def sxcal_obj_func(plist_fit,
plist_full,
param_flags,
dfuncs,
dparam_flags,
ndparams,
instr,
xyo_det,
hkls_idx,
bmat,
vinv_s,
ome_period,
bvec,
evec,
sim_only=False,
return_value_flag=None):
"""
"""
# stack flags and force bool repr
refine_flags = np.array(np.hstack([param_flags, dparam_flags]), dtype=bool)
# fill out full parameter list
# !!! no scaling for now
plist_full[refine_flags] = plist_fit
# instrument quantities
wavelength = plist_full[0]
chi = plist_full[1]
tvec_s = plist_full[2:5]
# calibration crystal quantities
rmat_c = xfcapi.makeRotMatOfExpMap(plist_full[5:8])
tvec_c = plist_full[8:11]
# right now just stuck on the end and assumed
# to all be the same length... FIX THIS
dparams_all = plist_full[-len(dparam_flags):]
xy_unwarped = {}
meas_omes = {}
calc_omes = {}
calc_xy = {}
ii = 11 # offset to start of panels...
jj = 0
npts_tot = 0
for det_key, panel in instr.detectors.iteritems():
xy_unwarped[det_key] = xyo_det[det_key][:, :2]
npts_tot += len(xyo_det[det_key])
dfunc = dfuncs[det_key]
len_these_dps = ndparams[det_key]
if dfunc is not None: # do unwarping
dparams = dparams_all[jj:jj + len_these_dps]
jj += len_these_dps
xy_unwarped[det_key] = dfunc(xy_unwarped[det_key], dparams)
pass
meas_omes[det_key] = xyo_det[det_key][:, 2]
# get these panel params for convenience
gparams = plist_full[ii:ii + 6]
rmat_d = xfcapi.makeDetectorRotMat(gparams[:3])
tvec_d = gparams[3:].reshape(3, 1)
# transform G-vectors:
# 1) convert inv. stretch tensor from MV notation in to 3x3
# 2) take reciprocal lattice vectors from CRYSTAL to SAMPLE frame
# 3) apply stretch tensor
# 4) normalize reciprocal lattice vectors in SAMPLE frame
# 5) transform unit reciprocal lattice vetors back to CRYSAL frame
gvec_c = np.dot(bmat, hkls_idx[det_key].T)
vmat_s = mutil.vecMVToSymm(vinv_s)
ghat_s = mutil.unitVector(np.dot(vmat_s, np.dot(rmat_c, gvec_c)))
ghat_c = np.dot(rmat_c.T, ghat_s)
match_omes, calc_omes_tmp = fitting.matchOmegas(xyo_det[det_key],
hkls_idx[det_key].T,
chi,
rmat_c,
bmat,
wavelength,
vInv=vinv_s,
beamVec=bvec,
etaVec=evec,
omePeriod=ome_period)
rmat_s_arr = xfcapi.makeOscillRotMatArray(
chi, np.ascontiguousarray(calc_omes_tmp))
calc_xy_tmp = xfcapi.gvecToDetectorXYArray(ghat_c.T, rmat_d,
rmat_s_arr, rmat_c, tvec_d,
tvec_s, tvec_c)
if np.any(np.isnan(calc_xy_tmp)):
print("infeasible parameters: " +
"may want to scale back finite difference step size")
calc_omes[det_key] = calc_omes_tmp
calc_xy[det_key] = calc_xy_tmp
ii += 6
pass
# return values
if sim_only:
retval = {}
for det_key in calc_xy.keys():
# ??? calc_xy is always 2-d
retval[det_key] = np.vstack(
[calc_xy[det_key].T, calc_omes[det_key]]).T
else:
meas_xy_all = []
calc_xy_all = []
meas_omes_all = []
calc_omes_all = []
for det_key in xy_unwarped.keys():
meas_xy_all.append(xy_unwarped[det_key])
calc_xy_all.append(calc_xy[det_key])
meas_omes_all.append(meas_omes[det_key])
calc_omes_all.append(calc_omes[det_key])
pass
meas_xy_all = np.vstack(meas_xy_all)
calc_xy_all = np.vstack(calc_xy_all)
meas_omes_all = np.hstack(meas_omes_all)
calc_omes_all = np.hstack(calc_omes_all)
diff_vecs_xy = calc_xy_all - meas_xy_all
diff_ome = xfcapi.angularDifference(calc_omes_all, meas_omes_all)
retval = np.hstack([diff_vecs_xy,
diff_ome.reshape(npts_tot, 1)]).flatten()
if return_value_flag == 1:
retval = sum(abs(retval))
elif return_value_flag == 2:
denom = npts_tot - len(plist_fit) - 1.
if denom != 0:
nu_fac = 1. / denom
else:
nu_fac = 1.
nu_fac = 1 / (npts_tot - len(plist_fit) - 1.)
retval = nu_fac * sum(retval**2)
return retval
def objFuncFitGrain(gFit,
gFull,
gFlag,
detectorParams,
xyo_det,
hkls_idx,
bMat,
wavelength,
bVec,
eVec,
dFunc,
dParams,
omePeriod,
simOnly=False,
return_value_flag=return_value_flag):
"""
gFull[0] = expMap_c[0]
gFull[1] = expMap_c[1]
gFull[2] = expMap_c[2]
gFull[3] = tVec_c[0]
gFull[4] = tVec_c[1]
gFull[5] = tVec_c[2]
gFull[6] = vInv_MV[0]
gFull[7] = vInv_MV[1]
gFull[8] = vInv_MV[2]
gFull[9] = vInv_MV[3]
gFull[10] = vInv_MV[4]
gFull[11] = vInv_MV[5]
detectorParams[0] = tiltAngles[0]
detectorParams[1] = tiltAngles[1]
detectorParams[2] = tiltAngles[2]
detectorParams[3] = tVec_d[0]
detectorParams[4] = tVec_d[1]
detectorParams[5] = tVec_d[2]
detectorParams[6] = chi
detectorParams[7] = tVec_s[0]
detectorParams[8] = tVec_s[1]
detectorParams[9] = tVec_s[2]
"""
npts = len(xyo_det)
gFull[gFlag] = gFit
xy_unwarped = dFunc(xyo_det[:, :2], dParams)
rMat_d = xfcapi.makeDetectorRotMat(detectorParams[:3])
tVec_d = detectorParams[3:6].reshape(3, 1)
chi = detectorParams[6]
tVec_s = detectorParams[7:10].reshape(3, 1)
rMat_c = xfcapi.makeRotMatOfExpMap(gFull[:3])
tVec_c = gFull[3:6].reshape(3, 1)
vInv_s = gFull[6:]
vMat_s = mutil.vecMVToSymm(vInv_s) # NOTE: Inverse of V from F = V * R
gVec_c = np.dot(bMat, hkls_idx) # gVecs with magnitudes in CRYSTAL frame
gVec_s = np.dot(vMat_s, np.dot(rMat_c,
gVec_c)) # stretched gVecs in SAMPLE frame
gHat_c = mutil.unitVector(np.dot(
rMat_c.T, gVec_s)) # unit reciprocal lattice vectors in CRYSTAL frame
match_omes, calc_omes = matchOmegas(xyo_det,
hkls_idx,
chi,
rMat_c,
bMat,
wavelength,
vInv=vInv_s,
beamVec=bVec,
etaVec=eVec,
omePeriod=omePeriod)
rMat_s = xfcapi.makeOscillRotMatArray(chi, calc_omes)
calc_xy = xfcapi.gvecToDetectorXYArray(gHat_c.T,
rMat_d,
rMat_s,
rMat_c,
tVec_d,
tVec_s,
tVec_c,
beamVec=bVec)
if np.any(np.isnan(calc_xy)):
print "infeasible pFull"
# return values
if simOnly:
retval = np.hstack([calc_xy, calc_omes.reshape(npts, 1)])
else:
diff_vecs_xy = calc_xy - xy_unwarped[:, :2]
diff_ome = xf.angularDifference(calc_omes, xyo_det[:, 2])
retval = np.hstack([diff_vecs_xy, diff_ome.reshape(npts, 1)]).flatten()
if return_value_flag == 1:
retval = sum(abs(retval))
elif return_value_flag == 2:
denom = npts - len(gFit) - 1.
