def evaluate_model(params, i, D, return_dict):
print("Starting sensitivity run %i out of %i" % (i + 1, len(D)))
# Defining model parameters
model_params, _ = data.get_params()
for var, val in zip(vars, params):
model_params[var] = val
# Initiate and run models
models = initiate_models(model_params)
run_models(models)
# Assign data to patches
patches = data.assign_data(models)
results = []
for t in range(measurements):
t += 1
mean = get_average_biomass(models, t)
loss = get_total_loss(patches, t)
results += [*mean, *loss]
return_dict[i] = results
print("Finishing sensitivity run %i out of %i" % (i + 1, len(D)))
return
def minimize_total(param, param_range, params_model, iterations, subset_nr, artificial, fname):
loss_list = []
optimalisation_list = []
values = np.linspace(*param_range, iterations)
for value in values:
print("%s value: %.3f" % (param, value))
# Collect parameters for fitting
these_params = copy.deepcopy(params_model)
these_params[param] = value
# Initiate and run models
models = initiate_models(these_params)
run_models(models)
# Assign data to patches
patches = data.assign_data(models, artificial)
# Subset patches for parameter fitting
if subset_nr == 5: subset_nr = 4
subsets = subset.all(patches)[subset_nr]
loss_list.append((
*get_total_loss(patches, 1), *get_subset_loss(subsets, 1),
*get_total_loss(patches, 2), *get_subset_loss(subsets, 2),
*get_total_loss(patches, 3), *get_subset_loss(subsets, 3)
))
# total_loss = get_total_loss(patches, 2)
# optimalisation_list.append(total_loss)
# opt_RL, opt_BR = zip(*optimalisation_list)
# i = opt_RL.index(min(opt_RL))
# j = opt_BR.index(min(opt_BR))
#
# if param in ['c_rr', 'c_rb']:
# params_model[param] = values[i]
# elif param in ['c_bb', 'c_br']:
# params_model[param] = values[j]
# else:
# params_model[param] = values[int(round((i+j)/2))]
np.savetxt(fname, loss_list)
return
def minimize_diam(param, param_range, params_model, iterations, artificial, fname):
loss_list = []
values = np.linspace(*param_range, iterations)
for value in values:
print("%s value: %.3f" % (param, value))
# Collect parameters for fitting
these_params = copy.deepcopy(params_model)
these_params[param] = value
# Initiate and run models
models = initiate_models(these_params)
run_models(models)
# Assign data to patches
patches = data.assign_data(models, artificial)
# Calculate loss
loss_list.append(get_diameter_loss(patches))
np.savetxt(fname, loss_list)
return
from growth import get_FL
from main import initiate_models
models = initiate_models()
# Get maximum global flowlength
FL_list = []
for model in models:
this_FL = 0
for cell in model.allVegetation:
this_FL += cell.FL
FL_list.append(this_FL)
FL_glob_max = max(FL_list)
print(FL_glob_max)
# Get maximum local flowlength
print(get_FL(200))
FL_list = []
for model in models:
for cell in model.allVegetation:
FL_list.append(cell.FL)
FL_loc_max = max(FL_list)
print(FL_loc_max)
# Update beta settings
step_size = init_beta * 0.5**b
for key in settings.keys():
old_beta, diff, cover = settings[key]
new_beta = old_beta + np.sign(diff) * step_size
settings[key] = (new_beta, diff, cover)
# Set up procedure for both species for all plots
proc = []
for cell_type in cell_types:
# Initiate models without runoff return
params, _ = data.get_params()
params["alpha"] = alpha
params["gamma"] = gamma
models = [
model for model in initiate_models(params)
if not model.runoff_return
]
# Run models and collect differences
for model in models:
p = Process(target=evaluate_model,
args=(model, settings, cell_type))
p.start()
proc.append(p)
# Run procedures in multi-processing
for p in proc:
p.join()
proc = []
fname = "../results/patchsize.txt"
if __name__ == '__main__':
artificial = False
prob_range = (.01, .04)
N = 6
probs = np.linspace(*prob_range, N)
errors = []
for value in probs:
print("Running model for seed probability ", value)
these_params, _ = data.get_params()
these_params['seed_prob'] = value
# Initiate and run models
models = initiate_models(these_params)
run_models(models)
# Assign data to patches
patches = data.assign_data(models, artificial)
error = 0
for patch in patches:
if len(patch.BR_original) != 0:
model_size = get_size(patch)
# TODO: Use also other diameters than the last one?
error += abs(model_size - patch.size[-1])
errors.append(error)
np.savetxt(fname, errors)