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 fit_grain_FF_reduced(grain_id):
"""
input parameters are [
plane_data, instrument, imgser_dict,
tth_tol, eta_tol, ome_tol, npdiv, threshold
]
"""
grains_table = paramMP['grains_table']
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']
refit = paramMP['refit']
threshold = paramMP['threshold']
eta_ranges = paramMP['eta_ranges']
ome_period = paramMP['ome_period']
analysis_dirname = paramMP['analysis_dirname']
spots_filename = paramMP['spots_filename']
grain = grains_table[grain_id]
grain_params = grain[3:15]
complvec, results = instrument.pull_spots(plane_data,
grain_params,
imgser_dict,
tth_tol=tth_tol[tol_loop_idx],
eta_tol=eta_tol[tol_loop_idx],
ome_tol=ome_tol[tol_loop_idx],
npdiv=npdiv,
threshold=threshold,
eta_ranges=eta_ranges,
ome_period=ome_period,
dirname=analysis_dirname,
filename=spots_filename %
grain_id,
save_spot_list=False,
quiet=True,
check_only=False,
interp='nearest')
# ======= DETERMINE VALID REFLECTIONS =======
culled_results = dict.fromkeys(results)
num_refl_tot = 0
num_refl_valid = 0
for det_key in culled_results:
panel = instrument.detectors[det_key]
presults = results[det_key]
valid_refl_ids = np.array([x[0] for x in presults]) >= 0
spot_ids = np.array([x[0] for x in presults])
# find unsaturated spots on this panel
if panel.saturation_level is None:
unsat_spots = np.ones(len(valid_refl_ids))
else:
unsat_spots = \
np.array([x[4] for x in presults]) < panel.saturation_level
idx = np.logical_and(valid_refl_ids, unsat_spots)
# if an overlap table has been written, load it and use it
overlaps = np.zeros_like(idx, dtype=bool)
try:
ot = np.load(
os.path.join(analysis_dirname,
os.path.join(det_key, 'overlap_table.npz')))
for key in ot.keys():
for this_table in ot[key]:
these_overlaps = np.where(this_table[:, 0] == grain_id)[0]
if len(these_overlaps) > 0:
mark_these = np.array(this_table[these_overlaps, 1],
dtype=int)
otidx = [
np.where(spot_ids == mt)[0] for mt in mark_these
]
overlaps[otidx] = True
idx = np.logical_and(idx, ~overlaps)
# print("found overlap table for '%s'" % det_key)
except (IOError, IndexError):
# print("no overlap table found for '%s'" % det_key)
pass
# attach to proper dict entry
culled_results[det_key] = [presults[i] for i in np.where(idx)[0]]
num_refl_tot += len(valid_refl_ids)
num_refl_valid += sum(valid_refl_ids)
pass # now we have culled data
# CAVEAT: completeness from pullspots only; incl saturated and overlaps
# <JVB 2015-12-15>
completeness = num_refl_valid / float(num_refl_tot)
# ======= DO LEASTSQ FIT =======
if num_refl_valid <= 12: # not enough reflections to fit... exit
grain_params_fit = grain_params
return grain_id, completeness, np.inf, grain_params_fit
else:
grain_params_fit = fitGrain(grain_params, instrument, culled_results,
plane_data.latVecOps['B'],
plane_data.wavelength)
