def __init__(self, spot_resolutions, firstBinCount, fractionCalculator,
image):
RingFinder.__init__(self, spot_resolutions, firstBinCount,
fractionCalculator)
# use Hough transform to locate ellipses
frame = hough()
frame.importData(image.linearintdata, image.pixel_size)
frame.setGeometry(float(fractionCalculator.pd['xbeam']),
float(fractionCalculator.pd['ybeam']),
float(fractionCalculator.pd['distance']),
float(fractionCalculator.pd['twotheta']))
frame.cannyEdge(2, 0.95, 0.97)
frame.findEllipse(2, 750.0, 3.5)
self.rings = frame.getRings()
self.col = 7
self.nRings = len(self.rings) // self.col
# add ellipses to existing list of rings
for i in xrange(self.nRings):
res = radius_to_resol(
self.rings[i * self.col + 6] * image.pixel_size,
fractionCalculator.pd)
RingFinder.add_interval(self, (res + 0.015, res - 0.015))
# ensure that rings from hough are definitely ice rings
self.impact = flex.int([0] * len(self.intervals))
for i in xrange(len(self.intervals)):
for j in xrange(self.nRings):
res = radius_to_resol(
self.rings[j * self.col + 6] * image.pixel_size,
fractionCalculator.pd)
if (res <= self.intervals[i][0]
and res >= self.intervals[i][1]):
self.impact[i] = 15
def __init__(self,spot_resolutions,firstBinCount,fractionCalculator,
image):
RingFinder.__init__(self,spot_resolutions,firstBinCount,fractionCalculator)
# use Hough transform to locate ellipses
frame = hough()
frame.importData(image.linearintdata,image.pixel_size)
frame.setGeometry( float(fractionCalculator.pd['xbeam']),
float(fractionCalculator.pd['ybeam']),
float(fractionCalculator.pd['distance']),
float(fractionCalculator.pd['twotheta']) )
frame.cannyEdge(2,0.95,0.97)
frame.findEllipse(2,750.0,3.5)
self.rings = frame.getRings()
self.col = 7
self.nRings = len(self.rings)//self.col
# add ellipses to existing list of rings
for i in xrange(self.nRings):
res = radius_to_resol(self.rings[i*self.col+6]*image.pixel_size,
fractionCalculator.pd)
RingFinder.add_interval(self,(res+0.015,res-0.015))
# ensure that rings from hough are definitely ice rings
self.impact = flex.int([0]*len(self.intervals))
for i in xrange(len(self.intervals)):
for j in xrange(self.nRings):
res = radius_to_resol(self.rings[j*self.col+6]*image.pixel_size,
fractionCalculator.pd)
if (res <= self.intervals[i][0] and
res >= self.intervals[i][1]):
self.impact[i] = 15
def get_indices_without(self, key, subtractive_key):
initial_set = list(self.get_indices(key))
subtractive_set = list(self.get_indices(subtractive_key))
initial_set.sort()
subtractive_set.sort()
#use an algorithm to subtract list B from list A
C = []
ptrA = 0
ptrB = 0
lenA = len(initial_set)
lenB = len(subtractive_set)
while ptrA < lenA and ptrB <= lenB:
if ptrB == lenB:
C.append(initial_set[ptrA])
ptrA += 1
continue
if initial_set[ptrA] == subtractive_set[ptrB]:
ptrA += 1
ptrB += 1
continue
if initial_set[ptrA] < subtractive_set[ptrB]:
C.append(initial_set[ptrA])
ptrA += 1
continue
ptrB += 1
return flex.int(
C) # returned indices will no longer be sorted by resolution
def calculate_saturation(self,fstats,image):
S = SaturationMeasure()
resolution_spots = fstats[fstats.most_recent_child()]
S.n_resolution_spots = len(resolution_spots)
S.n_goodspots = fstats['N_goodspots']
ispots = flex.int()
for i in xrange(S.n_resolution_spots):
spt = resolution_spots[i]
ispots.append( image.linearintdata[(spt.max_pxl_x(),spt.max_pxl_y())] )
S.OverCount = (ispots >= int(image.saturation)).count(True)
perm = flex.sort_permutation(ispots,True)
spot_peaks = flex.int([ispots[p] for p in perm[0:min(50,len(perm))]])
S.n_sample = len(spot_peaks)
if S.n_sample < 2: #no peaks on the image
S.p_saturation = 0.0; return S
S.average = float(reduce(lambda x,y:x+y, spot_peaks))/len(spot_peaks)
S.p_saturation = S.average/image.saturation
# for Raxis-II and Mar Image Plates, it has been noticed that image.linearintdata
# can contain pixel values greater than image.saturation. No doubt this is
# a shortcoming in how the pixel values are decoded for overloaded values (not fixed)
return S
def spot_filter(self,oldkey,newkey,apfunction):
#parent
parent = self.key[oldkey]
if parent == 1:
parentselection = xrange(self.master.size())
else:
parentselection = self.nodes[parent].data
nextkey = max(self.nodes.keys())+1
childselection = flex.int()
for x in parentselection:
if apfunction(self.master[x]):
childselection.append(x)
self.nodes[nextkey]=ListNode(childselection,newkey,parent)
self.key[newkey]=nextkey
def filtered(self, sorted_order):
# recoded in C++
from spotfinder.core_toolbox import bin_exclusion_and_impact
assert len(sorted_order) == len(self.ref_spots)
self.impact = flex.int([0] * len(self.intervals))
limits_low_hi_res = flex.double()
for x in xrange(len(self.intervals)):
limits_low_hi_res.append(self.intervals[x][0])
limits_low_hi_res.append(self.intervals[x][1])
N = bin_exclusion_and_impact(self.ref_spots, sorted_order,
limits_low_hi_res, self.impact)
return N
def __init__(self, ave_area, query_centers, fstats, ann_adaptor):
self.plot = {}
self.pmax = []
plot_to_spot_ratio = 20.
