def test_orientation_FF_reduced(quat):
"""
input parameters are [
plane_data, instrument, imgser_dict,
tth_tol, eta_tol, ome_tol, npdiv, threshold
]
"""
plane_data = paramMP['plane_data']
instrument = paramMP['instrument']
imgser_dict = paramMP['imgser_dict']
tth_tol = paramMP['tth_tol']
eta_tol = paramMP['eta_tol']
ome_tol = paramMP['ome_tol']
npdiv = paramMP['npdiv']
threshold = paramMP['threshold']
phi = 2*np.arccos(quat[0])
n = xfcapi.unitRowVector(quat[1:])
grain_params = np.hstack([
phi*n, cnst.zeros_3, cnst.identity_6x1,
])
compl, scrap = instrument.pull_spots(
plane_data, grain_params, imgser_dict,
tth_tol=tth_tol, eta_tol=eta_tol, ome_tol=ome_tol,
npdiv=npdiv, threshold=threshold,
eta_ranges=None, ome_period=(-np.pi, np.pi),
check_only=True)
return sum(compl)/float(len(compl))
def write_GOE(GOE_quats, compl, i, dump_dir):
phi = 2 * np.arccos(GOE_quats[0, :])
n = xfcapi.unitRowVector(GOE_quats[1:, :])
expmaps = phi * n
ori_data = np.hstack([np.expand_dims(compl, axis=1), expmaps.T])
delim = ' '
header_items = ('# completeness', 'exp_map_c[0]', 'exp_map_c[1]',
'exp_map_c[2]')
header = delim.join(np.tile('{:<12}', 4)).format(*header_items)
# print(output_str, file=
fname = os.path.join(dump_dir, 'grain_%04d.out' % i)
f = open(fname, 'w+')
print(header, file=f)
for i in range(len(phi)):
output_str = delim.join(['{:<12f}', '{:<12e}', '{:<12e}',
'{:<12e}']).format(*ori_data[i, :])
print(output_str, file=f)
f.close()
def calc_angles_from_beam_vec(bvec):
"""
Return the azimuth and polar angle from a beam
vector
"""
bvec = np.atleast_1d(bvec).flatten()
nvec = unitRowVector(-bvec)
azim = float(np.degrees(np.arctan2(nvec[2], nvec[0])))
pola = float(np.degrees(np.arccos(nvec[1])))
return azim, pola
def calc_angles_from_beam_vec(bvec):
"""
Return the azimuth and polar angle from a beam
vector
"""
bvec = np.atleast_1d(bvec).flatten()
nvec = unitRowVector(-bvec)
azim = float(
np.degrees(np.arctan2(nvec[2], nvec[0]))
)
pola = float(np.degrees(np.arccos(nvec[1])))
