else:
site_coords = [args.coords[0], args.coords[1]]
if args.pnames_file is not None:
with open(args.pnames_file) as f:
pnames_ens = f.read().splitlines()
else:
pnames_ens = ['nue_tree', 'gdd_crit'] # 'leafcn', 'slatop_decid']
ndim = len(pnames_ens)
nens = args.nens
out_prefix = args.out_prefix
# create model object
model = models.MyModel()
# Desired outputs
#myoutvars = ['gpp', 'lai', 'npp']
print('Generating ensemble')
model.generate_ensemble(nens,
pnames_ens,
fname=args.psam_file,
normalized=False)
assert (model.parm_ensemble.shape[0] == nens)
assert (model.parm_ensemble.shape[1] == ndim)
# Daily output
# model.run_selm(spinup_cycles=4,
# lon_bounds=[-82, -80], lat_bounds=[40, 42],
# prefix='regional', pft=0, use_MPI=False)
print("Dimensions #######################")
for ikey in dataset.dimensions.keys(): # time(360),lon(5),lat(7)
print(dataset.dimensions[ikey].name + ", size " +
str(dataset.dimensions[ikey].size)) # 7
print("Variables #######################")
for ikey in dataset.variables.keys():
print(dataset.variables[ikey].name + ", size " +
str(dataset.variables[ikey].shape))
lons = dataset.variables['lon'][:] - 360. # .shape
lats = dataset.variables['lat'][:] # .shape
# create model object
model = selm.MyModel()
qois = model.outvars
qois.remove('ctcpools')
years = 1980 + dataset.variables['time'][:] / 365
for qoi in qois:
qoi_var = utils.get_qoi_regave(dataset, qoi)
fig = plt.figure(figsize=(12, 6))
plt.plot(years, qoi_var.T)
plt.savefig(qoi+'_ens.png')
plt.clf()