def report(O, plot=None, xy_prefix=None):
from cctbx.array_family import flex
print "Number of shots:", O.completeness_history.size() - 1
print
print "Histogram of counts per reflection:"
flex.histogram(O.counts.as_double(),
n_slots=8).show(prefix=" ", format_cutoffs="%7.0f")
print
print "Observations per reflection:"
flex.show_count_stats(counts=O.counts, prefix=" ")
print " Median:", int(flex.median(O.counts.as_double()) + 0.5)
print
sys.stdout.flush()
if (xy_prefix is None):
xy_prefix = ""
elif (len(xy_prefix) != 0):
xy_prefix = xy_prefix + "_"
def dump_xy(name, array):
f = open(xy_prefix + "%s.xy" % name, "w")
for i, c in enumerate(array):
print >> f, i, c
dump_xy("completeness_history", O.completeness_history)
dump_xy("min_count_history", O.min_count_history)
if (O.use_symmetry): _ = O.i_calc.asu
else: _ = O.i_calc.p1_anom
_ = _.customized_copy(data=O.counts).sort(by_value="resolution")
sym_factors = _.space_group().order_p()
if (not O.i_calc.asu.anomalous_flag()):
sym_factors *= 2
sym_factors /= _.multiplicities().data()
counts_sorted_by_resolution = _.data().as_int() * sym_factors
dump_xy("counts_sorted_by_resolution", counts_sorted_by_resolution)
dump_xy("d_spacings_sorted_by_resolution", _.d_spacings().data())
if (plot == "completeness"):
from libtbx import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
_ = O.completeness_history
nx = _.size()
ax.plot(range(nx), _, "r-")
ax.axis([0, nx, 0, 1])
pyplot.show()
elif (plot == "redundancy"):
from libtbx import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
_ = counts_sorted_by_resolution
ax.plot(range(len(_)), _, "r-")
ax.axis([-_.size() * 0.05, _.size() * 1.05, 0, None])
pyplot.show()
elif (plot is not None):
raise RuntimeError('Unknown plot type: "%s"' % plot)
def report(O, plot=None, xy_prefix=None):
from cctbx.array_family import flex
print "Number of shots:", O.completeness_history.size()-1
print
print "Histogram of counts per reflection:"
flex.histogram(O.counts.as_double(), n_slots=8).show(
prefix=" ", format_cutoffs="%7.0f")
print
print "Observations per reflection:"
flex.show_count_stats(counts=O.counts, prefix=" ")
print " Median:", int(flex.median(O.counts.as_double())+0.5)
print
sys.stdout.flush()
if (xy_prefix is None):
xy_prefix = ""
elif (len(xy_prefix) != 0):
xy_prefix = xy_prefix + "_"
def dump_xy(name, array):
f = open(xy_prefix + "%s.xy" % name, "w")
for i,c in enumerate(array):
print >> f, i, c
dump_xy("completeness_history", O.completeness_history)
dump_xy("min_count_history", O.min_count_history)
if (O.use_symmetry): _ = O.i_calc.asu
else: _ = O.i_calc.p1_anom
_ = _.customized_copy(data=O.counts).sort(by_value="resolution")
sym_factors = _.space_group().order_p()
if (not O.i_calc.asu.anomalous_flag()):
sym_factors *= 2
sym_factors /= _.multiplicities().data()
counts_sorted_by_resolution = _.data().as_int() * sym_factors
dump_xy("counts_sorted_by_resolution", counts_sorted_by_resolution)
dump_xy("d_spacings_sorted_by_resolution", _.d_spacings().data())
if (plot == "completeness"):
from libtbx import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
_ = O.completeness_history
nx = _.size()
ax.plot(range(nx), _, "r-")
ax.axis([0, nx, 0, 1])
pyplot.show()
elif (plot == "redundancy"):
from libtbx import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
_ = counts_sorted_by_resolution
ax.plot(range(len(_)), _, "r-")
ax.axis([-_.size()*0.05, _.size()*1.05, 0, None])
pyplot.show()
elif (plot is not None):
raise RuntimeError('Unknown plot type: "%s"' % plot)
def run(args):
assert len(args) == 0
n_samples = 1000
from matplotlib.backends.backend_pdf import PdfPages
all_pdf = PdfPages("all.pdf")
from libtbx import pyplot
from scitbx.array_family import flex
for element in e_scattering.ito_vol_c_2011_table_4_3_2_2_elements():
fig = pyplot.figure()
fig.set_size_inches(11, 8.5)
ax = fig.add_subplot(1, 1, 1)
ax.set_title(element, fontsize=12)
def one_curv(g, code):
x = flex.double()
y = flex.double()
for i_stol in xrange(n_samples + 1):
stol = 6 * i_stol / n_samples
x.append(stol)
y.append(g.at_stol(stol))
ax.plot(x.as_numpy_array(), y.as_numpy_array(), code)
g = e_scattering.ito_vol_c_2011_table_4_3_2_2_entry_as_gaussian(