significant_vector_fraction_of_highest = 0.2
significant_vector_fraction_of_recent_child = 0.05
# make plot with area 20*average spot area (an arbitrary cutoff)
self.deltaxylimit = int(math.sqrt(plot_to_spot_ratio*ave_area)/2.)+1
for x in range(-self.deltaxylimit,self.deltaxylimit+1):
self.plot[x]=flex.int(2*self.deltaxylimit+1)
for d in range(len(query_centers)//2):
vector = (int( fstats.master[ann_adaptor.nn[d]].max_pxl_x()-query_centers[2*d] ),
int( fstats.master[ann_adaptor.nn[d]].max_pxl_y()-query_centers[2*d+1]))
if abs(vector[0])<=self.deltaxylimit and \
abs(vector[1])<=self.deltaxylimit:
self.plot[vector[0]][self.deltaxylimit+vector[1]]+=1
self.plot[-vector[0]][self.deltaxylimit-vector[1]]+=1
# get the maximum value of this "Fourier" plot
allx = ()
for x in range(-self.deltaxylimit+1,self.deltaxylimit):
allx=allx+tuple(self.plot[x])
maxp = max(allx) #"the largest peak is",maxp
# Search for all local maxima in the plot. Only look in positive hemisphere.
for locx in range(-self.deltaxylimit+1,self.deltaxylimit):
for locy in range(-self.deltaxylimit+1,self.deltaxylimit):
if locy>0 or (locy==0 and locx>0): #primary hemisphere
tlocy = self.deltaxylimit+locy
refval = self.plot[locx][tlocy]
#(Two important parameters subject to adjustment)
if refval > significant_vector_fraction_of_highest*maxp and \
refval > significant_vector_fraction_of_recent_child*\
fstats['N_%s'%fstats.most_recent_child()] and \
refval >= self.plot[locx][tlocy+1] and \
refval >= self.plot[locx+1][tlocy+1] and \
refval >= self.plot[locx+1][tlocy] and \
refval >= self.plot[locx+1][tlocy-1] and \
refval > self.plot[locx][tlocy-1] and \
refval > self.plot[locx-1][tlocy-1] and \
refval > self.plot[locx-1][tlocy] and \
refval > self.plot[locx-1][tlocy+1]:
self.pmax.append((locx,locy,refval))
def __init__(self, ave_area, query_centers, fstats, ann_adaptor):
self.plot = {}
self.pmax = []
plot_to_spot_ratio = 20.
significant_vector_fraction_of_highest = 0.2
significant_vector_fraction_of_recent_child = 0.05
# make plot with area 20*average spot area (an arbitrary cutoff)
self.deltaxylimit = int(math.sqrt(plot_to_spot_ratio*ave_area)/2.)+1
for x in xrange(-self.deltaxylimit,self.deltaxylimit+1):
self.plot[x]=flex.int(2*self.deltaxylimit+1)
for d in xrange(len(query_centers)//2):
vector = (int( fstats.master[ann_adaptor.nn[d]].max_pxl_x()-query_centers[2*d] ),
int( fstats.master[ann_adaptor.nn[d]].max_pxl_y()-query_centers[2*d+1]))
if abs(vector[0])<=self.deltaxylimit and \
abs(vector[1])<=self.deltaxylimit:
self.plot[vector[0]][self.deltaxylimit+vector[1]]+=1
self.plot[-vector[0]][self.deltaxylimit-vector[1]]+=1
# get the maximum value of this "Fourier" plot
allx = ()
for x in xrange(-self.deltaxylimit+1,self.deltaxylimit):
allx=allx+tuple(self.plot[x])
