def integration_concept(self,
image_number=0,
cb_op_to_primitive=None,
verbose=False,
**kwargs):
self.image_number = image_number
NEAR = 10
pxlsz = self.pixel_size
self.get_predictions_accounting_for_centering(cb_op_to_primitive,
**kwargs)
FWMOSAICITY = self.inputai.getMosaicity()
DOMAIN_SZ_ANG = kwargs.get("domain_size_ang",
self.__dict__.get("actual", 0))
refineflag = {True: 0, False: 1}[kwargs.get("domain_size_ang", 0) == 0]
self.inputpd["symmetry"].show_summary(
prefix="EXCURSION%1d REPORT FWMOS= %6.4f DOMAIN= %6.1f " %
(refineflag, FWMOSAICITY, DOMAIN_SZ_ANG))
from annlib_ext import AnnAdaptor
self.cell = self.inputai.getOrientation().unit_cell()
query = flex.double()
for pred in self.predicted: # predicted spot coord in pixels
query.append(pred[0] / pxlsz)
query.append(pred[1] / pxlsz)
self.reserve_hkllist_for_signal_search = self.hkllist
reference = flex.double()
spots = self.get_observations_with_outlier_removal()
assert len(
spots) > NEAR # Can't do spot/pred matching with too few spots
for spot in spots:
reference.append(spot.ctr_mass_x())
reference.append(spot.ctr_mass_y())
IS_adapt = AnnAdaptor(data=reference, dim=2, k=NEAR)
IS_adapt.query(query)
print "Calculate correction vectors for %d observations & %d predictions" % (
len(spots), len(self.predicted))
indexed_pairs_provisional = []
correction_vectors_provisional = []
c_v_p_flex = flex.vec3_double()
idx_cutoff = float(min(self.mask_focus[image_number]))
if verbose:
print "idx_cutoff distance in pixels", idx_cutoff
if not self.horizons_phil.integration.enable_one_to_one_safeguard:
# legacy code, no safeguard against many-to-one predicted-to-observation mapping
for i in range(len(self.predicted)): # loop over predicteds
#for n in range(NEAR): # loop over near spotfinder spots
for n in range(1): # only consider the nearest spotfinder spot
Match = dict(spot=IS_adapt.nn[i * NEAR + n], pred=i)
if n == 0 and math.sqrt(
IS_adapt.distances[i * NEAR + n]) < idx_cutoff:
indexed_pairs_provisional.append(Match)
vector = matrix.col([
spots[Match["spot"]].ctr_mass_x() -
self.predicted[Match["pred"]][0] / pxlsz,
spots[Match["spot"]].ctr_mass_y() -
self.predicted[Match["pred"]][1] / pxlsz
])
correction_vectors_provisional.append(vector)
c_v_p_flex.append((vector[0], vector[1], 0.))
else:
one_to_one = {}
for i in range(len(self.predicted)): # loop over predicteds
annresultidx = i * NEAR
obsidx = IS_adapt.nn[annresultidx]
this_distancesq = IS_adapt.distances[annresultidx]
if obsidx not in one_to_one or \
this_distancesq < one_to_one[obsidx]["distancesq"]:
if math.sqrt(this_distancesq) < idx_cutoff:
one_to_one[obsidx] = dict(spot=obsidx,
pred=i,
distancesq=this_distancesq)
for key, value in one_to_one.items():
indexed_pairs_provisional.append(value)
vector = matrix.col([
spots[value["spot"]].ctr_mass_x() -
self.predicted[value["pred"]][0] / pxlsz,
spots[value["spot"]].ctr_mass_y() -
self.predicted[value["pred"]][1] / pxlsz
])
correction_vectors_provisional.append(vector)
c_v_p_flex.append((vector[0], vector[1], 0.))
print "... %d provisional matches" % len(
correction_vectors_provisional),
print "r.m.s.d. in pixels: %5.2f" % (math.sqrt(
flex.mean(c_v_p_flex.dot(c_v_p_flex))))
if self.horizons_phil.integration.enable_residual_scatter:
from matplotlib import pyplot as plt
fig = plt.figure()
for cv in correction_vectors_provisional:
plt.plot([cv[1]], [-cv[0]], "b.")
plt.title(" %d matches, r.m.s.d. %5.2f pixels" %
(len(correction_vectors_provisional),
math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt, fig, "res")
plt.close()
if self.horizons_phil.integration.enable_residual_map:
from matplotlib import pyplot as plt
fig = plt.figure()
for match, cv in zip(indexed_pairs_provisional,
correction_vectors_provisional):
plt.plot([spots[match["spot"]].ctr_mass_y()],
[-spots[match["spot"]].ctr_mass_x()], "r.")
plt.plot([self.predicted[match["pred"]][1] / pxlsz],
[-self.predicted[match["pred"]][0] / pxlsz], "g.")
plt.plot([
spots[match["spot"]].ctr_mass_y(),
spots[match["spot"]].ctr_mass_y() + 10. * cv[1]
], [
-spots[match["spot"]].ctr_mass_x(),
-spots[match["spot"]].ctr_mass_x() - 10. * cv[0]
], 'b-')
plt.xlim([0, float(self.inputpd["size2"])])
plt.ylim([-float(self.inputpd["size1"]), 0])
plt.title(" %d matches, r.m.s.d. %5.2f pixels" %
(len(correction_vectors_provisional),
math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt, fig, "map")
plt.close()
# insert code here to remove correction length outliers...
# they are causing terrible
# problems for finding legitimate correction vectors (print out the list)
# also remove outliers for the purpose of reporting RMS
outlier_rejection = True
cache_refinement_spots = getattr(slip_callbacks.slip_callback,
"requires_refinement_spots", False)
if outlier_rejection:
correction_lengths = flex.double(
[v.length() for v in correction_vectors_provisional])
clorder = flex.sort_permutation(correction_lengths)
sorted_cl = correction_lengths.select(clorder)
ACCEPTABLE_LIMIT = 2
limit = int(
0.33 * len(sorted_cl)
) # best 1/3 of data are assumed to be correctly modeled.
