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Fig6.7_violin_regions.py
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Fig6.7_violin_regions.py
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import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
regionnum=1 #Southern Asia
#regionnum=2 #Europe
#regionnum=3 #Africa
#regionnum=4 #South-East Asia and Developing Pacific
#regionnum=5 #Latin America and Caribbean
#regionnum=6 #Eurasia
#regionnum=7 #Asia-Pacific Developed
#regionnum=8 #Middle East
#regionnum=9 #North America rural
#regionnum=10 #Eastern Asia
#regionnum=11 #North America urban
#regionnum=12 #example
AR6_file = Dataset('../input/AR6WG3_Atlas_IntermedRegions_1.0x1.0.nc','r')
AR6 = AR6_file.variables["AR6WG3RegionIntm"][:,:]
lat_in = AR6_file.variables['latitude'][:]
lon_in = AR6_file.variables['longitude'][:]
if regionnum==12:
SO4_file = Dataset('../input/Europe/EMEP/Aas/SO4mm_month_2000-2018_pm25.nc','r')
SO4 = SO4_file.variables["DATA"][:,:]
LON_SO4 = SO4_file.variables["LON"][:,:]
LAT_SO4 = SO4_file.variables["LAT"][:,:]
LAT=LAT_SO4
LON=LON_SO4
print(len(LAT[0,:]))
regi_SO4=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_SO4[:,j]=AR6[lt,ln]
if regionnum==1 or regionnum==3 or regionnum==5 or regionnum==8:
NH4_file = Dataset('../input/SPARTAN/NH4_month_2013-2019.nc','r')
NO3_file = Dataset('../input/SPARTAN/NO3_month_2013-2019.nc','r')
SO4_file = Dataset('../input/SPARTAN/SO4_month_2013-2019.nc','r')
Na_file = Dataset('../input/SPARTAN/Na_month_2013-2019.nc','r')
LON = SO4_file.variables["LON"][:,:]
LAT = SO4_file.variables["LAT"][:,:]
NH4 = NH4_file.variables["DATA"][:,:]
Na = Na_file.variables["DATA"][:,:]
NO3 = NO3_file.variables["DATA"][:,:]
SO4 = SO4_file.variables["DATA"][:,:]
print(len(LAT[0,:]))
regi=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi[:,j]=AR6[lt,ln]
if regionnum==2:
OC_file = Dataset('../input/Europe/EMEP/Aas/OC_month_2000-2018_pm25.nc','r')
NH4_file = Dataset('../input/Europe/EMEP/Aas/NH4p_month_2000-2018_pm25.nc','r')
Na_file = Dataset('../input/Europe/EMEP/Aas/Nap_month_2000-2018_pm25.nc','r')
Cl_file = Dataset('../input/Europe/EMEP/Aas/Clm_month_2000-2018_pm25.nc','r')
NO3_file = Dataset('../input/Europe/EMEP/Aas/NO3m_month_2000-2018_pm25.nc','r')
SO4_file = Dataset('../input/Europe/EMEP/Aas/SO4mm_month_2000-2018_pm25.nc','r')
EC_file = Dataset('../input/Europe/EMEP/Aas/EC_month_2000-2018_pm25.nc','r')
ORG = OC_file.variables["DATA"][:,:]
LON_ORG = OC_file.variables["LON"][:,:]
LAT_ORG = OC_file.variables["LAT"][:,:]
NH4 = NH4_file.variables["DATA"][:,:]
LON_NH4 = NH4_file.variables["LON"][:,:]
LAT_NH4 = NH4_file.variables["LAT"][:,:]
Na = Na_file.variables["DATA"][:,:]
LON_Na = Na_file.variables["LON"][:,:]
LAT_Na = Na_file.variables["LAT"][:,:]
Cl = Cl_file.variables["DATA"][:,:]
LON_Cl = Cl_file.variables["LON"][:,:]
LAT_Cl = Cl_file.variables["LAT"][:,:]
NO3 = NO3_file.variables["DATA"][:,:]
LON_NO3 = NO3_file.variables["LON"][:,:]
LAT_NO3 = NO3_file.variables["LAT"][:,:]
SO4 = SO4_file.variables["DATA"][:,:]
LON_SO4 = SO4_file.variables["LON"][:,:]
LAT_SO4 = SO4_file.variables["LAT"][:,:]
EC = EC_file.variables["DATA"][:,:]
LON_EC = EC_file.variables["LON"][:,:]
LAT_EC = EC_file.variables["LAT"][:,:]
LAT=LAT_ORG
LON=LON_ORG
print(len(LAT[0,:]))
regi_ORG=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_ORG[:,j]=AR6[lt,ln]
LAT=LAT_NH4
LON=LON_NH4
print(len(LAT[0,:]))
regi_NH4=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_NH4[:,j]=AR6[lt,ln]
LAT=LAT_Na
LON=LON_Na
print(len(LAT[0,:]))
regi_Na=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_Na[:,j]=AR6[lt,ln]
