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
def angs_of_xy(self, xy_det, *args):
"""
vstacked (tth, eta) pairs of cartesian coordinates
*) wrapper for transforms.detectorXYtoGvec
args: ome, chi
"""
xy_det = np.atleast_2d(
xy_det) # so len() gives right number even if one point
npts = len(xy_det)
if len(args) > 0:
ome = np.atleast_1d(args[0]).flatten()
assert len(ome) == npts, "ome must be the same length as xy_det!"
chi = args[1]
angles = np.zeros((npts, 2))
gVecs = np.zeros((npts, 3))
for i in range(npts):
rMat_s = xf.makeOscillRotMat([chi, ome[i]])
tmp = xf.detectorXYToGvec(xy_det,
self.rMat,
rMat_s,
self.tVec,
self.tVec_s,
self.tVec_c,
distortion=self.distortion,
beamVec=self.bVec,
etaVec=self.eVec)
angles[i, :] = tmp[0]
gVecs[i, :] = tmp[1]
pass
else:
angles, gVecs = xf.detectorXYToGvec(xy_det,
self.rMat,
self.rMat_s,
self.tVec,
self.tVec_s,
self.tVec_c,
distortion=self.distortion,
beamVec=self.bVec,
etaVec=self.eVec)
pass
# filter out NaNs
not_there = np.isnan(angles[:, 0])
tTh = angles[-not_there, 0]
eta = angles[-not_there, 1]
return tTh, eta
def angs_of_xy(self, xy_det, *args):
"""
vstacked (tth, eta) pairs of cartesian coordinates
*) wrapper for transforms.detectorXYtoGvec
args: ome, chi
"""
xy_det = np.atleast_2d(xy_det) # so len() gives right number even if one point
npts = len(xy_det)
if len(args) > 0:
ome = np.atleast_1d(args[0]).flatten()
assert len(ome) == npts, "ome must be the same length as xy_det!"
chi = args[1]
angles = np.zeros((npts, 2))
gVecs = np.zeros((npts, 3))
for i in range(npts):
rMat_s = xf.makeOscillRotMat([chi, ome[i]])
tmp = xf.detectorXYToGvec(xy_det,
self.rMat, rMat_s,
self.tVec, self.tVec_s, self.tVec_c,
distortion=self.distortion,
beamVec=self.bVec,
etaVec=self.eVec)
angles[i, :] = tmp[0]
gVecs[i, :] = tmp[1]
pass
else:
angles, gVecs = xf.detectorXYToGvec(xy_det,
self.rMat, self.rMat_s,
self.tVec, self.tVec_s, self.tVec_c,
distortion=self.distortion,
beamVec=self.bVec,
etaVec=self.eVec)
pass
# filter out NaNs
not_there = np.isnan(angles[:, 0])
tTh = angles[-not_there, 0]
eta = angles[-not_there, 1]
return tTh, eta
def objFuncFitGrain(gFit, gFull, gFlag,
detectorParams,
xyo_det, hkls_idx, bMat, wavelength,
bVec, eVec,
dFunc, dParams,
omePeriod,
simOnly=False, returnScalarValue=returnScalarValue):
"""
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)
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"
# 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 = mutil.rowNorm(
np.hstack([diff_vecs_xy,
diff_ome.reshape(npts, 1)
]) ).flatten()
if returnScalarValue:
retval = sum( retval )
return retval
def mockup_experiment():
# user options
# each grain is provided in the form of a quaternion.
# The following array contains the quaternions for the array. Note that the
# quaternions are in the columns, with the first row (row 0) being the real
# part w. We assume that we are dealing with unit quaternions
quats = np.array([[ 0.91836393, 0.90869942],
[ 0.33952917, 0.1834835 ],
[ 0.17216207, 0.10095837],
[ 0.10811041, 0.36111851]])
n_grains = quats.shape[-1] # last dimension provides the number of grains
phis = 2.*np.arccos(quats[0, :]) # phis are the angles for the quaternion
ns = mutil.unitVector(quats[1:, :]) # ns contains the rotation axis as an unit vector
exp_maps = np.array([phis[i]*ns[:, i] for i in range(n_grains)])
rMat_c = rot.rotMatOfQuat(quats)
cvec = np.arange(-25, 26)
X, Y, Z = np.meshgrid(cvec, cvec, cvec)
crd0 = 1e-3*np.vstack([X.flatten(), Y.flatten(), Z.flatten()]).T
crd1 = crd0 + np.r_[0.100, 0.100, 0]
crds = np.array([crd0, crd1])
# make grain parameters
grain_params = []
for i in range(n_grains):
for j in range(len(crd0)):
grain_params.append(
np.hstack([exp_maps[i, :], crds[i][j, :], xf.vInv_ref.flatten()])
)
# scan range and period
ome_period = (0, 2*np.pi)
ome_range = [ome_period,]
ome_step = np.radians(1.)
nframes = 0
for i in range(len(ome_range)):
del_ome = ome_range[i][1]-ome_range[i][0]
nframes += int((ome_range[i][1]-ome_range[i][0])/ome_step)
ome_edges = np.arange(nframes+1)*ome_step
# instrument
with open('./retiga.yml', 'r') as fildes:
instr_cfg = yaml.load(fildes)
tiltAngles = instr_cfg['detector']['transform']['tilt_angles']
tVec_d = np.array(instr_cfg['detector']['transform']['t_vec_d']).reshape(3,1)
chi = instr_cfg['oscillation_stage']['chi']
tVec_s = np.array(instr_cfg['oscillation_stage']['t_vec_s']).reshape(3,1)
rMat_d = xfcapi.makeDetectorRotMat(tiltAngles)
rMat_s = xfcapi.makeOscillRotMat([chi, 0.])
pixel_size = instr_cfg['detector']['pixels']['size']
nrows = instr_cfg['detector']['pixels']['rows']
ncols = instr_cfg['detector']['pixels']['columns']
col_ps = pixel_size[1]
row_ps = pixel_size[0]
row_dim = row_ps*nrows # in mm
col_dim = col_ps*ncols # in mm
panel_dims = [(-0.5*ncols*col_ps, -0.5*nrows*row_ps),
( 0.5*ncols*col_ps, 0.5*nrows*row_ps)]
x_col_edges = col_ps * (np.arange(ncols + 1) - 0.5*ncols)
y_row_edges = row_ps * (np.arange(nrows, -1, -1) - 0.5*nrows)
#x_col_edges = np.arange(panel_dims[0][0], panel_dims[1][0] + 0.5*col_ps, col_ps)
#y_row_edges = np.arange(panel_dims[0][1], panel_dims[1][1] + 0.5*row_ps, row_ps)
rx, ry = np.meshgrid(x_col_edges, y_row_edges)
gcrds = xfcapi.detectorXYToGvec(np.vstack([rx.flatten(), ry.flatten()]).T,
rMat_d, rMat_s,
tVec_d, tVec_s, np.zeros(3))
max_pixel_tth = np.amax(gcrds[0][0])
detector_params = np.hstack([tiltAngles, tVec_d.flatten(), chi,
tVec_s.flatten()])
distortion = None
# a different parametrization for the sensor (makes for faster quantization)
base = np.array([x_col_edges[0],
y_row_edges[0],
ome_edges[0]])
deltas = np.array([x_col_edges[1] - x_col_edges[0],
y_row_edges[1] - y_row_edges[0],
ome_edges[1] - ome_edges[0]])
inv_deltas = 1.0/deltas
clip_vals = np.array([ncols, nrows])
# dilation
max_diameter = np.sqrt(3)*0.005
row_dilation = np.ceil(0.5 * max_diameter/row_ps)
col_dilation = np.ceil(0.5 * max_diameter/col_ps)
# crystallography data
from hexrd import valunits
gold = material.Material('gold')
gold.sgnum = 225
gold.latticeParameters = [4.0782, ]
gold.hklMax = 200
gold.beamEnergy = valunits.valWUnit("wavelength", "ENERGY", 52, "keV")
gold.planeData.exclusions = None
gold.planeData.tThMax = max_pixel_tth #note this comes from info in the detector
ns = argparse.Namespace()
# grains related information
ns.n_grains = n_grains # this can be derived from other values...
ns.rMat_c = rMat_c # n_grains rotation matrices (one per grain)
ns.exp_maps = exp_maps # n_grains exp_maps -angle * rotation axis- (one per grain)
ns.plane_data = gold.planeData
ns.detector_params = detector_params
ns.pixel_size = pixel_size
ns.ome_range = ome_range
ns.ome_period = ome_period
ns.x_col_edges = x_col_edges
ns.y_row_edges = y_row_edges
ns.ome_edges = ome_edges
ns.ncols = ncols
ns.nrows = nrows
ns.nframes = nframes # used only in simulate...
ns.rMat_d = rMat_d
ns.tVec_d = tVec_d
ns.chi = chi # note this is used to compute S... why is it needed?
ns.tVec_s = tVec_s
# ns.rMat_s = rMat_s
# ns.tVec_s = tVec_s
ns.rMat_c = rMat_c
ns.row_dilation = row_dilation
ns.col_dilation = col_dilation
ns.distortion = distortion
ns.panel_dims = panel_dims # used only in simulate...
ns.base = base
ns.inv_deltas = inv_deltas
ns.clip_vals = clip_vals
return grain_params, ns
]])
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
def gen_trial_exp_data(grain_out_file,det_file,mat_file, x_ray_energy, mat_name, max_tth, comp_thresh, chi2_thresh, misorientation_bnd, \
misorientation_spacing,ome_range_deg, nframes, beam_stop_width):
print('Loading Grain Data.....')