if (limit <= ACCEPTABLE_LIMIT):
raise Sorry(
"Not enough indexed spots to reject outliers; have %d need >%d"
% (limit, ACCEPTABLE_LIMIT))
y_data = flex.double(len(sorted_cl))
for i in range(len(y_data)):
y_data[i] = float(i) / float(len(y_data))
# ideas are explained in Sauter & Poon (2010) J Appl Cryst 43, 611-616.
from rstbx.outlier_spots.fit_distribution import fit_cdf, rayleigh
fitted_rayleigh = fit_cdf(x_data=sorted_cl[0:limit],
y_data=y_data[0:limit],
distribution=rayleigh)
inv_cdf = [
fitted_rayleigh.distribution.inv_cdf(cdf) for cdf in y_data
]
#print "SORTED LIST OF ",len(sorted_cl), "with sigma",fitted_rayleigh.distribution.sigma
indexed_pairs = []
correction_vectors = []
self.correction_vectors = []
for icand in range(len(sorted_cl)):
# somewhat arbitrary sigma = 1.0 cutoff for outliers
if (sorted_cl[icand] - inv_cdf[icand]
) / fitted_rayleigh.distribution.sigma > 1.0:
break
indexed_pairs.append(indexed_pairs_provisional[clorder[icand]])
correction_vectors.append(
correction_vectors_provisional[clorder[icand]])
if cache_refinement_spots:
self.spotfinder.images[self.frame_numbers[
self.image_number]]["refinement_spots"].append(
spots[indexed_pairs[-1]["spot"]])
if kwargs.get("verbose_cv") == True:
print "CV OBSCENTER %7.2f %7.2f REFINEDCENTER %7.2f %7.2f" % (
float(self.inputpd["size1"]) / 2.,
float(self.inputpd["size2"]) / 2.,
self.inputai.xbeam() / pxlsz,
self.inputai.ybeam() / pxlsz),
print "OBSSPOT %7.2f %7.2f PREDSPOT %7.2f %7.2f" % (
spots[indexed_pairs[-1]["spot"]].ctr_mass_x(),
spots[indexed_pairs[-1]["spot"]].ctr_mass_y(),
self.predicted[indexed_pairs[-1]["pred"]][0] / pxlsz,
self.predicted[indexed_pairs[-1]["pred"]][1] / pxlsz),
the_hkl = self.hkllist[indexed_pairs[-1]["pred"]]
print "HKL %4d %4d %4d" % the_hkl, "%2d" % self.setting_id,
radial, azimuthal = spots[indexed_pairs[-1][
"spot"]].get_radial_and_azimuthal_size(
self.inputai.xbeam() / pxlsz,
self.inputai.ybeam() / pxlsz)
print "RADIALpx %5.3f AZIMUTpx %5.3f" % (radial, azimuthal)
# Store a list of correction vectors in self.
radial, azimuthal = spots[
indexed_pairs[-1]['spot']].get_radial_and_azimuthal_size(
self.inputai.xbeam() / pxlsz,
self.inputai.ybeam() / pxlsz)
self.correction_vectors.append(
dict(obscenter=(float(self.inputpd['size1']) / 2,
float(self.inputpd['size2']) / 2),
refinedcenter=(self.inputai.xbeam() / pxlsz,
self.inputai.ybeam() / pxlsz),
obsspot=(
spots[indexed_pairs[-1]['spot']].ctr_mass_x(),
spots[indexed_pairs[-1]['spot']].ctr_mass_y()),
predspot=(
self.predicted[indexed_pairs[-1]['pred']][0] /
pxlsz,
self.predicted[indexed_pairs[-1]['pred']][1] /
pxlsz),
hkl=(self.hkllist[indexed_pairs[-1]['pred']][0],
self.hkllist[indexed_pairs[-1]['pred']][1],
self.hkllist[indexed_pairs[-1]['pred']][2]),
setting_id=self.setting_id,
radial=radial,
azimuthal=azimuthal))
print "After outlier rejection %d indexed spotfinder spots remain." % len(
indexed_pairs)
if False:
rayleigh_cdf = [
fitted_rayleigh.distribution.cdf(x=sorted_cl[c])
for c in range(len(sorted_cl))
]
from matplotlib import pyplot as plt
plt.plot(sorted_cl, y_data, "r+")
#plt.plot(sorted_cl,rayleigh_cdf,"g.")
plt.plot(inv_cdf, y_data, "b.")
plt.show()
else:
indexed_pairs = indexed_pairs_provisional
correction_vectors = correction_vectors_provisional
########### finished with outlier rejection
self.inputpd["symmetry"].show_summary(prefix="SETTING ")
is_triclinic = (self.setting_id == 1)
if is_triclinic:
self.triclinic_pairs = [
dict(pred=self.hkllist[a["pred"]], spot=a["spot"])
for a in indexed_pairs
]
if self.horizons_phil.integration.model == "user_supplied":
if kwargs.get("user-reentrant", None) == None:
from cxi_user import post_outlier_rejection
self.indexed_pairs = indexed_pairs
self.spots = spots
post_outlier_rejection(self, image_number, cb_op_to_primitive,
self.horizons_phil, kwargs)
return
########### finished with user-supplied code
if self.horizons_phil.integration.spot_shape_verbose:
from rstbx.new_horizons.spot_shape import spot_shape_verbose
spot_shape_verbose(rawdata=self.imagefiles.images[
self.image_number].linearintdata,
beam_center_pix=matrix.col(
(self.inputai.xbeam() / pxlsz,
self.inputai.ybeam() / pxlsz)),
indexed_pairs=indexed_pairs,
spotfinder_observations=spots,
distance_mm=self.inputai.distance(),
mm_per_pixel=pxlsz,
hkllist=self.hkllist,
unit_cell=self.cell,
wavelength_ang=self.inputai.wavelength)
#Other checks to be implemented (future):
# spot is within active area of detector on a circular detector such as the Mar IP
# integration masks do not overlap; or deconvolute
correction_lengths = flex.double(
[v.length() for v in correction_vectors])
if verbose:
print "average correction %5.2f over %d vectors" % (
flex.mean(correction_lengths), len(correction_lengths)),
print "or %5.2f mm." % (pxlsz * flex.mean(correction_lengths))
self.r_residual = pxlsz * flex.mean(correction_lengths)
#assert len(indexed_pairs)>NEAR # must have enough indexed spots
if (len(indexed_pairs) <= NEAR):
raise Sorry("Not enough indexed spots, only found %d, need %d" %
(len(indexed_pairs), NEAR))
reference = flex.double()
for item in indexed_pairs:
reference.append(spots[item["spot"]].ctr_mass_x())
reference.append(spots[item["spot"]].ctr_mass_y())
PS_adapt = AnnAdaptor(data=reference, dim=2, k=NEAR)
PS_adapt.query(query)
self.BSmasks = []
#self.null_correction_mapping( predicted=self.predicted,
# correction_vectors = correction_vectors,
# IS_adapt = IS_adapt,
# spots = spots)
self.positional_correction_mapping(
predicted=self.predicted,
correction_vectors=correction_vectors,
PS_adapt=PS_adapt,
IS_adapt=IS_adapt,
spots=spots)