if denom != 0:
nu_fac = 1. / denom
else:
nu_fac = 1.
retval = nu_fac * sum(retval**2 / abs(
np.hstack([calc_xy, calc_omes.reshape(npts, 1)]).flatten()))
return retval
def _grand_loop_precomp(image_stack, all_angles, test_crds, experiment, controller):
"""grand loop precomputing the grown image stack"""
subprocess = 'dilate image_stack'
dilation_shape = np.ones((2*experiment.row_dilation + 1,
2*experiment.col_dilation + 1),
dtype=np.uint8)
image_stack_dilated = np.empty_like(image_stack)
n_images = len(image_stack)
controller.start(subprocess, n_images)
for i_image in range(n_images):
ski_dilation(image_stack[i_image], dilation_shape, out=image_stack_dilated[i_image])
controller.update(i_image+1)
controller.finish(subprocess)
n_grains = experiment.n_grains
n_coords = controller.limit('coords', len(test_crds))
_project = xrdutil._project_on_detector_plane
chunk_size = controller.get_chunk_size()
ncpus = controller.get_process_count()
# precompute per-grain stuff
subprocess = 'precompute gVec_cs'
controller.start(subprocess, len(all_angles))
gvec_cs_precomp = []
for i, angs in enumerate(all_angles):
rMat_ss = xfcapi.makeOscillRotMatArray(experiment.chi, angs[:,2])
gvec_cs = _anglesToGVec(angs, rMat_ss, experiment.rMat_c[i])
gvec_cs_precomp.append((gvec_cs, rMat_ss))
controller.finish(subprocess)
# split on coords
chunks = xrange(0, n_coords, chunk_size)
subprocess = 'grand_loop'
controller.start(subprocess, n_coords)
finished = 0
ncpus = min(ncpus, len(chunks))
if ncpus > 1:
shared_arr = multiprocessing.Array('d', n_grains * n_coords)
confidence = np.ctypeslib.as_array(shared_arr.get_obj()).reshape(n_grains, n_coords)
with multiproc_state(chunk_size, confidence, image_stack_dilated, all_angles,
gvec_cs_precomp, test_crds, experiment):
pool = multiprocessing.Pool(ncpus)
for count in pool.imap_unordered(multiproc_inner_loop, chunks):
finished += count
controller.update(finished)
del pool
else:
confidence = np.empty((n_grains, n_coords))
for chunk_start in chunks:
chunk_stop = min(n_coords, chunk_start+chunk_size)
count =_grand_loop_inner(confidence, image_stack_dilated,
all_angles, gvec_cs_precomp, test_crds,
experiment, start=chunk_start,
stop=chunk_stop)
finished += count
controller.update(finished)
controller.finish(subprocess)
controller.handle_result("confidence", confidence)
def _grand_loop_precomp(image_stack, all_angles, test_crds, experiment,
controller):
"""grand loop precomputing the grown image stack"""
subprocess = 'dilate image_stack'
dilation_shape = np.ones(
(2 * experiment.row_dilation + 1, 2 * experiment.col_dilation + 1),
dtype=np.uint8)
image_stack_dilated = np.empty_like(image_stack)
n_images = len(image_stack)
controller.start(subprocess, n_images)
for i_image in range(n_images):
ski_dilation(image_stack[i_image],
dilation_shape,
out=image_stack_dilated[i_image])
controller.update(i_image + 1)
controller.finish(subprocess)
n_grains = experiment.n_grains
n_coords = controller.limit('coords', len(test_crds))
_project = xrdutil._project_on_detector_plane
chunk_size = controller.get_chunk_size()
ncpus = controller.get_process_count()
# precompute per-grain stuff
subprocess = 'precompute gVec_cs'
controller.start(subprocess, len(all_angles))
gvec_cs_precomp = []
for i, angs in enumerate(all_angles):
rMat_ss = xfcapi.makeOscillRotMatArray(experiment.chi, angs[:, 2])
gvec_cs = _anglesToGVec(angs, rMat_ss, experiment.rMat_c[i])
gvec_cs_precomp.append((gvec_cs, rMat_ss))
controller.finish(subprocess)
# split on coords
chunks = xrange(0, n_coords, chunk_size)
subprocess = 'grand_loop'
controller.start(subprocess, n_coords)
finished = 0
ncpus = min(ncpus, len(chunks))
if ncpus > 1:
shared_arr = multiprocessing.Array('d', n_grains * n_coords)
confidence = np.ctypeslib.as_array(shared_arr.get_obj()).reshape(
n_grains, n_coords)
with multiproc_state(chunk_size, confidence, image_stack_dilated,
all_angles, gvec_cs_precomp, test_crds,
experiment):
pool = multiprocessing.Pool(ncpus)
for count in pool.imap_unordered(multiproc_inner_loop, chunks):
finished += count
controller.update(finished)
del pool
else:
confidence = np.empty((n_grains, n_coords))
for chunk_start in chunks:
chunk_stop = min(n_coords, chunk_start + chunk_size)
count = _grand_loop_inner(confidence,
image_stack_dilated,
all_angles,
gvec_cs_precomp,
test_crds,
experiment,
start=chunk_start,
stop=chunk_stop)
finished += count
controller.update(finished)
