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create_csv.py
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import numpy as np
import os
os.chdir('/Users/jesusvergaratemprado/Intercomparison_code')
from src import station_properties as stp
stp.names
from scipy.io import netcdf
import pandas as pd
from collections import OrderedDict
def find_nearest_vector_index(array, value):
n = np.array([abs(i-value) for i in array])
nindex=np.apply_along_axis(np.argmin,0,n)
return nindex
factor_OC2OM=1.9
nc_path='/Users/jesusvergaratemprado/OLD_GLOMAP_NC/'
total_TOA_file="total_organic_mass.nc"
total_MOA_file='wiom_acc.nc'
total_aerosol_file='total_aerosol_mass.nc'
total_sea_salt_file='total_sea_salt.nc'
total_dust_file='total_dust_mass.nc'
total_su_file='total_su_mass.nc'
total_bc_file='total_bc_mass.nc'
#%%
Ntotal_acc_file="Ntotal_acc.nc"
mb=netcdf.netcdf_file(nc_path+Ntotal_acc_file,'r')
mb.variables
Ntotal_acc=mb.variables['Ntotal_acc'][:]
Ntotal_coarse_file="Ntotal_coarse.nc"
mb=netcdf.netcdf_file(nc_path+Ntotal_coarse_file,'r')
mb.variables
Ntotal_coarse=mb.variables['Ntotal_coarse'][:]
radious_accmode_mo_file="radious_accmode_mo.nc"
mb=netcdf.netcdf_file(nc_path+radious_accmode_mo_file,'r')
mb.variables
radious_accmode_mo=mb.variables['radious_accmode_mo'][:]
radious_coarmode_mo_file="radious_coarmode_mo.nc"
mb=netcdf.netcdf_file(nc_path+radious_coarmode_mo_file,'r')
mb.variables
radious_coarmode_mo=mb.variables['radious_coarmode_mo'][:]
radious_accmode_file="radious_accmode.nc"
mb=netcdf.netcdf_file(nc_path+radious_accmode_file,'r')
mb.variables
radious_accmode=mb.variables['radious_accmode'][:]
radious_coarmode_file="radious_coarmode.nc"
mb=netcdf.netcdf_file(nc_path+radious_coarmode_file,'r')
mb.variables
radious_coarmode=mb.variables['radious_coarmode'][:]
submicron_sea_salt_file='sea_salt_acc.nc'
mb=netcdf.netcdf_file(nc_path+submicron_sea_salt_file,'r')
mb.variables
total_ss_acc=mb.variables['sea_salt_acc'][:]
coarse_sea_salt_file='sea_salt_coarse.nc'
mb=netcdf.netcdf_file(nc_path+coarse_sea_salt_file,'r')
mb.variables
total_ss_coar=mb.variables['sea_salt_coarse'][:]
mb=netcdf.netcdf_file(nc_path+total_TOA_file,'r')
mb.variables
total_TOA=mb.variables['total_organic_mass'][:]
mb=netcdf.netcdf_file(nc_path+total_MOA_file,'r')
mb.variables
total_MOA=mb.variables['wiom_acc'][:]
mb=netcdf.netcdf_file(nc_path+total_aerosol_file,'r')
mb.variables
total_AM=mb.variables['total_aerosol_mass'][:]
mb=netcdf.netcdf_file(nc_path+total_sea_salt_file,'r')
mb.variables
total_SS=mb.variables['total_sea_salt'][:]
mb=netcdf.netcdf_file(nc_path+total_dust_file,'r')
mb.variables
total_DUST=mb.variables['total_dust_mass'][:]
mb=netcdf.netcdf_file(nc_path+total_su_file,'r')
mb.variables
total_SU=mb.variables['total_su_mass'][:]
mb=netcdf.netcdf_file(nc_path+total_bc_file,'r')