# which spots are close enough to interfere with background?
MAXOVER = 6
OS_adapt = AnnAdaptor(data=query, dim=2, k=MAXOVER) #six near nbrs
OS_adapt.query(query)
if self.mask_focus[image_number] is None:
raise Sorry(
"No observed/predicted spot agreement; no Spotfinder masks; skip integration"
)
nbr_cutoff = 2.0 * max(self.mask_focus[image_number])
FRAME = int(nbr_cutoff / 2)
#print "The overlap cutoff is %d pixels"%nbr_cutoff
nbr_cutoff_sq = nbr_cutoff * nbr_cutoff
#print "Optimized C++ section...",
self.set_frame(FRAME)
self.set_background_factor(kwargs["background_factor"])
self.set_nbr_cutoff_sq(nbr_cutoff_sq)
self.set_guard_width_sq(self.horizons_phil.integration.guard_width_sq)
self.set_detector_gain(self.horizons_phil.integration.detector_gain)
flex_sorted = flex.int()
for item in self.sorted:
flex_sorted.append(item[0])
flex_sorted.append(item[1])
if self.horizons_phil.integration.mask_pixel_value is not None:
self.set_mask_pixel_val(
self.horizons_phil.integration.mask_pixel_value)
image_obj = self.imagefiles.imageindex(
self.frame_numbers[self.image_number])
image_obj.read()
rawdata = image_obj.linearintdata # assume image #1
if self.inputai.active_areas != None:
self.detector_xy_draft = self.safe_background(
rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted,
tiles=self.inputai.active_areas.IT,
tile_id=self.inputai.active_areas.tile_id)
else:
self.detector_xy_draft = self.safe_background(
rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted)
for i in range(len(self.predicted)): # loop over predicteds
B_S_mask = {}
keys = self.get_bsmask(i)
for k in range(0, len(keys), 2):
B_S_mask[(keys[k], keys[k + 1])] = True
self.BSmasks.append(B_S_mask)
#print "Done"
return
def integration_concept_detail(self, experiments, reflections, spots,image_number,cb_op_to_primitive,**kwargs):
detector = experiments[0].detector
crystal = experiments[0].crystal
from cctbx.crystal import symmetry
c_symmetry = symmetry(space_group = crystal.get_space_group(), unit_cell = crystal.get_unit_cell())
self.image_number = image_number
NEAR = 10
pxlsz = detector[0].get_pixel_size()
Predicted = self.get_predictions_accounting_for_centering(experiments,reflections,cb_op_to_primitive,**kwargs)
FWMOSAICITY = self.inputai.getMosaicity()
self.DOMAIN_SZ_ANG = kwargs.get("domain_size_ang", self.__dict__.get("actual",0) )
refineflag = {True:0,False:1}[kwargs.get("domain_size_ang",0)==0]
c_symmetry.show_summary(prefix="EXCURSION%1d REPORT FWMOS= %6.4f DOMAIN= %6.1f "%(refineflag,FWMOSAICITY,self.DOMAIN_SZ_ANG))
from annlib_ext import AnnAdaptor
self.cell = c_symmetry.unit_cell()
query = flex.double()
print len(self.predicted)
for pred in self.predicted: # predicted spot coord in pixels
query.append(pred[0]/pxlsz[0])
query.append(pred[1]/pxlsz[1])
self.reserve_hkllist_for_signal_search = self.hkllist
reference = flex.double()
assert self.length>NEAR# Can't do spot/pred matching with too few spots
for spot in spots:
reference.append(spot.ctr_mass_x())
reference.append(spot.ctr_mass_y())
IS_adapt = AnnAdaptor(data=reference,dim=2,k=NEAR)
IS_adapt.query(query)
idx_cutoff = float(min(self.mask_focus[image_number]))
from rstbx.apps.slip_helpers import slip_callbacks
cache_refinement_spots = getattr(slip_callbacks.slip_callback,"requires_refinement_spots",False)
indexed_pairs_provisional = []
correction_vectors_provisional = []
c_v_p_flex = flex.vec3_double()
this_setting_matched_indices = reflections["miller_index"]
for j,item in enumerate(this_setting_matched_indices):
this_setting_index = self.hkllist.first_index(item)
if this_setting_index:
Match = dict(spot=j,pred=this_setting_index)
indexed_pairs_provisional.append(Match)
vector = matrix.col(
[reflections["xyzobs.px.value"][j][0] - self.predicted[Match["pred"]][0]/pxlsz[0],
reflections["xyzobs.px.value"][j][1] - self.predicted[Match["pred"]][1]/pxlsz[1]])
correction_vectors_provisional.append(vector)
c_v_p_flex.append((vector[0],vector[1],0.))
self.N_correction_vectors = len(correction_vectors_provisional)
self.rmsd_px = math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))
print "... %d provisional matches"%self.N_correction_vectors,
print "r.m.s.d. in pixels: %6.3f"%(self.rmsd_px)
if self.horizons_phil.integration.enable_residual_scatter:
from matplotlib import pyplot as plt
fig = plt.figure()
for cv in correction_vectors_provisional:
plt.plot([cv[1]],[-cv[0]],"r.")
plt.title(" %d matches, r.m.s.d. %5.2f pixels"%(len(correction_vectors_provisional),math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt,fig,"res")
plt.close()
if self.horizons_phil.integration.enable_residual_map:
from matplotlib import pyplot as plt
PX = reflections["xyzobs.px.value"]
fig = plt.figure()
for match,cv in zip(indexed_pairs_provisional,correction_vectors_provisional):
plt.plot([PX[match["spot"]][1]],[-PX[match["spot"]][0]],"r.")
plt.plot([self.predicted[match["pred"]][1]/pxlsz[1]],[-self.predicted[match["pred"]][0]/pxlsz[0]],"g.")
plt.plot([PX[match["spot"]][1], PX[match["spot"]][1] + 10.*cv[1]],
[-PX[match["spot"]][0], -PX[match["spot"]][0] - 10.*cv[0]],'r-')
if kwargs.get("user-reentrant") != None and self.horizons_phil.integration.spot_prediction == "dials" \
and self.horizons_phil.integration.enable_residual_map_deltapsi:
from rstbx.apps.stills.util import residual_map_special_deltapsi_add_on
residual_map_special_deltapsi_add_on(
reflections = self.dials_spot_prediction,
matches = indexed_pairs_provisional, experiments=experiments,
hkllist = self.hkllist,
predicted = self.predicted, plot=plt, eta_deg=FWMOSAICITY, deff=self.DOMAIN_SZ_ANG
)
plt.xlim([0,detector[0].get_image_size()[1]])
plt.ylim([-detector[0].get_image_size()[0],0])
plt.title(" %d matches, r.m.s.d. %5.2f pixels"%(len(correction_vectors_provisional),math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt,fig,"map")
plt.close()