controller.finish(subprocess)
controller.handle_result("confidence", confidence)
def objFuncFitGrain(gFit, gFull, gFlag,
detectorParams,
xyo_det, hkls_idx, bMat, wavelength,
bVec, eVec,
dFunc, dParams,
omePeriod,
simOnly=False, return_value_flag=return_value_flag):
"""
gFull[0] = expMap_c[0]
gFull[1] = expMap_c[1]
gFull[2] = expMap_c[2]
gFull[3] = tVec_c[0]
gFull[4] = tVec_c[1]
gFull[5] = tVec_c[2]
gFull[6] = vInv_MV[0]
gFull[7] = vInv_MV[1]
gFull[8] = vInv_MV[2]
gFull[9] = vInv_MV[3]
gFull[10] = vInv_MV[4]
gFull[11] = vInv_MV[5]
detectorParams[0] = tiltAngles[0]
detectorParams[1] = tiltAngles[1]
detectorParams[2] = tiltAngles[2]
detectorParams[3] = tVec_d[0]
detectorParams[4] = tVec_d[1]
detectorParams[5] = tVec_d[2]
detectorParams[6] = chi
detectorParams[7] = tVec_s[0]
detectorParams[8] = tVec_s[1]
detectorParams[9] = tVec_s[2]
"""
npts = len(xyo_det)
gFull[gFlag] = gFit
xy_unwarped = dFunc(xyo_det[:, :2], dParams)
rMat_d = xfcapi.makeDetectorRotMat(detectorParams[:3])
tVec_d = detectorParams[3:6].reshape(3, 1)
chi = detectorParams[6]
tVec_s = detectorParams[7:10].reshape(3, 1)
rMat_c = xfcapi.makeRotMatOfExpMap(gFull[:3])
tVec_c = gFull[3:6].reshape(3, 1)
vInv_s = gFull[6:]
vMat_s = mutil.vecMVToSymm(vInv_s) # NOTE: Inverse of V from F = V * R
gVec_c = np.dot(bMat, hkls_idx) # gVecs with magnitudes in CRYSTAL frame
gVec_s = np.dot(vMat_s, np.dot(rMat_c, gVec_c)) # stretched gVecs in SAMPLE frame
gHat_c = mutil.unitVector(
np.dot(rMat_c.T, gVec_s)) # unit reciprocal lattice vectors in CRYSTAL frame
match_omes, calc_omes = matchOmegas(xyo_det, hkls_idx, chi, rMat_c, bMat, wavelength,
vInv=vInv_s, beamVec=bVec, etaVec=eVec,
omePeriod=omePeriod)
rMat_s = xfcapi.makeOscillRotMatArray(chi, calc_omes)
calc_xy = xfcapi.gvecToDetectorXYArray(gHat_c.T,
rMat_d, rMat_s, rMat_c,
tVec_d, tVec_s, tVec_c,
beamVec=bVec)
if np.any(np.isnan(calc_xy)):
print "infeasible pFull"
# return values
if simOnly:
retval = np.hstack([calc_xy, calc_omes.reshape(npts, 1)])
else:
diff_vecs_xy = calc_xy - xy_unwarped[:, :2]
diff_ome = xf.angularDifference( calc_omes, xyo_det[:, 2] )
retval = np.hstack([diff_vecs_xy,
diff_ome.reshape(npts, 1)
]).flatten()
if return_value_flag == 1:
retval = sum( abs(retval) )
elif return_value_flag == 2:
denom = npts - len(gFit) - 1.
if denom != 0:
nu_fac = 1. / denom
else:
nu_fac = 1.
retval = nu_fac * sum(retval**2 / abs(np.hstack([calc_xy, calc_omes.reshape(npts, 1)]).flatten()))
return retval
def test_orientations(image_stack, experiment, controller):
"""grand loop precomputing the grown image stack
image-stack -- is the image stack to be tested against.
experiment -- A bunch of experiment related parameters.
controller -- An external object implementing the hooks to notify progress
as well as figuring out what to do with results.
"""
# extract some information needed =========================================
# number of grains, number of coords (maybe limited by call), projection
# function to use, chunk size to use if multiprocessing and the number
# of cpus.
n_grains = experiment.n_grains
chunk_size = controller.get_chunk_size()
ncpus = controller.get_process_count()
# generate angles =========================================================
# all_angles will be a list containing arrays for the different angles to
# use, one entry per grain.
#
# Note that the angle generation is driven by the exp_maps in the experiment
all_angles = evaluate_diffraction_angles(experiment, controller)
# generate coords =========================================================
# The grid of coords to use to test
test_crds = generate_test_grid(-0.25, 0.25, 101)
n_coords = controller.limit('coords', len(test_crds))
# first, perform image dilation ===========================================
# perform image dilation (using scikit_image dilation)
subprocess = 'dilate image_stack'
dilation_shape = np.ones((2*experiment.row_dilation + 1,
2*experiment.col_dilation + 1),
dtype=np.uint8)
image_stack_dilated = np.empty_like(image_stack)
dilated = np.empty((image_stack.shape[-2], image_stack.shape[-1]<<3),
dtype=np.bool)
n_images = len(image_stack)
controller.start(subprocess, n_images)
for i_image in range(n_images):
to_dilate = np.unpackbits(image_stack[i_image], axis=-1)
ski_dilation(to_dilate, dilation_shape,
out=dilated)
image_stack_dilated[i_image] = np.packbits(dilated, axis=-1)
controller.update(i_image+1)
controller.finish(subprocess)
