def run(show_plots,args):
from xfel.command_line.cxi_merge import master_phil
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
datadir = "."
written_files = []
if work_params.levmar.compute_cc_half:
for half_data_flag in [1,2,0]:
case = execute_case(datadir, work_params, plot=show_plots, half_data_flag=half_data_flag)
assert len(case.Fit_I)==len(case.ordered_intensities.indices())==len(case.reference_millers.indices())
model_subset = case.reference_millers[0:len(case.Fit_I)]
fitted_miller_array = miller.array (miller_set = model_subset,
data = case.Fit_I, sigmas = case.Fit_I_stddev)
fitted_miller_array.set_observation_type_xray_intensity()
output_result = fitted_miller_array.select(case.I_visited==1)
outfile = "%s_s%1d_levmar.mtz"%(work_params.output.prefix,half_data_flag)
output_result.show_summary(prefix="%s: "%outfile)
mtz_out = output_result.as_mtz_dataset(column_root_label="Iobs",title=outfile,wavelength=None)
mtz_obj = mtz_out.mtz_object()
mtz_obj.write(outfile)
written_files.append(outfile)
print "OK s%1d"%half_data_flag
#raw_input("OK?")
"""Guest code to retrieve the modified orientations after rotational fitting is done"""
if "Rxy" in work_params.levmar.parameter_flags:
all_A = [e.crystal.get_A() for e in case.experiments.get_experiments()]
all_files = case.experiments.get_files()
all_x = case.Fit["Ax"]
all_y = case.Fit["Ay"]
from scitbx import matrix
x_axis = matrix.col((1.,0.,0.))
y_axis = matrix.col((0.,1.,0.))
out = open("aaaaa","w")
for x in xrange(len(all_A)):
Rx = x_axis.axis_and_angle_as_r3_rotation_matrix(angle=all_x[x], deg=True)
Ry = y_axis.axis_and_angle_as_r3_rotation_matrix(angle=all_y[x], deg=True)
modified_A = Rx * Ry * all_A[x]
filename = all_files[x]
print >>out, filename, " ".join([str(a) for a in modified_A.elems])
work_params.scaling.algorithm="levmar"
from xfel.cxi.cxi_cc import run_cc
run_cc(work_params,work_params.model_reindex_op,sys.stdout)
else:
execute_case(datadir, work_params, plot=show_plots)
def run(show_plots,args):
from xfel.command_line.cxi_merge import master_phil
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
datadir = "."
written_files = []
if work_params.levmar.compute_cc_half:
for half_data_flag in [1,2,0]:
case = execute_case(datadir, work_params, plot=show_plots, half_data_flag=half_data_flag)
assert len(case.Fit_I)==len(case.ordered_intensities.indices())==len(case.reference_millers.indices())
model_subset = case.reference_millers[0:len(case.Fit_I)]
fitted_miller_array = miller.array (miller_set = model_subset,
data = case.Fit_I, sigmas = case.Fit_I_stddev)
fitted_miller_array.set_observation_type_xray_intensity()
output_result = fitted_miller_array.select(case.I_visited==1)
outfile = "%s_s%1d_levmar.mtz"%(work_params.output.prefix,half_data_flag)
output_result.show_summary(prefix="%s: "%outfile)
mtz_out = output_result.as_mtz_dataset(column_root_label="Iobs",title=outfile,wavelength=None)
mtz_obj = mtz_out.mtz_object()
mtz_obj.write(outfile)
written_files.append(outfile)
print "OK s%1d"%half_data_flag
#raw_input("OK?")
"""Guest code to retrieve the modified orientations after rotational fitting is done"""
if "Rxy" in work_params.levmar.parameter_flags:
all_A = [e.crystal.get_A() for e in case.experiments.get_experiments()]
all_files = case.experiments.get_files()
all_x = case.Fit["Ax"]
all_y = case.Fit["Ay"]
from scitbx import matrix
x_axis = matrix.col((1.,0.,0.))
y_axis = matrix.col((0.,1.,0.))
out = open("aaaaa","w")
for x in xrange(len(all_A)):
Rx = x_axis.axis_and_angle_as_r3_rotation_matrix(angle=all_x[x], deg=True)
Ry = y_axis.axis_and_angle_as_r3_rotation_matrix(angle=all_y[x], deg=True)
modified_A = Rx * Ry * all_A[x]
filename = all_files[x]
print >>out, filename, " ".join([str(a) for a in modified_A.elems])
work_params.scaling.algorithm="levmar"
from xfel.cxi.cxi_cc import run_cc
run_cc(work_params,work_params.model_reindex_op,sys.stdout)
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 find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"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.")