return azim, pola
# =============================================================================
# %% FITTING
# =============================================================================
# make sure grains.out is there...
if not os.path.exists(grains_filename) or clobber_grains:
try:
qbar = np.loadtxt('accepted_orientations_' + analysis_id + '.dat',
ndmin=2).T
gw = instrument.GrainDataWriter(grains_filename)
grain_params_list = []
for i_g, q in enumerate(qbar.T):
phi = 2 * np.arccos(q[0])
n = xfcapi.unitRowVector(q[1:])
grain_params = np.hstack(
[phi * n, cnst.zeros_3, cnst.identity_6x1])
gw.dump_grain(int(i_g), 1., 0., grain_params)
grain_params_list.append(grain_params)
gw.close()
except (IOError):
if os.path.exists(cfg.fit_grains.estimate):
grains_filename = cfg.fit_grains.estimate
else:
raise (
RuntimeError,
"neither estimate nor %s exist!" % 'accepted_orientations_' +
analysis_id + '.dat')
pass
import sys, os, time, random import numpy as np from hexrd.xrd import transforms as xf from hexrd.xrd import transforms_CAPI as xfcapi epsf = 2.2e-16 vec = np.array([[random.uniform(-np.pi,np.pi),random.uniform(-np.pi,np.pi),random.uniform(-np.pi,np.pi)]]) vHat1 = xf.unitVector(vec.T) vHat2 = xfcapi.unitRowVector(vec) print "unitVector results match: ",np.linalg.norm(vHat1.T-vHat2)/np.linalg.norm(vHat1) < epsf tAng = np.array([0.0011546340766314521,-0.0040527538387122993,-0.0026221336905160211]) rMat1 = xf.makeDetectorRotMat(tAng) rMat2 = xfcapi.makeDetectorRotMat(tAng) print "makeDetectorRotMat results match: ",np.linalg.norm(rMat1-rMat2)/np.linalg.norm(rMat1) < epsf oAng = np.array([-0.0011591608938627839,0.0011546340766314521]) rMat1 = xf.makeOscillRotMat(oAng) rMat2 = xfcapi.makeOscillRotMat(oAng) print "makeOscillRotMat results match: ",np.linalg.norm(rMat1-rMat2)/np.linalg.norm(rMat1) < epsf eMap = np.array([ 0.66931818,-0.98578066,0.73593251]) rMat1 = xf.makeRotMatOfExpMap(eMap) rMat2 = xfcapi.makeRotMatOfExpMap(eMap) print "makeRotMatOfExpMap results match: ",np.linalg.norm(rMat1-rMat2)/np.linalg.norm(rMat1) < epsf axis = np.array([ 0.66931818,-0.98578066,0.73593251]) rMat1 = xf.makeBinaryRotMat(axis) rMat2 = xfcapi.makeBinaryRotMat(axis)
def vector(self, x):
assert len(x) == 3
self._vector = unitRowVector(np.atleast_1d(x).flatten())
def find_orientations(cfg, hkls=None, clean=False, profile=False, nsim=100):
print('ready to run find_orientations')
# %%
# =============================================================================
# SEARCH SPACE GENERATION
# =============================================================================
hedm = cfg.instrument.hedm
plane_data = cfg.material.plane_data
ncpus = cfg.multiprocessing
# for indexing
active_hkls = cfg.find_orientations.orientation_maps.active_hkls
fiber_ndiv = cfg.find_orientations.seed_search.fiber_ndiv
fiber_seeds = cfg.find_orientations.seed_search.hkl_seeds
on_map_threshold = cfg.find_orientations.threshold
# for clustering
cl_radius = cfg.find_orientations.clustering.radius
min_compl = cfg.find_orientations.clustering.completeness
compl_thresh = cfg.find_orientations.clustering.completeness
eta_ome = get_eta_ome(cfg, clean=clean)
print("INFO:\tgenerating search quaternion list using %d processes" %
ncpus)
start = timeit.default_timer()
qfib = generate_orientation_fibers(eta_ome,
hedm.chi,
on_map_threshold,
fiber_seeds,
fiber_ndiv,
ncpus=ncpus)
print("INFO:\t\t...took %f seconds" % (timeit.default_timer() - start))
print("INFO: will test %d quaternions using %d processes" %
(qfib.shape[1], ncpus))
# %%
# =============================================================================
# ORIENTATION SCORING
# =============================================================================
scoredq_filename = 'scored_orientations_' + analysis_id(cfg) + '.npz'
print("INFO:\tusing map search with paintGrid on %d processes" % ncpus)
start = timeit.default_timer()
completeness = indexer.paintGrid(
qfib,
eta_ome,
etaRange=np.radians(cfg.find_orientations.eta.range),
omeTol=np.radians(cfg.find_orientations.omega.tolerance),
etaTol=np.radians(cfg.find_orientations.eta.tolerance),
omePeriod=np.radians(cfg.find_orientations.omega.period),
threshold=on_map_threshold,
doMultiProc=ncpus > 1,
nCPUs=ncpus)
print("INFO:\t\t...took %f seconds" % (timeit.default_timer() - start))
completeness = np.array(completeness)
# export scored orientations
np.savez_compressed(scoredq_filename,
quaternions=qfib,
completeness=completeness)
print("INFO:\tsaved scored orientations to file: '%s'" %
(scoredq_filename))
# %%
# =============================================================================
# CLUSTERING AND GRAINS OUTPUT
# =============================================================================
if not os.path.exists(cfg.analysis_dir):
os.makedirs(cfg.analysis_dir)
qbar_filename = 'accepted_orientations_' + analysis_id(cfg) + '.dat'
print("INFO:\trunning clustering using '%s'" %
cfg.find_orientations.clustering.algorithm)
start = timeit.default_timer()
# Simulate N random grains to get neighborhood size
print("INFO:\trunning %d simulations to determine neighborhood size" %
nsim)
seed_hkl_ids = [
plane_data.hklDataList[active_hkls[i]]['hklID'] for i in fiber_seeds
]
