kBT = 0.5 # Move max 0.75 mu in any direction delta = M #int(0.75 / delta_x) nr_tries = 1000 # to avoid confusion vol_frac_goal = np.double(vol_frac_goal) L = np.double(L) M = np.int(M) mc_steps = np.int(mc_steps) kBT = np.double(kBT) nr_tries = np.int(nr_tries) trunc_triangles = ccb.prepare_triangles(1.0, L) # Using 1.0 instead of vol_frac_goal in order to obtain enough inputs. #ccb.optimize_midpoints(L, trunc_triangles) with open('trunc_triangles_0.data', 'wb') as f: pickle.dump(L, f, pickle.HIGHEST_PROTOCOL) pickle.dump(trunc_triangles, f, pickle.HIGHEST_PROTOCOL) grain_ids_0, overlaps_0, voxel_indices_0 = ccb_c.populate_voxels(M, L, trunc_triangles, nr_tries, delta, vol_frac_goal) phases_0, good_voxels_0, euler_angles_0, phase_volumes_0, grain_volumes_0 = ccb_c.calc_grain_prop(M, grain_ids_0, trunc_triangles) surface_voxels_0, gb_voxels_0, interface_voxels_0 = ccb_c.calc_surface_prop(M, grain_ids_0) vol_frac_WC_0 = phase_volumes_0[1]/np.float(np.sum(phase_volumes_0)) vol_frac_Co_0 = 1 - vol_frac_WC_0 mass_frac_WC_0 = ccb.mass_fraction(vol_frac_WC_0) d_eq_0 = ccb.volume_to_eq_d(grain_volumes_0*delta_x**3)
for sample in range(samples): np.random.seed(sample) # to avoid confusion vol_frac_goal = np.double(vol_frac_goal) L = np.double(L) kBT = np.double(kBT) nr_tries = np.int(nr_tries) delta_x = float(d_0)/float(m) M = np.int(m * L / d_0) print("Running vol fraction", vol_frac_goal, "with M", M) fname = 'mstat_{:.1f}_{}'.format(vol_frac_goal, sample) trunc_triangles = ccb.prepare_triangles(vol_frac_goal, L, r, k, d_eq) trunc_triangles.sort(key=lambda m: m.volume, reverse=True) print("Prepared", len(trunc_triangles), "triangles") if use_potential: ccb.optimize_midpoints(L, trunc_triangles) grain_ids_0, overlaps_0, voxel_indices_0 = ccb_c.populate_voxels(M, L, trunc_triangles, 1, 0, 1.0) else: grain_ids_0, overlaps_0, voxel_indices_0 = ccb_c.populate_voxels(M, L, trunc_triangles, nr_tries, M, 1.0) phases_0, good_voxels_0, euler_angles_0, phase_volumes_0, grain_volumes_0 = ccb_c.calc_grain_prop(M, grain_ids_0, trunc_triangles) surface_voxels_0, gb_voxels_0, interface_voxels_0 = ccb_c.calc_surface_prop(M, grain_ids_0) vol_frac_WC_0 = phase_volumes_0[1]/np.float(np.sum(phase_volumes_0)) vol_frac_Co_0.append( 1 - vol_frac_WC_0 ) sum_gb_voxels_0 = np.sum(gb_voxels_0) contiguity_0.append( sum_gb_voxels_0 / np.float(sum_gb_voxels_0 + np.sum(interface_voxels_0)) )
#fname = 'testfile_{}_Sayers'.format(L) #fname = 'testfile_{}_Mari'.format(L) #fname = 'testfile_{}_Cheng'.format(L) #fname = 'testfile_{}_Touloukian'.format(L) #fname = 'testfile_{}_Johansson_lowK'.format(L) #fname = 'testfile_{}_Lee'.format(L) #fname = 'testfile_{}_Golovchan'.format(L) #fname = 'testfile_{}_Johansson'.format(L) #fname = 'testfile_{}_Suetin'.format(L) #fname = 'testfile_{}_Li'.format(L) #fname = 'testfile_{}_Cheng_klow'.format(L) #fname = 'testfile' ######### Start of simulation ############# trunc_triangles = ccb.prepare_triangles(vol_frac_goal, L, r, k, d_eq) # Sort triangles w.r.t. volume, so that large triangles are added to the box first (better packing) trunc_triangles.sort(key=lambda m: m.volume, reverse=True) if use_potential: ccb.optimize_midpoints(L, trunc_triangles) print('Prepared', len(trunc_triangles), 'triangles') with open('trunc_triangles_0.data', 'wb') as f: pickle.dump(L, f, pickle.HIGHEST_PROTOCOL) pickle.dump(trunc_triangles, f, pickle.HIGHEST_PROTOCOL) if m_coarse == m: grain_ids_0, overlaps_0, voxel_indices_0 = ccb_c.populate_voxels(M, L, trunc_triangles, nr_tries, M, 1.0) else:
for sample in range(samples): np.random.seed(sample) print("Running vol fraction", vol_frac_goal) fname = 'volstat_{:.1f}_{}'.format(vol_frac_goal, sample) # to avoid confusion vol_frac_goal = np.double(vol_frac_goal) L = np.double(L) kBT = np.double(kBT) nr_tries = np.int(nr_tries) delta_x = float(d_0)/float(m) M = np.int(m * L / d_0) M_coarse = np.int(m_coarse * L / d_0) trunc_triangles = ccb.prepare_triangles(vol_frac_goal, L, r, k, d_eq) trunc_triangles.sort(key=lambda m: m.volume, reverse=True) print("Prepared", len(trunc_triangles), "triangles") if use_potential: ccb.optimize_midpoints(L, trunc_triangles) if nr_tries > 1: grain_ids_coarse_0, overlaps_coarse_0, voxel_indices_coarse_0 = ccb_c.populate_voxels(M_coarse, L, trunc_triangles, nr_tries, M_coarse, 1.) grain_ids_0, overlaps_0, voxel_indices_0 = ccb_c.populate_voxels(M, L, trunc_triangles, 1, 0, 1.) phases_0, good_voxels_0, euler_angles_0, phase_volumes_0, grain_volumes_0 = ccb_c.calc_grain_prop(M, grain_ids_0, trunc_triangles) surface_voxels_0, gb_voxels_0, interface_voxels_0 = ccb_c.calc_surface_prop(M, grain_ids_0) vol_frac_WC_0 = phase_volumes_0[1]/np.float(np.sum(phase_volumes_0)) vol_frac_Co_0.append( 1 - vol_frac_WC_0 ) sum_gb_voxels_0 = np.sum(gb_voxels_0)