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juli_functions.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 6 11:36:22 2019
@author: julian.giles
"""
#%% --------- mult_hd multiplicadores para ajustar unidades -----------------
def unit_multipliers(): #para ajustar las unidades de los datasets a un común
mult_hd = {'pre': {'RCA4':[24,0], 'RCA4CLIM':[24,0], 'LMDZ':[8,0], 'LMDZCLIM':[1,0], 'JRA-55':[8,0], 'ERA5':[24*1000,0], 'TRMM-3B42':[8,0], 'CMORPH':[8,0], 'CRU':[1/30,0], 'GLEAM+CMORPH+ERA5':[8,0], 'GLDASNOAH+CMORPH+ERA5':[8,0], 'GLDASNOAH+ERA5':[86400,0]},
'pre_nodrizzle': {'RCA4':[8,0], 'RCA4CLIM':[8,0], 'LMDZ':[8,0], 'LMDZCLIM':[1,0], 'JRA-55':[8,0], 'TRMM-3B42':[8,0], 'CMORPH':[8,0]},
'lhf': {'RCA4':[-1,0], 'RCA4CLIM':[-1,0], 'LMDZ':[-1,0],'LMDZCLIM':[-1,0], 'JRA-55':[1,0], 'ERA5':[-1,0]},
'shf': {'RCA4':[-1,0], 'RCA4CLIM':[-1,0], 'LMDZ':[-1,0],'LMDZCLIM':[-1,0], 'JRA-55':[1,0], 'ERA5':[-1,0]},
'lwr': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[-1,0], 'ERA5':[1,0]},
'swr': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[-1,0], 'ERA5':[1,0]},
't2m': {'RCA4':[1,-273.15], 'RCA4CLIM':[1,-273.15], 'LMDZ':[1,-273.15], 'LMDZCLIM':[1,-273.15], 'JRA-55':[1,-273.15], 'ERA5':[1,-273.15]},
'evapot': {'RCA4':[24,0], 'RCA4CLIM':[24,0], 'LMDZ':[86400,0], 'LMDZCLIM':[86400,0], 'JRA-55':[0.0352512,0], 'ERA5':[-60*60*24,0], 'GLEAM+CMORPH+ERA5':[1,0], 'GLDASNOAH+CMORPH+ERA5':[1,0]},
'evapotpot': {'RCA4':[8,0], 'RCA4CLIM':[8,0], 'LMDZ':[86400,0], 'LMDZCLIM':[86400,0], 'JRA-55':[0.0352512,0], 'ERA5':[-60*60*24,0], 'GLEAM+CMORPH+ERA5':[1,0], 'GLDASNOAH+CMORPH+ERA5':[1,0]},
'slp': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u1000': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u950': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u900': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u850': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u800': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u750': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u700': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u600': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u500': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'u200': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'v900': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'v850': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'v200': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'q2m': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'qu900': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'qv900': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'quv900conv': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'q900': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0],'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'qu2d': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0],'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'qv2d': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0],'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'vimfc2d': {'RCA4':[86400,0], 'RCA4CLIM':[86400,0], 'LMDZ':[86400,0],'LMDZCLIM':[86400,0], 'JRA-55':[86400,0], 'ERA5':[-86400,0], 'GLEAM+CMORPH+ERA5':[-86400,0], 'GLDASNOAH+CMORPH+ERA5':[-86400,0], 'GLDASNOAH+ERA5':[-86400,0]},
'uv900conv': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0],'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[-1,0]},
'uv850conv': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0],'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[-1,0]},
'u10m': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'v10m': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'uv10mconv': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'zi': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[0,0], 'ERA5':[1,0]},
'w1000': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w950': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w900': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w850': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w800': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w750': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w700': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w600': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'w500': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
't900': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
't850': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'gpot500': {'RCA4':[1/9.8,0], 'RCA4CLIM':[1/9.8,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'gpot1000': {'RCA4':[1/9.8,0], 'RCA4CLIM':[1/9.8,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'gpot900': {'RCA4':[1/9.8,0], 'RCA4CLIM':[1/9.8,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'gpot200': {'RCA4':[1/9.8,0], 'RCA4CLIM':[1/9.8,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'sm1': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0], 'GLEAM+CMORPH+ERA5':[1,0], 'GLDASNOAH+CMORPH+ERA5':[1,0], 'GLDASNOAH+ERA5':[1,0]},
'swa': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'EF': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]}, #para pasar los datos a mm/day y correr la T a Celsius
'EF_v2': {'RCA4':[1,0], 'RCA4CLIM':[1,0],'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'precwtr': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'cloudbot': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[1,0], 'ERA5':[1,0]},
'totcc': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[0.01,0], 'ERA5':[1,0]},
'lowcc': {'RCA4':[1,0], 'RCA4CLIM':[1,0], 'LMDZ':[1,0], 'LMDZCLIM':[1,0], 'JRA-55':[0.01,0], 'ERA5':[1,0]},
'orog': {'RCA4':[1/9.8,0], 'RCA4CLIM':[1/9.8,0], 'LMDZ':[1/9.8,0], 'LMDZCLIM':[1/9.8,0], 'JRA-55':[1/9.8,0], 'ERA5':[1/9.8,0], 'GLEAM+CMORPH+ERA5':[1/9.8,0], 'GLDASNOAH+CMORPH+ERA5':[1/9.8,0], 'GLDASNOAH+ERA5':[1/9.8,0]}}