# need ome_ranges from imageseries
# CAVEAT: assumes that all imageseries have same omega ranges!!!
oims = OmegaImageSeries(cfg.image_series.itervalues().next())
ome_ranges = [(np.radians([i['ostart'], i['ostop']]))
for i in oims.omegawedges.wedges]
if seed_hkl_ids is not None:
rand_q = mutil.unitVector(np.random.randn(4, nsim))
rand_e = np.tile(2.*np.arccos(rand_q[0, :]), (3, 1)) \
* mutil.unitVector(rand_q[1:, :])
refl_per_grain = np.zeros(nsim)
num_seed_refls = np.zeros(nsim)
grain_param_list = np.vstack([
rand_e,
np.zeros((3, nsim)),
np.tile(cnst.identity_6x1, (nsim, 1)).T
]).T
sim_results = hedm.simulate_rotation_series(
plane_data,
grain_param_list,
eta_ranges=np.radians(cfg.find_orientations.eta.range),
ome_ranges=ome_ranges,
ome_period=np.radians(cfg.find_orientations.omega.period))
refl_per_grain = np.zeros(nsim)
seed_refl_per_grain = np.zeros(nsim)
for sim_result in sim_results.itervalues():
for i, refl_ids in enumerate(sim_result[0]):
refl_per_grain[i] += len(refl_ids)
seed_refl_per_grain[i] += np.sum(
[sum(refl_ids == hkl_id) for hkl_id in seed_hkl_ids])
min_samples = max(
int(
np.floor(0.5 * cfg.find_orientations.clustering.completeness *
min(seed_refl_per_grain))), 2)
mean_rpg = int(np.round(np.average(refl_per_grain)))
else:
min_samples = 1
mean_rpg = 1
print("INFO:\tmean reflections per grain: %d" % mean_rpg)
print("INFO:\tneighborhood size: %d" % min_samples)
qbar, cl = run_cluster(completeness,
qfib,
plane_data.getQSym(),
cfg,
min_samples=min_samples,
compl_thresh=compl_thresh,
radius=cl_radius)
print("INFO:\t\t...took %f seconds" % (timeit.default_timer() - start))
print("INFO:\tfound %d grains; saved to file: '%s'" %
(qbar.shape[1], qbar_filename))
np.savetxt(qbar_filename, qbar.T, fmt='%.18e', delimiter='\t')
gw = instrument.GrainDataWriter(
os.path.join(cfg.analysis_dir, 'grains.out'))
grain_params_list = []
for gid, q in enumerate(qbar.T):
phi = 2 * np.arccos(q[0])
n = xfcapi.unitRowVector(q[1:])
grain_params = np.hstack([phi * n, cnst.zeros_3, cnst.identity_6x1])
gw.dump_grain(gid, 1., 0., grain_params)
grain_params_list.append(grain_params)
gw.close()
import sys, os, time, random
import numpy as np
from hexrd.xrd import transforms as xf
from hexrd.xrd import transforms_CAPI as xfcapi
epsf = 2.2e-16
vec = np.array([[
random.uniform(-np.pi, np.pi),
random.uniform(-np.pi, np.pi),
random.uniform(-np.pi, np.pi)
]])
vHat1 = xf.unitVector(vec.T)
vHat2 = xfcapi.unitRowVector(vec)
print "unitVector results match: ", np.linalg.norm(
vHat1.T - vHat2) / np.linalg.norm(vHat1) < epsf
tAng = np.array(
[0.0011546340766314521, -0.0040527538387122993, -0.0026221336905160211])
rMat1 = xf.makeDetectorRotMat(tAng)
rMat2 = xfcapi.makeDetectorRotMat(tAng)
print "makeDetectorRotMat results match: ", np.linalg.norm(
rMat1 - rMat2) / np.linalg.norm(rMat1) < epsf
oAng = np.array([-0.0011591608938627839, 0.0011546340766314521])
rMat1 = xf.makeOscillRotMat(oAng)
rMat2 = xfcapi.makeOscillRotMat(oAng)
print "makeOscillRotMat results match: ", np.linalg.norm(
rMat1 - rMat2) / np.linalg.norm(rMat1) < epsf
def vector(self, x):
assert len(x) == 3
