def objFunc(x, grainList, scl):
"""
"""
x = x / scl # remove scaling
xc = x[0] # beam x-center
zTilt = x[1] # zTilt --> inclination of oscill. axis on detector
chiTilt = x[2] # zTilt --> inclination of oscill. axis on detector
for i in range(len(grainList)):
grainList[i].detectorGeom.xc = xc
grainList[i].detectorGeom.zTilt = zTilt
grainList[i].detectorGeom.chiTilt = chiTilt
# need a fresh detector object to hand to spots
# use first grain by default (any will do)
tmpDG = grainList[0].detectorGeom.makeNew()
tmpDG.pVec = None
# reset the detector used by all spots
# each grain currently carried th
# ...PRIME CANDIDATE FOR OPTIMIZATION/CLEANUP/GENERALIZATION...
# ...perhaps loop over only the spots used by the grains in grainList?...
# grainList[0].spots.resetDetectorGeom(tmpDG)
# strip out quantities to hand off to the fit objective fuction to get residual contribution
resd = []
for i in range(len(grainList)):
spotIDs = grainList[i].grainSpots['iRefl']
spotIDs = spotIDs[spotIDs >= 0]
for j in range(len(spotIDs)):
spot = grainList[i].spots._Spots__spots[spotIDs[j]]
spot.setDetectorGeom(tmpDG, clobber=True)
angCOM = spot.angCOM()
grainList[i].spots._Spots__spotAngCoords[spotIDs[j]] = angCOM
grainList[i].spots._Spots__spotXYOCoords[
spotIDs[j]] = grainList[i].spots.detectorGeom.angToXYO(*angCOM)
pass
# refit grains to new detector -- mainly for fixing pVecs
grainList[i].updateGVecs()
grainList[i].fit(display=False)
angAxs = ROT.angleAxisOfRotMat(grainList[i].rMat)
biotT = matrixutil.symmToVecMV(grainList[i].vMat - numpy.eye(3))
pVec = grainList[i].detectorGeom.pVec
x = numpy.vstack(
[angAxs[0] * angAxs[1],
biotT.reshape(6, 1),
pVec.reshape(3, 1)])
resd.append(grainList[i]._fitF_objFunc(x))
pass
resd = numpy.hstack(resd).flatten()
return sum(resd**2)
def objFunc(x, grainList, scl):
"""
"""
x = x / scl # remove scaling
xc = x[0] # beam x-center
zTilt = x[1] # zTilt --> inclination of oscill. axis on detector
chiTilt = x[2] # zTilt --> inclination of oscill. axis on detector
for i in range(len(grainList)):
grainList[i].detectorGeom.xc = xc
grainList[i].detectorGeom.zTilt = zTilt
grainList[i].detectorGeom.chiTilt = chiTilt
# need a fresh detector object to hand to spots
# use first grain by default (any will do)
tmpDG = grainList[0].detectorGeom.makeNew()