indexed_pairs = indexed_pairs_provisional
correction_vectors = correction_vectors_provisional
########### skip outlier rejection for this derived class
### However must retain the ability to write out correction vectiors.
if True: # at Aaron's request; test later
correction_lengths = flex.double([v.length() for v in correction_vectors_provisional])
clorder = flex.sort_permutation(correction_lengths)
sorted_cl = correction_lengths.select(clorder)
indexed_pairs = []
correction_vectors = []
self.correction_vectors = []
for icand in xrange(len(sorted_cl)):
# somewhat arbitrary sigma = 1.0 cutoff for outliers
indexed_pairs.append(indexed_pairs_provisional[clorder[icand]])
correction_vectors.append(correction_vectors_provisional[clorder[icand]])
if cache_refinement_spots:
self.spotfinder.images[self.frame_numbers[self.image_number]]["refinement_spots"].append(
spots[reflections[indexed_pairs[-1]["spot"]]['spotfinder_lookup']])
if kwargs.get("verbose_cv")==True:
print "CV OBSCENTER %7.2f %7.2f REFINEDCENTER %7.2f %7.2f"%(
float(self.inputpd["size1"])/2.,float(self.inputpd["size2"])/2.,
self.inputai.xbeam()/pxlsz[0], self.inputai.ybeam()/pxlsz[1]),
print "OBSSPOT %7.2f %7.2f PREDSPOT %7.2f %7.2f"%(
reflections[indexed_pairs[-1]["spot"]]['xyzobs.px.value'][0],
reflections[indexed_pairs[-1]["spot"]]['xyzobs.px.value'][1],
self.predicted[indexed_pairs[-1]["pred"]][0]/pxlsz[0],
self.predicted[indexed_pairs[-1]["pred"]][1]/pxlsz[1]),
the_hkl = self.hkllist[indexed_pairs[-1]["pred"]]
print "HKL %4d %4d %4d"%the_hkl,"%2d"%self.setting_id,
radial, azimuthal = spots[indexed_pairs[-1]["spot"]].get_radial_and_azimuthal_size(
self.inputai.xbeam()/pxlsz[0], self.inputai.ybeam()/pxlsz[1])
print "RADIALpx %5.3f AZIMUTpx %5.3f"%(radial,azimuthal)
# Store a list of correction vectors in self.
radial, azimuthal = spots[indexed_pairs[-1]['spot']].get_radial_and_azimuthal_size(
self.inputai.xbeam()/pxlsz[0], self.inputai.ybeam()/pxlsz[1])
self.correction_vectors.append(
dict(obscenter=(float(self.inputpd['size1']) / 2,
float(self.inputpd['size2']) / 2),
refinedcenter=(self.inputai.xbeam() / pxlsz[0],
self.inputai.ybeam() / pxlsz[1]),
obsspot=(reflections[indexed_pairs[-1]['spot']]['xyzobs.px.value'][0],
reflections[indexed_pairs[-1]['spot']]['xyzobs.px.value'][1]),
predspot=(self.predicted[indexed_pairs[-1]['pred']][0] / pxlsz[0],
self.predicted[indexed_pairs[-1]['pred']][1] / pxlsz[1]),
hkl=(self.hkllist[indexed_pairs[-1]['pred']][0],
self.hkllist[indexed_pairs[-1]['pred']][1],
self.hkllist[indexed_pairs[-1]['pred']][2]),
setting_id=self.setting_id,
radial=radial,
azimuthal=azimuthal))
self.inputpd["symmetry"] = c_symmetry
self.inputpd["symmetry"].show_summary(prefix="SETTING ")
if self.horizons_phil.integration.model == "user_supplied":
# Not certain of whether the reentrant_* dictionary keys create a memory leak
if kwargs.get("user-reentrant",None)==None:
kwargs["reentrant_experiments"] = experiments
kwargs["reentrant_reflections"] = reflections
from cxi_user import post_outlier_rejection
self.indexed_pairs = indexed_pairs
self.spots = spots
post_outlier_rejection(self,image_number,cb_op_to_primitive,self.horizons_phil,kwargs)
return
########### finished with user-supplied code
correction_lengths=flex.double([v.length() for v in correction_vectors])
self.r_residual = pxlsz[0]*flex.mean(correction_lengths)
#assert len(indexed_pairs)>NEAR # must have enough indexed spots
if (len(indexed_pairs) <= NEAR):
raise Sorry("Not enough indexed spots, only found %d, need %d" % (len(indexed_pairs), NEAR))
reference = flex.double()
for item in indexed_pairs:
reference.append(spots[item["spot"]].ctr_mass_x())
reference.append(spots[item["spot"]].ctr_mass_y())
PS_adapt = AnnAdaptor(data=reference,dim=2,k=NEAR)
PS_adapt.query(query)
self.BSmasks = []
# do not use null: self.null_correction_mapping( predicted=self.predicted,
self.positional_correction_mapping( predicted=self.predicted,
correction_vectors = correction_vectors,
PS_adapt = PS_adapt,
IS_adapt = IS_adapt,
spots = spots)