# 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_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
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_min=work_params.d_min)
print 'Miller set size: %d' % len(miller_set.indices())
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
def nint(a):
return int(round(a))
from collections import defaultdict
i_scale = 0.1
i_hist = defaultdict(int)
for j, se in enumerate(zip(starts, ends)):
s, e = se
for i in intensi[s:e]:
i_hist[nint(i_scale * i)] += 1
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
fout = open('i_hist.dat', 'w')
for i in i_hist:
fout.write('%.2f %d\n' % (i / i_scale, i_hist[i]))
fout.close()
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
odd = 0
even = 0
for s, e in zip(starts, ends):
for x in range(s, e):
hkl = lookup[hkl_asu[x]]
if (hkl[0] + hkl[1] + hkl[2]) % 2 == 1:
odd += 1
else:
even += 1
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, intensities, sigmas))
# pre-scale the data - first determine average ln(k), B; then apply
kbs = [f.kb() for f in frames]
mn_k = sum([kb[0] for kb in kbs]) / len(kbs)
mn_B = sum([kb[1] for kb in kbs]) / len(kbs)
n_lt_500 = 0
n_gt_500 = 0
for j, f in enumerate(frames):
s_i = f.scale_to_kb(mn_k, mn_B)
fout = open('frame-s-i-%05d.dat' % j, 'w')
for s, i, si in s_i:
fout.write('%f %f %f\n' % (s, i, si))
if i < 500:
n_lt_500 += 1
else:
n_gt_500 += 1
fout.close()
from collections import defaultdict
hist = defaultdict(int)
fout = open('kb.dat', 'w')
for j, f in enumerate(frames):
kb = f.kb()
fout.write('%4d %6.3f %6.3f\n' % (j, kb[0], kb[1]))
hist[int(round(kb[1]))] += 1
fout.close()
for b in sorted(hist):
print b, hist[b]
print odd, even
print n_lt_500, n_gt_500
return
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
application(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) or
( (work_params.model is None) and work_params.scaling.algorithm != "mark1") ) :
raise Usage("cxi.merge "
"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")
log = open("%s_%s.log" % (work_params.output.prefix,work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
# Verify that the externally supplied isomorphous reference, if
# present, defines a suitable column of intensities, and exit with
# error if it does not. Then warn if it is necessary to generate
# Bijvoet mates. Failure to catch these issues here would lead to
# possibly obscure problems in cxi/cxi_cc.py later on.
try:
data_SR = mtz.object(work_params.scaling.mtz_file)
except RuntimeError:
pass
else:
array_SR = None
obs_labels = []
for array in data_SR.as_miller_arrays():
this_label = array.info().label_string().lower()
if array.observation_type() is not None:
obs_labels.append(this_label.split(',')[0])
if this_label.find('fobs')>=0:
array_SR = array.as_intensity_array()
break
if this_label.find('imean')>=0:
array_SR = array.as_intensity_array()
break
if this_label.find(work_params.scaling.mtz_column_F)==0:
array_SR = array.as_intensity_array()
break
if array_SR is None:
known_labels = ['fobs', 'imean', work_params.scaling.mtz_column_F]
raise Usage(work_params.scaling.mtz_file +
" does not contain any observations labelled [" +
", ".join(known_labels) +
"]. Please set scaling.mtz_column_F to one of [" +
",".join(obs_labels) + "].")
elif not work_params.merge_anomalous and not array_SR.anomalous_flag():
