def background(self,spotfinder):
self.addColumn('PxlBkgrd') # mean pixel background at center of windows in the shell
self.PxlBkgrd.format = "%.1f"
self.addColumn('MnWndwSz') # mean size (in pixels) of the scanbox windows in the shell
self.MnWndwSz.format = "%.0f"
for irow in xrange(self.S_table_rows):
self.PxlBkgrd[irow] = flex.double()
self.MnWndwSz[irow] = flex.double()
sortedidx = flex.sort_permutation(spotfinder.background_resolutions(),reverse=True)
reverse_resolutions = spotfinder.background_resolutions().select(sortedidx)
reverse_backgrounds = spotfinder.background_means().select(sortedidx)
reverse_wndw_sz = spotfinder.background_wndw_sz().select(sortedidx)
row = 0
for x in xrange(len(reverse_resolutions)):
while reverse_resolutions[x] < self.Limit[row]:
row += 1
if row >= self.S_table_rows: break
if row >= self.S_table_rows: break
self.PxlBkgrd[row].append(reverse_backgrounds[x])
self.MnWndwSz[row].append(reverse_wndw_sz[x])
for irow in xrange(self.S_table_rows):
#print irow, len(self.PxlBkgrd[irow])
#print list(self.PxlBkgrd[irow])
#print list(self.PxlSigma[irow])
if len(self.PxlBkgrd[irow])==0: self.PxlBkgrd[irow]=None; continue #No analysis without mean background
self.PxlBkgrd[irow] = flex.mean(self.PxlBkgrd[irow])
self.MnWndwSz[irow] = flex.mean(self.MnWndwSz[irow])
def background(self, spotfinder):
self.addColumn(
'PxlBkgrd'
) # mean pixel background at center of windows in the shell
self.PxlBkgrd.format = "%.1f"
self.addColumn(
'MnWndwSz'
) # mean size (in pixels) of the scanbox windows in the shell
self.MnWndwSz.format = "%.0f"
for irow in xrange(self.S_table_rows):
self.PxlBkgrd[irow] = flex.double()
self.MnWndwSz[irow] = flex.double()
sortedidx = flex.sort_permutation(spotfinder.background_resolutions(),
reverse=True)
reverse_resolutions = spotfinder.background_resolutions().select(
sortedidx)
reverse_backgrounds = spotfinder.background_means().select(sortedidx)
reverse_wndw_sz = spotfinder.background_wndw_sz().select(sortedidx)
row = 0
for x in xrange(len(reverse_resolutions)):
while reverse_resolutions[x] < self.Limit[row]:
row += 1
if row >= self.S_table_rows: break
if row >= self.S_table_rows: break
self.PxlBkgrd[row].append(reverse_backgrounds[x])
self.MnWndwSz[row].append(reverse_wndw_sz[x])
for irow in xrange(self.S_table_rows):
#print irow, len(self.PxlBkgrd[irow])
#print list(self.PxlBkgrd[irow])
#print list(self.PxlSigma[irow])
if len(self.PxlBkgrd[irow]) == 0:
self.PxlBkgrd[irow] = None
continue #No analysis without mean background
self.PxlBkgrd[irow] = flex.mean(self.PxlBkgrd[irow])
self.MnWndwSz[irow] = flex.mean(self.MnWndwSz[irow])
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 determine_maxcell(self,frame,pd):
if self.phil_params.codecamp.maxcell != None:
self.images[frame]['maxcel']=self.phil_params.codecamp.maxcell
return
if self.images[frame]['N_spots_inlier']>2:
neighbors = self.images[frame]['neighbors']
n_ave,n_std = scitbx_stats(neighbors)
average_nearest_neighbor = n_ave
NNBIN = self.NspotMin//2 # recommended bin size for nearest neighbor histogram
peak_of_interest = n_ave
for pss in xrange(1): #make two passes thru histo
fineness = max( [self.pixel_size / 2.,
peak_of_interest / (1.0+float(len(neighbors))/float(NNBIN))] )
min_neighbors = min(neighbors)
def histo_index_to_mm(index):
return min_neighbors+index*fineness
#neighbor_histogram
histogram = [0]*int(peak_of_interest*4/fineness)
for y in neighbors:
ibin = int( (y-min_neighbors)/fineness )
if ibin < len(histogram):
histogram[ibin]+=1
if TALLY2:
for row in xrange(len(histogram)):
low = histo_index_to_mm(row)
hi = histo_index_to_mm(row+1)
print "%.2f to %.2f, %5.1f Ang"%(low,hi,radius_to_resol(histo_index_to_mm(row+.5),pd)),
print "*"*histogram[row],
print
most_probable_neighbor = histo_index_to_mm(0.5 + histogram.index(max(histogram)))
#Compute yet another measure of unit cell--first peak in histogram
peak1 = 0; peak1i = 0
for peakpt in xrange(len(histogram)):
if histogram[peakpt]>peak1i:
peak1=peakpt; peak1i=histogram[peakpt]
if histogram[peakpt]<0.5*peak1i and peak1i> 0.1*(max(histogram)):
break
first_peak_if_any = histo_index_to_mm(0.5 + peak1)
peak_of_interest = min(first_peak_if_any,most_probable_neighbor)
# Another trial measure: 5th percentile neighbor
sort_perm = flex.sort_permutation(neighbors)
cutoff_mm = neighbors[sort_perm[ int(0.05*len(sort_perm)) ]]
#Caveat: this rough calculation becomes less accurate for non-zero two-theta
self.images[frame]['neighboring_spot_separation']=min(most_probable_neighbor , cutoff_mm)
MAXTOL = 1.5 # Margin of error for max unit cell estimate
# 11/19/02 old value 1.4; new value 2.0 to accomodate a
# pathological case where zone is down the large unit cell.
# Larger value is now tolerable because of post-mosflm
# check for systematic absences.
# 9/28/03 change back to 1.5 with new autoindexer because
# value of 2.0 can (infrequently) lead to misindexing;
# based on experience preparing figure 4.
maxcell = max(MAXTOL * radius_to_resol(most_probable_neighbor,pd),
0.0 * radius_to_resol(min(neighbors),pd),
0.0 * radius_to_resol(first_peak_if_any,pd),
MAXTOL * radius_to_resol(cutoff_mm,pd))
self.images[frame]['maxcel']=maxcell