# get chisq
# TODO: do this while evaluating fit???
chisq = objFuncFitGrain(grain_params_fit[gFlag_ref],
grain_params_fit,
gFlag_ref,
instrument,
culled_results,
plane_data.latVecOps['B'],
plane_data.wavelength,
ome_period,
simOnly=False,
return_value_flag=2)
if refit is not None:
# first get calculated x, y, ome from previous solution
# NOTE: this result is a dict
xyo_det_fit_dict = objFuncFitGrain(grain_params_fit[gFlag_ref],
grain_params_fit,
gFlag_ref,
instrument,
culled_results,
plane_data.latVecOps['B'],
plane_data.wavelength,
ome_period,
simOnly=True,
return_value_flag=2)
# make dict to contain new culled results
culled_results_r = dict.fromkeys(culled_results)
num_refl_valid = 0
for det_key in culled_results_r:
presults = culled_results[det_key]
ims = imgser_dict[det_key]
ome_step = sum(np.r_[-1, 1] * ims.metadata['omega'][0, :])
xyo_det = np.atleast_2d(
np.vstack([np.r_[x[7], x[6][-1]] for x in presults]))
xyo_det_fit = xyo_det_fit_dict[det_key]
xpix_tol = refit[0] * panel.pixel_size_col
ypix_tol = refit[0] * panel.pixel_size_row
fome_tol = refit[1] * ome_step
# define difference vectors for spot fits
x_diff = abs(xyo_det[:, 0] - xyo_det_fit['calc_xy'][:, 0])
y_diff = abs(xyo_det[:, 1] - xyo_det_fit['calc_xy'][:, 1])
ome_diff = np.degrees(
xfcapi.angularDifference(xyo_det[:, 2],
xyo_det_fit['calc_omes']))
# filter out reflections with centroids more than
# a pixel and delta omega away from predicted value
idx_new = np.logical_and(
x_diff <= xpix_tol,
np.logical_and(y_diff <= ypix_tol, ome_diff <= fome_tol))
# attach to proper dict entry
culled_results_r[det_key] = [
presults[i] for i in np.where(idx_new)[0]
]
num_refl_valid += sum(idx_new)
pass
# only execute fit if left with enough reflections
if num_refl_valid > 24:
grain_params_fit = fitGrain(grain_params_fit, instrument,
culled_results_r,
plane_data.latVecOps['B'],
plane_data.wavelength)
# get chisq
# TODO: do this while evaluating fit???
chisq = objFuncFitGrain(grain_params_fit[gFlag_ref],
grain_params_fit,
gFlag_ref,
instrument,
culled_results_r,
plane_data.latVecOps['B'],
plane_data.wavelength,
ome_period,
simOnly=False,
return_value_flag=2)
pass
pass # close refit conditional
return grain_id, completeness, chisq, grain_params_fit
def fit_grain_FF_reduced(grain_id):
"""
input parameters are [
plane_data, instrument, imgser_dict,
tth_tol, eta_tol, ome_tol, npdiv, threshold
]
"""
grains_table = paramMP['grains_table']
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']
eta_ranges = paramMP['eta_ranges']
ome_period = paramMP['ome_period']
analysis_dirname = paramMP['analysis_dirname']
spots_filename = paramMP['spots_filename']
grain = grains_table[grain_id]
grain_params = grain[3:15]
complvec, results = instrument.pull_spots(
plane_data, grain_params,
imgser_dict,
tth_tol=tth_tol[0], eta_tol=eta_tol[0], ome_tol=ome_tol[0],
npdiv=npdiv, threshold=threshold,
eta_ranges=eta_ranges,
ome_period=ome_period,
dirname=analysis_dirname, filename=spots_filename % grain_id,
save_spot_list=False,
quiet=True, lrank=1, check_only=False)
# ======= DETERMINE VALID REFLECTIONS =======
# CAVEAT: in the event of different stauration levels, can't mark saturated
# spots in aggregated results <JVB 2017-03-26>
culled_results = dict.fromkeys(results)
num_refl_tot = 0
num_refl_valid = 0
for det_key in culled_results:
presults = results[det_key]
valid_refl_ids = np.array([x[0] for x in presults]) >= 0
# FIXME: spot saturations will have to be handled differently
unsat_spots = np.ones(len(valid_refl_ids))
idx = np.logical_and(valid_refl_ids, unsat_spots)
# TODO: wire in reflection overlap tables
"""
# if an overlap table has been written, load it and use it
overlaps = np.zeros(len(refl_table), dtype=bool)
try:
ot = np.load(self._p['overlap_table'])
for key in ot.keys():
for this_table in ot[key]:
these_overlaps = np.where(
this_table[:, 0] == grain_id)[0]
if len(these_overlaps) > 0:
mark_these = np.array(
this_table[these_overlaps, 1], dtype=int
)
overlaps[mark_these] = True
idx = np.logical_and(idx, ~overlaps)
except IOError, IndexError:
#print "no overlap table found"
pass
"""
# attach to proper dict entry
culled_results[det_key] = [presults[i] for i in np.where(idx)[0]]
num_refl_tot += len(valid_refl_ids)
num_refl_valid += sum(valid_refl_ids)
pass
# CAVEAT: completeness from pullspots only; incl saturated and overlaps
# <JVB 2015-12-15>
completeness = num_refl_valid / float(num_refl_tot)
# ======= DO LEASTSQ FIT =======
if num_refl_valid <= 12: # not enough reflections to fit... exit
grain_params_fit = grain_params
return grain_id, completeness, np.inf, grain_params_fit
else:
grain_params_fit = fitGrain(
grain_params, instrument, culled_results,
plane_data.latVecOps['B'], plane_data.wavelength
)