mb.variables
total_BC=mb.variables['total_bc_mass'][:]
lat=mb.variables['lat'][:]
levels=mb.variables['levels'][:]
lon=mb.variables['lon'][:]
lon180=np.copy(lon)
lon180[lon>180]=lon[lon>180]-360
months=range(1,13)
#%%
d=OrderedDict()
d['model']=[]
d['OA']=[]
d['OAtype']=[]
d['OC']=[]
d['OCtype']=[]
d['aerosol']=[]
d['aerosoltype']=[]
d['month']=[]
d['station']=[]
d['lat']=[]
d['lon']=[]
iobs=54
isurf=30
types=["TOA","MOA"]
for iobs in range(len(stp.lons)):
for imonth in months:
for aer_type in types:
# print iobs
lat_point=stp.lats[iobs]
lon_point=stp.lons[iobs]
ilat=find_nearest_vector_index(lat,lat_point)
if lon_point<0:
ilon=find_nearest_vector_index(lon180,lon_point)
else:
ilon=find_nearest_vector_index(lon,lon_point)
if aer_type is "MOA":data=total_MOA[isurf,ilat,ilon,imonth-1]
if aer_type is "TOA":data=total_TOA[isurf,ilat,ilon,imonth-1]
d['model'].append('GLOMAP')
d['lat'].append(lat_point)
d['lon'].append(lon_point)
d['OA'].append(data*1e3)#ng/m3
d['OAtype'].append(aer_type)
d['OC'].append(data/factor_OC2OM*1e3)#ng/m3
d['OCtype'].append(aer_type[:-1]+'C')
d['aerosol'].append(data)
d['aerosoltype'].append(aer_type)
d['month'].append(imonth)
d['station'].append(stp.names[iobs])
df=pd.DataFrame(d)
df.to_csv('model_data/GLOMAP_TOA_station.csv',mode = 'w', index=False)
#%%
months_str=["JAN","FEB","MAR","APR","MAY","JUN","JUL","AUG","SEP","OCT","NOV","DEC"]
miami_dataset=pd.read_csv('model_data/ACMEv0-OCEANFILMS_mix3_UMiami_stations.csv')
# miami_dataset=miami_dataset[:]
# miami_dataset.shape
# sites=list(set(miami_dataset['site'][:]))
#
# sites_long_name=list(set(miami_dataset['site.long.name']))
# lats_miami=list(set(miami_dataset['lat']))
# lons_miami=list(set(miami_dataset['lon']))
sites=[miami_dataset['site'][i*12] for i in range(len(miami_dataset)/12)]
sites_long_name=[miami_dataset['site.long.name'][i*12] for i in range(len(miami_dataset)/12)]
lats_miami=[miami_dataset['lat'][i*12] for i in range(len(miami_dataset)/12)]
lons_miami=[miami_dataset['lon'][i*12] for i in range(len(miami_dataset)/12)]
len(lats_miami)
len(lons_miami)
len(sites_long_name)
len(sites)
dmia=OrderedDict()
dmia['month']=[]
dmia['site']=[]
dmia['site.long.name']=[]
dmia['lat']=[]
dmia['lon']=[]
dmia['MOA']=[]
dmia['TOA']=[]
dmia['BC']=[]
dmia['SO4']=[]
dmia['NCL']=[]
dmia['DST']=[]
dmia['smSS']=[]
dmia['supermSS']=[]
dmia['radious_accmode']=[]
dmia['radious_coarmode']=[]
dmia['Ntotal_acc']=[]
dmia['Ntotal_coarse']=[]
# dmia['radious_accmode_mo']=[]
# dmia['radious_coarmode_mo']=[]
dmia['total.aerosol']=[]
iobs
len(lats_miami)
for iobs in range(len(sites)):
for imonth in range(len(months_str)):
# print iobs
lat_point=lats_miami[iobs]
lon_point=lons_miami[iobs]
ilat=find_nearest_vector_index(lat,lat_point)
if lon_point<0:
ilon=find_nearest_vector_index(lon180,lon_point)
else:
ilon=find_nearest_vector_index(lon,lon_point)
data=total_MOA[isurf,ilat,ilon,imonth-1]
dmia['lat'].append(lat_point)
dmia['lon'].append(lon_point)
dmia['MOA'].append(total_MOA[isurf,ilat,ilon,imonth])
dmia['TOA'].append(total_TOA[isurf,ilat,ilon,imonth])
dmia['NCL'].append(total_SS[isurf,ilat,ilon,imonth])
dmia['DST'].append(total_DUST[isurf,ilat,ilon,imonth])
dmia['SO4'].append(total_SU[isurf,ilat,ilon,imonth])
dmia['BC'].append(total_BC[isurf,ilat,ilon,imonth])
dmia['smSS'].append(total_ss_acc[isurf,ilat,ilon,imonth])
dmia['supermSS'].append(total_ss_coar[isurf,ilat,ilon,imonth])
dmia['radious_accmode'].append(radious_accmode[isurf,ilat,ilon,imonth])
dmia['radious_coarmode'].append(radious_coarmode[isurf,ilat,ilon,imonth])
dmia['Ntotal_acc'].append(Ntotal_acc[isurf,ilat,ilon,imonth])
dmia['Ntotal_coarse'].append(Ntotal_coarse[isurf,ilat,ilon,imonth])
# dmia['radious_accmode_mo'].append(radious_accmode_mo[isurf,ilat,ilon,imonth])
# dmia['radious_coarmode_mo'].append(radious_coarmode_mo[isurf,ilat,ilon,imonth])
#
dmia['total.aerosol'].append(total_AM[isurf,ilat,ilon,imonth])
dmia['month'].append(months_str[imonth])
dmia['site'].append(sites[iobs])
dmia['site.long.name'].append(sites_long_name[iobs])
# print ilat, ilon, sites_long_name[iobs]
for iobs in range(len(stp.lons)):
for imonth in range(len(months_str)):
lat_point=stp.lats[iobs]
lon_point=stp.lons[iobs]
ilat=find_nearest_vector_index(lat,lat_point)
if lon_point<0:
ilon=find_nearest_vector_index(lon180,lon_point)
else:
ilon=find_nearest_vector_index(lon,lon_point)
data=total_TOA[isurf,ilat,ilon,imonth-1]
dmia['lat'].append(lat_point)
dmia['lon'].append(lon_point)
dmia['MOA'].append(total_MOA[isurf,ilat,ilon,imonth])
dmia['TOA'].append(total_TOA[isurf,ilat,ilon,imonth])
dmia['NCL'].append(total_SS[isurf,ilat,ilon,imonth])
dmia['DST'].append(total_DUST[isurf,ilat,ilon,imonth])
dmia['SO4'].append(total_SU[isurf,ilat,ilon,imonth])
dmia['BC'].append(total_BC[isurf,ilat,ilon,imonth])
dmia['smSS'].append(total_ss_acc[isurf,ilat,ilon,imonth])
dmia['supermSS'].append(total_ss_coar[isurf,ilat,ilon,imonth])
dmia['radious_accmode'].append(radious_accmode[isurf,ilat,ilon,imonth])
dmia['radious_coarmode'].append(radious_coarmode[isurf,ilat,ilon,imonth])
# dmia['radious_accmode_mo'].append(radious_accmode_mo[isurf,ilat,ilon,imonth])
# dmia['radious_coarmode_mo'].append(radious_coarmode_mo[isurf,ilat,ilon,imonth])
dmia['Ntotal_acc'].append(Ntotal_acc[isurf,ilat,ilon,imonth])
dmia['Ntotal_coarse'].append(Ntotal_coarse[isurf,ilat,ilon,imonth])
dmia['total.aerosol'].append(total_AM[isurf,ilat,ilon,imonth])
dmia['month'].append(months_str[imonth])
dmia['site'].append(stp.names[iobs])
dmia['site.long.name'].append(stp.names[iobs])
iobs
dmiaf=pd.DataFrame(dmia)
dmiaf.to_csv('model_data/GLOMAP_UMiami_stations.csv',mode = 'w', index=False)
#