.to_file(path.join(PATH,'inters.gpkg'),driver='GPKG',layer='inters_f
 
 # Fusion Géométries
 intersF = gpd.pd.concat([inters,inters_foret])
+intersF.to_csv(path.join(PATH,'intersection_finale_hab_zh.csv'))
 rgeo = (r_geom[r_geom.id_site.isin(intersF.id_site.unique())]
     .copy())
 for id_site in rgeo.id_site.unique():

3_AZALEE/tmp/maj_geom_belledo.py

@@ -3,7 +3,7 @@
 from pycen import con, update_to_sql
 import geopandas as gpd
 
-file = "/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PGSZH_Belledonne/Inventaire 2023/ZH_Belledonne_MAJ_2023_NB.shp"
+file = "/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PGSZH_Belledonne/Inventaire 2023/ZH_Belledonne_MAJ_2023_NB.shp"
 
 df = gpd.read_file(file)
 df = (df[df.site_code.isin(['38BB0028','38BB0055','38RD0170'])]

3_AZALEE/tmp/zh_plu_metro.py

@@ -445,7 +445,7 @@ def define_author(df,is_new=False):
 
 if __name__ == "__main__":
 
-    PATH = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PLUI METRO/INV_ZH_PLUI_METRO/Mosaique/MAJ-INV-ZH MOSAIQUE 2018-SIG_BDD'
+    PATH = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PLUI METRO/INV_ZH_PLUI_METRO/Mosaique/MAJ-INV-ZH MOSAIQUE 2018-SIG_BDD'
     shp = 'ZH_GAM_CC45.shp'
     data_file = 'BDD_ZH_GAM_database.xlsx'
     dict_cols = {
@@ -790,7 +790,7 @@ if __name__ == "__main__":
     insert_cnx(rpl_cnx)
     insertAttrsFct(rpl_fct,True)
     ## ADD GEOM 2023
-    PATH = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PLUI METRO/INV_ZH_PLUI_METRO/'
+    PATH = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PLUI METRO/INV_ZH_PLUI_METRO/'
     FILE_2023 = 'zh_gam_cc45_modifByMJ&GC.gpkg'
     df2023 = gpd.read_file(path.join(PATH,FILE_2023))
     df2023.rename_geometry('geom',inplace=True)
@@ -894,7 +894,7 @@ if __name__ == "__main__":
     insertAttrsFct(rpl_fct,True)
 
     ## ADD GEOM 2023
-    PATH = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PLUI METRO/INV_ZH_PLUI_METRO/'
+    PATH = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/PLUI METRO/INV_ZH_PLUI_METRO/'
     FILE_2023 = 'zh_gam_cc45_modifByMJ&GC.gpkg'
     df2023 = gpd.read_file(path.join(PATH,FILE_2023))
     df2023.rename_geometry('geom',inplace=True)
@@ -1162,7 +1162,7 @@ if __name__ == "__main__":
     ##########################
     ##### ADD 2010 GEOM ######
     ##########################
-    PATH = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/VECTEURS/ETUDES/ZONES HUMIDES/INVENTAIRE_ZH/'
+    PATH = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/VECTEURS/ETUDES/ZONES HUMIDES/INVENTAIRE_ZH/'
     FILE_2010 = 'ZH_2010 (re-save).shp'
     df2010 = gpd.read_file(PATH+FILE_2010)
     df2010.to_crs(2154,inplace=True)

3_AZALEE/v_zh_ponctuelle.py

@@ -0,0 +1,61 @@
+from pycen import con
+
+sql1 = """
+DROP VIEW IF EXISTS zones_humides.v_zh_ponctuelles;
+CREATE VIEW zones_humides.v_zh_ponctuelles AS
+SELECT 
+    id,
+    territoire,
+    commune,
+    lieu_dit,
+    observer,
+    datemodif,
+    x,
+    y,
+    code_insee,
+    descripti2,
+    geom,
+    qui,
+    statut,
+    source,
+    comment,
+    type,
+    ponctuelle,
+    classe
+FROM zones_humides.zh_ponctuelles
+WHERE in_zh is False
+"""
+
+with con.begin() as cnx:
+    cnx.execute(sql1)
+
+
+
+sql1 = """
+DROP VIEW IF EXISTS zones_humides.v_zh_ponctuelles;
+CREATE VIEW zones_humides.v_zh_ponctuelles AS
+SELECT 
+    id,
+    territoire,
+    commune,
+    lieu_dit,
+    observer,
+    datemodif,
+    x,
+    y,
+    code_insee,
+    descripti2,
+    geom,
+    qui,
+    statut,
+    source,
+    comment,
+    type,
+    ponctuelle,
+    classe
+FROM zones_humides.zh_ponctuelles
+WHERE in_zh is True
+"""
+
+with con.begin() as cnx:
+    cnx.execute(sql1)

5_GEONATURE/EXPORT/flore_export_sinp_with_metadata 2.py

@@ -1,3 +1,4 @@
+# SCRIPT NON VALIDE À CE JOUR
 from pycen import con_gn
 
 sql = '''

5_GEONATURE/EXPORT/get_zh_fromapi.py

@@ -6,6 +6,7 @@ import requests
 from os import path
 import io
 
+
 def get_zh(geom=None):
     url = 'https://geonature.cen-isere.fr/geonature/api/exports/api/9?limit=30000&{geo}'
     if not geom.empty :
@@ -17,6 +18,7 @@ def get_zh(geom=None):
             geometry = 'geometry={geom}'.format(geom=geom.unary_union.wkt)
     else:
         geom = ''
+    print(url.format(geo=geometry))
     r = requests.get(url.format(geo=geometry))    
     if r.status_code == 200:
         d = r.json()
@@ -27,9 +29,10 @@ def get_zh(geom=None):
         
         return df
 
+
 if __name__ == "__main__":
 
-    PATH = '/media/colas/Disk2/tmp/GEONATURE/ST MARTIN URIAGE'
+    PATH = '/media/cgeier/Disk2/tmp/GEONATURE/ST MARTIN URIAGE'
     FILE = 'ZH ENS POUR CEN 14102025.gpkg'
     f = gpd.read_file(path.join(PATH,FILE))
     df = get_zh(f)

5_GEONATURE/EXPORT/v_eee_n2000_platiere.py

@@ -0,0 +1,41 @@
+from pycen import con_gn
+
+sql = '''
+DROP VIEW IF EXISTS gn_exports.v_eee_n2000_platiere;
+
+CREATE OR REPLACE VIEW gn_exports.v_eee_n2000_platiere
+AS 
+SELECT 
+    v.*
+    FROM gn_synthese.v_synthese_for_export v
+    JOIN ref_geo.l_areas ON st_intersects(ST_GeomFromGeoJSON(v.geojson_4326),st_transform(l_areas.geom,4326))
+    JOIN ref_geo.bib_areas_types bib USING (id_type)
+    WHERE bib.type_code = 'ZPS'
+        AND area_name ilike '%%plati%%re%%'
+        AND v.cd_ref in (100330, 101055, 101237, 101286, 103139, 103543, 103545, 103547,
+       103557, 104074, 104805, 105017, 105433, 105615, 105689, 105960,
+       106252, 106571, 106742, 106748, 106754, 106965, 107446, 108628,
+       108642, 108810, 109141, 109911, 109926, 109954, 109980, 110762,
+       111863, 111881, 112100, 112111, 112130, 112195, 112463, 112467,
+       112482, 112712, 112790, 113418, 114024, 115527, 116089, 116137,
+       116485, 117503, 117505, 117723, 117860, 117937, 118477, 119474,
+       119558, 119595, 119854, 122630, 124025, 124164, 124168, 124378,
+       124635, 124646, 124719, 124730, 125331, 128504, 128748, 128863,
+       128954, 129468, 130484, 130491, 159690, 159937,   4419, 446169,
+       448413, 454932,   4872, 611690, 611753, 612522, 717136, 717180,
+        79766,  79877,  80086,  80824,  81955,  81978,  82018,  82080,
+        82093,  82164,  83938,  84057,  84251,  85186,  85469,  85949,
+        85957,  86167,  86513,  86817,  86869,  86975,  89452,  90192,
+        90234,  92572,  92649,  92663,  92793,  93020,  93129,  93613,
+        93923,  93924,  94168,  94489,  95679,  95823,  95831,  95965,
+        95975,  95980,  96149,  96585,  96644,  96739,  96749,  96775,
+        97346,  97594,  97666,  97961,  99260,  99359,  95983,  96814,
+       117507, 129959,  81992,  90532,  97623, 104353, 111886, 112483,
+       125369, 109037,  96624, 101056, 106800, 109949, 116762, 125324
+    )
+;
+'''
+
+with con_gn.begin() as cnx:
+    cnx.execute(sql)
+

