def iterate_data(dataset,weights,pixel_step=25,iter_no=5,pixel_mask=None):
start_gain = array.ones(len(dataset))
gain,first_ave,ar,esds,K = overlap.find_gain_fr(dataset,weights,pixel_step,start_gain,pixel_mask=pixel_mask)
chisquared,residual_map = overlap.get_statistics_fr(gain,first_ave,dataset,dataset.var,pixel_step,pixel_mask)
Plot1.set_dataset(Dataset(first_ave))
Plot2.set_dataset(zeros_like(first_ave))
old_result = first_ave #store for later
chisq_history = [chisquared]
if iter_no > 0:
no_iters = iter_no
else:
no_iters = abs(iter_no)
for cycle_no in range(no_iters+1):
esdflag = cycle_no == iter_no
if cycle_no > 3 and iter_no < 0:
esdflag = (esdflag or (abs(chisq_history[-2]-chisq_history[-1]))<0.005)
gain,interim_result,ar,esds,K = overlap.find_gain_fr(dataset,weights,pixel_step,gain,arminus1=ar,pixel_mask=pixel_mask,errors=esdflag)
chisquared,residual_map = overlap.get_statistics_fr(gain,interim_result,dataset,dataset.var,pixel_step,pixel_mask)
chisq_history.append(chisquared)
if not cycle_no % ((iter_no/2)+1): # +1 to avoid division by zero for single step iterations
print "Plotting cycle %d" % cycle_no
Plot1.add_dataset(Dataset(interim_result))#,label="%d" % cycle_no)
Plot2.add_dataset(Dataset(interim_result-first_ave))#,label="%d" % cycle_no)
old_result = interim_result
Plot3.set_dataset(Dataset(chisq_history))#,label="%d" % cycle_no)
print 'Maximum shift/error: %f' % max(ar/esds)
return gain,dataset,interim_result,residual_map,chisquared,esds,first_ave
def iterate_data(dataset,pixel_step=25,iter_no=5,pixel_mask=None,plot_clear=True,algo="FordRollett"):
"""Iteratively refine the gain. The pixel_step is the number of steps a tube takes before it
overlaps with the next tube. iter_no is the number of iterations. Pixel_mask has a zero for any
tube that should be excluded. Algo 'ford rollett' applies the algorithm of Ford and Rollet,
Acta Cryst. (1968) B24, p293"""
import overlap
start_gain = array.ones(len(dataset))
if algo == "FordRollett":
gain,first_ave,chisquared,residual_map,ar,esds = overlap.find_gain_fr(dataset,dataset,pixel_step,start_gain,pixel_mask=pixel_mask)
else:
gain,first_ave,chisquared,residual_map,esds = overlap.find_gain(dataset,dataset,pixel_step,start_gain,pixel_mask=pixel_mask)
old_result = first_ave #store for later
chisq_history = [chisquared]
for cycle_no in range(iter_no+1):
esdflag = (cycle_no == iter_no) # need esds as well
if algo == "FordRollett":
gain,interim_result,chisquared,residual_map,ar,esds = overlap.find_gain_fr(dataset,dataset,pixel_step,gain,arminus1=ar,pixel_mask=pixel_mask,errors=esdflag)
else:
gain,interim_result,chisquared,residual_map,ar,esds = overlap.find_gain(dataset,dataset,pixel_step,gain,pixel_mask=pixel_mask,errors=esdflag)
chisq_history.append(chisquared)
# Calculate the errors using the full, not truncated, dataset
print 'Chisquared: ' + `chisq_history`
return gain,dataset,interim_result,residual_map,chisquared,esds,first_ave