return mult_hd
def units_labels(): # diccionario con el nombre de las unidades a usar
units_labels={'pre': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'pre_nodrizzle': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'lhf': '$\mathregular{W⋅m^{-2}}$', #'W/m²',
'shf': '$\mathregular{W⋅m^{-2}}$', #'W/m²',
'lwr': '$\mathregular{W⋅m^{-2}}$', #'W/m²',
'swr': '$\mathregular{W⋅m^{-2}}$', #'W/m²',
't2m': '°C',
'evapot': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'evapotpot': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'slp': 'Pa',
'v900': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'uv900': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u1000': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u950': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u900': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u850': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u800': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u750': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u700': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u600': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u500': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'u200': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'v200': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'v850': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'uv850': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'uv200': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'q2m': 'kg/kg',
'q900': 'kg/kg',
'qu900': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'qv900': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'quv900conv': '$\mathregular{s^{-1}}$', #'1/s',
'qu2d': '$\mathregular{kg⋅m^{-1}⋅s^{-1}}$', #'kg/m/s',
'qv2d': '$\mathregular{kg⋅m^{-1}⋅s^{-1}}$', #'kg/m/s',
'quv2d': '$\mathregular{kg⋅m^{-1}⋅s^{-1}}$', #'kg/m/s',
'vimfc2d': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'uv900conv': '$\mathregular{s^{-1}}$', #'1/s',
'uv850conv': '$\mathregular{s^{-1}}$', #'1/s',
'u10m': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'v10m': '$\mathregular{m⋅s^{-1}}$', #'m/s',
'uv10mconv': '$\mathregular{s^{-1}}$', #'1/s',
'EF': '' ,
'EF_v2': '' ,
'swa': '' ,
'zi': 'm',
'w1000': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w950': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w900': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w850': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w800': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w750': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w700': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w600': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
'w500': '$\mathregular{Pa⋅s^{-1}}$', #'Pa/s',
't900': '°K',
't850': '°K',
'gpot500': 'gpm',
'gpot1000': 'gpm',
'gpot900': 'gpm',
'gpot200': 'gpm',
'sm1': 'm',
'precwtr': '$\mathregular{kg⋅m^{-2}}$', #kg/m2',
'cloudbot': 'm',
'lowcc': '',
'totcc': '',
'orog': 'm'}
return units_labels
def units_labels_old(): # diccionario con el nombre de las unidades a usar
units_labels={'pre': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'pre_nodrizzle': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'lhf': 'W/m²',
'shf': 'W/m²',
'lwr': 'W/m²',
'swr': 'W/m²',
't2m': 'C',
'evapot': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'evapotpot': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'slp': 'Pa',
'v900': 'm/s',
'uv900': 'm/s',
'u1000': 'm/s',
'u950': 'm/s',
'u900': 'm/s',
'u850': 'm/s',
'u800': 'm/s',
'u750': 'm/s',
'u700': 'm/s',
'u600': 'm/s',
'u500': 'm/s',
'u200': 'm/s',
'v200': 'm/s',
'v850': 'm/s',
'uv850': 'm/s',
'uv200': 'm/s',
'q2m': 'kg/kg',
'q900': 'kg/kg',
'qu900': 'm/s',
'qv900': 'm/s',
'quv900conv': '1/s',
'qu2d': 'kg/m/s',
'qv2d': 'kg/m/s',
'quv2d': 'kg/m/s',
'vimfc2d': '$\mathregular{mm⋅day^{-1}}$', #'mm/day',
'uv900conv': '1/s',
'uv850conv': '1/s',
'u10m': 'm/s',
'v10m': 'm/s',
'uv10mconv': '1/s',
'EF': '' ,
'EF_v2': '' ,
'swa': '' ,
'zi': 'm',
'w1000': 'Pa/s',
'w950': 'Pa/s',
'w900': 'Pa/s',
'w850': 'Pa/s',
'w800': 'Pa/s',
'w750': 'Pa/s',
'w700': 'Pa/s',
'w600': 'Pa/s',
'w500': 'Pa/s',
't900': 'K',
'gpot900': 'gpm',
'gpot200': 'gpm',
'sm1': 'm',
'precwtr': 'kg/m2',
'cloudbot': 'm',
'lowcc': '',
'totcc': '',
'orog': 'm'}
return units_labels
def regions():
regions = {'NEA':[-65,-57,-35,-25, "black"],
'JEXIT': [-62,-58,-30,-25, 'Red'],
'SALLJ':[-62,-54,-30,-20, "black"],
'BH':[-50,-40,-20,-10, "black"],
'SWA':[-70,-60,-12,-7, "black"]
}
return regions
#%% -------- VARIABLES FILES ------------
def files_dict():
# diccionario con los nombres de los archivos:
files =dict()
files['RCA4'] = {'pre': 'CTL/Data/1980-2012/pre/pre_1982-2012.nc',
'lhf': 'CTL/Data/1980-2012/lhf/lhf_198001_201212_sSA.nc',
'shf': 'CTL/Data/1980-2012/shf/shf_198001_201212_sSA.nc',
'lwr': 'CTL/Data/1980-2012/lwr/netlwr_198001_201212_sSA.nc',
'swr': 'CTL/Data/1980-2012/swr/netswr_198001_201212_sSA.nc',
't2m': 'CTL/Data/1980-2012/t2m/t2m_1982-2012.nc',
'evapot': 'CTL/Data/1980-2012/evapot/evapot_1982-2012.nc',
'evapotpot': 'CTL/Data/1980-2012/evapotpot/evapotpot_198001_201212_sSA.nc',
'slp': 'CTL/Data/1980-2012/slp/slp_198001_201212_sSA.nc',
'u900': 'CTL/Data/1980-2012/u-v/u900_1983-2012.nc',
'u200': 'CTL/Data/1980-2012/u-v/u200_1983-2012.nc',
'v900': 'CTL/Data/1980-2012/u-v/v900_1983-2012.nc',
'v200': 'CTL/Data/1980-2012/u-v/v200_1983-2012.nc',
'uv900conv': 'CTL/Data/1980-2012/u-v/uv900_conv_1980-2012.nc',
'u10m': 'CTL/Data/1980-2012/u-v_10m/u10m_198001_201212_sSA.nc',
'v10m': 'CTL/Data/1980-2012/u-v_10m/v10m_198001_201212_sSA.nc',
'uv10mconv': 'CTL/Data/1980-2012/u-v_10m/uv10m_conv_1980-2012.nc',
'q2m': 'CTL/Data/1980-2012/q2m/q2m_198001_201212_sSA.nc',
'q900': 'CTL/Data/1980-2012/q/q900_198001_201212_sSA.nc',
'qu900': 'CTL/Data/1980-2012/qu-qv/qu900_1983-2012.nc',
