def run(self):
hierarchy = self.model.get_hierarchy()
map_data, grid_unit_cell = None, None
# sanity check for map and model
if self.map_inp is not None:
base = map_and_model.input(map_manager=self.map_inp,
model=self.model,
crystal_symmetry=self.cs_consensus,
box=False)
hierarchy = base.model().get_hierarchy()
map_data = base.map_data()
grid_unit_cell = self.map_inp.grid_unit_cell()
hierarchy.atoms().reset_i_seq()
self.ringer_result = iterate_over_residues(
pdb_hierarchy=hierarchy,
map_coeffs=self.miller_array,
map_data=map_data,
unit_cell=grid_unit_cell,
params=self.params,
log=self.out).results
if (self.params.output_base is not None):
plots_dir = self.params.output_base + "_plots"
else:
plots_dir = 'emringer_plots'
import matplotlib
matplotlib.use("Agg")
self.scoring_result = em_scoring.main(
file_name=self.params.output_base,
ringer_result=self.ringer_result,
out_dir=plots_dir,
sampling_angle=self.params.sampling_angle,
quiet=self.params.quiet,
out=self.out)
rolling_window_threshold = self.params.rolling_window_threshold
self.rolling_result = em_rolling.main(
ringer_results=self.ringer_result,
dir_name=plots_dir,
threshold=rolling_window_threshold, #scoring.optimal_threshold,
graph=False,
save=not self.params.quiet,
out=self.out)
def run(args, out=None, verbose=True, plots_dir=None):
t0 = time.time()
if (out is None): out = sys.stdout
import iotbx.phil
cmdline = iotbx.phil.process_command_line_with_files(
args=args,
master_phil=master_phil,
pdb_file_def="model",
reflection_file_def="map_coeffs",
map_file_def="map_file",
usage_string="""\
phenix.emringer model.pdb map.mrc [cif_file ...] [options]
%s
""" % __doc__)
params = cmdline.work.extract()
validate_params(params)
pdb_in = cmdline.get_file(params.model)
pdb_in.check_file_type("pdb")
pdb_inp = iotbx.pdb.input(file_name=params.model)
model = mmtbx.model.manager(model_input=pdb_inp)
crystal_symmetry_model = model.crystal_symmetry()
if crystal_symmetry_model is not None:
crystal_symmetry_model.show_summary()
hierarchy = model.get_hierarchy()
map_coeffs = map_inp = None
map_data, unit_cell = None, None
if (params.map_coeffs is not None):
mtz_in = cmdline.get_file(params.map_coeffs)
mtz_in.check_file_type("hkl")
best_guess = None
best_labels = []
all_labels = []
for array in mtz_in.file_server.miller_arrays:
if (array.info().label_string() == params.map_label):
map_coeffs = array
break
elif (params.map_label is None):
if (array.is_complex_array()):
labels = array.info().label_string()
all_labels.append(labels)
if (labels.startswith("2FOFCWT")
or labels.startswith("2mFoDFc")
or labels.startswith("FWT")):
best_guess = array
best_labels.append(labels)
if (map_coeffs is None):
if (len(all_labels) == 0):
raise Sorry(
"No valid (pre-weighted) map coefficients found in file.")
elif (best_guess is None):
raise Sorry(
"Couldn't automatically determine appropriate map labels. "
+ "Choices:\n %s" % " \n".join(all_labels))
elif (len(best_labels) > 1):
raise Sorry(
"Multiple appropriate map coefficients found in file. " +
"Choices:\n %s" % "\n ".join(best_labels))
map_coeffs = best_guess
print(" Guessing %s for input map coefficients" % best_labels[0],
file=out)
else:
ccp4_map_in = cmdline.get_file(params.map_file)
ccp4_map_in.check_file_type("ccp4_map")
map_inp = ccp4_map_in.file_object
cs_consensus = mmtbx.utils.check_and_set_crystal_symmetry(
models=[model], map_inps=[map_inp])
base = map_and_model.input(map_data=map_inp.map_data(),
model=model,
box=False)
hierarchy = base.model().get_hierarchy()
map_data = base.map_data()
unit_cell = map_inp.grid_unit_cell()
hierarchy.atoms().reset_i_seq()
make_header("Iterating over residues", out=out)
t1 = time.time()
from mmtbx.ringer import iterate_over_residues
results = iterate_over_residues(pdb_hierarchy=hierarchy,
map_coeffs=map_coeffs,
map_data=map_data,
unit_cell=unit_cell,
params=params,
log=out).results
t2 = time.time()
if (verbose):
print("Time excluding I/O: %8.1fs" % (t2 - t1), file=out)
print("Overall runtime: %8.1fs" % (t2 - t0), file=out)
if (params.output_base is None):
pdb_base = os.path.basename(params.model)
params.output_base = os.path.splitext(pdb_base)[0] + "_emringer"
easy_pickle.dump("%s.pkl" % params.output_base, results)
print("Wrote %s.pkl" % params.output_base, file=out)
csv = "\n".join([r.format_csv() for r in results])
open("%s.csv" % params.output_base, "w").write(csv)
print("Wrote %s.csv" % params.output_base, file=out)
if (plots_dir is None):
plots_dir = params.output_base + "_plots"
if (not os.path.isdir(plots_dir)):
os.makedirs(plots_dir)
from mmtbx.ringer import em_rolling
from mmtbx.ringer import em_scoring
import matplotlib
matplotlib.use("Agg")
make_header("Scoring results", out=out)
scoring = em_scoring.main(file_name=params.output_base,
ringer_result=results,
out_dir=plots_dir,
sampling_angle=params.sampling_angle,
quiet=False,
out=out)
make_header("Inspecting chains", out=out)
rolling_window_threshold = params.rolling_window_threshold
rolling = em_rolling.main(
ringer_results=results,
dir_name=plots_dir,
threshold=rolling_window_threshold, #scoring.optimal_threshold,
graph=False,
save=True,
out=out)
scoring.show_summary(out=out)
print("\nReferences:", file=out)
references = """\
Barad BA, Echols N, Wang RYR, Cheng YC, DiMaio F, Adams PD, Fraser JS. (2015)
Side-chain-directed model and map validation for 3D Electron Cryomicroscopy.