# which spots are close enough to interfere with background?
MAXOVER=6
OS_adapt = AnnAdaptor(data=query,dim=2,k=MAXOVER) #six near nbrs
OS_adapt.query(query)
if self.mask_focus[image_number] is None:
raise Sorry("No observed/predicted spot agreement; no Spotfinder masks; skip integration")
nbr_cutoff = 2.0* max(self.mask_focus[image_number])
FRAME = int(nbr_cutoff/2)
#print "The overlap cutoff is %d pixels"%nbr_cutoff
nbr_cutoff_sq = nbr_cutoff * nbr_cutoff
#print "Optimized C++ section...",
self.set_frame(FRAME)
self.set_background_factor(kwargs["background_factor"])
self.set_nbr_cutoff_sq(nbr_cutoff_sq)
self.set_guard_width_sq(self.horizons_phil.integration.guard_width_sq)
self.set_detector_gain(self.horizons_phil.integration.detector_gain)
flex_sorted = flex.int()
for item in self.sorted:
flex_sorted.append(item[0]);flex_sorted.append(item[1]);
if self.horizons_phil.integration.mask_pixel_value is not None:
self.set_mask_pixel_val(self.horizons_phil.integration.mask_pixel_value)
image_obj = self.imagefiles.imageindex(self.frame_numbers[self.image_number])
image_obj.read()
rawdata = image_obj.linearintdata # assume image #1
if self.inputai.active_areas != None:
self.detector_xy_draft = self.safe_background( rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted,
tiles=self.inputai.active_areas.IT,
tile_id=self.inputai.active_areas.tile_id);
else:
self.detector_xy_draft = self.safe_background( rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted);
for i in xrange(len(self.predicted)): # loop over predicteds
B_S_mask = {}
keys = self.get_bsmask(i)
for k in xrange(0,len(keys),2):
B_S_mask[(keys[k],keys[k+1])]=True
self.BSmasks.append(B_S_mask)
#print "Done"
return
def integration_concept_detail(self, experiments, reflections, spots,
image_number, cb_op_to_primitive, **kwargs):
detector = experiments[0].detector
crystal = experiments[0].crystal
from cctbx.crystal import symmetry
c_symmetry = symmetry(space_group=crystal.get_space_group(),
unit_cell=crystal.get_unit_cell())
self.image_number = image_number
NEAR = 10
pxlsz = detector[0].get_pixel_size()
Predicted = self.get_predictions_accounting_for_centering(
experiments, reflections, cb_op_to_primitive, **kwargs)
FWMOSAICITY = self.inputai.getMosaicity()
self.DOMAIN_SZ_ANG = kwargs.get("domain_size_ang",
self.__dict__.get("actual", 0))
refineflag = {True: 0, False: 1}[kwargs.get("domain_size_ang", 0) == 0]
c_symmetry.show_summary(
prefix="EXCURSION%1d REPORT FWMOS= %6.4f DOMAIN= %6.1f " %
(refineflag, FWMOSAICITY, self.DOMAIN_SZ_ANG))
from annlib_ext import AnnAdaptor
self.cell = c_symmetry.unit_cell()
query = flex.double()
print len(self.predicted)
for pred in self.predicted: # predicted spot coord in pixels
query.append(pred[0] / pxlsz[0])
query.append(pred[1] / pxlsz[1])
self.reserve_hkllist_for_signal_search = self.hkllist
reference = flex.double()
assert self.length > NEAR # Can't do spot/pred matching with too few spots
for spot in spots:
reference.append(spot.ctr_mass_x())
reference.append(spot.ctr_mass_y())
IS_adapt = AnnAdaptor(data=reference, dim=2, k=NEAR)
IS_adapt.query(query)
idx_cutoff = float(min(self.mask_focus[image_number]))
from rstbx.apps.slip_helpers import slip_callbacks
cache_refinement_spots = getattr(slip_callbacks.slip_callback,
"requires_refinement_spots", False)
indexed_pairs_provisional = []
correction_vectors_provisional = []
c_v_p_flex = flex.vec3_double()
this_setting_matched_indices = reflections["miller_index"]
for j, item in enumerate(this_setting_matched_indices):
this_setting_index = self.hkllist.first_index(item)
if this_setting_index:
Match = dict(spot=j, pred=this_setting_index)
indexed_pairs_provisional.append(Match)
vector = matrix.col([
reflections["xyzobs.px.value"][j][0] -
self.predicted[Match["pred"]][0] / pxlsz[0],
reflections["xyzobs.px.value"][j][1] -
self.predicted[Match["pred"]][1] / pxlsz[1]
])
correction_vectors_provisional.append(vector)
c_v_p_flex.append((vector[0], vector[1], 0.))
self.N_correction_vectors = len(correction_vectors_provisional)
self.rmsd_px = math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))
print "... %d provisional matches" % self.N_correction_vectors,
print "r.m.s.d. in pixels: %6.3f" % (self.rmsd_px)
if self.horizons_phil.integration.enable_residual_scatter:
from matplotlib import pyplot as plt
fig = plt.figure()
for cv in correction_vectors_provisional:
plt.plot([cv[1]], [-cv[0]], "r.")
plt.title(" %d matches, r.m.s.d. %5.2f pixels" %
(len(correction_vectors_provisional),
math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt, fig, "res")
plt.close()
if self.horizons_phil.integration.enable_residual_map:
from matplotlib import pyplot as plt
PX = reflections["xyzobs.px.value"]
fig = plt.figure()
for match, cv in zip(indexed_pairs_provisional,
correction_vectors_provisional):
plt.plot([PX[match["spot"]][1]], [-PX[match["spot"]][0]], "r.")
plt.plot([self.predicted[match["pred"]][1] / pxlsz[1]],
[-self.predicted[match["pred"]][0] / pxlsz[0]], "g.")
plt.plot(
[PX[match["spot"]][1], PX[match["spot"]][1] + 10. * cv[1]],
[
-PX[match["spot"]][0],
-PX[match["spot"]][0] - 10. * cv[0]
], 'r-')
if kwargs.get("user-reentrant") != None and self.horizons_phil.integration.spot_prediction == "dials" \
and self.horizons_phil.integration.enable_residual_map_deltapsi:
from rstbx.apps.stills.util import residual_map_special_deltapsi_add_on
residual_map_special_deltapsi_add_on(
reflections=self.dials_spot_prediction,
matches=indexed_pairs_provisional,
experiments=experiments,
hkllist=self.hkllist,
predicted=self.predicted,
plot=plt,
eta_deg=FWMOSAICITY,
deff=self.DOMAIN_SZ_ANG)
plt.xlim([0, detector[0].get_image_size()[1]])
plt.ylim([-detector[0].get_image_size()[0], 0])
plt.title(" %d matches, r.m.s.d. %5.2f pixels" %
(len(correction_vectors_provisional),
math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt, fig, "map")
plt.close()
indexed_pairs = indexed_pairs_provisional
correction_vectors = correction_vectors_provisional
########### skip outlier rejection for this derived class
### However must retain the ability to write out correction vectiors.
if True: # at Aaron's request; test later
correction_lengths = flex.double(
[v.length() for v in correction_vectors_provisional])
clorder = flex.sort_permutation(correction_lengths)
sorted_cl = correction_lengths.select(clorder)
indexed_pairs = []
correction_vectors = []
self.correction_vectors = []
for icand in xrange(len(sorted_cl)):
# somewhat arbitrary sigma = 1.0 cutoff for outliers
indexed_pairs.append(indexed_pairs_provisional[clorder[icand]])
correction_vectors.append(
correction_vectors_provisional[clorder[icand]])
if cache_refinement_spots:
self.spotfinder.images[self.frame_numbers[
self.image_number]]["refinement_spots"].append(
spots[reflections[indexed_pairs[-1]["spot"]]
['spotfinder_lookup']])
if kwargs.get("verbose_cv") == True:
print "CV OBSCENTER %7.2f %7.2f REFINEDCENTER %7.2f %7.2f" % (
float(self.inputpd["size1"]) / 2.,
float(self.inputpd["size2"]) / 2.,
self.inputai.xbeam() / pxlsz[0],
self.inputai.ybeam() / pxlsz[1]),
print "OBSSPOT %7.2f %7.2f PREDSPOT %7.2f %7.2f" % (
reflections[indexed_pairs[-1]["spot"]]
['xyzobs.px.value'][0], reflections[
indexed_pairs[-1]["spot"]]['xyzobs.px.value'][1],
self.predicted[indexed_pairs[-1]["pred"]][0] /
pxlsz[0], self.predicted[indexed_pairs[-1]["pred"]][1]
/ pxlsz[1]),
the_hkl = self.hkllist[indexed_pairs[-1]["pred"]]
print "HKL %4d %4d %4d" % the_hkl, "%2d" % self.setting_id,
radial, azimuthal = spots[indexed_pairs[-1][
"spot"]].get_radial_and_azimuthal_size(
self.inputai.xbeam() / pxlsz[0],
self.inputai.ybeam() / pxlsz[1])
print "RADIALpx %5.3f AZIMUTpx %5.3f" % (radial, azimuthal)