# get chisq
# TODO: do this while evaluating fit???
chisq = objFuncFitGrain(
grain_params, grain_params, gFlag_ref,
instrument,
culled_results,
plane_data.latVecOps['B'], plane_data.wavelength,
ome_period,
simOnly=False, return_value_flag=2)
return grain_id, completeness, chisq, grain_params_fit
def fit_grain_FF_reduced(grain_id):
"""
Perform non-linear least-square fit for the specified grain.
Parameters
----------
grain_id : int
The grain id.
Returns
-------
grain_id : int
The grain id.
completeness : float
The ratio of predicted to measured (observed) Bragg reflections.
chisq: float
Figure of merit describing the sum of squared residuals for each Bragg
reflection in the form (x, y, omega) normalized by the total number of
degrees of freedom.
grain_params : array_like
The optimized grain parameters
[<orientation [3]>, <centroid [3]> <inverse stretch [6]>].
Notes
-----
input parameters are
[plane_data, instrument, imgser_dict,
tth_tol, eta_tol, ome_tol, npdiv, threshold]
"""
grains_table = paramMP['grains_table']
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']
refit = paramMP['refit']
threshold = paramMP['threshold']
eta_ranges = paramMP['eta_ranges']
ome_period = paramMP['ome_period']
analysis_dirname = paramMP['analysis_dirname']
prefix = paramMP['spots_filename']
spots_filename = None if prefix is None else prefix % grain_id
grain = grains_table[grain_id]
grain_params = grain[3:15]
for tols in zip(tth_tol, eta_tol, ome_tol):
complvec, results = instrument.pull_spots(plane_data,
grain_params,
imgser_dict,
tth_tol=tols[0],
eta_tol=tols[1],
ome_tol=tols[2],
npdiv=npdiv,
threshold=threshold,
eta_ranges=eta_ranges,
ome_period=ome_period,
dirname=analysis_dirname,
filename=spots_filename,
save_spot_list=False,
quiet=True,
check_only=False,
interp='nearest')
# ======= DETERMINE VALID REFLECTIONS =======
culled_results = dict.fromkeys(results)
num_refl_tot = 0
num_refl_valid = 0
for det_key in culled_results:
panel = instrument.detectors[det_key]
'''
grab panel results:
peak_id
hkl_id
hkl
sum_int
max_int,
pred_angs,
meas_angs,
meas_xy
'''
presults = results[det_key]
nrefl = len(presults)
# make data arrays
refl_ids = np.empty(nrefl)
max_int = np.empty(nrefl)
for i, spot_data in enumerate(presults):
refl_ids[i] = spot_data[0]
max_int[i] = spot_data[4]
valid_refl_ids = refl_ids >= 0
# find unsaturated spots on this panel
unsat_spots = np.ones(len(valid_refl_ids), dtype=bool)
if panel.saturation_level is not None:
unsat_spots[valid_refl_ids] = \
max_int[valid_refl_ids] < panel.saturation_level
idx = np.logical_and(valid_refl_ids, unsat_spots)
# if an overlap table has been written, load it and use it
overlaps = np.zeros_like(idx, dtype=bool)
try:
ot = np.load(
os.path.join(analysis_dirname,
os.path.join(det_key, 'overlap_table.npz')))
for key in ot.keys():
for this_table in ot[key]:
these_overlaps = np.where(this_table[:,
0] == grain_id)[0]
if len(these_overlaps) > 0:
mark_these = np.array(this_table[these_overlaps,
1],
dtype=int)
otidx = [
np.where(refl_ids == mt)[0]
for mt in mark_these
]
overlaps[otidx] = True
idx = np.logical_and(idx, ~overlaps)