#gen_grain_data
ff_data = np.loadtxt(grain_out_file)
#ff_data=np.atleast_2d(ff_data[2,:])
exp_maps = ff_data[:, 3:6]
t_vec_ds = ff_data[:, 6:9]
#
completeness = ff_data[:, 1]
chi2 = ff_data[:, 2]
n_grains = exp_maps.shape[0]
rMat_c = rot.rotMatOfExpMap(exp_maps.T)
cut = np.where(
np.logical_and(completeness > comp_thresh, chi2 < chi2_thresh))[0]
exp_maps = exp_maps[cut, :]
t_vec_ds = t_vec_ds[cut, :]
chi2 = chi2[cut]
# Add Misorientation
mis_amt = misorientation_bnd * np.pi / 180.
spacing = misorientation_spacing * np.pi / 180.
mis_steps = int(misorientation_bnd / misorientation_spacing)
ori_pts = np.arange(-mis_amt, (mis_amt + (spacing * 0.999)), spacing)
num_ori_grid_pts = ori_pts.shape[0]**3
num_oris = exp_maps.shape[0]
XsO, YsO, ZsO = np.meshgrid(ori_pts, ori_pts, ori_pts)
grid0 = np.vstack([XsO.flatten(), YsO.flatten(), ZsO.flatten()]).T
exp_maps_expanded = np.zeros([num_ori_grid_pts * num_oris, 3])
t_vec_ds_expanded = np.zeros([num_ori_grid_pts * num_oris, 3])
for ii in np.arange(num_oris):
pts_to_use = np.arange(num_ori_grid_pts) + ii * num_ori_grid_pts
exp_maps_expanded[pts_to_use, :] = grid0 + np.r_[exp_maps[ii, :]]
t_vec_ds_expanded[pts_to_use, :] = np.r_[t_vec_ds[ii, :]]
exp_maps = exp_maps_expanded
t_vec_ds = t_vec_ds_expanded
n_grains = exp_maps.shape[0]
rMat_c = rot.rotMatOfExpMap(exp_maps.T)
print('Loading Instrument Data.....')
instr_cfg = yaml.load(open(det_file, 'r'))
tiltAngles = instr_cfg['detector']['transform']['tilt_angles']
tVec_d = np.array(instr_cfg['detector']['transform']['t_vec_d']).reshape(
3, 1)
#tVec_d[0] = -0.05
chi = instr_cfg['oscillation_stage']['chi']
tVec_s = np.array(instr_cfg['oscillation_stage']['t_vec_s']).reshape(3, 1)
rMat_d = makeDetectorRotMat(tiltAngles)
rMat_s = makeOscillRotMat([chi, 0.])
pixel_size = instr_cfg['detector']['pixels']['size']
nrows = instr_cfg['detector']['pixels']['rows']
ncols = instr_cfg['detector']['pixels']['columns']
# row_dim = pixel_size[0]*nrows # in mm
# col_dim = pixel_size[1]*ncols # in mm
x_col_edges = pixel_size[1] * (np.arange(ncols + 1) - 0.5 * ncols)
y_row_edges = pixel_size[0] * (np.arange(nrows + 1) - 0.5 * nrows)[::-1]
panel_dims = [(-0.5 * ncols * pixel_size[1], -0.5 * nrows * pixel_size[0]),
(0.5 * ncols * pixel_size[1], 0.5 * nrows * pixel_size[0])]
# a bit overkill, but grab max two-theta from all pixel transforms
rx, ry = np.meshgrid(x_col_edges, y_row_edges)
gcrds = detectorXYToGvec(
np.vstack([rx.flatten(), ry.flatten()]).T, rMat_d, rMat_s, tVec_d,
tVec_s, np.zeros(3))
pixel_tth = gcrds[0][0]
detector_params = np.hstack(
[tiltAngles, tVec_d.flatten(), chi,
tVec_s.flatten()])
ome_period_deg = (ome_range_deg[0][0], (ome_range_deg[0][0] + 360.)
) #degrees
ome_step_deg = (ome_range_deg[0][1] -
ome_range_deg[0][0]) / nframes #degrees
ome_period = (ome_period_deg[0] * np.pi / 180.,
ome_period_deg[1] * np.pi / 180.)
ome_range = [(ome_range_deg[0][0] * np.pi / 180.,
ome_range_deg[0][1] * np.pi / 180.)]
ome_step = ome_step_deg * np.pi / 180.
ome_edges = np.arange(nframes +
1) * ome_step + ome_range[0][0] #fixed 2/26/17
base = np.array([x_col_edges[0], y_row_edges[0], ome_edges[0]])
deltas = np.array([
x_col_edges[1] - x_col_edges[0], y_row_edges[1] - y_row_edges[0],
ome_edges[1] - ome_edges[0]
])
inv_deltas = 1.0 / deltas
clip_vals = np.array([ncols, nrows])
print('Loading Material Data.....')
#Load Material Data
materials = cpl.load(open(mat_file, "rb"))
check = np.zeros(len(materials))
for ii in np.arange(len(materials)):
#print materials[ii].name
check[ii] = materials[ii].name == mat_name
mat_used = materials[np.where(check)[0][0]]
#niti_mart.beamEnergy = valunits.valWUnit("wavelength","ENERGY",61.332,"keV")
mat_used.beamEnergy = valunits.valWUnit("wavelength", "ENERGY",
x_ray_energy, "keV")
mat_used.planeData.exclusions = np.zeros(len(
mat_used.planeData.exclusions),
dtype=bool)
if max_tth > 0.:
mat_used.planeData.tThMax = np.amax(np.radians(max_tth))
else:
mat_used.planeData.tThMax = np.amax(pixel_tth)
pd = mat_used.planeData
print('Final Assembly.....')
experiment = argparse.Namespace()
# grains related information
experiment.n_grains = n_grains # this can be derived from other values...
experiment.rMat_c = rMat_c # n_grains rotation matrices (one per grain)
experiment.exp_maps = exp_maps # n_grains exp_maps -angle * rotation axis- (one per grain)
experiment.plane_data = pd
experiment.detector_params = detector_params
experiment.pixel_size = pixel_size
experiment.ome_range = ome_range
experiment.ome_period = ome_period
experiment.x_col_edges = x_col_edges
experiment.y_row_edges = y_row_edges
experiment.ome_edges = ome_edges
experiment.ncols = ncols
experiment.nrows = nrows
experiment.nframes = nframes # used only in simulate...
experiment.rMat_d = rMat_d
experiment.tVec_d = np.atleast_2d(detector_params[3:6]).T
experiment.chi = detector_params[
6] # note this is used to compute S... why is it needed?
experiment.tVec_s = np.atleast_2d(detector_params[7:]).T
experiment.rMat_c = rMat_c
experiment.distortion = None
experiment.panel_dims = panel_dims # used only in simulate...
