def objFuncSX(pFit, pFull, pFlag, dFunc, dFlag,
xyo_det, hkls_idx, bMat, vInv, wavelength,
bVec, eVec, omePeriod,
simOnly=False, returnScalarValue=returnScalarValue):
"""
"""
npts = len(xyo_det)
refineFlag = np.hstack([pFlag, dFlag])
# pFull[refineFlag] = pFit/scl[refineFlag]
pFull[refineFlag] = pFit
dParams = pFull[-len(dFlag):]
xy_unwarped = dFunc(xyo_det[:, :2], dParams)
# detector quantities
rMat_d = xf.makeDetectorRotMat(pFull[:3])
tVec_d = pFull[3:6].reshape(3, 1)
# sample quantities
chi = pFull[6]
tVec_s = pFull[7:10].reshape(3, 1)
# crystal quantities
rMat_c = xf.makeRotMatOfExpMap(pFull[10:13])
tVec_c = pFull[13:16].reshape(3, 1)
gVec_c = np.dot(bMat, hkls_idx)
vMat_s = mutil.vecMVToSymm(vInv) # stretch tensor comp matrix from MV notation in SAMPLE frame
gVec_s = np.dot(vMat_s, np.dot(rMat_c, gVec_c)) # reciprocal lattice vectors in SAMPLE frame
gHat_s = mutil.unitVector(gVec_s) # unit reciprocal lattice vectors in SAMPLE frame
gHat_c = np.dot(rMat_c.T, gHat_s) # unit reciprocal lattice vectors in CRYSTAL frame
match_omes, calc_omes = matchOmegas(xyo_det, hkls_idx, chi, rMat_c, bMat, wavelength,
vInv=vInv, beamVec=bVec, etaVec=eVec, omePeriod=omePeriod)
calc_xy = np.zeros((npts, 2))
for i in range(npts):
rMat_s = xfcapi.makeOscillRotMat([chi, calc_omes[i]])
calc_xy[i, :] = xfcapi.gvecToDetectorXY(gHat_c[:, i],
rMat_d, rMat_s, rMat_c,
tVec_d, tVec_s, tVec_c,
beamVec=bVec).flatten()
pass
if np.any(np.isnan(calc_xy)):
print "infeasible pFull: may want to scale back finite difference step size"
# 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 returnScalarValue:
retval = sum( retval )
return retval
def objFuncSX(pFit, pFull, pFlag, dFunc, dFlag,
xyo_det, hkls_idx, bMat, vInv, wavelength,
bVec, eVec, omePeriod,
simOnly=False, returnScalarValue=returnScalarValue):
"""
"""
npts = len(xyo_det)
refineFlag = np.hstack([pFlag, dFlag])
# pFull[refineFlag] = pFit/scl[refineFlag]
pFull[refineFlag] = pFit
dParams = pFull[-len(dFlag):]
xy_unwarped = dFunc(xyo_det[:, :2], dParams)
# detector quantities
rMat_d = xf.makeDetectorRotMat(pFull[:3])
tVec_d = pFull[3:6].reshape(3, 1)
# sample quantities
chi = pFull[6]
tVec_s = pFull[7:10].reshape(3, 1)
# crystal quantities
rMat_c = xf.makeRotMatOfExpMap(pFull[10:13])
tVec_c = pFull[13:16].reshape(3, 1)
gVec_c = np.dot(bMat, hkls_idx)
vMat_s = mutil.vecMVToSymm(vInv) # stretch tensor comp matrix from MV notation in SAMPLE frame
gVec_s = np.dot(vMat_s, np.dot(rMat_c, gVec_c)) # reciprocal lattice vectors in SAMPLE frame
gHat_s = mutil.unitVector(gVec_s) # unit reciprocal lattice vectors in SAMPLE frame
gHat_c = np.dot(rMat_c.T, gHat_s) # unit reciprocal lattice vectors in CRYSTAL frame
match_omes, calc_omes = matchOmegas(xyo_det, hkls_idx, chi, rMat_c, bMat, wavelength,
vInv=vInv, beamVec=bVec, etaVec=eVec, omePeriod=omePeriod)
calc_xy = np.zeros((npts, 2))
for i in range(npts):
rMat_s = xfcapi.makeOscillRotMat([chi, calc_omes[i]])
calc_xy[i, :] = xfcapi.gvecToDetectorXY(gHat_c[:, i],
rMat_d, rMat_s, rMat_c,
tVec_d, tVec_s, tVec_c,
beamVec=bVec).flatten()
pass
if np.any(np.isnan(calc_xy)):
print "infeasible pFull: may want to scale back finite difference step size"
# 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 returnScalarValue:
retval = sum( retval )
return retval
bVec_ref = xf.bVec_ref
rMat_d = xf.makeDetectorRotMat( ( 0.0011546340766314521,
-0.0040527538387122993,
-0.0026221336905160211 ) )
tVec_d = np.array( [ [ -1.44904 ],
[ -3.235616],
[-1050.74026 ] ] )
chi = -0.0011591608938627839
tVec_s = np.array([ [-0.15354144],
[ 0. ],
[-0.23294777] ] )
rMat_c = xf.makeRotMatOfExpMap(np.array( [ [ 0.66931818],
[-0.98578066],
[ 0.73593251] ] ) )
tVec_c = np.array( [ [ 0.07547626],
[ 0.08827523],
[-0.02131205] ] )
rMat_s = xf.makeOscillRotMat([chi, 0.])