# precompute per-grain stuff ==============================================
# gVec_cs and rmat_ss can be precomputed, do so.
subprocess = 'precompute gVec_cs'
controller.start(subprocess, len(all_angles))
precomp = []
for i, angs in enumerate(all_angles):
rmat_ss = xfcapi.makeOscillRotMatArray(experiment.chi, angs[:,2])
gvec_cs = _anglesToGVec(angs, rmat_ss, experiment.rMat_c[i])
precomp.append((gvec_cs, rmat_ss))
controller.finish(subprocess)
# grand loop ==============================================================
# The near field simulation 'grand loop'. Where the bulk of computing is
# performed. We are looking for a confidence matrix that has a n_grains
chunks = xrange(0, n_coords, chunk_size)
subprocess = 'grand_loop'
controller.start(subprocess, n_coords)
finished = 0
ncpus = min(ncpus, len(chunks))
logging.info('Checking confidence for %d coords, %d grains.',
n_coords, n_grains)
confidence = np.empty((n_grains, n_coords))
if ncpus > 1:
global _multiprocessing_start_method
logging.info('Running multiprocess %d processes (%s)',
ncpus, _multiprocessing_start_method)
with grand_loop_pool(ncpus=ncpus, state=(chunk_size,
image_stack_dilated,
all_angles, precomp, test_crds,
experiment)) as pool:
for rslice, rvalues in pool.imap_unordered(multiproc_inner_loop,
chunks):
count = rvalues.shape[1]
confidence[:, rslice] = rvalues
finished += count
controller.update(finished)
else:
logging.info('Running in a single process')
for chunk_start in chunks:
chunk_stop = min(n_coords, chunk_start+chunk_size)
rslice, rvalues = _grand_loop_inner(image_stack_dilated, all_angles,
precomp, test_crds, experiment,
start=chunk_start,
stop=chunk_stop)
count = rvalues.shape[1]
confidence[:, rslice] = rvalues
finished += count
controller.update(finished)
controller.finish(subprocess)
controller.handle_result("confidence", confidence)
def test_orientations(image_stack, experiment, test_crds, controller,
multiprocessing_start_method):
"""grand loop precomputing the grown image stack
image-stack -- is the image stack to be tested against.
experiment -- A bunch of experiment related parameters.
controller -- An external object implementing the hooks to notify progress
as well as figuring out what to do with results.
"""
# extract some information needed =========================================
# number of grains, number of coords (maybe limited by call), projection
# function to use, chunk size to use if multiprocessing and the number
# of cpus.
n_grains = experiment.n_grains
chunk_size = controller.get_chunk_size()
ncpus = controller.get_process_count()
# generate angles =========================================================
# all_angles will be a list containing arrays for the different angles to
# use, one entry per grain.
#
# Note that the angle generation is driven by the exp_maps in the experiment
all_angles = evaluate_diffraction_angles(experiment, controller)
# generate coords =========================================================
# The grid of coords to use to test
#test_crds = generate_test_grid(-0.25, 0.25, 101)
n_coords = controller.limit('coords', len(test_crds))
#
# # first, perform image dilation ===========================================
# # perform image dilation (using scikit_image dilation)
# subprocess = 'dilate image_stack'
# dilation_shape = np.ones((2*experiment.row_dilation + 1,
# 2*experiment.col_dilation + 1),
# dtype=np.uint8)
# image_stack_dilated = np.empty_like(image_stack)
# dilated = np.empty((image_stack.shape[-2], image_stack.shape[-1]<<3),
# dtype=np.bool)
# n_images = len(image_stack)
# controller.start(subprocess, n_images)
# for i_image in range(n_images):
# to_dilate = np.unpackbits(image_stack[i_image], axis=-1)
# ski_dilation(to_dilate, dilation_shape,
# out=dilated)
# image_stack_dilated[i_image] = np.packbits(dilated, axis=-1)
# controller.update(i_image+1)
# controller.finish(subprocess)
# precompute per-grain stuff ==============================================
# gVec_cs and rmat_ss can be precomputed, do so.
subprocess = 'precompute gVec_cs'
controller.start(subprocess, len(all_angles))
precomp = []
for i, angs in enumerate(all_angles):
rmat_ss = xfcapi.makeOscillRotMatArray(experiment.chi, angs[:, 2])
gvec_cs = _anglesToGVec(angs, rmat_ss, experiment.rMat_c[i])
precomp.append((gvec_cs, rmat_ss))
controller.finish(subprocess)
# grand loop ==============================================================
# The near field simulation 'grand loop'. Where the bulk of computing is
# performed. We are looking for a confidence matrix that has a n_grains
chunks = xrange(0, n_coords, chunk_size)
subprocess = 'grand_loop'
controller.start(subprocess, n_coords)
finished = 0
ncpus = min(ncpus, len(chunks))
logging.info('Checking confidence for %d coords, %d grains.', n_coords,
n_grains)
confidence = np.empty((n_grains, n_coords))
if ncpus > 1:
global _multiprocessing_start_method
_multiprocessing_start_method = multiprocessing_start_method
logging.info('Running multiprocess %d processes (%s)', ncpus,
_multiprocessing_start_method)
with grand_loop_pool(ncpus=ncpus,
state=(chunk_size, image_stack, all_angles,
precomp, test_crds, experiment)) as pool:
for rslice, rvalues in pool.imap_unordered(multiproc_inner_loop,
chunks):
count = rvalues.shape[1]
confidence[:, rslice] = rvalues
finished += count
controller.update(finished)
del _multiprocessing_start_method
pool.close()
else:
logging.info('Running in a single process')
for chunk_start in chunks:
chunk_stop = min(n_coords, chunk_start + chunk_size)
rslice, rvalues = _grand_loop_inner(image_stack,
all_angles,
precomp,
test_crds,
experiment,
start=chunk_start,
stop=chunk_stop)
count = rvalues.shape[1]
confidence[:, rslice] = rvalues
finished += count
controller.update(finished)
controller.finish(subprocess)
controller.handle_result("confidence", confidence)
#del _multiprocessing_start_method
#pool.close()
return confidence
def objFuncFitGrain(gFit, gFull, gFlag,
instrument,
reflections_dict,
bMat, wavelength,
omePeriod,
simOnly=False,
return_value_flag=return_value_flag):
"""
gFull[0] = expMap_c[0]
gFull[1] = expMap_c[1]
gFull[2] = expMap_c[2]
gFull[3] = tVec_c[0]
gFull[4] = tVec_c[1]
gFull[5] = tVec_c[2]
gFull[6] = vInv_MV[0]
gFull[7] = vInv_MV[1]
gFull[8] = vInv_MV[2]
gFull[9] = vInv_MV[3]
gFull[10] = vInv_MV[4]
gFull[11] = vInv_MV[5]
OLD CALL
objFuncFitGrain(gFit, gFull, gFlag,
detectorParams,
xyo_det, hkls_idx, bMat, wavelength,
bVec, eVec,
dFunc, dParams,
omePeriod,
simOnly=False, return_value_flag=return_value_flag)
"""
bVec = instrument.beam_vector
eVec = instrument.eta_vector
# fill out parameters
gFull[gFlag] = gFit
# map parameters to functional arrays
rMat_c = xfcapi.makeRotMatOfExpMap(gFull[:3])
tVec_c = gFull[3:6].reshape(3, 1)
vInv_s = gFull[6:]
