def discretefiber_reduced(params_in):
"""
input parameters are [hkl_id, com_ome, com_eta]
"""
bMat = paramMP['bMat']
chi = paramMP['chi']
csym = paramMP['csym']
fiber_ndiv = paramMP['fiber_ndiv']
hkl = params_in[:3].reshape(3, 1)
gVec_s = xfcapi.anglesToGVec(
np.atleast_2d(params_in[3:]),
chi=chi,
).T
tmp = mutil.uniqueVectors(
rot.discreteFiber(
hkl,
gVec_s,
B=bMat,
ndiv=fiber_ndiv,
invert=False,
csym=csym
)[0]
)
return tmp
def xy_of_angs(self, angs):
"""
Cartesion coordinates of vstacked (tth, eta) pairs
*) wrapper for transforms.anglesToGVec
"""
gVec_l = xf.anglesToGVec(angs, self.bVec, self.eVec,
rMat_s=None, rMat_c=None)
xy = xf.gvecToDetectorXY(gVec_l,
self.rMat, I3, I3,
self.tVec, self.tVec_s, self.tVec_c,
distortion=self.distortion,
beamVec=self.bVec,
etaVec=self.eVec)
return xy
def angles_to_cart(self, tth_eta):
"""
TODO: distortion
"""
rmat_s = rmat_c = ct.identity_3x3
tvec_s = tvec_c = ct.zeros_3
angs = np.hstack([tth_eta, np.zeros((len(tth_eta), 1))])
xy_det = gvecToDetectorXY(
anglesToGVec(angs, bHat_l=self.bvec, eHat_l=self.evec),
self.rmat, rmat_s, rmat_c,
self.tvec, tvec_s, tvec_c,
beamVec=self.bvec)
return xy_det
def angles_to_cart(self, tth_eta):
"""
TODO: distortion
"""
rmat_s = rmat_c = ct.identity_3x3
tvec_s = tvec_c = ct.zeros_3
angs = np.hstack([tth_eta, np.zeros((len(tth_eta), 1))])
xy_det = gvecToDetectorXY(anglesToGVec(angs,
bHat_l=self.bvec,
eHat_l=self.evec),
self.rmat,
rmat_s,
rmat_c,
self.tvec,
tvec_s,
tvec_c,
beamVec=self.bvec)
return xy_det
def xy_of_angs(self, angs):
"""
Cartesion coordinates of vstacked (tth, eta) pairs
*) wrapper for transforms.anglesToGVec
"""
gVec_l = xf.anglesToGVec(angs,
self.bVec,
self.eVec,
rMat_s=None,
rMat_c=None)
xy = xf.gvecToDetectorXY(gVec_l,
self.rMat,
I3,
I3,
self.tVec,
self.tVec_s,
self.tVec_c,
distortion=self.distortion,
beamVec=self.bVec,
etaVec=self.eVec)
return xy
def make_powder_rings(self,
pd,
merge_hkls=False,
delta_tth=None,
delta_eta=10.,
eta_period=None,
rmat_s=ct.identity_3x3,
tvec_s=ct.zeros_3,
tvec_c=ct.zeros_3,
full_output=False):
"""
"""
# in case you want to give it tth angles directly
if hasattr(pd, '__len__'):
tth = np.array(pd).flatten()
if delta_tth is None:
raise RuntimeError(
"If supplying a 2theta list as first arg, " +
"must supply a delta_tth")
sector_vertices = np.tile(
0.5 * np.radians([
-delta_tth, -delta_eta, -delta_tth, delta_eta, delta_tth,
delta_eta, delta_tth, -delta_eta, 0.0, 0.0
]), (len(tth), 1))
else:
# Okay, we have a PlaneData object
pd = PlaneData.makeNew(pd) # make a copy to munge
if delta_tth is not None:
pd.tThWidth = np.radians(delta_tth)
else:
delta_tth = np.degrees(pd.tThWidth)
# conversions, meh...
del_eta = np.radians(delta_eta)
# do merging if asked
if merge_hkls:
_, tth_ranges = pd.getMergedRanges()
tth = np.array([0.5 * sum(i) for i in tth_ranges])
else:
tth_ranges = pd.getTThRanges()
tth = pd.getTTh()
tth_pm = tth_ranges - np.tile(tth, (2, 1)).T
sector_vertices = np.vstack([[
i[0], -del_eta, i[0], del_eta, i[1], del_eta, i[1], -del_eta,
0.0, 0.0
] for i in tth_pm])
# for generating rings
if eta_period is None:
eta_period = (-np.pi, np.pi)
neta = int(360. / float(delta_eta))
eta = mapAngle(
np.radians(delta_eta *
(np.linspace(0, neta - 1, num=neta) + 0.5)) +
eta_period[0], eta_period)
angs = [
np.vstack([i * np.ones(neta), eta,
np.zeros(neta)]) for i in tth
]
# need xy coords and pixel sizes
valid_ang = []
valid_xy = []
map_indices = []
npp = 5 # [ll, ul, ur, lr, center]
for i_ring in range(len(angs)):
# expand angles to patch vertices
these_angs = angs[i_ring].T
patch_vertices = (np.tile(these_angs[:, :2], (1, npp)) +
np.tile(sector_vertices[i_ring],
(neta, 1))).reshape(npp * neta, 2)
# duplicate ome array
ome_dupl = np.tile(these_angs[:, 2],
(npp, 1)).T.reshape(npp * neta, 1)
# find vertices that all fall on the panel
gVec_ring_l = anglesToGVec(np.hstack([patch_vertices, ome_dupl]),
bHat_l=self.bvec)
all_xy = gvecToDetectorXY(gVec_ring_l,
self.rmat,
rmat_s,
ct.identity_3x3,
self.tvec,
tvec_s,
tvec_c,
beamVec=self.bvec)
_, on_panel = self.clip_to_panel(all_xy)