5_GEONATURE/EXPORT/v_synthese_zh_rhomeosite.py

@@ -0,0 +1,45 @@
+from pycen import con_gn
+
+sql = '''
+DROP VIEW IF EXISTS gn_exports.v_synthese_zh_rhomeosite;
+
+CREATE OR REPLACE VIEW gn_exports.v_synthese_zh_rhomeosite
+AS WITH t1 AS (
+    SELECT t_zh.code,
+    l_areas.area_name,
+    st_area(st_intersection(t_zh.geom, l_areas.geom_4326)) AS area_intersect
+    FROM pr_zh.t_zh
+        JOIN ref_geo.l_areas ON st_intersects(t_zh.geom, l_areas.geom_4326) 
+        JOIN ref_geo.bib_areas_types bib ON l_areas.id_type = bib.id_type and bib.type_name = 'Zones biogéographiques'
+), t2 AS (
+    SELECT t1.code,
+    t1.area_name,
+    row_number() OVER (PARTITION BY t1.code ORDER BY t1.area_intersect DESC) AS ismax_ter
+    FROM t1
+)
+ SELECT 
+ 	t_zh.id_zh,
+    t_zh.main_name AS "NAME",
+    concat(t_roles.nom_role, ' ', t_roles.prenom_role) AS "REFERENT",
+    bib.nom_organisme AS "ORG",
+    CASE 
+        WHEN t_zh.id_sage IS NULL
+        THEN ref_nomenclatures.get_cd_nomenclature(t_zh.id_sdage)::text 
+        ELSE ref_nomenclatures.get_cd_nomenclature(t_zh.id_sage)::text
+    END AS "TYPE",
+    CASE
+        WHEN t2.area_name = 'alpin'::text THEN '1'::text
+        WHEN t2.area_name = 'continental'::text THEN '2'::text
+        WHEN t2.area_name = 'mediterraneen'::text THEN '4'::text
+        ELSE NULL::text
+    END AS "ODONATE",
+    ST_TRANSFORM(t_zh.geom,2154)::geometry(geometry,2154) geom
+   FROM pr_zh.t_zh
+    LEFT JOIN utilisateurs.t_roles ON t_zh.update_author = t_roles.id_role
+    LEFT JOIN utilisateurs.bib_organismes bib USING (id_organisme)
+    JOIN t2 ON t_zh.code::text = t2.code::text AND t2.ismax_ter = 1;
+'''
+
+with con_gn.begin() as cnx:
+    cnx.execute(sql)
+

5_GEONATURE/EXPORT/v_synthese_zones_humides.py

@@ -1,9 +1,6 @@
 from pycen import con_gn
 
-sql = '''
+sql0 = '''
-DROP VIEW IF EXISTS gn_exports.v_synthese_zones_humides;
-
-CREATE VIEW gn_exports.v_synthese_zones_humides AS
 with habitats as (
     SELECT 
 	    zh_uuid,
@@ -25,8 +22,7 @@ with habitats as (
 	FROM pr_zh.t_zh 
 	LEFT JOIN pr_zh.cor_zh_lim_fs USING (id_zh)
 	group by 1 order by 1
-), activ_hum AS (
+), v_activity AS (
-    WITH v_activity AS (
         SELECT
             id_zh, 
             json_agg(ref_nomenclatures.get_nomenclature_label(cor_impact_types.id_impact)) impacts
@@ -34,38 +30,60 @@ with habitats as (
         LEFT JOIN pr_zh.cor_impact_list USING (id_impact_list)
         LEFT JOIN pr_zh.cor_impact_types USING (id_cor_impact_types)
         group by 1 order by 1
-    )
+), activ_hum AS (
     SELECT 
         zh_uuid,
-        json_agg(json_build_object(
+        json_agg(CASE WHEN t_activity.id_activity IS NULL
-            'activite', ref_nomenclatures.get_nomenclature_label(t_activity.id_activity),
+            THEN NULL
-            'position', ref_nomenclatures.get_nomenclature_label(t_activity.id_position),
+            ELSE json_build_object(
-            'impact', v_activity.impacts,
+                'activite', ref_nomenclatures.get_nomenclature_label(t_activity.id_activity),
-            'rmq_activ', t_activity.remark_activity
+                'position', ref_nomenclatures.get_nomenclature_label(t_activity.id_position),
-        )) AS activ_hum
+                'impact', v_activity.impacts,
+                'rmq_activ', t_activity.remark_activity
+            ) 
+        END) AS activ_hum
     FROM pr_zh.t_zh 
     LEFT JOIN pr_zh.t_activity USING (id_zh)
     LEFT JOIN v_activity USING (id_zh)
     group by zh_uuid order by 1
+--), activ_hum_test AS (
+--    SELECT 
+--        zh_uuid,
+--        CASE WHEN t_activity.id_activity IS NOT NULL
+--        	THEN jsonb_build_object(
+--	            ref_nomenclatures.get_nomenclature_label(t_activity.id_activity), v_activity.impacts
+--	        ) 
+--	       ELSE NULL 
+--        END AS activ_hum_test
+--    FROM pr_zh.t_zh 
+--    LEFT JOIN pr_zh.t_activity USING (id_zh)
+--    LEFT JOIN v_activity USING (id_zh)
+--    --group by zh_uuid order by 1
 ), inflow as (
     SELECT 
         zh_uuid,
-        json_agg(json_build_object(
+        json_agg(CASE WHEN t_inflow.id_inflow IS NULL
+            THEN NULL
+            ELSE json_build_object(
             'entree_eau', ref_nomenclatures.get_nomenclature_label(t_inflow.id_inflow),
             'perm', ref_nomenclatures.get_nomenclature_label(t_inflow.id_permanance),
             'topo', t_inflow.topo
-        )) AS entree_eau
+            ) 
+        END) AS entree_eau
     FROM pr_zh.t_zh 
     LEFT JOIN pr_zh.t_inflow USING (id_zh)
     group by zh_uuid
 ), outflow as (
     SELECT 
         zh_uuid,
-        json_agg(json_build_object(
+        json_agg(CASE WHEN t_outflow.id_outflow IS NULL
-            'sortie_eau', ref_nomenclatures.get_nomenclature_label(t_outflow.id_outflow),
+            THEN NULL
-            'perm', ref_nomenclatures.get_nomenclature_label(t_outflow.id_permanance),
+            ELSE json_build_object(
-            'topo', t_outflow.topo
+                'sortie_eau', ref_nomenclatures.get_nomenclature_label(t_outflow.id_outflow),
-        )) AS sortie_eau
+                'perm', ref_nomenclatures.get_nomenclature_label(t_outflow.id_permanance),
+                'topo', t_outflow.topo
+            ) 
+        END) AS sortie_eau
     FROM pr_zh.t_zh 
     LEFT JOIN pr_zh.t_outflow USING (id_zh)
     group by zh_uuid
@@ -247,11 +265,13 @@ SELECT
     zh_uuid,
     code,
     main_name,
-    CONCAT(crea_role.nom_role, ' ', crea_role.prenom_role) crea_author,
+    --concat(crea_role.nom_role, ' ', crea_role.prenom_role) AS crea_author,
-    CONCAT(updt_role.nom_role, ' ', updt_role.prenom_role) updt_author,
+    --bib_org.name AS organisme,
-    bib_org.name organisme,
+    --concat(updt_role.nom_role, ' ', updt_role.prenom_role) AS updt_author,
+    bib_org_user_updt.nom_organisme as org_updt,
     create_date crea_date,
     update_date updt_date,
+    --ST_TRANSFORM(geom,2154)::geometry(geometry,2154) geom,
     geom,
     area surface,
     rb.river_bassin,
@@ -259,27 +279,43 @@ SELECT
     zh_area_com.commune,
     ref_nomenclatures.get_nomenclature_label(id_sdage) sdage,
 	ref_nomenclatures.get_nomenclature_label(id_sage) sage,
-    habitats.habitats,
+    CASE WHEN habitats.habitats::text = '[null]'
+        THEN NULL
+        ELSE habitats.habitats
+    END AS habitats,
     remark_pres descript,
     crit_delim.crit_delim,
     remark_lim rmq_delilm,
-    esp_fs.crit_esp_fs,
+    CASE WHEN esp_fs.crit_esp_fs::text = '[null]'
+        THEN NULL
+        ELSE esp_fs.crit_esp_fs
+    END AS crit_esp_fs,
     remark_lim_fs rmq_esp_fs,
-    ef_area,
+    --ef_area,
-    v_habref v_hab,
+    --v_habref v_hab,
-    activ_hum.activ_hum,
+    CASE WHEN activ_hum.activ_hum::text = '[null]'
+        THEN NULL
+        ELSE activ_hum.activ_hum::text
+    END AS activ_hum,
+--    activ_hum_test.activ_hum_test,
     ref_nomenclatures.get_nomenclature_label(id_thread) bilan_menace,
-    inflow.entree_eau,
+    CASE WHEN inflow.entree_eau::text = '[null]'
-    outflow.sortie_eau,
+        THEN NULL
+        ELSE inflow.entree_eau::text
+    END AS entree_eau,
+    CASE WHEN outflow.sortie_eau::text = '[null]'
+        THEN NULL
+        ELSE outflow.sortie_eau::text
+    END AS sortie_eau,
     ref_nomenclatures.get_nomenclature_label(id_frequency) freq_sub,
     ref_nomenclatures.get_nomenclature_label(id_spread) extend_sub,
     ref_nomenclatures.get_nomenclature_label(id_connexion) connex,
     ref_nomenclatures.get_nomenclature_label(id_diag_hydro) diag_hydro,
     ref_nomenclatures.get_nomenclature_label(id_diag_bio) diag_bio,
-    functions.hydro,
+    functions.hydro::text,
-    functions.bio,
+    functions.bio::text,
-    functions.int_patrim,
+    functions.int_patrim::text,
-    functions.val_socio,
+    functions.val_socio::text,
     remark_eval_functions eval_fcts,
     remark_eval_heritage eval_heritage,
     remark_eval_thread eval_menace,
@@ -293,19 +329,21 @@ SELECT
     especes.nb_flora nb_flora_sp,
     especes.nb_vertebrates nb_vertebrate_sp,
     especes.nb_invertebrates nb_invertebrate_sp,
-    inventaire.inventaire,
+    inventaire.inventaire::text,
-    actions.prio_actions,
+    actions.prio_actions::text,
-    instrument.instrument,
+    instrument.instrument::text,
-    urba.docs_urba
+    urba.docs_urba::text
     --remark_is_other_inventory
 FROM pr_zh.t_zh
 JOIN utilisateurs.t_roles crea_role ON t_zh.create_author = crea_role.id_role
 LEFT JOIN utilisateurs.t_roles updt_role ON t_zh.update_author = updt_role.id_role
+LEFT JOIN utilisateurs.bib_organismes bib_org_user_updt on updt_role.id_organisme = bib_org_user_updt.id_organisme
 LEFT JOIN pr_zh.bib_organismes bib_org USING (id_org) 
 LEFT JOIN habitats USING (zh_uuid)
 LEFT JOIN crit_delim USING (zh_uuid)
 LEFT JOIN esp_fs USING (zh_uuid)
 LEFT JOIN activ_hum USING (zh_uuid)
+--LEFT JOIN activ_hum_test USING (zh_uuid)
 LEFT JOIN inflow USING (zh_uuid)
 LEFT JOIN outflow USING (zh_uuid)
 LEFT JOIN functions USING (zh_uuid)
@@ -317,11 +355,37 @@ LEFT JOIN actions USING (id_zh)
 LEFT JOIN zh_area_com USING (id_zh)
 LEFT JOIN dep USING (id_zh)
 LEFT JOIN inventaire USING (id_zh)
+'''
+
+sql_zh_all = '''
+DROP VIEW IF EXISTS gn_exports.v_synthese_zones_humides;
+
+CREATE VIEW gn_exports.v_synthese_zones_humides AS
+''' + sql0 + '''
 --group BY zh_uuid
-WHERE code not like '38CENIS%%'
+WHERE code not like '38CENIS%%' 
+    AND ref_nomenclatures.get_cd_nomenclature(id_sdage)::int > 0
+    --id_sdage > 0
 order by code
 ;
 '''
 