LAT=LAT_Cl
LON=LON_Cl
print(len(LAT[0,:]))
regi_Cl=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_Cl[:,j]=AR6[lt,ln]
LAT=LAT_NO3
LON=LON_NO3
print(len(LAT[0,:]))
regi_NO3=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_NO3[:,j]=AR6[lt,ln]
LAT=LAT_SO4
LON=LON_SO4
print(len(LAT[0,:]))
regi_SO4=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_SO4[:,j]=AR6[lt,ln]
LAT=LAT_EC
LON=LON_EC
print(len(LAT[0,:]))
regi_EC=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_EC[:,j]=AR6[lt,ln]
if regionnum==4 or regionnum==10:
NH4_spar = Dataset('../input/SPARTAN/NH4_month_2013-2019.nc','r')
NO3_spar = Dataset('../input/SPARTAN/NO3_month_2013-2019.nc','r')
SO4_spar = Dataset('../input/SPARTAN/SO4_month_2013-2019.nc','r')
Na_spar = Dataset('../input/SPARTAN/Na_month_2013-2019.nc','r')
LONs = SO4_spar.variables["LON"][:,:]
LATs = SO4_spar.variables["LAT"][:,:]
NH4s = NH4_spar.variables["DATA"][:,:]
Nas = Na_spar.variables["DATA"][:,:]
NO3s = NO3_spar.variables["DATA"][:,:]
SO4s = SO4_spar.variables["DATA"][:,:]
LAT=LATs
LON=LONs
print(len(LAT[0,:]))
regi=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi[:,j]=AR6[lt,ln]
OC_file = Dataset('../input/Asia/EANET/TotOC_month_2001-2010.nc','r')
NH4_file = Dataset('../input/Asia/EANET/NH4p_month_2001-2017.nc','r')
Na_file = Dataset('../input/Asia/EANET/Nap_month_2001-2017.nc','r')
Cl_file = Dataset('../input/Asia/EANET/Clm_month_2001-2017.nc','r')
NO3_file = Dataset('../input/Asia/EANET/NO3m_month_2001-2017.nc','r')
SO4_file = Dataset('../input/Asia/EANET/SO4mm_month_2001-2017.nc','r')
ORG = OC_file.variables["DATA"][:,:]
LON_ORG = OC_file.variables["LON"][:,:]
LAT_ORG = OC_file.variables["LAT"][:,:]
NH4 = NH4_file.variables["DATA"][:,:]
LON_NH4 = NH4_file.variables["LON"][:,:]
LAT_NH4 = NH4_file.variables["LAT"][:,:]
Na = Na_file.variables["DATA"][:,:]
LON_Na = Na_file.variables["LON"][:,:]
LAT_Na = Na_file.variables["LAT"][:,:]
Cl = Cl_file.variables["DATA"][:,:]
LON_Cl = Cl_file.variables["LON"][:,:]
LAT_Cl = Cl_file.variables["LAT"][:,:]
NO3 = NO3_file.variables["DATA"][:,:]
LON_NO3 = NO3_file.variables["LON"][:,:]
LAT_NO3 = NO3_file.variables["LAT"][:,:]
SO4 = SO4_file.variables["DATA"][:,:]
LON_SO4 = SO4_file.variables["LON"][:,:]
LAT_SO4 = SO4_file.variables["LAT"][:,:]
LAT=LAT_ORG
LON=LON_ORG
print(len(LAT[0,:]))
regie_ORG=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regie_ORG[:,j]=AR6[lt,ln]
LAT=LAT_NH4
LON=LON_NH4
print(len(LAT[0,:]))
regieanet=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regieanet[:,j]=AR6[lt,ln]
if regionnum==6:
OC_file = Dataset('../input/Europe/EMEP/Aas/OC_month_2000-2018_pm25.nc','r')
NH4_file = Dataset('../input/Europe/EMEP/Aas/NH4p_month_2000-2018_pm25.nc','r')
Na_file = Dataset('../input/Europe/EMEP/Aas/Nap_month_2000-2018_pm25.nc','r')
Cl_file = Dataset('../input/Europe/EMEP/Aas/Clm_month_2000-2018_pm25.nc','r')
NO3_file = Dataset('../input/Europe/EMEP/Aas/NO3m_month_2000-2018_pm25.nc','r')
SO4_file = Dataset('../input/Europe/EMEP/Aas/SO4mm_month_2000-2018_pm25.nc','r')
EC_file = Dataset('../input/Europe/EMEP/Aas/EC_month_2000-2018_pm25.nc','r')
ORG = OC_file.variables["DATA"][:,:]
LON_ORG = OC_file.variables["LON"][:,:]
LAT_ORG = OC_file.variables["LAT"][:,:]
NH4 = NH4_file.variables["DATA"][:,:]
LON_NH4 = NH4_file.variables["LON"][:,:]
LAT_NH4 = NH4_file.variables["LAT"][:,:]
Na = Na_file.variables["DATA"][:,:]
LON_Na = Na_file.variables["LON"][:,:]
LAT_Na = Na_file.variables["LAT"][:,:]
Cl = Cl_file.variables["DATA"][:,:]
LON_Cl = Cl_file.variables["LON"][:,:]
LAT_Cl = Cl_file.variables["LAT"][:,:]