Nature Methods, in press.
Lang PT, Ng HL, Fraser JS, Corn JE, Echols N, Sales M, Holton JM, Alber T.
Automated electron-density sampling reveals widespread conformational
polymorphism in proteins. Protein Sci. 2010 Jul;19(7):1420-31. PubMed PMID:
20499387"""
print(references, file=out)
if (params.show_gui):
run_app(results)
else:
return (results, scoring, rolling)
def run (args, out=None, verbose=True, plots_dir=None) :
t0 = time.time()
if (out is None) : out = sys.stdout
import iotbx.phil
cmdline = iotbx.phil.process_command_line_with_files(
args=args,
master_phil=master_phil,
pdb_file_def="model",
reflection_file_def="map_coeffs",
map_file_def="map_file",
usage_string="""\
phenix.emringer model.pdb map.mrc [cif_file ...] [options]
%s
""" % __doc__)
params = cmdline.work.extract()
validate_params(params)
pdb_in = cmdline.get_file(params.model)
pdb_in.check_file_type("pdb")
hierarchy = pdb_in.file_object.construct_hierarchy()
hierarchy.atoms().reset_i_seq()
map_coeffs = ccp4_map = None
if (params.map_coeffs is not None) :
mtz_in = cmdline.get_file(params.map_coeffs)
mtz_in.check_file_type("hkl")
best_guess = None
best_labels = []
all_labels = []
for array in mtz_in.file_server.miller_arrays :
if (array.info().label_string() == params.map_label) :
map_coeffs = array
break
elif (params.map_label is None) :
if (array.is_complex_array()) :
labels = array.info().label_string()
all_labels.append(labels)
if (labels.startswith("2FOFCWT") or labels.startswith("2mFoDFc") or
labels.startswith("FWT")) :
best_guess = array
best_labels.append(labels)
if (map_coeffs is None) :
if (len(all_labels) == 0) :
raise Sorry("No valid (pre-weighted) map coefficients found in file.")
elif (best_guess is None) :
raise Sorry("Couldn't automatically determine appropriate map labels. "+
"Choices:\n %s" % " \n".join(all_labels))
elif (len(best_labels) > 1) :
raise Sorry("Multiple appropriate map coefficients found in file. "+
"Choices:\n %s" % "\n ".join(best_labels))
map_coeffs = best_guess
print >> out, " Guessing %s for input map coefficients" % best_labels[0]
else :
ccp4_map_in = cmdline.get_file(params.map_file)
ccp4_map_in.check_file_type("ccp4_map")
ccp4_map = ccp4_map_in.file_object
make_header("Iterating over residues", out=out)
t1 = time.time()
from mmtbx.ringer import iterate_over_residues
results = iterate_over_residues(
pdb_hierarchy=hierarchy,
map_coeffs=map_coeffs,
ccp4_map=ccp4_map,
params=params,
log=out).results
t2 = time.time()
if (verbose) :
print >> out, "Time excluding I/O: %8.1fs" % (t2 - t1)
print >> out, "Overall runtime: %8.1fs" % (t2 - t0)
if (params.output_base is None) :
pdb_base = os.path.basename(params.model)
params.output_base = os.path.splitext(pdb_base)[0] + "_emringer"
easy_pickle.dump("%s.pkl" % params.output_base, results)
print >> out, "Wrote %s.pkl" % params.output_base
csv = "\n".join([ r.format_csv() for r in results ])
open("%s.csv" % params.output_base, "w").write(csv)
print >> out, "Wrote %s.csv" % params.output_base
if (plots_dir is None) :
plots_dir = params.output_base + "_plots"
if (not os.path.isdir(plots_dir)) :
os.makedirs(plots_dir)
from mmtbx.ringer import em_rolling
from mmtbx.ringer import em_scoring
import matplotlib
matplotlib.use("Agg")
make_header("Scoring results", out=out)
scoring = em_scoring.main(
file_name=params.output_base,
ringer_result=results,
out_dir=plots_dir,
sampling_angle=params.sampling_angle,
quiet=False,
out=out)
make_header("Inspecting chains", out=out)
rolling_window_threshold = params.rolling_window_threshold
rolling = em_rolling.main(
ringer_results=results,
dir_name=plots_dir,
threshold=rolling_window_threshold, #scoring.optimal_threshold,
graph=False,
save=True,
out=out)
scoring.show_summary(out=out)
print >> out, "\nReferences:"
print >> out, """\
Barad BA, Echols N, Wang RYR, Cheng YC, DiMaio F, Adams PD, Fraser JS. (2015)
Side-chain-directed model and map validation for 3D Electron Cryomicroscopy.
Nature Methods, in press.
Lang PT, Ng HL, Fraser JS, Corn JE, Echols N, Sales M, Holton JM, Alber T.
Automated electron-density sampling reveals widespread conformational
polymorphism in proteins. Protein Sci. 2010 Jul;19(7):1420-31. PubMed PMID:
20499387"""
if (params.show_gui) :
run_app(results)
else :
return (results, scoring, rolling)