vMat_s = mutil.vecMVToSymm(vInv_s) # NOTE: Inverse of V from F = V * R
# loop over instrument panels
# CAVEAT: keeping track of key ordering in the "detectors" attribute of
# instrument here because I am not sure if instatiating them using
# dict.fromkeys() preserves the same order if using iteration...
# <JVB 2017-10-31>
calc_omes_dict = dict.fromkeys(instrument.detectors, [])
calc_xy_dict = dict.fromkeys(instrument.detectors)
meas_xyo_all = []
det_keys_ordered = []
for det_key, panel in instrument.detectors.iteritems():
det_keys_ordered.append(det_key)
rMat_d, tVec_d, chi, tVec_s = extract_detector_transformation(
instrument.detector_parameters[det_key])
results = reflections_dict[det_key]
if len(results) == 0:
continue
"""
extract data from results list fields:
refl_id, gvec_id, hkl, sum_int, max_int, pred_ang, meas_ang, meas_xy
or array from spots tables:
0:5 ID PID H K L
5:7 sum(int) max(int)
7:10 pred tth pred eta pred ome
10:13 meas tth meas eta meas ome
13:15 pred X pred Y
15:17 meas X meas Y
"""
if isinstance(results, list):
# WARNING: hkls and derived vectors below must be columnwise;
# strictly necessary??? change affected APIs instead?
# <JVB 2017-03-26>
hkls = np.atleast_2d(
np.vstack([x[2] for x in results])
).T
meas_xyo = np.atleast_2d(
np.vstack([np.r_[x[7], x[6][-1]] for x in results])
)
elif isinstance(results, np.ndarray):
hkls = np.atleast_2d(results[:, 2:5]).T
meas_xyo = np.atleast_2d(results[:, [15, 16, 12]])
# FIXME: distortion handling must change to class-based
if panel.distortion is not None:
meas_omes = meas_xyo[:, 2]
xy_unwarped = panel.distortion[0](
meas_xyo[:, :2], panel.distortion[1])
meas_xyo = np.vstack([xy_unwarped.T, meas_omes]).T
pass
# append to meas_omes
meas_xyo_all.append(meas_xyo)
# G-vectors:
# 1. calculate full g-vector components in CRYSTAL frame from B
# 2. rotate into SAMPLE frame and apply stretch
# 3. rotate back into CRYSTAL frame and normalize to unit magnitude
# IDEA: make a function for this sequence of operations with option for
# choosing ouput frame (i.e. CRYSTAL vs SAMPLE vs LAB)
gVec_c = np.dot(bMat, hkls)
gVec_s = np.dot(vMat_s, np.dot(rMat_c, gVec_c))
gHat_c = mutil.unitVector(np.dot(rMat_c.T, gVec_s))
# !!!: check that this operates on UNWARPED xy
match_omes, calc_omes = matchOmegas(
meas_xyo, hkls, chi, rMat_c, bMat, wavelength,
vInv=vInv_s, beamVec=bVec, etaVec=eVec,
omePeriod=omePeriod)
# append to omes dict
calc_omes_dict[det_key] = calc_omes
# TODO: try Numba implementations
rMat_s = xfcapi.makeOscillRotMatArray(chi, calc_omes)
calc_xy = xfcapi.gvecToDetectorXYArray(gHat_c.T,
rMat_d, rMat_s, rMat_c,
tVec_d, tVec_s, tVec_c,
beamVec=bVec)
# append to xy dict
calc_xy_dict[det_key] = calc_xy
pass
# stack results to concatenated arrays
calc_omes_all = np.hstack([calc_omes_dict[k] for k in det_keys_ordered])
tmp = []
for k in det_keys_ordered:
if calc_xy_dict[k] is not None:
tmp.append(calc_xy_dict[k])
calc_xy_all = np.vstack(tmp)
meas_xyo_all = np.vstack(meas_xyo_all)
npts = len(meas_xyo_all)
if np.any(np.isnan(calc_xy)):
raise RuntimeError(
"infeasible pFull: may want to scale" +
"back finite difference step size")
# return values
if simOnly:
# return simulated values
if return_value_flag in [None, 1]:
retval = np.hstack([calc_xy_all, calc_omes_all.reshape(npts, 1)])
else:
rd = dict.fromkeys(det_keys_ordered)
for det_key in det_keys_ordered:
rd[det_key] = {'calc_xy': calc_xy_dict[det_key],
'calc_omes': calc_omes_dict[det_key]}
retval = rd
else:
# return residual vector
# IDEA: try angles instead of xys?
diff_vecs_xy = calc_xy_all - meas_xyo_all[:, :2]
diff_ome = xf.angularDifference(calc_omes_all, meas_xyo_all[:, 2])
retval = np.hstack([diff_vecs_xy,
diff_ome.reshape(npts, 1)
]).flatten()
if return_value_flag == 1:
# return scalar sum of squared residuals
retval = sum(abs(retval))
elif return_value_flag == 2:
# return DOF-normalized chisq
# TODO: check this calculation
denom = 3*npts - len(gFit) - 1.
if denom != 0:
nu_fac = 1. / denom
else:
nu_fac = 1.