# all vertices must be on...
patch_is_on = np.all(on_panel.reshape(neta, npp), axis=1)
patch_xys = all_xy.reshape(neta, 5, 2)[patch_is_on]
idx = np.where(patch_is_on)[0]
valid_ang.append(these_angs[patch_is_on, :2])
valid_xy.append(patch_xys[:, -1, :].squeeze())
map_indices.append(idx)
pass
# ??? is this option necessary?
if full_output:
return valid_ang, valid_xy, map_indices, eta
else:
return valid_ang, valid_xy
def generate_orientation_fibers(eta_ome, chi, threshold, seed_hkl_ids, fiber_ndiv, filt_stdev=0.8):
"""
From ome-eta maps and hklid spec, generate list of
quaternions from fibers
"""
# seed_hkl_ids must be consistent with this...
pd_hkl_ids = eta_ome.iHKLList[seed_hkl_ids]
# grab angular grid infor from maps
del_ome = eta_ome.omegas[1] - eta_ome.omegas[0]
del_eta = eta_ome.etas[1] - eta_ome.etas[0]
# labeling mask
structureNDI_label = ndimage.generate_binary_structure(2, 1)
# crystallography data from the pd object
pd = eta_ome.planeData
tTh = pd.getTTh()
bMat = pd.latVecOps['B']
csym = pd.getLaueGroup()
############################################
## Labeling of spots from seed hkls ##
############################################
qfib = []
labels = []
numSpots = []
coms = []
for i in seed_hkl_ids:
# First apply filter
this_map_f = -ndimage.filters.gaussian_laplace(eta_ome.dataStore[i], filt_stdev)
labels_t, numSpots_t = ndimage.label(
this_map_f > threshold,
structureNDI_label
)
coms_t = np.atleast_2d(
ndimage.center_of_mass(
this_map_f,
labels=labels_t,
index=np.arange(1, np.amax(labels_t)+1)
)
)
labels.append(labels_t)
numSpots.append(numSpots_t)
coms.append(coms_t)
pass
############################################
## Generate discrete fibers from labels ##
############################################
for i in range(len(pd_hkl_ids)):
ii = 0
qfib_tmp = np.empty((4, fiber_ndiv*numSpots[i]))
for ispot in range(numSpots[i]):
if not np.isnan(coms[i][ispot][0]):
ome_c = eta_ome.omeEdges[0] \
+ (0.5 + coms[i][ispot][0])*del_ome
eta_c = eta_ome.etaEdges[0] \
+ (0.5 + coms[i][ispot][1])*del_eta
#gVec_s = xrdutil.makeMeasuredScatteringVectors(
# tTh[pd_hkl_ids[i]], eta_c, ome_c
# )
gVec_s = xfcapi.anglesToGVec(
np.atleast_2d(
[tTh[pd_hkl_ids[i]], eta_c, ome_c]
),
chi=chi
).T
tmp = mutil.uniqueVectors(
rot.discreteFiber(
pd.hkls[:, pd_hkl_ids[i]].reshape(3, 1),
gVec_s,
B=bMat,
ndiv=fiber_ndiv,
invert=False,
csym=csym
)[0]
)
jj = ii + tmp.shape[1]
qfib_tmp[:, ii:jj] = tmp
ii += tmp.shape[1]
pass
pass
qfib.append(qfib_tmp[:, :ii])
pass
return np.hstack(qfib)
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
tth0 = np.degrees(tth_avg)
eta0 = 0.
tth_range = np.degrees(tth_hi - tth_lo)
eta_range = 360.
ntth = int(tth_range/tth_size)
neta = int(eta_range/eta_size)
tth_vec = tth_size*(np.arange(ntth) - 0.5*ntth - 1) + tth0
eta_vec = eta_size*(np.arange(neta) - 0.5*neta - 1) + eta0
angpts = np.meshgrid(eta_vec, tth_vec, indexing='ij')
gpts = xfc.anglesToGVec(
np.vstack([
np.radians(angpts[1].flatten()),
np.radians(angpts[0].flatten()),
np.zeros(neta*ntth)
]).T, bHat_l=d.bvec)
xypts = xfc.gvecToDetectorXY(
gpts,
d.rmat, np.eye(3), np.eye(3),
d.tvec, np.zeros(3), np.zeros(3),
beamVec=d.bvec)
img2 = d.interpolate_bilinear(xypts, average_frame).reshape(neta, ntth)
img3 = copy.deepcopy(img2)
borders = np.isnan(img2)
img2[borders] = 0.