print >> out, "Warning: Preserving anomalous contributors, but %s " \
"has anomalous contributors merged. Generating identical Bijvoet " \
"mates." % work_params.scaling.mtz_file
# 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!
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 = 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())
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all()
if scaler.n_accepted == 0:
return None
# --- End of x scaling
scaler.uc_values = unit_cell_distribution()
for icell in xrange(len(scaler.frames["unit_cell"])):
if scaler.params.model is None:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell])
else:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell],
rejected=(scaler.frames["cc"][icell] < scaler.params.min_corr))
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 = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all()
scaler.uc_values = unit_cell_distribution()
for icell in xrange(len(scaler.frames["unit_cell"])):
if scaler.params.model is None:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell])
else:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell],
rejected=(scaler.frames["cc"][icell] < scaler.params.min_corr))
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
application(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)
or ((work_params.model is None)
and work_params.scaling.algorithm != "mark1")):
raise Usage("cxi.merge "
"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")
log = open(
"%s_%s.log" %
(work_params.output.prefix, work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
# Verify that the externally supplied isomorphous reference, if
# present, defines a suitable column of intensities, and exit with
# error if it does not. Then warn if it is necessary to generate
# Bijvoet mates. Failure to catch these issues here would lead to
# possibly obscure problems in cxi/cxi_cc.py later on.
try:
data_SR = mtz.object(work_params.scaling.mtz_file)
except RuntimeError:
pass
else:
array_SR = None
obs_labels = []
for array in data_SR.as_miller_arrays():
this_label = array.info().label_string().lower()
if array.observation_type() is not None:
obs_labels.append(this_label.split(',')[0])
if this_label.find('fobs') >= 0:
array_SR = array.as_intensity_array()
break
if this_label.find('imean') >= 0:
array_SR = array.as_intensity_array()
break
if this_label.find(work_params.scaling.mtz_column_F) == 0:
array_SR = array.as_intensity_array()
break
if array_SR is None:
known_labels = ['fobs', 'imean', work_params.scaling.mtz_column_F]
raise Usage(work_params.scaling.mtz_file +
" does not contain any observations labelled [" +
", ".join(known_labels) +
"]. Please set scaling.mtz_column_F to one of [" +
",".join(obs_labels) + "].")
elif not work_params.merge_anomalous and not array_SR.anomalous_flag():
print >> out, "Warning: Preserving anomalous contributors, but %s " \
"has anomalous contributors merged. Generating identical Bijvoet " \
"mates." % work_params.scaling.mtz_file
# 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!
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)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all()
if scaler.n_accepted == 0:
return None
# --- End of x scaling
scaler.uc_values = unit_cell_distribution()
for icell in xrange(len(scaler.frames["unit_cell"])):
if scaler.params.model is None:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell])
else:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell],
rejected=(scaler.frames["cc"][icell] < scaler.params.min_corr))
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 = xscaling_manager(miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all()
scaler.uc_values = unit_cell_distribution()
for icell in xrange(len(scaler.frames["unit_cell"])):
if scaler.params.model is None:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell])
else:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell],
rejected=(scaler.frames["cc"][icell] <
scaler.params.min_corr))
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)
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")):
raise Usage("cxi.merge "
"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.")
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_min = work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set = miller_set,
i_model = i_model,
params = work_params)
scaler.read_all()
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
rational_ops = []
for symop in pg:
rational_ops.append((matrix.sqr(symop.r().transpose().as_rational()),
symop.r().as_hkl()))
# miller_set.show_summary()
uc = work_params.target_unit_cell
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
origH = scaler.observations["H"]
origK = scaler.observations["K"]
origL = scaler.observations["L"]
from cctbx.miller import map_to_asu
sgtype = miller_set.space_group_info().type()
aflag = miller_set.anomalous_flag()
from cctbx.array_family import flex
# FIXME in here perform the mapping to ASU for both the original and other
# index as an array-wise manipulation to make things a bunch faster...
# however this also uses a big chunk of RAM... FIXME also in here use
# cb_op.apply(indices) to get the indices reindexed...
original_indices = flex.miller_index()
for x in xrange(len(scaler.observations["hkl_id"])):
original_indices.append(lookup[hkl_asu[x]])
from cctbx.sgtbx import change_of_basis_op
I23 = change_of_basis_op('k, -h, l')
other_indices = I23.apply(original_indices)
map_to_asu(sgtype, aflag, original_indices)
map_to_asu(sgtype, aflag, other_indices)