'qv900': 'CTL/Data/1980-2012/qu-qv/qv900_1983-2012.nc',
'quv900conv': 'CTL/Data/1980-2012/qu-qv/quv900conv_1983-2012.nc',
'qu2d': 'CTL/Data/1980-2012/qu2d-qv2d/qu2d_198001_201212_sSA.nc',
'qv2d': 'CTL/Data/1980-2012/qu2d-qv2d/qv2d_198001_201212_sSA.nc',
'vimfc2d': 'CTL/Data/1980-2012/vimfc2d/vimfc2d_198001_201212_sSA.nc',
'swa': 'CTL/Data/1980-2012/swa/swa_198001_201212_sSA.nc',
'zi': 'CTL/Data/1980-2012/zi/zi_198001_201212_sSA.nc',
'w900': 'CTL/Data/1980-2012/w/w900_198001_201212_sSA.nc',
'w500': 'CTL/Data/1980-2012/w/w500_1983-2012.nc',
'cloudbot': 'CTL/Data/1980-2012/cloudbot/cloudbot_198001_201212_sSA.nc',
'lowcc': 'CTL/Data/1980-2012/lowcc/lowcc_198001_201212_sSA.nc',
'totcc': 'CTL/Data/1980-2012/totcc/totcc_198001_201212_sSA.nc',
'precwtr': 'CTL/Data/1980-2012/precwtr/precwtr_198001_201212_sSA.nc',
'sm1': 'CTL/Data/1980-2012/sm/sm1_1982-2012.nc',
't900': 'CTL/Data/1980-2012/t/t900_198001_201212_sSA.nc',
't850': 'CTL/Data/1980-2012/t/t850_1982-2012.nc',
'gpot500': 'CTL/Data/1980-2012/gpot/gpot500_198001_201212_sSA.nc',
'gpot1000': 'CTL/Data/1980-2012/gpot/gpot1000_198001_201212_sSA.nc',
'gpot900': 'CTL/Data/1980-2012/gpot/gpot900_198001_201212_sSA.nc',
'gpot200': 'CTL/Data/1980-2012/gpot/gpot200_198001_201212_sSA.nc',
'orog': 'CTL/Data/1980-2012/land_data/gpot.nc',
'lsmask': 'CTL/Data/1980-2012/land_data/frland.nc'}
files['RCA4CLIM'] = {'pre': 'CLIM/Data/1980-2012/pre/pre_1982-2012.nc',
'lhf': 'CLIM/Data/1980-2012/lhf/lhf_198001_201212_sSA.nc',
'shf': 'CLIM/Data/1980-2012/shf/shf_198001_201212_sSA.nc',
'lwr': 'CLIM/Data/1980-2012/lwr/netlwr_198001_201212_sSA.nc',
'swr': 'CLIM/Data/1980-2012/swr/netswr_198001_201212_sSA.nc',
't2m': 'CLIM/Data/1980-2012/t2m/t2m_1982-2012_3h.nc',
'evapot': 'CLIM/Data/1980-2012/evap_c/evap_c_1982-2012.nc',
'evapotpot': 'CLIM/Data/1980-2012/evapotpot/evapotpot_198001_201212_sSA.nc',
'slp': 'CLIM/Data/1980-2012/slp/slp_198001_201212_sSA.nc',
'u900': 'CLIM/Data/1980-2012/u-v/u900_1983-2012.nc',
'u200': 'CLIM/Data/1980-2012/u-v/u200_1983-2012.nc',
'v900': 'CLIM/Data/1980-2012/u-v/v900_1983-2012.nc',
'v200': 'CLIM/Data/1980-2012/u-v/v200_1983-2012.nc',
'uv900conv': 'CLIM/Data/1980-2012/u-v/uv900_conv_1980-2012.nc',
'u10m': 'CLIM/Data/1980-2012/u-v_10m/u10m_198001_201212_sSA.nc',
'v10m': 'CLIM/Data/1980-2012/u-v_10m/v10m_198001_201212_sSA.nc',
'uv10mconv': 'CLIM/Data/1980-2012/u-v_10m/uv10m_conv_1980-2012.nc',
'q2m': 'CLIM/Data/1980-2012/q2m/q2m_198001_201212_sSA.nc',
'q900': 'CLIM/Data/1980-2012/q/q900_1983-2012.nc',
'qu900': 'CLIM/Data/1980-2012/qu-qv/qu900_1983-2012.nc',
'qv900': 'CLIM/Data/1980-2012/qu-qv/qv900_1983-2012.nc',
'quv900conv': 'CLIM/Data/1980-2012/qu-qv/quv900conv_1983-2012.nc',
'qu2d': 'CLIM/Data/1980-2012/qu2d-qv2d/qu2d_198001_201212_sSA.nc',
'qv2d': 'CLIM/Data/1980-2012/qu2d-qv2d/qv2d_198001_201212_sSA.nc',
'vimfc2d': 'CLIM/Data/1980-2012/vimfc2d/vimfc2d_198001_201212_sSA.nc',
'swa': 'CLIM/Data/1980-2012/swa/swa_198001_201212_sSA.nc',
'zi': 'CLIM/Data/1980-2012/zi/zi_198001_201212_sSA.nc',
'w900': 'CLIM/Data/1980-2012/w/w900_198001_201212_sSA.nc',
'w500': 'CLIM/Data/1980-2012/w/w500_1983-2012.nc',
'cloudbot': 'CLIM/Data/1980-2012/cloudbot/cloudbot_198001_201212_sSA.nc',
'lowcc': 'CLIM/Data/1980-2012/lowcc/lowcc_198001_201212_sSA.nc',
'totcc': 'CLIM/Data/1980-2012/totcc/totcc_198001_201212_sSA.nc',
'precwtr': 'CLIM/Data/1980-2012/precwtr/precwtr_198001_201212_sSA.nc',
'sm1': 'CLIM/Data/1980-2012/sm/sm1_198001_201212_sSA_fixed.nc',
't900': 'CLIM/Data/1980-2012/t/t900_198001_201212_sSA.nc',
't850': 'CLIM/Data/1980-2012/t/t850_1982-2012.nc',
'gpot500': 'CLIM/Data/1980-2012/gpot/gpot500_198001_201212_sSA.nc',
'gpot1000': 'CLIM/Data/1980-2012/gpot/gpot1000_198001_201212_sSA.nc',
'gpot900': 'CLIM/Data/1980-2012/gpot/gpot900_198001_201212_sSA.nc',
'gpot200': 'CLIM/Data/1980-2012/gpot/gpot200_198001_201212_sSA.nc',
'orog': 'CTL/Data/1980-2012/land_data/gpot.nc',
'lsmask': 'CTL/Data/1980-2012/land_data/frland.nc'}
files['LMDZ'] = {'pre': 'LMDZ/CTL/rainsnow/LMDZ_3h_rainsnow_1979-2012_new.nc', #(LMDZ new esta en mm/3h)
'lhf': 'LMDZ/CTL/latf/latf_1983-2012_mon.nc',
'shf': 'LMDZ/CTL/senf/senf_1983-2012_mon.nc',
'lwr': 'LMDZ/CTL/lwr/lwr_1983-2012_mon.nc',
'swr': 'LMDZ/CTL/swr/swr_1983-2012_mon.nc',
't2m': 'LMDZ/CTL/t2m/LMDZ_3h_t2m_1979-2012.nc',
'evapot': 'LMDZ/CTL/evap/evap_c_1982-2012_day.nc',
'u900': 'LMDZ/CTL/vitu/LMDZ_day_vitu_1979-2012_000900.nc',
'v900': 'LMDZ/CTL/vitv/LMDZ_day_vitv_1979-2012_000900.nc',
'uv900conv': 'LMDZ/CTL/uvconv/LMDZ_day_uv900_convergence_1979-2012.nc',
'u850': 'LMDZ/CTL/vitu/LMDZ_day_vitu_1979-2012_000850.nc',
'v850': 'LMDZ/CTL/vitv/LMDZ_day_vitv_1979-2012_000850.nc',
'uv850conv': 'LMDZ/CTL/uvconv/LMDZ_day_uv850_convergence_1979-2012.nc',
'qu2d': 'LMDZ/CTL/vimtu/LMDZ_3h_vimtu_1979-2012.nc',
'qv2d': 'LMDZ/CTL/vimtv/LMDZ_3h_vimtv_1979-2012.nc',
'vimfc2d': 'LMDZ/CTL/vimfc2d/LMDZ_3h_vimfc2d_1979-2012.nc',
'w900': 'LMDZ/CTL/w/w_1983-2012000900.nc',
'w500': 'LMDZ/CTL/w/w_1983-2012000500.nc'}
files['LMDZCLIM'] = {'pre': 'LMDZ/CLIM/pre/pre_1982-2012_day_new.nc',
'lhf': 'LMDZ/CLIM/latf/latf_1983-2012_mon.nc',
'shf': 'LMDZ/CLIM/senf/senf_1983-2012_mon.nc',
'lwr': 'LMDZ/CLIM/lwr/lwr_1983-2012_mon.nc',
'swr': 'LMDZ/CLIM/swr/swr_1983-2012_mon.nc',
't2m': 'LMDZ/CLIM/t2m/t2m_1982-2012_day.nc',
'evapot': 'LMDZ/CLIM/evap_c/evap_c_1982-2012_day.nc',
'u900': 'LMDZ/CLIM/u-v/u_1983-2012000900.nc',
'v900': 'LMDZ/CLIM/u-v/v_1983-2012000900.nc',
'qu2d': 'LMDZ/CLIM/qu-qv/qu_1983-2012.nc',
'qv2d': 'LMDZ/CLIM/qu-qv/qv_1983-2012.nc',
'vimfc2d': 'LMDZ/CLIM/vimfc2d/',
'w900': 'LMDZ/CLIM/w/w_1983-2012000900.nc',
'w500': 'LMDZ/CLIM/w/w_1983-2012000500.nc'}
files['JRA-55'] = {'pre': 'JRA-55/pre/pre_forecast3h_198210-201301_new.nc', # CUIDADO CON EL CORRIMIENTO DE TIEMPO. LOS PROMEDIOS TIENEN SU TIEMPO AL INICIO DEL INTERVALO, EL RESTO SON INSTANTANEOS.