maxp = max(allx) #"the largest peak is",maxp
# Search for all local maxima in the plot. Only look in positive hemisphere.
for locx in xrange(-self.deltaxylimit+1,self.deltaxylimit):
for locy in xrange(-self.deltaxylimit+1,self.deltaxylimit):
if locy>0 or (locy==0 and locx>0): #primary hemisphere
tlocy = self.deltaxylimit+locy
refval = self.plot[locx][tlocy]
#(Two important parameters subject to adjustment)
if refval > significant_vector_fraction_of_highest*maxp and \
refval > significant_vector_fraction_of_recent_child*\
fstats['N_%s'%fstats.most_recent_child()] and \
refval >= self.plot[locx][tlocy+1] and \
refval >= self.plot[locx+1][tlocy+1] and \
refval >= self.plot[locx+1][tlocy] and \
refval >= self.plot[locx+1][tlocy-1] and \
refval > self.plot[locx][tlocy-1] and \
refval > self.plot[locx-1][tlocy-1] and \
refval > self.plot[locx-1][tlocy] and \
refval > self.plot[locx-1][tlocy+1]:
self.pmax.append((locx,locy,refval))
def filtered(self,sorted_order):
# recoded in C++
from spotfinder.core_toolbox import bin_exclusion_and_impact
assert len(sorted_order)==len(self.ref_spots)
self.impact = flex.int([0]*len(self.intervals))
limits_low_hi_res = flex.double();
for x in xrange(len(self.intervals)):
limits_low_hi_res.append(self.intervals[x][0]);
limits_low_hi_res.append(self.intervals[x][1]);
N = bin_exclusion_and_impact(self.ref_spots,
sorted_order,
limits_low_hi_res,
self.impact)
return N
def get_indices_without(self,key,subtractive_key):
initial_set = list(self.get_indices(key))
subtractive_set = list(self.get_indices(subtractive_key))
initial_set.sort()
subtractive_set.sort()
#use an algorithm to subtract list B from list A
C = []
ptrA = 0
ptrB = 0
lenA = len(initial_set)
lenB = len(subtractive_set)
while ptrA < lenA and ptrB <= lenB:
if ptrB == lenB:
C.append(initial_set[ptrA]); ptrA+=1; continue
if initial_set[ptrA]==subtractive_set[ptrB]:
ptrA+=1; ptrB+=1; continue
if initial_set[ptrA]<subtractive_set[ptrB]:
C.append(initial_set[ptrA]); ptrA+=1; continue
ptrB+=1;
return flex.int(C) # returned indices will no longer be sorted by resolution
# 5. eliminate multi-modal spots & punctate spots
fstats.c_spot_filter(
fstats.most_recent_child(),
'spots_unimodal',
'modal_test',
arguments=[self.phil_params.distl_profile_bumpiness,
self.phil_params.distl.minimum_spot_area or sf.spotbasesize()])
# parameters are bumpiness(max number of local maxima in peak),minimum pixels
if VERBOSE_COUNT: print "not bumpy & not punctate",fstats['N_spots_unimodal']
# 6. Compute distributions for outlier rejection
#Y = Timer("Inliers")#try to get this down from 232 seconds to 8 seconds
#(For HK97 sz=4)
inlier_idx_raw = flex.int()
inlier_neigh=[]
unimodal_spots = fstats['spots_unimodal']
if fstats['N_spots_unimodal']>2: # avoids divide-by-zero error in stats calls, below
intensities = flex.double([s.intensity() for s in unimodal_spots])
areas = flex.double([s.bodypixels.size() for s in unimodal_spots])
# Deprecate shapes because code is unstable; in rare cases spots can have
# body pixels but no border pixels ( RAW/APS_07_2005/pEI4/pEI4_8_1.0001 )
#shapes = flex.double([s.shape() for s in unimodal_spots])
eccentricity= flex.double([s.model_eccentricity() for s in unimodal_spots])
skewness = fstats.get_property(fstats.most_recent_child(),'skewness')
fstats['intensity'] = (i_ave,i_std) = scitbx_stats(intensities)
fstats['area'] = (a_ave,a_std) = scitbx_stats(areas)
#fstats['shape'] = (s_ave,s_std) = scitbx_stats(shapes)
def oneImage(self, framenumber, pd, image):
self.reporters[framenumber] = []
# The only way to get pixel size & pixel dimensions (currently) is from header
pimage = image
pimage.read()
#print "Detector type",type(pimage)
if 'endstation' not in pd:
from iotbx.detectors.context import endstation
pd['endstation'] = endstation.EndStation_from_ImageObject(
pimage, self.phil_params)
for key in pd['endstation'].mosflm():
pd[key] = pd['endstation'].mosflm()[key]
pd['vendortype'] = pimage.vendortype
pd['binning'] = "%d" % pimage.bin
pd['pixel_size'] = "%f" % pimage.pixel_size
self.pixel_size = float(pd['pixel_size'])
pd['size1'] = "%d" % pimage.size1
self.size1 = float(pd['size1'])
pd['size2'] = "%d" % pimage.size2
self.size2 = float(pd['size2'])
if 'osc_start' not in pd: pd['osc_start'] = {}
pd['osc_start'][framenumber] = "%f" % pimage.osc_start
if 'file' not in pd: pd['file'] = {}
pd['file'][framenumber] = pimage.filename
self.two_theta_degrees = float(pd['twotheta'])
if 'xbeam' not in pd or 'ybeam' not in pd:
raise SpotfinderError(
"Deprecation warning: inputs had no beam position", pd)
self.complex_nominal_center = complex(float(pd["xbeam"]),
float(pd["ybeam"]))
arguments = "" # distl_aggressive no longer supported, eg "-s2 5 -s3 8"
#allow for self.phil_params.distl_highres_limit
if self.phil_params.distl_highres_limit != None:
arguments = arguments + " -ro %.3f" % self.phil_params.distl_highres_limit
# test implementation of parallel processing for spotfinder
#from labelit.webice_support import parallel_distl
#pimage,pd = parallel_distl.split_image(pimage,pd)
try:
sf = Distl(
arguments,
pimage,
pd,
report_overloads=self.phil_params.distl_report_overloads,
params=self.phil_params)
except Sorry as e:
raise e
except Exception as e:
raise SpotfinderError("Spotfinder cannot analyze image %s :" %
pimage.filename + e.message)
#To support sublattice detection, make pixel-wise Z-scores persistent
if self.phil_params.distl_keep_Zdata:
pimage.linear_Z_data = sf.Z_data(
) #potentially uses a lot of memory
#************************************************************
#
# Very important. For the mar image plate (and allother circular detectors,
# must specify that these are embedded circular detectors and so adjust the
# percentage underloads. Or else libdistl must be changed so as not to
# search in these regions: yes, this would be better because I propose to
# change the search loop anyway. However, the getUnderload function must
# also be modified!!!
#
# ice ring search must also be modified to take this into account, but this
# part must be more precise than the above.
#
#************************************************************
if self.two_theta_degrees == 0.0:
mm_minimum_radius = resol_to_radius(
self.phil_params.distl_lowres_limit, pd)
sfa = SpotFilterAgent(
pixel_size=pimage.pixel_size,
xbeam=float(pd["xbeam"]),
ybeam=float(pd["ybeam"]),
distance=float(pd['distance']),
wavelength=float(pd['wavelength']),
icerings=sf.icerings,
)
#from libtbx import easy_pickle
#easy_pickle.dump("file.dmp",sfa)
#easy_pickle.load("file.dmp")
fstats = ListManager(masterlist=sf.spots,
masterkey='spots_total',
spotfilter=sfa)
fstats['distl_resolution'] = sf.imgresol()
# 1. Get all spots
fstats.alias(oldkey='spots_total', newkey='goodspots')
if VERBOSE_COUNT: print "total DISTL spots", fstats['N_spots_total']
fstats.c_spot_filter('goodspots', 'spots_non-ice', 'ice_ring_test')
fstats['ice-ring_impact'] = sf.nicerings()
fstats['ice-ring_bounds'] = [
(sf.icerings[i].lowerresol, sf.icerings[i].upperresol)
for i in xrange(fstats['ice-ring_impact'])
]
#**********************************************************************
# Known parts of code that are inefficient: use 35000-spot HK97 example
# 1. 3.8 seconds: sorting the resolution spots (item #4 in tnear2) (Corrected)
# 2. 9.7 seconds: spreadsheet bookkeeping; method2_resolution.py, xrow loop (Partly corrected 5/09)
# 3. 4.4 seconds: ice2::RingFinder::filtered() (Corrected)
# 3. omit spots too close to the beamstop
if self.two_theta_degrees == 0.0:
fstats.c_spot_filter('spots_non-ice',
'hi_pass_resolution_spots',
'resolution_test',
arguments=[
mm_minimum_radius,
])
else:
fstats.precompute_resolution(
self.two_theta_degrees * math.pi / 180., #two theta radians
pd['endstation'].rotation_axis(),
pd['endstation'].camera_convention())
fstats.c_spot_filter(
'spots_non-ice',
'hi_pass_resolution_spots',
'resolution_test_nztt',
arguments=[self.phil_params.distl_lowres_limit])
if VERBOSE_COUNT:
print "after lowres filter", fstats["N_hi_pass_resolution_spots"]
#start here.
#In the end, make sure these work:
#interface with mosflm: "TWOTHETA" keyword fails; "TILT" fix works with fudge factor
#James Holton's spots index (make unit test); anything with resolution_mm
#diffimage display!: ring_markup() method of webice_support/__init__
#report the two theta value in stats index
# 4. Calculate resolution cutoff
if self.two_theta_degrees == 0.0:
sorted_order, sorted_resolutions = fstats.resolution_sort(
"hi_pass_resolution_spots")
else:
sorted_order, sorted_resolutions = fstats.resolution_sort_nztt(
"hi_pass_resolution_spots")
# targetBinNumber: first try number of candidate Bragg spots per bin
targetBinNumber = max(self.BinMin, len(sorted_order) // 20)
# cutoff threshhold: expected number spots in highest resolution bin
cutoff_ratio = 100. / self.phil_params.distl.method2_cutoff_percentage
fstats['resolution_divisor'] = cutoff_ratio
lowerCutoffBinNumber = targetBinNumber / cutoff_ratio
if len(sorted_order) < targetBinNumber:
# So few spots that there is only one bin
# no resolution determination possible
fstats['resolution'] = None
fstats['resolution_detail'] = len(sorted_order)
elif False and sorted_resolutions[self.BinMin - 1] < 4.0:
'''This filter (that essentially says you must have low
resolution spots) hindered the ability to index the case
ana/procrun0000084148/sphN1_*.mar2300. Further investigation
showed that not a single one of the 94 reference cases in the
regression database was affected by this test, so the test is
being provisionally removed. It is not known if the test has a
beneficial effect on cases with no protein diffraction, i.e., just
ice rings.'''