# FIXME would be useful in here to have a less expensive way of finding the
# symmetry operation which gave the map to the ASU - perhaps best way is to
# make a new C++ map_to_asu which records this.
# FIXME in here recover the original frame structure of the data to
# logical frame objetcs - N.B. the frame will need to be augmented to test
# alternative indexings
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
print 'processing frame %d: %d to %d' % (j, se[0], se[1])
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
# then start running the comparison code
frames = []
for s, e in zip(starts, ends):
# FIXME need this from remap to ASU
misym = [0 for x in range(s, e)]
indices = [original_indices[x] for x in range(s, e)]
other = [other_indices[x] for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, other, intensities, sigmas))
reference = FrameFromReferenceMTZ()
fout = open('cc_reference.log', 'w')
for j, f in enumerate(frames):
_cc = reference.cc(f)
_oo = reference.cc_other(f)
print '%d %d %d %d %f %d %f' % (j, starts[j], ends[j], _cc[0], _cc[1],
_oo[0], _oo[1])
fout.write('%d %d %d %d %f %d %f\n' % (j, starts[j], ends[j],
_cc[0], _cc[1], _oo[0], _oo[1]))
fout.close()
return
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
application(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)
or ((work_params.model is None)
and work_params.scaling.algorithm != "mark1")):
raise Usage("cxi.merge "
"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")
if not work_params.include_negatives_fix_27May2018:
work_params.include_negatives = False # use old behavior
log = open(
"%s_%s.log" %
(work_params.output.prefix, work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
# Verify that the externally supplied isomorphous reference, if
# present, defines a suitable column of intensities, and exit with
# error if it does not. Then warn if it is necessary to generate
# Bijvoet mates. Failure to catch these issues here would lead to
# possibly obscure problems in cxi/cxi_cc.py later on.
try:
data_SR = mtz.object(work_params.scaling.mtz_file)
except RuntimeError:
pass
else:
array_SR = None
obs_labels = []
for array in data_SR.as_miller_arrays():
this_label = array.info().label_string().lower()
if array.observation_type() is not None:
obs_labels.append(this_label.split(',')[0])
if this_label.find('fobs') >= 0:
array_SR = array.as_intensity_array()
break
if this_label.find('imean') >= 0:
array_SR = array.as_intensity_array()
break
if this_label.find(work_params.scaling.mtz_column_F) == 0:
array_SR = array.as_intensity_array()
break
if array_SR is None:
known_labels = ['fobs', 'imean', work_params.scaling.mtz_column_F]
raise Usage(work_params.scaling.mtz_file +
" does not contain any observations labelled [" +
", ".join(known_labels) +
"]. Please set scaling.mtz_column_F to one of [" +
",".join(obs_labels) + "].")
elif not work_params.merge_anomalous and not array_SR.anomalous_flag():
print("Warning: Preserving anomalous contributors, but %s " \
"has anomalous contributors merged. Generating identical Bijvoet " \
"mates." % work_params.scaling.mtz_file, file=out)
# 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!
print("I model", file=out)
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("Target unit cell and space group:", file=out)
print(" ", work_params.target_unit_cell, file=out)
print(" ", work_params.target_space_group, file=out)
miller_set, i_model = consistent_set_and_model(work_params, i_model)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all()
if scaler.n_accepted == 0:
return None
# --- End of x scaling
scaler.uc_values = unit_cell_distribution()
for icell in range(len(scaler.frames["unit_cell"])):
if scaler.params.model is None:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell])
else:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell],
rejected=(scaler.frames["cc"][icell] < scaler.params.min_corr))
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("", file=out)
print("#" * 80, file=out)
print("RESCALING WITH NEW TARGET CELL", file=out)
print(" average cell: %g %g %g %g %g %g" % \
work_params.target_unit_cell.parameters(), file=out)
print("", file=out)
scaler.reset()
scaler = xscaling_manager(miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.scale_all()
scaler.uc_values = unit_cell_distribution()
for icell in range(len(scaler.frames["unit_cell"])):
if scaler.params.model is None:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell])
else:
scaler.uc_values.add_cell(
unit_cell=scaler.frames["unit_cell"][icell],
rejected=(scaler.frames["cc"][icell] <
scaler.params.min_corr))
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("\n", file=out)
reserve_prefix = work_params.output.prefix
for data_subset in [1, 2, 0]:
work_params.data_subset = data_subset
work_params.output.prefix = "%s_s%1d_%s" % (
reserve_prefix, data_subset, work_params.scaling.algorithm)
if work_params.data_subset == 0:
scaler.frames["data_subset"] = flex.bool(
scaler.frames["frame_id"].size(), True)
elif work_params.data_subset == 1:
scaler.frames["data_subset"] = scaler.frames["odd_numbered"]
elif work_params.data_subset == 2:
scaler.frames["data_subset"] = scaler.frames[
"odd_numbered"] == False
# --------- New code ------------------
#sanity check
for mod, obs in zip(miller_set.indices(),
scaler.millers["merged_asu_hkl"]):
if mod != obs:
raise Exception(
"miller index lists inconsistent--check d_min are equal for merge and xmerge scripts"
)
assert mod == obs
"""Sum the observations of I and I/sig(I) for each reflection.
