def run(self):
model = self.data_manager.get_model()
# Temporarly disable
# self.params.output.prefix=self.data_manager.get_default_model_name().split('.')[0]
# self.params.output.suffix='_helix'
# self.data_manager.set_default_output_filename(self.get_default_output_filename())
self.rama_z = rama_z.rama_z(model=model, log=self.logger)
# self.data_manager.write_model_file(model)
# self.params.output.suffix='_sheet'
# self.data_manager.set_default_output_filename(self.get_default_output_filename())
# self.data_manager.write_model_file(model)
result = self.get_results()
if result is None:
print("Calculation of z-score failed for some reason",
file=self.logger)
else:
for k in ["whole", "helix", "sheet", "loop"]:
rc = k[0].upper()
v = result.get(rc, None)
if v is None:
print("z-score %-5s: None, residues: %d" %
(k, result['residue_counts'][rc]),
file=self.logger)
else:
print("z-score %-5s: %6.3f (%5.3f), residues: %d" %
(k, v[0], v[1], result['residue_counts'][rc]),
file=self.logger)
def check_function():
inp = iotbx.pdb.input(fname)
model = mmtbx.model.manager(model_input=inp)
zs = rama_z(model, log=null_out())
z_scores = zs.get_z_scores()
ss_cont = zs.get_residue_counts()
# print (z_scores)
# print (ss_cont)
expected_z = {
'H': -2.7111077448509953,
'S': None,
'L': -0.7253113914835396,
'W': -1.0306993840276084
}
expeted_ss = {'H': 277, 'S': 0, 'L': 15041, 'W': 15318}
for k in expected_z:
if z_scores[k] is not None:
assert approx_equal(z_scores[k][0], expected_z[k])
if k == 'W':
assert 0.01 < z_scores[k][1] < 0.1, z_scores[k][1]
if k != 'weighted_mean':
assert approx_equal(ss_cont[k], expeted_ss[k])
# check how separate scores translate to whole
h_score = (z_scores['H'][0] * zs.calibration_values['H'][1] +
zs.calibration_values['H'][0]) * ss_cont['H']
l_score = (z_scores['L'][0] * zs.calibration_values['L'][1] +
zs.calibration_values['L'][0]) * ss_cont['L']
w_score = ((h_score + l_score) / (ss_cont['H'] + ss_cont['L']) -
zs.calibration_values['W'][0]) / zs.calibration_values['W'][1]
# print ("reconstructed:", w_score, z_scores['W'][0])
assert approx_equal(w_score, z_scores['W'][0])
def check_function():
inp = iotbx.pdb.input(fname)
model = mmtbx.model.manager(model_input=inp)
zs = rama_z([model], log=null_out())
z_scores = zs.get_z_scores()
ss_cont = zs.get_residue_counts()
# print (z_scores)
# print (ss_cont)
expected_z = {
'H': None,
'S': (-0.057428666470734, 0.5840017477579902),
'L': (-0.3588028726184504, 0.6941226745661744),
'W': (-0.4019606027769244, 0.6621289642029733)
}
expeted_ss = {'H': 0, 'S': 63, 'L': 71, 'W': 134}
for k in expected_z:
if z_scores[k] is not None:
assert approx_equal(z_scores[k], expected_z[k], eps=1e-5)
assert approx_equal(ss_cont[k], expeted_ss[k])
# check how separate scores translate to whole
s_score = (z_scores['S'][0] * zs.calibration_values['S'][1] +
zs.calibration_values['S'][0]) * ss_cont['S']
l_score = (z_scores['L'][0] * zs.calibration_values['L'][1] +
zs.calibration_values['L'][0]) * ss_cont['L']
w_score = ((s_score + l_score) / (ss_cont['S'] + ss_cont['L']) -
zs.calibration_values['W'][0]) / zs.calibration_values['W'][1]
# print ("reconstructed:", w_score, z_scores['W'][0])
assert approx_equal(w_score, z_scores['W'][0])
def run(self):
models = []
for model_name in self.data_manager.get_model_names():
models.append(self.data_manager.get_model(model_name))
# model = self.data_manager.get_model()
self.inp_fn = os.path.basename(
self.data_manager.get_default_model_name())[:-4]
self.rama_z = rama_z.rama_z(models=models, log=self.logger)
if len(models) == 1:
model = models[0]
cs = model.crystal_symmetry()
if cs is None:
model = shift_and_box_model(model)
self._write_plots_if_needed(model,
label='whole',
type_of_plot='whole')
helix_sel, sheet_sel, loop_sel = self.rama_z.get_ss_selections()
if model.get_hierarchy().models_size() != 1:
print(
"Warning! Outputting partial models and plots are not supported \
for multi-model files",
file=self.logger)
else:
for sel, label in [(helix_sel, "helix"), (sheet_sel, "sheet"),
(loop_sel, "loop")]:
selected_model = model.select(sel)
if self.params.write_HSL_models:
pdb_str = selected_model.model_as_pdb()
fn = "%s" % self.get_default_output_filename(
prefix='%s_' % self.inp_fn,
suffix=label,
serial=Auto)
print("Writing out partial model: %s" % fn,
file=self.logger)
self.data_manager.write_model_file(selected_model,
filename=fn)
self._write_plots_if_needed(selected_model,
label,
type_of_plot='HSL')
result = self.get_results()
res_info = self.rama_z.get_detailed_values()
print("Individual residues info:", file=self.logger)
