def read_models(self):
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s.log" % self.params.output.prefix, "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print("I model", file=out)
if self.params.model is not None:
from xfel.merging.general_fcalc import run as run_fmodel
i_model = run_fmodel(self.params)
self.params.target_unit_cell = i_model.unit_cell()
self.params.target_space_group = i_model.space_group_info()
i_model.show_summary()
else:
i_model = None
print("Target unit cell and space group:", file=out)
print(" ", self.params.target_unit_cell, file=out)
print(" ", self.params.target_space_group, file=out)
from xfel.command_line.cxi_merge import consistent_set_and_model
self.miller_set, self.i_model = consistent_set_and_model(
self.params, i_model)
self.frame_files = get_observations(self.params)
self.out = out
def run(args):
phil = iotbx.phil.process_command_line(args=args,
master_string=master_phil).show()
work_params = phil.work.extract()
from xfel.merging.phil_validation import application, samosa
application(work_params)
samosa(work_params)
if ("--help" in args):
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or (work_params.data is None)):
command_name = os.environ["LIBTBX_DISPATCHER_NAME"]
raise Usage(command_name + " "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell)
and (not work_params.set_average_unit_cell)):
raise Usage(
"If rescale_with_average_cell=True, you must also specify " +
"set_average_unit_cell=True.")
if work_params.raw_data.sdfac_auto and work_params.raw_data.sdfac_refine:
raise Usage("Cannot specify both sdfac_auto and sdfac_refine")
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s.log" % work_params.output.prefix, "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "I model"
if work_params.model is not None:
from xfel.merging.general_fcalc import run
i_model = run(work_params)
work_params.target_unit_cell = i_model.unit_cell()
work_params.target_space_group = i_model.space_group_info()
i_model.show_summary()
else:
i_model = None
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
miller_set, i_model = consistent_set_and_model(work_params, i_model)
frame_files = get_observations(work_params)
scaler = scaling_manager(miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all(frame_files)
if scaler.n_accepted == 0:
return None
scaler.show_unit_cell_histograms()
if (work_params.rescale_with_average_cell):
average_cell_abc = scaler.uc_values.get_average_cell_dimensions()
average_cell = uctbx.unit_cell(
list(average_cell_abc) +
list(work_params.target_unit_cell.parameters()[3:]))
work_params.target_unit_cell = average_cell
print >> out, ""
print >> out, "#" * 80
print >> out, "RESCALING WITH NEW TARGET CELL"
print >> out, " average cell: %g %g %g %g %g %g" % \
work_params.target_unit_cell.parameters()
print >> out, ""
scaler.reset()
scaler.scale_all(frame_files)
scaler.show_unit_cell_histograms()
if False: #(work_params.output.show_plots) :
try:
plot_overall_completeness(completeness)
except Exception as e:
print "ERROR: can't show plots"
print " %s" % str(e)
print >> out, "\n"
# Sum the observations of I and I/sig(I) for each reflection.
sum_I = flex.double(miller_set.size(), 0.)
sum_I_SIGI = flex.double(miller_set.size(), 0.)
for i in xrange(miller_set.size()):
index = miller_set.indices()[i]
if index in scaler.ISIGI:
for t in scaler.ISIGI[index]:
sum_I[i] += t[0]
sum_I_SIGI[i] += t[1]
miller_set_avg = miller_set.customized_copy(
unit_cell=work_params.target_unit_cell)
table1 = show_overall_observations(obs=miller_set_avg,
redundancy=scaler.completeness,
summed_wt_I=scaler.summed_wt_I,
summed_weight=scaler.summed_weight,
ISIGI=scaler.ISIGI,
n_bins=work_params.output.n_bins,
title="Statistics for all reflections",
out=out,
work_params=work_params)
print >> out, ""
n_refl, corr = ((scaler.completeness > 0).count(True), 0)
print >> out, "\n"
table2 = show_overall_observations(
obs=miller_set_avg,
redundancy=scaler.summed_N,
summed_wt_I=scaler.summed_wt_I,
summed_weight=scaler.summed_weight,
ISIGI=scaler.ISIGI,
n_bins=work_params.output.n_bins,
title="Statistics for reflections where I > 0",
out=out,
work_params=work_params)
#from libtbx import easy_pickle
#easy_pickle.dump(file_name="stats.pickle", obj=stats)
#stats.report(plot=work_params.plot)
#miller_counts = miller_set_p1.array(data=stats.counts.as_double()).select(
# stats.counts != 0)
#miller_counts.as_mtz_dataset(column_root_label="NOBS").mtz_object().write(
# file_name="nobs.mtz")
if work_params.data_subsubsets.subsubset is not None and work_params.data_subsubsets.subsubset_total is not None:
easy_pickle.dump(
"scaler_%d.pickle" % work_params.data_subsubsets.subsubset, scaler)
print >> out, ""
mtz_file, miller_array = scaler.finalize_and_save_data()
#table_pickle_file = "%s_graphs.pkl" % work_params.output.prefix
#easy_pickle.dump(table_pickle_file, [table1, table2])
loggraph_file = os.path.abspath("%s_graphs.log" %
work_params.output.prefix)
f = open(loggraph_file, "w")
f.write(table1.format_loggraph())
f.write("\n")
f.write(table2.format_loggraph())
f.close()
result = scaling_result(miller_array=miller_array,
plots=scaler.get_plot_statistics(),
mtz_file=mtz_file,
loggraph_file=loggraph_file,
obs_table=table1,
all_obs_table=table2,
n_reflections=n_refl,
overall_correlation=corr)
easy_pickle.dump("%s.pkl" % work_params.output.prefix, result)
return result
def run(args):
phil = iotbx.phil.process_command_line(args=args,
master_string=master_phil).show()
work_params = phil.work.extract()
from xfel.merging.phil_validation import application, samosa
application(work_params)
samosa(work_params)
if ("--help" in args):
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or (work_params.data is None)):
command_name = os.environ["LIBTBX_DISPATCHER_NAME"]
raise Usage(command_name + " "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell)
and (not work_params.set_average_unit_cell)):
raise Usage(
"If rescale_with_average_cell=True, you must also specify " +
"set_average_unit_cell=True.")