sum_I = flex.double(i_model.size(), 0.)
sum_I_SIGI = flex.double(i_model.size(), 0.)
scaler.completeness = flex.int(i_model.size(), 0)
scaler.summed_N = flex.int(i_model.size(), 0)
scaler.summed_wt_I = flex.double(i_model.size(), 0.)
scaler.summed_weight = flex.double(i_model.size(), 0.)
scaler.n_rejected = flex.double(scaler.frames["frame_id"].size(), 0.)
scaler.n_obs = flex.double(scaler.frames["frame_id"].size(), 0.)
scaler.d_min_values = flex.double(scaler.frames["frame_id"].size(), 0.)
scaler.ISIGI = {}"""
from xfel import scaling_results, get_scaling_results, get_isigi_dict
results = scaling_results(scaler._observations, scaler._frames,
scaler.millers["merged_asu_hkl"],
scaler.frames["data_subset"],
work_params.include_negatives)
results.__getattribute__(work_params.scaling.algorithm)(
scaler.params.min_corr, scaler.params.target_unit_cell)
sum_I, sum_I_SIGI, \
scaler.completeness, scaler.summed_N, \
scaler.summed_wt_I, scaler.summed_weight, scaler.n_rejected, scaler.n_obs, \
scaler.d_min_values, hkl_ids, i_sigi_list = get_scaling_results(results)
scaler.ISIGI = get_isigi_dict(results)
if work_params.merging.refine_G_Imodel:
from xfel.cxi.merging.refine import find_scale
my_find_scale = find_scale(scaler, work_params)
sum_I, sum_I_SIGI, \
scaler.completeness, scaler.summed_N, \
scaler.summed_wt_I, scaler.summed_weight, scaler.n_rejected, \
scaler.n_obs, scaler.d_min_values, hkl_ids, i_sigi_list \
= my_find_scale.get_scaling_results(results, scaler)
scaler.ISIGI = get_isigi_dict(results)
scaler.wavelength = scaler.frames["wavelength"]
scaler.corr_values = scaler.frames["cc"]
scaler.rejected_fractions = flex.double(
scaler.frames["frame_id"].size(), 0.)
for irej in range(len(scaler.rejected_fractions)):
if scaler.n_obs[irej] > 0:
scaler.rejected_fractions = scaler.n_rejected[
irej] / scaler.n_obs[irej]
# ---------- End of new code ----------------
if work_params.raw_data.sdfac_refine or work_params.raw_data.errors_from_sample_residuals:
if work_params.raw_data.sdfac_refine:
if work_params.raw_data.error_models.sdfac_refine.minimizer == 'simplex':
from xfel.merging.algorithms.error_model.sdfac_refine import sdfac_refine as error_modeler
elif work_params.raw_data.error_models.sdfac_refine.minimizer == 'lbfgs':
from xfel.merging.algorithms.error_model.sdfac_refine_lbfgs import sdfac_refine_refltable_lbfgs as error_modeler
elif self.params.raw_data.error_models.sdfac_refine.minimizer == 'LevMar':
from xfel.merging.algorithms.error_model.sdfac_refine_levmar import sdfac_refine_refltable_levmar as error_modeler
if work_params.raw_data.errors_from_sample_residuals:
from xfel.merging.algorithms.error_model.errors_from_residuals import errors_from_residuals as error_modeler
error_modeler(scaler).adjust_errors()
if work_params.raw_data.reduced_chi_squared_correction:
from xfel.merging.algorithms.error_model.reduced_chi_squared import reduced_chi_squared
reduced_chi_squared(scaler).compute()
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,
redundancy_to_edge=None,
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)
if table1 is None:
raise Exception("table could not be constructed")
print("", file=out)
if work_params.scaling.algorithm == 'mark0':
n_refl, corr = scaler.get_overall_correlation(sum_I)
else:
n_refl, corr = ((scaler.completeness > 0).count(True), 0)
print("\n", file=out)
table2 = show_overall_observations(
obs=miller_set_avg,
redundancy=scaler.summed_N,
redundancy_to_edge=None,
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)
if table2 is None:
raise Exception("table could not be constructed")
print("", file=out)
mtz_file, miller_array = scaler.finalize_and_save_data()
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)
work_params.output.prefix = reserve_prefix
# Output table with number of images contribution reflections per
# resolution bin.
from libtbx import table_utils
miller_set_avg.setup_binner(d_max=100000,
d_min=work_params.d_min,
n_bins=work_params.output.n_bins)
table_data = [["Bin", "Resolution Range", "# images", "%accept"]]
if work_params.model is None:
appropriate_min_corr = -1.1 # lowest possible c.c.