NO3 = NO3_file.variables["DATA"][:,:]
LON_NO3 = NO3_file.variables["LON"][:,:]
LAT_NO3 = NO3_file.variables["LAT"][:,:]
SO4 = SO4_file.variables["DATA"][:,:]
LON_SO4 = SO4_file.variables["LON"][:,:]
LAT_SO4 = SO4_file.variables["LAT"][:,:]
EC = EC_file.variables["DATA"][:,:]
LON_EC = EC_file.variables["LON"][:,:]
LAT_EC = EC_file.variables["LAT"][:,:]
LAT=LAT_ORG
LON=LON_ORG
print(len(LAT[0,:]))
regi_ORG=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_ORG[:,j]=AR6[lt,ln]
LAT=LAT_NH4
LON=LON_NH4
print(len(LAT[0,:]))
regi_NH4=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_NH4[:,j]=AR6[lt,ln]
LAT=LAT_Na
LON=LON_Na
print(len(LAT[0,:]))
regi_Na=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_Na[:,j]=AR6[lt,ln]
LAT=LAT_Cl
LON=LON_Cl
print(len(LAT[0,:]))
regi_Cl=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_Cl[:,j]=AR6[lt,ln]
LAT=LAT_NO3
LON=LON_NO3
print(len(LAT[0,:]))
regi_NO3=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_NO3[:,j]=AR6[lt,ln]
LAT=LAT_SO4
LON=LON_SO4
print(len(LAT[0,:]))
regi_SO4=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_SO4[:,j]=AR6[lt,ln]
LAT=LAT_EC
LON=LON_EC
print(len(LAT[0,:]))
regi_EC=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi_EC[:,j]=AR6[lt,ln]
NH4e_file = Dataset('../input/Asia/EANET/NH4p_month_2001-2017.nc','r')
Nae_file = Dataset('../input/Asia/EANET/Nap_month_2001-2017.nc','r')
Cle_file = Dataset('../input/Asia/EANET/Clm_month_2001-2017.nc','r')
NO3e_file = Dataset('../input/Asia/EANET/NO3m_month_2001-2017.nc','r')
SO4e_file = Dataset('../input/Asia/EANET/SO4mm_month_2001-2017.nc','r')
NH4e = NH4e_file.variables["DATA"][:,:]
LONe_NH4 = NH4e_file.variables["LON"][:,:]
LATe_NH4 = NH4e_file.variables["LAT"][:,:]
Nae = Nae_file.variables["DATA"][:,:]
Cle = Cle_file.variables["DATA"][:,:]
NO3e = NO3e_file.variables["DATA"][:,:]
SO4e = SO4e_file.variables["DATA"][:,:]
LAT=LATe_NH4
LON=LONe_NH4
print(len(LAT[0,:]))
regieanet=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regieanet[:,j]=AR6[lt,ln]
if regionnum==7:
ORGe_file = Dataset('../input/Asia/EANET/TotOC_month_2001-2010.nc','r')
NH4e_file = Dataset('../input/Asia/EANET/NH4p_month_2001-2017.nc','r')
Nae_file = Dataset('../input/Asia/EANET/Nap_month_2001-2017.nc','r')
Cle_file = Dataset('../input/Asia/EANET/Clm_month_2001-2017.nc','r')
NO3e_file = Dataset('../input/Asia/EANET/NO3m_month_2001-2017.nc','r')
SO4e_file = Dataset('../input/Asia/EANET/SO4mm_month_2001-2017.nc','r')
ORGe = ORGe_file.variables["DATA"][:,:]
LONe_ORG = ORGe_file.variables["LON"][:,:]
LATe_ORG = ORGe_file.variables["LAT"][:,:]
NH4e = NH4e_file.variables["DATA"][:,:]
LONe_NH4 = NH4e_file.variables["LON"][:,:]
LATe_NH4 = NH4e_file.variables["LAT"][:,:]
Nae = Nae_file.variables["DATA"][:,:]
Cle = Cle_file.variables["DATA"][:,:]
NO3e = NO3e_file.variables["DATA"][:,:]
SO4e = SO4e_file.variables["DATA"][:,:]
LAT=LATe_NH4
LON=LONe_NH4
print(len(LAT[0,:]))
regieanet=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regieanet[:,j]=AR6[lt,ln]
LAT=LATe_ORG
LON=LONe_ORG
print(len(LAT[0,:]))
regia_ORG=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regia_ORG[:,j]=AR6[lt,ln]
if regionnum==9:
OC_file = Dataset('../input/North_America/IMPROVE/OC_month_2000-2018.nc','r')
NH4_file = Dataset('../input/North_America/IMPROVE/NH4_month_2000-2018.nc','r')
Na_file = Dataset('../input/North_America/IMPROVE/Na_month_2000-2018.nc','r')
Cl_file = Dataset('../input/North_America/IMPROVE/Cl_month_2000-2018.nc','r')
NO3_file = Dataset('../input/North_America/IMPROVE/NO3_month_2000-2018.nc','r')
SO4_file = Dataset('../input/North_America/IMPROVE/SO4_month_2000-2018.nc','r')
