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

from statistics import geometric_mean
import geopandas as gpd
import pandas as pd
from sqlalchemy.engine import URL
from sqlalchemy import create_engine
# from shapely.geometry import Polygon
import pycen
from os import listdir, chdir
from pathlib import Path
from zipfile import ZipFile
from rasterstats import zonal_stats


# Path MNT
path0 = '/home/colas/Documents/9_PROJETS/2_PS/'
path0_mnt = '/home/colas/Documents/9_PROJETS/3_PGZH/'
path  = path0_mnt + 'SIG/'
p_mltifct = 'multi_fonctions/IGN - BD Alti 25M/'
path2_mnt = path0+'MNT/'
file_mnt25 = 'MNT_25m.tif' 
file_mnt5  = 'MNT_5m.tif' 

# Parametres bdd IN
user   = 'cen_admin'
pwd    = '#CEN38@venir'
adr    = '192.168.0.189'
base   = 'bd-cen-38'
url = URL.create('postgresql+psycopg2', 
  username=user,
  password=pwd,
  host=adr,
  database=base,
)
con = create_engine(url)
# con = create_engine('postgresql+psycopg2://{0}:{1}@{2}/{3}'.format(user,pwd,adr,base), echo=False)

def keepgeom_indept(df):
  dnat = pycen.ref.get_districtNat().geom.unary_union
  df['in_isere'] = df.intersects(dnat)
  df = df[df.in_isere].copy()
  df.dropna(axis=1, how='all', inplace=True)
  del df['in_isere']
  return df
# df = gpd.read_postgis(
#   sql = 'SELECT * FROM pelouse_seche."cr_PS_CBNA_habitats_aggreg_06_2020"',
#   con = con
# )

lst_tab = ['"cr_VERCORS_habitats_CBNA_1999-2007"','"cr_TRIEVES+VERCORS_habitats_CBNA_2014"','"cr_ECRIN_habitats_CBNA_2014"','"cr_CHARTREUSE_habitats_CBNA_2000-2012"',]
table = lst_tab[3]
df = gpd.read_postgis(
  sql = 'SELECT * FROM habitat.%s'%table,
  con = con
)

df.set_index('idfinal', inplace=True)
# df.set_index('data_id', inplace=True)
colnhab = df.columns[df.columns.str.startswith('n_hab')]
nhab = df[colnhab].copy()
nhab['SUMM'] = nhab.sum(axis=1)
nhab['SUMM'].unique()

lst_hab = ['34.1','34.3','34.4','34.7','35.2','62.3','64.1']
df2 = pd.DataFrame()
for h in lst_hab:
  tmp = df[
    (df.code_hab1.str.contains(h,na=False))|
    (df.code_hab2.str.contains(h,na=False)&(df.n_hab2 >= 20))|
    (df.code_hab3.str.contains(h,na=False)&(df.n_hab3 >= 20))|
    (df.code_hab4.str.contains(h,na=False)&(df.n_hab4 >= 20))
    ]
  df2 = pd.concat([df2,tmp])

df3 = df2.copy()

# Before 1385 / After 1422
lst_hab2 = ['41','42','43','44','81','82','83']

for h2 in lst_hab2:
  tmp2 = df2[
    (df2.code_hab1.str.contains('&'+h2,na=False)&(df2.n_hab1 >= 60))
  ]
  df2.drop(labels=tmp2.index,inplace=True)
for h2 in lst_hab2:
  tmp2 = df2[
    (df2.code_hab1.str.contains(h2+'.',na=False)&(df2.n_hab1 >= 60))
  ]
  df2.drop(labels=tmp2.index,inplace=True)
  

for h2 in lst_hab2:
  tmp3 = df3[
    (df3.code_hab1.str.contains(h2,na=False)&(df3.n_hab1 >= 60))
  ]
  df3.drop(labels=tmp3.index,inplace=True)


df2 = keepgeom_indept(df2)
df3 = keepgeom_indept(df3)


df = df2.copy()
df.reset_index(inplace=True,drop=False)
# ps = pycen.ps.get_sitesGeom()
sql = 'SELECT * FROM ps.v_pelouseseches_all'
PS = gpd.read_postgis(sql,pycen.con)
ps = PS[PS.source==table.replace('"','')].copy()
# 1385



not_in_azalee = df2[~df2.index.isin(df3.index)].copy()
in_azalee = df2[df2.index.isin(df3.index)].copy()

not_in_azalee.to_file(path0+'CBNA/MISSING_DATA_INDB/chartreuse.gpkg',layer='not_in_azalee')
in_azalee.to_file(path0+'CBNA/MISSING_DATA_INDB/chartreuse.gpkg',layer='in_azalee')

ps.columns

# Drop poly who intersect poly non-CBNA
# ps_autre = ps[~ps.auteur_geom.str.contains('CBNA')]
# intersect  = gpd.sjoin(df,ps_autre,op='intersects').idfinal.tolist()
# drop_index = df[df.idfinal.isin(intersect)].index
# df.drop(drop_index,inplace=True)

# Drop poly who intersect poly CBNA
# ps_cbna = ps[ps.auteur_geom.str.contains('CBNA')]
# intersect  = gpd.sjoin(
#   df,
#   gpd.GeoDataFrame( geometry=ps_cbna.centroid),
#   op='intersects').idfinal.tolist()
# drop_index = df[df.idfinal.isin(intersect)].index
# df.drop(drop_index,inplace=True)
from os.path import join
PATH = '/home/colas/Documents/9_PROJETS/2_PS/CBNA/MISSING_DATA_INDB'
FILE = 'CHARTREUSE.gpkg'
df = gpd.read_file(join(PATH,FILE))


if 'date' not in df.columns and 'annee' in df.columns:
  df.loc[~df.annee.isna(),'date'] = df.loc[~df.annee.isna(),'annee'].astype(str) + '-01-01'
  df['date'] = pd.to_datetime(df['date'])

colnhab2 = df2.columns[df2.columns.str.startswith('n_hab')]
nhab2 = df2[colnhab2].copy()
nhab2['SUMM'] = nhab2.sum(axis=1)
nhab2['SUMM'].unique()

if table == '"cr_CHARTREUSE_habitats_CBNA_2000-2012"':
  dic = {
    'JCV':'VILLARET Jean-charles',
    'GP' :'PACHE Gilles',
    'AM' :'MIKOLAJCZAK Alexis',
    'TL' :'LEGLAND Thomas',
    'HM' :'MERLE Hugues',
    'CC' :'Crassous Claire',
    # 'AL'
    # 'LA'
    # 'JC'
  }
  df.obs = df.obs.replace(dic)
  df.loc[~df.obs.isin(['ONF38',*dic.values()]),'obs'] = 'CBNA'
  df.loc[df.structur=='ONF 38','structur'] = 'ONF38'
if table == '"cr_ECRIN_habitats_CBNA_2014"':
  df['auteur'] = 'PnE'
  df['structure'] = 'PnE'
  df['date'] = '2014-01-01'
  df['date'] = pd.to_datetime(df['date'])
if table == '"cr_TRIEVES+VERCORS_habitats_CBNA_2014"':
  df['auteur'] = 'CBNA'
  df['structure'] = 'CBNA'
  df['date'] = '2014-01-01'
  df['date'] = pd.to_datetime(df['date'])
if table == '"cr_VERCORS_habitats_CBNA_1999-2007"':
  df['structure'] = 'CBNA'
if table in [
  '"cr_VERCORS_habitats_CBNA_1999-2007"','"cr_TRIEVES+VERCORS_habitats_CBNA_2014"'
]:
  df.loc[df.statut=="Habitat d'intérêt communautaire",'statut'] = \
    "Communautaire"
  df.loc[df.statut=="Habitat d'intérêt communautaire retenu prioritaire",'statut'] = \
    "Prioritaire"
  df.loc[df.statut=="Habitat d'intérêt communautaire, retenu prioritaire",'statut'] = \
    "Prioritaire"
  df.loc[
    df.statut=="""Habitat d'intérêt communautaire, retenu prioritaire pour les sites riches en orchidées""",
    'statut'] = "Prioritaire"
  df.loc[
    df.statut=="""Habitat d'intérêt communautaire retenu prioritaire pour les sites riches en orchidées""",
    'statut'] = "Prioritaire"
  df.loc[
    df.statut=="""Habitat communautaire, retenu prioritaire pour les sites riches en orchidées""",
    'statut'] = "Prioritaire"
  df.loc[df.statut=="Habitat d'intérêt communautaire retenu prioritaire",'statut'] = \
    "Prioritaire"
if table in [
  '"cr_CHARTREUSE_habitats_CBNA_2000-2012"', '"cr_ECRIN_habitats_CBNA_2014"']:
  dic = {'PR':'Prioritaire','IC':'Communautaire','NC':'Non communautaire'}
  df.statut.replace(dic,inplace=True)

df.reset_index(inplace=True, drop=True)

df.to_postgis(
  'ps'+table[3:-1],pycen.con_bdcen,'pelouse_seche',geom_col='geom',if_exists='replace'
)

# [CROSS_MNT]
ddf = df[['idfinal','geom']].copy()
ddf.set_geometry('geom',inplace=True)

# home = str(Path.home())
# chdir(path+p_mltifct)
# Dir = listdir()
# Dir = [x for x in Dir if '.zip' in x]
# # stats = pd.DataFrame()
# for i, d in enumerate(Dir):
#   zip = ZipFile(d).namelist()
#   z = [z for z in zip if 'MNT' in z][0]
#   print(z)
#   tmp = zonal_stats(ddf.geom,'/vsizip/{zip}/{mnt}'.format(zip=d,mnt=z),stats="max")
#   tmp = pd.DataFrame(tmp)
#   tmp.columns = ['max%s'%i]
#   if i == 0 :
#     stats = tmp
#   else:
#     stats = pd.merge(stats,tmp,how='left',left_index=True,right_index=True)

# stats['bilan'] = stats.max(axis=1)

# tmp = pd.merge(df,stats['bilan'],left_index=True,right_index=True)
# tmp.bilan = tmp.bilan.round()

# zonal_tmp  = zonal_stats(ddf.geom,path2_mnt+file_mnt25,stats="mean max",nodata=0,all_touched=True)

# import rasterio
# with rasterio.open(path2_mnt+file_mnt5) as src:
#   affine = src.transform
#   array = src.read(1)
# zonal_tmp3 = zonal_stats(ddf, array, affine=affine,stats="mean max",nodata=0,all_touched=True)


# zonal_tmp  = pd.DataFrame(zonal_tmp)
# zonal_tmp3 = pd.DataFrame(zonal_tmp3)
# zonal_tmp.columns  = ['max_alti','mean_alti']
# zonal_tmp3.columns = ['max_alti','mean_alti']
# tmp = pd.merge(df,zonal_tmp2,left_index=True,right_index=True)
from pycen import con as con_aza
sql = 'SELECT * FROM ps.v_pelouseseches_noalti'
ddf = gpd.read_postgis(sql,con_aza)
zonal_tmp2 = zonal_stats(ddf,path2_mnt+file_mnt5,stats="max",nodata=0,all_touched=True)
zonal_tmp2 = pd.DataFrame(zonal_tmp2)
zonal_tmp2.columns = ['max_alti']
tmp = pd.concat([ddf,zonal_tmp2], axis=1)
tmp = tmp.set_geometry('geom', crs=2154)
tmp['infeq_1200'] = tmp.max_alti <= 1200
tmp2 = tmp[['site_code','infeq_1200']].copy()
tmp2.rename(columns={'site_code':'id_site'},inplace=True)
tmp2.to_sql('r_infeq_1200m',con_aza,'ps',if_exists='append',index=False)

df.to_postgis(
  'ps'+table[3:-1],pycen.con_bdcen,'pelouse_seche',geom_col='geom',if_exists='replace'
)

PSinfeq1200 = tmp[tmp.max_alti <= 1200].copy()
PSsup1200 = tmp[tmp.max_alti > 1200].copy()




# PSinfeq1200 = tmp[tmp.bilan <= 1200].copy()
# PSsup1200 = tmp[tmp.bilan > 1200].copy()
# PSinfeq1200.rename(columns={'bilan':'max_alti'}, inplace=True)
# PSsup1200.rename(columns={'bilan':'max_alti'}, inplace=True)
PSinfeq1200.dropna(axis=1, how='all',inplace=True)
if table in [
  '"cr_VERCORS_habitats_CBNA_1999-2007"','"cr_TRIEVES+VERCORS_habitats_CBNA_2014"']:
  PSinfeq1200.loc[PSinfeq1200.statut=="Habitat d'intérêt communautaire",'statut'] = \
    "Communautaire"
  PSinfeq1200.loc[PSinfeq1200.statut=="Habitat d'intérêt communautaire retenu prioritaire",'statut'] = \
    "Prioritaire"
  PSinfeq1200.loc[PSinfeq1200.statut=="Habitat d'intérêt communautaire, retenu prioritaire",'statut'] = \
    "Prioritaire"
  PSinfeq1200.loc[
    PSinfeq1200.statut=="""Habitat d'intérêt communautaire, retenu prioritaire pour les sites riches en orchidées""",
    'statut'] = "Prioritaire"
  PSinfeq1200.loc[
    PSinfeq1200.statut=="""Habitat d'intérêt communautaire retenu prioritaire pour les sites riches en orchidées""",
    'statut'] = "Prioritaire"
  PSinfeq1200.loc[
    PSinfeq1200.statut=="""Habitat communautaire, retenu prioritaire pour les sites riches en orchidées""",
    'statut'] = "Prioritaire"
  PSinfeq1200.loc[PSinfeq1200.statut=="Habitat d'intérêt communautaire retenu prioritaire",'statut'] = \
    "Prioritaire"
if table in [
  '"cr_CHARTREUSE_habitats_CBNA_2000-2012"', '"cr_ECRIN_habitats_CBNA_2014"']:
  dic = {'PR':'Prioritaire','IC':'Communautaire','NC':'Non communautaire'}
  PSinfeq1200.statut.replace(dic,inplace=True)

DF_INI = PSinfeq1200.copy()
df = DF_INI.copy()
# df['structure'] = 'CBNA'


# sql = """
# DELETE FROM sites.sites 
# WHERE sites.id in (SELECT id_site from sites.r_sites_geom where id_lot = 1);
# """
# with pycen.con.begin() as cnx:
#   cnx.execute(sql)
path0 = '/home/colas/Documents/9_PROJETS/2_PS/TO IMPORT/'
cols_date = PSsup1200.columns[PSsup1200.columns.str.contains('date')]
PSsup1200[cols_date] = PSsup1200[cols_date].astype(str)
PSsup1200.to_file(path0 + 'PS_ECRIN_CBNA_2014_SUP1200.shp')

cols_date = DF_INI.columns[DF_INI.columns.str.contains('date')]
DF_INI[cols_date] = DF_INI[cols_date].astype(str)
DF_INI.to_file(path0 + 'PS_ECRIN_CBNA_2014_INF1200.shp')


op = '''
gdalwarp -overwrite -s_srs EPSG:2154 -t_srs EPSG:2154 -co FORMAT=GPKG -of GTiff -tr 25.0 -25.0 -tap -cutline \
  "PG:dbname='bd-cen-38' host=192.168.0.189 port=5432 sslmode=disable user='cgeier' password='adm1n*bdCen'" \
  -csql "SELECT * FROM habitat."cr_TRIEVES_VERCORS_habitats_CBNA_2014" WHERE idfinal in (24060)" \
  "{mnt}" {out}
'''.format(
  mnt=path2_mnt+'out.tif', 
  out=path2_mnt+'24060.tif')
system(op)
mnt = rio.open(Path_tmp+mnt_out)



sql = 'SELECT * FROM pelouse_seche."PB_codehab_nonPresent_dans_corineBiotope"'
df  = gpd.read_postgis(sql,con)
lst = ['cr_TRIEVES+VERCORS_habitats_CBNA_2014','cr_VERCORS_habitats_CBNA_1999-2007','cr_ECRIN_habitats_CBNA_2014','cr_CHARTREUSE_habitats_CBNA_2000-2012']
df = df[df.table_org.isin(lst)]
df.loc[df.auteur=='PnE','structure'] = 'PnE'


sql = 'SELECT * FROM pelouse_seche."PB_codehabCBNA_nonPresent_dans_corineBiotope"'
df2 = gpd.read_postgis(sql,con)

df3 = pd.concat([df,df2])
df3.set_geometry('geom',inplace=True,crs=2154)

df3.to_postgis(
  name='PB_codehabCBNA_nonPresent_dans_corineBiotope', 
  con=con, 
  schema='pelouse_seche',
  if_exists='replace',
  geom_col='geom'
)