if work_params.raw_data.sdfac_auto and work_params.raw_data.sdfac_refine:
raise Usage("Cannot specify both sdfac_auto and sdfac_refine")
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s.log" % work_params.output.prefix, "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "I model"
if work_params.model is not None:
from xfel.merging.general_fcalc import run
i_model = run(work_params)
work_params.target_unit_cell = i_model.unit_cell()
work_params.target_space_group = i_model.space_group_info()
i_model.show_summary()
else:
i_model = None
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
miller_set, i_model = consistent_set_and_model(work_params, i_model)
frame_files = get_observations(work_params)
scaler = scaling_manager(miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all(frame_files)
if scaler.n_accepted == 0:
return None
scaler.show_unit_cell_histograms()
if (work_params.rescale_with_average_cell):
average_cell_abc = scaler.uc_values.get_average_cell_dimensions()
average_cell = uctbx.unit_cell(
list(average_cell_abc) +
list(work_params.target_unit_cell.parameters()[3:]))
work_params.target_unit_cell = average_cell
print >> out, ""
print >> out, "#" * 80
print >> out, "RESCALING WITH NEW TARGET CELL"
print >> out, " average cell: %g %g %g %g %g %g" % \
work_params.target_unit_cell.parameters()
print >> out, ""
scaler.reset()
scaler.scale_all(frame_files)
scaler.show_unit_cell_histograms()
if False: #(work_params.output.show_plots) :
try:
plot_overall_completeness(completeness)
except Exception, e:
print "ERROR: can't show plots"
print " %s" % str(e)
def run(args):
phil = iotbx.phil.process_command_line(args=args, master_string=master_phil).show()
work_params = phil.work.extract()
from xfel.merging.phil_validation import application,samosa
application(work_params)
samosa(work_params)
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) ) :
command_name = os.environ["LIBTBX_DISPATCHER_NAME"]
raise Usage(command_name + " "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell) and
(not work_params.set_average_unit_cell)) :
raise Usage("If rescale_with_average_cell=True, you must also specify "+
"set_average_unit_cell=True.")
if work_params.raw_data.sdfac_auto and work_params.raw_data.sdfac_refine:
raise Usage("Cannot specify both sdfac_auto and sdfac_refine")
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s.log" % work_params.output.prefix, "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "I model"
if work_params.model is not None:
from xfel.merging.general_fcalc import run
i_model = run(work_params)
work_params.target_unit_cell = i_model.unit_cell()
work_params.target_space_group = i_model.space_group_info()
i_model.show_summary()
else:
i_model = None
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
# Adjust the minimum d-spacing of the generated Miller set to assure
# that the desired high-resolution limit is included even if the
# observed unit cell differs slightly from the target. If a
# reference model is present, ensure that Miller indices are ordered
# identically.
miller_set = symmetry(
unit_cell=work_params.target_unit_cell,
space_group_info=work_params.target_space_group
).build_miller_set(
anomalous_flag=not work_params.merge_anomalous,
d_max=work_params.d_max,
d_min=work_params.d_min / math.pow(
1 + work_params.unit_cell_length_tolerance, 1 / 3))
miller_set = miller_set.change_basis(
work_params.model_reindex_op).map_to_asu()
if i_model is not None:
matches = miller.match_indices(i_model.indices(), miller_set.indices())
assert not matches.have_singles()
miller_set = miller_set.select(matches.permutation())
frame_files = get_observations(work_params)
scaler = scaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all(frame_files)
if scaler.n_accepted == 0:
return None
scaler.show_unit_cell_histograms()
if (work_params.rescale_with_average_cell) :
average_cell_abc = scaler.uc_values.get_average_cell_dimensions()
average_cell = uctbx.unit_cell(list(average_cell_abc) +
list(work_params.target_unit_cell.parameters()[3:]))
work_params.target_unit_cell = average_cell
print >> out, ""
print >> out, "#" * 80
print >> out, "RESCALING WITH NEW TARGET CELL"
print >> out, " average cell: %g %g %g %g %g %g" % \
work_params.target_unit_cell.parameters()
print >> out, ""
scaler.reset()
scaler.scale_all(frame_files)
scaler.show_unit_cell_histograms()
if False : #(work_params.output.show_plots) :
try :
plot_overall_completeness(completeness)
except Exception, e :
print "ERROR: can't show plots"
print " %s" % str(e)