self._vector = unitRowVector(np.atleast_1d(x).flatten())
image_stack=gen_nf_cleaned_image_stack(data_folder,img_nums,dark,ome_dilation_iter,threshold,experiment.nrows,experiment.ncols,num_digits=6,grey_bnds=(5,5),gaussian=4.5)
#%%
test_crds_load = np.load(output_dir + missing_grain_coordinates)
test_crds = test_crds_load[:,:]
n_crds = test_crds.shape[0]
random_quaternions = np.load(output_dir + quaternion_test_list)
n_grains = random_quaternions.shape[1]
rMat_c = rot.rotMatOfQuat(random_quaternions)
exp_maps = np.zeros([random_quaternions.shape[1],3])
for i in range(0,random_quaternions.shape[1]):
phi = 2*np.arccos(random_quaternions[0,i])
n = xfcapi.unitRowVector(random_quaternions[1:,i])
exp_maps[i,:] = phi*n
#%%
experiment.n_grains = n_grains
experiment.rMat_c = rMat_c
experiment.exp_maps = exp_maps
#==============================================================================
# %% INSTANTIATE CONTROLLER - RUN BLOCK NO EDITING
#==============================================================================
progress_handler = nfutil.progressbar_progress_observer()
save_handler=nfutil.forgetful_result_handler()
def find_orientations(cfg, hkls=None, clean=False, profile=False, nsim=100):
print('ready to run find_orientations')
# %%
# =============================================================================
# SEARCH SPACE GENERATION
# =============================================================================
hedm = cfg.instrument.hedm
plane_data = cfg.material.plane_data
ncpus = cfg.multiprocessing
# for indexing
active_hkls = cfg.find_orientations.orientation_maps.active_hkls
fiber_ndiv = cfg.find_orientations.seed_search.fiber_ndiv
fiber_seeds = cfg.find_orientations.seed_search.hkl_seeds
on_map_threshold = cfg.find_orientations.threshold
# for clustering
cl_radius = cfg.find_orientations.clustering.radius
min_compl = cfg.find_orientations.clustering.completeness
compl_thresh = cfg.find_orientations.clustering.completeness
eta_ome = get_eta_ome(cfg, clean=clean)
print("INFO:\tgenerating search quaternion list using %d processes" % ncpus)
start = timeit.default_timer()
qfib = generate_orientation_fibers(
eta_ome, hedm.chi, on_map_threshold,
fiber_seeds, fiber_ndiv,
ncpus=ncpus
)
print("INFO:\t\t...took %f seconds" % (timeit.default_timer() - start))
print("INFO: will test %d quaternions using %d processes"
% (qfib.shape[1], ncpus))
# %%
# =============================================================================
# ORIENTATION SCORING
# =============================================================================
scoredq_filename = 'scored_orientations_' + analysis_id(cfg) + '.npz'
print("INFO:\tusing map search with paintGrid on %d processes"
% ncpus)
start = timeit.default_timer()
completeness = indexer.paintGrid(
qfib,
eta_ome,
etaRange=np.radians(cfg.find_orientations.eta.range),
omeTol=np.radians(cfg.find_orientations.omega.tolerance),
etaTol=np.radians(cfg.find_orientations.eta.tolerance),
omePeriod=np.radians(cfg.find_orientations.omega.period),
threshold=on_map_threshold,
doMultiProc=ncpus > 1,
nCPUs=ncpus