# Store a list of correction vectors in self.
radial, azimuthal = spots[
indexed_pairs[-1]['spot']].get_radial_and_azimuthal_size(
self.inputai.xbeam() / pxlsz[0],
self.inputai.ybeam() / pxlsz[1])
self.correction_vectors.append(
dict(obscenter=(float(self.inputpd['size1']) / 2,
float(self.inputpd['size2']) / 2),
refinedcenter=(self.inputai.xbeam() / pxlsz[0],
self.inputai.ybeam() / pxlsz[1]),
obsspot=(reflections[indexed_pairs[-1]
['spot']]['xyzobs.px.value'][0],
reflections[indexed_pairs[-1]
['spot']]['xyzobs.px.value'][1]),
predspot=(
self.predicted[indexed_pairs[-1]['pred']][0] /
pxlsz[0],
self.predicted[indexed_pairs[-1]['pred']][1] /
pxlsz[1]),
hkl=(self.hkllist[indexed_pairs[-1]['pred']][0],
self.hkllist[indexed_pairs[-1]['pred']][1],
self.hkllist[indexed_pairs[-1]['pred']][2]),
setting_id=self.setting_id,
radial=radial,
azimuthal=azimuthal))
self.inputpd["symmetry"] = c_symmetry
self.inputpd["symmetry"].show_summary(prefix="SETTING ")
if self.horizons_phil.integration.model == "user_supplied":
# Not certain of whether the reentrant_* dictionary keys create a memory leak
if kwargs.get("user-reentrant", None) == None:
kwargs["reentrant_experiments"] = experiments
kwargs["reentrant_reflections"] = reflections
from cxi_user import post_outlier_rejection
self.indexed_pairs = indexed_pairs
self.spots = spots
post_outlier_rejection(self, image_number, cb_op_to_primitive,
self.horizons_phil, kwargs)
return
########### finished with user-supplied code
correction_lengths = flex.double(
[v.length() for v in correction_vectors])
self.r_residual = pxlsz[0] * flex.mean(correction_lengths)
#assert len(indexed_pairs)>NEAR # must have enough indexed spots
if (len(indexed_pairs) <= NEAR):
raise Sorry("Not enough indexed spots, only found %d, need %d" %
(len(indexed_pairs), NEAR))
reference = flex.double()
for item in indexed_pairs:
reference.append(spots[item["spot"]].ctr_mass_x())
reference.append(spots[item["spot"]].ctr_mass_y())
PS_adapt = AnnAdaptor(data=reference, dim=2, k=NEAR)
PS_adapt.query(query)
self.BSmasks = []
# do not use null: self.null_correction_mapping( predicted=self.predicted,
self.positional_correction_mapping(
predicted=self.predicted,
correction_vectors=correction_vectors,
PS_adapt=PS_adapt,
IS_adapt=IS_adapt,
spots=spots)
# which spots are close enough to interfere with background?
MAXOVER = 6
OS_adapt = AnnAdaptor(data=query, dim=2, k=MAXOVER) #six near nbrs
OS_adapt.query(query)
if self.mask_focus[image_number] is None:
raise Sorry(
"No observed/predicted spot agreement; no Spotfinder masks; skip integration"
)
nbr_cutoff = 2.0 * max(self.mask_focus[image_number])
FRAME = int(nbr_cutoff / 2)
#print "The overlap cutoff is %d pixels"%nbr_cutoff
nbr_cutoff_sq = nbr_cutoff * nbr_cutoff
#print "Optimized C++ section...",
self.set_frame(FRAME)
self.set_background_factor(kwargs["background_factor"])
self.set_nbr_cutoff_sq(nbr_cutoff_sq)
self.set_guard_width_sq(self.horizons_phil.integration.guard_width_sq)
self.set_detector_gain(self.horizons_phil.integration.detector_gain)
flex_sorted = flex.int()
for item in self.sorted:
flex_sorted.append(item[0])
flex_sorted.append(item[1])
if self.horizons_phil.integration.mask_pixel_value is not None:
self.set_mask_pixel_val(
self.horizons_phil.integration.mask_pixel_value)
image_obj = self.imagefiles.imageindex(
self.frame_numbers[self.image_number])
image_obj.read()
rawdata = image_obj.linearintdata # assume image #1
if self.inputai.active_areas != None:
self.detector_xy_draft = self.safe_background(
rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted,
tiles=self.inputai.active_areas.IT,
tile_id=self.inputai.active_areas.tile_id)
else:
self.detector_xy_draft = self.safe_background(
rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted)
for i in xrange(len(self.predicted)): # loop over predicteds
B_S_mask = {}
keys = self.get_bsmask(i)
for k in xrange(0, len(keys), 2):
B_S_mask[(keys[k], keys[k + 1])] = True
self.BSmasks.append(B_S_mask)
#print "Done"
return
def integration_concept(self,image_number=0,cb_op_to_primitive=None,verbose=False,**kwargs):
self.image_number = image_number
NEAR = 10
pxlsz = self.pixel_size
self.get_predictions_accounting_for_centering(cb_op_to_primitive,**kwargs)
FWMOSAICITY = self.inputai.getMosaicity()
DOMAIN_SZ_ANG = kwargs.get("domain_size_ang", self.__dict__.get("actual",0) )
refineflag = {True:0,False:1}[kwargs.get("domain_size_ang",0)==0]
self.inputpd["symmetry"].show_summary(prefix="EXCURSION%1d REPORT FWMOS= %6.4f DOMAIN= %6.1f "%(refineflag,FWMOSAICITY,DOMAIN_SZ_ANG))
from annlib_ext import AnnAdaptor
self.cell = self.inputai.getOrientation().unit_cell()
query = flex.double()
for pred in self.predicted: # predicted spot coord in pixels
query.append(pred[0]/pxlsz)
query.append(pred[1]/pxlsz)
self.reserve_hkllist_for_signal_search = self.hkllist
reference = flex.double()
spots = self.get_observations_with_outlier_removal()
assert len(spots)>NEAR# Can't do spot/pred matching with too few spots
for spot in spots:
reference.append(spot.ctr_mass_x())
reference.append(spot.ctr_mass_y())
IS_adapt = AnnAdaptor(data=reference,dim=2,k=NEAR)
IS_adapt.query(query)
print "Calculate correction vectors for %d observations & %d predictions"%(len(spots),len(self.predicted))
indexed_pairs_provisional = []
correction_vectors_provisional = []
c_v_p_flex = flex.vec3_double()
idx_cutoff = float(min(self.mask_focus[image_number]))
if verbose:
print "idx_cutoff distance in pixels",idx_cutoff
if not self.horizons_phil.integration.enable_one_to_one_safeguard:
# legacy code, no safeguard against many-to-one predicted-to-observation mapping
for i in xrange(len(self.predicted)): # loop over predicteds
#for n in xrange(NEAR): # loop over near spotfinder spots
for n in xrange(1): # only consider the nearest spotfinder spot
Match = dict(spot=IS_adapt.nn[i*NEAR+n],pred=i)
if n==0 and math.sqrt(IS_adapt.distances[i*NEAR+n]) < idx_cutoff:
indexed_pairs_provisional.append(Match)
vector = matrix.col(
[spots[Match["spot"]].ctr_mass_x() - self.predicted[Match["pred"]][0]/pxlsz,
spots[Match["spot"]].ctr_mass_y() - self.predicted[Match["pred"]][1]/pxlsz])
correction_vectors_provisional.append(vector)
c_v_p_flex.append((vector[0],vector[1],0.))