label=element, exact=True)
one_curv(g, "b-")
one_curv(e_scattering.gaussian(g.array_of_a()[:4],
g.array_of_b()[:4]), "r-")
all_pdf.savefig(fig, bbox_inches="tight")
all_pdf.close()
print "plots written to file: all.pdf"
def run(args):
assert len(args) == 0
n_samples = 1000
from matplotlib.backends.backend_pdf import PdfPages
all_pdf = PdfPages("all.pdf")
from libtbx import pyplot
from scitbx.array_family import flex
for element in e_scattering.ito_vol_c_2011_table_4_3_2_2_elements():
fig = pyplot.figure()
fig.set_size_inches(11, 8.5)
ax = fig.add_subplot(1, 1, 1)
ax.set_title(element, fontsize=12)
def one_curv(g, code):
x = flex.double()
y = flex.double()
for i_stol in xrange(n_samples+1):
stol = 6 * i_stol / n_samples
x.append(stol)
y.append(g.at_stol(stol))
ax.plot(x.as_numpy_array(), y.as_numpy_array(), code)
g = e_scattering.ito_vol_c_2011_table_4_3_2_2_entry_as_gaussian(
label=element, exact=True)
one_curv(g, "b-")
one_curv(e_scattering.gaussian(
g.array_of_a()[:4],
g.array_of_b()[:4]), "r-")
all_pdf.savefig(fig, bbox_inches="tight")
all_pdf.close()
print "plots written to file: all.pdf"
def run(args, log=sys.stdout, as_gui_program=False):
if (len(args) == 0):
parsed = defaults(log=log)
parsed.show(prefix=" ", out=log)
return
command_line = (option_parser().enable_symmetry_comprehensive().option(
"-q",
"--quiet",
action="store_true",
default=False,
help="suppress output").option("--output_plots",
action="store_true",
default=False)).process(args=args)
parsed = defaults(log=log)
processed_args = mmtbx.utils.process_command_line_args(
args=command_line.args,
cmd_cs=command_line.symmetry,
master_params=parsed,
log=log,
suppress_symmetry_related_errors=True)
processed_args.params.show(out=log)
params = processed_args.params.extract().density_modification
output_plots = command_line.options.output_plots
crystal_symmetry = crystal.symmetry(
unit_cell=params.input.unit_cell,
space_group_info=params.input.space_group)
reflection_files = {}
for rfn in (params.input.reflection_data.file_name,
params.input.experimental_phases.file_name,
params.input.map_coefficients.file_name):
if os.path.isfile(str(rfn)) and rfn not in reflection_files:
reflection_files.setdefault(
rfn,
iotbx.reflection_file_reader.any_reflection_file(
file_name=rfn, ensure_read_access=False))
# TODO is reflection_files a dict ?
server = iotbx.reflection_file_utils.reflection_file_server(
crystal_symmetry=crystal_symmetry,
reflection_files=list(reflection_files.values()))
fo = mmtbx.utils.determine_data_and_flags(
server,
parameters=params.input.reflection_data,
extract_r_free_flags=False,
log=log).f_obs
hl_coeffs = mmtbx.utils.determine_experimental_phases(
server,
params.input.experimental_phases,
log=log,
parameter_scope="",
working_point_group=None,
symmetry_safety_check=True,
ignore_all_zeros=True)
if params.input.map_coefficients.file_name is not None:
map_coeffs = server.get_phases_deg(
file_name=params.input.map_coefficients.file_name,
labels=params.input.map_coefficients.labels,
convert_to_phases_if_necessary=False,
original_phase_units=None,
parameter_scope="",
parameter_name="labels").map_to_asu()
else:
map_coeffs = None
ncs_object = None
if params.input.ncs_file_name is not None:
ncs_object = ncs.ncs()
ncs_object.read_ncs(params.input.ncs_file_name)
ncs_object.display_all(log=log)
fo = fo.map_to_asu()
hl_coeffs = hl_coeffs.map_to_asu()
fo = fo.eliminate_sys_absent().average_bijvoet_mates()
hl_coeffs = hl_coeffs.eliminate_sys_absent().average_bijvoet_mates()
model_map = None
model_map_coeffs = None
if len(processed_args.pdb_file_names):
pdb_inp = mmtbx.utils.pdb_inp_from_multiple_files(
pdb_files=processed_args.pdb_file_names, log=log)
xs = pdb_inp.xray_structure_simple()
fo_, hl_ = fo, hl_coeffs
if params.change_basis_to_niggli_cell:
change_of_basis_op = xs.change_of_basis_op_to_niggli_cell()
xs = xs.change_basis(change_of_basis_op)
fo_ = fo_.change_basis(change_of_basis_op).map_to_asu()
hl_ = hl_.change_basis(change_of_basis_op).map_to_asu()
#fo_, hl_ = fo_.common_sets(hl_)
fmodel_refined = mmtbx.utils.fmodel_simple(
f_obs=fo_,
scattering_table=
"wk1995", #XXX pva: 1) neutrons? 2) move up as a parameter.
