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 update_detail(self, horizon_phil, current_status, first_time_through,
verbose):
assert (len(self.observed_spots) == len(self.predicted_spots))
if horizon_phil.indexing.outlier_detection.verbose:
classes = [
str(current_status[i]) for i in range(len(self.observed_spots))
]
class_types = set(classes)
class_counts = dict([[item, classes.count(item)]
for item in class_types])
flex_counts = flex.int(class_counts.values())
assert flex.sum(flex_counts) == len(self.observed_spots)
#for pair in class_counts.items():
# print "%10s %6d"%pair
#print "%10s %6d"%("TOTAL",len(self.observed_spots))
if status_with_marked_outliers == None:
# status_with_marked_outliers==None is shorthand for identifying the first run through
print """After indexing on a subset of %d spots (from all images), %d were reclassified as
either lying on the spindle, or potential overlapped spots or ice rings.""" % (
len(self.observed_spots),
len(self.observed_spots) - class_counts["GOOD"])
else:
print """Rerefinement on just the well-fit spots followed by spot reclassification
leaves %d good spots on which to calculate a triclinic rmsd.""" % (
class_counts["GOOD"])
# check good spots
if (self.good is not None):
match = 0
for i in range(len(self.observed_spots)):
if ((current_status[i] == SpotClass.GOOD) and self.good[i]):
match = match + 1
if self.verbose:
print "Number of GOOD spots matched with previous model =", match
# calculate differences for all spots
self.sorted_observed_spots = {}
self.dr = flex.double()
self.not_good_dr = flex.double()
self.dx = [0.0 for i in range(len(self.observed_spots))]
self.dy = [0.0 for i in range(len(self.observed_spots))]
for i in range(len(self.observed_spots)):
o = self.observed_spots[i]
p = self.predicted_spots[i]
self.dx[i] = o[0] - p[0]
self.dy[i] = o[1] - p[1]
self.sorted_observed_spots[math.sqrt(self.dx[i] * self.dx[i] +
self.dy[i] * self.dy[i])] = i
# separate GOOD spots
spotclasses = {
SpotClass.GOOD: 0,
SpotClass.SPINDLE: 0,
SpotClass.OVERLAP: 0,
SpotClass.ICE: 0,
SpotClass.OUTLIER: 0,
SpotClass.NONE: 0
}
for key in sorted(self.sorted_observed_spots.keys()):
spotclass = current_status[self.sorted_observed_spots[key]]
spotclasses[spotclass] += 1
if (current_status[self.sorted_observed_spots[key]] ==
SpotClass.GOOD):
self.dr.append(key)
else:
self.not_good_dr.append(key)
if verbose:
print ", ".join([
"=".join([str(i[0]), "%d" % i[1]])
for i in spotclasses.items()
]),
totalsp = sum(
[spotclasses.values()[iidx] for iidx in range(len(spotclasses))])
if verbose:
print "Total=%d" % (totalsp), "# observed spots", len(
self.observed_spots)
assert totalsp == len(
self.observed_spots
), "Some spot pairs have the same predicted-observed distances. Do you have duplicated images?"
self.x = flex.double(len(self.dr))
for i in range(len(self.x)):
self.x[i] = float(i) / float(len(self.x))
limit = int(self.fraction * len(self.dr))
if limit < 4:
return # Basic sanity check, need at least a few good spots to fit the distribution
fitted_rayleigh = fit_cdf(x_data=self.dr[0:limit],
y_data=self.x[0:limit],
distribution=rayleigh_cpp)
if False:
y_data = self.x[0:limit]
inv_cdf = [
fitted_rayleigh.distribution.inv_cdf(cdf) for cdf in y_data
]
from matplotlib import pyplot as plt
plt.plot(self.dr[0:limit], self.x[0:limit], "r+")
plt.plot(inv_cdf, y_data, "b.")