 with con_gn.begin() as cnx:
-    cnx.execute(sql)
+    cnx.execute(sql_zh_all)
+
+
+sql_zh_38 = '''
+DROP VIEW IF EXISTS gn_exports.v_synthese_zones_humides_isere;
+
+CREATE VIEW gn_exports.v_synthese_zones_humides_isere AS
+''' + sql0 + '''
+--group BY zh_uuid
+WHERE code not like '38CENIS%%' 
+    AND dep.departement::jsonb @> '"Isère"'
+    AND ref_nomenclatures.get_cd_nomenclature(id_sdage)::int > 0
+order by code
+;
+'''
+
+with con_gn.begin() as cnx:
+    cnx.execute(sql_zh_38)

5_GEONATURE/EXPORT/v_synthese_zones_humides_isere.py

@@ -1,236 +0,0 @@
-from pycen import con_gn
-
-sql = '''
-DROP VIEW IF EXISTS gn_exports.v_synthese_zones_humides_isere;
-
-CREATE VIEW gn_exports.v_synthese_zones_humides_isere AS
-with habitats as (
-    SELECT 
-	    zh_uuid,
-	    array_agg(lb_code) AS habitats
-	FROM pr_zh.t_zh 
-	LEFT JOIN pr_zh.cor_zh_cb USING (id_zh)
-	group by 1 order by 1
-), crit_delim as (
-	SELECT 
-	    zh_uuid,
-	    array_agg(ref_nomenclatures.get_nomenclature_label(cor_lim_list.id_lim)) AS crit_delim
-	FROM pr_zh.t_zh 
-	LEFT JOIN pr_zh.cor_lim_list USING (id_lim_list)
-	group by 1 order by 1
-), esp_fs as (
-	SELECT 
-	    zh_uuid,
-	    array_agg(ref_nomenclatures.get_nomenclature_label(cor_zh_lim_fs.id_lim_fs)) AS crit_esp_fs
-	FROM pr_zh.t_zh 
-	LEFT JOIN pr_zh.cor_zh_lim_fs USING (id_zh)
-	group by 1 order by 1
-), activ_hum AS (
-    WITH v_activity AS (
-        SELECT
-            id_zh, 
-            array_agg(ref_nomenclatures.get_nomenclature_label(cor_impact_types.id_impact)) impacts
-        FROM pr_zh.t_activity
-        LEFT JOIN pr_zh.cor_impact_list USING (id_impact_list)
-        LEFT JOIN pr_zh.cor_impact_types USING (id_cor_impact_types)
-        group by 1 order by 1
-    )
-    SELECT 
-        zh_uuid,
-        json_agg(json_build_object(
-            'activite', ref_nomenclatures.get_nomenclature_label(t_activity.id_activity),
-            'position', ref_nomenclatures.get_nomenclature_label(t_activity.id_position),
-            'impact', v_activity.impacts,
-            'rmq_activ', t_activity.remark_activity
-        )) AS activ_hum
-    FROM pr_zh.t_zh 
-    LEFT JOIN pr_zh.t_activity USING (id_zh)
-    LEFT JOIN v_activity USING (id_zh)
-    group by zh_uuid order by 1
-), inflow as (
-    SELECT 
-        zh_uuid,
-        json_agg(json_build_object(
-            'entree_eau', ref_nomenclatures.get_nomenclature_label(t_inflow.id_inflow),
-            'perm', ref_nomenclatures.get_nomenclature_label(t_inflow.id_permanance),
-            'topo', t_inflow.topo
-        )) AS entree_eau
-    FROM pr_zh.t_zh 
-    LEFT JOIN pr_zh.t_inflow USING (id_zh)
-    group by zh_uuid
-), outflow as (
-    SELECT 
-        zh_uuid,
-        json_agg(json_build_object(
-            'sortie_eau', ref_nomenclatures.get_nomenclature_label(t_outflow.id_outflow),
-            'perm', ref_nomenclatures.get_nomenclature_label(t_outflow.id_permanance),
-            'topo', t_outflow.topo
-        )) AS sortie_eau
-    FROM pr_zh.t_zh 
-    LEFT JOIN pr_zh.t_outflow USING (id_zh)
-    group by zh_uuid
-), functions as (
-    WITH hydro as (
-            SELECT 
-                zh_uuid,
-                json_agg(json_build_object(
-                    'fonctions',     ref_nomenclatures.get_nomenclature_label(t_functions.id_function),
-                    'qualification', ref_nomenclatures.get_nomenclature_label(t_functions.id_qualification),
-                    'connaissance',  ref_nomenclatures.get_nomenclature_label(t_functions.id_knowledge),
-                    'justification', t_functions.justification
-                )) AS hydro
-            FROM pr_zh.t_zh 
-            LEFT JOIN pr_zh.t_functions USING (id_zh)
-            WHERE id_function IN (
-                SELECT 
-                    id_nomenclature 
-                FROM ref_nomenclatures.t_nomenclatures 
-                JOIN ref_nomenclatures.bib_nomenclatures_types bib USING (id_type)
-                WHERE bib.mnemonique = 'FONCTIONS_HYDRO')
-            group by zh_uuid
-    ), bio as (
-        SELECT 
-            zh_uuid,
-            json_agg(json_build_object(
-                'fonctions',     ref_nomenclatures.get_nomenclature_label(t_functions.id_function),
-                'qualification', ref_nomenclatures.get_nomenclature_label(t_functions.id_qualification),
-                'connaissance',  ref_nomenclatures.get_nomenclature_label(t_functions.id_knowledge),
-                'justification', t_functions.justification
-            )) AS bio
-        FROM pr_zh.t_zh 
-        LEFT JOIN pr_zh.t_functions USING (id_zh)
-        WHERE id_function IN (
-            SELECT 
-                id_nomenclature 
-            FROM ref_nomenclatures.t_nomenclatures 
-            JOIN ref_nomenclatures.bib_nomenclatures_types bib USING (id_type)
-            WHERE bib.mnemonique = 'FONCTIONS_BIO')
-        group by zh_uuid
-    ), int_patrim as (
-        SELECT 
-            zh_uuid,
-            json_agg(json_build_object(
-                'fonctions',     ref_nomenclatures.get_nomenclature_label(t_functions.id_function),
-                'qualification', ref_nomenclatures.get_nomenclature_label(t_functions.id_qualification),
-                'connaissance',  ref_nomenclatures.get_nomenclature_label(t_functions.id_knowledge),
-                'justification', t_functions.justification
-            )) AS int_patrim
-        FROM pr_zh.t_zh 
-        LEFT JOIN pr_zh.t_functions USING (id_zh)
-        WHERE id_function IN (
-            SELECT 
-                id_nomenclature 
-            FROM ref_nomenclatures.t_nomenclatures 
-            JOIN ref_nomenclatures.bib_nomenclatures_types bib USING (id_type)
-            WHERE bib.mnemonique = 'INTERET_PATRIM')
-        group by zh_uuid
-    ), val_socio as (
-        SELECT 
-            zh_uuid,
-            json_agg(json_build_object(
-                'fonctions',     ref_nomenclatures.get_nomenclature_label(t_functions.id_function),
-                'qualification', ref_nomenclatures.get_nomenclature_label(t_functions.id_qualification),
-                'connaissance',  ref_nomenclatures.get_nomenclature_label(t_functions.id_knowledge),
-                'justification', t_functions.justification
-            )) AS val_socio
-        FROM pr_zh.t_zh 
-        LEFT JOIN pr_zh.t_functions USING (id_zh)
-        WHERE id_function IN (
-            SELECT 
-                id_nomenclature 
-            FROM ref_nomenclatures.t_nomenclatures 
-            JOIN ref_nomenclatures.bib_nomenclatures_types bib USING (id_type)
-            WHERE bib.mnemonique = 'VAL_SOC_ECO')
-        group by zh_uuid
-    )
-    SELECT 
-        zh_uuid,
-        hydro.hydro,
-        bio.bio,
-        int_patrim.int_patrim,
-        val_socio.val_socio
-    FROM pr_zh.t_zh 
-    LEFT JOIN hydro USING (zh_uuid)
-    LEFT JOIN bio USING (zh_uuid)
-    LEFT JOIN int_patrim USING (zh_uuid)
-    LEFT JOIN val_socio USING (zh_uuid)
-    --group by zh_uuid
-), especes as (
-    SELECT
-        zh_uuid,
-        (select distinct count(vm_flora.cd_nom) from pr_zh.vm_flora where id_zh = t.id_zh) nb_flora,
-        (select distinct count(vm_vertebrates.cd_nom) from pr_zh.vm_vertebrates where id_zh = t.id_zh) nb_vertebrates,
-        (select distinct count(vm_invertebrates.cd_nom) from pr_zh.vm_invertebrates where id_zh = t.id_zh) nb_invertebrates
-        --count(vm_vertebrates.cd_nom) nb_vertebrates,
-        --count(vm_invertebrates.cd_nom) nb_invertebrates
-    FROM pr_zh.t_zh t
-    --GROUP BY zh_uuid
-)
-SELECT 
-    zh_uuid,
-    code,
-    main_name,
-    CONCAT(crea_role.nom_role, ' ', crea_role.prenom_role) crea_author,
-    CONCAT(updt_role.nom_role, ' ', updt_role.prenom_role) updt_author,
-    bib_org.name organisme,
-    create_date crea_date,
-    update_date updt_date,
-    geom,
-    area surface,
-    ref_nomenclatures.get_nomenclature_label(id_sdage) sdage,
-	ref_nomenclatures.get_nomenclature_label(id_sage) sage,
-    habitats.habitats,
-    remark_pres descript,
-    crit_delim.crit_delim,
-    remark_lim rmq_delilm,
-    esp_fs.crit_esp_fs,
-    remark_lim_fs rmq_esp_fs,
-    ef_area,
-    v_habref v_hab,
-    activ_hum.activ_hum,
-    ref_nomenclatures.get_nomenclature_label(id_thread) bilan_menace,
-    inflow.entree_eau,
-    outflow.sortie_eau,
-    ref_nomenclatures.get_nomenclature_label(id_frequency) freq_sub,
-    ref_nomenclatures.get_nomenclature_label(id_spread) extend_sub,
-    ref_nomenclatures.get_nomenclature_label(id_connexion) connex,
-    ref_nomenclatures.get_nomenclature_label(id_diag_hydro) diag_hydro,
-    ref_nomenclatures.get_nomenclature_label(id_diag_bio) diag_bio,
-    functions.hydro,
-    functions.bio,
-    functions.int_patrim,
-    functions.val_socio,
-    remark_eval_functions eval_fcts,
-    remark_eval_heritage eval_heritage,
-    remark_eval_thread eval_menace,
-    remark_eval_actions eval_actions,
-    ref_nomenclatures.get_nomenclature_label(id_strat_gestion) strat_gestion,
-    remark_diag rmq_gestion,
-    --is_other_inventory,
-    --is_carto_hab,
-    --nb_hab,
-    --total_hab_cover,
-    especes.nb_flora nb_flora_sp,
-    especes.nb_vertebrates nb_vertebrate_sp,
-    especes.nb_invertebrates nb_invertebrate_sp
-    --remark_is_other_inventory
-FROM pr_zh.t_zh
-JOIN utilisateurs.t_roles crea_role ON t_zh.create_author = crea_role.id_role
-LEFT JOIN utilisateurs.t_roles updt_role ON t_zh.update_author = updt_role.id_role
-LEFT JOIN pr_zh.bib_organismes bib_org USING (id_org) 
-LEFT JOIN habitats USING (zh_uuid)
-LEFT JOIN crit_delim USING (zh_uuid)
-LEFT JOIN esp_fs USING (zh_uuid)
-LEFT JOIN activ_hum USING (zh_uuid)
-LEFT JOIN inflow USING (zh_uuid)
-LEFT JOIN outflow USING (zh_uuid)
-LEFT JOIN functions USING (zh_uuid)
-LEFT JOIN especes USING (zh_uuid)
---group BY zh_uuid
-WHERE code not like '38CENIS%%' AND code like '38%%'
-order by code
-;
-'''
-
-with con_gn.begin() as cnx:
-    cnx.execute(sql)

