def run_indexer(self):
config = create_indexing_config()
# Find orientations
self.update_progress_text('Running indexer (paintGrid)')
ncpus = config.multiprocessing
self.completeness = indexer.paintGrid(
self.qfib,
self.ome_maps,
etaRange=np.radians(config.find_orientations.eta.range),
omeTol=np.radians(config.find_orientations.omega.tolerance),
etaTol=np.radians(config.find_orientations.eta.tolerance),
omePeriod=np.radians(config.find_orientations.omega.period),
threshold=config.find_orientations.threshold,
doMultiProc=ncpus > 1,
nCPUs=ncpus)
print('paintGrid complete')
orientations_cfg = HexrdConfig().indexing_config['find_orientations']
if orientations_cfg.get('_write_scored_orientations'):
# Write out the scored orientations
results = {}
results['scored_orientations'] = {
'test_quaternions': self.qfib,
'score': self.completeness
}
print(f'Writing scored orientations in {config.working_dir} ...')
write_scored_orientations(results, config)
def run_indexer(self, config):
# Generate the orientation fibers
self.update_progress_text('Generating orientation fibers')
self.qfib = generate_orientation_fibers(config, self.ome_maps)
# Find orientations
self.update_progress_text('Running indexer (paintGrid)')
ncpus = config.multiprocessing
self.completeness = indexer.paintGrid(
self.qfib,
self.ome_maps,
etaRange=np.radians(config.find_orientations.eta.range),
omeTol=np.radians(config.find_orientations.omega.tolerance),
etaTol=np.radians(config.find_orientations.eta.tolerance),
omePeriod=np.radians(config.find_orientations.omega.period),
threshold=config.find_orientations.threshold,
doMultiProc=ncpus > 1,
nCPUs=ncpus)
print('Indexing complete')
def find_orientations(cfg,
hkls=None,
clean=False,
profile=False,
use_direct_testing=False):
"""
Parameters
----------
cfg : TYPE
DESCRIPTION.
hkls : TYPE, optional
DESCRIPTION. The default is None.
clean : TYPE, optional
DESCRIPTION. The default is False.
profile : TYPE, optional
DESCRIPTION. The default is False.
use_direct_search : TYPE, optional
DESCRIPTION. The default is False.
Returns
-------
None.
"""
# grab objects from config
plane_data = cfg.material.plane_data
imsd = cfg.image_series
instr = cfg.instrument.hedm
eta_ranges = cfg.find_orientations.eta.range
# tolerances
tth_tol = plane_data.tThWidth
eta_tol = np.radians(cfg.find_orientations.eta.tolerance)
ome_tol = np.radians(cfg.find_orientations.omega.tolerance)
# handle omega period
ome_period, ome_ranges = _process_omegas(imsd)
# for multiprocessing
ncpus = cfg.multiprocessing
# thresholds
image_threshold = cfg.find_orientations.orientation_maps.threshold
on_map_threshold = cfg.find_orientations.threshold
compl_thresh = cfg.find_orientations.clustering.completeness
# clustering
cl_algorithm = cfg.find_orientations.clustering.algorithm
cl_radius = cfg.find_orientations.clustering.radius
# =========================================================================
# ORIENTATION SCORING
# =========================================================================
do_grid_search = cfg.find_orientations.use_quaternion_grid is not None
if use_direct_testing:
npdiv_DFLT = 2
params = dict(plane_data=plane_data,
instrument=instr,
imgser_dict=imsd,
tth_tol=tth_tol,
eta_tol=eta_tol,
ome_tol=ome_tol,
eta_ranges=np.radians(eta_ranges),
ome_period=np.radians(ome_period),
npdiv=npdiv_DFLT,
threshold=image_threshold)
logger.info("\tusing direct search on %d processes", ncpus)
# handle search space
if cfg.find_orientations.use_quaternion_grid is None:
# doing seeded search
logger.info("Will perform seeded search")
logger.info(
"\tgenerating search quaternion list using %d processes",
ncpus)
start = timeit.default_timer()
# need maps
eta_ome = load_eta_ome_maps(cfg,
plane_data,
imsd,
hkls=hkls,
clean=clean)
# generate trial orientations
qfib = generate_orientation_fibers(cfg, eta_ome)
logger.info("\t\t...took %f seconds",
timeit.default_timer() - start)
else:
# doing grid search
try:
qfib = np.load(cfg.find_orientations.use_quaternion_grid)
except (IOError):
raise RuntimeError(
"specified quaternion grid file '%s' not found!" %
cfg.find_orientations.use_quaternion_grid)
# execute direct search
pool = mp.Pool(ncpus, indexer.test_orientation_FF_init, (params, ))
completeness = pool.map(indexer.test_orientation_FF_reduced, qfib.T)
pool.close()
else:
logger.info("\tusing map search with paintGrid on %d processes", ncpus)
start = timeit.default_timer()
# handle eta-ome maps
eta_ome = load_eta_ome_maps(cfg,
plane_data,
imsd,
hkls=hkls,
clean=clean)
# handle search space
if cfg.find_orientations.use_quaternion_grid is None:
# doing seeded search
logger.info(
"\tgenerating search quaternion list using %d processes",
ncpus)
start = timeit.default_timer()
qfib = generate_orientation_fibers(cfg, eta_ome)
logger.info("\t\t...took %f seconds",
timeit.default_timer() - start)
else:
# doing grid search
try:
qfib = np.load(cfg.find_orientations.use_quaternion_grid)
except (IOError):
raise RuntimeError(
"specified quaternion grid file '%s' not found!" %
cfg.find_orientations.use_quaternion_grid)
# do map-based indexing
start = timeit.default_timer()
logger.info("will test %d quaternions using %d processes",
qfib.shape[1], ncpus)
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)
logger.info("\t\t...took %f seconds", timeit.default_timer() - start)
completeness = np.array(completeness)
logger.info("\tSaving %d scored orientations with max completeness %f%%",
qfib.shape[1], 100 * np.max(completeness))
results = {}
results['scored_orientations'] = {
'test_quaternions': qfib,
'score': completeness
}
# =========================================================================
# CLUSTERING AND GRAINS OUTPUT
# =========================================================================
logger.info("\trunning clustering using '%s'", cl_algorithm)
start = timeit.default_timer()
if do_grid_search:
min_samples = 1
mean_rpg = 1
else:
min_samples, mean_rpg = create_clustering_parameters(cfg, eta_ome)
logger.info("\tmean reflections per grain: %d", mean_rpg)
logger.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)
logger.info("\t\t...took %f seconds", (timeit.default_timer() - start))
logger.info("\tfound %d grains", qbar.shape[1])
results['qbar'] = qbar
return results