plt.show()
# store indices for spots used for fitting
self.fraction_spot_indices = []
for dr in self.dr[0:limit]:
self.fraction_spot_indices.append(self.sorted_observed_spots[dr])
# generate points for fitted distributions
rayleigh_cdf_x = flex.double(range(500))
rayleigh_cdf_x /= float(len(rayleigh_cdf_x))
rayleigh_cdf = fitted_rayleigh.distribution.cdf(x=rayleigh_cdf_x)
# generate points for pdf
dr_bins, dr_histogram = make_histogram_data(data=self.dr, n_bins=100)
rayleigh_pdf = flex.double(len(dr_bins))
for i in range(len(dr_bins)):
rayleigh_pdf[i] = fitted_rayleigh.distribution.pdf(x=dr_bins[i])
rayleigh_pdf = rayleigh_pdf / flex.sum(rayleigh_pdf)
dr_bins = flex.double(dr_bins)
dr_histogram = flex.double(dr_histogram)
# standard deviation for cdf
sd = math.sqrt((4.0 - math.pi) / (2.0) * fitted_rayleigh.x[0] *
fitted_rayleigh.x[0])
if self.verbose:
print 'Standard deviation of Rayleigh fit = %4.3f' % sd
sd_data = None
radius_outlier_index = None
limit_outlier = None
# --- Quoted code superceeded by extension module call to find_green_bar
"""
for i in range(len(rayleigh_cdf_x)):
mx = rayleigh_cdf_x[i]
my = rayleigh_cdf[i]
for j in range(1,len(self.dr)):
upper_x = self.dr[j]
upper_y = self.x[j]
lower_x = self.dr[j-1]
lower_y = self.x[j-1]
if ((my >= lower_y) and (my < upper_y)):
if ((sd <= (upper_x - mx)) and ((lower_x - mx) > 0.0)):
sd_data = ((mx,my),(lower_x,lower_y))
radius_outlier_index = j-1
limit_outlier = lower_x
if self.verbose:print "Width of green bar = %4.3f"%(lower_x - mx)
break
if (sd_data is not None):
break
"""
from rstbx.indexing_api import find_green_bar
green = find_green_bar(rayleigh_cdf_x=rayleigh_cdf_x,
rayleigh_cdf=rayleigh_cdf,
dr=self.dr,
x=self.x,
sd=sd)
if green.is_set:
#assert radius_outlier_index == green.radius_outlier_index
#assert limit_outlier == green.limit_outlier
#assert sd_data[0][0] == green.sd_mx
#assert sd_data[0][1] == green.sd_my
#assert sd_data[1][0] == green.sd_lower_x
#assert sd_data[1][1] == green.sd_lower_y
if self.verbose:
print "Width of green bar = %4.3f" % (green.sd_lower_x -
green.sd_mx)
radius_outlier_index = green.radius_outlier_index
limit_outlier = green.limit_outlier
sd_data = ((green.sd_mx, green.sd_my), (green.sd_lower_x,
green.sd_lower_y))
if (radius_outlier_index is None):
radius_outlier_index = len(self.dr)
if (limit_outlier is None):
limit_outlier = self.dr[-1]
radius_95 = None
for i in range(len(rayleigh_cdf)):
if (rayleigh_cdf[i] >= 0.95):
radius_95 = rayleigh_cdf_x[i]
break
if (radius_95 is None):
radius_95 = rayleigh_cdf_x[-1]
upper_circle = []
lower_circle = []
d_radius = 2.0 * radius_95 / 100.0
x = -radius_95
r2 = radius_95 * radius_95
for i in range(100):
y = math.sqrt(r2 - x * x)
upper_circle.append((x, y))
lower_circle.append((x, -y))
x = x + d_radius
y = 0.0
upper_circle.append((x, y))
lower_circle.append((x, -y))
self.sqrtr2 = math.sqrt(r2)
# color code dx dy
dxdy_fraction = []
dxdy_inliers = []
dxdy_outliers = []
limit = self.dr[int(self.fraction * len(self.dr))]
trifold = dict(fraction=0, inlier=0, outlier=0, total=0)
for key in self.dr:
trifold["total"] += 1
i = self.sorted_observed_spots[key]
if (key < limit):
trifold["fraction"] += 1
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