5_GEONATURE/GN_ZH/tmp/epode.py

@@ -2,7 +2,7 @@ from pycen import con_gn,update_to_sql
 from os import path
 import geopandas as gpd
 
-PATH = '/media/colas/SRV/FICHIERS'
+PATH = '/media/cgeier/SRV/FICHIERS'
 FILE = 'OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/Zones humides/ACTUALISATION 2024/BE EPODE - LEZE/KIT INVENTAIRE38 MODIFIE/a envoyer prêt/GEOM/38CG0105_Epode2025 (à intégrer).gpkg'
 
 tmp = (gpd.read_file(path.join(PATH, FILE))

5_GEONATURE/GN_ZH/tmp/maj_geom.py

@@ -4,8 +4,8 @@ import geopandas as gpd
 from pycen import con_gn, update_to_sql
 from os import path
 
-PATH = '/media/colas/SRV/'
+PATH = '/media/cgeier/SRV/FICHIERS'
-FILE = 'FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/Zones humides/diag_zh_GAM/ZH_Séchilienne/zh_sapey.gpkg'
+FILE = 'OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/Zones humides/diag_zh_GAM/ZH_Séchilienne/zh_sapey.gpkg'
 df = (gpd.read_file(path.join(PATH, FILE))
   .rename(columns={'code_zh':'code'})
   .rename_geometry('geom'))

5_GEONATURE/IMPORTS/CHAR_amphi_2011.py

@@ -1,7 +1,7 @@
 import geopandas as gpd
 import os
 
-PATH = '/media/colas/SRV/FICHIERS/SITES/SITES GERES/CHAR_CHARVAS/Scientifique-technique/Faune/AMPHIBIENS'
+PATH = '/media/cgeier/SRV/FICHIERS/SITES/SITES GERES/CHAR_CHARVAS/Scientifique-technique/Faune/AMPHIBIENS'
 file = 'Inventaires amphibiens Charvas 2011.xlsx'
 
 

5_GEONATURE/MIGRATION/PLATIERE/correct_cdnom.py

@@ -43,7 +43,7 @@ dict_taxo2 = {
 
 if __name__ == "__main__":
 
-    PATH = '/media/colas/Disk2/tmp'
+    PATH = '/media/cgeier/Disk2/tmp'
     file = '(RNN Platière) Inventaire Bryophytes L93.csv'
     file_out = '(RNN Platière) Inventaire Bryophytes L93 (correct cdnom).csv'
     df = pd.read_csv(os.path.join(PATH,file),encoding='Windows-1252',sep=';')

5_GEONATURE/MIGRATION/TAXREF/taxref_14to16.py

@@ -11,7 +11,7 @@ def get_synthese(con,lst):
     return pd.read_sql_query(sql,con)
 
 if __name__ == "__main__":
-    PATH = '/media/colas/SRV/homer_geonat/home/geonatureadmin/taxhub/tmp'
+    PATH = '/media/cgeier/SRV/homer_geonat/home/geonatureadmin/taxhub/tmp'
     file = 'liste_changements.csv'
     chgt = pd.read_csv(os.path.join(PATH,file))
 

5_GEONATURE/MONITORINGS/get_data.py

@@ -87,7 +87,7 @@ if __name__ == "__main__":
     module_name = "chiro"
     id_tax_list = 100      # taxons occtax 
 
-    PATH = '/media/colas/SRV/homer_geonat/home/geonatureadmin/protocoles_suivi/{mod}/odk_form/'.format(mod=module_name)
+    PATH = '/media/cgeier/SRV/homer_geonat/home/geonatureadmin/protocoles_suivi/{mod}/odk_form/'.format(mod=module_name)
     PATH = '/home/colas/Documents/9_PROJETS/6_GEONATURE/MODULES/ODK/test/CHIRO/'
     csv_site_name = 'gn_sites.csv'
     csv_jdd_name = 'gn_jdds.csv'

5_GEONATURE/pivot_bdc_status.py

@@ -4,7 +4,7 @@ from pycen import con_gn
 
 
 NAME_OUT = '/home/colas/Documents/tmp/v_bdc_status2.xlsx'
-FileIn = '/media/colas/SRV/FICHIERS/TRANSFERTS-EQUIPE/CG/BDC_STATUTS_15.xls'
+FileIn = '/media/cgeier/SRV/FICHIERS/TRANSFERTS-EQUIPE/CG/BDC_STATUTS_15.xls'
 db = False
 
 if db :

5_GEONATURE/pivot_bdc_status_v2.py

@@ -24,6 +24,7 @@ def get_status(lst,con):
             t.nom_valide,
             t.lb_nom,
             --s.*
+            s.cd_sig,
             s.rq_statut,
             s.code_statut,
             s.cd_type_statut,
@@ -202,12 +203,65 @@ def get_taxon_status(lst,api):
     }
     return pd.DataFrame({**phylo,**cd_status})
 
+
+def filter_bio_geo(df,zone_bio):
+    idNotBioGeo = []
+    # Filtre du dommaine biogeographique sur la plaine Rhodanienne et Alpine
+    test_rhod = df.rq_statut.str.contains('rhodanienne : Non déterminante',na=False)
+    test_alpi = df.rq_statut.str.contains('Alpine : Non déterminante',na=False)
+    
+    if zone_bio == 'rhod':
+        idNotBioGeo = df[test_rhod].index
+    elif zone_bio == 'alpi':
+        idNotBioGeo = df[test_alpi].index
+    elif zone_bio == 'all':
+        idNotBioGeo = df[test_rhod&test_alpi].index
+
+    if not idNotBioGeo.empty:
+        df.drop(idNotBioGeo, inplace=True)
+
+    return df
+
+
+def form_territoire(df,terr):
+    is_dep = df.cd_sig.str.contains('INSEED')
+    dep = (
+        ['38','42','07','26'] if terr == 'platiere' else
+        ['38','69','01'] if terr == 'negria' else ['38']
+    )
+    keep_not = df[is_dep&(~df.cd_sig.str[-2:].isin(dep))].index
+    df.drop(keep_not,inplace=True)
+    
+    is_dep = df.cd_sig.str.contains('INSEED')
+    is_38  = df.cd_sig=='INSEED38'
+
+    if terr == 'isere':
+        filter_id = df[is_dep&(~is_38)].index
+        df.drop(filter_id,inplace=True)
+    else:
+        lst_stat = df[is_dep&(~is_38)].cd_type_statut.unique()
+        # is_lst = df.cd_type_statut.isin(lst_stat)
+        df.loc[is_dep&(~is_38),'cd_type_statut'] = ( df[is_dep&(~is_38)]
+            .cd_type_statut + '_' + df[is_dep&(~is_38)].cd_sig.str.strip('INSEED')    
+        )
+        df.loc[is_38,'cd_type_statut'] = ( df[is_38]
+            .cd_type_statut + '_38'    
+        )
+
+    return df
+
+
 dict_dep = {
     '38':'Isère',
     '42':'Loire',
     '07':'Ardèche',
     '26':'Drôme',
 }
+
+
+cols_rename = {
+    'nom_vernaculaire': 'nom_vern'
+}
     
 
 if __name__ == "__main__":
@@ -217,21 +271,35 @@ if __name__ == "__main__":
     api_taxref = 'https://geonature.cen-isere.fr/taxhub/api/taxref'
 