else:
appropriate_min_corr = work_params.min_corr
n_frames = (scaler.frames['cc'] > appropriate_min_corr).count(True)
iselect = 1
while iselect < work_params.output.n_bins:
col_count1 = results.count_frames(
appropriate_min_corr,
miller_set_avg.binner().selection(iselect))
print("colcount1", col_count1)
if col_count1 > 0: break
iselect += 1
if col_count1 == 0: raise Exception("no reflections in any bins")
for i_bin in miller_set_avg.binner().range_used():
col_count = '%8d' % results.count_frames(
appropriate_min_corr,
miller_set_avg.binner().selection(i_bin))
col_legend = '%-13s' % miller_set_avg.binner().bin_legend(
i_bin=i_bin,
show_bin_number=False,
show_bin_range=False,
show_d_range=True,
show_counts=False)
xpercent = results.count_frames(
appropriate_min_corr,
miller_set_avg.binner().selection(i_bin)) / float(n_frames)
percent = '%5.2f' % (100. * xpercent)
table_data.append(['%3d' % i_bin, col_legend, col_count, percent])
table_data.append([""] * len(table_data[0]))
table_data.append(["All", "", '%8d' % n_frames])
print(file=out)
print(table_utils.format(table_data,
has_header=1,
justify='center',
delim=' '),
file=out)
reindexing_ops = {
"h,k,l": 0
} # get a list of all reindexing ops for this dataset
if work_params.merging.reverse_lookup is not None:
for key in scaler.reverse_lookup:
if reindexing_ops.get(scaler.reverse_lookup[key], None) is None:
reindexing_ops[scaler.reverse_lookup[key]] = 0
reindexing_ops[scaler.reverse_lookup[key]] += 1
from xfel.cxi.cxi_cc import run_cc
for key in reindexing_ops.keys():
run_cc(work_params, reindexing_op=key, output=out)
if isinstance(scaler.ISIGI, dict):
from xfel.merging import isigi_dict_to_reflection_table
refls = isigi_dict_to_reflection_table(scaler.miller_set.indices(),
scaler.ISIGI)
else:
refls = scaler.ISIGI
easy_pickle.dump("%s.refl" % work_params.output.prefix, refls)
return result
def find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"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.")
# 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_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
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_min=work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
for s, e in zip(starts, ends):
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, intensities, sigmas))
cycle = 0
total_nref = sum([len(f.get_indices()) for f in frames])
# pre-scale the data - first determine average ln(k), B; then apply
kbs = [f.kb() for f in frames]
mn_k = sum([kb[0] for kb in kbs]) / len(kbs)
mn_B = sum([kb[1] for kb in kbs]) / len(kbs)
for f in frames:
f.scale_to_kb(mn_k, mn_B)
while True:
print 'Analysing %d frames' % len(frames)
print 'Cycle %d' % cycle
cycle += 1
print 'Power spectrum'
fn = frame_numbers(frames)
for j in sorted(fn):
print '%4d %4d' % (j, fn[j])
nref_cycle = sum([len(f.get_indices()) for f in frames])
assert(nref_cycle == total_nref)
common_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
from cctbx.sgtbx import rt_mx, change_of_basis_op
oh = change_of_basis_op(rt_mx('h,l,k'))
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
# work through unique observations ignoring those which include no
# hand information
for hkl in obs:
if hkl == oh.apply(hkl):
continue
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
common_reflections[(f1, f2)] += 1
cmn_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1 + 1, len(frames)):
if common_reflections[(f1, f2)] > 20:
cmn_rfl_list.append((common_reflections[(f1, f2)], f1, f2))
cmn_rfl_list.sort()
cmn_rfl_list.reverse()
joins = []
used = []
for n, f1, f2 in cmn_rfl_list:
if f1 in used or f2 in used:
continue
_cc = frames[f1].cc(frames[f2])