dxdy_fraction.append((self.dx[i], self.dy[i]))
elif (key < limit_outlier):
trifold["inlier"] += 1
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
dxdy_inliers.append((self.dx[i], self.dy[i]))
else:
trifold["outlier"] += 1
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
dxdy_outliers.append((self.dx[i], self.dy[i]))
if verbose:
print ", ".join(
["=".join([str(i[0]), "%d" % i[1]]) for i in trifold.items()])
# color code observed fractions
o_fraction = []
o_inliers = []
o_outliers = []
mr = format_data(x_data=rayleigh_cdf_x, y_data=rayleigh_cdf)
limit = int(self.fraction * len(self.dr))
for i in range(len(self.dr)):
if (self.dr[i] <= 1.0):
if (i < limit):
o_fraction.append((self.dr[i], self.x[i]))
elif (i < radius_outlier_index):
o_inliers.append((self.dr[i], self.x[i]))
else:
o_outliers.append((self.dr[i], self.x[i]))
if horizon_phil.indexing.outlier_detection.verbose:
o_outliers_for_severity = []
for i in range(radius_outlier_index, len(self.dr)):
o_outliers_for_severity.append((self.dr[i], self.x[i]))
# limit data range
for i in range(len(dr_bins)):
if (dr_bins[i] > 1.0):
dr_bins.resize(i)
dr_histogram.resize(i)
rayleigh_pdf.resize(i)
break
ho = format_data(x_data=dr_bins, y_data=dr_histogram)
hr = format_data(x_data=dr_bins, y_data=rayleigh_pdf)
# format data for graphing
self.plot_dxdy_data = [
dxdy_fraction, dxdy_inliers, dxdy_outliers, [(0.0, 0.0)], [], [],
[], []
]
self.framework = {
4: dict(status=SpotClass.SPINDLE),
5: dict(status=SpotClass.OVERLAP),
6: dict(status=SpotClass.OUTLIER),
7: dict(status=SpotClass.ICE),
}
for key in self.not_good_dr:
i = self.sorted_observed_spots[key]
status = current_status[i]
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
statuskey = [
k for k in self.framework.keys()
if self.framework[k]["status"] == status
][0]
self.plot_dxdy_data[statuskey].append((self.dx[i], self.dy[i]))
self.plot_cdf_data = [mr, o_fraction, o_inliers, o_outliers]
if (sd_data is not None):
self.plot_cdf_data.append(sd_data)
self.plot_pdf_data = [ho, hr]
# mark outliers
if (first_time_through): #i.e., first time through the update() method
if (radius_outlier_index < len(self.dr)):
for i in range(radius_outlier_index, len(self.dr)):
current_status[self.sorted_observed_spots[
self.dr[i]]] = SpotClass.OUTLIER
# reset good spots
self.good = [False for i in range(len(self.observed_spots))]
for i in range(len(self.observed_spots)):
if (current_status[i] == SpotClass.GOOD):
self.good[i] = True
count_outlier = 0
count_good = 0
for i in range(len(self.observed_spots)):
if (current_status[i] == SpotClass.OUTLIER):
count_outlier = count_outlier + 1
elif (current_status[i] == SpotClass.GOOD):
count_good = count_good + 1
if self.verbose: print 'Old GOOD =', len(self.dr),\
'OUTLIER =', count_outlier,\
'New GOOD =', count_good
if horizon_phil.indexing.outlier_detection.verbose and status_with_marked_outliers is None:
print "\nOf the remaining %d spots, %.1f%% were lattice outliers, leaving %d well-fit spots" % (
len(self.dr), 100. * count_outlier / len(self.dr), count_good)
if count_outlier == 0: return
#width of green bar is sd
delta_spread = o_outliers_for_severity[1][
1] - o_outliers_for_severity[0][1]
severity = 0.