tmpDG.pVec = None
# reset the detector used by all spots
# each grain currently carried th
# ...PRIME CANDIDATE FOR OPTIMIZATION/CLEANUP/GENERALIZATION...
# ...perhaps loop over only the spots used by the grains in grainList?...
# grainList[0].spots.resetDetectorGeom(tmpDG)
# strip out quantities to hand off to the fit objective fuction to get residual contribution
resd = []
for i in range(len(grainList)):
spotIDs = grainList[i].grainSpots['iRefl']
spotIDs = spotIDs[spotIDs >= 0]
for j in range(len(spotIDs)):
spot = grainList[i].spots._Spots__spots[spotIDs[j]]
spot.setDetectorGeom(tmpDG, clobber=True)
angCOM = spot.angCOM()
grainList[i].spots._Spots__spotAngCoords[spotIDs[j]] = angCOM
grainList[i].spots._Spots__spotXYOCoords[spotIDs[j]] = grainList[i].spots.detectorGeom.angToXYO( *angCOM )
pass
# refit grains to new detector -- mainly for fixing pVecs
grainList[i].updateGVecs()
grainList[i].fit(display=False)
angAxs = ROT.angleAxisOfRotMat(grainList[i].rMat)
biotT = matrixutil.symmToVecMV(grainList[i].vMat - numpy.eye(3))
pVec = grainList[i].detectorGeom.pVec
x = numpy.vstack([angAxs[0]*angAxs[1], biotT.reshape(6, 1), pVec.reshape(3, 1)])
resd.append(grainList[i]._fitF_objFunc(x))
pass
resd = numpy.hstack(resd).flatten()
return sum(resd**2)
def dump_grain(self, grain_id, completeness, chisq, grain_params):
assert len(grain_params) == 12, \
"len(grain_params) must be 12, not %d" % len(grain_params)
# extract strain
emat = logm(np.linalg.inv(mutil.vecMVToSymm(grain_params[6:])))
evec = mutil.symmToVecMV(emat, scale=False)
res = [int(grain_id), completeness, chisq] \
+ grain_params.tolist() \
+ evec.tolist()
output_str = self._delim.join([
self._delim.join(['{:<12d}', '{:<12f}',
'{:<12e}']).format(*res[:3]),
self._delim.join(np.tile('{:<23.16e}',
len(res) - 3)).format(*res[3:])
])
print(output_str, file=self.fid)
return output_str
def dump_grain(self, grain_id, completeness, chisq,
grain_params):
assert len(grain_params) == 12, \
"len(grain_params) must be 12, not %d" % len(grain_params)
# extract strain
emat = logm(np.linalg.inv(mutil.vecMVToSymm(grain_params[6:])))
evec = mutil.symmToVecMV(emat, scale=False)
res = [int(grain_id), completeness, chisq] \
+ grain_params.tolist() \
+ evec.tolist()
output_str = self._delim.join(
[self._delim.join(
['{:<12d}', '{:<12f}', '{:<12e}']
).format(*res[:3]),
self._delim.join(
np.tile('{:<23.16e}', len(res) - 3)
).format(*res[3:])]
)
print(output_str, file=self.fid)
return output_str
def inverse_stretch(self):
m = np.array(self.stretch_matrix).reshape(3, 3)
return matrixutil.symmToVecMV(np.linalg.inv(m), scale=True)
def compare_grain_fits(fit_grain_params,
ref_grain_params,
mtol=1.e-4,
ctol=1.e-3,
vtol=1.e-4):
"""
Executes comparison between reference and fit grain parameters for ff-HEDM
for the same initial parameters.
Parameters
----------
fit_grain_params : array_like, (n, 12)
The fit grain parameters to be tested.
ref_grain_params : array_like, (n, 12)
The reference grain parameters (see Notes below).
Returns
-------
bool
True is successful comparison
Notes
-----
The fitgrains action currently returns
grain_id, completeness, chisq, grain_params.
We will have to assume that the grain_ids are in the *same order* as the
reference, which can be enforces by running the comparison using the
reference orientation list.
"""
fit_grain_params = np.atleast_2d(fit_grain_params)
ref_grain_params = np.atleast_2d(ref_grain_params)
cresult = False
ii = 0
for fg, rg in zip(fit_grain_params, ref_grain_params):
# test_orientation
quats = rot.quatOfExpMap(np.vstack([fg[:3], rg[:3]]).T)
ang, mis = rot.misorientation(quats[:, 0].reshape(4, 1),
quats[:, 1].reshape(4, 1))
if ang <= mtol:
cresult = True
else:
print("orientations for grain %d do not agree." % ii)
return cresult
# test position
if np.linalg.norm(fg[3:6] - rg[3:6]) > ctol:
print("centroidal coordinates for grain %d do not agree." % ii)
return False
# test strain
vmat_fit = mutil.symmToVecMV(np.linalg.inv(mutil.vecMVToSymm(fg[6:])),
scale=False)
vmat_ref = mutil.symmToVecMV(np.linalg.inv(mutil.vecMVToSymm(rg[6:])),
scale=False)
if np.linalg.norm(vmat_fit - vmat_ref, ord=1) > vtol:
print("stretch components for grain %d do not agree." % ii)
return False
# index grain id
ii += 1
return cresult