EC_file = Dataset('../input/North_America/IMPROVE/EC_month_2000-2018.nc','r')
ORG = OC_file.variables["DATA"][:,:]
LON_ORG = OC_file.variables["LON"][:,:]
LAT_ORG = OC_file.variables["LAT"][:,:]
NH4 = NH4_file.variables["DATA"][:,:]
Na = Na_file.variables["DATA"][:,:]
Cl = Cl_file.variables["DATA"][:,:]
NO3 = NO3_file.variables["DATA"][:,:]
SO4 = SO4_file.variables["DATA"][:,:]
EC = EC_file.variables["DATA"][:,:]
# LAT=LAT_ORG
# LON=LON_ORG
# print(len(LAT[0,:]))
# regimprove=LAT*0
# for lt in range(len(lat_in[:])):
# for ln in range(len(lon_in[:])):
# for j in range(len(LAT[0,:])):
# if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
# regimprove[:,j]=AR6[lt,ln]
if regionnum==11:
NH4_spar = Dataset('../input/SPARTAN/NH4_month_2013-2019.nc','r')
NO3_spar = Dataset('../input/SPARTAN/NO3_month_2013-2019.nc','r')
SO4_spar = Dataset('../input/SPARTAN/SO4_month_2013-2019.nc','r')
Na_spar = Dataset('../input/SPARTAN/Na_month_2013-2019.nc','r')
LONs = SO4_spar.variables["LON"][:,:]
LATs = SO4_spar.variables["LAT"][:,:]
NH4s = NH4_spar.variables["DATA"][:,:]
Nas = Na_spar.variables["DATA"][:,:]
NO3s = NO3_spar.variables["DATA"][:,:]
SO4s = SO4_spar.variables["DATA"][:,:]
LAT=LATs
LON=LONs
print(len(LAT[0,:]))
regi=LAT*0
for lt in range(len(lat_in[:])):
for ln in range(len(lon_in[:])):
for j in range(len(LAT[0,:])):
if lat_in[lt] <= (LAT[0,j]+0.5) and lat_in[lt] >= (LAT[0,j]-0.5) and lon_in[ln] >= (LON[0,j]-0.5) and lon_in[ln] <= (LON[0,j]+0.5):
regi[:,j]=AR6[lt,ln]
OC_file = Dataset('../input/North_America/EPA/OC_month_2000-2018.nc','r')
NH4_file = Dataset('../input/North_America/EPA/NH4_month_2000-2018.nc','r')
Na_file = Dataset('../input/North_America/EPA/Na_month_2000-2018.nc','r')
Cl_file = Dataset('../input/North_America/EPA/Cl_month_2000-2018.nc','r')
NO3_file = Dataset('../input/North_America/EPA/NO3_month_2000-2018.nc','r')
SO4_file = Dataset('../input/North_America/EPA/SO4_month_2000-2018.nc','r')
EC_file = Dataset('../input/North_America/EPA/EC_month_2000-2018.nc','r')
ORG = OC_file.variables["DATA"][:,:]
LON_ORG = OC_file.variables["LON"][:,:]
LAT_ORG = OC_file.variables["LAT"][:,:]
NH4 = NH4_file.variables["DATA"][:,:]
Na = Na_file.variables["DATA"][:,:]
Cl = Cl_file.variables["DATA"][:,:]
NO3 = NO3_file.variables["DATA"][:,:]
SO4 = SO4_file.variables["DATA"][:,:]
EC = EC_file.variables["DATA"][:,:]
if regionnum==12:
sub_SO4=SO4[(regi_SO4==2)]
fl_SO4=sub_SO4.flatten()
# Filter data using np.isnan
fl_SO4[fl_SO4 == -1e+34] = np.nan
example_SO4 = 3*fl_SO4[~np.isnan(fl_SO4)]
#for i in example_SO4:
# if i >=1:
# little_high_SO4 = 5*example_SO4
#for i in example_SO4:
# if i >=10:
# very_high_SO4 = 10*example_SO4
for m in example_SO4:
if m <=10:
little_low_SO4 = 0.1*example_SO4
for m in example_SO4:
if m <=1:
very_low_SO4 = 0.01*example_SO4
low_SO4 = np.concatenate((very_low_SO4,little_low_SO4))
#high_SO4 = np.concatenate((very_high_SO4,little_high_SO4))
#mask_SO4 = np.concatenate((low_SO4,high_SO4))
mask_SO4 = np.concatenate((low_SO4,example_SO4))
if regionnum==1 or regionnum==8:
sub_NH4=NH4[(regi==regionnum)]
sub_Na = Na[(regi==regionnum)]
sub_NO3=NO3[(regi==regionnum)]
sub_SO4=SO4[(regi==regionnum)]
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
# Filter data using np.isnan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
spartan_NH4 = fl_NH4[~np.isnan(fl_NH4)]
spartan_Na = fl_Na[~np.isnan(fl_Na)]
spartan_NO3 = fl_NO3[~np.isnan(fl_NO3)]
spartan_SO4 = fl_SO4[~np.isnan(fl_SO4)]
mask_NH4 = spartan_NH4
mask_Na = spartan_Na
mask_NO3 = spartan_NO3
mask_SO4 = spartan_SO4
if regionnum==2:
sub_ORG=ORG[(regi_ORG==regionnum)]
sub_NH4=NH4[(regi_NH4==regionnum)]
sub_Na = Na[(regi_Na==regionnum)]
sub_Cl = Cl[(regi_Cl==regionnum)]
sub_NO3=NO3[(regi_NO3==regionnum)]
sub_SO4=SO4[(regi_SO4==regionnum)]
sub_EC= EC[(regi_EC==regionnum)]
fl_ORG=sub_ORG.flatten()
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_Cl =sub_Cl.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
fl_EC =sub_EC.flatten()
# Filter data using np.isnan
fl_ORG[fl_ORG == -1e+34] = np.nan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_Cl[fl_Cl == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
fl_EC[fl_EC == -1e+34] = np.nan
EMEP_ORG = fl_ORG[~np.isnan(fl_ORG)]
EMEP_NH4 = 18/14*fl_NH4[~np.isnan(fl_NH4)]
EMEP_Na = fl_Na[~np.isnan(fl_Na)]
EMEP_Cl = fl_Cl[~np.isnan(fl_Cl)]
EMEP_NO3 = 62/14*fl_NO3[~np.isnan(fl_NO3)]
EMEP_SO4 = 3*fl_SO4[~np.isnan(fl_SO4)]
EMEP_EC = fl_EC[~np.isnan(fl_EC)]
#print(mask_NH4.shape)
mask_NH4 = EMEP_NH4
mask_Na = EMEP_Na
mask_NO3 = EMEP_NO3
mask_SO4 = EMEP_SO4
mask_Cl = EMEP_Cl
mask_ORG = EMEP_ORG
mask_EC = EMEP_EC
if regionnum==3:
sub_NH4=NH4[(regi==regionnum)]
sub_Na = Na[(regi==regionnum)]
sub_NO3=NO3[(regi==regionnum)]
sub_SO4=SO4[(regi==regionnum)]
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
# Filter data using np.isnan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
spartan_NH4 = fl_NH4[~np.isnan(fl_NH4)]
spartan_Na = fl_Na[~np.isnan(fl_Na)]
spartan_NO3 = fl_NO3[~np.isnan(fl_NO3)]
spartan_SO4 = fl_SO4[~np.isnan(fl_SO4)]
field_NH4 = np.array([2.3, 4.3, 1.9, 1.15, 4.95, 0.15, 0.23, 0.12, 0.09, 0.11, 0.05, 0.53, 0.24, 0.29])
field_Na = np.array([6.3, 6.45, 3.8, 3.45, 6.6, 0.41, 1.02, 0.13, 1.57, 3, 0.88, 0.26, 2.5, 0.29])
field_Cl = np.array([10, 19.35, 9.5, 4.6, 18.15, 0.37, 1.06, 0.11, 2.4, 5.1, 1.5, 0.55, 5.2, 0.46])
field_NO3 = np.array([4.9, 6.45, 5.7, 5.75, 6.6, 0.43, 1.53, 0.18, 0.75, 0.82, 0.34, 0.96, 1.43, 0.62])
field_ORG = np.array([36.55, 26.6, 26.45, 46.2, 6.5, 11, 3.9, 11, 14, 9.5,])
field_SO4 = np.array([14.0, 15.05, 13.3, 13.8, 16.5, 0.77, 1.85, 0.41, 2.1, 2.5, 1.44, 1.47, 1.6, 2.14])
field_EC = np.array([10.75, 5.7, 5.75, 8.25, 0.46, 0.82, 0.51, 4.3, 4.4, 4.3])
mask_NH4 = np.concatenate((spartan_NH4,field_NH4))
mask_Na = np.concatenate((spartan_Na ,field_Na ))
mask_NO3 = np.concatenate((spartan_NO3,field_NO3))
mask_SO4 = np.concatenate((spartan_SO4,field_SO4))
mask_Cl = field_Cl
mask_ORG = field_ORG
mask_EC = field_EC
if regionnum==4 or regionnum==10:
subs_NH4=NH4s[(regi==regionnum)]
subs_Na = Nas[(regi==regionnum)]
subs_NO3=NO3s[(regi==regionnum)]
subs_SO4=SO4s[(regi==regionnum)]
fls_NH4=subs_NH4.flatten()
fls_Na =subs_Na.flatten()
fls_NO3=subs_NO3.flatten()
fls_SO4=subs_SO4.flatten()
# Filter data using np.isnan
fls_NH4[fls_NH4 == -1e+34] = np.nan
fls_Na[fls_Na == -1e+34] = np.nan
fls_NO3[fls_NO3 == -1e+34] = np.nan
fls_SO4[fls_SO4 == -1e+34] = np.nan
spartan_NH4 = fls_NH4[~np.isnan(fls_NH4)]
spartan_Na = fls_Na[~np.isnan(fls_Na)]
spartan_NO3 = fls_NO3[~np.isnan(fls_NO3)]
spartan_SO4 = fls_SO4[~np.isnan(fls_SO4)]
sub_ORG=ORG[(regie_ORG==regionnum)]
sub_NH4=NH4[(regieanet==regionnum)]
sub_Na = Na[(regieanet==regionnum)]
sub_Cl = Cl[(regieanet==regionnum)]
sub_NO3=NO3[(regieanet==regionnum)]
sub_SO4=SO4[(regieanet==regionnum)]
fl_ORG=sub_ORG.flatten()
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_Cl =sub_Cl.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
# Filter data using np.isnan
fl_ORG[fl_ORG == -1e+34] = np.nan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_Cl[fl_Cl == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
EANET_ORG = fl_ORG[~np.isnan(fl_ORG)]