)
print("INFO:\t\t...took %f seconds" % (timeit.default_timer() - start))
completeness = np.array(completeness)
# export scored orientations
np.savez_compressed(scoredq_filename,
quaternions=qfib,
completeness=completeness)
print("INFO:\tsaved scored orientations to file: '%s'"
% (scoredq_filename))
# %%
# =============================================================================
# CLUSTERING AND GRAINS OUTPUT
# =============================================================================
if not os.path.exists(cfg.analysis_dir):
os.makedirs(cfg.analysis_dir)
qbar_filename = 'accepted_orientations_' + analysis_id(cfg) + '.dat'
print("INFO:\trunning clustering using '%s'"
% cfg.find_orientations.clustering.algorithm
)
start = timeit.default_timer()
# Simulate N random grains to get neighborhood size
print("INFO:\trunning %d simulations to determine neighborhood size"
% nsim
)
seed_hkl_ids = [
plane_data.hklDataList[active_hkls[i]]['hklID'] for i in fiber_seeds
]
# need ome_ranges from imageseries
# CAVEAT: assumes that all imageseries have same omega ranges!!!
oims = OmegaImageSeries(cfg.image_series.itervalues().next())
ome_ranges = [
(np.radians([i['ostart'], i['ostop']])) for i in oims.omegawedges.wedges
]
if seed_hkl_ids is not None:
rand_q = mutil.unitVector(np.random.randn(4, nsim))
rand_e = np.tile(2.*np.arccos(rand_q[0, :]), (3, 1)) \
* mutil.unitVector(rand_q[1:, :])
refl_per_grain = np.zeros(nsim)
num_seed_refls = np.zeros(nsim)
grain_param_list = np.vstack([rand_e,
np.zeros((3, nsim)),
np.tile(cnst.identity_6x1, (nsim, 1)).T]).T
sim_results = hedm.simulate_rotation_series(
plane_data, grain_param_list,
eta_ranges=np.radians(cfg.find_orientations.eta.range),
ome_ranges=ome_ranges,
ome_period=np.radians(cfg.find_orientations.omega.period)
)
refl_per_grain = np.zeros(nsim)
seed_refl_per_grain = np.zeros(nsim)
for sim_result in sim_results.itervalues():
for i, refl_ids in enumerate(sim_result[0]):
refl_per_grain[i] += len(refl_ids)
seed_refl_per_grain[i] += np.sum([sum(refl_ids == hkl_id) for hkl_id in seed_hkl_ids])
min_samples = max(
int(np.floor(0.5*cfg.find_orientations.clustering.completeness*min(seed_refl_per_grain))),
2
)
mean_rpg = int(np.round(np.average(refl_per_grain)))
else:
min_samples = 1
mean_rpg = 1
print("INFO:\tmean reflections per grain: %d" % mean_rpg)
print("INFO:\tneighborhood size: %d" % min_samples)
qbar, cl = run_cluster(
completeness, qfib, plane_data.getQSym(), cfg,
min_samples=min_samples,
compl_thresh=compl_thresh,
radius=cl_radius
)
print("INFO:\t\t...took %f seconds" % (timeit.default_timer() - start))
print("INFO:\tfound %d grains; saved to file: '%s'"
% (qbar.shape[1], qbar_filename))
np.savetxt(qbar_filename, qbar.T,
fmt='%.18e', delimiter='\t')
gw = instrument.GrainDataWriter(os.path.join(cfg.analysis_dir, 'grains.out'))
grain_params_list = []
for gid, q in enumerate(qbar.T):
phi = 2*np.arccos(q[0])
n = xfcapi.unitRowVector(q[1:])
grain_params = np.hstack([phi*n, cnst.zeros_3, cnst.identity_6x1])
gw.dump_grain(gid, 1., 0., grain_params)
grain_params_list.append(grain_params)
gw.close()