xray_structures=[xs],
bulk_solvent_correction=True,
anisotropic_scaling=True,
r_free_flags=fo_.array(data=flex.bool(fo_.size(), False)))
fmodel_refined.update(abcd=hl_)
master_phil = mmtbx.maps.map_and_map_coeff_master_params()
map_params = master_phil.fetch(
iotbx.phil.parse("""\
map_coefficients {
map_type = 2mFo-DFc
isotropize = True
}
""")).extract().map_coefficients[0]
model_map_coeffs = mmtbx.maps.map_coefficients_from_fmodel(
fmodel=fmodel_refined, params=map_params)
model_map = model_map_coeffs.fft_map(
resolution_factor=params.grid_resolution_factor).real_map_unpadded(
)
import time
t0 = time.time()
dm = density_modify(params,
fo,
hl_coeffs,
ncs_object=ncs_object,
map_coeffs=map_coeffs,
model_map_coeffs=model_map_coeffs,
log=log,
as_gui_program=as_gui_program)
time_dm = time.time() - t0
print("Time taken for density modification: %.2fs" % time_dm, file=log)
# run cns
if 0:
from cctbx.development import cns_density_modification
cns_result = cns_density_modification.run(params, fo, hl_coeffs)
print(cns_result.modified_map.all())
print(dm.map.all())
dm_map_coeffs = dm.map_coeffs_in_original_setting
from cctbx import maptbx, miller
crystal_gridding = maptbx.crystal_gridding(
dm_map_coeffs.unit_cell(),
space_group_info=dm_map_coeffs.space_group().info(),
pre_determined_n_real=cns_result.modified_map.all())
dm_map = miller.fft_map(crystal_gridding,
dm_map_coeffs).apply_sigma_scaling()
corr = flex.linear_correlation(cns_result.modified_map.as_1d(),
dm_map.real_map_unpadded().as_1d())
print("CNS dm/mmtbx dm correlation:")
corr.show_summary()
if dm.model_map_coeffs is not None:
model_map = miller.fft_map(
crystal_gridding,
dm.miller_array_in_original_setting(
dm.model_map_coeffs)).apply_sigma_scaling()
corr = flex.linear_correlation(
cns_result.modified_map.as_1d(),
model_map.real_map_unpadded().as_1d())
print("CNS dm/model correlation:")
corr.show_summary()
if output_plots:
plots_to_make = (
"fom",
"skewness",
"r1_factor",
"r1_factor_fom",
"mean_solvent_density",
"mean_protein_density",
"f000_over_v",
"k_flip",
"rms_solvent_density",
"rms_protein_density",
"standard_deviation_local_rms",
"mean_delta_phi",
"mean_delta_phi_initial",
)
from matplotlib.backends.backend_pdf import PdfPages
from libtbx import pyplot
stats = dm.get_stats()
pdf = PdfPages("density_modification.pdf")
if len(dm.correlation_coeffs) > 1:
if 0:
start_coeffs, model_coeffs = dm.map_coeffs_start.common_sets(
model_map_coeffs)
model_phases = model_coeffs.phases(deg=True).data()
exptl_phases = nearest_phase(
model_phases,
start_coeffs.phases(deg=True).data(),
deg=True)
corr = flex.linear_correlation(exptl_phases, model_phases)
corr.show_summary()
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title("phases start")
ax.set_xlabel("Experimental phases")
ax.set_ylabel("Phases from refined model")
ax.scatter(exptl_phases, model_phases, marker="x", s=10)
pdf.savefig(fig)
#
dm_coeffs, model_coeffs = dm.map_coeffs.common_sets(
model_map_coeffs)
model_phases = model_coeffs.phases(deg=True).data()
dm_phases = nearest_phase(model_phases,
dm_coeffs.phases(deg=True).data(),
deg=True)
corr = flex.linear_correlation(dm_phases, model_phases)
corr.show_summary()
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title("phases dm")
ax.set_xlabel("Phases from density modification")
ax.set_ylabel("Phases from refined model")
ax.scatter(dm_phases, model_phases, marker="x", s=10)
pdf.savefig(fig)
#
data = dm.correlation_coeffs
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title("correlation coefficient")
ax.plot(list(range(1, dm.i_cycle + 2)), data)
pdf.savefig(fig)
#
data = dm.mean_phase_errors
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title("Mean effective phase errors")
ax.plot(list(range(1, dm.i_cycle + 2)), data)
pdf.savefig(fig)
for plot in plots_to_make:
data = [
getattr(stats.get_cycle_stats(i), plot)
for i in range(1, dm.i_cycle + 2)
]
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title(plot.replace("_", " "))
ax.plot(list(range(1, dm.i_cycle + 2)), data)
pdf.savefig(fig)
data = [
stats.get_cycle_stats(i).rms_solvent_density /
stats.get_cycle_stats(i).rms_protein_density
for i in range(1, dm.i_cycle + 2)
]
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title("RMS solvent/protein density ratio")
ax.plot(list(range(1, dm.i_cycle + 2)), data)
pdf.savefig(fig)
pdf.close()
dm_map_coeffs = dm.map_coeffs_in_original_setting
dm_hl_coeffs = dm.hl_coeffs_in_original_setting
# output map if requested
map_params = params.output.map
if map_params.file_name is not None:
fft_map = dm_map_coeffs.fft_map(
resolution_factor=params.grid_resolution_factor)
if map_params.scale == "sigma":
fft_map.apply_sigma_scaling()
else:
fft_map.apply_volume_scaling()
gridding_first = gridding_last = None
title_lines = []
if map_params.format == "xplor":
fft_map.as_xplor_map(file_name=map_params.file_name,
title_lines=title_lines,
gridding_first=gridding_first,
gridding_last=gridding_last)
else:
fft_map.as_ccp4_map(file_name=map_params.file_name,
gridding_first=gridding_first,
gridding_last=gridding_last,
labels=title_lines)
# output map coefficients if requested
mtz_params = params.output.mtz
# Decide if we are going to actually write the mtz
if mtz_params.file_name is not None:
orig_fom, final_fom = dm.start_and_end_fom()
if mtz_params.skip_output_if_worse and final_fom < orig_fom:
ok_to_write_mtz = False
print(
"Not writing out mtz. Final FOM (%7.3f) worse than start (%7.3f)"
% (final_fom, orig_fom))
else: # usual
ok_to_write_mtz = True
else:
ok_to_write_mtz = True
if mtz_params.file_name is not None and ok_to_write_mtz:
label_decorator = iotbx.mtz.ccp4_label_decorator()
fo = dm.miller_array_in_original_setting(
dm.f_obs_complete).common_set(dm_map_coeffs)
mtz_dataset = fo.as_mtz_dataset(column_root_label="F",
label_decorator=label_decorator)
mtz_dataset.add_miller_array(dm_map_coeffs,
column_root_label="FWT",
label_decorator=label_decorator)
phase_source = dm.miller_array_in_original_setting(
dm.phase_source).common_set(dm_map_coeffs)
mtz_dataset.add_miller_array(
phase_source.array(data=flex.abs(phase_source.data())),
column_root_label="FOM",
column_types='W',
label_decorator=label_decorator)
mtz_dataset.add_miller_array(
phase_source.array(data=phase_source.phases(deg=True).data()),
column_root_label="PHIB",
column_types='P',
label_decorator=None)
if mtz_params.output_hendrickson_lattman_coefficients:
mtz_dataset.add_miller_array(dm_hl_coeffs,
column_root_label="HL",
label_decorator=label_decorator)
mtz_dataset.mtz_object().write(mtz_params.file_name)
return result(map_file=map_params.file_name,
mtz_file=mtz_params.file_name,
stats=dm.get_stats())
def run(args, log = sys.stdout, as_gui_program=False):
if(len(args)==0):
parsed = defaults(log=log)
parsed.show(prefix=" ", out=log)
return
command_line = (option_parser()
.enable_symmetry_comprehensive()
.option("-q", "--quiet",
action="store_true",
default=False,
help="suppress output")
.option("--output_plots",
action="store_true",
default=False)
).process(args=args)
parsed = defaults(log=log)
processed_args = mmtbx.utils.process_command_line_args(
args=command_line.args,
cmd_cs=command_line.symmetry,
master_params=parsed,
log=log,
suppress_symmetry_related_errors=True)
processed_args.params.show(out=log)
params = processed_args.params.extract().density_modification
output_plots = command_line.options.output_plots
crystal_symmetry = crystal.symmetry(
unit_cell=params.input.unit_cell,
space_group_info=params.input.space_group)
reflection_files = {}
for rfn in (params.input.reflection_data.file_name,
params.input.experimental_phases.file_name,
params.input.map_coefficients.file_name):
if os.path.isfile(str(rfn)) and rfn not in reflection_files:
reflection_files.setdefault(
rfn, iotbx.reflection_file_reader.any_reflection_file(
file_name=rfn, ensure_read_access=False))
server = iotbx.reflection_file_utils.reflection_file_server(
crystal_symmetry=crystal_symmetry,
reflection_files=reflection_files.values())
fo = mmtbx.utils.determine_data_and_flags(
server,
parameters=params.input.reflection_data,
extract_r_free_flags=False,log=log).f_obs
hl_coeffs = mmtbx.utils.determine_experimental_phases(
server,
params.input.experimental_phases,
log=log,
parameter_scope="",
working_point_group=None,
symmetry_safety_check=True,
ignore_all_zeros=True)
if params.input.map_coefficients.file_name is not None:
map_coeffs = server.get_phases_deg(
file_name=params.input.map_coefficients.file_name,
labels=params.input.map_coefficients.labels,
convert_to_phases_if_necessary=False,
original_phase_units=None,
parameter_scope="",
parameter_name="labels").map_to_asu()
else:
map_coeffs = None
ncs_object = None
if params.input.ncs_file_name is not None:
ncs_object = ncs.ncs()
ncs_object.read_ncs(params.input.ncs_file_name)
ncs_object.display_all(log=log)
fo = fo.map_to_asu()
hl_coeffs = hl_coeffs.map_to_asu()
fo = fo.eliminate_sys_absent().average_bijvoet_mates()
hl_coeffs = hl_coeffs.eliminate_sys_absent().average_bijvoet_mates()
model_map = None
model_map_coeffs = None
if len(processed_args.pdb_file_names):
pdb_file = mmtbx.utils.pdb_file(
pdb_file_names=processed_args.pdb_file_names)
xs = pdb_file.pdb_inp.xray_structure_simple()
fo_, hl_ = fo, hl_coeffs
if params.change_basis_to_niggli_cell:
change_of_basis_op = xs.change_of_basis_op_to_niggli_cell()
xs = xs.change_basis(change_of_basis_op)
fo_ = fo_.change_basis(change_of_basis_op).map_to_asu()
hl_ = hl_.change_basis(change_of_basis_op).map_to_asu()
#fo_, hl_ = fo_.common_sets(hl_)
fmodel_refined = mmtbx.utils.fmodel_simple(
f_obs=fo_,
scattering_table="wk1995",#XXX pva: 1) neutrons? 2) move up as a parameter.
xray_structures=[xs],
bulk_solvent_correction=True,
anisotropic_scaling=True,
r_free_flags=fo_.array(data=flex.bool(fo_.size(), False)))
fmodel_refined.update(abcd=hl_)
master_phil = mmtbx.maps.map_and_map_coeff_master_params()
map_params = master_phil.fetch(iotbx.phil.parse("""\
map_coefficients {
map_type = 2mFo-DFc
isotropize = True
}
""")).extract().map_coefficients[0]
model_map_coeffs = mmtbx.maps.map_coefficients_from_fmodel(
fmodel=fmodel_refined, params=map_params)
model_map = model_map_coeffs.fft_map(
resolution_factor=params.grid_resolution_factor).real_map_unpadded()
import time
t0 = time.time()
dm = density_modify(
params,
fo,
hl_coeffs,
ncs_object=ncs_object,
map_coeffs=map_coeffs,
model_map_coeffs=model_map_coeffs,
log=log,
as_gui_program=as_gui_program)
time_dm = time.time()-t0
print >> log, "Time taken for density modification: %.2fs" %time_dm
# run cns
if 0:
from cctbx.development import cns_density_modification
cns_result = cns_density_modification.run(params, fo, hl_coeffs)
print cns_result.modified_map.all()
print dm.map.all()
dm_map_coeffs = dm.map_coeffs_in_original_setting
from cctbx import maptbx, miller
crystal_gridding = maptbx.crystal_gridding(
dm_map_coeffs.unit_cell(),
space_group_info=dm_map_coeffs.space_group().info(),
pre_determined_n_real=cns_result.modified_map.all())
dm_map = miller.fft_map(crystal_gridding, dm_map_coeffs).apply_sigma_scaling()
corr = flex.linear_correlation(cns_result.modified_map.as_1d(), dm_map.real_map_unpadded().as_1d())
print "CNS dm/mmtbx dm correlation:"
corr.show_summary()
if dm.model_map_coeffs is not None:
model_map = miller.fft_map(
crystal_gridding,
dm.miller_array_in_original_setting(dm.model_map_coeffs)).apply_sigma_scaling()
corr = flex.linear_correlation(cns_result.modified_map.as_1d(), model_map.real_map_unpadded().as_1d())
print "CNS dm/model correlation:"
corr.show_summary()
if output_plots:
plots_to_make = (
"fom", "skewness",
"r1_factor", "r1_factor_fom", "mean_solvent_density", "mean_protein_density",
"f000_over_v", "k_flip", "rms_solvent_density", "rms_protein_density",
"standard_deviation_local_rms", "mean_delta_phi", "mean_delta_phi_initial",
)
from matplotlib.backends.backend_pdf import PdfPages
from libtbx import pyplot
stats = dm.get_stats()
pdf = PdfPages("density_modification.pdf")
if len(dm.correlation_coeffs) > 1:
if 0:
start_coeffs, model_coeffs = dm.map_coeffs_start.common_sets(model_map_coeffs)
model_phases = model_coeffs.phases(deg=True).data()
exptl_phases = nearest_phase(
model_phases, start_coeffs.phases(deg=True).data(), deg=True)
corr = flex.linear_correlation(exptl_phases, model_phases)
corr.show_summary()
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("phases start")
ax.set_xlabel("Experimental phases")
ax.set_ylabel("Phases from refined model")
ax.scatter(exptl_phases,
model_phases,
marker="x", s=10)
pdf.savefig(fig)
#
dm_coeffs, model_coeffs = dm.map_coeffs.common_sets(model_map_coeffs)
model_phases = model_coeffs.phases(deg=True).data()
dm_phases = nearest_phase(
model_phases, dm_coeffs.phases(deg=True).data(), deg=True)
corr = flex.linear_correlation(dm_phases, model_phases)
corr.show_summary()
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("phases dm")
ax.set_xlabel("Phases from density modification")
ax.set_ylabel("Phases from refined model")
ax.scatter(dm_phases,
model_phases,
marker="x", s=10)
pdf.savefig(fig)
#
data = dm.correlation_coeffs
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("correlation coefficient")
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
#
data = dm.mean_phase_errors
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("Mean effective phase errors")
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
for plot in plots_to_make:
data = [getattr(stats.get_cycle_stats(i), plot) for i in range(1, dm.i_cycle+2)]
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title(plot.replace("_", " "))
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
data = [stats.get_cycle_stats(i).rms_solvent_density/
stats.get_cycle_stats(i).rms_protein_density
for i in range(1, dm.i_cycle+2)]
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("RMS solvent/protein density ratio")
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
pdf.close()
dm_map_coeffs = dm.map_coeffs_in_original_setting
dm_hl_coeffs = dm.hl_coeffs_in_original_setting
# output map if requested
map_params = params.output.map
if map_params.file_name is not None:
fft_map = dm_map_coeffs.fft_map(resolution_factor=params.grid_resolution_factor)
if map_params.scale == "sigma":
fft_map.apply_sigma_scaling()
else:
fft_map.apply_volume_scaling()
gridding_first = gridding_last = None
title_lines = []
if map_params.format == "xplor":
fft_map.as_xplor_map(
file_name = map_params.file_name,
title_lines = title_lines,
gridding_first = gridding_first,
gridding_last = gridding_last)
else :
fft_map.as_ccp4_map(
file_name = map_params.file_name,
gridding_first = gridding_first,
gridding_last = gridding_last,
labels=title_lines)
# output map coefficients if requested
mtz_params = params.output.mtz
# Decide if we are going to actually write the mtz
if mtz_params.file_name is not None:
orig_fom,final_fom=dm.start_and_end_fom()
if mtz_params.skip_output_if_worse and final_fom < orig_fom:
ok_to_write_mtz=False
print "Not writing out mtz. Final FOM (%7.3f) worse than start (%7.3f)" %(
final_fom,orig_fom)
else: # usual
ok_to_write_mtz=True
else:
ok_to_write_mtz=True
if mtz_params.file_name is not None and ok_to_write_mtz:
label_decorator=iotbx.mtz.ccp4_label_decorator()
fo = dm.miller_array_in_original_setting(dm.f_obs_complete).common_set(dm_map_coeffs)
mtz_dataset = fo.as_mtz_dataset(
column_root_label="F",
label_decorator=label_decorator)
mtz_dataset.add_miller_array(
dm_map_coeffs,
column_root_label="FWT",
label_decorator=label_decorator)
phase_source = dm.miller_array_in_original_setting(dm.phase_source).common_set(dm_map_coeffs)
mtz_dataset.add_miller_array(
phase_source.array(data=flex.abs(phase_source.data())),
column_root_label="FOM",
column_types='W',
label_decorator=label_decorator)
mtz_dataset.add_miller_array(
phase_source.array(data=phase_source.phases(deg=True).data()),
column_root_label="PHIB",
column_types='P',
label_decorator=None)
if mtz_params.output_hendrickson_lattman_coefficients:
mtz_dataset.add_miller_array(
dm_hl_coeffs,
column_root_label="HL",
label_decorator=label_decorator)
mtz_dataset.mtz_object().write(mtz_params.file_name)
return result(
map_file=map_params.file_name,
mtz_file=mtz_params.file_name,
stats=dm.get_stats())
def plot_samples_ix(O, stage, ix):
p = O.params.plot_samples
i_sc, x_type = O.x_info[ix]
def f_calc_without_moving_scatterer():
sc = O.xray_structure.scatterers()[i_sc]
occ = sc.occupancy
try:
sc.occupancy = 0
result = O.get_f_calc().data()
finally:
sc.occupancy = occ
return result
f_calc_fixed = f_calc_without_moving_scatterer()
xs = O.xray_structure.select(flex.size_t([i_sc]))
def f_calc_moving():
return O.f_obs.structure_factors_from_scatterers(
xray_structure=xs,
algorithm="direct",
cos_sin_table=False).f_calc().data()
sc = xs.scatterers()[0]
ss = xs.site_symmetry_table().get(0)
def build_info_str():
return "%s|%03d|%03d|%s|%s|occ=%.2f|%s|%s" % (
O.plot_samples_id,
ix,
i_sc,
sc.label,
sc.scattering_type,
sc.occupancy,
ss.special_op_simplified(),
x_type)
info_str = build_info_str()
print "plot_samples:", info_str
sys.stdout.flush()
ys = []
def ys_append():
tg = O.__get_tg(
f_cbs=O.get_f_cbs(
f_calc=O.f_obs.customized_copy(data=f_calc_moving()+f_calc_fixed)),
derivatives_depth=0,
target=O.ps_target,
weights=O.ps_weights)
y = tg.target_work()
ys.append(y)
return y
xyv = []
if (x_type == "u"):
assert p.u_min < p.u_max
assert p.u_steps > 0
for i_step in xrange(p.u_steps+1):
u = p.u_min + i_step / p.u_steps * (p.u_max - p.u_min)
sc.u_iso = u
y = ys_append()
xyv.append((u,y,sc.u_iso))
else:
assert p.x_radius > 0
assert p.x_steps > 0
ss_constr = ss.site_constraints()
ss_np = ss_constr.n_independent_params()
ss_ip = "xyz".find(x_type)
assert ss_ip >= 0
ixx = ix - ss_ip
indep = list(O.x[ixx:ixx+ss_np])
i_inp = indep[ss_ip]
from libtbx.test_utils import approx_equal
assert approx_equal(
ss_constr.all_params(independent_params=indep), sc.site)
site_inp = sc.site
indep[ss_ip] = i_inp + 1
sc.site = ss_constr.all_params(independent_params=indep)
dist = xs.unit_cell().distance(sc.site, site_inp)
assert dist != 0
x_scale = p.x_radius / dist
for i_step in xrange(-p.x_steps//2, p.x_steps//2+1):
x = i_step / p.x_steps * 2 * x_scale
indep[ss_ip] = i_inp + x
sc.site = ss_constr.all_params(independent_params=indep)
y = ys_append()
dist = xs.unit_cell().distance(sc.site, site_inp)
if (i_step < 0): dist *= -1
xyv.append((dist,y,indep[ss_ip]))
#
base_name_plot_files = "%s_%03d_%s" % (p.file_prefix, ix, stage)
def write_xyv():
if (p.file_prefix is None): return
f = open(base_name_plot_files+".xy", "w")
print >> f, "# %s" % info_str
print >> f, "# %s" % str(xs.unit_cell())
