def merge(pres_result, iparams, txt_out_prefix, output_prefix, avg_mode):
#pre-merge task
if rank == 0:
pres_results = sum(pres_result, [])
print "Scaling/post-refinement is done on %d cores for %d frames" % (
size, len(pres_results))
master((pres_results, avg_mode), iparams, "pre_merge")
premerge_result = []
else:
pres_results = None
premerge_result = client()
premerge_result = comm.gather(premerge_result, root=0)
comm.Barrier()
#merge task
if rank == 0:
print "Pre-merge is done on %d cores" % (len(premerge_result))
master((premerge_result, avg_mode), iparams, "merge")
merge_result = []
else:
merge_result = client()
#finalize merge
merge_result = comm.gather(merge_result, root=0)
comm.Barrier()
if rank == 0:
print "Merge completed on %d cores" % (len(merge_result))
merge_results = sum(merge_result, [])
mdh = merge_data_handler()
txt_out_rejection = ""
for _mdh, _txt_out_rejection in merge_results:
mdh.extend(_mdh)
txt_out_rejection += _txt_out_rejection
#selet only indices with non-Inf non-Nan stats
selections = flex.bool([
False if (math.isnan(r0) or math.isinf(r0) or math.isnan(r1)
or math.isinf(r1)) else True
for r0, r1 in zip(mdh.r_meas_div, mdh.r_meas_divisor)
])
mdh.reduce_by_selection(selections)
its = intensities_scaler()
mdh, txt_merge_mean_table = its.write_output(
mdh, iparams, os.path.join(iparams.run_no, output_prefix),
avg_mode)
print txt_merge_mean_table
#write log output
with open(os.path.join(iparams.run_no, 'log.txt'), 'a') as f:
f.write(txt_out_prefix + txt_merge_mean_table)
with open(os.path.join(iparams.run_no, 'rejections.txt'), 'a') as f:
f.write(txt_out_rejection)
else:
merge_results = None
mdh = None
return pres_results, mdh
def run(argv):
comm.Barrier()
start_time = MPI.Wtime()
# broadcast parameters
if rank == 0:
iparams, txt_out_input = process_input(argv)
iparams.flag_volume_correction = False
iparams.flag_hush = True
print(txt_out_input)
frame_files = read_pickles(iparams.data)
else:
iparams = None
frame_files = None
comm.Barrier()
# assign scaling task
if rank == 0:
master(frame_files, iparams, "scale")
result = []
else:
result = client()
result = comm.gather(result, root=0)
comm.Barrier()
# pre-merge task
if rank == 0:
results = sum(result, [])
print("Scaling is done on %d cores for %d frames" %
(size, len(results)))
master(results, iparams, "pre_merge")
result = []
else:
result = client()
result = comm.gather(result, root=0)
comm.Barrier()
# merge task
if rank == 0:
print("Pre-merge is done on %d cores" % (len(result)))
master(result, iparams, "merge")
result = []
else:
result = client()
# finalize merge
result = comm.gather(result, root=0)
comm.Barrier()
if rank == 0:
print("Merge completed on %d cores" % (len(result)))
results = sum(result, [])
mdh = merge_data_handler()
txt_out_rejection = ""
for _mdh, _txt_out_rejection in results:
mdh.extend(_mdh)
txt_out_rejection += _txt_out_rejection
# selet only indices with non-Inf non-Nan stats
selections = flex.bool([
False if (math.isnan(r0) or math.isinf(r0) or math.isnan(r1)
or math.isinf(r1)) else True
for r0, r1 in zip(mdh.r_meas_div, mdh.r_meas_divisor)
])
mdh.reduce_by_selection(selections)
its = intensities_scaler()
mdh, txt_merge_mean_table = its.write_output(mdh, iparams, "test",
"average")
print(txt_merge_mean_table)
# collect time profile
comm.Barrier()
end_time = MPI.Wtime()
txt_time = "Elapsed Time (s):%10.2f\n" % (end_time - start_time)
# write log output
if rank == 0:
print(txt_time)
with open(os.path.join(iparams.run_no, "log.txt"), "w") as f:
f.write(txt_out_input + txt_merge_mean_table + txt_time)
with open(os.path.join(iparams.run_no, "rejections.txt"), "w") as f:
f.write(txt_out_rejection)
MPI.Finalize()