EANET_NH4 = fl_NH4[~np.isnan(fl_NH4)]
EANET_Na = fl_Na[~np.isnan(fl_Na)]
EANET_Cl = fl_Cl[~np.isnan(fl_Cl)]
EANET_NO3 = fl_NO3[~np.isnan(fl_NO3)]
EANET_SO4 = fl_SO4[~np.isnan(fl_SO4)]
#print(mask_NH4.shape)
if regionnum==10:
field_EC = np.array([17.0,10.0,3.1,18.0,13.0,2.8,28.0,19.0,3.9,1.2,1.5,1.9,1.8,6.6,1.4,1.5,2.0,2.0,6.4,10.4,7.1])
field_ORG = np.array([39.0,30.0,12.0,45.0,26.0,15.0,77.0,49.0,21.0,22.0,15.0,12.0,37.0,27.0,20.0,27.6,22.7,18.5,45.2,61.7,31.0])
field_SO4 = np.array([17.0,15.0, 8.7,27.0,17.0,4.2,34.0,24.0,5.8,11.0,9.3,9.4,19.0,15.0,3.5,15.7,14.4,15.6,22.8,17.8,14.0])
field_NO3 = np.array([27.0, 21.0, 14.0,28.0,21.0,11.0,28.0,21.0,10.0,3.5,3.4,3.5,3.8,12.0,6.1,5.8,6.2,6.0,17.9,17.5,16.0])
field_NH4 = np.array([19.0, 13.0, 9.00,24.0,13.0,6.6,28.0,13.0,7.0,5.7,6.9,5.6,7.5,10.0,4.7,8.2,9.4,8.0,8.5,7.6,6.4])
mask_NH4 = np.concatenate((field_NH4,spartan_NH4,EANET_NH4))
mask_Na = np.concatenate((spartan_Na,EANET_Na))
mask_NO3 = np.concatenate((field_NO3,spartan_NO3,EANET_NO3))
mask_SO4 = np.concatenate((field_SO4,spartan_SO4,EANET_SO4))
mask_Cl = EANET_Cl
mask_EC = field_EC
mask_ORG = np.concatenate((field_ORG,EANET_ORG))
else:
mask_NH4 = np.concatenate((spartan_NH4,EANET_NH4))
mask_Na = np.concatenate((spartan_Na,EANET_Na))
mask_NO3 = np.concatenate((spartan_NO3,EANET_NO3))
mask_SO4 = np.concatenate((spartan_SO4,EANET_SO4))
mask_Cl = EANET_Cl
mask_ORG = EANET_ORG
if regionnum==5:
sub_NH4=NH4[(regi==regionnum)]
sub_Na = Na[(regi==regionnum)]
sub_NO3=NO3[(regi==regionnum)]
sub_SO4=SO4[(regi==regionnum)]
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
# Filter data using np.isnan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
spartan_NH4 = fl_NH4[~np.isnan(fl_NH4)]
spartan_Na = fl_Na[~np.isnan(fl_Na)]
spartan_NO3 = fl_NO3[~np.isnan(fl_NO3)]
spartan_SO4 = fl_SO4[~np.isnan(fl_SO4)]
field_NH4 = np.array([0.04, 0.05, 0.02, 0.19, 0.14, 0.09, 1.23, 6.28, 0.94, 2.2, 0.59, 0.19, 0.30, 3.65, 3.11, 6.35, 7.96])
field_Na = np.array([2.76, 2.36, 1.94, 0.04, 0.27, 0.12, 0.05, 0.1, 1.73, 0.33, 0.13])
field_Cl = np.array([2.05, 2.20, 1.24, 0.03, 0.18, 0.02, 0.01, 0.04, 0.06, 1.26, 0.29, 0.12])
field_NO3 = np.array([3.34, 3.88, 2.14, 0.1, 0.02, 0.04, 1.20, 0.79, 0.40, 2.49, 0.35, 1.60, 0.15, 0.43, 4.73, 6.42, 17.29, 11.32])
field_ORG = np.array([2.62, 3.86, 3.09, 37.66, 11.2, 1.99, 45.76, 15.3, 3.96, 12.91, 17.15, 39.67, 34.42])
field_SO4 = np.array([3.69, 3.32, 2.12, 0.22, 0.69, 3.06, 0.55, 2.45, 2.05, 2.67, 2.56, 2.94, 0.32, 1.20, 2.05, 1.76, 6.12, 6.67])
field_EC = np.array([5.40, 5.84, 6.63, 5.87])
mask_NH4 = np.concatenate((spartan_NH4,field_NH4))
mask_Na = np.concatenate((spartan_Na ,field_Na ))
mask_NO3 = np.concatenate((spartan_NO3,field_NO3))
mask_SO4 = np.concatenate((spartan_SO4,field_SO4))
mask_Cl = field_Cl
mask_ORG = field_ORG
mask_EC = field_EC
if regionnum==6:
sub_ORG=ORG[(regi_ORG==regionnum)]
sub_NH4=NH4[(regi_NH4==regionnum)]
sub_Na = Na[(regi_Na==regionnum)]
sub_Cl = Cl[(regi_Cl==regionnum)]
sub_NO3=NO3[(regi_NO3==regionnum)]
sub_SO4=SO4[(regi_SO4==regionnum)]
sub_EC= EC[(regi_EC==regionnum)]
fl_ORG=sub_ORG.flatten()
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_Cl =sub_Cl.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
fl_EC =sub_EC.flatten()
# Filter data using np.isnan
fl_ORG[fl_ORG == -1e+34] = np.nan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_Cl[fl_Cl == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
fl_EC[fl_EC == -1e+34] = np.nan
EMEP_ORG = fl_ORG[~np.isnan(fl_ORG)]
EMEP_NH4 = 18/14*fl_NH4[~np.isnan(fl_NH4)]
EMEP_Na = fl_Na[~np.isnan(fl_Na)]
EMEP_Cl = fl_Cl[~np.isnan(fl_Cl)]
