def assemble_grain_data(grain_data_list,
pos_offset=None,
rotation_offset=None):
num_grain_files = len(grain_data_list)
num_grains_list = [None] * num_grain_files
for i in np.arange(num_grain_files):
num_grains_list[i] = grain_data_list[i].shape[0]
num_grains = np.sum(num_grains_list)
grain_data = np.zeros([num_grains, grain_data_list[0].shape[1]])
for i in np.arange(num_grain_files):
tmp = copy.copy(grain_data_list[i])
if pos_offset is not None:
pos_tile = np.tile(pos_offset[:, i], [num_grains_list[i], 1])
tmp[:, 6:9] = tmp[:, 6:9] + pos_tile
#Needs Testing
if rotation_offset is not None:
rot_tile = np.tile(
np.atleast_2d(rotation_offset[:, i]).T,
[1, num_grains_list[i]])
quat_tile = rot.quatOfExpMap(rot_tile)
grain_quats = rot.quatOfExpMap(tmp[:, 3:6].T)
new_quats = rot.quatProduct(grain_quats, quat_tile)
sinang = mutil.columnNorm(new_quats[1:, :])
ang = 2. * np.arcsin(sinang)
axis = mutil.unitVector(new_quats[1:, :])
tmp[:, 3:6] = np.tile(np.atleast_2d(ang).T, [1, 3]) * axis.T
grain_data[int(np.sum(num_grains_list[:i])
):int(np.sum(num_grains_list[:(i + 1)])), :] = tmp
old_grain_numbers = copy.copy(grain_data[:, 0])
grain_data[:, 0] = np.arange(num_grains)
return grain_data, old_grain_numbers
def assemble_grain_data(grain_data_list,pos_offset=None,rotation_offset=None):
num_grain_files=len(grain_data_list)
num_grains_list=[None]*num_grain_files
for i in np.arange(num_grain_files):
num_grains_list[i]=grain_data_list[i].shape[0]
num_grains=np.sum(num_grains_list)
grain_data=np.zeros([num_grains,grain_data_list[0].shape[1]])
for i in np.arange(num_grain_files):
tmp=copy.copy(grain_data_list[i])
if pos_offset is not None:
pos_tile=np.tile(pos_offset[:,i],[num_grains_list[i],1])
tmp[:,6:9]=tmp[:,6:9]+pos_tile
#Needs Testing
if rotation_offset is not None:
rot_tile=np.tile(np.atleast_2d(rotation_offset[:,i]).T,[1,num_grains_list[i]])
quat_tile=rot.quatOfExpMap(rot_tile)
grain_quats=rot.quatOfExpMap(tmp[:,3:6].T)
new_quats=rot.quatProduct(grain_quats,quat_tile)
sinang = mutil.columnNorm(new_quats[1:,:])
ang=2.*np.arcsin(sinang)
axis = mutil.unitVector(new_quats[1:,:])
tmp[:,3:6]=np.tile(np.atleast_2d(ang).T,[1,3])*axis.T
grain_data[int(np.sum(num_grains_list[:i])):int(np.sum(num_grains_list[:(i+1)])),:]=tmp
old_grain_numbers=copy.copy(grain_data[:,0])
grain_data[:,0]=np.arange(num_grains)
return grain_data,old_grain_numbers
def remove_duplicate_grains(grain_data,
qsyms,
dist_thresh=0.01,
misorient_thresh=0.1,
comp_diff=0.1):
total_grains = grain_data.shape[0]
all_comp = grain_data[:, 1]
grain_quats = rot.quatOfExpMap(grain_data[:, 3:6].T)
dup_list = np.array([])
print 'Removing duplicate grains'
for i in np.arange(total_grains - 1):
cur_pos = grain_data[i, 3:6]
other_pos = grain_data[(i + 1):, 3:6]
xdist = cur_pos[0] - other_pos[:, 0]
ydist = cur_pos[1] - other_pos[:, 1]
zdist = cur_pos[2] - other_pos[:, 2]
dist = np.sqrt(xdist**2. + ydist**2. + zdist**2.)