experiment.base = base
experiment.inv_deltas = inv_deltas
experiment.clip_vals = clip_vals
experiment.bsw = beam_stop_width
if mis_steps == 0:
nf_to_ff_id_map = cut
else:
nf_to_ff_id_map = np.tile(cut, 27 * mis_steps)
return experiment, nf_to_ff_id_map
def pull_spots(self,
plane_data,
grain_params,
imgser_dict,
tth_tol=0.25,
eta_tol=1.,
ome_tol=1.,
npdiv=2,
threshold=10,
eta_ranges=[
(-np.pi, np.pi),
],
ome_period=(-np.pi, np.pi),
dirname='results',
filename=None,
output_format='text',
save_spot_list=False,
quiet=True,
check_only=False,
interp='nearest'):
"""
Exctract reflection info from a rotation series encoded as an
OmegaImageseries object
"""
# grain parameters
rMat_c = makeRotMatOfExpMap(grain_params[:3])
tVec_c = grain_params[3:6]
# grab omega ranges from first imageseries
#
# WARNING: all imageseries AND all wedges within are assumed to have
# the same omega values; put in a check that they are all the same???
oims0 = imgser_dict[imgser_dict.keys()[0]]
ome_ranges = [
np.radians([i['ostart'], i['ostop']])
for i in oims0.omegawedges.wedges
]
# delta omega in DEGREES grabbed from first imageseries in the dict
delta_ome = oims0.omega[0, 1] - oims0.omega[0, 0]
# make omega grid for frame expansion around reference frame
# in DEGREES
ndiv_ome, ome_del = make_tolerance_grid(
delta_ome,
ome_tol,
1,
adjust_window=True,
)
# generate structuring element for connected component labeling
if ndiv_ome == 1:
label_struct = ndimage.generate_binary_structure(2, 2)
else:
label_struct = ndimage.generate_binary_structure(3, 3)
# simulate rotation series
sim_results = self.simulate_rotation_series(plane_data, [
grain_params,
],
eta_ranges=eta_ranges,
ome_ranges=ome_ranges,
ome_period=ome_period)
# patch vertex generator (global for instrument)
tol_vec = 0.5 * np.radians([
-tth_tol, -eta_tol, -tth_tol, eta_tol, tth_tol, eta_tol, tth_tol,
-eta_tol
])
# prepare output if requested
if filename is not None and output_format.lower() == 'hdf5':
this_filename = os.path.join(dirname, filename)
writer = io.GrainDataWriter_h5(os.path.join(dirname, filename),
self.write_config(), grain_params)
# =====================================================================
# LOOP OVER PANELS
# =====================================================================
iRefl = 0
compl = []
output = dict.fromkeys(self.detectors)
for detector_id in self.detectors:
# initialize text-based output writer
if filename is not None and output_format.lower() == 'text':
output_dir = os.path.join(dirname, detector_id)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
this_filename = os.path.join(output_dir, filename)
writer = io.PatchDataWriter(this_filename)
# grab panel
panel = self.detectors[detector_id]
instr_cfg = panel.config_dict(self.chi, self.tvec)
native_area = panel.pixel_area # pixel ref area
# pull out the OmegaImageSeries for this panel from input dict
ome_imgser = imgser_dict[detector_id]
# extract simulation results
sim_results_p = sim_results[detector_id]
hkl_ids = sim_results_p[0][0]
hkls_p = sim_results_p[1][0]
ang_centers = sim_results_p[2][0]
xy_centers = sim_results_p[3][0]
ang_pixel_size = sim_results_p[4][0]
# now verify that full patch falls on detector...
# ???: strictly necessary?
#
# patch vertex array from sim
nangs = len(ang_centers)
patch_vertices = (np.tile(ang_centers[:, :2],
(1, 4)) + np.tile(tol_vec,
(nangs, 1))).reshape(
4 * nangs, 2)
ome_dupl = np.tile(ang_centers[:, 2],
(4, 1)).T.reshape(len(patch_vertices), 1)
# find vertices that all fall on the panel
det_xy, _ = xrdutil._project_on_detector_plane(
np.hstack([patch_vertices, ome_dupl]), panel.rmat, rMat_c,
self.chi, panel.tvec, tVec_c, self.tvec, panel.distortion)
_, on_panel = panel.clip_to_panel(det_xy, buffer_edges=True)
# all vertices must be on...
patch_is_on = np.all(on_panel.reshape(nangs, 4), axis=1)
patch_xys = det_xy.reshape(nangs, 4, 2)[patch_is_on]
# re-filter...
hkl_ids = hkl_ids[patch_is_on]
hkls_p = hkls_p[patch_is_on, :]
ang_centers = ang_centers[patch_is_on, :]
xy_centers = xy_centers[patch_is_on, :]
ang_pixel_size = ang_pixel_size[patch_is_on, :]
# TODO: add polygon testing right here!
# done <JVB 06/21/16>
if check_only:
patch_output = []
for i_pt, angs in enumerate(ang_centers):
# the evaluation omegas;
# expand about the central value using tol vector
ome_eval = np.degrees(angs[2]) + ome_del
# ...vectorize the omega_to_frame function to avoid loop?
frame_indices = [
ome_imgser.omega_to_frame(ome)[0] for ome in ome_eval
]
if -1 in frame_indices:
if not quiet:
msg = """
window for (%d%d%d) falls outside omega range
""" % tuple(hkls_p[i_pt, :])
print(msg)
continue
else:
these_vertices = patch_xys[i_pt]
ijs = panel.cartToPixel(these_vertices)
ii, jj = polygon(ijs[:, 0], ijs[:, 1])
contains_signal = False
for i_frame in frame_indices:
contains_signal = contains_signal or np.any(
ome_imgser[i_frame][ii, jj] > threshold)
compl.append(contains_signal)
patch_output.append((ii, jj, frame_indices))
else:
# make the tth,eta patches for interpolation
patches = xrdutil.make_reflection_patches(
instr_cfg,
ang_centers[:, :2],
ang_pixel_size,
omega=ang_centers[:, 2],
tth_tol=tth_tol,
eta_tol=eta_tol,
rMat_c=rMat_c,
tVec_c=tVec_c,
distortion=panel.distortion,
npdiv=npdiv,
quiet=True,
beamVec=self.beam_vector)
# GRAND LOOP over reflections for this panel
patch_output = []
for i_pt, patch in enumerate(patches):
# strip relevant objects out of current patch
vtx_angs, vtx_xy, conn, areas, xy_eval, ijs = patch
prows, pcols = areas.shape
nrm_fac = areas / float(native_area)
nrm_fac = nrm_fac / np.min(nrm_fac)
# grab hkl info
hkl = hkls_p[i_pt, :]
hkl_id = hkl_ids[i_pt]
# edge arrays
tth_edges = vtx_angs[0][0, :]
delta_tth = tth_edges[1] - tth_edges[0]
eta_edges = vtx_angs[1][:, 0]
delta_eta = eta_edges[1] - eta_edges[0]
# need to reshape eval pts for interpolation
xy_eval = np.vstack(
[xy_eval[0].flatten(), xy_eval[1].flatten()]).T
# the evaluation omegas;
# expand about the central value using tol vector
ome_eval = np.degrees(ang_centers[i_pt, 2]) + ome_del
# ???: vectorize the omega_to_frame function to avoid loop?
frame_indices = [
ome_imgser.omega_to_frame(ome)[0] for ome in ome_eval
]
if -1 in frame_indices:
if not quiet:
msg = """
window for (%d%d%d) falls outside omega range
""" % tuple(hkl)
print(msg)
continue
else:
# initialize spot data parameters
# !!! maybe change these to nan to not f**k up writer
peak_id = -999
sum_int = None
max_int = None
meas_angs = None
meas_xy = None
# quick check for intensity
contains_signal = False
patch_data_raw = []
for i_frame in frame_indices:
tmp = ome_imgser[i_frame][ijs[0], ijs[1]]
contains_signal = contains_signal or np.any(
tmp > threshold)
patch_data_raw.append(tmp)
pass
patch_data_raw = np.stack(patch_data_raw, axis=0)
compl.append(contains_signal)
if contains_signal:
# initialize patch data array for intensities
if interp.lower() == 'bilinear':
patch_data = np.zeros(
(len(frame_indices), prows, pcols))
for i, i_frame in enumerate(frame_indices):
patch_data[i] = \
panel.interpolate_bilinear(
xy_eval,
ome_imgser[i_frame],
pad_with_nans=False
).reshape(prows, pcols) # * nrm_fac
elif interp.lower() == 'nearest':
patch_data = patch_data_raw # * nrm_fac
else:
msg = "interpolation option " + \
"'%s' not understood"
raise (RuntimeError, msg % interp)
# now have interpolated patch data...
labels, num_peaks = ndimage.label(
patch_data > threshold, structure=label_struct)
slabels = np.arange(1, num_peaks + 1)
if num_peaks > 0:
peak_id = iRefl
coms = np.array(
ndimage.center_of_mass(patch_data,
labels=labels,
index=slabels))
if num_peaks > 1:
center = np.r_[patch_data.shape] * 0.5
center_t = np.tile(center, (num_peaks, 1))
com_diff = coms - center_t
closest_peak_idx = np.argmin(
np.sum(com_diff**2, axis=1))
else:
closest_peak_idx = 0
pass # end multipeak conditional
coms = coms[closest_peak_idx]
# meas_omes = \
# ome_edges[0] + (0.5 + coms[0])*delta_ome
meas_omes = \
ome_eval[0] + coms[0]*delta_ome
meas_angs = np.hstack([
tth_edges[0] + (0.5 + coms[2]) * delta_tth,
eta_edges[0] + (0.5 + coms[1]) * delta_eta,
mapAngle(np.radians(meas_omes), ome_period)
])
# intensities
# - summed is 'integrated' over interpolated
# data
# - max is max of raw input data
sum_int = np.sum(patch_data[
labels == slabels[closest_peak_idx]])
max_int = np.max(patch_data_raw[
labels == slabels[closest_peak_idx]])
