#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:
if nr_tries > 0:
# Optimization: Use coarser grid for packing, then insert packed grains into fine grid
grain_ids_coarse_0, overlaps_coarse_0, voxel_indices_coarse_0 = ccb_c.populate_voxels(M_coarse, L, trunc_triangles, nr_tries, M_coarse, 1.0)
grain_ids_0, overlaps_0, voxel_indices_0 = ccb_c.populate_voxels(M, L, trunc_triangles, 1, 0, 1.0)