# This test indicates that there are so few spots at low
# resolution that the first bin extends past 4.0 Angstroms,
# into the region where ice rings might be found
# since there are too few low resolution data
# conclude that these are false Bragg spots
fstats['resolution'] = None
sorted_resolutions = []
# deprecated; would need to be recoded:
fstats['N_spots_resolution'] = 0
else:
from spotfinder.diffraction.geometry import Geom2d
frac_calc = Geom2d(pd)
#spot-based ice-ring filtering, added Aug 2004
'''The case ana/procrun0000084148/sphN1_*.mar2300 (image 090) shows the
limits of this filter as presently implemented. In that particular
case, this ice-ring filter eliminates spots from a large area in the
2-3 Angstrom resolution range. However, to be believed, the purported
ice-spots should define a circle or ellipse centered at the beam position
with a resolution spread that is very narrow. Code could be
written much more effectively to search and elimate these rings'''
from spotfinder.applications.heuristic_tbx.ice2 import RingFinder
from spotfinder.applications.heuristic_tbx.ice_nztt import RingFinder_nztt
if (abs(float(pd['twotheta'])) > 0.0):
# Very inefficient--8 seconds per call; will need to be optimized
#PP = Profiler("nztt")
Ring = RingFinder_nztt(sorted_resolutions, targetBinNumber,
frac_calc, image)
#del PP
else:
Ring = RingFinder(sorted_resolutions, targetBinNumber,
frac_calc)
fstats.add_child("hi_pass_resolution_spots",
"ice_free_resolution_spots",
Ring.filtered(sorted_order))
fstats['ice-ring_impact'] += Ring.ice_ring_impact()
fstats['ice-ring_bounds'] += Ring.ice_ring_bounds()
#*************************the filtering of existing spots
if VERBOSE_COUNT:
print "after spot_based ice-ring filter", fstats[
'N_ice_free_resolution_spots']
if fstats['N_ice_free_resolution_spots'] < targetBinNumber:
# So few spots that there is only one bin
# no resolution determination possible
fstats['resolution'] = None
fstats['resolution_detail'] = fstats[
'N_ice_free_resolution_spots']
else:
from spotfinder.applications.heuristic_tbx.method2_resolution\
import ResolutionShells
sorted_resolutions = fstats.spotfilter.get_resolution(
fstats.master,
fstats.get_indices('ice_free_resolution_spots'))
Shell = ResolutionShells(
sorted_resolutions,
sorted_resolutions[targetBinNumber - 1], frac_calc)
#Shell.show()
if self.phil_params.distl.bins.verbose:
ShellR = spotreporter(
sorted_resolutions,
self.phil_params,
sorted_resolutions[targetBinNumber - 1],
wavelength=float(pd['wavelength']),
max_total_rows=self.phil_params.distl.bins.N,
fractionCalculator=frac_calc,
use_binning_of=self)
ShellR.total_signal(
fstats,
fstats.get_indices('ice_free_resolution_spots'))
ShellR.background(sf)
ShellR.sigma_analysis()
print
ShellR.show(
message=
"Analysis of spots after ice removal, but prior to resolution cutoff, for image \n%s"
% pimage.filename)
self.reporters[framenumber].append(ShellR)
# slight adjustment so that we don't focus on the lowest
# resolution data shell (spends too much time in Ewald sphere)
# But don't make this adjustment if we are limited by low spot count
if Shell.rows() > 2 and \
Shell.Population[1] > 2*lowerCutoffBinNumber and \
Shell.Population[1] > self.BinMin:
lowerCutoffBinNumber = Shell.Population[1] / cutoff_ratio
# first determination of cutoff ignoring corner effect
for x in xrange(Shell.rows()):
idx = Shell.rows() - x - 1
if Shell.Population[idx] > lowerCutoffBinNumber:
lastshell = idx
break
# eliminate pathological case where there are a lot of low resolution
# spots and then some ice rings at high resolution. If there are ten
# empty shells in a row (empty defined as having fewer than
# VetterCut spots), redetermine lastshell.
#
# Originally, VetterCut := lowerCutoffBinNumber
# Modify the heuristic to cover two extremes:
# Case 1. Based on the HK97 virus work; there is a class of diffraction
# cases showing a distinct dip in diffraction intensity in the
# 5-Angstrom regime. If lowerCutoffBinNumber is used (usually
# 20% of the spot count in the 2nd bin), the algorithm can
# misbehave and reject all the high-resolution Bragg spots.
# For one case in particular a tiny change in beam position
# flips the resolution cutoff from a 6.5- to 4.4-Angstrom.
# Case 2. There are many cases where the diffraction clearly
# drops off, and at higher resolutions there are random-signal
# spots plus ice rings. The spot count in these high-res bins
# is typically small, <20. Use "20" as a heuristic cutoff
# between Case 1 & Case 2.
# In future, it may be productive to focus on smarter algorithms to
# execute ellipse recognition in the "Ringfinder" section above
if lowerCutoffBinNumber <= 20:
VetterCut = lowerCutoffBinNumber
else:
VetterCut = 20 + lowerCutoffBinNumber // 5
lastshell = Shell.vetter(VetterCut, lastshell)
#option for overriding the resolution analysis based on falloff
# of spot count. Force spots at least this far out
if self.phil_params.force_method2_resolution_limit is not None:
for x in xrange(Shell.rows()):
if Shell.Limit[
x] < self.phil_params.force_method2_resolution_limit and lastshell < x:
lastshell = x
break
# extend resolution cutoff taking into account corner cutoff
while Shell.Fract[
lastshell] < 1.0 and lastshell + 1 < Shell.rows():
nextshell = lastshell + 1
if Shell.adjustPop[nextshell] > lowerCutoffBinNumber:
lastshell += 1
else:
break
fstats['resolution'] = Shell.Limit[lastshell]
if self.phil_params.distl_highres_limit != None:
fstats['resolution'] = max(
fstats['resolution'],
self.phil_params.distl_highres_limit)
for x in xrange(lastshell + 1):
if self.phil_params.spotfinder_verbose:
print "(%.2f,%d)" % (Shell.Limit[x],
Shell.Population[x])
if self.two_theta_degrees == 0.0:
fstats.c_spot_filter( #hi-resolution radius
'ice_free_resolution_spots',
'lo_pass_resolution_spots',
'lo_pass_resolution_test',
arguments=[
resol_to_radius(fstats['resolution'], self.pd),
])
else:
fstats.c_spot_filter( #hi-resolution radius
'ice_free_resolution_spots',
'lo_pass_resolution_spots',
'lo_pass_resolution_test_nztt',
arguments=[
fstats['resolution'],
])
if VERBOSE_COUNT:
print "ice_free_resolution_spots ", fstats[
'N_ice_free_resolution_spots']
fstats['shells'] = Shell
if VERBOSE_COUNT:
print "after resolution-shell cutoff", fstats[
'N_lo_pass_resolution_spots']
fstats['resolution_mm'] = resol_to_radius(
fstats['resolution'], pd)
fstats['saturation'] = self.calculate_saturation(fstats, image)
if self.phil_params.distl.bins.verbose:
try:
subset = fstats.spotfilter.get_resolution(
fstats.master,
fstats.get_indices('lo_pass_resolution_spots'))
ShellR = spotreporter(subset,
self.phil_params,
use_binning_of=self)
ShellR.total_signal(
fstats, fstats.get_indices('lo_pass_resolution_spots'))
ShellR.background(sf)
ShellR.sigma_analysis()
print
ShellR.show(
message=
"Analysis of spots after resolution filtering, but prior to spot quality heuristics, for image \n%s"
% pimage.filename)
self.reporters[framenumber].append(ShellR)
except Exception: # in case the low-pass filter step was skipped; e.g., blank image.
pass
# 5. eliminate multi-modal spots & punctate spots
fstats.c_spot_filter(fstats.most_recent_child(),
'spots_unimodal',
'modal_test',
arguments=[
self.phil_params.distl_profile_bumpiness,
self.phil_params.distl.minimum_spot_area
or sf.spotbasesize()
])
# parameters are bumpiness(max number of local maxima in peak),minimum pixels
if VERBOSE_COUNT:
print "not bumpy & not punctate", fstats['N_spots_unimodal']
# 6. Compute distributions for outlier rejection
#Y = Timer("Inliers")#try to get this down from 232 seconds to 8 seconds
#(For HK97 sz=4)
inlier_idx_raw = flex.int()
inlier_neigh = []
unimodal_spots = fstats['spots_unimodal']
if fstats[
'N_spots_unimodal'] > 2: # avoids divide-by-zero error in stats calls, below
intensities = flex.double([s.intensity() for s in unimodal_spots])
areas = flex.double([s.bodypixels.size() for s in unimodal_spots])
# Deprecate shapes because code is unstable; in rare cases spots can have
# body pixels but no border pixels ( RAW/APS_07_2005/pEI4/pEI4_8_1.0001 )
#shapes = flex.double([s.shape() for s in unimodal_spots])
eccentricity = flex.double(
[s.model_eccentricity() for s in unimodal_spots])
skewness = fstats.get_property(fstats.most_recent_child(),
'skewness')
fstats['intensity'] = (i_ave, i_std) = scitbx_stats(intensities)
fstats['area'] = (a_ave, a_std) = scitbx_stats(areas)
#fstats['shape'] = (s_ave,s_std) = scitbx_stats(shapes)
fstats['eccen'] = (e_ave, e_std) = scitbx_stats(eccentricity)
fstats['skewness'] = (k_ave, k_std) = scitbx_stats(skewness)
#Filtering on skewness is important when using center-of-mass positions
# for autoindexing. The goal is to eliminate pairs of unresolved Bragg
# spots. Condition is flagged when the max_pixel to center-of-mass
# vector is more than 45% of the semi-major axis
fstats.c_spot_filter(fstats.most_recent_child(),
'spots_low_skew',
'low_skew',
arguments=[
min(0.45, 2.0 * k_std + k_ave),
])
#Filter on the intensity distribution.
