def get_compressed(year,month,day,hour):
prmsl=twcr.load('prmsl',datetime.datetime(year,month,day,hour),
version='2c')
prmsl=to_analysis_grid(prmsl.extract(iris.Constraint(member=1)))
t2m=twcr.load('air.2m',datetime.datetime(year,month,day,hour),
version='2c')
t2m=to_analysis_grid(t2m.extract(iris.Constraint(member=1)))
u10m=twcr.load('uwnd.10m',datetime.datetime(year,month,day,hour),
version='2c')
u10m=to_analysis_grid(u10m.extract(iris.Constraint(member=1)))
v10m=twcr.load('vwnd.10m',datetime.datetime(year,month,day,hour),
version='2c')
v10m=to_analysis_grid(v10m.extract(iris.Constraint(member=1)))
insol=to_analysis_grid(load_insolation(year,month,day,hour))
# Convert the validation data into tensor format
t2m_t = tf.convert_to_tensor(normalise_t2m(t2m.data),numpy.float32)
t2m_t = tf.reshape(t2m_t,[79,159,1])
prmsl_t = tf.convert_to_tensor(normalise_prmsl(prmsl.data),numpy.float32)
prmsl_t = tf.reshape(prmsl_t,[79,159,1])
u10m_t = tf.convert_to_tensor(normalise_wind(u10m.data),numpy.float32)
u10m_t = tf.reshape(u10m_t,[79,159,1])
v10m_t = tf.convert_to_tensor(normalise_wind(v10m.data),numpy.float32)
v10m_t = tf.reshape(v10m_t,[79,159,1])
insol_t = tf.convert_to_tensor(normalise_insolation(insol.data),numpy.float32)
insol_t = tf.reshape(insol_t,[79,159,1])
ict = tf.concat([t2m_t,prmsl_t,u10m_t,v10m_t,insol_t],2) # Now [79,159,5]
ict = tf.reshape(ict,[1,79,159,5])
result = autoencoder.predict_on_batch(ict)
result = tf.reshape(result,[79,159,5])
ls = encoder.predict_on_batch(ict)
# Convert the encoded fields back to unnormalised cubes
t2m_r=t2m.copy()
t2m_r.data = tf.reshape(result.numpy()[:,:,0],[79,159]).numpy()
t2m_r.data = unnormalise_t2m(t2m_r.data)
prmsl_r=prmsl.copy()
prmsl_r.data = tf.reshape(result.numpy()[:,:,1],[79,159]).numpy()
prmsl_r.data = unnormalise_prmsl(prmsl_r.data)
u10m_r=u10m.copy()
u10m_r.data = tf.reshape(result.numpy()[:,:,2],[79,159]).numpy()
u10m_r.data = unnormalise_wind(u10m_r.data)
v10m_r=v10m.copy()
v10m_r.data = tf.reshape(result.numpy()[:,:,3],[79,159]).numpy()
v10m_r.data = unnormalise_wind(v10m_r.data)
return {'t2m':t2m_r,'prmsl':prmsl_r,'u10m':u10m_r,'v10m':v10m_r,'ls':ls}
zorder=200)
# Load the validation data
prmsl = twcr.load('prmsl', datetime.datetime(2010, 3, 12, 18), version='2c')
prmsl = to_analysis_grid(prmsl.extract(iris.Constraint(member=1)))
t2m = twcr.load('air.2m', datetime.datetime(2010, 3, 12, 18), version='2c')
t2m = to_analysis_grid(t2m.extract(iris.Constraint(member=1)))
u10m = twcr.load('uwnd.10m', datetime.datetime(2010, 3, 12, 18), version='2c')
u10m = to_analysis_grid(u10m.extract(iris.Constraint(member=1)))
v10m = twcr.load('vwnd.10m', datetime.datetime(2010, 3, 12, 18), version='2c')
v10m = to_analysis_grid(v10m.extract(iris.Constraint(member=1)))
insol = to_analysis_grid(load_insolation(2010, 3, 12, 18))
# Convert the validation data into tensor format
t2m_t = tf.convert_to_tensor(normalise_t2m(t2m.data), numpy.float32)
t2m_t = tf.reshape(t2m_t, [79, 159, 1])
prmsl_t = tf.convert_to_tensor(normalise_prmsl(prmsl.data), numpy.float32)
prmsl_t = tf.reshape(prmsl_t, [79, 159, 1])
u10m_t = tf.convert_to_tensor(normalise_wind(u10m.data), numpy.float32)
u10m_t = tf.reshape(u10m_t, [79, 159, 1])
v10m_t = tf.convert_to_tensor(normalise_wind(v10m.data), numpy.float32)
v10m_t = tf.reshape(v10m_t, [79, 159, 1])
insol_t = tf.convert_to_tensor(normalise_insolation(insol.data), numpy.float32)
insol_t = tf.reshape(insol_t, [79, 159, 1])
# Get autoencoded versions of the validation data
model_save_file = ("%s/ML_GCM/autoencoder.tst/" + "Epoch_%04d/autoencoder") % (
os.getenv('SCRATCH'), args.epoch)
autoencoder = tf.keras.models.load_model(model_save_file, compile=False)
ict = tf.concat([t2m_t, prmsl_t, u10m_t, v10m_t, insol_t], 2) # Now [79,159,5]
if not os.path.isdir(os.path.dirname(args.opfile)):
os.makedirs(os.path.dirname(args.opfile))
# Load and standardise data
if args.source == '20CR2c':
ic = twcr.load(
args.variable,
(datetime.datetime(args.year, args.month, args.day, args.hour) +
datetime.timedelta(hours=6)),
version='2c')
ic = ic.extract(iris.Constraint(member=args.member))
ic = to_analysis_grid(ic)
if args.variable == 'uwnd.10m' or args.variable == 'vwnd.10m':
ic.data = normalise_wind(ic.data)
elif args.variable == 'air.2m':
ic.data = normalise_t2m(ic.data)
elif args.variable == 'prmsl':
ic.data = normalise_prmsl(ic.data)
else:
raise ValueError('Variable %s is not supported' % args.variable)
else:
raise ValueError('Source %s is not supported' % args.source)
# Convert to Tensor
ict = tf.convert_to_tensor(ic.data, numpy.float32)
# Write to file
sict = tf.serialize_tensor(ict)
tf.write_file(args.opfile, sict)