img3[borders] = 0.
img3 += np.min(img3) + 1
def make_powder_rings(
self, pd, merge_hkls=False, delta_tth=None,
delta_eta=10., eta_period=None,
rmat_s=ct.identity_3x3, tvec_s=ct.zeros_3,
tvec_c=ct.zeros_3, full_output=False):
"""
"""
# in case you want to give it tth angles directly
if hasattr(pd, '__len__'):
tth = np.array(pd).flatten()
if delta_tth is None:
raise RuntimeError(
"If supplying a 2theta list as first arg, "
+ "must supply a delta_tth")
sector_vertices = np.tile(
0.5*np.radians([-delta_tth, -delta_eta,
-delta_tth, delta_eta,
delta_tth, delta_eta,
delta_tth, -delta_eta,
0.0, 0.0]), (len(tth), 1)
)
else:
# Okay, we have a PlaneData object
pd = PlaneData.makeNew(pd) # make a copy to munge
if delta_tth is not None:
pd.tThWidth = np.radians(delta_tth)
else:
delta_tth = np.degrees(pd.tThWidth)
# conversions, meh...
del_eta = np.radians(delta_eta)
# do merging if asked
if merge_hkls:
_, tth_ranges = pd.getMergedRanges()
tth = np.array([0.5*sum(i) for i in tth_ranges])
else:
tth_ranges = pd.getTThRanges()
tth = pd.getTTh()
tth_pm = tth_ranges - np.tile(tth, (2, 1)).T
sector_vertices = np.vstack(
[[i[0], -del_eta,
i[0], del_eta,
i[1], del_eta,
i[1], -del_eta,
0.0, 0.0]
for i in tth_pm])
# for generating rings
if eta_period is None:
eta_period = (-np.pi, np.pi)
neta = int(360./float(delta_eta))
eta = mapAngle(
np.radians(delta_eta*(np.linspace(0, neta - 1, num=neta) + 0.5)) +
eta_period[0], eta_period
)
angs = [np.vstack([i*np.ones(neta), eta, np.zeros(neta)]) for i in tth]
# need xy coords and pixel sizes
valid_ang = []
valid_xy = []
map_indices = []
npp = 5 # [ll, ul, ur, lr, center]
for i_ring in range(len(angs)):
# expand angles to patch vertices
these_angs = angs[i_ring].T
patch_vertices = (
np.tile(these_angs[:, :2], (1, npp))
+ np.tile(sector_vertices[i_ring], (neta, 1))
).reshape(npp*neta, 2)
# duplicate ome array
ome_dupl = np.tile(
these_angs[:, 2], (npp, 1)
).T.reshape(npp*neta, 1)
# find vertices that all fall on the panel
gVec_ring_l = anglesToGVec(
np.hstack([patch_vertices, ome_dupl]),
bHat_l=self.bvec)
all_xy = gvecToDetectorXY(
gVec_ring_l,
self.rmat, rmat_s, ct.identity_3x3,
self.tvec, tvec_s, tvec_c,
beamVec=self.bvec)
_, on_panel = self.clip_to_panel(all_xy)
# all vertices must be on...
patch_is_on = np.all(on_panel.reshape(neta, npp), axis=1)
patch_xys = all_xy.reshape(neta, 5, 2)[patch_is_on]
idx = np.where(patch_is_on)[0]
valid_ang.append(these_angs[patch_is_on, :2])
valid_xy.append(patch_xys[:, -1, :].squeeze())
map_indices.append(idx)
pass
# ??? is this option necessary?
if full_output:
return valid_ang, valid_xy, map_indices, eta
else:
return valid_ang, valid_xy
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
tth0 = np.degrees(tth_avg)
eta0 = 0.
tth_range = np.degrees(tth_hi - tth_lo)
eta_range = 360.
ntth = int(tth_range / tth_size)
neta = int(eta_range / eta_size)
tth_vec = tth_size * (np.arange(ntth) - 0.5 * ntth - 1) + tth0
eta_vec = eta_size * (np.arange(neta) - 0.5 * neta - 1) + eta0
angpts = np.meshgrid(eta_vec, tth_vec, indexing='ij')
gpts = xfc.anglesToGVec(np.vstack([
np.radians(angpts[1].flatten()),
np.radians(angpts[0].flatten()),
np.zeros(neta * ntth)
]).T,
bHat_l=d.bvec)
xypts = xfc.gvecToDetectorXY(gpts,
d.rmat,
np.eye(3),
np.eye(3),
d.tvec,
np.zeros(3),
np.zeros(3),
beamVec=d.bvec)
img2 = d.interpolate_bilinear(xypts, average_frame).reshape(neta, ntth)
img3 = copy.deepcopy(img2)
borders = np.isnan(img2)