fstats.c_spot_filter(fstats.most_recent_child(),
'spots_good_intensity',
'intensity_inlier',
arguments=[
i_ave - 5.0 * i_std,
i_ave + 5.0 * i_std,
image.saturation,
])
#special code for expecting a high MOSFLM RESID based on the
# presence of very high signal/noise ratio (essentially lysozyme strength).
# This is all just a supposition based on one dataset, offset_1 from Ana.
# The assumption may be wrong; e.g. the high resid may be due to very low
# background instead of high s/n; or due to some geometrical distortion.
#This breaks encapsulation and will have to be re-organized in the future.
if 'special_resid' not in pd: pd['special_resid'] = ''
if fstats['intensity'][0] > 160.:
pd['special_resid'] = 'RESID 10.0 #High s/n'
#The most unusual thing about the procrun0000077831/TMC114_WT2_run77831_1_001
#dataset is its large differential between spot areas of the largest spots
# and the smallest spots. Hypothesize that this also leads to high
# weighted residuals.
if stats_profile(areas) > 10.:
pd['special_resid'] = 'RESID 10.0 #High s/n'
from annlib_ext import AnnAdaptor
data = fstats.get_property(
'goodspots', self.phil_params.distl_spotcenter_algorithm)
query = fstats.get_property(
fstats.most_recent_child(),
self.phil_params.distl_spotcenter_algorithm)
A = AnnAdaptor(data, 2) # construct k-d tree for reference set
A.query(query) # find nearest neighbors of query points
neighbors = (self.pixel_size) * flex.sqrt(A.distances)
"""Explanation: Distance to the nearest neighbor is math.sqrt(A.distances[i]), in
units of pixels. The vector to the nearest neighbor, in units of pixels, is
( fstats.master[A.nn[i]].x()-query[2*i], fstats.master[A.nn[i]].y()-query[2*i+1])
"""
fstats['neighbor'] = (n_ave, n_std) = scitbx_stats(neighbors)
sep_input_spots = fstats[fstats.most_recent_child()]
sep_input_indices = fstats.get_indices(fstats.most_recent_child())
overlapping_count = 0
for idx in xrange(len(sep_input_spots)):
try:
if float(pd['pixel_size'])*sep_input_spots[idx].majoraxis() * \
self.phil_params.overlapping_spot_criterion > neighbors[idx]:
overlapping_count += 1
except Exception:
pass
if len(sep_input_spots) == 0: percent_overlap = 0
else:
percent_overlap = 100 * overlapping_count / len(
sep_input_spots)
#print "overlap %2.0f%% vs. cutoff %2.0f%%"%(percent_overlap,self.phil_params.percent_overlap_forcing_detail)
from spotfinder.core_toolbox.close_spots_detail import NearNeighborVectors
Afull = AnnAdaptor(data, 2)
Afull.query(data)
NV = NearNeighborVectors(ave_area=a_ave,
query_centers=data,
fstats=fstats,
ann_adaptor=Afull)
#NV.show_vector_map()
#NV.show_maxima()
DEVELOP_ASSERT = True
self.force_detail = percent_overlap > self.phil_params.percent_overlap_forcing_detail
if DEVELOP_ASSERT or self.force_detail:
self.overlapping = True
#extraordinary procedure, when many spots are close. Include closely
# spaced spots in autoindexing, if it appears that they truly
# reflect lattice spacing.
if self.phil_params.spotfinder_verbose:
print len(sep_input_spots
), "spot count before close neighbor analysis;",
print overlapping_count, "(%2.0f%%) rejected on neighbors;" % (
percent_overlap)
pmax = NV.vectors()
#filter out spots that are potentially large enough to contain
#two spots, now that we know a candidate projected unit-cell vector
# expect big time savings if this section is pushed down to C++
compact_idx = []
if 0 < len(pmax) <= 3:
sq_vectors = [v[0] * v[0] + v[1] * v[1] for v in pmax]
for idx in xrange(len(sep_input_spots)):
spot_compact = True
for iv, vector in enumerate(pmax):
thisspot = sep_input_spots[idx]
#more efficient code--disallowed spots (based on nearest neighbor
# vector) are now flagged based on the width of the ellipse model
# rather than a more time-consuming body-pixel match:
#calculate angle theta between major axis and neighbor vector
dot = thisspot.eigenvector(1)[0] * vector[
0] + thisspot.eigenvector(1)[1] * vector[1]
costheta = dot / math.sqrt(sq_vectors[iv])
costhetasq = min(costheta * costheta, 1.0)
sintheta = math.sqrt(1 - costhetasq)
spota = thisspot.a()
spotb = thisspot.b()
a_sin_theta = spota * sintheta
b_cos_theta = spotb * costheta
#evaluate the ellipse full width along neighbor vector direction
width_sq = 4. * (spota * spota * spotb * spotb /
(a_sin_theta * a_sin_theta +
b_cos_theta * b_cos_theta))
if width_sq >= sq_vectors[iv]:
spot_compact = False
