def read_instance(self, forward_folder):
fileio = FileIO()
fileio.assign_forward_folder(forward_folder)
i = 1
self.fhn_model_instances = fileio.read_physics_model_instance(i, 'fhn')
self.diffusion_model_instances = fileio.read_physics_model_instance(
i, 'diffusion')
self.point_cloud_instances = fileio.read_point_cloud_instance(i)
# ========================== get variable ================================ #
self.coord = self.point_cloud_instances['coord']
self.no_pt = self.point_cloud_instances['no_pt']
self.t = self.fhn_model_instances['t']
self.V = self.fhn_model_instances['V']
self.v = self.fhn_model_instances['v']
self.a = self.fhn_model_instances['a']
self.delta = self.fhn_model_instances['delta']
self.gamma = self.fhn_model_instances['gamma']
self.stimulated_current = np.max(
self.fhn_model_instances['applied_current'])
self.D = self.diffusion_model_instances['D']
self.c = self.diffusion_model_instances['c']
return
predicted.std()))
print('error(%): {} \u00B1 {}'.format(
(abs(predicted - true_value) / true_value).mean() * 100,
(abs(predicted - true_value) / true_value).std() * 100))
print('quartile error(%): {} \u00B1 {}'.format(
np.quantile((abs(predicted - true_value) / true_value), 0.25) * 100,
np.quantile((abs(predicted - true_value) / true_value), 0.75) * 100))
if __name__ == '__main__':
# case1_1D_D1_c0, case2_sphere_D1_c0, case3_2D_D1_c0,
forward_folder = '../data/case3_sphere/forward1/'
inverse_folder = '../data/case3_sphere/inverse1/'
fileio = FileIO()
fileio.assign_forward_folder(forward_folder)
fileio.assign_inverse_folder(inverse_folder)
i = 1
fhn_model_instances = fileio.read_physics_model_instance(i, model='fhn')
fhn_dl_model_instances = fileio.read_inverse_physics_model_instance(
i, model='fhn')
diffusion_model_instances = fileio.read_physics_model_instance(
i, model='diffusion')
diffusion_dl_model_instances = fileio.read_inverse_physics_model_instance(
i, model='diffusion')
point_cloud_instances = fileio.read_point_cloud_instance(i)
coord = point_cloud_instances['coord']
t = fhn_model_instances['t']
V = fhn_model_instances['V']