else:
one_to_one = {}
for i in xrange(len(self.predicted)): # loop over predicteds
annresultidx = i*NEAR
obsidx = IS_adapt.nn[annresultidx]
this_distancesq = IS_adapt.distances[annresultidx]
if not one_to_one.has_key(obsidx) or \
this_distancesq < one_to_one[obsidx]["distancesq"]:
if math.sqrt(this_distancesq) < idx_cutoff:
one_to_one[obsidx] = dict(spot=obsidx,pred=i,distancesq=this_distancesq)
for key,value in one_to_one.items():
indexed_pairs_provisional.append(value)
vector = matrix.col(
[spots[value["spot"]].ctr_mass_x() - self.predicted[value["pred"]][0]/pxlsz,
spots[value["spot"]].ctr_mass_y() - self.predicted[value["pred"]][1]/pxlsz])
correction_vectors_provisional.append(vector)
c_v_p_flex.append((vector[0],vector[1],0.))
print "... %d provisional matches"%len(correction_vectors_provisional),
print "r.m.s.d. in pixels: %5.2f"%(math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex))))
if self.horizons_phil.integration.enable_residual_scatter:
from matplotlib import pyplot as plt
fig = plt.figure()
for cv in correction_vectors_provisional:
plt.plot([cv[1]],[-cv[0]],"b.")
plt.title(" %d matches, r.m.s.d. %5.2f pixels"%(len(correction_vectors_provisional),math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt,fig,"res")
plt.close()
if self.horizons_phil.integration.enable_residual_map:
from matplotlib import pyplot as plt
fig = plt.figure()
for match,cv in zip(indexed_pairs_provisional,correction_vectors_provisional):
plt.plot([spots[match["spot"]].ctr_mass_y()],[-spots[match["spot"]].ctr_mass_x()],"r.")
plt.plot([self.predicted[match["pred"]][1]/pxlsz],[-self.predicted[match["pred"]][0]/pxlsz],"g.")
plt.plot([spots[match["spot"]].ctr_mass_y(), spots[match["spot"]].ctr_mass_y() + 10.*cv[1]],
[-spots[match["spot"]].ctr_mass_x(), -spots[match["spot"]].ctr_mass_x() - 10.*cv[0]],'b-')
plt.xlim([0,float(self.inputpd["size2"])])
plt.ylim([-float(self.inputpd["size1"]),0])
plt.title(" %d matches, r.m.s.d. %5.2f pixels"%(len(correction_vectors_provisional),math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt,fig,"map")
plt.close()
# insert code here to remove correction length outliers...
# they are causing terrible
# problems for finding legitimate correction vectors (print out the list)
# also remove outliers for the purpose of reporting RMS
outlier_rejection = True
cache_refinement_spots = getattr(slip_callbacks.slip_callback,"requires_refinement_spots",False)
if outlier_rejection:
correction_lengths = flex.double([v.length() for v in correction_vectors_provisional])
clorder = flex.sort_permutation(correction_lengths)
sorted_cl = correction_lengths.select(clorder)
ACCEPTABLE_LIMIT = 2
limit = int(0.33 * len(sorted_cl)) # best 1/3 of data are assumed to be correctly modeled.
if (limit <= ACCEPTABLE_LIMIT):
raise Sorry("Not enough indexed spots to reject outliers; have %d need >%d" % (limit, ACCEPTABLE_LIMIT))
y_data = flex.double(len(sorted_cl))
for i in xrange(len(y_data)):
y_data[i] = float(i)/float(len(y_data))
# ideas are explained in Sauter & Poon (2010) J Appl Cryst 43, 611-616.
from rstbx.outlier_spots.fit_distribution import fit_cdf,rayleigh
fitted_rayleigh = fit_cdf(x_data = sorted_cl[0:limit],
y_data = y_data[0:limit],
distribution=rayleigh)
inv_cdf = [fitted_rayleigh.distribution.inv_cdf(cdf) for cdf in y_data]
#print "SORTED LIST OF ",len(sorted_cl), "with sigma",fitted_rayleigh.distribution.sigma
indexed_pairs = []
correction_vectors = []
self.correction_vectors = []
for icand in xrange(len(sorted_cl)):
# somewhat arbitrary sigma = 1.0 cutoff for outliers
if (sorted_cl[icand]-inv_cdf[icand])/fitted_rayleigh.distribution.sigma > 1.0:
break
indexed_pairs.append(indexed_pairs_provisional[clorder[icand]])
correction_vectors.append(correction_vectors_provisional[clorder[icand]])
if cache_refinement_spots:
self.spotfinder.images[self.frame_numbers[self.image_number]]["refinement_spots"].append(
spots[indexed_pairs[-1]["spot"]])
if kwargs.get("verbose_cv")==True:
print "CV OBSCENTER %7.2f %7.2f REFINEDCENTER %7.2f %7.2f"%(
float(self.inputpd["size1"])/2.,float(self.inputpd["size2"])/2.,
self.inputai.xbeam()/pxlsz, self.inputai.ybeam()/pxlsz),
print "OBSSPOT %7.2f %7.2f PREDSPOT %7.2f %7.2f"%(
spots[indexed_pairs[-1]["spot"]].ctr_mass_x(),
spots[indexed_pairs[-1]["spot"]].ctr_mass_y(),
self.predicted[indexed_pairs[-1]["pred"]][0]/pxlsz,
self.predicted[indexed_pairs[-1]["pred"]][1]/pxlsz),
the_hkl = self.hkllist[indexed_pairs[-1]["pred"]]
print "HKL %4d %4d %4d"%the_hkl,"%2d"%self.setting_id,
radial, azimuthal = spots[indexed_pairs[-1]["spot"]].get_radial_and_azimuthal_size(
self.inputai.xbeam()/pxlsz, self.inputai.ybeam()/pxlsz)
print "RADIALpx %5.3f AZIMUTpx %5.3f"%(radial,azimuthal)