'lhf': 'JRA-55/lhf/lhf_1983-2012.nc',
'shf': 'JRA-55/shf/shf_1983-2012.nc',
'lwr': 'JRA-55/lwr/netlwr_1983-2012.nc',
'swr': 'JRA-55/swr/netswr_1983-2012.nc',
't2m': 'JRA-55/t2m/t2m_forecast3h_1980-2013.nc',
'evapot': 'JRA-55/evapot/evapot_1982-2012.nc',
'slp': 'JRA-55/sea_lvl_pressure/slp_diagnostic3h_1980-2012.nc',
'u900': 'JRA-55/u-v/u900_1983-2012_new.nc',
'v900': 'JRA-55/u-v/v900_1983-2012_new.nc',
'uv900conv': 'JRA-55/u-v/uv900_convergence_1983-2012.nc',
'u10m': 'JRA-55/u-v_10m/u10m_diagnostic3h_1980-2012.nc',
'v10m': 'JRA-55/u-v_10m/v10m_diagnostic3h_1980-2012.nc',
'uv10mconv': 'JRA-55/u-v_10m/uv10m_convergence_1980-2012.nc',
'q2m': 'JRA-55/q2m/q2m_diagnostic3h_1980-2012.nc',
'q900': 'JRA-55/q/q900_analysis6h_1980-2012.nc',
'qu900': 'JRA-55/qu_qv/qu900_1983-2012.nc',
'qv900': 'JRA-55/qu_qv/qv900_1983-2012.nc',
'quv900conv': 'JRA-55/qu_qv/quv900conv_1983-2012.nc',
'qu2d': 'JRA-55/qu2d_qv2d/qu2d_forecast3h_1980-2013.nc',
'qv2d': 'JRA-55/qu2d_qv2d/qv2d_forecast3h_1980-2013.nc',
'vimfc2d': 'JRA-55/vimfc2d/vimfc2d_forecast3h_1980-2013.nc',
'zi': 'JRA-55/',
'w900': 'JRA-55/w/vvel900_1983-2012.nc',
'sm1': 'JRA-55/sm/sm_198210-201301.nc',
'precwtr': 'JRA-55/precwtr/precwtr_1982-2012.nc',
'lowcc': 'JRA-55/lowcc/lowcc_1982-2012.nc',
'totcc': 'JRA-55/totcc/totcc_1982-2012.nc',
'orog': 'JRA-55/land_data/gpot.nc',
'lsmask': 'JRA-55/sm/sm_198210-201301.nc',
}
files['ERA5'] = {'pre': 'ERA5/single_level_vars/total_precipitation/total_precipitation_year_*.nc',
'lhf': 'ERA5/single_level_vars/mean_surface_latent_heat_flux/mean_surface_latent_heat_flux_year_*.nc',
'shf': 'ERA5/single_level_vars/mean_surface_sensible_heat_flux/mean_surface_sensible_heat_flux_year_*.nc',
'lwr': 'ERA5/single_level_vars/mean_surface_net_long_wave_radiation_flux/mean_surface_net_long_wave_radiation_flux_year_*.nc',
'swr': 'ERA5/single_level_vars/mean_surface_net_short_wave_radiation_flux/mean_surface_net_short_wave_radiation_flux_year_*.nc',
't2m': 'ERA5/single_level_vars/2m_temperature/2m_temperature_year_*.nc',
'evapot': 'ERA5/single_level_vars/mean_evaporation_rate/mean_evaporation_rate_year_*.nc',
'slp': 'ERA5/single_level_vars/mean_sea_level_pressure/mean_sea_level_pressure_year_*.nc',
'u1000': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_1000_year_*.nc',
'u950': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_950_year_*.nc',
'u900': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_900_year_*.nc',
'u850': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_850_year_*.nc',
'u800': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_800_year_*.nc',
'u750': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_750_year_*.nc',
'u700': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_700_year_*.nc',
'u600': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_600_year_*.nc',
'u500': 'ERA5/pressure_level_vars/u_component_of_wind/u_component_of_wind_500_year_*.nc',
'w1000': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_1000_year_*.nc',
'w950': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_950_year_*.nc',
'w900': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_900_year_*.nc',
'w850': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_850_year_*.nc',
'w800': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_800_year_*.nc',
'w750': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_750_year_*.nc',
'w700': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_700_year_*.nc',
'w600': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_600_year_*.nc',
'w500': 'ERA5/pressure_level_vars/vertical_velocity/vertical_velocity_500_year_*.nc',
'v900': 'ERA5/pressure_level_vars/v_component_of_wind/v_component_of_wind_900_year_*.nc',
'uv900conv': 'ERA5/pressure_level_vars/divergence/divergence_900_year_*.nc',
'v850': 'ERA5/pressure_level_vars/v_component_of_wind/v_component_of_wind_850_year_*.nc',
'uv850conv': 'ERA5/pressure_level_vars/divergence/divergence_850_year_*.nc',
'u10m': 'ERA5/single_level_vars/10m_u_component_of_wind/10m_u_component_of_wind_year_*.nc',
'v10m': 'ERA5/single_level_vars/10m_v_component_of_wind/10m_v_component_of_wind_year_*.nc',
'uv10mconv': 'ERA5/single_level_vars/10m_uv_convergence/10m_uv_convergence_year_*.nc',
'q900': 'ERA5/pressure_level_vars/specific_humidity/specific_humidity_900_year_*.nc',
'qu900': 'ERA5/pressure_level_vars/qu_qv/qu_900_year_*.nc',
'qv900': 'ERA5/pressure_level_vars/qu_qv/qv_900_year_*.nc',
'qu2d': 'ERA5/single_level_vars/vertical_integral_of_eastward_water_vapour_flux/vertical_integral_of_eastward_water_vapour_flux_year_*.nc',
'qv2d': 'ERA5/single_level_vars/vertical_integral_of_northward_water_vapour_flux/vertical_integral_of_northward_water_vapour_flux_year_*.nc',
'vimfc2d': 'ERA5/single_level_vars/mean_vertically_integrated_moisture_divergence/mean_vertically_integrated_moisture_divergence_year_*.nc',
'quv900conv': 'ERA5/pressure_level_vars/qu_qv/quv900conv_*.nc',
'zi': 'ERA5/single_level_vars/boundary_layer_height/boundary_layer_height_year_*.nc',
'sm1': 'ERA5/single_level_vars/volumetric_soil_water_layer_1/volumetric_soil_water_layer_1_year_*.nc',
'precwtr': 'ERA5/single_level_vars/total_column_water_vapour/total_column_water_vapour_year_*.nc',
'cloudbot': 'ERA5/single_level_vars/cloud_base_height/cloud_base_height_year_*.nc',
'lowcc': 'ERA5/single_level_vars/low_cloud_cover/low_cloud_cover_year_*.nc',
'totcc': 'ERA5/single_level_vars/total_cloud_cover/total_cloud_cover_year_*.nc',
'orog': 'ERA5/single_level_vars/orography_geopotential_rotated.nc',
'lsmask': 'ERA5/single_level_vars/land-sea_mask_rotated.nc'}
files['TRMM-3B42'] = {'pre': 'TRMM_3B42/TRMM_3B42_3H_1998-2014_7_cut_fixed3h.nc'} #fixed3h es cambiado al formato del resto (acumulado en las 3h previas, in_mm3h es multiplicado por 3 el archivo original)
files['CMORPH'] = {'pre': 'CMORPH/CMORPH_V1.0_CRT_0.25deg-3HLY_cut.nc'}
files['CRU'] = {'pre': 'CRU/cru_ts3.23.1971.2014.pre.dat.nc'}
files['GLEAM'] = {'sm1': 'GLEAM/v3.5a/daily/*/SMsurf_*_GLEAM_v3.5a.nc',
'evapot': 'GLEAM/v3.5a/daily/*/E_*_GLEAM_v3.5a.nc'}
files['GLDASNOAH'] = {'sm1': 'GLDAS_Noah_v2/soil_moisture_content_1/*.nc',
'pre': 'GLDAS_Noah_v2/total_precipitation_rate/*.nc'}
files['ESACCI'] = {'sm1': 'ESA_CCI_SM-v06.1/COMBINED/*/ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-*-fv06.1.nc'}
files['GLEAM+CMORPH+ERA5'] = {'pre': 'CMORPH/CMORPH_V1.0_CRT_0.25deg-3HLY_cut.nc',
'sm1': 'GLEAM/v3.5a/daily/*/SMsurf_*_GLEAM_v3.5a.nc',
'vimfc2d': 'ERA5/single_level_vars/mean_vertically_integrated_moisture_divergence/mean_vertically_integrated_moisture_divergence_year_*.nc',
'orog': 'ERA5/single_level_vars/orography_geopotential_rotated.nc',
'lsmask': 'ERA5/single_level_vars/land-sea_mask_rotated.nc'}
files['GLDASNOAH+CMORPH+ERA5'] = {'pre': 'CMORPH/CMORPH_V1.0_CRT_0.25deg-3HLY_cut.nc',
'sm1': 'GLDAS_Noah_v2/*.nc',
'vimfc2d': 'ERA5/single_level_vars/mean_vertically_integrated_moisture_divergence/mean_vertically_integrated_moisture_divergence_year_*.nc',