for item in o_outliers_for_severity:
delta_r = item[0] # obs - predicted deviation in mm
spread = item[
1] # order of observed deviation on a scale from 0 to 1
# now invert the cdf to find expected delta r:
expected_delta_r = fitted_rayleigh.distribution.sigma * math.sqrt(
-2. * math.log(1. - spread))
#print item, expected_delta_r, (delta_r - expected_delta_r) / sd
severity += ((delta_r - expected_delta_r) / sd)
severity *= delta_spread
print "The outlier severity is %.2f sigma [defined in J Appl Cryst (2010) 43, p.611 sec. 4].\n" % severity
return current_status
def update_detail(self,horizon_phil,current_status,first_time_through,verbose):
assert(len(self.observed_spots) == len(self.predicted_spots))
if horizon_phil.indexing.outlier_detection.verbose:
classes=[str(current_status[i]) for i in xrange(len(self.observed_spots))]
class_types = set(classes)
class_counts = dict([[item,classes.count(item)] for item in class_types])
flex_counts = flex.int(class_counts.values())
assert flex.sum(flex_counts) == len(self.observed_spots)
#for pair in class_counts.items():
# print "%10s %6d"%pair
#print "%10s %6d"%("TOTAL",len(self.observed_spots))
if status_with_marked_outliers == None:
# status_with_marked_outliers==None is shorthand for identifying the first run through
print """After indexing on a subset of %d spots (from all images), %d were reclassified as
either lying on the spindle, or potential overlapped spots or ice rings."""%(
len(self.observed_spots),len(self.observed_spots)-class_counts["GOOD"])
else:
print """Rerefinement on just the well-fit spots followed by spot reclassification
leaves %d good spots on which to calculate a triclinic rmsd."""%(class_counts["GOOD"])
# check good spots
if (self.good is not None):
match = 0
for i in xrange(len(self.observed_spots)):
if ((current_status[i] == SpotClass.GOOD) and self.good[i]):
match = match + 1
if self.verbose:print "Number of GOOD spots matched with previous model =",match
# calculate differences for all spots
self.sorted_observed_spots = {}
self.dr = flex.double()
self.not_good_dr = flex.double()
self.dx = [0.0 for i in xrange(len(self.observed_spots))]
self.dy = [0.0 for i in xrange(len(self.observed_spots))]
for i in xrange(len(self.observed_spots)):
o = self.observed_spots[i]
p = self.predicted_spots[i]
self.dx[i] = o[0] - p[0]
self.dy[i] = o[1] - p[1]
self.sorted_observed_spots[
math.sqrt(self.dx[i]*self.dx[i] + self.dy[i]*self.dy[i])] = i
# separate GOOD spots
spotclasses = {SpotClass.GOOD:0,SpotClass.SPINDLE:0,SpotClass.OVERLAP:0,SpotClass.ICE:0,SpotClass.OUTLIER:0,SpotClass.NONE:0}
for key in sorted(self.sorted_observed_spots.keys()):
spotclass = current_status[self.sorted_observed_spots[key]]
spotclasses[spotclass]+=1
if (current_status[self.sorted_observed_spots[key]] == SpotClass.GOOD):
self.dr.append(key)
else:
self.not_good_dr.append(key)
if verbose: print ", ".join(["=".join([str(i[0]),"%d"%i[1]]) for i in spotclasses.items()]),
totalsp = sum([spotclasses.values()[iidx] for iidx in xrange(len(spotclasses))])
if verbose: print "Total=%d"%(totalsp),"# observed spots",len(self.observed_spots)
assert totalsp == len(self.observed_spots)
self.x = flex.double(len(self.dr))
for i in xrange(len(self.x)):
self.x[i] = float(i)/float(len(self.x))
limit = int(self.fraction*len(self.dr))
if limit < 4: return # Basic sanity check, need at least a few good spots to fit the distribution
fitted_rayleigh = fit_cdf(x_data=self.dr[0:limit],
y_data=self.x[0:limit],distribution=rayleigh)
if False:
y_data=self.x[0:limit]
inv_cdf = [fitted_rayleigh.distribution.inv_cdf(cdf) for cdf in y_data]
from matplotlib import pyplot as plt
plt.plot(self.dr[0:limit],self.x[0:limit],"r+")
plt.plot(inv_cdf,y_data,"b.")