if np.min(dist) < dist_thresh:
q1 = sym.toFundamentalRegion(np.atleast_2d(grain_quats[:, i]).T,
crysSym=qsyms)
q2 = sym.toFundamentalRegion(np.atleast_2d(
grain_quats[:, np.argmin(dist) + i + 1]).T,
crysSym=qsyms)
misorientation = rot.misorientation(q1, q2)[0] * 180. / np.pi
if misorientation < misorient_thresh:
dup_list = np.append(dup_list, np.argmin(dist) + i + 1)
if (np.abs(all_comp[i] - all_comp[np.argmin(dist) + i + 1]) <=
comp_diff):
grain_data[
i, :] = (grain_data[i, :] +
grain_data[np.argmin(dist) + i + 1, :]) / 2.
elif ((all_comp[np.argmin(dist) + i + 1] - all_comp[i]) > 0):
grain_data[i, :] = grain_data[np.argmin(dist) + i + 1, :]
grain_data = np.delete(grain_data, dup_list, axis=0)
print 'Removed %d Grains' % (len(dup_list))
grain_data[:, 0] = np.arange(grain_data.shape[0])
return grain_data, dup_list
def remove_duplicate_grains(grain_data,qsyms,dist_thresh=0.01,misorient_thresh=0.1,comp_diff=0.1):
total_grains=grain_data.shape[0]
all_comp=grain_data[:,1]
grain_quats=rot.quatOfExpMap(grain_data[:,3:6].T)
dup_list=np.array([])
print 'Removing duplicate grains'
for i in np.arange(total_grains-1):
cur_pos=grain_data[i,3:6]
other_pos=grain_data[(i+1):,3:6]
xdist=cur_pos[0]-other_pos[:,0]
ydist=cur_pos[1]-other_pos[:,1]
zdist=cur_pos[2]-other_pos[:,2]
dist=np.sqrt(xdist**2.+ydist**2.+zdist**2.)
if np.min(dist)<dist_thresh:
q1=sym.toFundamentalRegion(np.atleast_2d(grain_quats[:,i]).T,crysSym=qsyms)
q2=sym.toFundamentalRegion(np.atleast_2d(grain_quats[:,np.argmin(dist)+i+1]).T,crysSym=qsyms)
misorientation=rot.misorientation(q1,q2)[0]*180./np.pi
if misorientation < misorient_thresh:
dup_list=np.append(dup_list,np.argmin(dist)+i+1)
if (np.abs(all_comp[i] - all_comp[np.argmin(dist)+i+1]) <=comp_diff):
grain_data[i,:]=(grain_data[i,:]+grain_data[np.argmin(dist)+i+1,:])/2.
elif ( (all_comp[np.argmin(dist)+i+1]-all_comp[i]) >0):
grain_data[i,:]=grain_data[np.argmin(dist)+i+1,:]
grain_data=np.delete(grain_data,dup_list,axis=0)
print 'Removed %d Grains' % (len(dup_list))
grain_data[:,0]=np.arange(grain_data.shape[0])
return grain_data,dup_list
#==============================================================================
# MISORIENTATION
#==============================================================================
print('calculate misorientation')
tmp_data_avg = np.loadtxt(grain_out_file)
tmp_data_grain = np.loadtxt(grain_id_out_file)
id_avg = tmp_data_avg[:, 0]
ori_avg = tmp_data_avg[:, 3:6]
id_mis = tmp_data_grain[:, 0]
ori_mis = tmp_data_grain[:, 3:6]
mis = np.zeros(grain_map_g.shape)
q_mor = rot.quatOfExpMap(ori_mis.T)
q_avg = rot.quatOfExpMap(ori_avg.T)
material_file_loc = mat_file # hexrd material file in cpickle format
mat_name = 'ti7al'
mat_list = cpl.load(open(material_file_loc, 'r'))
mat_idx = np.where([
mat_list[i].name == mat_name for i in range(len(mat_list))
])[0]
# grab plane data, and useful things hanging off of it
pd = mat_list[mat_idx[0]].planeData
qsyms = sym.quatOfLaueGroup(pd.getLaueGroup())
for w in range(0, len(test[0])):
fc_file = sorted(
glob.glob(os.path.join(data_dir, fc_stem % det_key.lower())))
if len(fc_file) != 1:
raise (RuntimeError, 'cache file not found, or multiple found')
else:
ims = load_images(fc_file[0])
imsd[det_key] = OmegaImageSeries(ims)
eta_ome = instrument.GenerateEtaOmeMaps(
imsd,
instr,
pd,
