dj_df.columns = columns_dJ
# merge
opt_df = pd.merge(params_df, dj_df, on='J', how='outer')
return opt_df
reduced_functional = fenics.ReducedFunctional(
J,
controls,
derivative_cb_post=derivative_cb_post,
eval_cb_post=eval_cb_post)
dJdu = fenics.compute_gradient(J, controls)
bounds = [[0.05, 0.01, 0.05], [0.5, 0.2, 0.05]]
m_opt = fenics.minimize(reduced_functional,
bounds=bounds,
method='SLSQP',
options={'disp': True})
opt_df = create_opt_progress_df(opt_param_progress_post, opt_dj_progress_post,
['D', 'rho', 'c'])
opt_df.to_excel(os.path.join(output_path, 'optimization.xls'))
opt_df.to_pickle(os.path.join(output_path, 'optimization.pkl'))
# ==============================================================================
# Plotting
# ==============================================================================
plott.show_img_seg_f(function=m_target,
path=os.path.join(output_path, 'disp_target_orig.png'))
plott.show_img_seg_f(function=w_target,
path=os.path.join(output_path, 'conc_target_orig.png'))
thresh(w_target, thresh_1), # thresholded concentration
thresh(w, thresh_1) - thresh(w_target, thresh_1)) * sim.subdomains.dx)
controls = [fenics.ConstantControl(param) for param in params_init]
def eval_cb(j, a):
params = [param.values() for param in a]
print(j, *params)
reduced_functional = fenics.ReducedFunctional(J,
controls,
eval_cb_post=eval_cb)
m_opt = fenics.minimize(reduced_functional)
for var in m_opt:
print(var.values())
# ==============================================================================
# RESULTS
# ==============================================================================
# Plot when adjoint computation has finished to avoid recording of function projections
# sim.plotting.plot_all(sim_time)
#
# output_path = os.path.join(test_config.output_path, 'test_case_simulation_tumor_growth_2D_uniform_adjoint')
# fu.ensure_dir_exists(output_path)
#
# sim.init_postprocess(os.path.join(output_path, 'postprocess', 'plots'))
#
params_df.columns = columns_params
dj_df = pd.DataFrame(opt_dj_list)
dj_df.columns = columns_dJ
# merge
opt_df = pd.merge(params_df, dj_df, on='J', how='outer')
return opt_df
reduced_functional = fenics.ReducedFunctional(
J,
controls,
derivative_cb_post=derivative_cb_post,
eval_cb_post=eval_cb_post)
dJdu = fenics.compute_gradient(J, controls)
bounds = [[0.05, 0.01, 0.05], [0.5, 0.2, 0.5]]
m_opt = fenics.minimize(reduced_functional,
bounds=bounds,
options={
'disp': True,
'gtol': 1e-10
},
tol=1e-10)
opt_df = create_opt_progress_df(opt_param_progress_post, opt_dj_progress_post,
['D', 'rho', 'c'])
opt_df.to_excel(os.path.join(output_path, 'optimization.xls'))
opt_df.to_pickle(os.path.join(output_path, 'optimization.pkl'))
for var in m_opt:
print(var.values())