     # Paramètres de chargement du fichier des taxons
-    PATH0 = '/media/colas/SRV/FICHIERS'
+    PATH0 = '/home/cgeier/Téléchargements'
-    PATH = 'SITES/SITES GERES/PLAN_PLANCHETTES/Scientifique et technique/Flore et habitats/Suivi flore patrimoniale 2025'
+    PATH = ''
-    file = 'donnes_sp_suivi2025.xlsx'
+    file = 'synthese_observations_2026-04-23.xlsx'
-    sheet = 'liste sp'
+    sheet = 'observations'
+    zone_bio_znieff = 'rhod' # ['rhod', 'alpi', 'all']
+    territoire = 'negria' # ['isere', 'platiere','negria']
+    # [GEOMETRY PARAMS]
+    keep_geomtype = None # ['polygon', 'point', 'ligne', None]
+    geom_col = 'geometrie_wkt_4326'
 
     # Liste des CD_NOM en entrée
-    cd_col = 'cd_ref'   # Nom de la colonne à utiliser dans le feuillet ``sheet``
+    cd_col = 'cd_nom'   # Nom de la colonne à utiliser dans le feuillet ``sheet``
+
 
     # Lecture des données
     taxlist = pd.read_excel(os.path.join(PATH0,PATH,file),sheet,usecols=[cd_col],header=0)
     tab_sp = pd.read_excel(os.path.join(PATH0,PATH,file),sheet,index_col=cd_col)
+    tab_sp.rename(columns=cols_rename,inplace=True)
+
+    # Exclusion d'un type de géométrie
+    if keep_geomtype is not None:
+        as_geom = tab_sp[geom_col].str.contains(keep_geomtype.upper(),na=False)
+        tab_sp = tab_sp[as_geom]
+        taxlist = taxlist[taxlist.cd_nom.isin(tab_sp.index.tolist())]
+
     lst =  taxlist[cd_col]
     
     # Récupération des statuts
-    df = get_status(taxlist[cd_col].astype(str),con_gn)
+    df = get_status(taxlist[cd_col].astype(str).unique(),con_gn)
     typ = get_type_status(con_gn)
     typ = typ[typ.cd_type_statut.isin(df.cd_type_statut.unique())]
 
@@ -241,32 +309,70 @@ if __name__ == "__main__":
     df.loc[is_lrr & (df.niveau_admin == 'Ancienne région'),'cd_type_statut'] = 'LRR_RA'
     del df['niveau_admin']
 
-    for c in ['cd_ref','cd_nom','lb_nom']:
+    # Filtre du dommaine biogeographique sur la plaine Rhodanienne et Alpine
-        if c in tab_sp.columns:
+    df = filter_bio_geo(df,zone_bio_znieff)
-        # if 'cd_nom' not in df.columns and c == 'cd_ref': continue
-            tab_sp.drop(c,axis=1,inplace=True)
 
-    pivot = pd.pivot_table(
+    # Filtre des statuts vis à vis du territoire d'intérêts
+    # Conservation des statuts Adrèche, Drôme et Loire pour la Platière
+    df = form_territoire(df,territoire)
+
+    # for c in ['cd_ref','cd_nom','lb_nom']:
+    #     if c in tab_sp.columns:
+    #     # if 'cd_nom' not in df.columns and c == 'cd_ref': continue
+    #         tab_sp.drop(c,axis=1,inplace=True)
+
+    keep_cols = same_col = df.columns[df.columns.isin(tab_sp.reset_index(drop=False).columns)]
+    as_vern = keep_cols.str.contains('vern').any()
+    as_numb = keep_cols.str.contains('nombre').any()
+    if not as_vern:
+        keep_cols = [*keep_cols,'nom_vern']
+    if not as_numb:
+        keep_cols = [*keep_cols,'nombre_min','nombre_max']
+    else:
+        same_col = [x for x in keep_cols if x not in ['nombre_min','nombre_max']]
+
+    piv = pd.pivot_table(
         df,
         values='code_statut',
-        index=['cd_nom', 'cd_ref','lb_nom'#,'niveau_admin','lb_adm_tr'
+        index=['cd_nom', 'cd_ref','lb_nom','nom_vern'#,'niveau_admin','lb_adm_tr'
         ],
         columns=['cd_type_statut'],
         aggfunc=list,fill_value=None)
 
-    for c in pivot.columns:
+    for c in piv.columns:
-        pivot[c] = [x[0] if x is not np.NaN and len(x)==1 else x for x in pivot[c]]
+        piv[c] = [x[0] if x is not np.NaN and len(x)==1 else x for x in piv[c]]
-    if 'DH' in pivot.columns:
+    if 'DH' in piv.columns:
-        pivot['DH'] = [','.join(x) if (x is not np.NaN) and (len(x)==2) else x for x in pivot['DH']]
+        piv['DH'] = [','.join(x) if (x is not np.NaN) and (len(x)==2) else x for x in piv['DH']]
-        pivot.DH.replace({'CDH':''},regex=True,inplace=True)
+        piv.DH.replace({'CDH':''},regex=True,inplace=True)
+    # piv.reset_index(-1, inplace=True)
 
-    pivot = tab_sp.merge(pivot,on=[cd_col],how='left')
+    # tabsp_nb = tab_sp.reset_index(drop=False)[[*same_col,'nombre_min','nombre_max']].groupby([*same_col]).sum()
+    tabsp_nb = (
+        tab_sp.reset_index(drop=False)[same_col]
+        .drop_duplicates()
+        .merge(
+            tab_sp.reset_index(drop=False)[[cd_col,'nombre_min','nombre_max','date_debut','date_fin']]
+            .groupby([cd_col])
+            .agg({
+                'nombre_min':'sum',
+                'nombre_max':'sum',
+                'date_debut':'min',
+                'date_fin':'max'
+            }),
+            on = cd_col
+        )
+    )
+    tabsp_nb.set_index(cd_col,inplace=True)
+    tabsp_nb.sort_index(inplace=True)
+    
+    # pivot = tab_sp.merge(piv,on=[cd_col],how='left')
+    pivot = tabsp_nb.merge(piv,on=[cd_col],how='left')
 
     pivlib = pd.pivot_table(
         df,
         values='label_statut',
         index=[
-            'cd_nom', 'cd_ref','lb_nom'#,'niveau_admin','lb_adm_tr'
+            'cd_nom', 'cd_ref','lb_nom','nom_vern'#,'niveau_admin','lb_adm_tr'
         ],
         columns=['cd_type_statut'],
         aggfunc=list,fill_value=None)
@@ -276,10 +382,18 @@ if __name__ == "__main__":
         pivlib['DH'] = [','.join(x) if (x is not np.NaN) and (len(x)==2) else x for x in pivlib['DH']]
         pivlib.DH.replace({'CDH':''},regex=True,inplace=True)
 
-    pivlib = tab_sp.merge(pivlib,on=[cd_col],how='left')
+    # pivlib = tab_sp.merge(pivlib,on=[cd_col],how='left')
+    pivotlib = tabsp_nb.merge(pivlib,on=[cd_col],how='left')
+
+    tx_nb_missing = df[~df.cd_nom.isin(tabsp_nb.index)].shape[0]
+    if tx_nb_missing > 0:
+        print('WARNING : %i taxon(s) is MISING !! \n'%tx_nb_missing)
 
     print('INIT writer')
-    NAME_OUT = os.path.join(PATH0,PATH,sheet+'_status.xlsx')
+    NAME_OUT = os.path.join('~/',sheet+'_status.xlsx')
+    if keep_geomtype is not None:
+        NAME_OUT = NAME_OUT[:-5]+' (%s only).xlsx'%keep_geomtype.lower()
+
     with pd.ExcelWriter(NAME_OUT) as writer:
         df.to_excel(
             writer,sheet_name='v_bdc_status',index=False
@@ -291,7 +405,7 @@ if __name__ == "__main__":
             )
         # writer.save()
         print('pivot_table OK !')
-        pivlib.to_excel(
+        pivotlib.to_excel(
             writer,sheet_name='pivot_libel'
             )
         # writer.save()
@@ -301,3 +415,5 @@ if __name__ == "__main__":
             )
         # writer.save()
         print('dic_type_statut OK !')
+    print('END writing %s'%NAME_OUT)
+

5_GEONATURE/tmp/clean_negria_partenaire.py

@@ -0,0 +1,98 @@
+import geopandas as gpd
+from pycen import con_gn
+
+uuid_gn38 = gpd.pd.read_sql('SELECT unique_id_sinp uuid_perm_sinp FROM gn_synthese.synthese',con_gn)
+lst_gn38 = uuid_gn38.uuid_perm_sinp.astype(str).tolist()
+
+PATH = '/media/cgeier/SRV/FICHIERS'
+biodiv = gpd.read_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/0.Données transmises/BiodivAura_Synthese_Export_2026-04-21.geojson')
+cenra_point = gpd.read_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/0.Données transmises/CENRA_20260421_gn_export_lones_cen38.gpkg',layer = 'points')
+cenra_poly = gpd.read_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/0.Données transmises/CENRA_20260421_gn_export_lones_cen38.gpkg',layer = 'polygones')
+# synt = gpd.pd.read_csv(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/0.Données transmises/synthese_observations_2025-04-24T12_11_45.488Z (completer).csv',sep=',')
+
+# synt['code_com'] = synt.communes.str.split('(',expand=True)[1].str[:5]
+# synt['code_dep'] = synt.communes.str.split('(',expand=True)[1].str[:2]
+# synt.nombre_min = synt.nombre_min.astype(str).str.strip('.0').replace('nan',None)
+# synt.nombre_max = synt.nombre_max.astype(str).str.strip('.0').replace('nan',None)
+# synt.precision_geographique = synt.precision_geographique.astype(str).str.strip('.0').replace('nan',None)
+# synt.alti_min = synt.alti_min.astype(str).str.strip('.0').replace('nan',None)
+
+for cenra in [cenra_point,cenra_poly]:
+    test_nb_cenra = cenra.nombre_min.astype('float') > cenra.nombre_max.astype('float')
+    if test_nb_cenra.any():
+        cenra.loc[test_nb_cenra,'nb_tmp'] = cenra[test_nb_cenra].nombre_min
+        cenra.loc[test_nb_cenra,'nombre_min'] = cenra[test_nb_cenra].nombre_max
+        cenra.loc[test_nb_cenra,'nombre_max'] = cenra[test_nb_cenra].nb_tmp
+        cenra.drop(columns='nb_tmp',inplace=True)
+
+    # cenra.nombre_min = cenra.nombre_min.astype(str).str.strip('.0').replace('nan',None)
+    # cenra.nombre_max = cenra.nombre_max.astype(str).str.strip('.0').replace('nan',None)
+    cenra.profondeur_min = cenra.profondeur_min.astype(str).str.strip('.0').replace('nan',None)
+    cenra.profondeur_max = cenra.profondeur_max.astype(str).str.strip('.0').replace('nan',None)
+    cenra.precision = cenra.precision.astype(str).str.strip('.0').replace('nan',None)
+    cenra.code_habitat = cenra.code_habitat.astype(str).str.strip('.0').replace('nan',None)
+    # cenra.altitude_min = cenra.altitude_min.astype(str).str.strip('.0').replace('nan',None)
+    # cenra.altitude_max = cenra.altitude_max.astype(str).str.strip('.0').replace('nan',None)
+    cenra.precision_diffusion.fillna('Non déterminé', inplace=True)
+
+
+# synt_l93 = synt[synt.x_centroid_4326>500000]
+# synt_l93_geom = gpd.GeoDataFrame(synt_l93,geometry=gpd.geoseries.points_from_xy(synt_l93.x_centroid_4326,synt_l93.y_centroid_4326),crs=2154)
+# synt_l93_geom.to_crs(4326,inplace=True)
+# synt_l93_geom.x_centroid_4326 = synt_l93_geom.geometry.x
+# synt_l93_geom.y_centroid_4326 = synt_l93_geom.geometry.y
+# synt_w84 = synt[synt.x_centroid_4326<500000]
+# synt = gpd.pd.concat([synt_l93_geom,synt_w84])
+
+
+
+biodiv.nombre_min = biodiv.nombre_min.astype(str).str.strip('.0').replace('nan',None).replace('',None)
+biodiv.nombre_max = biodiv.nombre_max.astype(str).str.strip('.0').replace('nan',None).replace('',None)
+biodiv.alti_min = biodiv.alti_min.astype(str).str.strip('.0').replace('nan',None)
+biodiv.precision_geographique = biodiv.precision_geographique.astype(str).str.strip('.0').replace('nan',None)
+biodiv.observateurs.fillna('Inconnu', inplace=True)
+biodiv.stade_vie.fillna('Inconnu', inplace=True)
+biodiv.loc[biodiv.cd_ref == 1078938, 'cd_ref'] = 5289     # Geheebia lurida (Hornsch. ex Spreng.) J.A.Jiménez & M.J.Cano, 2021
+biodiv.loc[biodiv.cd_ref == 1049429, 'cd_ref'] = 108898   # Muscari neglectum Guss. ex Ten. & Sangiov., 1841
+
+test_nb_biodiv = biodiv.nombre_min.astype('float') > biodiv.nombre_max.astype('float')
+if test_nb_biodiv.any():
+    biodiv.loc[test_nb_biodiv,'nb_tmp'] = biodiv[test_nb_biodiv].nombre_min
+    biodiv.loc[test_nb_biodiv,'nombre_min'] = biodiv[test_nb_biodiv].nombre_max
+    biodiv.loc[test_nb_biodiv,'nombre_max'] = biodiv[test_nb_biodiv].nb_tmp
+    biodiv.drop(columns='nb_tmp',inplace=True)
+
+lst_cenra = [
+    *cenra_point.id_perm_sinp.tolist(),
+    *cenra_poly.id_perm_sinp.tolist()
+]
+
+# synt = synt[~synt.uuid_perm_sinp.isin(lst_cenra)]
+biodiv = biodiv[~biodiv.uuid_perm_sinp.isin(lst_cenra)]
+biodiv = biodiv[~biodiv.uuid_perm_sinp.isin(lst_cenra)]
+
+# lst_synt  = synt.uuid_perm_sinp.tolist()
+lst_biodiv = biodiv.uuid_perm_sinp.tolist()
+
+# biodiv = biodiv[~biodiv.uuid_perm_sinp.isin(lst_synt)]
+biodiv = biodiv[~biodiv.uuid_perm_sinp.isin(lst_gn38)]
+# synt[~synt.uuid_perm_sinp.isin(lst_biodiv)]
+
+from shapely.geometry import shape
+import json
+biodiv['geom'] = biodiv.apply(lambda row: shape(json.loads(row['geojson_4326'])), axis=1).set_crs(4326)
+biodiv.set_geometry('geom',inplace=True)
+biodiv.drop(columns='geometry',inplace=True)
+biodiv['geom_4326'] = biodiv.geom.to_wkt()
+
+gpd.pd.concat([cenra_point,cenra_poly]).to_csv(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/CENRA_20260421_gn_export_lones_cen38.csv',index=False)
+# cenra.to_csv(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/CENRA_synthese_observations_geonature_2026-03-16T15_25_59.351Z.csv',index=False)
+# synt.to_csv(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/synthese_observations_2025-04-24T12_11_45.488Z.csv',index=False)
+biodiv.to_csv(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/BiodivAura_Synthese_Export_2026-04-21.csv',index=False)
+
+gpd.pd.concat([cenra_point,cenra_poly]).to_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/CENRA_20260421_gn_export_lones_cen38.geojson',index=False)
+# cenra.to_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/CENRA_synthese_observations_geonature_2026-03-16T15_25_59.351Z.geojson',index=False)
+# synt.to_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/synthese_observations_2025-04-24T12_11_45.488Z.geojson',index=False)
+# synt_geom = gpd.GeoDataFrame(synt,geometry=gpd.geoseries.points_from_xy(synt.x_centroid_4326,synt.y_centroid_4326),crs=4326)
+# synt_geom.to_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/synthese_observations_2025-04-24T12_11_45.488Z.geojson',index=False)
+biodiv.to_file(PATH+'/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Données partenaires/1.Données nettoyées/BiodivAura_Synthese_Export_2026-04-21.geojson',index=False)

6_SQL/create_view_c_acquisition_flore.sql

@@ -0,0 +1,64 @@
+CREATE OR REPLACE VIEW gn_exports.v_synthese_sinp_acquisition_flore_with_metadata_for_gn2pg AS 
+WITH af_actors AS
+	(SELECT cafa.*
+		FROM gn_meta.cor_acquisition_framework_actor cafa
+		LEFT JOIN utilisateurs.bib_organismes borg ON cafa.id_organism = borg.id_organisme
+		WHERE borg.uuid_organisme = '5a433bd0-2070-25d9-e053-2614a8c026f8'::uuid 
+		--OR borg.uuid_organisme = 'a3c44538-f22a-4134-9e21-28646a73986b'::uuid 
+	), act AS
+		(SELECT *
+			FROM crosstab( 'SELECT id_acquisition_framework, id_nomenclature_actor_role, id_organism
+		FROM gn_meta.cor_acquisition_framework_actor
+		WHERE id_acquisition_framework = 1 OR id_acquisition_framework = 6
+		ORDER  BY 1,2' , $$VALUES ('359'::text), ('361')$$ ) AS ct ("id_acquisition_framework" int, "Contact principal" int, "Producteur du jeu de données" int))
+SELECT taf.id_acquisition_framework AS id_ca,
+	taf.unique_acquisition_framework_id AS id_sinp_ca,
+	taf.acquisition_framework_name AS libelle,
+	NULL::text AS cadre_acquisition_parent,
+	taf.acquisition_framework_desc AS description,
+	taf.keywords AS mots_cles,
+	ref1.label_default as niveau_territorial,
+	taf.territory_desc AS description_territoire,
+	ref2.label_default AS objectifs_cadre_acquisition,
+	ref4.label_default AS volet_SINP,
+	ref3.label_default AS type_financement,
+	taf.target_description AS description_cible,
+	to_char(taf.meta_create_date, 'DD-MM-YYYY')::date AS date_debut,
+	NULL::date AS date_fin,
+	ref5.nom_organisme AS acteur1_organisme,
+	NULL::text AS acteur1_personne_groupe,
+	'Contact principal' AS acteur1_type_role,
+	ref6.nom_organisme AS acteur2_organisme,
+	NULL::text AS acteur2_personne_groupe,
+	CASE
+		WHEN ref6.nom_organisme IS NOT NULL THEN 'Producteur du jeu de données'
+		ELSE NULL
+	END AS acteur2_type_role,
+	NULL::text AS acteur3_organisme,
+	NULL::text AS acteur3_personne_groupe,
+	NULL::text AS acteur3_type_role
+FROM gn_meta.t_acquisition_frameworks taf
+JOIN af_actors ON af_actors.id_acquisition_framework = taf.id_acquisition_framework
+JOIN gn_meta.cor_acquisition_framework_objectif obj ON obj.id_acquisition_framework = taf.id_acquisition_framework
+JOIN ref_nomenclatures.t_nomenclatures ref1 ON ref1.id_nomenclature = taf.id_nomenclature_territorial_level
+JOIN ref_nomenclatures.t_nomenclatures ref2 ON ref2.id_nomenclature = obj.id_nomenclature_objectif
+JOIN ref_nomenclatures.t_nomenclatures ref3 ON ref3.id_nomenclature = taf.id_nomenclature_financing_type
+JOIN gn_meta.cor_acquisition_framework_voletsinp sinp ON sinp.id_acquisition_framework = taf.id_acquisition_framework
+JOIN ref_nomenclatures.t_nomenclatures ref4 ON ref4.id_nomenclature = sinp.id_nomenclature_voletsinp
+JOIN act ON act.id_acquisition_framework = taf.id_acquisition_framework
+LEFT JOIN utilisateurs.bib_organismes ref5 ON ref5.id_organisme = act."Contact principal"
+LEFT JOIN utilisateurs.bib_organismes ref6 ON ref6.id_organisme = act."Producteur du jeu de données"
+GROUP BY taf.id_acquisition_framework,
+	taf.unique_acquisition_framework_id,
+	taf.acquisition_framework_name,
+	taf.acquisition_framework_desc,
+	taf.keywords,
+	ref1.label_default,
+	taf.territory_desc,
+	ref2.label_default,
+	ref4.label_default,
+	ref3.label_default,
+	taf.target_description,
+	to_char(taf.meta_create_date, 'DD-MM-YYYY')::date,
+	ref5.nom_organisme,
+	ref6.nom_organisme

6_SQL/create_view_jeu_donnees_flore.sql

@@ -0,0 +1,77 @@
+CREATE OR REPLACE VIEW gn_exports.v_synthese_sinp_jeu_donnees_flore_with_metadata_for_gn2pg AS 
+WITH ds_actors AS (
+	SELECT cda.id_dataset,
+		borg.nom_organisme,
+		cda.id_role,
+		borg.uuid_organisme,
+		tro.nom_role,
+		tro.prenom_role,
+		borg.email_organisme,
+		tro.email
+	FROM gn_meta.cor_dataset_actor cda
+		LEFT JOIN utilisateurs.bib_organismes borg ON cda.id_organism = borg.id_organisme
+		LEFT JOIN utilisateurs.t_roles tro ON cda.id_role = tro.id_role
+		JOIN ref_nomenclatures.t_nomenclatures tn ON cda.id_nomenclature_actor_role = tn.id_nomenclature
+	-- WHERE borg.uuid_organisme = '5a433bd0-2070-25d9-e053-2614a8c026f8'::uuid 
+	-- 	OR borg.uuid_organisme = 'a3c44538-f22a-4134-9e21-28646a73986b'::uuid
+	),
+	actor AS (
+		SELECT *
+		FROM crosstab( 'SELECT id_dataset, id_nomenclature_actor_role, id_organism
+		  FROM gn_meta.cor_dataset_actor
+		  ORDER  BY 1,2' ,
+		  $$VALUES ('359'), ('364'), ('365'), ('395')$$ ) AS ct ("id_dataset" int, "Contact principal" int, "Producteur du jeu de données" int, "Point de contact base de données de production" int, "Point de contact pour les métadonnées" int)
+	),
+	ds AS (
+	 SELECT tds.id_dataset AS id_jdd,
+		tds.unique_dataset_id AS id_sinp_jdd,
+	    acq.acquisition_framework_name AS cadre_acquisiton,
+		tds.dataset_name AS nom_jeu_donnees,
+		tds.dataset_shortname AS nom_court,
+		tds.dataset_desc AS description,
+		ndso.label_default AS objectif,
+		ncm.label_default AS methode_collecte,
+		ndo.label_default AS origine_donnees,
+		nss.label_default AS statut_source,
+		tds.keywords AS mots_cles,
+		act1.nom_organisme AS acteur1_organisme,
+		NULL::text AS acteur1_personne_groupe,
+		'Contact principal' AS acteur1_type_role,
+		act2.nom_organisme AS acteur2_organisme,
+		NULL::text AS acteur2_personne_groupe,
+		CASE
+			WHEN act2.nom_organisme IS NOT NULL THEN 'Producteur du jeu de données'
+			ELSE NULL
+		END AS acteur2_type_role,
+		act3.nom_organisme AS acteur3_organisme,
+		NULL::text AS acteur3_personne_groupe,
+		CASE
+			WHEN act3.nom_organisme IS NOT NULL THEN 'Point de contact base de données de production'
+			ELSE NULL
+		END AS acteur3_type_role,
+		act4.nom_organisme AS acteur4_organisme,
+		NULL::text AS acteur4_personne_groupe,
+		CASE
+			WHEN act4.nom_organisme IS NOT NULL THEN 'Point de contact pour les métadonnées'
+			ELSE NULL
+		END AS acteur4_type_role
+	    FROM gn_meta.t_datasets tds
+			JOIN ds_actors ON ds_actors.id_dataset = tds.id_dataset
+			JOIN actor ON actor.id_dataset = tds.id_dataset
+			JOIN gn_meta.t_acquisition_frameworks acq ON tds.id_acquisition_framework = acq.id_acquisition_framework
+			LEFT JOIN gn_meta.cor_dataset_territory cdt ON cdt.id_dataset = tds.id_dataset
+			LEFT JOIN ref_nomenclatures.t_nomenclatures ndt ON tds.id_nomenclature_data_type = ndt.id_nomenclature
+			LEFT JOIN ref_nomenclatures.t_nomenclatures ncm ON tds.id_nomenclature_collecting_method = ncm.id_nomenclature
+			LEFT JOIN ref_nomenclatures.t_nomenclatures ndo ON tds.id_nomenclature_data_origin = ndo.id_nomenclature
+			LEFT JOIN ref_nomenclatures.t_nomenclatures ndso ON tds.id_nomenclature_dataset_objectif = ndso.id_nomenclature
+			LEFT JOIN ref_nomenclatures.t_nomenclatures nrt ON tds.id_nomenclature_resource_type = nrt.id_nomenclature
+			LEFT JOIN ref_nomenclatures.t_nomenclatures nss ON tds.id_nomenclature_source_status = nss.id_nomenclature
+			LEFT JOIN utilisateurs.bib_organismes act1 ON actor."Contact principal" = act1.id_organisme
+			LEFT JOIN utilisateurs.bib_organismes act2 ON actor."Producteur du jeu de données" = act2.id_organisme
+			LEFT JOIN utilisateurs.bib_organismes act3 ON actor."Point de contact base de données de production" = act3.id_organisme
+			LEFT JOIN utilisateurs.bib_organismes act4 ON actor."Point de contact pour les métadonnées" = act4.id_organisme
+	  GROUP BY tds.id_dataset, acq.acquisition_framework_name, tds.unique_dataset_id, tds.dataset_name, tds.dataset_shortname, tds.dataset_desc, ndso.label_default, ncm.label_default, ndo.label_default, nss.label_default, ds_actors.nom_organisme, act1.nom_organisme, act2.nom_organisme, act3.nom_organisme, act4.nom_organisme
+	)
+SELECT * FROM ds
+--WHERE acteur1_organisme IN ('Conservatoire d’espaces naturels Isère')
+;

9_PGSZH

@@ -0,0 +1 @@
+Subproject commit 250831edc17987766cb4ca2fb4370adca02a4a74

analyse_rps.py

@@ -136,7 +136,7 @@ def make_plot(data):
 if __name__ == "__main__":
 
 
-    PATH = '/media/colas/SRV/FICHIERS/Délégués Personnel_CSE/6_Enquêtes/Enquêtes RPS'
+    PATH = '/media/cgeier/SRV/FICHIERS/Délégués Personnel_CSE/6_Enquêtes/Enquêtes RPS'
     file = 'Enquete_Nat_RPS_2023_CEN_ISERE.xlsx'
 
     df = pd.read_excel(os.path.join(PATH,file))

createMNT_intersects.py

@@ -22,7 +22,7 @@ out   = path_tmp + 'etandLong_MNT1m.tif'
 read = 'file'       # 'file' or 'bdd'
 # if read == 'file'
 # file_geom = 'cheminversmageometrie/monfichier'
-file_geom = "/media/colas/SRV/FICHIERS/TRANSFERTS-EQUIPE/CG/Etang des longs - zone d'étude/Zone d'étude.shp"
+file_geom = "/media/cgeier/SRV/FICHIERS/TRANSFERTS-EQUIPE/CG/Etang des longs - zone d'étude/Zone d'étude.shp"
 # if read == 'bdd'
 sql_select = "SELECT code_site, geom FROM sites.c_sites_zonages WHERE code_site='GOUR' AND type_zonage = 'ZO'"
 user   = 'cen_admin'

create_mbtile.py

@@ -46,7 +46,7 @@ def gen_mbtiles2(xml,output,format='mbtiles'):
 