# ???: Should this only use labeled pixels?
# Those are segmented from interpolated data,
# not raw; likely ok in most cases.
# need MEASURED xy coords
gvec_c = anglesToGVec(meas_angs,
chi=self.chi,
rMat_c=rMat_c,
bHat_l=self.beam_vector)
rMat_s = makeOscillRotMat(
[self.chi, meas_angs[2]])
meas_xy = gvecToDetectorXY(
gvec_c,
panel.rmat,
rMat_s,
rMat_c,
panel.tvec,
self.tvec,
tVec_c,
beamVec=self.beam_vector)
if panel.distortion is not None:
# FIXME: distortion handling
meas_xy = panel.distortion[0](
np.atleast_2d(meas_xy),
panel.distortion[1],
invert=True).flatten()
pass
# FIXME: why is this suddenly necessary???
meas_xy = meas_xy.squeeze()
pass # end num_peaks > 0
else:
patch_data = patch_data_raw
pass # end contains_signal
# write output
if filename is not None:
if output_format.lower() == 'text':
writer.dump_patch(peak_id, hkl_id, hkl,
sum_int, max_int,
ang_centers[i_pt], meas_angs,
xy_centers[i_pt], meas_xy)
elif output_format.lower() == 'hdf5':
xyc_arr = xy_eval.reshape(prows, pcols,
2).transpose(
2, 0, 1)
writer.dump_patch(
detector_id, iRefl, peak_id, hkl_id,
hkl, tth_edges, eta_edges,
np.radians(ome_eval), xyc_arr, ijs,
frame_indices, patch_data,
ang_centers[i_pt], xy_centers[i_pt],
meas_angs, meas_xy)
pass # end conditional on write output
pass # end conditional on check only
patch_output.append([
peak_id,
hkl_id,
hkl,
sum_int,
max_int,
ang_centers[i_pt],
meas_angs,
meas_xy,
])
iRefl += 1
pass # end patch conditional
pass # end patch loop
output[detector_id] = patch_output
if filename is not None and output_format.lower() == 'text':
writer.close()
pass # end detector loop
if filename is not None and output_format.lower() == 'hdf5':
writer.close()
return compl, output
def gen_trial_exp_data(grain_out_file,det_file,mat_file, x_ray_energy, mat_name, max_tth, comp_thresh, chi2_thresh, misorientation_bnd, \
misorientation_spacing,ome_range_deg, nframes, beam_stop_width):
print('Loading Grain Data.....')
#gen_grain_data
ff_data=np.loadtxt(grain_out_file)
#ff_data=np.atleast_2d(ff_data[2,:])
exp_maps=ff_data[:,3:6]
t_vec_ds=ff_data[:,6:9]
#
completeness=ff_data[:,1]
chi2=ff_data[:,2]
n_grains=exp_maps.shape[0]
rMat_c = rot.rotMatOfExpMap(exp_maps.T)
cut=np.where(np.logical_and(completeness>comp_thresh,chi2<chi2_thresh))[0]
exp_maps=exp_maps[cut,:]
t_vec_ds=t_vec_ds[cut,:]
chi2=chi2[cut]
# Add Misorientation
mis_amt=misorientation_bnd*np.pi/180.
spacing=misorientation_spacing*np.pi/180.
ori_pts = np.arange(-mis_amt, (mis_amt+(spacing*0.999)),spacing)
num_ori_grid_pts=ori_pts.shape[0]**3
num_oris=exp_maps.shape[0]
XsO, YsO, ZsO = np.meshgrid(ori_pts, ori_pts, ori_pts)
grid0 = np.vstack([XsO.flatten(), YsO.flatten(), ZsO.flatten()]).T
exp_maps_expanded=np.zeros([num_ori_grid_pts*num_oris,3])
t_vec_ds_expanded=np.zeros([num_ori_grid_pts*num_oris,3])
for ii in np.arange(num_oris):
pts_to_use=np.arange(num_ori_grid_pts)+ii*num_ori_grid_pts
exp_maps_expanded[pts_to_use,:]=grid0+np.r_[exp_maps[ii,:] ]
t_vec_ds_expanded[pts_to_use,:]=np.r_[t_vec_ds[ii,:] ]
exp_maps=exp_maps_expanded
t_vec_ds=t_vec_ds_expanded
n_grains=exp_maps.shape[0]
rMat_c = rot.rotMatOfExpMap(exp_maps.T)
print('Loading Instrument Data.....')
instr_cfg = yaml.load(open(det_file, 'r'))
tiltAngles = instr_cfg['detector']['transform']['tilt_angles']
tVec_d = np.array(instr_cfg['detector']['transform']['t_vec_d']).reshape(3, 1)
#tVec_d[0] = -0.05
chi = instr_cfg['oscillation_stage']['chi']
tVec_s = np.array(instr_cfg['oscillation_stage']['t_vec_s']).reshape(3, 1)
rMat_d = makeDetectorRotMat(tiltAngles)
rMat_s = makeOscillRotMat([chi, 0.])
pixel_size = instr_cfg['detector']['pixels']['size']
nrows = instr_cfg['detector']['pixels']['rows']
ncols = instr_cfg['detector']['pixels']['columns']
# row_dim = pixel_size[0]*nrows # in mm
# col_dim = pixel_size[1]*ncols # in mm
x_col_edges = pixel_size[1]*(np.arange(ncols+1) - 0.5*ncols)
y_row_edges = pixel_size[0]*(np.arange(nrows+1) - 0.5*nrows)[::-1]
panel_dims = [(-0.5*ncols*pixel_size[1],
-0.5*nrows*pixel_size[0]),
( 0.5*ncols*pixel_size[1],
0.5*nrows*pixel_size[0])]
# a bit overkill, but grab max two-theta from all pixel transforms
rx, ry = np.meshgrid(x_col_edges, y_row_edges)
gcrds = detectorXYToGvec(np.vstack([rx.flatten(), ry.flatten()]).T,
rMat_d, rMat_s,
tVec_d, tVec_s, np.zeros(3))
pixel_tth = gcrds[0][0]
detector_params = np.hstack([tiltAngles, tVec_d.flatten(), chi, tVec_s.flatten()])
ome_period_deg=(ome_range_deg[0][0], (ome_range_deg[0][0]+360.)) #degrees
ome_step_deg=(ome_range_deg[0][1]-ome_range_deg[0][0])/nframes #degrees
ome_period = (ome_period_deg[0]*np.pi/180.,ome_period_deg[1]*np.pi/180.)
ome_range = [(ome_range_deg[0][0]*np.pi/180.,ome_range_deg[0][1]*np.pi/180.)]
ome_step = ome_step_deg*np.pi/180.
ome_edges = np.arange(nframes+1)*ome_step+ome_range[0][0]#fixed 2/26/17
base = np.array([x_col_edges[0],
y_row_edges[0],
ome_edges[0]])
deltas = np.array([x_col_edges[1] - x_col_edges[0],
y_row_edges[1] - y_row_edges[0],
ome_edges[1] - ome_edges[0]])
inv_deltas = 1.0/deltas
clip_vals = np.array([ncols, nrows])
print('Loading Material Data.....')
