def sisa_optimize_mproc(micro_cycle_no, stack_no, miller_arrays,
indices_selected, cdf_set, iparams):
from mmtbx.sisa.optimize.mod_optimize import sisa_optimizer
somer = sisa_optimizer()
result = somer.run_optimize(micro_cycle_no, stack_no, miller_arrays,
indices_selected, cdf_set, iparams)
return result
def sisa_optimize_mproc(micro_cycle_no, stack_no, miller_arrays, indices_selected, cdf_set, iparams): from mmtbx.sisa.optimize.mod_optimize import sisa_optimizer somer = sisa_optimizer() result = somer.run_optimize(micro_cycle_no, stack_no, miller_arrays, indices_selected, cdf_set, iparams) return result
from mmtbx.sisa.optimize.mod_mtz import mtz_handler
mtzh = mtz_handler()
miller_arrays, fp_sort_index_stacks, txt_out_format = mtzh.format_miller_arrays(iparams)
print txt_out_format
txt_out += txt_out_format
for i in range(iparams.n_macro_cycles):
txt_out += 'Macrocycle no. %4.0f\n'%(i+1)
print 'Macrocycle no. %4.0f\n'%(i+1)
for j in range(len(fp_sort_index_stacks)):
#select the index group
i_sel = fp_sort_index_stacks[j]
#generate cdf_set for selected reflections
from mmtbx.sisa.optimize.mod_optimize import sisa_optimizer
somer = sisa_optimizer()
hl_selected = flex.hendrickson_lattman([miller_arrays[3].data()[ii_sel] for ii_sel in i_sel])
cdf_set = somer.calc_pdf_cdf_from_hl(hl_selected)
def sisa_optimize_mproc_wrapper(arg):
return sisa_optimize_mproc(arg, j, miller_arrays, i_sel, cdf_set, iparams)
sisa_optimize_results = pool_map(
args=range(iparams.n_micro_cycles),
func=sisa_optimize_mproc_wrapper,
processes=iparams.n_processors)
list_phis = []
foms_sum = None
list_skews = []
for result in sisa_optimize_results:
mtzh = mtz_handler()
miller_arrays, fp_sort_index_stacks, txt_out_format = mtzh.format_miller_arrays(
iparams)
print(txt_out_format)
txt_out += txt_out_format
for i in range(iparams.n_macro_cycles):
txt_out += 'Macrocycle no. %4.0f\n' % (i + 1)
print('Macrocycle no. %4.0f\n' % (i + 1))
for j in range(len(fp_sort_index_stacks)):
#select the index group
i_sel = fp_sort_index_stacks[j]
#generate cdf_set for selected reflections
from mmtbx.sisa.optimize.mod_optimize import sisa_optimizer
somer = sisa_optimizer()
hl_selected = flex.hendrickson_lattman(
[miller_arrays[3].data()[ii_sel] for ii_sel in i_sel])
cdf_set = somer.calc_pdf_cdf_from_hl(hl_selected)
def sisa_optimize_mproc_wrapper(arg):
return sisa_optimize_mproc(arg, j, miller_arrays, i_sel,
cdf_set, iparams)
sisa_optimize_results = pool_map(args=range(
iparams.n_micro_cycles),
func=sisa_optimize_mproc_wrapper,
processes=iparams.n_processors)
list_phis = []
foms_sum = None