# really only need to worry about f2 which will get merged...
# merging multiple files together should be OK provided they are
# correctly sorted (though the order should not matter anyhow?)
# anyhow they are sorted anyway... ah as f2 > f1 then just sorting
# the list by f2 will make sure the data cascase correctly.
# p-value very small for cc > 0.75 for > 20 observations - necessary
# as will be correlated due to Wilson curves
if _cc[0] > 20 and _cc[1] > 0.75:
print '%4d %.3f' % _cc, f1, f2
joins.append((f2, f1))
# used.append(f1)
used.append(f2)
if not joins:
print 'No pairs found'
break
joins.sort()
joins.reverse()
for j2, j1 in joins:
rmerge = frames[j1].merge(frames[j2])
if rmerge:
print 'R: %4d %4d %6.3f' % (j1, j2, rmerge)
else:
print 'R: %4d %4d ------' % (j1, j2)
continue
frames.sort()
print 'Biggest few: #frames; #unique refl'
j = -1
while frames[j].get_frames() > 1:
print frames[j].get_frames(), frames[j].get_unique_indices()
j -= 1
return
def run(args):
phil = iotbx.phil.process_command_line(
args = args, master_string = master_phil)
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")):
raise Usage("cxi.merge "
"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.")
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_min = work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set = miller_set,
i_model = i_model,
params = work_params)
scaler.read_all()
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
rational_ops = []
for symop in pg:
rational_ops.append((matrix.sqr(symop.r().transpose().as_rational()),
symop.r().as_hkl()))
# miller_set.show_summary()
uc = work_params.target_unit_cell
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
origH = scaler.observations["H"]
origK = scaler.observations["K"]
origL = scaler.observations["L"]
from cctbx.miller import map_to_asu
sgtype = miller_set.space_group_info().type()
aflag = miller_set.anomalous_flag()
from cctbx.array_family import flex
# FIXME in here perform the mapping to ASU for both the original and other
# index as an array-wise manipulation to make things a bunch faster...
# however this also uses a big chunk of RAM... FIXME also in here use
# cb_op.apply(indices) to get the indices reindexed...
original_indices = flex.miller_index()
for x in xrange(len(scaler.observations["hkl_id"])):
original_indices.append(lookup[hkl_asu[x]])
from cctbx.sgtbx import change_of_basis_op
I23 = change_of_basis_op('k, -h, l')
other_indices = I23.apply(original_indices)
map_to_asu(sgtype, aflag, original_indices)
map_to_asu(sgtype, aflag, other_indices)
# FIXME would be useful in here to have a less expensive way of finding the
# symmetry operation which gave the map to the ASU - perhaps best way is to
# make a new C++ map_to_asu which records this.
# FIXME in here recover the original frame structure of the data to
# logical frame objetcs - N.B. the frame will need to be augmented to test
# alternative indexings
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
print 'processing frame %d: %d to %d' % (j, se[0], se[1])
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
# then start running the comparison code
frames = []
for s, e in zip(starts, ends):
# FIXME need this from remap to ASU
misym = [0 for x in range(s, e)]
indices = [original_indices[x] for x in range(s, e)]
other = [other_indices[x] for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, other, intensities, sigmas))
cycle = 0
total_nref = sum([len(f.get_indices()) for f in frames])
# pre-scale the data - first determine average ln(k), B; then apply
kbs = [f.kb() for f in frames]
mn_k = sum([kb[0] for kb in kbs]) / len(kbs)
mn_B = sum([kb[1] for kb in kbs]) / len(kbs)
for f in frames:
f.scale_to_kb(mn_k, mn_B)
while True:
print 'Analysing %d frames' % len(frames)
print 'Cycle %d' % cycle
cycle += 1
print 'Power spectrum'
fn = frame_numbers(frames)
for j in sorted(fn):
print '%4d %4d' % (j, fn[j])
nref_cycle = sum([len(f.get_indices()) for f in frames])
assert(nref_cycle == total_nref)
# first work on the original indices
import numpy
common_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
# for other hand add -j
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
for hkl in obs:
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
if f1 * f2 > 0:
common_reflections[(abs(f1), abs(f2))] += 1
cmn_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1 + 1, len(frames)):
if common_reflections[(f1, f2)] > 10:
cmn_rfl_list.append((common_reflections[(f1, f2)], f1, f2))
cmn_rfl_list.sort()
cmn_rfl_list.reverse()
joins = []
used = []
for n, f1, f2 in cmn_rfl_list:
if f1 in used or f2 in used:
continue
_cc = frames[f1].cc(frames[f2])