retval = nu_fac * sum(retval**2)
return retval
def check_indexing_plots(cfg_filename,
plot_trials=False,
plot_from_grains=False):
cfg = config.open(cfg_filename)[0] # use first block, like indexing
working_dir = cfg.working_dir
analysis_dir = os.path.join(working_dir, cfg.analysis_name)
#instrument parameters
icfg = get_instrument_parameters(cfg)
chi = icfg['oscillation_stage']['chi']
# load maps that were used
oem = cPickle.load(open(cfg.find_orientations.orientation_maps.file, 'r'))
nmaps = len(oem.dataStore)
omeEdges = np.degrees(oem.omeEdges)
nome = len(omeEdges) - 1
etaEdges = np.degrees(oem.etaEdges)
neta = len(etaEdges) - 1
delta_ome = abs(omeEdges[1] - omeEdges[0])
full_ome_range = xf.angularDifference(omeEdges[0], omeEdges[-1]) == 0
full_eta_range = xf.angularDifference(etaEdges[0], etaEdges[-1]) == 0
# grab plane data and figure out IDs of map HKLS
pd = oem.planeData
gvids = [
pd.hklDataList[i]['hklID']
for i in np.where(pd.exclusions == False)[0].tolist()
]
# load orientations
quats = np.atleast_2d(
np.loadtxt(os.path.join(working_dir, 'accepted_orientations.dat')))
if plot_trials:
scored_trials = np.load(
os.path.join(working_dir, 'scored_orientations.dat'))
quats = scored_orientations[:4, scored_orientations[
-1, :] >= cfg.find_orientations.clustering.completeness]
pass
expMaps = np.tile(2. * np.arccos(quats[:, 0]),
(3, 1)) * unitVector(quats[:, 1:].T)
##########################################
# SPECIAL CASE FOR FIT GRAINS #
##########################################
if plot_from_grains:
distortion = (GE_41RT, icfg['detector']['distortion']['parameters'])
#
grain_table = np.atleast_2d(
np.loadtxt(os.path.join(analysis_dir, 'grains.out')))
ngrains = len(grain_table)
#
expMaps = grain_table[:, 3:6]
tVec_c = grain_table[:, 6:9]
vInv = grain_table[:, 6:12]
#
rMat_d = xf.makeDetectorRotMat(
icfg['detector']['transform']['tilt_angles'])
tVec_d = np.vstack(icfg['detector']['transform']['t_vec_d'])
#
chi = icfg['oscillation_stage']['chi']
tVec_s = np.vstack(icfg['oscillation_stage']['t_vec_s'])
#
oes = np.zeros(oem.dataStore.shape)
for i_grn in range(ngrains):
spots_table = np.loadtxt(
os.path.join(analysis_dir, 'spots_%05d.out' % i_grn))
idx_m = spots_table[:, 0] >= 0
for i_map in range(nmaps):
idx_g = spots_table[:, 1] == gvids[i_map]
idx = np.logical_and(idx_m, idx_g)
nrefl = sum(idx)
omes_fit = xf.mapAngle(spots_table[idx, 9],
np.radians(
cfg.find_orientations.omega.period),
units='radians')
xy_det = spots_table[idx, -3:]
xy_det[:, 2] = np.zeros(nrefl)
rMat_s_array = xfcapi.makeOscillRotMatArray(chi, omes_fit)
# form in-plane vectors for detector points list in DETECTOR FRAME
P2_d = xy_det.T
# in LAB FRAME
P2_l = np.dot(rMat_d, P2_d) + tVec_d # point on detector
P0_l = np.hstack([
tVec_s +
np.dot(rMat_s_array[j], tVec_c[i_grn, :].reshape(3, 1))
for j in range(nrefl)
]) # origin of CRYSTAL FRAME
# diffraction unit vector components in LAB FRAME
dHat_l = unitVector(P2_l - P0_l)
P2_l = np.dot(rMat_d, xy_det.T) + tVec_d
# angles for reference frame
dHat_ref_l = unitVector(P2_l)
# append etas and omes
etas_fit = np.arctan2(dHat_ref_l[1, :],
dHat_ref_l[0, :]).flatten()
# find indices, then truncate or wrap
i_ome = cellIndices(oem.omeEdges, omes_fit)
if full_ome_range:
i_ome[i_ome < 0] = np.mod(i_ome, nome) + 1
i_ome[i_ome >= nome] = np.mod(i_ome, nome)
else:
incl = np.logical_or(i_ome >= 0, i_ome < nome)
i_ome = i_ome[incl]
j_eta = cellIndices(oem.etaEdges, etas_fit)
if full_eta_range:
j_eta[j_eta < 0] = np.mod(j_eta, neta) + 1
j_eta[j_eta >= neta] = np.mod(j_eta, neta)
else:
incl = np.logical_or(j_eta >= 0, j_eta < neta)
j_eta = j_eta[incl]
#if np.max(i_ome) >= nome or np.min(i_ome) < 0 or np.max(j_eta) >= neta or np.min(j_eta) < 0:
# import pdb; pdb.set_trace()
# add to map
oes[i_map][i_ome, j_eta] = 1
pass
pass
# simulate quaternion points
if not plot_from_grains:
oes = simulateOmeEtaMaps(omeEdges,
etaEdges,
pd,
expMaps,
chi=chi,
etaTol=0.01,
omeTol=0.01,
etaRanges=None,
omeRanges=None,
bVec=xf.bVec_ref,
eVec=xf.eta_ref,
vInv=xf.vInv_ref)
# tick labling
omes = np.degrees(oem.omeEdges)
etas = np.degrees(oem.etaEdges)
num_ticks = 7
xmin = np.amin(etas)
xmax = np.amax(etas)
dx = (xmax - xmin) / (num_ticks - 1.)
dx1 = (len(etas) - 1) / (num_ticks - 1.)
xtlab = ["%.0f" % (xmin + i * dx) for i in range(num_ticks)]
xtloc = np.array([i * dx1 for i in range(num_ticks)]) - 0.5
ymin = np.amin(omes)
ymax = np.amax(omes)
dy = (ymax - ymin) / (num_ticks - 1.)
dy1 = (len(omes) - 1) / (num_ticks - 1.)