active_hkls=active_hkls,
threshold=cfg.find_orientations.orientation_maps.threshold,
ome_period=cfg.find_orientations.omega.period)
grains_data = np.loadtxt(ref_grains_out)
expmaps = grains_data[:, 3:6]
quats = rot.quatOfExpMap(expmaps.T)
#%%
for i in range(0, len(expmaps)):
this_quat = quats[:, i]
qball = quaternion_ball(this_quat, mis_thresh)
test_compl = test_GOE(cfg, qball, eta_ome, eta_tol=0.25)
GOE, compl = thresh_GOE(qball, test_compl, comp_thresh)
print('Writing Grain %d' % i)
write_GOE(GOE, compl, i, dump_dir)
cmaplist = [cmap(i) for i in range(cmap.N)]
#%%
symmetry_symbol = 'd6h' #cubic high, d6h for hex high
crystal_symmetry = sym.quatOfLaueGroup(symmetry_symbol)
# grain = 97
# grain_id = int(grain_data['scan21'][grain,0])
# mis_ang = []
# rotmats_grain = []
# expmaps_grain = []
# Eulers_grain = np.zeros([len(no_load_scans),3])
quats_init = rot.quatOfExpMap(grain_data['scan21'][:, 3:6].T)
expmaps_init = grain_data['scan21'][:, 3:6].T
quats_final = rot.quatOfExpMap(
(grain_data['scan%d' % no_load_scans[-1]][:, 3:6].T))
expmaps_final = grain_data['scan111'][:, 3:6].T
grainlist = []
for i in range(ngrains):
mis = rot.misorientation(quats_init[:, i:i + 1], quats_final[:, i:i + 1],
(crystal_symmetry, ))[0]
mis_deg = np.degrees(mis)
if mis_deg > 0.05:
grainlist.append(i)
for i in range(
len(scanIDs)): #puts all of the data from the FF into dictionaries
grain_fname = '/Users/rachellim/Documents/Research/Dye_CHESS_Jan20/fd1-q-1_filtered/filtered_scan_%04d_grains.out' % scanIDs[
i]
stress_fname = '/Users/rachellim/Documents/Research/Dye_CHESS_Jan20/fd1-q-1_filtered_stress/scan_%04d_stress.cpl' % scanIDs[
i]
grain_data['scan%d' % scanIDs[i]] = np.loadtxt(grain_fname)
stress_data['scan%d' % scanIDs[i]] = cpl.load(open(stress_fname))
RSS_all[i, :, :] = stress_data['scan%d' % scanIDs[i]]['RSS'] #RSS
ngrains = len(RSS)
quats_init = rot.quatOfExpMap(grain_data['scan12'][:, 3:6].T)
#==============================================================================
# %% Make color list
#==============================================================================
colors = np.arange(0, len(scanIDs))
colornums = np.arange(10, 256, 246 / len(scanIDs))
cmap = plt.cm.inferno_r
cmaplist = [cmap(i) for i in range(cmap.N)]
#%%
symmetry_symbol = 'd6h' #cubic high, d6h for hex high
crystal_symmetry = sym.quatOfLaueGroup(symmetry_symbol)
#%%
delta_strain11 = np.diff(strain11)
delta_strain22 = np.diff(strain22)
delta_strain33 = np.diff(strain33)
delta_stress11 = np.diff(stress11)
delta_stress22 = np.diff(stress22)
delta_stress33 = np.diff(stress33)
delta_vonmises = np.diff(vonmises)
delta_hydrostatic = np.diff(hydrostatic)
#%% Calculate misorientation from initial orientation
mis = np.zeros([ngrains, len(scanIDs)])
quats_init = rot.quatOfExpMap(grain_data['scan%d' % scanIDs[0]][:, 3:6].T)
for i in range(len(scanIDs)):
quats_this = rot.quatOfExpMap(grain_data['scan%d' % scanIDs[i]][:, 3:6].T)
for j in range(len(quats_init.T)):
mis[j, i] = np.degrees(
rot.misorientation(quats_init[:, j:j + 1], quats_this[:,
j:j + 1])[0])
#%% Stress reasonability check
mean_stress11 = np.mean(stress11, axis=0)
mean_stress22 = np.mean(stress22, axis=0)
mean_stress33 = np.mean(stress33, axis=0)
mean_vonmises = np.mean(vonmises, axis=0)
mean_hydrostatic = np.mean(hydrostatic, axis=0)