(unknowns, inpt)
| 118 | |
| 119 | |
| 120 | def forwardModel(unknowns, inpt): |
| 121 | t = np.reshape(inpt, (inpt.shape[0], 1)) |
| 122 | dummyR = params["Rs_c"] * np.ones((inpt.shape[0], 1)) |
| 123 | var_dict = {} |
| 124 | params_dict = {} |
| 125 | |
| 126 | var_dict["phis_c"] = [t / resc_t] |
| 127 | var_dict["phis_c_unr"] = [t] |
| 128 | var_dict["phis_c_full"] = [t / resc_t, dummyR / resc_r] |
| 129 | var_dict["phis_c_unr_full"] = [t, dummyR] |
| 130 | |
| 131 | params_dict["phis_c_unr"] = [ |
| 132 | np.clip( |
| 133 | unknowns[i] * np.ones((len(t), 1)).astype("float64"), |
| 134 | nn.params_min[i], |
| 135 | nn.params_max[i], |
| 136 | ) |
| 137 | for i in range(len(unknowns)) |
| 138 | ] |
| 139 | params_dict["phis_c"] = rescale_param_list(nn, params_dict["phis_c_unr"]) |
| 140 | |
| 141 | pred_dict = pinn_pred_phis_c(nn, var_dict, params_dict) |
| 142 | |
| 143 | phis_c_rescaled = pred_dict["phis_c"] |
| 144 | |
| 145 | return phis_c_rescaled[:, 0] |
| 146 | |
| 147 | |
| 148 | def hypercube_combinations(val_list): |
no test coverage detected