ytlab = ["%.0f" % (ymin + i * dy) for i in range(num_ticks)]
ytloc = np.array([i * dy1 for i in range(num_ticks)]) - 0.5
# Plot the three kernel density estimates
n_maps = len(oem.iHKLList)
fig_list = [plt.figure(num=i + 1) for i in range(n_maps)]
ax_list = [fig_list[i].gca() for i in range(n_maps)]
for i_map in range(n_maps):
y, x = np.where(oes[i_map] > 0)
ax_list[i_map].hold(True)
ax_list[i_map].imshow(oem.dataStore[i_map] > 0.1, cmap=cm.bone)
ax_list[i_map].set_title(r'Map for $\{%d %d %d\}$' %
tuple(pd.hkls[:, i_map]))
ax_list[i_map].set_xlabel(
r'Azimuth channel, $\eta$; $\Delta\eta=%.3f$' % delta_ome)
ax_list[i_map].set_ylabel(
r'Rotation channel, $\omega$; $\Delta\omega=%.3f$' % delta_ome)
ax_list[i_map].plot(x, y, 'c+')
ax_list[i_map].xaxis.set_ticks(xtloc)
ax_list[i_map].xaxis.set_ticklabels(xtlab)
ax_list[i_map].yaxis.set_ticks(ytloc)
ax_list[i_map].yaxis.set_ticklabels(ytlab)
ax_list[i_map].axis('tight')
plt.show()
return fig_list, oes
def check_indexing_plots(cfg_filename, plot_trials=False, plot_from_grains=False):
cfg = config.open(cfg_filename)[0] # use first block, like indexing
working_dir = cfg.working_dir
analysis_dir = os.path.join(working_dir, cfg.analysis_name)
#instrument parameters
icfg = get_instrument_parameters(cfg)
chi = icfg['oscillation_stage']['chi']
# load maps that were used
oem = cPickle.load(
open(cfg.find_orientations.orientation_maps.file, 'r')
)
nmaps = len(oem.dataStore)
omeEdges = np.degrees(oem.omeEdges); nome = len(omeEdges) - 1
etaEdges = np.degrees(oem.etaEdges); neta = len(etaEdges) - 1
delta_ome = abs(omeEdges[1]-omeEdges[0])
full_ome_range = xf.angularDifference(omeEdges[0], omeEdges[-1]) == 0
full_eta_range = xf.angularDifference(etaEdges[0], etaEdges[-1]) == 0
# grab plane data and figure out IDs of map HKLS
pd = oem.planeData
gvids = [pd.hklDataList[i]['hklID'] for i in np.where(pd.exclusions == False)[0].tolist()]
# load orientations
quats = np.atleast_2d(np.loadtxt(os.path.join(working_dir, 'accepted_orientations.dat')))
if plot_trials:
scored_trials = np.load(os.path.join(working_dir, 'scored_orientations.dat'))
quats = scored_orientations[:4, scored_orientations[-1, :] >= cfg.find_orientations.clustering.completeness]
pass
expMaps = np.tile(2. * np.arccos(quats[:, 0]), (3, 1))*unitVector(quats[:, 1:].T)
##########################################
# SPECIAL CASE FOR FIT GRAINS #
##########################################
if plot_from_grains:
distortion = (GE_41RT, icfg['detector']['distortion']['parameters'])
#
grain_table = np.atleast_2d(np.loadtxt(os.path.join(analysis_dir, 'grains.out')))
ngrains = len(grain_table)
#
expMaps = grain_table[:, 3:6]
tVec_c = grain_table[:, 6:9]
vInv = grain_table[:, 6:12]
#
rMat_d = xf.makeDetectorRotMat(icfg['detector']['transform']['tilt_angles'])
tVec_d = np.vstack(icfg['detector']['transform']['t_vec_d'])
#
chi = icfg['oscillation_stage']['chi']
tVec_s = np.vstack(icfg['oscillation_stage']['t_vec_s'])
#
oes = np.zeros(oem.dataStore.shape)
for i_grn in range(ngrains):
spots_table = np.loadtxt(os.path.join(analysis_dir, 'spots_%05d.out' %i_grn))
idx_m = spots_table[:, 0] >= 0
for i_map in range(nmaps):
idx_g = spots_table[:, 1] == gvids[i_map]
idx = np.logical_and(idx_m, idx_g)
nrefl = sum(idx)
omes_fit = xf.mapAngle(spots_table[idx, 9], np.radians(cfg.find_orientations.omega.period), units='radians')
xy_det = spots_table[idx, -3:]
xy_det[:, 2] = np.zeros(nrefl)
rMat_s_array = xfcapi.makeOscillRotMatArray(chi, omes_fit)
# form in-plane vectors for detector points list in DETECTOR FRAME
P2_d = xy_det.T
# in LAB FRAME
P2_l = np.dot(rMat_d, P2_d) + tVec_d # point on detector
P0_l = np.hstack(
[tVec_s + np.dot(rMat_s_array[j], tVec_c[i_grn, :].reshape(3, 1)) for j in range(nrefl)]
) # origin of CRYSTAL FRAME
# diffraction unit vector components in LAB FRAME
dHat_l = unitVector(P2_l - P0_l)
P2_l = np.dot(rMat_d, xy_det.T) + tVec_d
# angles for reference frame
dHat_ref_l = unitVector(P2_l)
# append etas and omes
etas_fit = np.arctan2(dHat_ref_l[1, :], dHat_ref_l[0, :]).flatten()
# find indices, then truncate or wrap
i_ome = cellIndices(oem.omeEdges, omes_fit)
if full_ome_range:
i_ome[i_ome < 0] = np.mod(i_ome, nome) + 1
i_ome[i_ome >= nome] = np.mod(i_ome, nome)
else:
incl = np.logical_or(i_ome >= 0, i_ome < nome)
i_ome = i_ome[incl]
j_eta = cellIndices(oem.etaEdges, etas_fit)
if full_eta_range:
j_eta[j_eta < 0] = np.mod(j_eta, neta) + 1
j_eta[j_eta >= neta] = np.mod(j_eta, neta)
else:
incl = np.logical_or(j_eta >= 0, j_eta < neta)
j_eta = j_eta[incl]
#if np.max(i_ome) >= nome or np.min(i_ome) < 0 or np.max(j_eta) >= neta or np.min(j_eta) < 0:
# import pdb; pdb.set_trace()
# add to map
oes[i_map][i_ome, j_eta] = 1
pass
pass
# simulate quaternion points
if not plot_from_grains:
oes = simulateOmeEtaMaps(omeEdges, etaEdges, pd,
expMaps,
chi=chi,
etaTol=0.01, omeTol=0.01,
etaRanges=None, omeRanges=None,
bVec=xf.bVec_ref, eVec=xf.eta_ref, vInv=xf.vInv_ref)
# tick labling
omes = np.degrees(oem.omeEdges)
etas = np.degrees(oem.etaEdges)
num_ticks = 7
xmin = np.amin(etas); xmax = np.amax(etas)
dx = (xmax - xmin) / (num_ticks - 1.); dx1 = (len(etas) - 1) / (num_ticks - 1.)
xtlab = ["%.0f" % (xmin + i*dx) for i in range(num_ticks)]
xtloc = np.array([i*dx1 for i in range(num_ticks)]) - 0.5
ymin = np.amin(omes); ymax = np.amax(omes)
dy = (ymax - ymin) / (num_ticks - 1.); dy1 = (len(omes) - 1) / (num_ticks - 1.)