EMEP_NO3 = 62/14*fl_NO3[~np.isnan(fl_NO3)]
EMEP_SO4 = 3*fl_SO4[~np.isnan(fl_SO4)]
EMEP_EC = fl_EC[~np.isnan(fl_EC)]
sube_NH4=NH4e[(regieanet==regionnum)]
sube_Na = Nae[(regieanet==regionnum)]
sube_Cl = Cle[(regieanet==regionnum)]
sube_NO3=NO3e[(regieanet==regionnum)]
sube_SO4=SO4e[(regieanet==regionnum)]
fle_NH4=sube_NH4.flatten()
fle_Na =sube_Na.flatten()
fle_Cl =sube_Cl.flatten()
fle_NO3=sube_NO3.flatten()
fle_SO4=sube_SO4.flatten()
# Filter data using np.isnan
fle_NH4[fle_NH4 == -1e+34] = np.nan
fle_Na[fle_Na == -1e+34] = np.nan
fle_Cl[fle_Cl == -1e+34] = np.nan
fle_NO3[fle_NO3 == -1e+34] = np.nan
fle_SO4[fle_SO4 == -1e+34] = np.nan
EANET_NH4 = fle_NH4[~np.isnan(fle_NH4)]
EANET_Na = fle_Na[~np.isnan(fle_Na)]
EANET_Cl = fle_Cl[~np.isnan(fle_Cl)]
EANET_NO3 = fle_NO3[~np.isnan(fle_NO3)]
EANET_SO4 = fle_SO4[~np.isnan(fle_SO4)]
#print(mask_NH4.shape)
mask_NH4 = np.concatenate((EMEP_NH4,EANET_NH4))
mask_Na = np.concatenate((EMEP_Na,EANET_Na))
mask_NO3 = np.concatenate((EMEP_NO3,EANET_NO3))
mask_SO4 = np.concatenate((EMEP_SO4,EANET_SO4))
mask_Cl = np.concatenate((EMEP_Cl,EANET_Cl))
mask_ORG = EMEP_ORG
mask_EC = EMEP_EC
if regionnum==7:
sube_ORG=ORGe[(regia_ORG==regionnum)]
sube_NH4=NH4e[(regieanet==regionnum)]
sube_Na = Nae[(regieanet==regionnum)]
sube_Cl = Cle[(regieanet==regionnum)]
sube_NO3=NO3e[(regieanet==regionnum)]
sube_SO4=SO4e[(regieanet==regionnum)]
fle_ORG=sube_ORG.flatten()
fle_NH4=sube_NH4.flatten()
fle_Na =sube_Na.flatten()
fle_Cl =sube_Cl.flatten()
fle_NO3=sube_NO3.flatten()
fle_SO4=sube_SO4.flatten()
# Filter data using np.isnan
fle_ORG[fle_ORG == -1e+34] = np.nan
fle_NH4[fle_NH4 == -1e+34] = np.nan
fle_Na[fle_Na == -1e+34] = np.nan
fle_Cl[fle_Cl == -1e+34] = np.nan
fle_NO3[fle_NO3 == -1e+34] = np.nan
fle_SO4[fle_SO4 == -1e+34] = np.nan
EANET_ORG = fle_ORG[~np.isnan(fle_ORG)]
EANET_NH4 = fle_NH4[~np.isnan(fle_NH4)]
EANET_Na = fle_Na[~np.isnan(fle_Na)]
EANET_Cl = fle_Cl[~np.isnan(fle_Cl)]
EANET_NO3 = fle_NO3[~np.isnan(fle_NO3)]
EANET_SO4 = fle_SO4[~np.isnan(fle_SO4)]
field_NH4 = np.array([0.22, 0.31, 0.36, 0.02, 0.22, 0.11])
field_Na = np.array([0.31, 1.71, 2.99, 2.27, 1.68, 0.40])
field_Cl = np.array([0.14, 3.09, 4.94, 3.38, 2.47, 0.70])
field_NO3 = np.array([0.45, 0.73, 0.48, 0.74, 1.03, 0.41])
field_ORG = np.array([14.5, 2.95])
field_SO4 = np.array([0.66, 1.08, 1.18, 1.29, 1.76, 0.37])
field_EC = np.array([5.99, 2.03])
mask_NH4 = np.concatenate((field_NH4,EANET_NH4))
mask_Na = np.concatenate((field_Na,EANET_Na))
mask_NO3 = np.concatenate((field_NO3,EANET_NO3))
mask_SO4 = np.concatenate((field_SO4,EANET_SO4))
mask_Cl = np.concatenate((field_Cl,EANET_Cl))
mask_ORG = np.concatenate((field_ORG,EANET_ORG))
mask_EC = field_EC
if regionnum==9:
sub_ORG=ORG
sub_NH4=NH4
sub_Na = Na
sub_Cl = Cl
sub_NO3=NO3
sub_SO4=SO4
sub_EC= EC
fl_ORG=sub_ORG.flatten()
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_Cl =sub_Cl.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
fl_EC =sub_EC.flatten()
# Filter data using np.isnan
fl_ORG[fl_ORG == -1e+34] = np.nan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_Cl[fl_Cl == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
fl_EC[fl_EC == -1e+34] = np.nan
IMPROVE_ORG = fl_ORG[~np.isnan(fl_ORG)]
IMPROVE_NH4 = fl_NH4[~np.isnan(fl_NH4)]
IMPROVE_Na = fl_Na[~np.isnan(fl_Na)]
IMPROVE_Cl = fl_Cl[~np.isnan(fl_Cl)]
IMPROVE_NO3 = fl_NO3[~np.isnan(fl_NO3)]
IMPROVE_SO4 = fl_SO4[~np.isnan(fl_SO4)]
IMPROVE_EC = fl_EC[~np.isnan(fl_EC)]
#print(mask_NH4.shape)
mask_NH4 = IMPROVE_NH4
mask_Na = IMPROVE_Na
mask_NO3 = IMPROVE_NO3
mask_SO4 = IMPROVE_SO4
mask_Cl = IMPROVE_Cl
mask_ORG = IMPROVE_ORG
mask_EC = IMPROVE_EC
