#!/usr/bin/env python3
# -*- coding: UTF-8 -*-

from pycen import update_to_sql, con_sicen,con_gn
import geopandas as gpd
import os

DIR = '/home/colas/Documents/9_PROJETS/4_SICEN/GN_MIGRATION'

def form_cdnom(data):
    dict_cdnom = {
        116744:521658,  # Flore : Quercus petraea
    }
    data.replace({'cd_nom':dict_cdnom},inplace=True)
    data.replace({'cd_ref':dict_cdnom},inplace=True)

def form_complx_grp(data):
    dict_cdnom = {
        9999005:105817, # Leucanthemum vulgare (#groupe)
        9999014:119097, # Rubus fruticosus (#groupe)
        9999017:717630, # Taraxacum officinale (#groupe)
        9999019:126573, # Thymus serpyllum (#groupe)
        9999020:129298, # Vicia sativa (#groupe)
        9999023:188772, # Acrocephalus palustris / scirpaceus (#complexe)
        9999031:441709, # Cairina moschata f. domestica (#forme)
        9999033:190350, # Carduelis flammea flammea / cabaret / Carduelis hornemanni (#complexe)
        9999037:191029, # Colias alfacariensis / hyale (#complexe)
        9999041:4503, # Corvus corone corone / cornix (#complexe)
        9999042:186239, # Eptesicus / Nyctalus sp. (#complexe)
        9999046:192539, # Felis silvestris / catus (#complexe)
        9999050:193993, # Leptidea sinapis / reali (#complexe)
        9999054:194481, # Martes martes / foina (#complexe)
        9999057:195005, # Myotis myotis / blythii (#complexe)
        9999063:444436, # Pelophylax kl. esculentus / lessonae (#complexe)
        9999064:4280, # Phylloscopus collybita tristis / "abietinus" (#complexe)
        9999066:196980, # Pyrgus malvae / malvoides (#complexe)
        9999074:197040, # Rana dalmatina / temporaria (#complexe)
        9999075:196296, # Pipistrellus nathusii / kuhlii (#complexe)
        9999080:194357, # Lysandra coridon / hispana (#complexe)
        9999082:195005, # Myotis daubentonii / Myotis mystacinus (#complexe)
        9999083:699094, # Pipistrellus / Miniopterus (#complexe)
    }
    lst_cdnom_old = [*dict_cdnom.keys()]

    if data.cd_nom.isin(lst_cdnom_old).any():
        data.loc[data.cd_nom.isin(lst_cdnom_old),'complexe_groupe'] = data[data.cd_nom.isin(lst_cdnom_old)].nom_complet
        data.replace({'cd_nom':dict_cdnom},inplace=True)

        sql = 'SELECT cd_nom, nom_complet nom_new FROM taxonomie.taxref where cd_nom in {}'.format(tuple(dict_cdnom.values()))
        tax = gpd.pd.read_sql(sql, con_gn)
        data = data.merge(tax,how='left',on='cd_nom')
        data.loc[data.nom_new.notna(),'nom_latin'] = data[data.nom_new.notna()].nom_new
        data.drop(columns='nom_new',inplace=True)

def form_precision(data):
    dict_pre = {
        'GPS':0,
        '0 à 10m':10,
        '10 à 100m':100,
        '100 à 500m':500,
        'lieu-dit':750,
        'commune':None,
    }
    is_com = data.precision =='commune'
    rmq_null = data.rmq_localisation.isnull()
    data.loc[is_com&rmq_null,'rmq_localisation'] = 'Localisation : commune'
    data.loc[is_com&~rmq_null,'rmq_localisation'] = 'Localisation : commune ;'+data[is_com].rmq_localisation
    data.replace({'precision':dict_pre},inplace=True)
    data.precision = data.precision.astype('Int64')


def form_effectif(data):
    eff_notna = data.effectif.notna()
    efmin_notna = data.effectif_min.notna()
    efmax_isna = data.effectif_max.isna()
    data.loc[(~eff_notna)&efmin_notna,'effectif'] = data[(~eff_notna)&efmin_notna].effectif_min
    data.loc[(~eff_notna)&(~efmax_isna),'effectif'] = data[(~eff_notna)&(~efmax_isna)].effectif_max
    # data.loc[efmax_isna,'effectif_max'] = data[efmax_isna].effectif
    data.effectif = data.effectif.astype('Int64')
    data.effectif_max = data.effectif_max.astype('Int64')

def form_date(data):
    cols = data.columns[data.columns.str.contains('date')]
    for col in cols:
        data.loc[data[col].isna(),col] = None

def recup_stadevie(data):
    dict_repro = {
        'ODO_Exuvie/émergence':'Exuvie/émergence',
        'ODO_Immature':'Immature',
        **dict.fromkeys(['ODO_Mâles+Femelles','ODO_Tandem','ODO_Territorial','ODO_Ponte'],'Adulte'),
        **dict.fromkeys(['CHIR_Indéterminé','ODO_Indéterminé'],'Indéterminé')
    }
    if 'age_faune' in data.columns:
        age_isna = data.age_faune.isna()
        age_inrmq = data.rmq_observation.str.contains('Stade de vie')
        data.loc[age_isna&age_inrmq,'age_faune'] = (data[age_isna&age_inrmq].rmq_observation
            .str.split('Stade de vie :')
            .str[1]
            .str.split('|')
            .str[0]
            .str.strip())
        double_info = data.age_faune.isin(['Imago, adulte', 'Nymphe, immature'])
        d2 = data[double_info].copy()

        data.age_faune = data.age_faune.str.replace('Imago, adulte','Imago')
        data.age_faune = data.age_faune.str.replace('Nymphe, immature','Nymphe')

        d2.age_faune = d2.age_faune.str.replace('Imago, adulte','Adulte')
        d2.age_faune = d2.age_faune.str.replace('Nymphe, immature','Immature')

        data = gpd.pd.concat([data,d2])

        lst_age = [*dict_repro.keys()]
        age_isna = data.age_faune.isna()
        t1 = age_isna&(data.reprostatut_faune.isin(lst_age))
        data.loc[t1,'age_faune'] = data[t1].reprostatut_faune.replace(dict_repro)

        t2 = data.age_faune=='Exuvie/émergence'
        is_exuvie = (data.rmq_observation
            .replace(['é','E','É'],'e',regex=True)
            .str.contains('exuvie',na=False))
        is_emerge = (data.rmq_observation
            .replace(['é','E','É'],'e',regex=True)
            .str.contains('emerge',na=False))
        rmq_isna = data.rmq_observation.isna()
        
        data.loc[is_exuvie&~is_emerge&t2,'age_faune'] = 'Exuvie'
        data.loc[~is_exuvie&is_emerge&t2,'age_faune'] = 'Emergent'
        data.loc[is_exuvie&is_emerge&t2,'age_faune']  = 'Exuvie'
        data.loc[is_exuvie&is_emerge&rmq_isna&t2,'age_faune'] = 'Emergent'

        t3 = data.age_faune=='Oeuf/ponte/larve/nymphe/chenille...'
        # Odonate
        is_odo = data.ordre == 'Odonata'
        data.loc[t3&is_odo,'age_faune'] = 'Exuvie'
        # Amphibiens
        is_amphi = data.group2_inpn == 'Amphibiens'
        is_larve = data.rmq_observation.str.contains('larve',na=False)
        is_ponte = data.rmq_observation.str.contains('ponte',na=False)
        is_tetar = data.rmq_observation.replace('ê','e',regex=True).str.contains('ponte',na=False)
        is_urode = data.ordre=='Urodela'
        is_anure = data.ordre=='Anura'
        rmq_na   = data.rmq_observation.isna()
        if 'obj_denombre' not in data.columns:
            data['obj_denombre'] = None
        data.loc[t3&is_amphi&is_larve,'age_faune'] = 'Larve'
        data.loc[t3&is_amphi&is_ponte,'age_faune'] = 'Oeufs'
        data.loc[t3&is_amphi&is_ponte,'obj_denombre'] = 'Ponte'
        data.loc[t3&is_amphi&is_tetar,'age_faune'] = 'Têtard'
        data.loc[t3&is_amphi&is_urode&rmq_na,'age_faune'] = 'Larve'
        data.loc[t3&is_amphi&is_anure&rmq_na,'age_faune'] = 'Ponte'
        return data


def recup_comptmt(data):
    if 'reprostatut_faune' in data.columns:
        compt_isna = data.reprostatut_faune.isna()
        in_rmq = data.rmq_observation.str.contains('Comp. ind')
        data.loc[compt_isna&in_rmq,'comportement'] = (data[compt_isna&in_rmq].rmq_observation
            .str.split('Comp. ind. :')
            .str[1]
            .str.split('|')
            .str[0]
            .str.strip())
    
        sex_isna  = data.sexe_faune.isna()
        comp_isna = data.comportement.isna()
        lst_comp = ['ODO_Tandem','ODO_Territorial','INV_Accouplement','INV_Chant (orthoptères)','ODO_Ponte']
        t3 = (~compt_isna)&comp_isna&sex_isna&(data.reprostatut_faune.isin(lst_comp))
        data.loc[t3,'comportement'] = data[t3].reprostatut_faune


def isole_rnngl():
    return


def format_faune(data):
    recup_comptmt(data)
    return recup_stadevie(data)

def format_data(data):
    form_precision(data)
    form_effectif(data)
    form_cdnom(data)
    form_date(data)
    form_complx_grp(data)
    return format_faune(data)

def export(path_name, data, format='csv'):
    detect_date = data.columns[data.columns.str.startswith('date')]
    data[detect_date] = data[detect_date].astype(str)
    df = data.to_wkt().drop(columns='geom',errors='ignore')
    if format=='xlsx':
        df.to_excel(path_name+'.%s'%format)
    elif format=='csv':
        df.to_csv(path_name+'.%s'%format)
    else:
        raise('format non prévu')
    
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']))

if __name__ == "__main__":

    v_synthese_invertebre = 'v_synthese_invertebre'
    v_synthese_vertebre = 'v_synthese_vertebre'
    v_synthese_flore = 'v_synthese_flore'

    sit_rnngl = serena_rnngl_site()
    sql_exclude_rnngl = " (rmq_localisation NOT ILIKE '%%lemps%%' OR NOT ST_INTERSECTS(geom,'SRID=2154;{}'))".format(sit_rnngl.unary_union)
    sql_exclude_rnngl = " rmq_localisation NOT ILIKE '%%grand%%lemps%%'"

    sql_inv = "SELECT * FROM saisie.%s WHERE cd_nom <> '9999081'"%v_synthese_invertebre     # 9999081 : Heterocera sp.
    v_inv = gpd.read_postgis(sql_inv+" AND"+sql_exclude_rnngl,con_sicen)
    sql_ver = "SELECT * FROM saisie.%s WHERE cd_nom <> '9999056'"%v_synthese_vertebre       # 9999056 : Micromammalia sp.
    v_ver = gpd.read_postgis(sql_ver+" AND"+sql_exclude_rnngl,con_sicen)
    sql_flo = 'SELECT * FROM saisie.%s'%v_synthese_flore
    v_flo = gpd.read_postgis(sql_flo+" WHERE"+sql_exclude_rnngl,con_sicen).dropna(how='all',axis=1)

    v_inv = format_data(v_inv)
    format_data(v_ver)
    format_data(v_flo)

    # INVERTEBRE
    for etude in v_inv.etude.unique():
        exp_inv = v_inv[v_inv.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',etude+'_GRP'),exp_inv1,format='csv')
            export(os.path.join(DIR,'INVERTEBRE',etude),exp_inv2,format='csv')
        else : 
            export(os.path.join(DIR,'INVERTEBRE',etude),exp_inv,format='csv')
    
    # VERTEBRE
    for etude in v_ver.etude.unique():
        exp_ver = v_ver[v_ver.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',etude+'_GRP'),exp_ver1,format='csv')
            export(os.path.join(DIR,'VERTEBRE',etude),exp_ver2,format='csv')
        else : 
            export(os.path.join(DIR,'VERTEBRE',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',etude+'_GRP'),exp_flo1,format='csv')
            export(os.path.join(DIR,'FLORE',etude),exp_flo2,format='csv')
        else : 
            export(os.path.join(DIR,'FLORE',etude),exp_flo,format='csv')


# export(os.path.join(DIR,v_synthese_invertebre+'2'),v_inv.dropna(how='all',axis=1))
# 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()