#Load Material Data
materials=cpl.load(open( mat_file, "rb" ))
check=np.zeros(len(materials))
for ii in np.arange(len(materials)):
#print materials[ii].name
check[ii]=materials[ii].name==mat_name
mat_used=materials[np.where(check)[0][0]]
#niti_mart.beamEnergy = valunits.valWUnit("wavelength","ENERGY",61.332,"keV")
mat_used.beamEnergy = valunits.valWUnit("wavelength","ENERGY",x_ray_energy,"keV")
mat_used.planeData.exclusions = np.zeros(len(mat_used.planeData.exclusions), dtype=bool)
if max_tth>0.:
mat_used.planeData.tThMax = np.amax(np.radians(max_tth))
else:
mat_used.planeData.tThMax = np.amax(pixel_tth)
pd=mat_used.planeData
print('Final Assembly.....')
experiment = argparse.Namespace()
# grains related information
experiment.n_grains = n_grains # this can be derived from other values...
experiment.rMat_c = rMat_c # n_grains rotation matrices (one per grain)
experiment.exp_maps = exp_maps # n_grains exp_maps -angle * rotation axis- (one per grain)
experiment.plane_data = pd
experiment.detector_params = detector_params
experiment.pixel_size = pixel_size
experiment.ome_range = ome_range
experiment.ome_period = ome_period
experiment.x_col_edges = x_col_edges
experiment.y_row_edges = y_row_edges
experiment.ome_edges = ome_edges
experiment.ncols = ncols
experiment.nrows = nrows
experiment.nframes = nframes# used only in simulate...
experiment.rMat_d = rMat_d
experiment.tVec_d = np.atleast_2d(detector_params[3:6]).T
experiment.chi = detector_params[6] # note this is used to compute S... why is it needed?
experiment.tVec_s = np.atleast_2d(detector_params[7:]).T
experiment.rMat_c = rMat_c
experiment.distortion = None
experiment.panel_dims = panel_dims # used only in simulate...
experiment.base = base
experiment.inv_deltas = inv_deltas
experiment.clip_vals = clip_vals
experiment.bsw = beam_stop_width
nf_to_ff_id_map=cut
return experiment, nf_to_ff_id_map
pass
ome_edges = np.arange(nframes + 1) * ome_step
#==============================================================================
# %% INSTRUMENT
#==============================================================================
# load config
instr_cfg = yaml.load(open('./retiga.yml', 'r'))
tiltAngles = instr_cfg['detector']['transform']['tilt_angles']
tVec_d = np.array(instr_cfg['detector']['transform']['t_vec_d']).reshape(3, 1)
chi = instr_cfg['oscillation_stage']['chi']
tVec_s = np.array(instr_cfg['oscillation_stage']['t_vec_s']).reshape(3, 1)
rMat_d = makeDetectorRotMat(tiltAngles)
rMat_s = makeOscillRotMat([chi, 0.])
pixel_size = instr_cfg['detector']['pixels']['size']
nrows = instr_cfg['detector']['pixels']['rows']
ncols = instr_cfg['detector']['pixels']['columns']
row_dim = pixel_size[0] * nrows # in mm
col_dim = pixel_size[1] * ncols # in mm
x_col_edges = pixel_size[1] * (np.arange(ncols + 1) - 0.5 * ncols)
y_row_edges = pixel_size[0] * (np.arange(nrows + 1) - 0.5 * nrows)[::-1]
panel_dims = [(-0.5 * ncols * pixel_size[1], -0.5 * nrows * pixel_size[0]),
(0.5 * ncols * pixel_size[1], 0.5 * nrows * pixel_size[0])]
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 make_reflection_patches(
instr_cfg,
tth_eta,
ang_pixel_size,
omega=None,
tth_tol=0.2,
eta_tol=1.0,
rMat_c=np.eye(3),
tVec_c=np.c_[0.0, 0.0, 0.0].T,
distortion=distortion,
npdiv=1,
quiet=False,
compute_areas_func=compute_areas,
):
"""
prototype function for making angular patches on a detector
panel_dims are [(xmin, ymin), (xmax, ymax)] in mm
pixel_pitch is [row_size, column_size] in mm
DISTORTION HANDING IS STILL A KLUDGE
patches are:
delta tth
d ------------- ... -------------
e | x | x | x | ... | x | x | x |
l ------------- ... -------------
t .
a .
.
e ------------- ... -------------
t | x | x | x | ... | x | x | x |
a ------------- ... -------------
"""
npts = len(tth_eta)
# detector frame
rMat_d = xfcapi.makeDetectorRotMat(instr_cfg["detector"]["transform"]["tilt_angles"])
tVec_d = np.r_[instr_cfg["detector"]["transform"]["t_vec_d"]]
pixel_size = instr_cfg["detector"]["pixels"]["size"]
frame_nrows = instr_cfg["detector"]["pixels"]["rows"]
frame_ncols = instr_cfg["detector"]["pixels"]["columns"]
panel_dims = (
-0.5 * np.r_[frame_ncols * pixel_size[1], frame_nrows * pixel_size[0]],
0.5 * np.r_[frame_ncols * pixel_size[1], frame_nrows * pixel_size[0]],
)
row_edges = np.arange(frame_nrows + 1)[::-1] * pixel_size[1] + panel_dims[0][1]
col_edges = np.arange(frame_ncols + 1) * pixel_size[0] + panel_dims[0][0]
# sample frame
chi = instr_cfg["oscillation_stage"]["chi"]
tVec_s = np.r_[instr_cfg["oscillation_stage"]["t_vec_s"]]
# data to loop
# ...WOULD IT BE CHEAPER TO CARRY ZEROS OR USE CONDITIONAL?
if omega is None:
full_angs = np.hstack([tth_eta, np.zeros((npts, 1))])
else:
full_angs = np.hstack([tth_eta, omega.reshape(npts, 1)])
patches = []
for angs, pix in zip(full_angs, ang_pixel_size):
# need to get angular pixel size
rMat_s = xfcapi.makeOscillRotMat([chi, angs[2]])
ndiv_tth = npdiv * np.ceil(tth_tol / np.degrees(pix[0]))
ndiv_eta = npdiv * np.ceil(eta_tol / np.degrees(pix[1]))
tth_del = np.arange(0, ndiv_tth + 1) * tth_tol / float(ndiv_tth) - 0.5 * tth_tol
eta_del = np.arange(0, ndiv_eta + 1) * eta_tol / float(ndiv_eta) - 0.5 * eta_tol
# store dimensions for convenience
# * etas and tths are bin vertices, ome is already centers
sdims = [len(eta_del) - 1, len(tth_del) - 1]
# meshgrid args are (cols, rows), a.k.a (fast, slow)
m_tth, m_eta = np.meshgrid(tth_del, eta_del)
npts_patch = m_tth.size
# calculate the patch XY coords from the (tth, eta) angles
# * will CHEAT and ignore the small perturbation the different
# omega angle values causes and simply use the central value
gVec_angs_vtx = np.tile(angs, (npts_patch, 1)) + np.radians(
np.vstack([m_tth.flatten(), m_eta.flatten(), np.zeros(npts_patch)]).T
)
# will need this later
rMat_s = xfcapi.makeOscillRotMat([chi, angs[2]])
# FOR ANGULAR MESH
conn = gutil.cellConnectivity(sdims[0], sdims[1], origin="ll")
gVec_c = xf.anglesToGVec(gVec_angs_vtx, xf.bVec_ref, xf.eta_ref, rMat_s=rMat_s, rMat_c=rMat_c)
xy_eval_vtx = xfcapi.gvecToDetectorXY(gVec_c.T, rMat_d, rMat_s, rMat_c, tVec_d, tVec_s, tVec_c)
if distortion is not None and len(distortion) == 2:
xy_eval_vtx = distortion[0](xy_eval_vtx, distortion[1], invert=True)
pass
areas = compute_areas_func(xy_eval_vtx, conn)
# EVALUATION POINTS
# * for lack of a better option will use centroids
tth_eta_cen = gutil.cellCentroids(np.atleast_2d(gVec_angs_vtx[:, :2]), conn)
gVec_angs = np.hstack([tth_eta_cen, np.tile(angs[2], (len(tth_eta_cen), 1))])
gVec_c = xf.anglesToGVec(gVec_angs, xf.bVec_ref, xf.eta_ref, rMat_s=rMat_s, rMat_c=rMat_c)
xy_eval = xfcapi.gvecToDetectorXY(gVec_c.T, rMat_d, rMat_s, rMat_c, tVec_d, tVec_s, tVec_c)
if distortion is not None and len(distortion) == 2:
xy_eval = distortion[0](xy_eval, distortion[1], invert=True)
pass
row_indices = gutil.cellIndices(row_edges, xy_eval[:, 1])
col_indices = gutil.cellIndices(col_edges, xy_eval[:, 0])
patches.append(
(
(gVec_angs_vtx[:, 0].reshape(m_tth.shape), gVec_angs_vtx[:, 1].reshape(m_tth.shape)),
(xy_eval_vtx[:, 0].reshape(m_tth.shape), xy_eval_vtx[:, 1].reshape(m_tth.shape)),
conn,
areas.reshape(sdims[0], sdims[1]),
(row_indices.reshape(sdims[0], sdims[1]), col_indices.reshape(sdims[0], sdims[1])),
)
)
pass
return patches
def objFuncFitGrain(gFit,
gFull,
gFlag,
detectorParams,
xyo_det,
hkls_idx,
bMat,
wavelength,
bVec,
eVec,
dFunc,
dParams,
omePeriod,
simOnly=False,
returnScalarValue=returnScalarValue):
"""
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)
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"
# 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
pass
ome_edges = np.arange(nframes+1)*ome_step
#==============================================================================
# %% INSTRUMENT
#==============================================================================
# load config
instr_cfg = yaml.load(open('./retiga.yml', 'r'))
tiltAngles = instr_cfg['detector']['transform']['tilt_angles']
tVec_d = np.array(instr_cfg['detector']['transform']['t_vec_d']).reshape(3, 1)
chi = instr_cfg['oscillation_stage']['chi']
tVec_s = np.array(instr_cfg['oscillation_stage']['t_vec_s']).reshape(3, 1)
rMat_d = makeDetectorRotMat(tiltAngles)
rMat_s = makeOscillRotMat([chi, 0.])
