def convert(unit,var):
mmrtovmr=1/(variables_attributes.attributes(var)[4])
molmass=variables_attributes.attributes(var)[-1]
if unit=='ppmv':
conversion = 1E6*mmrtovmr
elif unit=='ppbv':
conversion = 1E9*mmrtovmr
elif unit=='pptv':
conversion = 1E12*mmrtovmr
elif unit=='h':
conversion = (1/3600.0) # from s
elif unit=='days':
conversion = (1/(3600.0*24)) # from s
else:
conversion=1
return conversion
def convert(unit, var):
mmrtovmr = 1 / (variables_attributes.attributes(var)[4])
molmass = variables_attributes.attributes(var)[-1]
if unit == 'ppmv':
conversion = 1E6 * mmrtovmr
elif unit == 'ppbv':
conversion = 1E9 * mmrtovmr
elif unit == 'pptv':
conversion = 1E12 * mmrtovmr
elif unit == 'h':
conversion = (1 / 3600.0) # from s
elif unit == 'days':
conversion = (1 / (3600.0 * 24)) # from s
else:
conversion = 1
return conversion
def convert2(unit,var,airmass):
mmrtovmr=1/(variables_attributes.attributes(var)[4])
molmass=variables_attributes.attributes(var)[-1]
if unit=='ppmv':
conversion = 1E6*mmrtovmr
elif unit=='ppbv':
conversion = 1E9*mmrtovmr
elif unit=='pptv':
conversion = 1E12*mmrtovmr
elif unit==r'$\mathrm{molecules\ cm^{-3}}$':
# OH(kg/kg) * mAir(kg) * Na(mol-1) / (Vgridcell(cm-3) * mMol(kg.mol-1) )
volfile = ncdf.Dataset('/homes/pjt50/plotting/idl/newncfiles/ukca_geovol.nc','r')
Vcell = np.array(volfile.variables['vol_theta'], dtype=np.float64)[:]
conversion = airmass*6.022E23/(1E6*Vcell*molmass*1E-3)
elif unit=='h':
conversion = (1/3600.0) # from s
elif unit=='days':
conversion = (1/(3600.0*24)) # from s
else:
conversion=1
return conversion
def convert2(unit, var, airmass):
mmrtovmr = 1 / (variables_attributes.attributes(var)[4])
molmass = variables_attributes.attributes(var)[-1]
if unit == 'ppmv':
conversion = 1E6 * mmrtovmr
elif unit == 'ppbv':
conversion = 1E9 * mmrtovmr
elif unit == 'pptv':
conversion = 1E12 * mmrtovmr
elif unit == r'$\mathrm{molecules\ cm^{-3}}$':
# OH(kg/kg) * mAir(kg) * Na(mol-1) / (Vgridcell(cm-3) * mMol(kg.mol-1) )
volfile = ncdf.Dataset(
'/homes/pjt50/plotting/idl/newncfiles/ukca_geovol.nc', 'r')
Vcell = np.array(volfile.variables['vol_theta'], dtype=np.float64)[:]
conversion = airmass * 6.022E23 / (1E6 * Vcell * molmass * 1E-3)
elif unit == 'h':
conversion = (1 / 3600.0) # from s
elif unit == 'days':
conversion = (1 / (3600.0 * 24)) # from s
else:
conversion = 1
return conversion
file1 = '/' + disk + '/ih280/um/' + jobid + '/' + jobid + stash + '.nc' ncbase = ncdf.Dataset(file1, 'r') # time,model_level_number,latitude,longitude # dimensions lat = np.array(ncbase.variables['latitude'], dtype=np.float64)[:] hgt = np.array(ncbase.variables['level_height'], dtype=np.float64)[:] data = ncbase.variables[var] print data #import numpy.ma as ma #new_data = np.ma.masked_where(np.ma.getmask(mask), data) # attributes attributes = variables_attributes.attributes(var) name = attributes[0] formula = attributes[2] # Convert unit conversion = convert_unit.convert(unit, var) data = data[:] * conversion data = data[:] * mask[:] #data_m = data[:] #np.ma.set_fill_value(data_m,0.) #data_m[data_m.mask] = data_m.fill_value # Zonal mean data1 = np.mean(np.mean(\ data[:,:,:,:]\ , axis=3, dtype=np.float64)\
cmap = cm.bwr cnorm=clrs.Normalize(cmap,clip=False) cmap.set_under(color=cmap(0.0),alpha=1.0) cmap.set_over(color=cmap(1.0),alpha=1.0) #************************************************************ # Read in files # time,model_level_number,latitude,longitude file1 = '/'+disk+'/ih280/um/'+jobid+'/'+jobid+stash+'.nc' ncbase = ncdf.Dataset(file1,'r') data = ncbase.variables[var] print data # attributes attributes = variables_attributes.attributes(var) varname = attributes[0] formula = attributes[2] # convert to unit conversion = convert_unit.convert(unit,var) data = data[:]*conversion #*********************************************************** # Tropospheric mask: 1 for troposphere, 0 other #INPUT: #ncfile = file containing tropopause height #LTROP = 1 for troposphere (i.e. mask out stratosphere); 0 otherwise #tmean = 0 (no time meaning), # 1 (climatological monthly mean), # 2 (full time mean),