# Store a list of correction vectors in self.
radial, azimuthal = spots[indexed_pairs[-1]['spot']].get_radial_and_azimuthal_size(
self.inputai.xbeam()/pxlsz, self.inputai.ybeam()/pxlsz)
self.correction_vectors.append(
dict(obscenter=(float(self.inputpd['size1']) / 2,
float(self.inputpd['size2']) / 2),
refinedcenter=(self.inputai.xbeam() / pxlsz,
self.inputai.ybeam() / pxlsz),
obsspot=(spots[indexed_pairs[-1]['spot']].ctr_mass_x(),
spots[indexed_pairs[-1]['spot']].ctr_mass_y()),
predspot=(self.predicted[indexed_pairs[-1]['pred']][0] / pxlsz,
self.predicted[indexed_pairs[-1]['pred']][1] / pxlsz),
hkl=(self.hkllist[indexed_pairs[-1]['pred']][0],
self.hkllist[indexed_pairs[-1]['pred']][1],
self.hkllist[indexed_pairs[-1]['pred']][2]),
setting_id=self.setting_id,
radial=radial,
azimuthal=azimuthal))
print "After outlier rejection %d indexed spotfinder spots remain."%len(indexed_pairs)
if False:
rayleigh_cdf = [
fitted_rayleigh.distribution.cdf(x=sorted_cl[c]) for c in xrange(len(sorted_cl))]
from matplotlib import pyplot as plt
plt.plot(sorted_cl,y_data,"r+")
#plt.plot(sorted_cl,rayleigh_cdf,"g.")
plt.plot(inv_cdf,y_data,"b.")
plt.show()
else:
indexed_pairs = indexed_pairs_provisional
correction_vectors = correction_vectors_provisional
########### finished with outlier rejection
self.inputpd["symmetry"].show_summary(prefix="SETTING ")
is_triclinic = (self.setting_id==1)
if is_triclinic:
self.triclinic_pairs = [ dict(pred=self.hkllist[a["pred"]],spot=a["spot"])
for a in indexed_pairs ]
if self.horizons_phil.integration.model == "user_supplied":
if kwargs.get("user-reentrant",None)==None:
from cxi_user import post_outlier_rejection
self.indexed_pairs = indexed_pairs
self.spots = spots
post_outlier_rejection(self,image_number,cb_op_to_primitive,self.horizons_phil,kwargs)
return
########### finished with user-supplied code
if self.horizons_phil.integration.spot_shape_verbose:
from rstbx.new_horizons.spot_shape import spot_shape_verbose
spot_shape_verbose(rawdata = self.imagefiles.images[self.image_number].linearintdata,
beam_center_pix = matrix.col((self.inputai.xbeam()/pxlsz, self.inputai.ybeam()/pxlsz)),
indexed_pairs = indexed_pairs,
spotfinder_observations = spots,
distance_mm = self.inputai.distance(),
mm_per_pixel = pxlsz,
hkllist = self.hkllist,
unit_cell = self.cell,
wavelength_ang = self.inputai.wavelength
)
#Other checks to be implemented (future):
# spot is within active area of detector on a circular detector such as the Mar IP
# integration masks do not overlap; or deconvolute
correction_lengths=flex.double([v.length() for v in correction_vectors])
if verbose:
print "average correction %5.2f over %d vectors"%(flex.mean(correction_lengths),
len(correction_lengths)),
print "or %5.2f mm."%(pxlsz*flex.mean(correction_lengths))
self.r_residual = pxlsz*flex.mean(correction_lengths)
#assert len(indexed_pairs)>NEAR # must have enough indexed spots
if (len(indexed_pairs) <= NEAR):
raise Sorry("Not enough indexed spots, only found %d, need %d" % (len(indexed_pairs), NEAR))
reference = flex.double()
for item in indexed_pairs:
reference.append(spots[item["spot"]].ctr_mass_x())
reference.append(spots[item["spot"]].ctr_mass_y())
PS_adapt = AnnAdaptor(data=reference,dim=2,k=NEAR)
PS_adapt.query(query)
self.BSmasks = []
#self.null_correction_mapping( predicted=self.predicted,
# correction_vectors = correction_vectors,
# IS_adapt = IS_adapt,
# spots = spots)
self.positional_correction_mapping( predicted=self.predicted,
correction_vectors = correction_vectors,
PS_adapt = PS_adapt,
IS_adapt = IS_adapt,
spots = spots)
# which spots are close enough to interfere with background?
MAXOVER=6
OS_adapt = AnnAdaptor(data=query,dim=2,k=MAXOVER) #six near nbrs
OS_adapt.query(query)
if self.mask_focus[image_number] is None:
raise Sorry("No observed/predicted spot agreement; no Spotfinder masks; skip integration")
nbr_cutoff = 2.0* max(self.mask_focus[image_number])
FRAME = int(nbr_cutoff/2)
#print "The overlap cutoff is %d pixels"%nbr_cutoff
nbr_cutoff_sq = nbr_cutoff * nbr_cutoff
#print "Optimized C++ section...",
self.set_frame(FRAME)
self.set_background_factor(kwargs["background_factor"])
self.set_nbr_cutoff_sq(nbr_cutoff_sq)
self.set_guard_width_sq(self.horizons_phil.integration.guard_width_sq)
self.set_detector_gain(self.horizons_phil.integration.detector_gain)
flex_sorted = flex.int()
for item in self.sorted:
flex_sorted.append(item[0]);flex_sorted.append(item[1]);
if self.horizons_phil.integration.mask_pixel_value is not None:
self.set_mask_pixel_val(self.horizons_phil.integration.mask_pixel_value)
image_obj = self.imagefiles.imageindex(self.frame_numbers[self.image_number])
image_obj.read()
rawdata = image_obj.linearintdata # assume image #1
if self.inputai.active_areas != None:
self.detector_xy_draft = self.safe_background( rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted,
tiles=self.inputai.active_areas.IT,
tile_id=self.inputai.active_areas.tile_id);
else:
self.detector_xy_draft = self.safe_background( rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted);
for i in xrange(len(self.predicted)): # loop over predicteds
B_S_mask = {}
keys = self.get_bsmask(i)
for k in xrange(0,len(keys),2):
B_S_mask[(keys[k],keys[k+1])]=True
self.BSmasks.append(B_S_mask)
#print "Done"
return