if regionnum==11:
subs_NH4=NH4s[(regi==regionnum)]
subs_Na = Nas[(regi==regionnum)]
subs_NO3=NO3s[(regi==regionnum)]
subs_SO4=SO4s[(regi==regionnum)]
fls_NH4=subs_NH4.flatten()
fls_Na =subs_Na.flatten()
fls_NO3=subs_NO3.flatten()
fls_SO4=subs_SO4.flatten()
# Filter data using np.isnan
fls_NH4[fls_NH4 == -1e+34] = np.nan
fls_Na[fls_Na == -1e+34] = np.nan
fls_NO3[fls_NO3 == -1e+34] = np.nan
fls_SO4[fls_SO4 == -1e+34] = np.nan
spartan_NH4 = fls_NH4[~np.isnan(fls_NH4)]
spartan_Na = fls_Na[~np.isnan(fls_Na)]
spartan_NO3 = fls_NO3[~np.isnan(fls_NO3)]
spartan_SO4 = fls_SO4[~np.isnan(fls_SO4)]
sub_ORG=ORG
sub_NH4=NH4
sub_Na = Na
sub_Cl = Cl
sub_NO3=NO3
sub_SO4=SO4
sub_EC= EC
fl_ORG=sub_ORG.flatten()
fl_NH4=sub_NH4.flatten()
fl_Na =sub_Na.flatten()
fl_Cl =sub_Cl.flatten()
fl_NO3=sub_NO3.flatten()
fl_SO4=sub_SO4.flatten()
fl_EC =sub_EC.flatten()
# Filter data using np.isnan
fl_ORG[fl_ORG == -1e+34] = np.nan
fl_NH4[fl_NH4 == -1e+34] = np.nan
fl_Na[fl_Na == -1e+34] = np.nan
fl_Cl[fl_Cl == -1e+34] = np.nan
fl_NO3[fl_NO3 == -1e+34] = np.nan
fl_SO4[fl_SO4 == -1e+34] = np.nan
fl_EC[fl_EC == -1e+34] = np.nan
EPA_ORG = fl_ORG[~np.isnan(fl_ORG)]
EPA_NH4 = fl_NH4[~np.isnan(fl_NH4)]
EPA_Na = fl_Na[~np.isnan(fl_Na)]
EPA_Cl = fl_Cl[~np.isnan(fl_Cl)]
EPA_NO3 = fl_NO3[~np.isnan(fl_NO3)]
EPA_SO4 = fl_SO4[~np.isnan(fl_SO4)]
EPA_EC = fl_EC[~np.isnan(fl_EC)]
mask_NH4 = np.concatenate((spartan_NH4,EPA_NH4))
mask_Na = np.concatenate((spartan_Na,EPA_Na))
mask_NO3 = np.concatenate((spartan_NO3,EPA_NO3))
mask_SO4 = np.concatenate((spartan_SO4,EPA_SO4))
mask_Cl = EPA_Cl
mask_ORG = EPA_ORG
mask_EC = EPA_EC
if regionnum==12:
data_to_box = [mask_SO4]
data_to_violin = [example_SO4]
if regionnum==1 or regionnum==8:
data_to_box = [mask_SO4,mask_NO3,mask_NH4,mask_Na,0,0,0]
data_to_violin = [mask_SO4,mask_NO3,mask_NH4,mask_Na,0,0,0]
if regionnum==2 or regionnum==3 or regionnum==5 or regionnum==6 or regionnum==7 or regionnum==9 or regionnum==10 or regionnum==11:
data_to_box = [mask_SO4,mask_NO3,mask_NH4,mask_Na,mask_Cl,mask_ORG,mask_EC]
data_to_violin = [mask_SO4,mask_NO3,mask_NH4,mask_Na,mask_Cl,mask_ORG,mask_EC]
if regionnum==4:
data_to_box = [mask_SO4,mask_NO3,mask_NH4,mask_Na,mask_Cl,0,0]
data_to_violin = [mask_SO4,mask_NO3,mask_NH4,mask_Na,mask_Cl,0,0]
fig = plt.figure(1, figsize=(16,9))
# Create an axes instance
ax = fig.add_subplot(111)
# Create the boxplot
## add patch_artist=True option to get fill color
if regionnum<=11:
bp = ax.boxplot(data_to_box,widths=0.7,whis=[10,90],showfliers=False, patch_artist=True)
print(mask_SO4.shape)
print(mask_NO3.shape)
print(mask_NH4.shape)
print(mask_Na.shape)
print(np.nanmean(mask_SO4))
print(np.nanmean(mask_NO3))
print(np.nanmean(mask_NH4))
print(np.nanmean(mask_Na))
if regionnum==4:
print(mask_Cl.shape)
print(np.nanmean(mask_Cl))
if regionnum==2 or regionnum==3 or regionnum==5 or regionnum==6 or regionnum==7 or regionnum==9 or regionnum==10 or regionnum==11:
print(mask_Cl.shape)
print(mask_ORG.shape)
print(mask_EC.shape)
print(np.nanmean(mask_Cl))
print(np.nanmean(mask_ORG))
print(np.nanmean(mask_EC))
else:
bp = ax.boxplot(data_to_box,widths=0.3,whis=[10,90],showfliers=False, patch_artist=True)
if regionnum<=11 :
colors = ['Red','Brown','Orange','rebeccapurple','Blue','Green','Black']
else:
colors = ['Black']
for patch, color in zip(bp['boxes'], colors):
patch.set_color(color)
## change color and linewidth of the whiskers
for whisker in bp['whiskers']:
whisker.set(color='black', linewidth=1)
## change color and linewidth of the caps
for cap in bp['caps']:
cap.set(color='black', linewidth=1)
for median in bp['medians']: