861 lines
39 KiB
Python
861 lines
39 KiB
Python
#!/usr/bin/env python3
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# -*- coding: UTF-8 -*-
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from pycen import update_to_sql, con_sicen,con_gn
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import geopandas as gpd
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import os
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DIR = '/home/colas/Documents/9_PROJETS/4_SICEN/GN_MIGRATION'
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def form_cdnom(data):
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dict_cdnom = {
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116744:521658, # Flore : Quercus petraea
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}
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data.replace({'cd_nom':dict_cdnom},inplace=True)
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data.replace({'cd_ref':dict_cdnom},inplace=True)
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def form_complx_grp(data):
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dict_cdnom = {
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9999005:105817, # Leucanthemum vulgare (#groupe)
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9999014:119097, # Rubus fruticosus (#groupe)
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9999017:717630, # Taraxacum officinale (#groupe)
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9999019:126573, # Thymus serpyllum (#groupe)
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9999020:129298, # Vicia sativa (#groupe)
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9999023:188772, # Acrocephalus palustris / scirpaceus (#complexe)
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9999031:441709, # Cairina moschata f. domestica (#forme)
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9999033:190350, # Carduelis flammea flammea / cabaret / Carduelis hornemanni (#complexe)
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9999037:191029, # Colias alfacariensis / hyale (#complexe)
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9999041:4503, # Corvus corone corone / cornix (#complexe)
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9999042:186239, # Eptesicus / Nyctalus sp. (#complexe)
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9999046:192539, # Felis silvestris / catus (#complexe)
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9999050:193993, # Leptidea sinapis / reali (#complexe)
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9999054:194481, # Martes martes / foina (#complexe)
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9999057:195005, # Myotis myotis / blythii (#complexe)
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9999063:444436, # Pelophylax kl. esculentus / lessonae (#complexe)
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9999064:4280, # Phylloscopus collybita tristis / "abietinus" (#complexe)
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9999066:196980, # Pyrgus malvae / malvoides (#complexe)
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9999074:197040, # Rana dalmatina / temporaria (#complexe)
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9999075:196296, # Pipistrellus nathusii / kuhlii (#complexe)
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9999080:194357, # Lysandra coridon / hispana (#complexe)
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9999082:195005, # Myotis daubentonii / Myotis mystacinus (#complexe)
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9999083:699094, # Pipistrellus / Miniopterus (#complexe)
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}
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lst_cdnom_old = [*dict_cdnom.keys()]
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if data.cd_nom.isin(lst_cdnom_old).any():
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data.loc[data.cd_nom.isin(lst_cdnom_old),'complexe_groupe'] = data[data.cd_nom.isin(lst_cdnom_old)].nom_complet
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data.replace({'cd_nom':dict_cdnom},inplace=True)
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sql = 'SELECT cd_nom, nom_complet nom_new FROM taxonomie.taxref where cd_nom in {}'.format(tuple(dict_cdnom.values()))
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tax = gpd.pd.read_sql(sql, con_gn)
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data = data.merge(tax,how='left',on='cd_nom')
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data.loc[data.nom_new.notna(),'nom_latin'] = data[data.nom_new.notna()].nom_new
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data.drop(columns='nom_new',inplace=True)
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def correct_taxonomie(data):
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dict_taxo = {
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444440:'Pelophylax kl\. esculentus \(Linnaeus, 1758\)'
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}
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for k,v in dict_taxo.items():
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is_saisie = data.rmq_observation.str.contains(v,na=False)
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if not is_saisie.any():
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continue
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data.loc[is_saisie,'nom_latin'] = v
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data.loc[is_saisie,'cd_nom'] = k
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data.loc[is_saisie,'cd_ref'] = k
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def form_precision(data):
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dict_pre = {
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'GPS':0,
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'0 à 10m':10,
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'10 à 100m':100,
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'100 à 500m':500,
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'lieu-dit':750,
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'commune':None,
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}
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is_com = data.precision =='commune'
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rmq_null = data.rmq_localisation.isnull()
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data.loc[is_com&rmq_null,'rmq_localisation'] = 'Localisation : commune'
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data.loc[is_com&~rmq_null,'rmq_localisation'] = 'Localisation : commune ;'+data[is_com].rmq_localisation
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data.replace({'precision':dict_pre},inplace=True)
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data.precision = data.precision.astype('Int64')
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def form_effectif(data):
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eff_notna = data.effectif.notna()
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efmin_notna = data.effectif_min.notna()
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efmax_isna = data.effectif_max.isna()
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data.loc[(~eff_notna)&efmin_notna,'effectif'] = data[(~eff_notna)&efmin_notna].effectif_min
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data.loc[(~eff_notna)&(~efmax_isna),'effectif'] = data[(~eff_notna)&(~efmax_isna)].effectif_max
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# data.loc[efmax_isna,'effectif_max'] = data[efmax_isna].effectif
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data.effectif = data.effectif.astype('Int64')
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data.effectif_max = data.effectif_max.astype('Int64')
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t1 = data.effectif_min < data.effectif
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data.loc[t1,'effectif_max'] = data[t1].effectif
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data.loc[t1,'effectif'] = data[t1].effectif_min
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# Est trop incertain... Les effectifs récupérés sont < aux effectifs déclarés
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# ==> Incompréhansion, Ne sera pas utilisé
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def recup_faune_eff(data):
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# Récupération des effectifs standardisés du champ rmq_observation
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ismixte = data.rmq_observation.str.contains('\\d.00 Mâle\(s\) et \\d.00 Femelle\(s\)',na=False)
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ismal = data.rmq_observation.str.contains('\\d.00 Mâle\(s\) et \? Femelle\(s\)',na=False)
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isfem = data.rmq_observation.str.contains('\? Mâle\(s\) et \\d.00 Femelle\(s\)',na=False)
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data.loc[ismixte,'extract_eff'] = (data[ismixte].rmq_observation
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.str.extract('(\d+).00 Mâle\(s\) et (\d+).00 Femelle\(s\)').astype(int).sum(axis=1))
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data.loc[ismal,'extract_eff'] = (data[ismal].rmq_observation
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.str.extract('(\d+).00 Mâle\(s\) et \? Femelle\(s\)').astype(int).sum(axis=1))
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data.loc[isfem,'extract_eff'] = (data[isfem].rmq_observation
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.str.extract('\? Mâle\(s\) et (\d+).00 Femelle\(s\)').astype(int).sum(axis=1))
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extr = data.extract_eff.notna()
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extr_sup = data.effectif < data.extract_eff
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data[extr_sup]
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age_na = data.age_faune.isna()
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no_0 = data.extract_eff > 0
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if any(age_na&no_0&ismixte):
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data.loc[age_na&no_0&ismixte,'age_faune'] = 'mixte'
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if any(age_na&no_0&ismal):
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data.loc[age_na&no_0&ismal,'age_faune'] = 'male'
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if any(age_na&no_0&isfem):
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data.loc[age_na&no_0&isfem,'age_faune'] = 'femelle'
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def form_date(data):
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cols = data.columns[data.columns.str.contains('date')]
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for col in cols:
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data.loc[data[col].isna(),col] = None
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def recup_stadevie(data):
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dict_repro = {
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'ODO_Exuvie/émergence':'Exuvie/émergence',
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'ODO_Immature':'Immature',
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**dict.fromkeys(['ODO_Mâles+Femelles','ODO_Tandem','ODO_Territorial','ODO_Ponte'],'Adulte'),
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**dict.fromkeys(['CHIR_Indéterminé','ODO_Indéterminé'],'Indéterminé')
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}
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if 'age_faune' in data.columns:
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age_isna = data.age_faune.isna()
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age_inrmq = data.rmq_observation.str.contains('Stade de vie')
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data.loc[age_isna&age_inrmq,'age_faune'] = (data[age_isna&age_inrmq].rmq_observation
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.str.split('Stade de vie :')
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.str[1]
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.str.split('|')
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.str[0]
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.str.strip())
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double_info = data.age_faune.isin(['Imago, adulte', 'Nymphe, immature'])
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d2 = data[double_info].copy()
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data.age_faune = data.age_faune.str.replace('Imago, adulte','Imago')
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data.age_faune = data.age_faune.str.replace('Nymphe, immature','Nymphe')
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d2.age_faune = d2.age_faune.str.replace('Imago, adulte','Adulte')
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d2.age_faune = d2.age_faune.str.replace('Nymphe, immature','Immature')
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d2.id_obs = ('9999'+d2.id_obs.astype(str)).astype(int)
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data = gpd.pd.concat([data,d2])
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lst_age = [*dict_repro.keys()]
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age_isna = data.age_faune.isna()
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t1 = age_isna&(data.reprostatut_faune.isin(lst_age))
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data.loc[t1,'age_faune'] = data[t1].reprostatut_faune.replace(dict_repro)
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t2 = data.age_faune=='Exuvie/émergence'
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is_exuvie = (data.rmq_observation
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.replace(['é','E','É'],'e',regex=True)
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.str.contains('exuvie',na=False))
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is_emerge = (data.rmq_observation
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.replace(['é','E','É'],'e',regex=True)
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.str.contains('emerge',na=False))
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rmq_isna = data.rmq_observation.isna()
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data.loc[is_exuvie&~is_emerge&t2,'age_faune'] = 'Exuvie'
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data.loc[~is_exuvie&is_emerge&t2,'age_faune'] = 'Emergent'
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data.loc[is_exuvie&is_emerge&t2,'age_faune'] = 'Exuvie'
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data.loc[is_exuvie&is_emerge&rmq_isna&t2,'age_faune'] = 'Emergent'
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t3 = data.age_faune=='Oeuf/ponte/larve/nymphe/chenille...'
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# Odonate
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is_odo = data.ordre == 'Odonata'
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data.loc[t3&is_odo,'age_faune'] = 'Exuvie'
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# Amphibiens
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is_amphi = data.group2_inpn == 'Amphibiens'
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is_larve = data.rmq_observation.str.contains('larve',na=False)
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is_ponte = data.rmq_observation.str.contains('ponte|Ponte',na=False)
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is_tetar = data.rmq_observation.replace(['ê','é'],'e',regex=True).str.contains('tetard',na=False)
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is_urode = data.ordre=='Urodela'
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is_anure = data.ordre=='Anura'
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rmq_na = data.rmq_observation.isna()
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data.loc[t3&is_amphi&is_larve,'age_faune'] = 'Larve'
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data.loc[t3&is_amphi&is_ponte,'age_faune'] = 'Oeufs'
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data.loc[t3&is_amphi&is_tetar,'age_faune'] = 'Têtard'
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data.loc[t3&is_amphi&is_urode&rmq_na,'age_faune'] = 'Larve'
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data.loc[t3&is_amphi&is_anure&rmq_na,'age_faune'] = 'Ponte'
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# Corresction de 3 données de Nicolas Biron
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t3 = data.age_faune=='Oeuf/ponte/larve/nymphe/chenille...'
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data.loc[t3&(is_urode|is_anure),'age_faune'] = 'Ponte'
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# Correction de la dernière donnée restante (Marjorie Simean)
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t3 = data.age_faune=='Oeuf/ponte/larve/nymphe/chenille...'
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data.loc[t3,'age_faune'] = 'Oeufs'
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# Définition de l'objet du dénombrement si ponte
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if 'obj_denombre' not in data.columns:
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data['obj_denombre'] = None
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isnot_ponte = ~data.rmq_observation.str.contains('nbre pontes:0',na=False)
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data.loc[is_amphi&is_ponte&isnot_ponte,'obj_denombre'] = 'Ponte'
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# Récup stade_vie derrirère le terme @NSA
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age_isna = data.age_faune.isna()
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as_nsa = data.rmq_observation.str.contains('@NSA',na=False)
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# data.loc[age_isna&as_nsa,'test'] = (data[age_isna&as_nsa].rmq_observation
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# .str.split('@NSA :|@NSA:')
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# .str[1]
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# .str.split('/|;')
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# .str[0]
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# .str.strip())
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data.loc[age_isna&as_nsa,'age_faune'] = (data[age_isna&as_nsa].rmq_observation
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.str.split('@NSA :|@NSA:')
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.str[1]
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.str.split('/|;')
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.str[0]
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.str.strip()
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.str.lower()
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.str.extract('(imm|juv|ad|jeune|larve)')[0])
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# Récup stade_vie derrirère le terme Commentaires
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age_isna = data.age_faune.isna()
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as_com = data.rmq_observation.str.contains('Commentaires : \\d',na=False)
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data.loc[age_isna&as_com,'age_faune'] = (data[age_isna&as_com].rmq_observation
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.str.split('[,(.]|Code Atlas')
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.str[0].str.strip()
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.str.lower()
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.replace(
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{'femelles':'femelle','mâles':'mâle','adultes':'adulte',
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'jeunes':'jeune','juvéniles':'juvénile','larves':'larve',
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'couples':'couple','x':''},regex=True)
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.str.extract('[Commentaires : \d+ ](jeune volant|juvénile|juv|couple|adulte|ad|crapelet|femelle adulte|larve|mâle juvénile|mâle adulte|subadulte|larve \/ têtard)$')[0])
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age_isna = data.age_faune.isna()
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data.loc[age_isna&as_com,'age_faune'] = (data[age_isna&as_com].rmq_observation
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.str.split('[,(.]|Code Atlas')
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.str[0].str.strip()
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.str.lower()
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.replace(
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{'femelles':'femelle','mâles':'mâle','adultes':'adulte',
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'jeunes':'jeune','juvéniles':'juvénile','larves':'larve',
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'têtards':'têtard','couples':'couple','x':''},regex=True)
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.str.extract('[Commentaires : \d+](jeune volant|juvénile|juv|couple|adulte|ad|crapelet|femelle adulte|larve|mâle adulte|subadulte|larve / têtard)$')[0])
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# Précision de larve / têtard
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is_impreci = data.age_faune=='larve / têtard'
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data.loc[is_impreci&is_anure,'age_faune'] = 'têtard'
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data.loc[is_impreci&is_urode,'age_faune'] = 'larve'
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data.age_faune = (data.age_faune
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.replace({
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'ad':'Adulte',
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'Oeufs':'Oeuf',
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'imm':'Immature',
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'jeune':'Jeune',
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'juv':'Juvénile',
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'subadulte':'Sub-adulte',
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'Subadulte':'Sub-adulte',})
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.str.capitalize()
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)
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sex_isna = data.sexe_faune.isna()
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is_male = data.age_faune.str.contains('Mâle',na=False)
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is_feml = data.age_faune.str.contains('Femelle',na=False)
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is_copl = data.age_faune.str.contains('Couple',na=False)
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is_immat = data.age_faune.str.contains('Immature',na=False)
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data.loc[sex_isna&is_male,'sexe_faune'] = 'Mâle'
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data.loc[sex_isna&is_feml,'sexe_faune'] = 'Femelle'
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data.loc[sex_isna&is_copl,'sexe_faune'] = 'Couple'
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data.loc[sex_isna&is_immat,'sexe_faune'] = 'Immature'
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return data
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def complete_objdenomb(data):
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asnot_denomb = data.obj_denombre.isna()
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isnot_dead = data.etat_bio != 'mort'
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as_statbio = data.statut_bio.notna()
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as_compt = data.comportement.notna()
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as_atlas = data.code_atlas.notna()
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as_age = data.age_faune.notna()
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as_sexe = data.sexe_faune.notna()
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as_repro = data.reprostatut_faune.notna()
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as_determ = data.determination.notna()
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data.loc[asnot_denomb&isnot_dead&(as_statbio|as_compt|as_atlas|as_age|as_sexe|as_repro|as_determ),'obj_denombre'] = 'individu'
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return data
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def recup_comptmt(data):
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if 'reprostatut_faune' in data.columns:
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# Récupération du comportement après `Comp. ind` dans la colonne `rmq_observation`
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compt_isna = data.reprostatut_faune.isna()
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in_rmq = data.rmq_observation.str.contains('Comp. ind')
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data.loc[compt_isna&in_rmq,'comportement'] = (data[compt_isna&in_rmq].rmq_observation
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.str.split('Comp. ind. :')
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.str[1]
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.str.split('|')
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.str[0]
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.str.strip())
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# Récupération du comportement dans la colonne `reprostatut_faune`
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sex_isna = data.sexe_faune.isna()
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comp_isna = data.comportement.isna()
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lst_comp_inv = [
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'ODO_Tandem','ODO_Territorial','INV_Accouplement','INV_Chant (orthoptères)','ODO_Ponte']
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lst_comp_ver = ['OIS_Reproduction certaine (patrim)','MAM_Fécès - épreinte - urine', 'OIS_Nid avec jeune(s)',
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'OIS_Couvaison', 'CHIR_Hivernant', 'AMP_Mise bas - ponte','CHIR_Lethargie', 'OIS_Chant', # 'AMP_Indéterminé',
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'AMP_Chant','CHIR_Estivant', 'CHIR_Transit', 'OIS_Reproduction possible',
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'MAM_Alarme', 'AMP_Accouplement', 'OIS_Fuite - envol','OIS_Accouplement']
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if data.reprostatut_faune.isin(lst_comp_inv).any():
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t3 = (~compt_isna)&comp_isna&sex_isna&(data.reprostatut_faune.isin(lst_comp_inv))
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elif data.reprostatut_faune.isin(lst_comp_ver).any():
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t3 = (~compt_isna)&comp_isna&(data.reprostatut_faune.isin(lst_comp_ver))
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data.loc[t3,'comportement'] = data[t3].reprostatut_faune
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# Récupération du comportement après `Comp. ind` dans la colonne `rmq_observation`
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# dictionnaire des transports
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dict_transport = {
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**dict.fromkeys(['transport de branche','transport de matériaux','transport de brindille','transporte une branche',"transport d'une branche",'transportant des matériaux','transportant une branche','transporte des feuilles'],'transport matériaux'),
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**dict.fromkeys(['transport de nourriture','transport nourriture','transport de poisson','transportant nourriture','Transporte une proie','transport de proie'],'transport nourriture'),
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}
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lst_rmq_comp = "chant|houspille|en chasse|{}|en vol|en survol|posé|parade|s'envole|se nourrit|se pose|tambourinage|vol migratoire|hivernage|nourrissage|couveur|nourrit|cht|cris|estivant|hivernant|Construction de nid|alarme|migration active".format('|'.join(dict_transport.keys()))
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comp_isna = data.comportement.isna()
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in_rmq = data.rmq_observation.str.contains('[%s]'%lst_rmq_comp,case=False,na=False)
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data.loc[comp_isna&in_rmq,'comportement'] = (data[comp_isna&in_rmq].rmq_observation
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.str.lower()
|
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.str.extract('(%s)'%lst_rmq_comp)[0]
|
||
.replace(dict_transport))
|
||
|
||
comp_isna = data.comportement.isna()
|
||
envol = data.rmq_observation.str.contains('Statut:Passage.')
|
||
if any(comp_isna&envol):
|
||
data.loc[comp_isna&envol,'comportement'] = 'en vol'
|
||
|
||
comp_isna = data.comportement.isna()
|
||
data.loc[comp_isna,'comportement'] = (data[comp_isna].rmq_observation
|
||
.replace({'x',''},regex=True)
|
||
.str.lower()
|
||
.str.extract('[détails : \d+ ]\((en transit)\)')[0])
|
||
|
||
def _get_gn_atlas():
|
||
sch = 'ref_nomenclatures'
|
||
tab = ['t_nomenclatures','bib_nomenclatures_types']
|
||
bib_mnemo='CODE_ATLAS'
|
||
sql = """
|
||
SELECT
|
||
a.id_nomenclature,
|
||
a.cd_nomenclature,
|
||
a.mnemonique mnemo,
|
||
a.label_default as label,
|
||
a.label_fr,
|
||
a.definition_default definition,
|
||
a.definition_fr def_fr,
|
||
b.mnemonique bib_mnemo,
|
||
b.label_default bib_label,
|
||
b.definition_default bib_def,
|
||
a.active
|
||
FROM {sch}.{tab0} a
|
||
JOIN {sch}.{tab1} b USING (id_type)
|
||
""".format(sch=sch,tab0=tab[0],tab1=tab[1])
|
||
if bib_mnemo is not None:
|
||
sql += " WHERE "
|
||
sql += "b.mnemonique = '%s'"%bib_mnemo
|
||
return gpd.pd.read_sql_query(sql,con_gn)
|
||
|
||
|
||
def recup_atlas(data):
|
||
is_atlas = data.rmq_observation.str.contains('Code atlas',na=False)
|
||
# is_atlas = data.rmq_observation.str.contains('Code atlas :',na=False)
|
||
|
||
if any(is_atlas):
|
||
atlas = _get_gn_atlas()
|
||
dict_code = dict(zip(atlas.cd_nomenclature.astype(int).astype(str),atlas.label_fr))
|
||
dict_label = dict(zip(atlas.label.str.removesuffix('.'),atlas.label_fr))
|
||
dict_autre = {
|
||
'Mâle chanteur (ou cris de nidification) ou tambourinage en période de reproduction':'Mâle chanteur présent en période de nidification, cris nuptiaux ou tambourinage entendus, mâle vu en parade',
|
||
'Jeunes fraîchement envolés (espèces nidicoles) ou poussins (espèces nidifuges)':'Jeunes en duvet ou jeunes venant de quitter le nid et incapables de soutenir le vol sur de longues distances'
|
||
}
|
||
data.loc[is_atlas,'code_atlas'] = (data[is_atlas].rmq_observation
|
||
.str.split('Code atlas')
|
||
.str[1]
|
||
# .str.split('\||@|;|\(')
|
||
.str.split(r'[|@;]')
|
||
.str[0]
|
||
.replace({
|
||
**dict.fromkeys([':','Nidification possible \(','Nidification certaine \(','Nidification probable \('],''),
|
||
'\s+':' ',
|
||
' - ':'; '},regex=True)
|
||
.str.strip()
|
||
.str.replace('nuptial parades','nuptial: parades')
|
||
.str.removesuffix(')')
|
||
.replace('',None)
|
||
.replace(dict_code)
|
||
.replace(dict_autre)
|
||
.replace(dict_label))
|
||
|
||
data.loc[is_atlas,'rmq1'] = (data[is_atlas].rmq_observation
|
||
.str.split('Code atlas')
|
||
.str[0]
|
||
.str.strip()
|
||
.replace(dict.fromkeys(['Commentaires :','Remarques :','Détails : @','','nan'],None))
|
||
)
|
||
data.loc[is_atlas,'rmq2'] = (data[is_atlas].rmq_observation
|
||
.str.split('Code atlas')
|
||
.str[1]
|
||
.str.split(r'[|@;]')
|
||
.str[1]
|
||
.str.strip()
|
||
.str.removesuffix(';')
|
||
.str.strip()
|
||
.replace(dict.fromkeys(['','nan'],None))
|
||
)
|
||
data.loc[is_atlas,'rmq_observation'] = [
|
||
' ; '.join([x,y]) if not gpd.pd.isna(y) and not gpd.pd.isna(x)
|
||
else y if gpd.pd.isna(x)
|
||
else x
|
||
for x,y in zip(data[is_atlas].rmq1,data[is_atlas].rmq2)
|
||
]
|
||
data.rmq_observation.replace('nan',None,inplace=True)
|
||
data.drop(columns=['rmq1','rmq2'],inplace=True)
|
||
|
||
atlas_isna = data.code_atlas.isna()
|
||
rmq_atlas = {
|
||
'2':"Observation de l'espèce pendant la période de nidification dans un biotope adéquat",
|
||
'2':"Observation de l'espèce pendant la période de nidification",
|
||
'4':'Couple pendant la période de nidification dans un biotope adéquat',
|
||
}
|
||
for k,v in rmq_atlas.items():
|
||
as_atlas = data.rmq_observation.str.contains(v,na=False)
|
||
data.loc[atlas_isna&as_atlas,'code_atlas'] = dict_code[k]
|
||
|
||
# Récupération du code atlas 99
|
||
for i in ['99','1.00','2','3.00','3 ','4 ','4.00','5.00','5 ',
|
||
'6','7','8','9.00','9 ','10','11 ','12 ','12.00','13',
|
||
'14.00','14 ','15.00','16','17','18','19']:
|
||
is_atlas = data.rmq_observation.str.contains('Code Atlas : %s'%i,na=False)
|
||
atlas_isna = data.code_atlas.isna()
|
||
if is_atlas.any():
|
||
data.loc[is_atlas&atlas_isna,'code_atlas'] = str(int(float('%s'%i)))
|
||
data.loc[is_atlas&atlas_isna,'code_atlas'] = data[is_atlas&atlas_isna].code_atlas.replace(dict_code)
|
||
|
||
# définition du code_atlas par le statut repro
|
||
lst_repro = [
|
||
'Reproduction possible','Reproduction probable','Reproduction confirmée','Nicheur possible'
|
||
]
|
||
|
||
atlas_isna = data.code_atlas.isna()
|
||
is_aves = data.group2_inpn == 'Oiseaux'
|
||
is_repro = data.statut_bio.isin(lst_repro)
|
||
if any(is_aves&is_repro&atlas_isna):
|
||
data.loc[is_aves&is_repro&atlas_isna,'code_atlas'] = (data[is_aves&is_repro&atlas_isna].statut_bio
|
||
.replace(['Reproduction','Nicheur'],'Nidification')
|
||
.replace(dict_label))
|
||
|
||
# définition du code atlas par le statut repro
|
||
atlas_isna = data.code_atlas.isna()
|
||
dict_repro = {
|
||
**dict.fromkeys(['couvaison','couveur','ois_couvaison'],'18'),
|
||
**dict.fromkeys(['accouplement','ois_accouplement','parade'],'6'),
|
||
**dict.fromkeys(['alarme'],'8'),
|
||
**dict.fromkeys(['chant','cht','ois_chant'],'3'),
|
||
**dict.fromkeys(['nourrissage','nourrissage des jeunes'],'16'),
|
||
**dict.fromkeys(['ois_nid avec jeune(s)'],'19'),
|
||
**dict.fromkeys(['ois_reproduction certaine (patrim)'],'50'),
|
||
**dict.fromkeys(['ois_reproduction possible'],'30'),
|
||
**dict.fromkeys(['transport matériaux'],'10'),
|
||
}
|
||
for k,v in dict_repro.items():
|
||
is_repro = data.comportement.str.lower()==k
|
||
if any(is_repro&atlas_isna):
|
||
data.loc[is_repro&is_aves&atlas_isna,'code_atlas'] = dict_code[v]
|
||
|
||
# Suppression des codes atlas érronés
|
||
is_dead = data.etat_bio=='mort'
|
||
atlas_notna = data.code_atlas.notna()
|
||
if any(is_dead&atlas_notna):
|
||
data.loc[is_dead&atlas_notna,'code_atlas'] = None
|
||
|
||
|
||
def form_observator(data):
|
||
return data.observateurs_v2.replace({
|
||
' \(CEN Isère\) \(CEN Isère\)':'',
|
||
' \(GRPLS\) \(GRPLS\)':'',
|
||
' \(CD Isère\) \(CD Isère\)':'',
|
||
' \(BIOTOPE\) \(BIOTOPE\)':'',
|
||
' \(Ecosphère\) \(Ecosphère\)':'',
|
||
' \(FMBDS\) \(FMBDS\)':'',
|
||
' \(Lo Parvi\) \(Lo Parvi\)':'',
|
||
' \(LPO Rhône\) \(LPO Rhône\)':'',
|
||
' \(Personnel\) \(Personnel\)':'',
|
||
' \(ONCFS\) \(ONCFS\)':'',
|
||
' \(Nature et Vie Sociale\) \(Nature et Vie Sociale\)':'',
|
||
' \(Conservatoire d’Espaces Naturels Isère\) \(Conservatoire d’Espaces Naturels Isère\)':'',
|
||
' \(Société Batrachologique de France\) \(Société Batrachologique de France\)':'',
|
||
'CEN Isère':'Conservatoire d’Espaces Naturels Isère',
|
||
},regex=True)
|
||
|
||
def form_coord(data):
|
||
t1 = data.longitude_x > 10
|
||
t2 = data.latitude_y > 50
|
||
data.loc[t1,'longitude_x'] = data[t1].to_crs(epsg=4326).geom.x
|
||
data.loc[t2,'latitude_y'] = data[t2].to_crs(epsg=4326).geom.y
|
||
|
||
def form_rmqobs(data):
|
||
data.rmq_observation.replace({
|
||
'Commentaires : Détails :':'Détails :',
|
||
'Commentaires : , localisation :':'localisation :',
|
||
},inplace=True,regex=True)
|
||
|
||
def recup_determ(data):
|
||
determ_isna = data.determination.isna()
|
||
is_statut = data.rmq_observation.str.contains('Indice de présence')
|
||
if is_statut.any():
|
||
data.loc[determ_isna&is_statut,'determination'] = 'Indice de présence'
|
||
data.loc[determ_isna&is_statut,'preuve_detrm'] = (data[determ_isna&is_statut].rmq_observation
|
||
.str.split('Indice de présence')
|
||
.str[1]
|
||
.str.split('|')
|
||
.str[0]
|
||
.str.strip()
|
||
.str.extract('.*\((.*)\).*')[0]
|
||
)
|
||
|
||
determ_isna = data.determination.isna()
|
||
data.loc[determ_isna,'determination'] = (data[determ_isna].rmq_observation
|
||
.str.extract('.*\((en main|observation indirecte|vu|contact auditif|analyse de pelotes)\).*')[0]
|
||
)
|
||
|
||
# determ_isna = data.determination.isna()
|
||
# in_hand = data.rmq_observation.str.contains('\(en main\)')
|
||
# if any(determ_isna&in_hand):
|
||
# data.loc[determ_isna&in_hand,'determination'] = 'en main'
|
||
|
||
determ_isna = data.determination.isna()
|
||
vu = data.rmq_observation.str.contains('Individu vu|Vu vivant|Commentaires \: Vu')
|
||
if any(determ_isna&vu):
|
||
data.loc[determ_isna&vu,'determination'] = 'Individu vu'
|
||
|
||
determ_isna = data.determination.isna()
|
||
entendu = data.rmq_observation.str.contains('Individu entendu')
|
||
if any(determ_isna&entendu):
|
||
data.loc[determ_isna&entendu,'determination'] = 'Individu entendu'
|
||
|
||
determ_isna = data.determination.isna()
|
||
photo = data.rmq_observation.str.contains('piège photo')
|
||
if any(determ_isna&photo):
|
||
data.loc[determ_isna&photo,'determination'] = 'Observation par piège photographique'
|
||
|
||
def recup_statutbio(data):
|
||
data['statut_bio'] = None
|
||
is_stat1 = data.rmq_observation.str.contains('Caractéristique',na=False)
|
||
if is_stat1.any():
|
||
statbio_isna = data.statut_bio.isna()
|
||
data.loc[statbio_isna&is_stat1,'statut_bio'] = (data[statbio_isna&is_stat1].rmq_observation
|
||
.str.split('Caractéristique :')
|
||
.str[1]
|
||
.str.split('\|')
|
||
.str[0]
|
||
.str.strip()
|
||
)
|
||
is_stat2 = data.rmq_observation.str.contains('Statut',na=False)
|
||
if is_stat2.any():
|
||
statbio_isna = data.statut_bio.isna()
|
||
data.loc[statbio_isna&is_stat2,'statut_bio'] = (data[statbio_isna&is_stat2].rmq_observation
|
||
.str.split('Statut')
|
||
.str[1]
|
||
.str.strip()
|
||
.str.removeprefix(':').str.strip()
|
||
.str.split('\||/|;')
|
||
.str[0]
|
||
.str.strip()
|
||
.str.extract('(Migrateur|Estivage|Reproduction probable|Reproduction certaine|Reproduction possible|Hivernant)')[0]
|
||
)
|
||
is_stat3 = data.rmq_observation.str.contains('Critere',na=False)
|
||
if is_stat3.any():
|
||
statbio_isna = data.statut_bio.isna()
|
||
data.loc[statbio_isna&is_stat3,'statut_bio'] = (data[statbio_isna&is_stat3].rmq_observation
|
||
.str.split('Critere')
|
||
.str[1]
|
||
.str.strip()
|
||
.str.removeprefix(':').str.strip()
|
||
.str.split('\||/|;')
|
||
.str[0]
|
||
.str.strip()
|
||
.str.extract('(nicheurs possibles|reproducteur)')[0]
|
||
)
|
||
is_stat4 = data.rmq_observation.str.contains('Commentaires',na=False)
|
||
if is_stat4.any():
|
||
statbio_isna = data.statut_bio.isna()
|
||
data.loc[statbio_isna&is_stat4,'statut_bio'] = (data[statbio_isna&is_stat4].rmq_observation
|
||
.str.split('Commentaires')
|
||
.str[1]
|
||
.str.strip()
|
||
.str.removeprefix(':').str.strip()
|
||
.str.lower()
|
||
.str.extract('(estivant|nicheur certain|nicheur probable|nicheur|semble nicher|niche|Reproduction probable|Reproduction certaine|Reproduction possible)')[0]
|
||
)
|
||
|
||
def recup_etatbio(data):
|
||
data['etat_bio'] = None
|
||
# Récupération de la mortalité dans le champs rmq_observation
|
||
is_dead1 = (data.rmq_observation
|
||
.str.lower()
|
||
.str.contains('individu mort|trouvé mort|mort : oui',na=False))
|
||
if is_dead1.any():
|
||
data.loc[is_dead1,'etat_bio'] = 'mort'
|
||
|
||
is_dead2 = data.comportement.str.lower()=='cadavre'
|
||
if is_dead2.any():
|
||
data.loc[is_dead2,'etat_bio'] = 'mort'
|
||
|
||
# nettoyage des comportements si individu mort
|
||
is_dead = is_dead1|is_dead2
|
||
compt_notna = data.comportement.notna()
|
||
if any(is_dead&compt_notna):
|
||
data.loc[is_dead&compt_notna,'comportement'] = None
|
||
|
||
# adaptation de la détermination
|
||
detm_hand = data.determination=='en main'
|
||
if any(detm_hand&is_dead):
|
||
data.loc[detm_hand&is_dead,'comportement'] = 'cadavre en main'
|
||
|
||
def recup_sexefaune(data):
|
||
is_male = data.rmq_observation.replace({'[x.0s]':''},regex=True).str.lower().str.contains('\\d mâle',na=False)
|
||
is_feml = data.rmq_observation.replace({'[x.0s]':''},regex=True).str.lower().str.contains('\\d femelle',na=False)
|
||
sexe_isna = data.sexe_faune.isna()
|
||
data.loc[sexe_isna&is_male&~is_feml,'sexe_faune'] = 'mâle'
|
||
data.loc[sexe_isna&~is_male&is_feml,'sexe_faune'] = 'femelle'
|
||
data.loc[sexe_isna&is_male&is_feml,'sexe_faune'] = 'mixte'
|
||
|
||
def format_faune(data):
|
||
recup_sexefaune(data)
|
||
recup_comptmt(data)
|
||
recup_etatbio(data)
|
||
recup_statutbio(data)
|
||
recup_atlas(data)
|
||
res = recup_stadevie(data)
|
||
return complete_objdenomb(res)
|
||
|
||
def format_data(data):
|
||
form_rmqobs(data)
|
||
data.observateurs_v2 = form_observator(data)
|
||
form_precision(data)
|
||
form_effectif(data)
|
||
correct_taxonomie(data)
|
||
form_cdnom(data)
|
||
form_date(data)
|
||
form_coord(data)
|
||
form_complx_grp(data)
|
||
recup_determ(data)
|
||
return format_faune(data)
|
||
|
||
def _export(path_name, data, format='csv'):
|
||
if format=='xlsx':
|
||
data.to_excel(path_name+'.%s'%format)
|
||
elif format=='csv':
|
||
data.to_csv(path_name+'.%s'%format)
|
||
else:
|
||
raise('format non prévu')
|
||
|
||
def _filter_diffusable(path_name,data):
|
||
if any(data.diffusable=='non'):
|
||
no_diff = data[data.diffusable=='non'].copy()
|
||
diff = data[data.diffusable=='oui'].copy()
|
||
to_export = {
|
||
path_name+'_diff':diff,
|
||
path_name+'_no_diff':no_diff
|
||
}
|
||
else:
|
||
to_export = {
|
||
path_name:data
|
||
}
|
||
return to_export
|
||
|
||
|
||
def _filter_uuid_sinp(to_export):
|
||
res = {}
|
||
for k,v in to_export.items():
|
||
if 'unique_id_sinp' in v.columns:
|
||
uuid_sinp = v[v.unique_id_sinp.notna()]
|
||
not_uuid = v[v.unique_id_sinp.isna()]
|
||
res |= {
|
||
k+'_uuid':uuid_sinp,
|
||
k+'_not_uuid':not_uuid
|
||
}
|
||
else:
|
||
res |= {
|
||
k:v
|
||
}
|
||
return res
|
||
|
||
def export(path_name, data, format='csv'):
|
||
# Formatage des dates avant écriture
|
||
detect_date = data.columns[data.columns.str.startswith('date')]
|
||
data.loc[:,detect_date] = data[detect_date].astype(str)
|
||
# Suppression de la colonne geom
|
||
df = data.to_wkt().drop(columns='geom',errors='ignore')
|
||
# Export
|
||
_to_export = _filter_diffusable(path_name,df)
|
||
to_export = _filter_uuid_sinp(_to_export)
|
||
for k,v in to_export.items():
|
||
if v.empty:
|
||
continue
|
||
_export(k,v,format)
|
||
|
||
def serena_rnngl_site():
|
||
from sqlalchemy import create_engine # pour lecture de la bd
|
||
from sqlalchemy.engine import URL
|
||
from shapely.geometry import Polygon
|
||
|
||
usr = 'postgres'
|
||
pdw = 'postgres'
|
||
bdd = 'serenadb'
|
||
host = '172.17.0.2'
|
||
eng = URL.create('postgresql+psycopg2',username=usr,password=pdw,host=host,database=bdd)
|
||
conn = create_engine(eng)
|
||
sit = gpd.pd.read_sql_table('rnf_site',con=conn,schema='serenabase')
|
||
sit['poly'] = (sit
|
||
.site_poly.str[9:]
|
||
.str.split(','))
|
||
|
||
sit.loc[sit.poly.notna(),'geom'] = (sit.loc[sit.poly.notna(),'poly']
|
||
.apply(lambda x: [xx.split(' ') for xx in x if xx])
|
||
.apply(lambda x: [[float(xxx) for xxx in xx] for xx in x ])
|
||
.apply(lambda x: Polygon(x)))
|
||
|
||
return (sit
|
||
.set_geometry('geom',crs=4326)
|
||
.to_crs(2154)
|
||
.dropna(subset=['geom']))
|
||
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if __name__ == "__main__":
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v_synthese_invertebre = 'v_synthese_invertebre'
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v_synthese_vertebre = 'v_synthese_vertebre'
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v_synthese_flore = 'v_synthese_flore'
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sit_rnngl = serena_rnngl_site()
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# sql_exclude_rnngl = " (rmq_localisation NOT ILIKE '%%lemps%%' OR NOT ST_INTERSECTS(geom,'SRID=2154;{}'))".format(sit_rnngl.unary_union)
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sql_exclude_rnngl = " rmq_localisation NOT ILIKE '%%grand%%lemps%%'"
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sql_rmqloc_null = " rmq_localisation IS NULL"
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sql_inv = "SELECT * FROM saisie.%s WHERE cd_nom <> '9999081'"%v_synthese_invertebre # 9999081 : Heterocera sp.
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v_inv = gpd.read_postgis(sql_inv+" AND"+sql_exclude_rnngl,con_sicen).dropna(how='all',axis=1)
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v_inv_autre = gpd.read_postgis(sql_inv+" AND"+sql_rmqloc_null,con_sicen).dropna(how='all',axis=1)
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v_inv = gpd.pd.concat([v_inv,v_inv_autre],ignore_index=True).reset_index(drop=True)
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v_inv_rnngl = gpd.read_postgis(sql_inv+" AND"+sql_exclude_rnngl.replace('NOT ',''),con_sicen).dropna(how='all',axis=1)
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t1_inv = v_inv_rnngl.etude.str.contains('Haute Bourbre 2014')
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t2_inv = v_inv_rnngl.observateurs_v2.isin(['ZARADZKI Lise (CEN Isère)','BIRON Nicolas (CEN Isère)'])
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t3_inv = v_inv.observateurs_v2=='LUCAS Jérémie (CEN Isère)'
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t4_inv = v_inv.etude!='Echange de données - GRPLS'
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hb_inv = v_inv_rnngl[t1_inv|t2_inv].copy()
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no_stg = v_inv[t3_inv&t4_inv].copy()
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v_inv = gpd.pd.concat([v_inv,hb_inv],ignore_index=True).drop_duplicates()
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v_inv_rnngl.drop(hb_inv.index,inplace=True)
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v_inv_rnngl = gpd.pd.concat([v_inv_rnngl,no_stg],ignore_index=True).drop_duplicates()
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v_inv.drop(no_stg.index,inplace=True)
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#v_synthese_invertebre = 37915
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#v_inv.shape[0]+v_inv_rnngl.shape[0] = 37914
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#différence de 1 donnée. Correspond au taxon 9999081 dans la bdd
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sql_ver = "SELECT * FROM saisie.%s WHERE cd_nom <> '9999056'"%v_synthese_vertebre # 9999056 : Micromammalia sp. (17 données)
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v_ver = gpd.read_postgis(sql_ver+" AND"+sql_exclude_rnngl,con_sicen).dropna(how='all',axis=1)
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v_ver_autre = gpd.read_postgis(sql_ver+" AND"+sql_rmqloc_null,con_sicen).dropna(how='all',axis=1)
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v_ver = gpd.pd.concat([v_ver,v_ver_autre],ignore_index=True).reset_index(drop=True)
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v_ver_rnngl = gpd.read_postgis(sql_ver+" AND"+sql_exclude_rnngl.replace('NOT ',''),con_sicen).dropna(how='all',axis=1)
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t1_ver = v_ver_rnngl.etude.str.contains('Haute Bourbre 2014')
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t2_ver = v_ver_rnngl.observateurs_v2.isin(['ZARADZKI Lise (CEN Isère)','BIRON Nicolas (CEN Isère)'])
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t3_ver = v_ver.observateurs_v2.isin(['LUCAS Jérémie (CEN Isère)','PRUNIER Jérôme (Personnel)', 'MAILLET Grégory (CEN Isère)','LAFON Arnaud (CEN Isère)'])
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t4_ver = v_ver.etude!='Echange de données - GRPLS'
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hb_ver = v_ver_rnngl[t1_ver|t2_ver].copy()
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sg_ver = v_ver[t3_ver&t4_ver].copy()
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v_ver = gpd.pd.concat([v_ver,hb_ver],ignore_index=True).drop_duplicates()
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v_ver_rnngl.drop(hb_ver.index,inplace=True)
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v_ver_rnngl = gpd.pd.concat([v_ver_rnngl,sg_ver],ignore_index=True).drop_duplicates()
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v_ver.drop(sg_ver.index,inplace=True)
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#v_synthese_vertebre = 102175
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#v_ver.shape[0]+v_ver_rnngl.shape[0] = 102158
|
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#différence de 17 donnée. Correspond au taxon 9999056 dans la bdd
|
||
|
||
sql_flo = 'SELECT * FROM saisie.%s'%v_synthese_flore
|
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v_flo = gpd.read_postgis(sql_flo+" WHERE"+sql_exclude_rnngl,con_sicen).dropna(how='all',axis=1)
|
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v_flo_autre = gpd.read_postgis(sql_flo+" WHERE"+sql_rmqloc_null,con_sicen).dropna(how='all',axis=1)
|
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v_flo = gpd.pd.concat([v_flo,v_flo_autre],ignore_index=True).reset_index(drop=True)
|
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v_flo_rnngl = gpd.read_postgis(sql_flo+" WHERE"+sql_exclude_rnngl.replace('NOT ',''),con_sicen).dropna(how='all',axis=1)
|
||
t1_flo = v_flo_rnngl.etude.str.contains('Haute Bourbre 2014')
|
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t2_flo = v_flo_rnngl.observateurs_v2=='ZARADZKI Lise (CEN Isère)'
|
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t3_flo = v_flo.observateurs_v2=='LUCAS Jérémie (CEN Isère)'
|
||
t4_flo = v_flo.etude!='Echange de données - GRPLS'
|
||
hb_flo = v_flo_rnngl[t1_flo|t2_flo].copy()
|
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sg_flo = v_flo[t3_flo&t4_flo].copy()
|
||
v_flo = gpd.pd.concat([v_flo,hb_flo],ignore_index=True).drop_duplicates()
|
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v_flo_rnngl.drop(hb_flo.index,inplace=True)
|
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idflo_drop = v_flo[v_flo.id_obs.isin(v_flo_rnngl.id_obs)].index
|
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v_flo.drop(idflo_drop,inplace=True)
|
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v_flo_rnngl = gpd.pd.concat([v_flo_rnngl,sg_flo],ignore_index=True).drop_duplicates()
|
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v_flo.drop(sg_flo.index,inplace=True)
|
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#v_synthese_flore = 113556
|
||
#v_flo.shape[0]+v_flo_rnngl.shape[0] = 113556
|
||
|
||
|
||
v_rnngl = gpd.pd.concat([v_ver_rnngl,v_flo_rnngl,v_inv_rnngl],ignore_index=True)
|
||
|
||
v_inv_explode = format_data(v_inv)
|
||
V_VER = v_ver.copy()
|
||
v_ver = V_VER.copy()
|
||
v_ver_explode = format_data(v_ver)
|
||
format_data(v_flo)
|
||
|
||
inv_uuid = (gpd.pd.read_csv(os.path.join(DIR,'INVERTEBRE/','ORB_UUID','donnees_importees_cen38.csv'),sep=';')
|
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.rename(columns={'entity_source_pk_value':'id_obs'}))
|
||
v_inv_explode = v_inv_explode.merge(inv_uuid[['id_obs', 'unique_id_sinp']],how='left',on='id_obs')
|
||
# list_inv = v_inv_explode[v_inv_explode.unique_id_sinp.notna()].etude.unique()
|
||
# INVERTEBRE
|
||
for etude in v_inv_explode.etude.unique():
|
||
exp_inv = v_inv_explode[v_inv_explode.etude==etude].copy()
|
||
exp_inv.dropna(how='all',inplace=True,axis=1)
|
||
if 'complexe_groupe' in exp_inv.columns:
|
||
exp_inv1 = exp_inv[exp_inv.complexe_groupe.notna()]
|
||
exp_inv2 = exp_inv[exp_inv.complexe_groupe.isna()].dropna(how='all',axis=1)
|
||
export(os.path.join(DIR,'INVERTEBRE','INV_'+etude+'_GRP'),exp_inv1,format='csv')
|
||
export(os.path.join(DIR,'INVERTEBRE','INV_'+etude),exp_inv2,format='csv')
|
||
else :
|
||
export(os.path.join(DIR,'INVERTEBRE','INV_'+etude),exp_inv,format='csv')
|
||
|
||
# VERTEBRE
|
||
for etude in v_ver_explode.etude.unique():
|
||
exp_ver = v_ver_explode[v_ver_explode.etude==etude].copy()
|
||
exp_ver.dropna(how='all',inplace=True,axis=1)
|
||
if 'complexe_groupe' in exp_ver.columns:
|
||
exp_ver1 = exp_ver[exp_ver.complexe_groupe.notna()]
|
||
exp_ver2 = exp_ver[exp_ver.complexe_groupe.isna()].dropna(how='all',axis=1)
|
||
export(os.path.join(DIR,'VERTEBRE','VER_'+etude+'_GRP'),exp_ver1,format='csv')
|
||
export(os.path.join(DIR,'VERTEBRE','VER_'+etude),exp_ver2,format='csv')
|
||
else :
|
||
export(os.path.join(DIR,'VERTEBRE','VER_'+etude),exp_ver,format='csv')
|
||
|
||
# FLORE
|
||
for etude in v_flo.etude.unique():
|
||
exp_flo = v_flo[v_flo.etude==etude].copy()
|
||
exp_flo.dropna(how='all',inplace=True,axis=1)
|
||
if 'complexe_groupe' in exp_flo.columns:
|
||
exp_flo1 = exp_flo[exp_flo.complexe_groupe.notna()]
|
||
exp_flo2 = exp_flo[exp_flo.complexe_groupe.isna()].dropna(how='all',axis=1)
|
||
export(os.path.join(DIR,'FLORE','FLO_'+etude+'_locNULL'+'_GRP'),exp_flo1,format='csv')
|
||
export(os.path.join(DIR,'FLORE','FLO_'+etude+'_locNULL'),exp_flo2,format='csv')
|
||
else :
|
||
export(os.path.join(DIR,'FLORE','FLO_'+etude+'_locNULL'),exp_flo,format='csv')
|
||
|
||
|
||
# _export(os.path.join(DIR,v_synthese_invertebre+'2'),v_inv_explode.dropna(how='all',axis=1))
|
||
# _export(os.path.join(DIR,v_synthese_invertebre),v_inv.dropna(how='all',axis=1))
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||
# _export(os.path.join(DIR,v_synthese_vertebre+'2'),v_ver_explode.dropna(how='all',axis=1))
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||
# _export(os.path.join(DIR,v_synthese_vertebre),v_ver.dropna(how='all',axis=1))
|
||
# _export(os.path.join(DIR,v_synthese_flore),v_flo)
|
||
|
||
# v_ver.etude.unique()
|
||
# v_ver.protocole.unique()
|
||
# v_ver.lot_donnee.unique()
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