ytlab = ["%.0f" % (ymin + i*dy) for i in range(num_ticks)]
ytloc = np.array([i*dy1 for i in range(num_ticks)]) - 0.5
# Plot the three kernel density estimates
n_maps = len(oem.iHKLList)
fig_list =[plt.figure(num=i+1) for i in range(n_maps)]
ax_list = [fig_list[i].gca() for i in range(n_maps)]
for i_map in range(n_maps):
y, x = np.where(oes[i_map] > 0)
ax_list[i_map].hold(True)
ax_list[i_map].imshow(oem.dataStore[i_map] > 0.1, cmap=cm.bone)
ax_list[i_map].set_title(r'Map for $\{%d %d %d\}$' %tuple(pd.hkls[:, i_map]))
ax_list[i_map].set_xlabel(r'Azimuth channel, $\eta$; $\Delta\eta=%.3f$' %delta_ome)
ax_list[i_map].set_ylabel(r'Rotation channel, $\omega$; $\Delta\omega=%.3f$' %delta_ome)
ax_list[i_map].plot(x, y, 'c+')
ax_list[i_map].xaxis.set_ticks(xtloc)
ax_list[i_map].xaxis.set_ticklabels(xtlab)
ax_list[i_map].yaxis.set_ticks(ytloc)
ax_list[i_map].yaxis.set_ticklabels(ytlab)
ax_list[i_map].axis('tight')
plt.show()
return fig_list, oes
def objFuncFitGrain(gFit,
gFull,
gFlag,
instrument,
reflections_dict,
bMat,
wavelength,
omePeriod,
simOnly=False,
return_value_flag=return_value_flag):
"""
gFull[0] = expMap_c[0]
gFull[1] = expMap_c[1]
gFull[2] = expMap_c[2]
gFull[3] = tVec_c[0]
gFull[4] = tVec_c[1]
gFull[5] = tVec_c[2]
gFull[6] = vInv_MV[0]
gFull[7] = vInv_MV[1]
gFull[8] = vInv_MV[2]
gFull[9] = vInv_MV[3]
gFull[10] = vInv_MV[4]
gFull[11] = vInv_MV[5]
OLD CALL
objFuncFitGrain(gFit, gFull, gFlag,
detectorParams,
xyo_det, hkls_idx, bMat, wavelength,
bVec, eVec,
dFunc, dParams,
omePeriod,
simOnly=False, return_value_flag=return_value_flag)
"""
bVec = instrument.beam_vector
eVec = instrument.eta_vector
# fill out parameters
gFull[gFlag] = gFit
# map parameters to functional arrays
rMat_c = xfcapi.makeRotMatOfExpMap(gFull[:3])
tVec_c = gFull[3:6].reshape(3, 1)
vInv_s = gFull[6:]
vMat_s = mutil.vecMVToSymm(vInv_s) # NOTE: Inverse of V from F = V * R
# loop over instrument panels
# CAVEAT: keeping track of key ordering in the "detectors" attribute of
# instrument here because I am not sure if instatiating them using
# dict.fromkeys() preserves the same order if using iteration...
# <JVB 2017-10-31>
calc_omes_dict = dict.fromkeys(instrument.detectors, [])
calc_xy_dict = dict.fromkeys(instrument.detectors)
meas_xyo_all = []
det_keys_ordered = []
for det_key, panel in instrument.detectors.iteritems():
det_keys_ordered.append(det_key)
# extract transformation quantities
rMat_d = instrument.detectors[det_key].rmat
tVec_d = instrument.detectors[det_key].tvec
chi = instrument.chi
tVec_s = instrument.tvec
results = reflections_dict[det_key]
if len(results) == 0:
continue
"""
extract data from results list
fields:
refl_id, gvec_id, hkl, sum_int, max_int, pred_ang, meas_ang, meas_xy
"""
# WARNING: hkls and derived vectors below must be columnwise;
# strictly necessary??? change affected APIs instead?
# <JVB 2017-03-26>
hkls = np.atleast_2d(np.vstack([x[2] for x in results])).T
meas_xyo = np.atleast_2d(
np.vstack([np.r_[x[7], x[6][-1]] for x in results]))
# FIXME: distortion handling must change to class-based
if panel.distortion is not None:
meas_omes = meas_xyo[:, 2]
xy_unwarped = panel.distortion[0](meas_xyo[:, :2],
panel.distortion[1])
meas_xyo = np.vstack([xy_unwarped.T, meas_omes]).T
pass
# append to meas_omes
meas_xyo_all.append(meas_xyo)
# G-vectors:
# 1. calculate full g-vector components in CRYSTAL frame from B
# 2. rotate into SAMPLE frame and apply stretch
# 3. rotate back into CRYSTAL frame and normalize to unit magnitude
# IDEA: make a function for this sequence of operations with option for
# choosing ouput frame (i.e. CRYSTAL vs SAMPLE vs LAB)
gVec_c = np.dot(bMat, hkls)
gVec_s = np.dot(vMat_s, np.dot(rMat_c, gVec_c))
gHat_c = mutil.unitVector(np.dot(rMat_c.T, gVec_s))
# !!!: check that this operates on UNWARPED xy
match_omes, calc_omes = matchOmegas(meas_xyo,
hkls,
chi,
rMat_c,
bMat,
wavelength,
vInv=vInv_s,
beamVec=bVec,
etaVec=eVec,
omePeriod=omePeriod)
# append to omes dict
calc_omes_dict[det_key] = calc_omes
# TODO: try Numba implementations
rMat_s = xfcapi.makeOscillRotMatArray(chi, calc_omes)
calc_xy = xfcapi.gvecToDetectorXYArray(gHat_c.T,
rMat_d,
rMat_s,
rMat_c,
tVec_d,
tVec_s,
tVec_c,
beamVec=bVec)
# append to xy dict
calc_xy_dict[det_key] = calc_xy
pass
# stack results to concatenated arrays
calc_omes_all = np.hstack([calc_omes_dict[k] for k in det_keys_ordered])
tmp = []
for k in det_keys_ordered:
if calc_xy_dict[k] is not None:
tmp.append(calc_xy_dict[k])
calc_xy_all = np.vstack(tmp)
meas_xyo_all = np.vstack(meas_xyo_all)
npts = len(meas_xyo_all)
if np.any(np.isnan(calc_xy)):
raise RuntimeError("infeasible pFull: may want to scale" +
"back finite difference step size")
# return values
if simOnly:
# return simulated values
if return_value_flag in [None, 1]:
retval = np.hstack([calc_xy_all, calc_omes_all.reshape(npts, 1)])
else:
rd = dict.fromkeys(det_keys_ordered)
for det_key in det_keys_ordered:
rd[det_key] = {
'calc_xy': calc_xy_dict[det_key],
'calc_omes': calc_omes_dict[det_key]
}
retval = rd
else:
# return residual vector
# IDEA: try angles instead of xys?
diff_vecs_xy = calc_xy_all - meas_xyo_all[:, :2]
diff_ome = xf.angularDifference(calc_omes_all, meas_xyo_all[:, 2])
retval = np.hstack([diff_vecs_xy, diff_ome.reshape(npts, 1)]).flatten()
if return_value_flag == 1:
# return scalar sum of squared residuals
retval = sum(abs(retval))
elif return_value_flag == 2:
# return DOF-normalized chisq
# TODO: check this calculation
denom = 3 * npts - len(gFit) - 1.
if denom != 0:
nu_fac = 1. / denom
else:
nu_fac = 1.
retval = nu_fac * sum(retval**2)
return retval
