def generate_samples(sp, n_runs, VPD=2.0, tmax=180):
var_vals = [traits[sp][get_part(v)][v] for v in var_names[:-1]]
var_vals.extend([Rmax])
problem_bounds = [[min(0.5 * v, 2.0 * v),
max(0.5 * v, 2.0 * v)] for v in var_vals]
# problem_bounds = [[min(0.25*v,4.0*v), max(0.25*v,4.0*v)] for v in var_vals]
problem = {
'num_vars': n_vars,
'names': var_names,
'bounds': problem_bounds
}
# generate samples
param_values = saltelli.sample(problem, n_runs, calc_second_order=False)
print len(param_values)
''' parallel processing '''
ncores = mp.cpu_count()
print('There are %s cores on this machine ' % (str(ncores), ))
pool = mp.Pool()
param_cores = np.array_split(param_values, ncores, axis=0)
param_augmented = [(p, VPD, tmax) for p in param_cores]
Y_pooled = pool.map(evaluate_model, param_augmented)
Y = np.vstack(Y_pooled) # output shape (len(param_values), 2)
with open(
'/Users/xuefeng/Dropbox/Projects/Isohydricity/Sobol/Si_' + sp +
'_vpd' + str(int(VPD)) + '_tmax' + str(tmax) + '_outcomes.pickle',
'wb') as handle:
pickle.dump(Y, handle)
with open(
'/Users/xuefeng/Dropbox/Projects/Isohydricity/Sobol/Si_' + sp +
'_vpd' + str(int(VPD)) + '_tmax' + str(tmax) + '_params.pickle',
'wb') as handle:
pickle.dump(param_values, handle)
return Y, param_values
def define_problem(sp):
var_vals = [traits[sp][get_part(v)][v] for v in var_names]
problem_bounds = [[min(0.5 * v, 2.0 * v),
max(0.5 * v, 2.0 * v)] for v in var_vals]
problem = {
'num_vars': n_vars,
'names': var_names,
'bounds': problem_bounds
}
return problem
def Sobol(sp, n_runs):
var_vals = [traits[sp][get_part(v)][v] for v in var_names[:-1]]
var_vals.extend([Rmax])
problem_bounds = [[min(0.5 * v, 2.0 * v),
max(0.5 * v, 2.0 * v)] for v in var_vals]
problem = {
'num_vars': n_vars,
'names': var_names,
'bounds': problem_bounds
}
# generate samples
param_values = saltelli.sample(problem, n_runs, calc_second_order=False)
print len(param_values)
VPD = 2.0
gridsize = 10
Si_depo = np.zeros(
(gridsize, n_vars, 4)) # number of increments, parameters, Assm/HF/CIs
for i, tmax in enumerate(np.linspace(60, 240, gridsize)):
print i,
''' parallel processing '''
ncores = mp.cpu_count()
print('There are %s cores on this machine ' % (str(ncores), ))
pool = mp.Pool()
param_cores = np.array_split(param_values, ncores, axis=0)
param_augmented = [(p, VPD, tmax) for p in param_cores]
Y_pooled = pool.map(evaluate_model, param_augmented)
Y = np.vstack(Y_pooled) # output shape (len(param_values), 2)
# perform analysis
Si_A = sobol.analyze(problem,
Y[:, 1],
calc_second_order=False,
print_to_console=True)
try:
Si_H = sobol.analyze(problem,
Y[:, 0],
calc_second_order=False,
print_to_console=True)
except:
pass
# store
Si_depo[i, :, 0] = Si_H['ST']
Si_depo[i, :, 1] = Si_A['ST_conf']
Si_depo[i, :, 2] = Si_H['ST']
Si_depo[i, :, 3] = Si_A['ST_conf']
with open(
'/Users/xuefeng/Dropbox/Projects/Isohydricity/Sobol/Si_' + sp +
'_vpd' + str(int(VPD)) + '_tmax' + str(tmax) + '.pickle',
'wb') as handle:
pickle.dump(Si_depo, handle)
return Si_depo
def define_problem(sp='JUNI'):
var_vals = [traits[sp][get_part(v)][v] for v in var_names[:-1]]
var_vals.extend([Rmax])
problem_bounds = [[min(0.5 * v, 2.0 * v),
max(0.5 * v, 2.0 * v)] for v in var_vals]
# problem_bounds = [[min(0.25*v,4.0*v), max(0.25*v,4.0*v)] for v in var_vals]
problem = {
'num_vars': n_vars,
'names': var_names,
'bounds': problem_bounds
}
return problem