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prepare_country_data.py
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# -*- coding: utf-8 -*-
import sys,glob,os,pickle
import numpy as np
from netCDF4 import Dataset,netcdftime,num2date
import pandas as pd
sys.path.append('/p/projects/tumble/carls/shared_folder/country_analysis/')
try:del sys.modules['country_analysis']
except:pass
from country_analysis import country_analysis
sys.path.append('/p/projects/tumble/carls/shared_folder/country_analysis/')
all_isos=['AGO', 'DZA', 'EGY', 'GNQ', 'BEN', 'NGA', 'NER', 'ZWE', 'NAM', 'GNB', 'SWZ', 'GHA', 'COG', 'SLE', 'ETH', 'COM', 'ERI', 'CPV', 'LBR', 'LBY', 'LSO', 'UGA', 'RWA', 'SOM', 'MDG', 'CMR', 'TZA', 'BWA', 'SEN', 'TCD', 'GAB', 'BFA', 'MWI', 'MOZ', 'MRT', 'GMB', 'MLI', 'BDI', 'STP', 'DJI', 'GIN', 'ESH', 'KEN', 'MAR', 'COD', 'ZMB', 'ZAF', 'TGO', 'TUN', 'CAF', 'SSD', 'SDN', 'CIV']
#all_isos=['EGY', 'GNQ', 'ZWE', 'NAM', 'SWZ', 'COG', 'ETH', 'COM', 'ERI', 'CPV', 'UGA', 'RWA', 'SOM', 'MDG', 'CMR', 'TZA', 'SEN', 'MWI', 'MOZ', 'STP', 'ESH', 'KEN', 'MAR', 'COD', 'ZAF', 'CAF', 'SSD', 'SDN']
try:
job_id=int(os.environ["SLURM_ARRAY_TASK_ID"])
print 'job_id=',job_id
print job_id
isos=all_isos[(job_id*6):((job_id+1)*6)]
print isos
except:
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--country",'-c', help="country iso",required=False)
parser.add_argument("--overwrite",'-o', help="overwrite output files",action="store_true")
parser.add_argument("--get_shapefile", help="overwrite output files",action="store_true")
args = parser.parse_args()
if args.overwrite:
overwrite=True
else:
overwrite=False
if args.country is not None:
isos=[args.country]
else:
isos=['CPV']
if args.get_shapefile:
for country_iso in isos:
print country_iso
COU=country_analysis(country_iso,'data/'+country_iso+'/',additional_tag='')
os.chdir(COU._working_directory)
os.system('wget biogeo.ucdavis.edu/data/gadm2.8/shp/'+country_iso+'_adm_shp.zip')
os.system('mkdir '+country_iso+'_adm_shp')
os.system('ls')
os.chdir(COU._working_directory+country_iso+'_adm_shp')
os.system('unzip ../'+country_iso+'_adm_shp.zip')
for country_iso in isos:
print country_iso
#if os.path.isdir('/p/projects/tumble/carls/shared_folder/country_analysis/data/'+country_iso+'.tar.gz')==False:
if True:
os.chdir('/p/projects/tumble/carls/shared_folder/country_analysis/')
COU=country_analysis(country_iso,'data/'+country_iso+'/',additional_tag='')
# ##############
# # MASKS
# ##############
COU.create_mask_country('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/pr/mon_pr_ECEARTH_rcp45_.nc4','pr',overwrite=overwrite)
COU.create_mask_country('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/pr/mon_pr_ECEARTH_rcp45_.nc4','pr',mask_style='pop1990_weighted',pop_mask_file='/home/pepflei/CA/masks/population/population_1990_incrLat.nc',overwrite=overwrite)
COU.create_mask_country('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/pr/mon_pr_ECEARTH_rcp45_.nc4','pr',mask_style='pop2015_weighted',pop_mask_file='/home/pepflei/CA/masks/population/population_2015_incrLat.nc',overwrite=overwrite)
COU.create_mask_admin('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/pr/mon_pr_ECEARTH_rcp45_.nc4','pr',overwrite=overwrite)
COU.create_mask_country('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_tas_EWEMBI_1979-2014.nc4','tas',overwrite=overwrite)
COU.create_mask_country('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_tas_EWEMBI_1979-2014.nc4','tas',mask_style='pop1990_weighted',pop_mask_file='/home/pepflei/CA/masks/population/population_1990_incrLat.nc',overwrite=overwrite)
COU.create_mask_country('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_tas_EWEMBI_1979-2014.nc4','tas',mask_style='pop2015_weighted',pop_mask_file='/home/pepflei/CA/masks/population/population_2015_incrLat.nc',overwrite=overwrite)
COU.create_mask_admin('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_tas_EWEMBI_1979-2014.nc4','tas',overwrite=overwrite)
# COU.create_mask_country('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/tas/mon_tas_HadGEM2-ES_historical_0-2004_BC.nc4','tas',overwrite=overwrite)
# COU.create_mask_country('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/tas/mon_tas_HadGEM2-ES_historical_0-2004_BC.nc4','tas',mask_style='pop1990_weighted',pop_mask_file='/home/pepflei/CA/masks/population/population_1990_incrLat.nc',overwrite=overwrite)
# COU.create_mask_country('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/tas/mon_tas_HadGEM2-ES_historical_0-2004_BC.nc4','tas',mask_style='pop2015_weighted',pop_mask_file='/home/pepflei/CA/masks/population/population_2015_incrLat.nc',overwrite=overwrite)
# COU.create_mask_admin('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/tas/mon_tas_HadGEM2-ES_historical_0-2004_BC.nc4','tas',COU._working_directory+country_iso+'_adm_shp/'+country_iso+'_adm1',overwrite=overwrite)
# ##############
# #CMIP5_BC
# ##############
#
# #tas
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/tas/mon_tas_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='tas',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #TXx
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/TXx/mon_TXx_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='tasmax',given_var_name='TXx',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #pr
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/pr/mon_pr_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #RX5
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/RX5/mon_RX5_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',given_var_name='RX5',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #RX1
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/RX1/mon_RX1_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',given_var_name='RX1',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
# #year_CDD
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/CDD/year_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[4]
# rcp=in_file.split('/')[-1].split('_')[5]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='year_cdd',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #Apr_Jul_cdd
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/CDD/Apr-Jul_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[4]
# rcp=in_file.split('/')[-1].split('_')[5]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='Apr_Jul_cdd',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #Jun_Sep_cdd
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/CDD/Jun-Sep_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[4]
# rcp=in_file.split('/')[-1].split('_')[5]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='Jun_Sep_cdd',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #May_Oct_cdd
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/CDD/May-Oct_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[4]
# rcp=in_file.split('/')[-1].split('_')[5]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='May_Oct_cdd',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #10day_pr
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/10_day_pr/10day_mean_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[4]
# rcp=in_file.split('/')[-1].split('_')[5]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',given_var_name='10day_pr',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='10day',overwrite=overwrite)
# #SPEI
# for months in [1,3,6,12]:
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/SPEI/SPEI_*'+str(months)+'m_*thronthwaite*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[-2]
# rcp=in_file.split('/')[-1].split('_')[-1].split('.')[0]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='spei',given_var_name='SPEI_'+str(months)+'m',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
# #SPEI
# for months in [1,3,6,12]:
# all_files=glob.glob('/p/projects/tumble/carls/shared_folder/CMIP5_monthly/SPEI/SPEI_*'+str(months)+'m_*penman*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[-2]
# rcp=in_file.split('/')[-1].split('_')[-1].split('.')[0]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='spei',given_var_name='SPEI_'+str(months)+'m_penman',data_type='CMIP5_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
# ##############
# # CORDEX
# ##############
# #tas
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/tas/mon_tas_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='tas',data_type='CORDEX',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #TXx
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/TXx/mon_TXx_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='tasmax',given_var_name='TXx',data_type='CORDEX',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #pr
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/pr/mon_pr_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',data_type='CORDEX',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #RX5
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/RX5/mon_RX5_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',given_var_name='RX5',data_type='CORDEX',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#
# #RX1
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/RX1/mon_RX1_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',given_var_name='RX1',data_type='CORDEX',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
# #year_CDD
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/CDD/year_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='year_cdd',data_type='CORDEX',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #Apr_Jul_cdd
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/CDD/Apr-Jul_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='Apr_Jul_cdd',data_type='CORDEX',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #Jun_Sep_cdd
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/CDD/Jun-Sep_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='Jun_Sep_cdd',data_type='CORDEX',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #May_Oct_cdd
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/CDD/May-Oct_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='May_Oct_cdd',data_type='CORDEX',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #10day_pr
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/10_day_pr/10day_mean_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',given_var_name='10day_pr',data_type='CORDEX',scenario=rcp,model=model,time_format='10day',overwrite=overwrite)
# #SPEI
# for months in [1,3,6,12]:
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMinput/monthly/SPEI/SPEI_*'+str(months)+'m_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[-2]
# rcp=in_file.split('/')[-1].split('_')[-1].split('.')[0]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='spei',given_var_name='SPEI_'+str(months)+'m',data_type='CORDEX',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
# ##############
# #CORDEX BC
# ##############
#tas
all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/tas/mon_tas_*')
for in_file in all_files:
model=in_file.split('/')[-1].split('_')[2]
rcp=in_file.split('/')[-1].split('_')[3]
print rcp,model,in_file
COU.country_zoom(in_file,var_name='tas',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#TXx
all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/TXx/mon_TXx_*')
for in_file in all_files:
model=in_file.split('/')[-1].split('_')[2]
rcp=in_file.split('/')[-1].split('_')[3]
print rcp,model,in_file
COU.country_zoom(in_file,var_name='tasmax',given_var_name='TXx',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#pr
all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/pr/mon_pr_*')
for in_file in all_files:
model=in_file.split('/')[-1].split('_')[2]
rcp=in_file.split('/')[-1].split('_')[3]
print rcp,model,in_file
COU.country_zoom(in_file,var_name='pr',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#RX5
all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/RX5/mon_RX5_*')
for in_file in all_files:
model=in_file.split('/')[-1].split('_')[2]
rcp=in_file.split('/')[-1].split('_')[3]
print rcp,model,in_file
COU.country_zoom(in_file,var_name='pr',given_var_name='RX5',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
#RX1
all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/RX1/mon_RX1_*')
for in_file in all_files:
model=in_file.split('/')[-1].split('_')[2]
rcp=in_file.split('/')[-1].split('_')[3]
print rcp,model,in_file
COU.country_zoom(in_file,var_name='pr',given_var_name='RX1',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
# #year_CDD
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/CDD/year_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='year_cdd',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #Apr_Jul_cdd
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/CDD/Apr-Jul_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='Apr_Jul_cdd',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #Jun_Sep_cdd
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/CDD/Jun-Sep_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='Jun_Sep_cdd',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #May_Oct_cdd
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/CDD/May-Oct_cdd_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='consecutive_dry_days_index_per_time_period',given_var_name='May_Oct_cdd',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='yearly',overwrite=overwrite)
# #10day_pr
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/10_day_pr/10day_mean_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[2]
# rcp=in_file.split('/')[-1].split('_')[3]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='pr',given_var_name='10day_pr',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='10day',overwrite=overwrite)
# # SPEI
# for months in [1,3,6,12]:
# all_files=glob.glob('/p/projects/ikiimp/RCM_BC/ISIMIP2b_bc/GCMoutput/monthly/SPEI/SPEI_*'+str(months)+'m_*')
# for in_file in all_files:
# model=in_file.split('/')[-1].split('_')[-2]
# rcp=in_file.split('/')[-1].split('_')[-1].split('.')[0]
# print rcp,model,in_file
# COU.country_zoom(in_file,var_name='spei',given_var_name='SPEI_'+str(months)+'m',data_type='CORDEX_BC',scenario=rcp,model=model,time_format='monthly',overwrite=overwrite)
# ##############
# # EWEMBI
# ##############
COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_tas_EWEMBI_1979-2014.nc4',var_name='tas',data_type='EWEMBI',time_format='monthly',overwrite=overwrite)
COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_TXx_EWEMBI_1979-2014.nc4',var_name='tasmax',given_var_name='TXx',data_type='EWEMBI',time_format='monthly',overwrite=overwrite)
COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_pr_EWEMBI_1979-2014.nc4',var_name='pr',data_type='EWEMBI',time_format='monthly',overwrite=overwrite)
COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_RX5_EWEMBI_1979-2014.nc4',var_name='pr',given_var_name='RX5',data_type='EWEMBI',time_format='monthly',overwrite=overwrite)
COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/mon_RX1_EWEMBI_1979-2014.nc4',var_name='pr',given_var_name='RX1',data_type='EWEMBI',time_format='monthly',overwrite=overwrite)
# COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/10day_pr/pr_10day_EWEMBI_1979-2013.nc4',var_name='pr',given_var_name='10day_pr',data_type='EWEMBI',time_format='10day',overwrite=overwrite)
# COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/year_CDD_EWEMBI_1979-2014.nc4',var_name='consecutive_dry_days_index_per_time_period',given_var_name='year_cdd',data_type='EWEMBI',time_format='yearly',overwrite=overwrite)
# COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/Jun-Sep_CDD_EWEMBI_1979-2014.nc4',var_name='consecutive_dry_days_index_per_time_period',given_var_name='Jun_Sep_cdd',data_type='EWEMBI',time_format='yearly',overwrite=overwrite)
# COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/May-Oct_CDD_EWEMBI_1979-2014.nc4',var_name='consecutive_dry_days_index_per_time_period',given_var_name='May_Oct_cdd',data_type='EWEMBI',time_format='yearly',overwrite=overwrite)
# COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/mon_year/Apr-Jul_CDD_EWEMBI_1979-2014.nc4',var_name='consecutive_dry_days_index_per_time_period',given_var_name='Apr_Jul_cdd',data_type='EWEMBI',time_format='yearly',overwrite=overwrite)
# for months in [1,3,6,12]:
# COU.country_zoom('/p/projects/tumble/carls/shared_folder/EWEMBI/PET/SPEI_'+str(months)+'m_thornthwaite_EWEMBI_1979-2014.nc4',var_name='spei',given_var_name='SPEI_'+str(months)+'m',data_type='EWEMBI',time_format='monthly',overwrite=overwrite)
COU.hist_merge()
COU.ensemble_mean()
COU.summary()
if len(COU.selection(['year_RX5']))<2:
for data in COU.selection(['RX5']): data.year_max('year_RX5')
for data in COU.selection(['year_RX5','ensemble_mean']): COU._DATA.remove(data)
COU.ensemble_mean()
COU.summary()
COU.area_average('lat_weighted',overwrite=overwrite)
# COU.area_average('pop2015_weighted',overwrite=False)
# COU.area_average('pop1990_weighted',overwrite=False)
COU.zip_it()
# except Exception,e:
# print str(e)
# print 'issues with '+country_iso