Hi Blaine, I would be surprised if there is no better way of doing this, but here is what I could type in 5 minutes: *** from cctbx.development import random_structure from cctbx import sgtbx from cctbx.array_family import flex def exercise(): # generate random structure xrs = random_structure.xray_structure( space_group_info = sgtbx.space_group_info("P212121"), elements = ["N","C","O"]*10) # compute structure factors up to given resolution f_calc = xrs.structure_factors(d_min=1.5).f_calc() # obtain selection for miller indices you are interested in selection = flex.bool() for mi in f_calc.indices(): h,k,l = mi if(h==1 or h==3): selection.append(True) else: selection.append(False) print "Number of reflections selected %d out of total %d"%( selection.count(True), selection.size()) # apply obtained selection f_calc_selected = f_calc.select(selection) # do binning f_calc_selected.setup_binner(reflections_per_bin = 50) for i_bin in f_calc_selected.binner().range_used(): sel_i_bin = f_calc_selected.binner().selection(i_bin) d_range = f_calc_selected.binner().bin_legend( i_bin=i_bin, show_bin_number=True, show_counts=True) print d_range if(__name__ == "__main__"): exercise() *** Hope this does the job for you! Pavel On 4/24/15 10:22 AM, Mooers, Blaine H.M. (HSC) wrote:
Dear cctbxbb:
I have a simple question.
I have a data set with a strong off origin Patterson peak (0,0,0.11). I want to plot the average I/sig per zone l = n. I expect to see enhanced values at l=9n. How do you bin data by the L Miller index in iotbx?
Best regards,
Blaine
Blaine Mooers, Ph.D. Assistant Professor Director of the Laboratory of Biomolecular Structure and Function Department of Biochemistry and Molecular Biology University of Oklahoma Health Sciences Center S.L. Young Biomedical Research Center Rm. 466
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