print >> f, "# %s" % str(xs.space_group_info().symbol_and_number())
for x,y,v in xyv:
print >> f, x, y, v
write_xyv()
if (len(p.pyplot) != 0):
from libtbx import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
x,y,v = zip(*xyv)
ax.plot(x,y, "r-")
x = O.x[ix]
ax.plot([x, x], [min(ys), max(ys)], "k--")
if (O.x_reference is not None):
x = O.x_reference[ix]
ax.plot([x, x], [min(ys), max(ys)], "r--")
ax.set_title(info_str, fontsize=12)
ax.axis([xyv[0][0], xyv[-1][0], None, None])
def write_pdf():
if (p.file_prefix is None): return
fig.savefig(base_name_plot_files+".pdf", bbox_inches="tight")
if ("pdf" in p.pyplot):
write_pdf()
if ("gui" in p.pyplot):
pyplot.show()
def plot_samples_ix(O, stage, ix):
p = O.params.plot_samples
i_sc, x_type = O.x_info[ix]
def f_calc_without_moving_scatterer():
sc = O.xray_structure.scatterers()[i_sc]
occ = sc.occupancy
try:
sc.occupancy = 0
result = O.get_f_calc().data()
finally:
sc.occupancy = occ
return result
f_calc_fixed = f_calc_without_moving_scatterer()
xs = O.xray_structure.select(flex.size_t([i_sc]))
def f_calc_moving():
return O.f_obs.structure_factors_from_scatterers(
xray_structure=xs,
algorithm="direct",
cos_sin_table=False).f_calc().data()
sc = xs.scatterers()[0]
ss = xs.site_symmetry_table().get(0)
def build_info_str():
return "%s|%03d|%03d|%s|%s|occ=%.2f|%s|%s" % (
O.plot_samples_id,
ix,
i_sc,
sc.label,
sc.scattering_type,
sc.occupancy,
ss.special_op_simplified(),
x_type)
info_str = build_info_str()
print "plot_samples:", info_str
sys.stdout.flush()
ys = []
def ys_append():
tg = O.__get_tg(
f_cbs=O.get_f_cbs(
f_calc=O.f_obs.customized_copy(data=f_calc_moving()+f_calc_fixed)),
derivatives_depth=0,
target=O.ps_target,
weights=O.ps_weights)
y = tg.target_work()
ys.append(y)
return y
xyv = []
if (x_type == "u"):
assert p.u_min < p.u_max
assert p.u_steps > 0
for i_step in xrange(p.u_steps+1):
u = p.u_min + i_step / p.u_steps * (p.u_max - p.u_min)
sc.u_iso = u
y = ys_append()
xyv.append((u,y,sc.u_iso))
else:
assert p.x_radius > 0
assert p.x_steps > 0
ss_constr = ss.site_constraints()
ss_np = ss_constr.n_independent_params()
ss_ip = "xyz".find(x_type)
assert ss_ip >= 0
ixx = ix - ss_ip
indep = list(O.x[ixx:ixx+ss_np])
i_inp = indep[ss_ip]
from libtbx.test_utils import approx_equal
assert approx_equal(
ss_constr.all_params(independent_params=indep), sc.site)
site_inp = sc.site
indep[ss_ip] = i_inp + 1
sc.site = ss_constr.all_params(independent_params=indep)
dist = xs.unit_cell().distance(sc.site, site_inp)
assert dist != 0
x_scale = p.x_radius / dist
for i_step in xrange(-p.x_steps//2, p.x_steps//2+1):
x = i_step / p.x_steps * 2 * x_scale
indep[ss_ip] = i_inp + x
sc.site = ss_constr.all_params(independent_params=indep)
y = ys_append()
dist = xs.unit_cell().distance(sc.site, site_inp)
if (i_step < 0): dist *= -1
xyv.append((dist,y,indep[ss_ip]))
#
base_name_plot_files = "%s_%03d_%s" % (p.file_prefix, ix, stage)
def write_xyv():
if (p.file_prefix is None): return
f = open(base_name_plot_files+".xy", "w")
print >> f, "# %s" % info_str
print >> f, "# %s" % str(xs.unit_cell())
print >> f, "# %s" % str(xs.space_group_info().symbol_and_number())
for x,y,v in xyv:
print >> f, x, y, v
write_xyv()
if (len(p.pyplot) != 0):
from libtbx import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
x,y,v = zip(*xyv)
ax.plot(x,y, "r-")
x = O.x[ix]
ax.plot([x, x], [min(ys), max(ys)], "k--")
if (O.x_reference is not None):
x = O.x_reference[ix]
ax.plot([x, x], [min(ys), max(ys)], "r--")
ax.set_title(info_str, fontsize=12)
ax.axis([xyv[0][0], xyv[-1][0], None, None])
def write_pdf():
if (p.file_prefix is None): return
fig.savefig(base_name_plot_files+".pdf", bbox_inches="tight")
if ("pdf" in p.pyplot):
write_pdf()
if ("gui" in p.pyplot):
pyplot.show()