plt.show()
# store indices for spots used for fitting
self.fraction_spot_indices = []
for dr in self.dr[0:limit]:
self.fraction_spot_indices.append(self.sorted_observed_spots[dr])
# generate points for fitted distributions
rayleigh_cdf_x = flex.double(500)
for i in xrange(len(rayleigh_cdf_x)):
rayleigh_cdf_x[i] = float(i)/float(len(rayleigh_cdf_x))
rayleigh_cdf = flex.double(len(rayleigh_cdf_x))
for i in xrange(len(rayleigh_cdf_x)):
rayleigh_cdf[i] = fitted_rayleigh.distribution.cdf(x=rayleigh_cdf_x[i])
# generate points for pdf
dr_bins,dr_histogram = make_histogram_data(data=self.dr,n_bins=100)
rayleigh_pdf = flex.double(len(dr_bins))
for i in xrange(len(dr_bins)):
rayleigh_pdf[i] = fitted_rayleigh.distribution.pdf(x=dr_bins[i])
rayleigh_pdf = rayleigh_pdf/flex.sum(rayleigh_pdf)
dr_bins = flex.double(dr_bins)
dr_histogram = flex.double(dr_histogram)
# standard deviation for cdf
sd = math.sqrt((4.0-math.pi)/(2.0)*
fitted_rayleigh.x[0]*fitted_rayleigh.x[0])
if self.verbose:print 'Standard deviation of Rayleigh fit = %4.3f'%sd
sd_data = None
radius_outlier_index = None
limit_outlier = None
for i in xrange(len(rayleigh_cdf_x)):
mx = rayleigh_cdf_x[i]
my = rayleigh_cdf[i]
for j in xrange(1,len(self.dr)):
upper_x = self.dr[j]
upper_y = self.x[j]
lower_x = self.dr[j-1]
lower_y = self.x[j-1]
if ((my >= lower_y) and (my < upper_y)):
if ((sd <= (upper_x - mx)) and ((lower_x - mx) > 0.0)):
sd_data = ((mx,my),(lower_x,lower_y))
radius_outlier_index = j-1
limit_outlier = lower_x
if self.verbose:print "Width of green bar = %4.3f"%(lower_x - mx)
break
if (sd_data is not None):
break
if (radius_outlier_index is None):
radius_outlier_index = len(self.dr)
if (limit_outlier is None):
limit_outlier = self.dr[-1]
radius_95 = None
for i in xrange(len(rayleigh_cdf)):
if (rayleigh_cdf[i] >= 0.95):
radius_95 = rayleigh_cdf_x[i]
break
if (radius_95 is None):
radius_95 = rayleigh_cdf_x[-1]
upper_circle = []
lower_circle = []
d_radius = 2.0*radius_95/100.0
x = -radius_95
r2 = radius_95*radius_95
for i in xrange(100):
y = math.sqrt(r2 - x*x)
upper_circle.append((x,y))
lower_circle.append((x,-y))
x = x + d_radius
y = 0.0
upper_circle.append((x,y))
lower_circle.append((x,-y))
self.sqrtr2 = math.sqrt(r2)
# color code dx dy
dxdy_fraction = []
dxdy_inliers = []
dxdy_outliers = []
limit = self.dr[int(self.fraction*len(self.dr))]
trifold = dict(fraction=0,inlier=0,outlier=0,total=0)
for key in self.dr:
trifold["total"]+=1
i = self.sorted_observed_spots[key]
if (key < limit):
trifold["fraction"]+=1
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
dxdy_fraction.append((self.dx[i],self.dy[i]))
elif (key < limit_outlier):
trifold["inlier"]+=1
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
dxdy_inliers.append((self.dx[i],self.dy[i]))
else:
trifold["outlier"]+=1
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
dxdy_outliers.append((self.dx[i],self.dy[i]))
if verbose: print ", ".join(["=".join([str(i[0]),"%d"%i[1]]) for i in trifold.items()])
# color code observed fractions
o_fraction = []
o_inliers = []
o_outliers = []
mr = format_data(x_data=rayleigh_cdf_x,y_data=rayleigh_cdf)
limit = int(self.fraction*len(self.dr))
for i in xrange(len(self.dr)):