# really only need to worry about f2 which will get merged...
# merging multiple files together should be OK provided they are
# correctly sorted (though the order should not matter anyhow?)
# anyhow they are sorted anyway... ah as f2 > f1 then just sorting
# the list by f2 will make sure the data cascase correctly.
# p-value small (3% ish) for cc > 0.6 for > 10 observations -
# necessary as will be correlated due to Wilson curves though
# with B factor < 10 this is less of an issue
if _cc[0] > 10 and _cc[1] > 0.6:
print '%4d %.3f' % _cc, f1, f2
joins.append((f2, f1))
used.append(f2)
if not joins:
print 'No pairs found'
break
joins.sort()
joins.reverse()
for j2, j1 in joins:
rmerge = frames[j1].merge(frames[j2])
if rmerge:
print 'R: %4d %4d %6.3f' % (j1, j2, rmerge)
else:
print 'R: %4d %4d ------' % (j1, j2)
all_joins = [j for j in joins]
# then do the same for the alternative indices
other_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
# for other hand add -j
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
indices = set(f.get_other())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(-j)
for hkl in obs:
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
if f1 * f2 < 0:
other_reflections[(abs(f1), abs(f2))] += 1
oth_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1 + 1, len(frames)):
if other_reflections[(f1, f2)] > 10:
oth_rfl_list.append((other_reflections[(f1, f2)], f1, f2))
joins = []
oth_rfl_list.sort()
oth_rfl_list.reverse()
for n, f1, f2 in oth_rfl_list:
if f1 in used or f2 in used:
continue
_cc = frames[f1].cc_other(frames[f2])
# really only need to worry about f2 which will get merged...
# merging multiple files together should be OK provided they are
# correctly sorted (though the order should not matter anyhow?)
# anyhow they are sorted anyway... ah as f2 > f1 then just sorting
# the list by f2 will make sure the data cascase correctly.
# p-value small (3% ish) for cc > 0.6 for > 10 observations -
# necessary as will be correlated due to Wilson curves though
# with B factor < 10 this is less of an issue
if _cc[0] > 10 and _cc[1] > 0.6:
print '%4d %.3f' % _cc, f1, f2
joins.append((f2, f1))
used.append(f2)
all_joins += joins
if not all_joins:
break
joins.sort()
joins.reverse()
for j2, j1 in joins:
frames[j2].reindex()
rmerge = frames[j1].merge(frames[j2])
if rmerge:
print 'R: %4d %4d %6.3f' % (j1, j2, rmerge)
else:
print 'R: %4d %4d ------' % (j1, j2)
continue
frames.sort()
print 'Biggest few: #frames; #unique refl'
j = -1
while frames[j].get_frames() > 1:
print frames[j].get_frames(), frames[j].get_unique_indices()
frames[j].output_as_scalepack(sg, 'scalepack-%d.sca' % j)
j -= 1
return
def find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"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.")
# 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_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
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_min=work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
for s, e in zip(starts, ends):
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, intensities, sigmas))
from collections import defaultdict
hist = defaultdict(int)
fout = open('common_oh.dat', 'w')
for j, f in enumerate(frames):
hp = f.hand_pairs()
fout.write('%4d %d\n' % (j, hp))
hist[int(hp)] += 1
fout.close()
for b in sorted(hist):
print b, hist[b]
return
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)
def find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"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.")
# 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_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
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_min=work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
for s, e in zip(starts, ends):
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(indices, intensities, sigmas))
while True:
print 'Analysing %d frames' % len(frames)
common_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
for hkl in obs:
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
common_reflections[(f1, f2)] += 1
cmn_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1, len(frames)):
cmn_rfl_list.append((common_reflections[(f1, f2)], f1, f2))
cmn_rfl_list.sort()
cmn_rfl_list.reverse()
joins = []
for n, f1, f2 in cmn_rfl_list:
_cc = frames[f1].cc(frames[f2])
if _cc[0] > 20 and _cc[1] > 0.75:
# print '===> %d %d' % (n, frames[f1].common(frames[f2]))
print '%4d %.3f' % _cc, f1, f2
joins.append((f1, f2))
if not joins:
print 'No pairs found'
break
joins.sort()
joins.reverse()
for j1, j2 in joins:
frames[j1].merge(frames[j2])
frames.remove(frames[j2])
continue
return