# ######################################################################
# Calculate pixel coordinates
#
pvec = 204.8 * np.linspace(-1, 1, 512)
#pvec = 204.8 * np.linspace(-1, 1, 2048)
#pvec = 204.8 * np.linspace(-1, 1, 4096)
#pvec = 204.8 * np.linspace(-1, 1, 8192)
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) print "makeBinaryRotMat results match: ",np.linalg.norm(rMat1-rMat2)/np.linalg.norm(rMat1) < epsf bHat = np.array([0.0,0.0,-1.0]) eta = np.array([1.0,0.0,0.0]) rMat1 = xf.makeEtaFrameRotMat(bHat,eta) rMat2 = xfcapi.makeEtaFrameRotMat(bHat,eta) print "makeEtaFrameRotMat results match: ",np.linalg.norm(rMat1-rMat2)/np.linalg.norm(rMat1) < epsf angles = np.array([random.uniform(-np.pi,np.pi),random.uniform(-np.pi,np.pi),random.uniform(-np.pi,np.pi)])
from hexrd.xrd import transforms as xf
from hexrd.xrd import transforms_CAPI as xfcapi
# input parameters
bVec_ref = xf.bVec_ref
tilt_angles = (0.0011546340766314521, -0.0040527538387122993,
-0.0026221336905160211)
rMat_d = xf.makeDetectorRotMat(tilt_angles)
tVec_d = np.array([[-1.44904], [-3.235616], [-1050.74026]])
chi = -0.0011591608938627839
tVec_s = np.array([[-0.15354144], [0.], [-0.23294777]])
rMat_c = xf.makeRotMatOfExpMap(
np.array([[0.66931818], [-0.98578066], [0.73593251]]))
tVec_c = np.array([[0.07547626], [0.08827523], [-0.02131205]])
rMat_s = xf.makeOscillRotMat([chi, 0.])
# ######################################################################
# Calculate pixel coordinates
#
pvec = 204.8 * np.linspace(-1, 1, 2048)
dcrds = np.meshgrid(pvec, pvec)
XY = np.ascontiguousarray(
np.vstack([dcrds[0].flatten(), dcrds[1].flatten()]).T)
# Check the timings
start1 = time.clock() # time this
dangs1 = xf.detectorXYToGvec(XY,
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)
print "makeBinaryRotMat results match: ", np.linalg.norm(
rMat1 - rMat2) / np.linalg.norm(rMat1) < epsf
bHat = np.array([0.0, 0.0, -1.0])
eta = np.array([1.0, 0.0, 0.0])
rMat1 = xf.makeEtaFrameRotMat(bHat, eta)
rMat2 = xfcapi.makeEtaFrameRotMat(bHat, eta)
print "makeEtaFrameRotMat results match: ", np.linalg.norm(
def objFuncSX(pFit, pFull, pFlag, dFunc, dFlag,
xyo_det, hkls_idx, bMat, vInv,
bVec, eVec, omePeriod,
simOnly=False, return_value_flag=return_value_flag):
"""
"""
npts = len(xyo_det)
refineFlag = np.array(np.hstack([pFlag, dFlag]), dtype=bool)
print refineFlag
# pFull[refineFlag] = pFit/scl[refineFlag]
pFull[refineFlag] = pFit
if dFunc is not None:
dParams = pFull[-len(dFlag):]
xys = dFunc(xyo_det[:, :2], dParams)
else:
xys = xyo_det[:, :2]
# detector quantities
wavelength = pFull[0]
rMat_d = xf.makeDetectorRotMat(pFull[1:4])
tVec_d = pFull[4:7].reshape(3, 1)
# sample quantities
chi = pFull[7]
tVec_s = pFull[8:11].reshape(3, 1)
# crystal quantities
rMat_c = xf.makeRotMatOfExpMap(pFull[11:14])
tVec_c = pFull[14:17].reshape(3, 1)
# stretch tensor comp matrix from MV notation in SAMPLE frame
vMat_s = mutil.vecMVToSymm(vInv)
# 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_idx)
gVec_s = np.dot(vMat_s, np.dot(rMat_c, gVec_c))
gHat_c = mutil.unitVector(np.dot(rMat_c.T, gVec_s))
match_omes, calc_omes = matchOmegas(
xyo_det, hkls_idx, chi, rMat_c, bMat, wavelength,
vInv=vInv, beamVec=bVec, etaVec=eVec, omePeriod=omePeriod)
calc_xy = np.zeros((npts, 2))
for i in range(npts):
rMat_s = xfcapi.makeOscillRotMat([chi, calc_omes[i]])
calc_xy[i, :] = xfcapi.gvecToDetectorXY(gHat_c[:, i],
rMat_d, rMat_s, rMat_c,
tVec_d, tVec_s, tVec_c,
beamVec=bVec).flatten()
pass
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
retval = np.hstack([calc_xy, calc_omes.reshape(npts, 1)])
else:
# return residual vector
# IDEA: try angles instead of xys?
diff_vecs_xy = calc_xy - xys[:, :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:
# return scalar sum of squared residuals
retval = sum(abs(retval))
elif return_value_flag == 2:
# return DOF-normalized chisq
# TODO: check this calculation
denom = npts - len(pFit) - 1.
if denom != 0:
nu_fac = 1. / denom
else:
nu_fac = 1.
nu_fac = 1 / (npts - len(pFit) - 1.)
retval = nu_fac * sum(retval**2)
return retval
def objFuncSX(pFit,
pFull,
pFlag,
dFunc,
dFlag,
xyo_det,
hkls_idx,
bMat,
vInv,
bVec,
eVec,
omePeriod,
simOnly=False,
return_value_flag=return_value_flag):
"""
"""
npts = len(xyo_det)
refineFlag = np.array(np.hstack([pFlag, dFlag]), dtype=bool)
print refineFlag
# pFull[refineFlag] = pFit/scl[refineFlag]
pFull[refineFlag] = pFit
if dFunc is not None:
dParams = pFull[-len(dFlag):]
xys = dFunc(xyo_det[:, :2], dParams)
else:
xys = xyo_det[:, :2]
# detector quantities
wavelength = pFull[0]
rMat_d = xf.makeDetectorRotMat(pFull[1:4])
tVec_d = pFull[4:7].reshape(3, 1)
# sample quantities
chi = pFull[7]
tVec_s = pFull[8:11].reshape(3, 1)
# crystal quantities
rMat_c = xf.makeRotMatOfExpMap(pFull[11:14])
tVec_c = pFull[14:17].reshape(3, 1)
# stretch tensor comp matrix from MV notation in SAMPLE frame
vMat_s = mutil.vecMVToSymm(vInv)
# 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_idx)
gVec_s = np.dot(vMat_s, np.dot(rMat_c, gVec_c))
gHat_c = mutil.unitVector(np.dot(rMat_c.T, gVec_s))
match_omes, calc_omes = matchOmegas(xyo_det,
hkls_idx,
chi,
rMat_c,
bMat,
wavelength,
vInv=vInv,
beamVec=bVec,
etaVec=eVec,
omePeriod=omePeriod)
calc_xy = np.zeros((npts, 2))
for i in range(npts):
rMat_s = xfcapi.makeOscillRotMat([chi, calc_omes[i]])
calc_xy[i, :] = xfcapi.gvecToDetectorXY(gHat_c[:, i],
rMat_d,
rMat_s,
rMat_c,
tVec_d,
tVec_s,
tVec_c,
beamVec=bVec).flatten()
pass
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
retval = np.hstack([calc_xy, calc_omes.reshape(npts, 1)])
else:
# return residual vector
# IDEA: try angles instead of xys?
diff_vecs_xy = calc_xy - xys[:, :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:
# return scalar sum of squared residuals
retval = sum(abs(retval))
elif return_value_flag == 2:
# return DOF-normalized chisq
# TODO: check this calculation
denom = npts - len(pFit) - 1.
if denom != 0:
nu_fac = 1. / denom
else:
nu_fac = 1.
nu_fac = 1 / (npts - len(pFit) - 1.)
retval = nu_fac * sum(retval**2)
return retval