if (self.dr[i] <= 1.0):
if (i < limit):
o_fraction.append((self.dr[i],self.x[i]))
elif (i < radius_outlier_index):
o_inliers.append((self.dr[i],self.x[i]))
else:
o_outliers.append((self.dr[i],self.x[i]))
if horizon_phil.indexing.outlier_detection.verbose:
o_outliers_for_severity = []
for i in xrange(radius_outlier_index, len(self.dr)):
o_outliers_for_severity.append((self.dr[i],self.x[i]))
# limit data range
for i in xrange(len(dr_bins)):
if (dr_bins[i] > 1.0):
dr_bins.resize(i)
dr_histogram.resize(i)
rayleigh_pdf.resize(i)
break
ho = format_data(x_data=dr_bins,y_data=dr_histogram)
hr = format_data(x_data=dr_bins,y_data=rayleigh_pdf)
# format data for graphing
self.plot_dxdy_data = [dxdy_fraction,dxdy_inliers,dxdy_outliers,
[(0.0,0.0)],[],[],[],[]]
self.framework = {4:dict(status=SpotClass.SPINDLE),
5:dict(status=SpotClass.OVERLAP),
6:dict(status=SpotClass.OUTLIER),
7:dict(status=SpotClass.ICE),
}
for key in self.not_good_dr:
i = self.sorted_observed_spots[key]
status = current_status[i]
if (not ((self.dx[i] > 1.0) or (self.dx[i] < -1.0) or
(self.dy[i] > 1.0) or (self.dy[i] < -1.0))):
statuskey = [k for k in self.framework.keys() if self.framework[k]["status"]==status][0]
self.plot_dxdy_data[statuskey].append((self.dx[i],self.dy[i]))
self.plot_cdf_data = [mr,o_fraction,o_inliers,o_outliers]
if (sd_data is not None):
self.plot_cdf_data.append(sd_data)
self.plot_pdf_data = [ho,hr]
# mark outliers
if (first_time_through): #i.e., first time through the update() method
if (radius_outlier_index < len(self.dr)):
for i in xrange(radius_outlier_index,len(self.dr)):
current_status[self.sorted_observed_spots[self.dr[i]]] = SpotClass.OUTLIER
# reset good spots
self.good = [False for i in xrange(len(self.observed_spots))]
for i in xrange(len(self.observed_spots)):
if (current_status[i] == SpotClass.GOOD):
self.good[i] = True
count_outlier = 0
count_good = 0
for i in xrange(len(self.observed_spots)):
if (current_status[i] == SpotClass.OUTLIER):
count_outlier = count_outlier + 1
elif (current_status[i] == SpotClass.GOOD):
count_good = count_good + 1
if self.verbose:print 'Old GOOD =', len(self.dr),\
'OUTLIER =', count_outlier,\
'New GOOD =', count_good
if horizon_phil.indexing.outlier_detection.verbose and status_with_marked_outliers is None:
print "\nOf the remaining %d spots, %.1f%% were lattice outliers, leaving %d well-fit spots"%(
len(self.dr),100.*count_outlier/len(self.dr), count_good )
if count_outlier==0:return
#width of green bar is sd
delta_spread = o_outliers_for_severity[1][1]-o_outliers_for_severity[0][1]
severity = 0.
for item in o_outliers_for_severity:
delta_r = item[0] # obs - predicted deviation in mm
spread = item[1] # order of observed deviation on a scale from 0 to 1
# now invert the cdf to find expected delta r:
expected_delta_r = fitted_rayleigh.distribution.sigma * math.sqrt(
-2.* math.log(1.-spread) )
#print item, expected_delta_r, (delta_r - expected_delta_r) / sd
severity += ((delta_r - expected_delta_r) / sd)
severity *= delta_spread
print "The outlier severity is %.2f sigma [defined in J Appl Cryst (2010) 43, p.611 sec. 4].\n"%severity
return current_status
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