break
if spot_compact: compact_idx.append(idx)
else:
pass #print "eliminate the spot at",thisspot.x(),thisspot.y()
else:
compact_idx = xrange(len(sep_input_spots))
if self.phil_params.spotfinder_verbose:
print len(sep_input_spots) - len(
compact_idx), "large spots rejected"
# finally, allow certain close spots (but not all of them) to be included
for idx in compact_idx:
try:
S = (int(fstats.master[A.nn[idx]].max_pxl_x() -
query[2 * idx]),
int(fstats.master[A.nn[idx]].max_pxl_y() -
query[2 * idx + 1]))
#accept spot by default
reject_spot = False
#if close to nearest neighbor reject
if sep_input_spots[idx].majoraxis() * \
self.phil_params.overlapping_spot_criterion > math.sqrt(S[0]*S[0]+S[1]*S[1]):
# with normal procedure, spot would be rejected at this point
reject_spot = True
if len(pmax) <= 3:
for vector in pmax:
if abs(vector[0] - S[0]) <= 1 and abs(
vector[1] - S[1]) <= 1:
#but allow if the proximity is to a candidate cell near neighbor
reject_spot = False
break
if reject_spot: continue
inlier_idx_raw.append(sep_input_indices[idx])
inlier_neigh.append(neighbors[idx])
except Exception:
pass
if self.phil_params.spotfinder_verbose:
print len(compact_idx) - len(
inlier_idx_raw), "close spots rejected"
print len(sep_input_spots
), "input for spot separation analysis;",
jj = len(sep_input_spots) - len(inlier_idx_raw)
print jj, "(%2.0f%%) rejected on special criteria;" % (
100. * jj / len(sep_input_spots))
else: #normal procedure, assuming not too many close spots
for idx in xrange(len(sep_input_spots)):
try:
if math.fabs(intensities[idx]-i_ave) <= 5.0*i_std and \
intensities[idx]<image.saturation and \
float(pd['pixel_size'])*sep_input_spots[idx].majoraxis() * \
self.phil_params.overlapping_spot_criterion<=neighbors[idx]:
inlier_idx_raw.append(sep_input_indices[idx])
inlier_neigh.append(neighbors[idx])
except Exception:
#print "REJECT spot on exception"
pass #sometimes throw an error when majoraxis is requested (edge spots)
if len(inlier_idx_raw) < self.NspotMin:
for idx in xrange(len(sep_input_spots)):
try:
proximal_radius = 2.0 * self.phil_params.overlapping_spot_criterion * float(
pd['pixel_size']
) * sep_input_spots[idx].majoraxis()
if math.fabs(intensities[idx]-i_ave) <= 5.0*i_std and \
intensities[idx]<image.saturation and \
float(pd['pixel_size'])*sep_input_spots[idx].majoraxis() *self.phil_params.overlapping_spot_criterion > neighbors[idx] and \
sf.isIsolated(sep_input_spots[idx],proximal_radius):
#print "Very few Bragg spots; forced to accept spot with neighbor distance",neighbors[idx]/(float(pd['pixel_size'])*sep_input_spots[idx].majoraxis())
inlier_idx_raw.append(sep_input_indices[idx])
inlier_neigh.append(neighbors[idx])
except Exception:
print "REJECT spot on exception"
pass #sometimes throw an error when majoraxis is requested (edge spots)
if fstats.has_extended_key('lo_pass_resolution_spots'):
fstats.alias('lo_pass_resolution_spots', 'spots_resolution')
elif fstats.has_extended_key('ice_free_resolution_spots'):
fstats.alias('ice_free_resolution_spots', 'spots_resolution')
else:
fstats.alias('hi_pass_resolution_spots', 'spots_resolution')
fstats.add_child('spots_unimodal', 'spots_separated', inlier_idx_raw)
fstats.alias(fstats.most_recent_child(), 'spots_inlier')
fstats.alias('spots_inlier',
'inlier_spots') #for backward compatibility
if self.phil_params.distl.bins.verbose:
try:
subset = fstats.spotfilter.get_resolution(
fstats.master, fstats.get_indices('inlier_spots'))
ShellR = spotreporter(subset,
self.phil_params,
use_binning_of=self)
ShellR.total_signal(fstats, fstats.get_indices('inlier_spots'))
ShellR.background(sf)
ShellR.sigma_analysis()
print
ShellR.show(
message=
"Analysis of good Bragg spots after quality heuristics, for image \n%s"
% pimage.filename)
self.reporters[framenumber].append(ShellR)
except Exception:
pass #if there aren't enough spots, just skip the tabular printout
if 'masks' not in pd: pd['masks'] = {}
pd['masks'][framenumber] = None
if fstats['N_spots_inlier'] > ( #guard against C++ hard-coded minimum
self.phil_params.codecamp.minimum_spot_count or 25):
Msk = fstats.single_mask('inlier_spots', self.phil_params)
pd['masks'][framenumber] = [Msk.x, Msk.y]
if VERBOSE_COUNT: print "inlier spots", fstats["N_inlier_spots"]
#need this for later calculation of maxcell
fstats['neighbors'] = flex.double(inlier_neigh)
return fstats