pixel_size = instr_cfg['detector']['pixels']['size']
nrows = instr_cfg['detector']['pixels']['rows']
ncols = instr_cfg['detector']['pixels']['columns']
row_dim = pixel_size[0]*nrows # in mm
col_dim = pixel_size[1]*ncols # in mm
x_col_edges = pixel_size[1]*(np.arange(ncols+1) - 0.5*ncols)
y_row_edges = pixel_size[0]*(np.arange(nrows+1) - 0.5*nrows)[::-1]
panel_dims = [(-0.5*ncols*pixel_size[1],
-0.5*nrows*pixel_size[0]),
( 0.5*ncols*pixel_size[1],
def mockup_experiment():
# user options
# each grain is provided in the form of a quaternion.
# The following array contains the quaternions for the array. Note that the
# quaternions are in the columns, with the first row (row 0) being the real
# part w. We assume that we are dealing with unit quaternions
quats = np.array([[0.91836393, 0.90869942], [0.33952917, 0.1834835],
[0.17216207, 0.10095837], [0.10811041, 0.36111851]])
n_grains = quats.shape[-1] # last dimension provides the number of grains
phis = 2. * np.arccos(
quats[0, :]) # phis are the angles for the quaternion
ns = mutil.unitVector(
quats[1:, :]) # ns contains the rotation axis as an unit vector
exp_maps = np.array([phis[i] * ns[:, i] for i in range(n_grains)])
rMat_c = rot.rotMatOfQuat(quats)
cvec = np.arange(-25, 26)
X, Y, Z = np.meshgrid(cvec, cvec, cvec)
crd0 = 1e-3 * np.vstack([X.flatten(), Y.flatten(), Z.flatten()]).T
crd1 = crd0 + np.r_[0.100, 0.100, 0]
crds = np.array([crd0, crd1])
# make grain parameters
grain_params = []
for i in range(n_grains):
for j in range(len(crd0)):
grain_params.append(
np.hstack(
[exp_maps[i, :], crds[i][j, :],
xf.vInv_ref.flatten()]))
# scan range and period
ome_period = (0, 2 * np.pi)
ome_range = [
ome_period,
]
ome_step = np.radians(1.)
nframes = 0
for i in range(len(ome_range)):
del_ome = ome_range[i][1] - ome_range[i][0]
nframes += int((ome_range[i][1] - ome_range[i][0]) / ome_step)
ome_edges = np.arange(nframes + 1) * ome_step
# instrument
with open('./retiga.yml', 'r') as fildes:
instr_cfg = yaml.load(fildes)
tiltAngles = instr_cfg['detector']['transform']['tilt_angles']
tVec_d = np.array(instr_cfg['detector']['transform']['t_vec_d']).reshape(
3, 1)
chi = instr_cfg['oscillation_stage']['chi']
tVec_s = np.array(instr_cfg['oscillation_stage']['t_vec_s']).reshape(3, 1)
rMat_d = xfcapi.makeDetectorRotMat(tiltAngles)
rMat_s = xfcapi.makeOscillRotMat([chi, 0.])
pixel_size = instr_cfg['detector']['pixels']['size']
nrows = instr_cfg['detector']['pixels']['rows']
ncols = instr_cfg['detector']['pixels']['columns']
col_ps = pixel_size[1]
row_ps = pixel_size[0]
row_dim = row_ps * nrows # in mm
col_dim = col_ps * ncols # in mm
panel_dims = [(-0.5 * ncols * col_ps, -0.5 * nrows * row_ps),
(0.5 * ncols * col_ps, 0.5 * nrows * row_ps)]
x_col_edges = col_ps * (np.arange(ncols + 1) - 0.5 * ncols)
y_row_edges = row_ps * (np.arange(nrows, -1, -1) - 0.5 * nrows)
#x_col_edges = np.arange(panel_dims[0][0], panel_dims[1][0] + 0.5*col_ps, col_ps)
#y_row_edges = np.arange(panel_dims[0][1], panel_dims[1][1] + 0.5*row_ps, row_ps)
rx, ry = np.meshgrid(x_col_edges, y_row_edges)
gcrds = xfcapi.detectorXYToGvec(
np.vstack([rx.flatten(), ry.flatten()]).T, rMat_d, rMat_s, tVec_d,
tVec_s, np.zeros(3))
max_pixel_tth = np.amax(gcrds[0][0])
detector_params = np.hstack(
[tiltAngles, tVec_d.flatten(), chi,
tVec_s.flatten()])
distortion = None
# a different parametrization for the sensor (makes for faster quantization)
base = np.array([x_col_edges[0], y_row_edges[0], ome_edges[0]])
deltas = np.array([
x_col_edges[1] - x_col_edges[0], y_row_edges[1] - y_row_edges[0],
ome_edges[1] - ome_edges[0]
])
inv_deltas = 1.0 / deltas
clip_vals = np.array([ncols, nrows])
# dilation
max_diameter = np.sqrt(3) * 0.005
row_dilation = np.ceil(0.5 * max_diameter / row_ps)
col_dilation = np.ceil(0.5 * max_diameter / col_ps)
# crystallography data
from hexrd import valunits
gold = material.Material('gold')
gold.sgnum = 225
gold.latticeParameters = [
4.0782,
]
gold.hklMax = 200
gold.beamEnergy = valunits.valWUnit("wavelength", "ENERGY", 52, "keV")
gold.planeData.exclusions = None
gold.planeData.tThMax = max_pixel_tth #note this comes from info in the detector
ns = argparse.Namespace()
# grains related information
ns.n_grains = n_grains # this can be derived from other values...
ns.rMat_c = rMat_c # n_grains rotation matrices (one per grain)
ns.exp_maps = exp_maps # n_grains exp_maps -angle * rotation axis- (one per grain)
ns.plane_data = gold.planeData
ns.detector_params = detector_params
ns.pixel_size = pixel_size
ns.ome_range = ome_range
ns.ome_period = ome_period
ns.x_col_edges = x_col_edges
ns.y_row_edges = y_row_edges
ns.ome_edges = ome_edges
ns.ncols = ncols
ns.nrows = nrows
ns.nframes = nframes # used only in simulate...
ns.rMat_d = rMat_d
ns.tVec_d = tVec_d
ns.chi = chi # note this is used to compute S... why is it needed?
ns.tVec_s = tVec_s
# ns.rMat_s = rMat_s
# ns.tVec_s = tVec_s
ns.rMat_c = rMat_c
ns.row_dilation = row_dilation
ns.col_dilation = col_dilation
ns.distortion = distortion
ns.panel_dims = panel_dims # used only in simulate...
ns.base = base
ns.inv_deltas = inv_deltas
ns.clip_vals = clip_vals
return grain_params, ns
epsf = 2.2e-16 vec = np.array([[random.uniform(-np.pi,np.pi),random.uniform(-np.pi,np.pi),random.uniform(-np.pi,np.pi)]]) 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)
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 make_reflection_patches(instr_cfg,
tth_eta,
ang_pixel_size,
omega=None,
tth_tol=0.2,
eta_tol=1.0,
rMat_c=np.eye(3),
tVec_c=np.c_[0., 0., 0.].T,
distortion=distortion,
npdiv=1,
quiet=False,
compute_areas_func=compute_areas):
"""
prototype function for making angular patches on a detector
panel_dims are [(xmin, ymin), (xmax, ymax)] in mm
pixel_pitch is [row_size, column_size] in mm
DISTORTION HANDING IS STILL A KLUDGE
patches are:
delta tth
d ------------- ... -------------
e | x | x | x | ... | x | x | x |
l ------------- ... -------------
t .
a .
.
e ------------- ... -------------
t | x | x | x | ... | x | x | x |
a ------------- ... -------------
"""
npts = len(tth_eta)
# detector frame
rMat_d = xfcapi.makeDetectorRotMat(
instr_cfg['detector']['transform']['tilt_angles'])
tVec_d = np.r_[instr_cfg['detector']['transform']['t_vec_d']]
pixel_size = instr_cfg['detector']['pixels']['size']
frame_nrows = instr_cfg['detector']['pixels']['rows']
frame_ncols = instr_cfg['detector']['pixels']['columns']
panel_dims = (
-0.5 * np.r_[frame_ncols * pixel_size[1], frame_nrows * pixel_size[0]],
0.5 * np.r_[frame_ncols * pixel_size[1], frame_nrows * pixel_size[0]])
row_edges = np.arange(frame_nrows +
1)[::-1] * pixel_size[1] + panel_dims[0][1]
col_edges = np.arange(frame_ncols + 1) * pixel_size[0] + panel_dims[0][0]
# sample frame
chi = instr_cfg['oscillation_stage']['chi']
tVec_s = np.r_[instr_cfg['oscillation_stage']['t_vec_s']]
# data to loop
# ...WOULD IT BE CHEAPER TO CARRY ZEROS OR USE CONDITIONAL?
if omega is None:
full_angs = np.hstack([tth_eta, np.zeros((npts, 1))])
else:
full_angs = np.hstack([tth_eta, omega.reshape(npts, 1)])
patches = []
for angs, pix in zip(full_angs, ang_pixel_size):
# need to get angular pixel size
rMat_s = xfcapi.makeOscillRotMat([chi, angs[2]])
ndiv_tth = npdiv * np.ceil(tth_tol / np.degrees(pix[0]))
ndiv_eta = npdiv * np.ceil(eta_tol / np.degrees(pix[1]))
tth_del = np.arange(
0, ndiv_tth + 1) * tth_tol / float(ndiv_tth) - 0.5 * tth_tol
eta_del = np.arange(
0, ndiv_eta + 1) * eta_tol / float(ndiv_eta) - 0.5 * eta_tol
# store dimensions for convenience
# * etas and tths are bin vertices, ome is already centers
sdims = [len(eta_del) - 1, len(tth_del) - 1]
# meshgrid args are (cols, rows), a.k.a (fast, slow)
m_tth, m_eta = np.meshgrid(tth_del, eta_del)
npts_patch = m_tth.size
# calculate the patch XY coords from the (tth, eta) angles
# * will CHEAT and ignore the small perturbation the different
# omega angle values causes and simply use the central value
gVec_angs_vtx = np.tile(angs, (npts_patch, 1)) \
+ np.radians(
np.vstack([m_tth.flatten(),
m_eta.flatten(),
np.zeros(npts_patch)
]).T
)
# will need this later
rMat_s = xfcapi.makeOscillRotMat([chi, angs[2]])
# FOR ANGULAR MESH
conn = gutil.cellConnectivity(sdims[0], sdims[1], origin='ll')
gVec_c = xf.anglesToGVec(gVec_angs_vtx,
xf.bVec_ref,
xf.eta_ref,
rMat_s=rMat_s,
rMat_c=rMat_c)
xy_eval_vtx = xfcapi.gvecToDetectorXY(gVec_c.T, rMat_d, rMat_s, rMat_c,
tVec_d, tVec_s, tVec_c)
if distortion is not None and len(distortion) == 2:
xy_eval_vtx = distortion[0](xy_eval_vtx,
distortion[1],
invert=True)
pass
areas = compute_areas_func(xy_eval_vtx, conn)
# EVALUATION POINTS
# * for lack of a better option will use centroids
tth_eta_cen = gutil.cellCentroids(np.atleast_2d(gVec_angs_vtx[:, :2]),
conn)
gVec_angs = np.hstack(
[tth_eta_cen, np.tile(angs[2], (len(tth_eta_cen), 1))])
gVec_c = xf.anglesToGVec(gVec_angs,
xf.bVec_ref,
xf.eta_ref,
rMat_s=rMat_s,
rMat_c=rMat_c)
xy_eval = xfcapi.gvecToDetectorXY(gVec_c.T, rMat_d, rMat_s, rMat_c,
tVec_d, tVec_s, tVec_c)
if distortion is not None and len(distortion) == 2:
xy_eval = distortion[0](xy_eval, distortion[1], invert=True)
pass
row_indices = gutil.cellIndices(row_edges, xy_eval[:, 1])
col_indices = gutil.cellIndices(col_edges, xy_eval[:, 0])
patches.append(
((gVec_angs_vtx[:, 0].reshape(m_tth.shape),
gVec_angs_vtx[:, 1].reshape(m_tth.shape)),
(xy_eval_vtx[:, 0].reshape(m_tth.shape),
xy_eval_vtx[:, 1].reshape(m_tth.shape)), conn,
areas.reshape(sdims[0],
sdims[1]), (row_indices.reshape(sdims[0], sdims[1]),
col_indices.reshape(sdims[0],
sdims[1]))))
pass
return patches
def pull_spots(self, plane_data, grain_params,
imgser_dict,
tth_tol=0.25, eta_tol=1., ome_tol=1.,
npdiv=2, threshold=10,
eta_ranges=[(-np.pi, np.pi), ],
ome_period=(-np.pi, np.pi),
dirname='results', filename=None, output_format='text',
save_spot_list=False,
quiet=True, check_only=False,
interp='nearest'):
"""
Exctract reflection info from a rotation series encoded as an
OmegaImageseries object
"""
# grain parameters
rMat_c = makeRotMatOfExpMap(grain_params[:3])
tVec_c = grain_params[3:6]
# grab omega ranges from first imageseries
#
# WARNING: all imageseries AND all wedges within are assumed to have
# the same omega values; put in a check that they are all the same???
oims0 = imgser_dict[imgser_dict.keys()[0]]
ome_ranges = [np.radians([i['ostart'], i['ostop']])
for i in oims0.omegawedges.wedges]
# delta omega in DEGREES grabbed from first imageseries in the dict
delta_ome = oims0.omega[0, 1] - oims0.omega[0, 0]
# make omega grid for frame expansion around reference frame
# in DEGREES
ndiv_ome, ome_del = make_tolerance_grid(
delta_ome, ome_tol, 1, adjust_window=True,
)
# generate structuring element for connected component labeling
if ndiv_ome == 1:
label_struct = ndimage.generate_binary_structure(2, 2)
else:
label_struct = ndimage.generate_binary_structure(3, 3)
# simulate rotation series
sim_results = self.simulate_rotation_series(
plane_data, [grain_params, ],
eta_ranges=eta_ranges,
ome_ranges=ome_ranges,
ome_period=ome_period)
# patch vertex generator (global for instrument)
tol_vec = 0.5*np.radians(
[-tth_tol, -eta_tol,
-tth_tol, eta_tol,
tth_tol, eta_tol,
tth_tol, -eta_tol])
# prepare output if requested
if filename is not None and output_format.lower() == 'hdf5':
this_filename = os.path.join(dirname, filename)
writer = io.GrainDataWriter_h5(
os.path.join(dirname, filename),
self.write_config(), grain_params)
# =====================================================================
# LOOP OVER PANELS
# =====================================================================
iRefl = 0
compl = []
output = dict.fromkeys(self.detectors)
for detector_id in self.detectors:
# initialize text-based output writer
if filename is not None and output_format.lower() == 'text':
output_dir = os.path.join(
dirname, detector_id
)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
this_filename = os.path.join(
output_dir, filename
)
writer = io.PatchDataWriter(this_filename)
# grab panel
panel = self.detectors[detector_id]
instr_cfg = panel.config_dict(self.chi, self.tvec)
native_area = panel.pixel_area # pixel ref area
# pull out the OmegaImageSeries for this panel from input dict
ome_imgser = imgser_dict[detector_id]
# extract simulation results
sim_results_p = sim_results[detector_id]
hkl_ids = sim_results_p[0][0]
hkls_p = sim_results_p[1][0]
ang_centers = sim_results_p[2][0]
xy_centers = sim_results_p[3][0]
ang_pixel_size = sim_results_p[4][0]
# now verify that full patch falls on detector...
# ???: strictly necessary?
#
# patch vertex array from sim
nangs = len(ang_centers)
patch_vertices = (
np.tile(ang_centers[:, :2], (1, 4)) +
np.tile(tol_vec, (nangs, 1))
).reshape(4*nangs, 2)
ome_dupl = np.tile(
ang_centers[:, 2], (4, 1)
).T.reshape(len(patch_vertices), 1)
# find vertices that all fall on the panel
det_xy, _ = xrdutil._project_on_detector_plane(
np.hstack([patch_vertices, ome_dupl]),
panel.rmat, rMat_c, self.chi,
panel.tvec, tVec_c, self.tvec,
panel.distortion)
_, on_panel = panel.clip_to_panel(det_xy, buffer_edges=True)
# all vertices must be on...
patch_is_on = np.all(on_panel.reshape(nangs, 4), axis=1)
patch_xys = det_xy.reshape(nangs, 4, 2)[patch_is_on]
# re-filter...
hkl_ids = hkl_ids[patch_is_on]
hkls_p = hkls_p[patch_is_on, :]
ang_centers = ang_centers[patch_is_on, :]
xy_centers = xy_centers[patch_is_on, :]
ang_pixel_size = ang_pixel_size[patch_is_on, :]
# TODO: add polygon testing right here!
# done <JVB 06/21/16>
if check_only:
patch_output = []
for i_pt, angs in enumerate(ang_centers):
# the evaluation omegas;
# expand about the central value using tol vector
ome_eval = np.degrees(angs[2]) + ome_del
# ...vectorize the omega_to_frame function to avoid loop?
frame_indices = [
ome_imgser.omega_to_frame(ome)[0] for ome in ome_eval
]
if -1 in frame_indices:
if not quiet:
msg = """
window for (%d%d%d) falls outside omega range
""" % tuple(hkls_p[i_pt, :])
print(msg)
continue
else:
these_vertices = patch_xys[i_pt]
ijs = panel.cartToPixel(these_vertices)
ii, jj = polygon(ijs[:, 0], ijs[:, 1])
contains_signal = False
for i_frame in frame_indices:
contains_signal = contains_signal or np.any(
ome_imgser[i_frame][ii, jj] > threshold
)
compl.append(contains_signal)
patch_output.append((ii, jj, frame_indices))
else:
# make the tth,eta patches for interpolation
patches = xrdutil.make_reflection_patches(
instr_cfg, ang_centers[:, :2], ang_pixel_size,
omega=ang_centers[:, 2],
tth_tol=tth_tol, eta_tol=eta_tol,
rMat_c=rMat_c, tVec_c=tVec_c,
distortion=panel.distortion,
npdiv=npdiv, quiet=True,
beamVec=self.beam_vector)
# GRAND LOOP over reflections for this panel
patch_output = []
for i_pt, patch in enumerate(patches):
# strip relevant objects out of current patch
vtx_angs, vtx_xy, conn, areas, xy_eval, ijs = patch
prows, pcols = areas.shape
nrm_fac = areas/float(native_area)
nrm_fac = nrm_fac / np.min(nrm_fac)
# grab hkl info
hkl = hkls_p[i_pt, :]
hkl_id = hkl_ids[i_pt]
# edge arrays
tth_edges = vtx_angs[0][0, :]
delta_tth = tth_edges[1] - tth_edges[0]
eta_edges = vtx_angs[1][:, 0]
delta_eta = eta_edges[1] - eta_edges[0]
# need to reshape eval pts for interpolation
xy_eval = np.vstack([xy_eval[0].flatten(),
xy_eval[1].flatten()]).T
# the evaluation omegas;
# expand about the central value using tol vector
ome_eval = np.degrees(ang_centers[i_pt, 2]) + ome_del
# ???: vectorize the omega_to_frame function to avoid loop?
frame_indices = [
ome_imgser.omega_to_frame(ome)[0] for ome in ome_eval
]
if -1 in frame_indices:
if not quiet:
msg = """
window for (%d%d%d) falls outside omega range
""" % tuple(hkl)
print(msg)
continue
else:
# initialize spot data parameters
# !!! maybe change these to nan to not f**k up writer
peak_id = -999
sum_int = None
max_int = None
meas_angs = None
meas_xy = None
# quick check for intensity
contains_signal = False
patch_data_raw = []
for i_frame in frame_indices:
tmp = ome_imgser[i_frame][ijs[0], ijs[1]]
contains_signal = contains_signal or np.any(
tmp > threshold
)
patch_data_raw.append(tmp)
pass
patch_data_raw = np.stack(patch_data_raw, axis=0)
compl.append(contains_signal)
if contains_signal:
# initialize patch data array for intensities
if interp.lower() == 'bilinear':
patch_data = np.zeros(
(len(frame_indices), prows, pcols))
for i, i_frame in enumerate(frame_indices):
patch_data[i] = \
panel.interpolate_bilinear(
xy_eval,
ome_imgser[i_frame],
pad_with_nans=False
).reshape(prows, pcols) # * nrm_fac
elif interp.lower() == 'nearest':
patch_data = patch_data_raw # * nrm_fac
else:
msg = "interpolation option " + \
"'%s' not understood"
raise(RuntimeError, msg % interp)
# now have interpolated patch data...
labels, num_peaks = ndimage.label(
patch_data > threshold, structure=label_struct
)
slabels = np.arange(1, num_peaks + 1)
if num_peaks > 0:
peak_id = iRefl
coms = np.array(
ndimage.center_of_mass(
patch_data,
labels=labels,
index=slabels
)
)
if num_peaks > 1:
center = np.r_[patch_data.shape]*0.5
center_t = np.tile(center, (num_peaks, 1))
com_diff = coms - center_t
closest_peak_idx = np.argmin(
np.sum(com_diff**2, axis=1)
)
else:
closest_peak_idx = 0
pass # end multipeak conditional
coms = coms[closest_peak_idx]
# meas_omes = \
# ome_edges[0] + (0.5 + coms[0])*delta_ome
meas_omes = \
ome_eval[0] + coms[0]*delta_ome
meas_angs = np.hstack(
[tth_edges[0] + (0.5 + coms[2])*delta_tth,
eta_edges[0] + (0.5 + coms[1])*delta_eta,
mapAngle(
np.radians(meas_omes), ome_period
)
]
)
# intensities
# - summed is 'integrated' over interpolated
# data
# - max is max of raw input data
sum_int = np.sum(
patch_data[
labels == slabels[closest_peak_idx]
]
)
max_int = np.max(
patch_data_raw[
labels == slabels[closest_peak_idx]
]
)
# ???: Should this only use labeled pixels?
# Those are segmented from interpolated data,
# not raw; likely ok in most cases.
# need MEASURED xy coords
gvec_c = anglesToGVec(
meas_angs,
chi=self.chi,
rMat_c=rMat_c,
bHat_l=self.beam_vector)
rMat_s = makeOscillRotMat(
[self.chi, meas_angs[2]]
)
meas_xy = gvecToDetectorXY(
gvec_c,
panel.rmat, rMat_s, rMat_c,
panel.tvec, self.tvec, tVec_c,
beamVec=self.beam_vector)
if panel.distortion is not None:
# FIXME: distortion handling
meas_xy = panel.distortion[0](
np.atleast_2d(meas_xy),
panel.distortion[1],
invert=True).flatten()
pass
# FIXME: why is this suddenly necessary???
meas_xy = meas_xy.squeeze()
pass # end num_peaks > 0
else:
patch_data = patch_data_raw
pass # end contains_signal
# write output
if filename is not None:
if output_format.lower() == 'text':
writer.dump_patch(
peak_id, hkl_id, hkl, sum_int, max_int,
ang_centers[i_pt], meas_angs,
xy_centers[i_pt], meas_xy)
elif output_format.lower() == 'hdf5':
xyc_arr = xy_eval.reshape(
prows, pcols, 2
).transpose(2, 0, 1)
writer.dump_patch(
detector_id, iRefl, peak_id, hkl_id, hkl,
tth_edges, eta_edges, np.radians(ome_eval),
xyc_arr, ijs, frame_indices, patch_data,
ang_centers[i_pt], xy_centers[i_pt],
meas_angs, meas_xy)
pass # end conditional on write output
pass # end conditional on check only
patch_output.append([
peak_id, hkl_id, hkl, sum_int, max_int,
ang_centers[i_pt], meas_angs, meas_xy,
])
iRefl += 1
pass # end patch conditional
pass # end patch loop
output[detector_id] = patch_output
if filename is not None and output_format.lower() == 'text':
writer.close()
pass # end detector loop
if filename is not None and output_format.lower() == 'hdf5':
writer.close()
return compl, output