 def gdalwarp(vrt,mbtiles):
     # exemple : Découpage d'un raster à partir d'un polygon
-    # gdalwarp -overwrite -s_srs "EPSG:2154" -t_srs "EPSG:2154" -of GTiff -cutline "PG:dbname='azalee' host=91.134.194.221 port=5432 sslmode=disable user='cgeier' password='adm1n*bdCen'" -cl "ref_territoire.isere_platiere" -crop_to_cutline -multi -co "COMPRESS=JPEG" -co "JPEG_QUALITY=75" "/media/colas/Disk2/5_BDD/ZH_prob/ZH_probS_Auvergne-Rhone-Alpes/ZH_probS_region84.tif" "/media/colas/Disk2/5_BDD/ZH_prob/ZH_probS_Auvergne-Rhone-Alpes/ZH_probS_dept38.tif"
+    # gdalwarp -overwrite -s_srs "EPSG:2154" -t_srs "EPSG:2154" -of GTiff -cutline "PG:dbname='azalee' host=91.134.194.221 port=5432 sslmode=disable user='cgeier' password='adm1n*bdCen'" -cl "ref_territoire.isere_platiere" -crop_to_cutline -multi -co "COMPRESS=JPEG" -co "JPEG_QUALITY=75" "/media/cgeier/Disk2/5_BDD/ZH_prob/ZH_probS_Auvergne-Rhone-Alpes/ZH_probS_region84.tif" "/media/cgeier/Disk2/5_BDD/ZH_prob/ZH_probS_Auvergne-Rhone-Alpes/ZH_probS_dept38.tif"
     cmd = '''
     gdalwarp -of MBTiles -s_srs epsg:2154 -t_srs epsg:3857 %s %s
     ''' % (vrt,mbtiles)
@@ -71,7 +71,7 @@ if __name__ == "__main__":
     flux = 'http://tiles.craig.fr/ortho/service?VERSION=1.3.0&crs=EPSG:2154&featureCount=10&format=image/jpeg&layers=ortho_2021&maxHeight=256&maxWidth=256'
 
     # flux = 'http://mt.google.com/vt/lyrs=s&x=${x}&y=${y}&z=${z}'
-    PATH = '/media/colas/Disk2'
+    PATH = '/media/cgeier/Disk2'
     xml = 'googlemaps.xml'
     xml = 'craig.xml'
     xml = 'output.xml'
@@ -94,4 +94,4 @@ if __name__ == "__main__":
 
     # ds = gdal.Open(os.path.join(PATH,vrt))
 
-# gdal_translate -outsize 50% 50% -projwin 631397 5672590 639669 5659275 -of MBTILES -co "COMPRESS=YES" -co "TILE_FORMAT=JPEG" -co "QUALITY=80" "/media/colas/Disk2/output.xml" "/media/colas/Disk2/ign_ortho2024_38.mbtiles"
+# gdal_translate -outsize 50% 50% -projwin 631397 5672590 639669 5659275 -of MBTILES -co "COMPRESS=YES" -co "TILE_FORMAT=JPEG" -co "QUALITY=80" "/media/cgeier/Disk2/output.xml" "/media/cgeier/Disk2/ign_ortho2024_38.mbtiles"

hydro_analyse.py

@@ -108,7 +108,7 @@ if __name__ == "__main__":
     # if nr_worker_threads != 7:
     #     set_nr_worker_threads(7)
 
-    PATH = "/media/colas/SRV/FICHIERS/SITES/DISTRICTS NATURELS/BASSE VALLEE DE L'ISERE/SONE/TUFI_Sone-à-Soi/SIG/"
+    PATH = "/media/cgeier/SRV/FICHIERS/SITES/DISTRICTS NATURELS/BASSE VALLEE DE L'ISERE/SONE/TUFI_Sone-à-Soi/SIG/"
     raster = PATH+"MNT_1m.tif"
     dst_filename = '/home/colas/Documents/tmp/OUTPT.map'
     xyz_filename = '/home/colas/Documents/tmp/OUTPT.xyz'
@@ -188,4 +188,4 @@ if __name__ == "__main__":
 
     # # processing.run("pcraster:comparisonoperators", {'INPUT':'%3Fcrs%3DEPSG%3A2154%26extent%3D868999.5%2C6447000.5%2C885999.5%2C6467000.5%26width%3D17000%26height%3D20000%26formula%3D%2522strahler%401%2522%2520%253E%253D5%2520%26strahler%3Auri%3D%2Fhome%2Fcolas%2FDocuments%2Ftmp%2Fstrahler.map%26strahler%3Aprovider%3Dgdal','INPUT1':0,'INPUT2':'%3Fcrs%3DEPSG%3A2154%26extent%3D868999.5%2C6447000.5%2C885999.5%2C6467000.5%26width%3D17000%26height%3D20000%26formula%3D%2522strahler%401%2522%2520%253E%253D5%2520%26strahler%3Auri%3D%2Fhome%2Fcolas%2FDocuments%2Ftmp%2Fstrahler.map%26strahler%3Aprovider%3Dgdal','OUTPUT':'TEMPORARY_OUTPUT'})
 
-    # # processing.run("pcraster:col2map", {'INPUT':'/home/colas/Documents/tmp/bilan_sites.csv','INPUT1':"/media/colas/SRV/FICHIERS/SITES/DISTRICTS NATURELS/BASSE VALLEE DE L'ISERE/SONE/TUFI_Sone-à-Soi/SIG/MNT_1m.tif",'INPUT2':0,'OUTPUT':'TEMPORARY_OUTPUT'})
+    # # processing.run("pcraster:col2map", {'INPUT':'/home/colas/Documents/tmp/bilan_sites.csv','INPUT1':"/media/cgeier/SRV/FICHIERS/SITES/DISTRICTS NATURELS/BASSE VALLEE DE L'ISERE/SONE/TUFI_Sone-à-Soi/SIG/MNT_1m.tif",'INPUT2':0,'OUTPUT':'TEMPORARY_OUTPUT'})

maj_site.py

@@ -2,7 +2,7 @@ from pycen import con_bdcen,con_fon,update_to_sql
 from pycen.tools import Polygons_to_MultiPolygon
 import geopandas as gpd
 
-PATHIN = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/LEZE/PG2022/'
+PATHIN = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/LEZE/PG2022/'
 FILEIN = 'LEZE_zonage_2022.shp'
 SITE_NAME = 'LEZE'
 SITE_TYPE = ''

manage_bdd.py

@@ -55,7 +55,7 @@ def copy_2another_server(host_in,base_in,user_in,host_out,base_out,user_out,pass
 if __name__ == "__main__":
 
     dump_bdd(
-        file_out='/media/colas/SRV/FICHIERS/OUTILS/BASES DE DONNEES/FONCIER/CEN73/V4/_backup_V3/bd_cen_V3.sql',
+        file_out='/media/cgeier/SRV/FICHIERS/OUTILS/BASES DE DONNEES/FONCIER/CEN73/V4/_backup_V3/bd_cen_V3.sql',
         host='91.134.194.221',
         base='bd_cen',
         user='cgeier',

order_polygon.py

@@ -0,0 +1,14 @@
+import geopandas as gpd
+from os import path
+
+PATH = '/media/cgeier/SRV/FICHIERS'
+file = 'OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/SITES/Lone de la Negria/Faune-Flore/Synthèse/synthese_observations_2026-04-23.geojson'
+
+df = gpd.read_file(path.join(PATH,file), use_arrow=True)
+
+df['ha'] = df.to_crs(2154).area
+df.sort_values('ha', ascending=False, inplace=True)
+df.reset_index(inplace=True)
+df.drop(columns='ha', inplace=True)
+
+df.to_file(path.join(PATH,file))

tmp/AVALON - aggreg data.py

@@ -21,7 +21,7 @@ dict_gn = {
     'group2_inp':'group2_inpn'
 }
 
-PATH = "/media/colas/SRV/FICHIERS/SITES/SITES GERES/AVAL_AVALON/Scientifique et technique/Données historiques/Marais d'Avalon"
+PATH = "/media/cgeier/SRV/FICHIERS/SITES/SITES GERES/AVAL_AVALON/Scientifique et technique/Données historiques/Marais d'Avalon"
 
 biodiv1 = gpd.read_file(path.join(PATH,'Biodi_Aura_Mare Ratier','POINT_2023_12_13_08h35m51.shp'))
 # biodiv2 = gpd.read_file(path.join(PATH,'Biodiv_Aura_Marais','POINT_2023_12_13_08h41m24.shp'))

tmp/CVB_bourbre_MC_penible.py

@@ -7,8 +7,8 @@ import pandas as pd
 import geopandas as gpd
 
 
-path_fichsite = '/media/colas/SRV/FICHIERS/OPERATIONS (EX ETUDES)/ETUDES EN COURS/CORRIDORS/CVB_Bourbre/#42_Mesures compensatoires/BM/Evaluation des sites'
+path_fichsite = '/media/cgeier/SRV/FICHIERS/OPERATIONS (EX ETUDES)/ETUDES EN COURS/CORRIDORS/CVB_Bourbre/#42_Mesures compensatoires/BM/Evaluation des sites'
-path_geom = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/CVB_Bourbre/Cartographie CVB Bourbre 2021/Mesures compensatoires/0_FINAL'
+path_geom = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/CVB_Bourbre/Cartographie CVB Bourbre 2021/Mesures compensatoires/0_FINAL'
 c_geom = 'mc_isere_CVBbourbre.gpkg'
 c_geom_det = 'mc_isere_CVBbourbre_détaillé.gpkg'
 pathout = '/home/colas/Documents/tmp/CVB_Bourbre'
@@ -192,7 +192,7 @@ gdf.to_file(pathout+'/'+ c_geom, driver='GPKG')
 #####################
 #####################
 #####################
-path = '/media/colas/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/CVB_Bourbre/Cartographie CVB Bourbre 2021/Mesures compensatoires'
+path = '/media/cgeier/SRV/FICHIERS/OUTILS/CARTOGRAPHIE/ESPACE DE TRAVAIL/ETUDES/CVB_Bourbre/Cartographie CVB Bourbre 2021/Mesures compensatoires'
 l1 = 'LOT 1.shp'
 l2 = 'LOT 2.shp'
 l3 = 'LOT 3.shp'