# logger.info("found overlap table for '%s'", det_key)
except (IOError, IndexError):
# logger.info("no overlap table found for '%s'", det_key)
pass
# attach to proper dict entry
# FIXME: want to avoid looping again here
culled_results[det_key] = [presults[i] for i in np.where(idx)[0]]
num_refl_tot += len(valid_refl_ids)
num_refl_valid += sum(valid_refl_ids)
pass # now we have culled data
# CAVEAT: completeness from pullspots only; incl saturated and overlaps
# <JVB 2015-12-15>
completeness = num_refl_valid / float(num_refl_tot)
# ======= DO LEASTSQ FIT =======
if num_refl_valid <= 12: # not enough reflections to fit... exit
return grain_id, completeness, np.inf, grain_params
else:
grain_params = fitGrain(grain_params, instrument, culled_results,
plane_data.latVecOps['B'],
plane_data.wavelength)
# get chisq
# TODO: do this while evaluating fit???
chisq = objFuncFitGrain(grain_params[gFlag_ref],
grain_params,
gFlag_ref,
instrument,
culled_results,
plane_data.latVecOps['B'],
plane_data.wavelength,
ome_period,
simOnly=False,
return_value_flag=2)
pass # end conditional on fit
pass # end tolerance looping
if refit is not None:
# first get calculated x, y, ome from previous solution
# NOTE: this result is a dict
xyo_det_fit_dict = objFuncFitGrain(grain_params[gFlag_ref],
grain_params,
gFlag_ref,
instrument,
culled_results,
plane_data.latVecOps['B'],
plane_data.wavelength,
ome_period,
simOnly=True,
return_value_flag=2)
# make dict to contain new culled results
culled_results_r = dict.fromkeys(culled_results)
num_refl_valid = 0
for det_key in culled_results_r:
presults = culled_results[det_key]
if not presults:
culled_results_r[det_key] = []
continue
ims = imgser_dict[det_key]
ome_step = sum(np.r_[-1, 1] * ims.metadata['omega'][0, :])
xyo_det = np.atleast_2d(
np.vstack([np.r_[x[7], x[6][-1]] for x in presults]))
xyo_det_fit = xyo_det_fit_dict[det_key]
xpix_tol = refit[0] * panel.pixel_size_col
ypix_tol = refit[0] * panel.pixel_size_row
fome_tol = refit[1] * ome_step
# define difference vectors for spot fits
x_diff = abs(xyo_det[:, 0] - xyo_det_fit['calc_xy'][:, 0])
y_diff = abs(xyo_det[:, 1] - xyo_det_fit['calc_xy'][:, 1])
ome_diff = np.degrees(
xfcapi.angularDifference(xyo_det[:, 2],
xyo_det_fit['calc_omes']))
# filter out reflections with centroids more than
# a pixel and delta omega away from predicted value
idx_new = np.logical_and(
x_diff <= xpix_tol,
np.logical_and(y_diff <= ypix_tol, ome_diff <= fome_tol))
# attach to proper dict entry
culled_results_r[det_key] = [
presults[i] for i in np.where(idx_new)[0]
]
num_refl_valid += sum(idx_new)
pass
# only execute fit if left with enough reflections
if num_refl_valid > 12:
grain_params = fitGrain(grain_params, instrument, culled_results_r,
plane_data.latVecOps['B'],
plane_data.wavelength)
# get chisq
# TODO: do this while evaluating fit???
chisq = objFuncFitGrain(grain_params[gFlag_ref],
grain_params,
gFlag_ref,
instrument,
culled_results_r,
plane_data.latVecOps['B'],
plane_data.wavelength,
ome_period,
simOnly=False,
return_value_flag=2)
pass
pass # close refit conditional
return grain_id, completeness, chisq, grain_params