def _get_aramis_field_data(self): t_fail = self.time_asc[-1] ad = AramisFieldData(aramis_info=self.aramis_info) current_step = (np.abs(ad.step_times - t_fail).argmin()) # print 'ad.step_times - t_fail', ad.step_times - t_fail ad.current_step = int(current_step * 0.95) return ad
def _get_aramis_field_data(self): print '_get_aramis_field_data' t_fail = self.t_asc[-1] ad = AramisFieldData(aramis_info=self.aramis_info, integ_radius=self.integ_radius, integ_radius_crack=self.integ_radius_crack, transform_data=True, scale_data_factor=self.scale_data_factor) current_step = (np.abs(ad.step_times - t_fail).argmin()) # print 'ad.step_times - t_fail', ad.step_times - t_fail ad.current_step = current_step return ad
p.subplot(224) for e in e_list: aramis_file_path = e.get_cached_aramis_file('Xf15s3-Yf15s3') AI = AramisInfo(data_dir=aramis_file_path) ad = AramisFieldData(aramis_info=AI, integ_radius=3) max_step = e.n_steps a = e.crack_bridge_strain_all n_fa = ad.d_ux.shape[0] h = np.linspace(e.pos_fa[0], e.pos_fa[1], num=n_fa) # print 'h', h for step in range(0, max_step, 10): ad.current_step = step if a == None: mid_idx = ad.d_ux.shape[1] / 2 eps_range = 3 eps = np.mean(ad.d_ux[:, mid_idx - eps_range:mid_idx + eps_range], axis=1) # print 'eps', eps p.title('strain in the middle of the measuring field') else: ux = ad.ux_arr x_und = ad.x_arr_0 idx_border1 = e.idx_failure_crack[1] idx_border2 = e.idx_failure_crack[2] eps_range = 1 ux1 = np.mean(ux[:, idx_border1 - eps_range: idx_border1 + eps_range ], axis=1)
absa = AramisCDT(aramis_info=AI, aramis_data=ad) max_step = e.n_steps a = e.crack_bridge_strain_all n_fa = ad.d_ux.shape[0] h = np.linspace(e.pos_fa[0], e.pos_fa[1], num=n_fa) t_N = e.t_N_arr if len(t_N) == 0: N_end_idx = 0 else: N_end_idx = np.shape(t_N) [0] for step in range(0, N_end_idx, 5): ad.current_step = step if a == None: mid_idx = ad.d_ux.shape[1] / 2 eps_range = 3 eps = np.mean(ad.d_ux[:, mid_idx - eps_range:mid_idx + eps_range], axis=1) p.title('crack bridge strain(N) in the middle of the measuring field') else: ux = ad.ux_arr x_0 = ad.x_arr_0 idx_border1 = e.idx_failure_crack[1] idx_border2 = e.idx_failure_crack[2] eps_range = 2 ux1 = np.mean(ux[:, idx_border1 - eps_range: idx_border1 + eps_range ], axis=1) ux2 = np.mean(ux[:, idx_border2 - eps_range: idx_border2 + eps_range ], axis=1) x_0_1 = np.mean(x_0[:, idx_border1 - eps_range: idx_border1 + eps_range ], axis=1)