def vertdata(ncbase,jobid,z,var,latbounds):
lat = np.array(ncbase.variables['latitude'],dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'],dtype=np.float64)[:]
hgt = np.array(ncbase.variables['level_height'],dtype=np.float64)[:]
mon1 = np.array(ncbase.variables['time'],dtype=np.float64)
mon1 = mon1[jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
mon = mon1[:]
high = np.where(hgt>=z[1]*1E3)[0][0]+1
low = np.where(hgt>=z[0]*1E3)[0][0]
south = np.where(lat==latbounds[0])[0][0]
north = np.where(lat==latbounds[1])[0][0]+1
# attributes=variables_attributes.attributes(var)
# Convert unit
unit1=units.diagnostic(var)
if unit1=='':
unit1 = data.unit
conversion = convert_unit.convert(unit1,var)
# DATA
data = ncbase.variables[var][jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
data = data[:]*conversion
# Vertical profile
years=[]
for y in range(0,len(data)/12):
years.append(np.mean(np.mean(np.mean(data[y*12:(y+1)*12,low:high,south:north,:], axis=3, dtype=np.float64), axis=2, dtype=np.float64), axis=0, dtype=np.float64))
data1 = np.mean(years, axis=0, dtype=np.float64)
sd = np.std(years, axis=0, dtype=np.float64)
# Standard deviation
sdbelow = data1[:]-sd[:]
sdabove = data1[:]+sd[:]
return data1,sdbelow,sdabove
def dimensions(ncbase,jobid):
lat = np.array(ncbase.variables['latitude'],dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'],dtype=np.float64)[:]
hgt = np.array(ncbase.variables['level_height'],dtype=np.float64)[:]
mon = np.array(ncbase.variables['time'],dtype=np.float64)\
[jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
return lat,lon,hgt,mon
def mapdata(ncbase,jobid,z,var):
# ncbase = time,model_level_number,latitude,longitude
lat = np.array(ncbase.variables['latitude'],dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'],dtype=np.float64)[:]
hgt = np.array(ncbase.variables['level_height'],dtype=np.float64)[:]
mon = np.array(ncbase.variables['time'],dtype=np.float64)\
[jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
high = np.where(hgt>=z[1]*1E3)[0][0]+1
low = np.where(hgt>=z[0]*1E3)[0][0]
# Airmass
file1 = '/scratch/ih280/um/'+jobid+'/pm_'+jobid+'_oh.nc'
ncbase1 = ncdf.Dataset(file1,'r')
airmass = np.array(ncbase1.variables['field643'], dtype=np.float64)[:]
# Convert unit
unit1=units.diagnostic(var)
conversion = convert_unit.convert2(unit1,var,airmass)
# DATA
data = ncbase.variables[var][jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
if np.shape(conversion)>(1,):
conversion = np.resize(conversion,(len(data), 60, 73, 96))
data = data[:]*conversion
# Map
years=[]
for y in range(0,len(data)/12):
years.append(np.mean(np.mean(data[y*12:(y+1)*12,low:high,:,:], axis=1, dtype=np.float64), axis=0, dtype=np.float64))
data1 = np.mean(years, axis=0, dtype=np.float64)
sd = np.std(years, axis=0, dtype=np.float64)
sdbelow = data1[:]-sd[:]
sdabove = data1[:]+sd[:]
return data1,sdbelow,sdabove
def dimensions(ncbase, jobid):
lat = np.array(ncbase.variables['latitude'], dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'], dtype=np.float64)[:]
hgt = np.array(ncbase.variables['level_height'], dtype=np.float64)[:]
mon = np.array(ncbase.variables['time'],dtype=np.float64)\
[jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
return lat, lon, hgt, mon
def season(ncbase, jobid, var):
data = ncbase.variables[var][jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
for y in range(0, len(data) / 12 - 1):
mam = np.mean(data[2 + y * 12:5 + y * 12], axis=0)
jja = np.mean(data[5 + y * 12:8 + y * 12], axis=0)
son = np.mean(data[8 + y * 12:12 + y * 12], axis=0)
d = np.mean(data[12 + y * 12:13 + y * 12], axis=0)
jf = np.mean(data[0 + y * 12:2 + y * 12], axis=0)
djf = np.mean([d, jf], axis=0)
return djf, mam, jja, son
def season(ncbase,jobid, var):
data = ncbase.variables[var][jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
for y in range(0,len(data)/12-1):
mam = np.mean(data[2+y*12:5+y*12],axis=0)
jja = np.mean(data[5+y*12:8+y*12],axis=0)
son = np.mean(data[8+y*12:12+y*12],axis=0)
d = np.mean(data[12+y*12:13+y*12],axis=0)
jf = np.mean(data[0+y*12:2+y*12],axis=0)
djf = np.mean([d,jf],axis=0)
return djf,mam,jja,son
def vertdata(ncbase, jobid, z, var, latbounds):
lat = np.array(ncbase.variables['latitude'], dtype=np.float64)[:]
lon = np.array(ncbase.variables['longitude'], dtype=np.float64)[:]
hgt = np.array(ncbase.variables['level_height'], dtype=np.float64)[:]
mon1 = np.array(ncbase.variables['time'], dtype=np.float64)
mon1 = mon1[jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
mon = mon1[:]
high = np.where(hgt >= z[1] * 1E3)[0][0] + 1
low = np.where(hgt >= z[0] * 1E3)[0][0]
south = np.where(lat == latbounds[0])[0][0]
north = np.where(lat == latbounds[1])[0][0] + 1
# attributes=variables_attributes.attributes(var)
# Convert unit
unit1 = units.diagnostic(var)
if unit1 == '':
unit1 = data.unit
conversion = convert_unit.convert(unit1, var)
# DATA
data = ncbase.variables[var][jobs.spinup(jobid)[0]:jobs.spinup(jobid)[1]]
data = data[:] * conversion
# Vertical profile
years = []
for y in range(0, len(data) / 12):
years.append(
np.mean(np.mean(np.mean(data[y * 12:(y + 1) * 12, low:high,
south:north, :],
axis=3,
dtype=np.float64),
axis=2,
dtype=np.float64),
axis=0,
dtype=np.float64))
data1 = np.mean(years, axis=0, dtype=np.float64)
sd = np.std(years, axis=0, dtype=np.float64)
# Standard deviation
sdbelow = data1[:] - sd[:]
sdabove = data1[:] + sd[:]
return data1, sdbelow, sdabove