'orog': 'ERA5/single_level_vars/orography_geopotential_rotated.nc',
'lsmask': 'ERA5/single_level_vars/land-sea_mask_rotated.nc'}
return files
#%% ------------ CARGA DE DATOS -------------------
def load_datasets (models, var_list, start_date, end_date, data_path, homepath, files, chunksize, seas_warm, seas_cold, latfix, lonfix, forward_timestep=False, interpolated='no', latlims=(-50.3, 13.5), triRCA=False):
# forward_timestep=True si quiero que los timesteps indiquen el inicio del intervalo de datos que representa (False para que esté al final)
# ---------- TOMA DE DATOS -----------
import xarray as xr
import numpy as np
data_xr = dict()
data = dict()
lon = dict()
lat = dict()
lonproj = dict()
latproj = dict()
for model in models.keys():
print("---------------------------- Cargando "+model)
data_xr[model] = dict()
data[model] = dict()
for var in var_list:
if var in ['EF']:
data[model][var] = dict()
continue
print("Cargando "+var)
if model == 'JRA-55':
try:
if var == 'orog':
data_xr[model][var] = xr.open_mfdataset(data_path+files[model][var], combine='by_coords', chunks={'time':int(chunksize*10)})
else:
data_xr[model][var] = xr.open_mfdataset(data_path+files[model][var], combine='by_coords', chunks={'initial_time0_hours':int(chunksize*10)})
except:
print(' '+var+' not available for '+model+', ignoring')
continue
elif model in ['ERA5', 'GLEAM', 'ESACCI', 'GLDASNOAH']:
try: data_xr[model][var] = xr.open_mfdataset(data_path+files[model][var], combine='by_coords', chunks={'time':chunksize})
except:
print(' '+var+' not available for '+model+', ignoring')
continue
elif model == 'CRU':
try: data_xr[model][var] = xr.open_mfdataset(data_path+files[model][var], combine='by_coords', chunks={'time':chunksize})
except:
print(' '+var+' not available for '+model+', ignoring')
continue
else:
try: data_xr[model][var] = xr.open_mfdataset(homepath+files[model][var], combine='by_coords', chunks={'time':(chunksize*10)})
except:
print(' '+var+' not available for '+model+', ignoring')
continue
var_temp = dict(data_xr[model][var].data_vars)
# get coords names
lon_name = [coord for coord in set(data_xr[model][var].coords.keys()) if "lon" in coord][0]
lat_name = [coord for coord in set(data_xr[model][var].coords.keys()) if "lat" in coord][0]
data[model][var] = dict()
nn=1
if var in ['orog','lsmask']: #para estas hay un solo paso temporal , cambio temporalmente la variable para agarrarlo
start_aux = start_date
start_date = '1980-01-01'
if model == 'RCA4' or model == 'RCA4CLIM':
if triRCA and var in ['zi', 'lhf', 'shf', 'swr', 'lwr', 't2m'] : nn=3 # Para tomar variables horarias de forma trihoraria. Para mejorar: buscarle una forma mas elegante
if forward_timestep and var not in ['orog', 'lsmask']:
if int(data_xr[model][var].coords['time'][2]-data_xr[model][var].coords['time'][1]) < 24*3600000000000 and int(data_xr[model][var].coords['time'][8]-data_xr[model][var].coords['time'][7]) < 24*3600000000000:
tshift = data_xr[model][var].coords['time'][3]-data_xr[model][var].coords['time'][2]
data_xr[model][var].coords['time'] = data_xr[model][var].coords['time'] - tshift
else:
print('!!!!!!!!!!!!! something is not right, unable to shift timesteps !!!!!!!!!!!!!!!!')
if len(data_xr[model][var].coords) == 4:
data[model][var][''] = data_xr[model][var].sel(time=slice(start_date, end_date), lat=slice(latlims[0], latlims[1]))[[str(var_name) for var_name in set(var_temp.keys())][0]][::nn,0,:,:]
else:
data[model][var][''] = data_xr[model][var].sel(time=slice(start_date, end_date), lat=slice(latlims[0], latlims[1]))[[str(var_name) for var_name in set(var_temp.keys())][0]][::nn,:,:]
data[model][var][''].coords[lon_name] = data[model][var][''][lon_name] -360
if model == 'LMDZ' or model == 'LMDZCLIM':
if len(data_xr[model][var].coords) == 4:
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1], latfix[interpolated]), lon_name:slice(278+lonfix[interpolated][model], 326+lonfix[interpolated][model])}][[str(var_name) for var_name in set(var_temp.keys()) if var_name != 'time_bnds'][0]][:,0,:,:]
if len(data_xr[model][var].coords) == 3:
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1], latfix[interpolated]), lon_name:slice(278+lonfix[interpolated][model], 326+lonfix[interpolated][model])}][[str(var_name) for var_name in set(var_temp.keys()) if var_name != 'time_bnds'][0]][:,:,:]
data[model][var][''].coords['time'] = data[model][var]['']['time'] + int(1.5*3600000000000) #sumo 1.5h (en LMDZ las cuentas trihorarias están ubicadas en el centro del intervalo)
if interpolated=='True':
data[model][var][''].coords[lon_name] = data[model][var][''][lon_name] -360
if model == 'JRA-55':
if forward_timestep and var not in ['orog', 'lsmask']:
if int(data_xr[model][var].coords['initial_time0_hours'][2]-data_xr[model][var].coords['initial_time0_hours'][1]) < 24*3600000000000 and int(data_xr[model][var].coords['initial_time0_hours'][8]-data_xr[model][var].coords['initial_time0_hours'][7]) < 24*3600000000000:
tshift = data_xr[model][var].coords['initial_time0_hours'][3]-data_xr[model][var].coords['initial_time0_hours'][2]
data_xr[model][var].coords['initial_time0_hours'] = data_xr[model][var].coords['initial_time0_hours'] - tshift
else:
print('!!!!!!!!!!!!! something is not right, unable to shift timesteps !!!!!!!!!!!!!!!!')
if var=='orog':
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278, 326)}][[str(var_name) for var_name in set(var_temp.keys()) if 'time' not in var_name][0]].rename({'time': 'initial_time0_hours'}).compute()
elif var=='lsmask':
data[model][var][''] = (~np.isnan(data_xr[model][var].loc[{lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278, 326)}][[str(var_name) for var_name in set(var_temp.keys()) if 'time' not in var_name][0]][10:12,0,:,:])).compute()
elif len(data_xr[model][var].coords) == 4:
data[model][var][''] = data_xr[model][var].loc[{'initial_time0_hours':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278, 326)}][[str(var_name) for var_name in set(var_temp.keys()) if 'time' not in var_name][0]][1:,0,:,:]
else:
data[model][var][''] = data_xr[model][var].loc[{'initial_time0_hours':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278, 326)}][[str(var_name) for var_name in set(var_temp.keys()) if 'time' not in var_name][0]][1:,:,:]
data[model][var][''].coords[lon_name] = data[model][var][''][lon_name] -360
if model == 'ERA5':
if forward_timestep and var not in ['orog', 'lsmask']:
if int(data_xr[model][var].coords['time'][2]-data_xr[model][var].coords['time'][1]) < 24*3600000000000 and int(data_xr[model][var].coords['time'][8]-data_xr[model][var].coords['time'][7]) < 24*3600000000000:
tshift = data_xr[model][var].coords['time'][3]-data_xr[model][var].coords['time'][2]
data_xr[model][var].coords['time'] = data_xr[model][var].coords['time'] - tshift
else:
print('!!!!!!!!!!!!! something is not right, unable to shift timesteps !!!!!!!!!!!!!!!!')
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1],-1), lon_name:slice(278-360, 326-360)}][[str(var_name) for var_name in set(var_temp.keys())][0]][:,:,:]
if model == 'TRMM-3B42':
if forward_timestep and var not in ['orog', 'lsmask']:
if int(data_xr[model][var].coords['time'][2]-data_xr[model][var].coords['time'][1]) < 24*3600000000000 and int(data_xr[model][var].coords['time'][8]-data_xr[model][var].coords['time'][7]) < 24*3600000000000:
tshift = data_xr[model][var].coords['time'][3]-data_xr[model][var].coords['time'][2]
data_xr[model][var].coords['time'] = data_xr[model][var].coords['time'] - tshift
else:
print('!!!!!!!!!!!!! something is not right, unable to shift timesteps !!!!!!!!!!!!!!!!')
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278+lonfix[interpolated][model], 326+lonfix[interpolated][model])}]['pcp'][1:,:,:]
if interpolated=='True':
data[model][var][''].coords[lon_name] = data[model][var][''][lon_name] -360
if model == 'CMORPH':
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278, 326)}][[str(var_name) for var_name in set(var_temp.keys())][0]][:-1,0,:,:]
if not forward_timestep: data[model][var][''].coords['time'] = data[model][var]['']['time'] + int(3*3600000000000) #Muevo 3 horas para adelante, el timestep de CMORPH indica el inicio del intervalo de acumulación.
data[model][var][''].coords[lon_name] = data[model][var][''][lon_name] -360
if model == 'CRU':
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278-360, 326-360)}][[str(var_name) for var_name in set(var_temp.keys())][0]][:,:,:]
if model == 'GLEAM':
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1],-1), lon_name:slice(278-360, 326-360)}][[str(var_name) for var_name in set(var_temp.keys())][0]][:,:,:]
if model == 'GLDASNOAH':
if forward_timestep and var not in ['orog', 'lsmask']:
if int(data_xr[model][var].coords['time'][2]-data_xr[model][var].coords['time'][1]) < 24*3600000000000 and int(data_xr[model][var].coords['time'][8]-data_xr[model][var].coords['time'][7]) < 24*3600000000000:
tshift = data_xr[model][var].coords['time'][3]-data_xr[model][var].coords['time'][2]
data_xr[model][var].coords['time'] = data_xr[model][var].coords['time'] - tshift
else:
print('!!!!!!!!!!!!! something is not right, unable to shift timesteps !!!!!!!!!!!!!!!!')
try:
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278-360, 326-360)}][[str(var_name) for var_name in set(var_temp.keys())][0]][:,:,:]
except:
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1]), lon_name:slice(278-360, 326-360)}][[str(var_name) for var_name in set(var_temp.keys())][1]][:,:,:]
if model == 'ESACCI':
if var == 'sm1':
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1],-1), lon_name:slice(278-360, 326-360)}]['sm'][:,:,:]
else:
data[model][var][''] = data_xr[model][var].loc[{'time':slice(start_date, end_date), lat_name:slice(latlims[0], latlims[1],-1), lon_name:slice(278-360, 326-360)}][[str(var_name) for var_name in var_temp.keys()][0]][:,:,:]
if var in ['orog','lsmask']: #vuelvo al start date original
start_date = start_aux
# extraigo lon y lats
lon[model] = data[model][var][''][lon_name]
lat[model] = data[model][var][''][lat_name]
lonproj[model], latproj[model] = np.meshgrid(lon[model], lat[model])
# separo semestres calido y frio
print("Separando semestres cálido y frío")
def is_warm(month):
return (month >= seas_warm[3]) | (month <= seas_warm[2])
def is_cold(month):
return (month >= seas_cold[0]) & (month <= seas_cold[5])
def is_summer(month):
return (month >= 12) | (month <= 2)
def is_winter(month):
return (month >= 6) & (month <= 8)
def is_spring(month):
return (month >= 9) & (month <= 11)
def is_fall(month):
return (month >= 3) & (month <= 5)
if model == 'JRA-55':
if var=='lsmask': continue
data[model][var]['ONDJFM'] = data[model][var][''].sel(initial_time0_hours=is_warm(data[model][var]['']['initial_time0_hours.month']))
data[model][var]['AMJJAS'] = data[model][var][''].sel(initial_time0_hours=is_cold(data[model][var]['']['initial_time0_hours.month']))
data[model][var]['DJF'] = data[model][var][''].sel(initial_time0_hours=is_summer(data[model][var]['']['initial_time0_hours.month']))
data[model][var]['JJA'] = data[model][var][''].sel(initial_time0_hours=is_winter(data[model][var]['']['initial_time0_hours.month']))
data[model][var]['MAM'] = data[model][var][''].sel(initial_time0_hours=is_fall(data[model][var]['']['initial_time0_hours.month']))
data[model][var]['SON'] = data[model][var][''].sel(initial_time0_hours=is_spring(data[model][var]['']['initial_time0_hours.month']))
data[model][var]['OND'] = data[model][var][''].sel(initial_time0_hours=is_month(data[model][var]['']['initial_time0_hours.month'], [10,11,12]))
data[model][var]['JFM'] = data[model][var][''].sel(initial_time0_hours=is_month(data[model][var]['']['initial_time0_hours.month'], [1,2,3]))
else:
data[model][var]['ONDJFM'] = data[model][var][''].sel(time=is_warm(data[model][var]['']['time.month']))
data[model][var]['AMJJAS'] = data[model][var][''].sel(time=is_cold(data[model][var]['']['time.month']))
data[model][var]['DJF'] = data[model][var][''].sel(time=is_summer(data[model][var]['']['time.month']))
data[model][var]['JJA'] = data[model][var][''].sel(time=is_winter(data[model][var]['']['time.month']))
data[model][var]['MAM'] = data[model][var][''].sel(time=is_fall(data[model][var]['']['time.month']))
data[model][var]['SON'] = data[model][var][''].sel(time=is_spring(data[model][var]['']['time.month']))
data[model][var]['OND'] = data[model][var][''].sel(time=is_month(data[model][var]['']['time.month'], [10,11,12]))
data[model][var]['JFM'] = data[model][var][''].sel(time=is_month(data[model][var]['']['time.month'], [1,2,3]))
if 'EF' in var_list:
try:
if model == 'JRA-55':
data[model]['EF'][''] = data[model]['lhf']['']/(data[model]['lhf']['']+data[model]['shf'][''])
data[model]['EF']['ONDJFM'] = data[model]['EF'][''].sel(initial_time0_hours=is_warm(data[model]['EF']['']['initial_time0_hours.month']))
data[model]['EF']['AMJJAS'] = data[model]['EF'][''].sel(initial_time0_hours=is_cold(data[model]['EF']['']['initial_time0_hours.month']))
data[model]['EF']['DJF'] = data[model]['EF'][''].sel(initial_time0_hours=is_summer(data[model]['EF']['']['initial_time0_hours.month']))
data[model]['EF']['JJA'] = data[model]['EF'][''].sel(initial_time0_hours=is_winter(data[model]['EF']['']['initial_time0_hours.month']))
data[model]['EF']['MAM'] = data[model]['EF'][''].sel(initial_time0_hours=is_fall(data[model]['EF']['']['initial_time0_hours.month']))
data[model]['EF']['SON'] = data[model]['EF'][''].sel(initial_time0_hours=is_spring(data[model]['EF']['']['initial_time0_hours.month']))
data[model]['EF']['OND'] = data[model]['EF'][''].sel(initial_time0_hours=is_month(data[model]['EF']['']['initial_time0_hours.month'], [10,11,12]))
data[model]['EF']['JFM'] = data[model]['EF'][''].sel(initial_time0_hours=is_month(data[model]['EF']['']['initial_time0_hours.month'], [1,2,3]))
else:
data[model]['EF'][''] = data[model]['lhf']['']/(data[model]['lhf']['']+data[model]['shf'][''])
data[model]['EF']['ONDJFM'] = data[model]['EF'][''].sel(time=is_warm(data[model]['EF']['']['time.month']))
data[model]['EF']['AMJJAS'] = data[model]['EF'][''].sel(time=is_cold(data[model]['EF']['']['time.month']))
data[model]['EF']['DJF'] = data[model]['EF'][''].sel(time=is_summer(data[model]['EF']['']['time.month']))
data[model]['EF']['JJA'] = data[model]['EF'][''].sel(time=is_winter(data[model]['EF']['']['time.month']))
data[model]['EF']['MAM'] = data[model]['EF'][''].sel(time=is_fall(data[model]['EF']['']['time.month']))
data[model]['EF']['SON'] = data[model]['EF'][''].sel(time=is_spring(data[model]['EF']['']['time.month']))
data[model]['EF']['OND'] = data[model]['EF'][''].sel(time=is_month(data[model]['EF']['']['time.month'], [10,11,12]))
data[model]['EF']['JFM'] = data[model]['EF'][''].sel(time=is_month(data[model]['EF']['']['time.month'], [1,2,3]))
except KeyError:
print('lhf or shf missing')
del(data[model]['EF'])
return data_xr, data, lon, lat, lonproj, latproj
#%%
# ------------- FUNCIONES PARA SEPARAR EN ESTACIONES ------
def is_warm(month, seas_warm = [1,2,3,10,11,12]):
return (month >= seas_warm[3]) | (month <= seas_warm[2])
def is_cold(month, seas_cold = [4,5,6,7,8,9]):
return (month >= seas_cold[0]) & (month <= seas_cold[5])
def is_summer(month):
return (month >= 12) | (month <= 2)
def is_winter(month):
return (month >= 6) & (month <= 8)
def is_spring(month):
return (month >= 9) & (month <= 11)
def is_fall(month):
return (month >= 3) & (month <= 5)
def is_month(time, month):
# time = es el time.month
# month = qué mes o meses quiero evaluar si es. Tiene que ser una lista de enteros o una sigla reconocible como DJF
import numpy as np
allmonths = 'JFMAMJJASONDJFMAMJJASOND'
allmonthsn = [1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12]
if isinstance(month, str):
if month == '':
month_list = [1,2,3,4,5,6,7,8,9,10,11,12]
else:
month_list = allmonthsn[allmonths.find(month):allmonths.find(month)+len(month)]
else:
month_list = month.copy()
cond = month_list.copy()
for m,mm in enumerate(month_list):
cond[m] = (time==mm)
suma = cond[0]
if len(month_list)>1:
for m,mm in enumerate(month_list):
if m >0:
suma = suma+cond[m]
return suma
#return (np.sum(cond, axis=0)==1)
#%% ------ PLOT DE FIGURAS -------
def plot_3h_clims(model, seas, var, var_list, filename, clims3h, models, clevs, lon, lat, lonproj, latproj, proj,
images_path, units_labels, arrow_scale=4000, arrow_spacing=3): #figuras cada 3h
# arrow_scale: a smaller scale parameter makes the arrow longer
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
import matplotlib.colors as mcolors
import cartopy
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import os
from numpy import ma
fig1 = plt.figure( figsize=(8,5),dpi=300) #fig size in inches (width, height)
ax = fig1.add_axes([0.1,0.1,0.9,0.9])
ax.set_visible(False)
gs = gridspec.GridSpec(2,4, wspace=0.4, hspace=0.2)
if 'anom' in filename:
fig1.suptitle(models[model]+' '+seas+' '+var+' anomalies', fontsize=12)
else:
fig1.suptitle(models[model]+' '+seas+' '+var, fontsize=12)
if len(clims3h[model][var][seas].hour) >8:
times = clims3h[model][var][seas].hour[::3]
else:
times = clims3h[model][var][seas].hour
for m, time in enumerate(np.array(times)):
barra = matplotlib.cm.get_cmap('RdYlBu_r') # premade colorbar
norm = mcolors.BoundaryNorm(boundaries=clevs[var], ncolors=256)
barra= barra_whitecenter(clevs[var] ,colormap=matplotlib.cm.get_cmap('RdYlBu_r'))
ax1 = plt.subplot(gs[m], projection = proj)
ax1.set_extent([lon[model][0], lon[model][-1], lat[model][0], lat[model][-1]], crs=proj)
index = m #int(m*len(clims3h[model][var][seas].hour)/len(times))
if len(clims3h[model][var][seas].hour) >8:
CS1 = ax1.pcolormesh(lonproj[model], latproj[model],
clims3h[model][var][seas].roll(hour=-1, roll_coords=False).groupby_bins('hour', 8).mean(axis=0).roll(hour_bins=1, roll_coords=False)[m],
norm=norm, transform=proj, cmap=barra)
else:
CS1 = ax1.pcolormesh(lonproj[model], latproj[model], clims3h[model][var][seas][index], norm=norm, transform=proj, cmap=barra)
#color lower and upper colorbar triangles
barra.set_over('Maroon')
barra.set_under('Navy')
ax1.coastlines(resolution='50m', linewidth=1) # coast
ax1.add_feature(cartopy.feature.BORDERS, linestyle='-', linewidth=1, alpha=0.7) #countries
parallels = np.arange(-60,30,10.)
meridians = np.arange(-90,-20,10.)
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax1.xaxis.set_major_formatter(lon_formatter)
ax1.yaxis.set_major_formatter(lat_formatter)
ax1.set_xticks(meridians[1:-1:2], crs=proj)
ax1.set_yticks(parallels[1:-1], crs=proj)
ax1.gridlines(crs=proj, xlocs=meridians, ylocs=parallels, linewidth=1, linestyle='--')
ax1.set_title(str(time)+' UTC', fontsize=12)
if var== 'vimfc2d' and 'qu2d' and 'qv2d' in var_list or \
var== 'quv900conv' and 'qu900' and 'qv900' in var_list or \
var== 'uv900conv' and 'u900' and 'v900' in var_list or \
var== 'uv850conv' and 'u850' and 'v850' in var_list or \
var== 'uv10mconv' and 'u10m' and 'v10m' in var_list:
if var=='vimfc2d': vect1= 'qu2d'; vect2= 'qv2d'
if var=='quv900conv': vect1= 'qu900'; vect2= 'qv900'
if var=='uv900conv': vect1= 'u900'; vect2= 'v900'
if var=='uv850conv': vect1= 'u850'; vect2= 'v850'
if var=='uv10mconv': vect1= 'u10m'; vect2= 'v10m'
# Vectores
u = np.ones(clims3h[model][vect1][seas][index].shape,dtype=bool)
v = np.ones(clims3h[model][vect2][seas][index].shape,dtype=bool)
u[0:np.shape(u)[0]:arrow_spacing,0:np.shape(u)[1]:arrow_spacing]=0
v[0:np.shape(v)[0]:arrow_spacing,0:np.shape(v)[1]:arrow_spacing]=0
uu = ma.array(clims3h[model][vect1][seas][index],mask = u)
vv = ma.array(clims3h[model][vect2][seas][index],mask = v)
Q = ax1.quiver(lonproj[model],latproj[model],uu,vv,width=3e-3,
headwidth=3,#headwidht (default3)
headlength=2.5, scale = arrow_scale[var],
transform=proj) # (default5)
if m ==3:
# busco para la quiverkey la magnitud del percentil 90 y redondeo al decimal significativo
p90 = np.percentile(np.abs(clims3h[model][vect1][seas]), 90)
deci = int(('%e' % p90).split('e')[1])
p90round = round(p90, deci)
qk = plt.quiverkey(Q, 1, 1.03, p90round, str(p90round)+' '+units_labels[vect1], labelpos='E')
# add colorbar
fig1.subplots_adjust(right=0.92)
cbar_ax = fig1.add_axes([0.94, 0.3, 0.01, 0.4]) #[*left*, *bottom*, *width*, *height*]
cb =fig1.colorbar(CS1, cax=cbar_ax, extend='both')
axis_labels = units_labels[var]
cb.set_label(axis_labels, labelpad = -2)
cb.formatter.set_powerlimits((0, 0)) # scientific notation
if not os.path.exists(str(images_path)+'hourly_mean/'+model+'/'+seas):
os.makedirs(str(images_path)+'hourly_mean/'+model+'/'+seas)
fig1.savefig(str(images_path)+'hourly_mean/'+model+'/'+seas+'/'+str(filename),dpi=300,bbox_inches='tight',orientation='landscape',papertype='A4')
plt.close(fig1)
def plot_3h_anim_frames(model, seas, var, var_list, filename, clims3h, models, clevs, lon, lat, lonproj, latproj, proj,
images_path, units_labels, arrow_scale=4000, arrow_spacing=3):
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import cartopy
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import os
from numpy import ma
if len(clims3h[model][var][seas].hour) >8:
times = clims3h[model][var][seas].hour[::3]
else:
times = clims3h[model][var][seas].hour
for m, time in enumerate(np.array(times)):
fig1 = plt.figure( figsize=(7,5),dpi=300) #fig size in inches (width, height)
ax = fig1.add_axes([0.1,0.1,0.9,0.9], projection=proj)
# fig1.set_title(models[model]+' '+seas+' '+var, fontsize=12)
barra = barra_whitecenter(clevs[var] ,colormap=matplotlib.cm.get_cmap('RdYlBu_r'))
norm = mcolors.BoundaryNorm(boundaries=clevs[var], ncolors=256)
ax.set_extent([lon[model][0], lon[model][-1], lat[model][0], lat[model][-1]], crs=proj)
index = m #int(m*len(clims3h[model][var][seas].hour)/len(times))
if len(clims3h[model][var][seas].hour) >8:
CS1 = ax.pcolormesh(lonproj[model], latproj[model],
clims3h[model][var][seas].roll(hour=-1, roll_coords=False).groupby_bins('hour', 8).mean(axis=0).roll(hour_bins=1, roll_coords=False)[m],
norm=norm, transform=proj, cmap=barra)
else:
CS1 = ax.pcolormesh(lonproj[model], latproj[model], clims3h[model][var][seas][index], norm=norm, transform=proj, cmap=barra)
#color lower and upper colorbar triangles
barra.set_over('Maroon')
barra.set_under('Navy')
ax.coastlines(resolution='50m', linewidth=1) # coast
ax.add_feature(cartopy.feature.BORDERS, linestyle='-', linewidth=1, alpha=0.7) #countries
parallels = np.arange(-60,30,10.)
meridians = np.arange(-90,-20,10.)
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.set_xticks(meridians[1:-1:2], crs=proj)
ax.set_yticks(parallels[1:-1], crs=proj)
ax.gridlines(crs=proj, xlocs=meridians, ylocs=parallels, linewidth=1, linestyle='--')
if 'anom' in filename:
ax.set_title(models[model]+' '+seas+' '+var+' anomalies'+'\n'+str(time)+' UTC', fontsize=12)
else:
ax.set_title(models[model]+' '+seas+' '+var+'\n'+str(time)+' UTC', fontsize=12)
if var== 'vimfc2d' and 'qu2d' and 'qv2d' in var_list or \
var== 'quv900conv' and 'qu900' and 'qv900' in var_list or \
var== 'uv900conv' and 'u900' and 'v900' in var_list or \
var== 'uv850conv' and 'u850' and 'v850' in var_list or \
var== 'uv10mconv' and 'u10m' and 'v10m' in var_list:
if var=='vimfc2d': vect1= 'qu2d'; vect2= 'qv2d'
if var=='quv900conv': vect1= 'qu900'; vect2= 'qv900'
if var=='uv900conv': vect1= 'u900'; vect2= 'v900'
if var=='uv850conv': vect1= 'u850'; vect2= 'v850'
if var=='uv10mconv': vect1= 'u10m'; vect2= 'v10m'
# Vectores
u = np.ones(clims3h[model][vect1][seas][index].shape,dtype=bool)
v = np.ones(clims3h[model][vect2][seas][index].shape,dtype=bool)
u[0:np.shape(u)[0]:arrow_spacing,0:np.shape(u)[1]:arrow_spacing]=0
v[0:np.shape(v)[0]:arrow_spacing,0:np.shape(v)[1]:arrow_spacing]=0
uu = ma.array(clims3h[model][vect1][seas][index],mask = u)
vv = ma.array(clims3h[model][vect2][seas][index],mask = v)
Q = ax.quiver(lonproj[model],latproj[model],uu,vv,width=3e-3,
headwidth=3,#headwidht (default3)
headlength=2.5, scale = arrow_scale[var],
transform=proj) # (default5)
# busco para la quiverkey la magnitud del percentil 90 y redondeo al decimal significativo
p90 = np.percentile(np.abs(clims3h[model][vect1][seas]), 90)
deci = int(('%e' % p90).split('e')[1])
p90round = round(p90, deci)
qk = plt.quiverkey(Q, 1, 1.03, p90round, str(p90round)+' '+units_labels[vect1], labelpos='E')
# add colorbar
fig1.subplots_adjust(right=0.92)
cbar_ax = fig1.add_axes([0.94, 0.3, 0.01, 0.4]) #[*left*, *bottom*, *width*, *height*]
cb =fig1.colorbar(CS1, cax=cbar_ax, extend='both')
axis_labels = units_labels[var]
cb.set_label(axis_labels, labelpad = -2)
cb.formatter.set_powerlimits((0, 0)) # scientific notation
if not os.path.exists(str(images_path)+'hourly_mean/'+model+'/'+seas+'/animation'):
os.makedirs(str(images_path)+'hourly_mean/'+model+'/'+seas+'/animation')
fig1.savefig(str(images_path)+'hourly_mean/'+model+'/'+seas+'/animation/'+str(filename)+str(time)+'.png',dpi=300,bbox_inches='tight',orientation='landscape',papertype='A4')
plt.clf()
plt.cla()
plt.close(fig1)
def plot_simple_SA_map(model, seas, var, var_list, filename, clims, models, clevs, lon, lat, lonproj, latproj, proj,
images_path, units_labels, arrow_scale=4000, arrow_spacing=3, extend='both'):
import matplotlib