print("Residue name, type, SS, (phi, psi), Z", file=self.logger)
for i in res_info:
print('%4s %10s %1s (%7.2f, %7.2f) %7.4f' %
(i[2], res_type_labels[i[1]], i[3], i[4], i[5], i[6]),
file=self.logger)
print(result.as_string(prefix=""), file=self.logger)
def run(self):
model = self.data_manager.get_model()
self.inp_fn = os.path.basename(
self.data_manager.get_default_model_name())[:-4]
self.rama_z = rama_z.rama_z(model=model, log=self.logger)
self._write_plots_if_needed(model, label='whole', type_of_plot='whole')
helix_sel, sheet_sel, loop_sel = self.rama_z.get_ss_selections()
for sel, label in [(helix_sel, "helix"), (sheet_sel, "sheet"),
(loop_sel, "loop")]:
selected_model = model.select(sel)
if self.params.write_HSL_models:
pdb_str = selected_model.model_as_pdb()
fn = "%s" % self.get_default_output_filename(
prefix='%s_' % self.inp_fn, suffix=label, serial=Auto)
print("Writing out partial model: %s" % fn, file=self.logger)
self.data_manager.write_model_file(selected_model, filename=fn)
self._write_plots_if_needed(selected_model,
label,
type_of_plot='HSL')
result = self.get_results()
if result is None:
print("Calculation of z-score failed for some reason",
file=self.logger)
else:
for k in ["whole", "helix", "sheet", "loop"]:
rc = k[0].upper()
v = result.get(rc, None)
if v is None:
print("z-score %-5s: None, residues: %d" %
(k, result['residue_counts'][rc]),
file=self.logger)
else:
print("z-score %-5s: %5.2f (%4.2f), residues: %d" %
(k, v[0], v[1], result['residue_counts'][rc]),
file=self.logger)
def get_statistics(self):
if self.json_data['success_composition'] is False:
return
make_sub_header('Get statistics', out=self.logger)
self.save_json()
success = True
self.json_data['success_statistics'] = None
sc, r_sc = None, None
self.pickle_data.min_reso = None
self.pickle_data.d_inv = None
self.pickle_data.r_d_inv = None
self.pickle_data.d_inv_half = None
self.pickle_data.fsc_model = None
self.pickle_data.r_fsc_model = None
self.pickle_data.fsc_half = None
if self.json_data['restraints_file']:
cif_file = self.json_data['restraints_file']
cif_objects = monomer_library.server.read_cif(file_name=to_str(cif_file))
cif_objects = [(cif_file, cif_objects)]
else: cif_objects = None
for p, fn in zip(('', 'r_'),
[self.json_data['pdb_file_updated'], self.json_data['pdb_file_refined']]):
# TODO which file to use for initial!!
if (not fn or not os.path.isfile(fn)): continue
if p=='': print('Initial model', file=self.logger)
if p=='r_': print('\nRefined model', file=self.logger)
dm = DataManager()
dm.process_real_map_file(self.json_data['map_file'])
map_inp = dm.get_real_map(self.json_data['map_file'])
#map_inp = iotbx.ccp4_map.map_reader(file_name = self.json_data['map_file'])
map_inp_1, map_inp_2 = None, None
if self.json_data['map_file_1'] is not None:
#map_inp_1 = iotbx.ccp4_map.map_reader(file_name = self.json_data['map_file_1'])
dm.process_real_map_file(self.json_data['map_file_1'])
map_inp_1 = dm.get_real_map(self.json_data['map_file_1'])
#map_data_1 = map_inp_1.map_data()
if self.json_data['map_file_2'] is not None:
dm.process_real_map_file(self.json_data['map_file_2'])
map_inp_2 = dm.get_real_map(self.json_data['map_file_2'])
#map_inp_2 = iotbx.ccp4_map.map_reader(file_name = self.json_data['map_file_2'])
#map_data_2 = map_inp_2.map_data()
print('\tGet model class with restraints...', file=self.logger)
pdb_inp = iotbx.pdb.input(file_name = fn)
try:
model = mmtbx.model.manager(
model_input = pdb_inp,
restraint_objects = cif_objects,
build_grm = True,
stop_for_unknowns = False,
crystal_symmetry = map_inp.crystal_symmetry(),
log = null_out())
except Exception as e:
#success = False
self.json_data['success_statistics'] = False
msg = traceback.format_exc()
print(msg, file=self.logger)
self.write_log(
step = 'Statistics: Model class (with restraints) from pdb ',
msg = msg)
self.save_json()
continue
if not success: continue
# Emringer
if self.resolution < 4.0:
print('\tCalculate Emringer score', file=self.logger)
try:
emringer_score = self.get_emringer_score(
model = model.deep_copy(),
map_inp = map_inp.deep_copy())
self.json_data[p+'emringer'] = emringer_score
except Exception as e:
msg = traceback.format_exc()
print(msg, file=self.logger)
self.write_log(step = 'EMRinger failed ', msg = msg)
# TODO: save as success_statistics False?
# Rama-z score
try:
rama_z_score = rama_z.rama_z(
models = [model],
log = self.logger)
z_scores = rama_z_score.get_z_scores()
self.json_data[p+'z_score'] = z_scores['W'][0]
except Exception as e:
msg = traceback.format_exc()
print(msg, file=self.logger)
self.write_log(step = 'Rama z-score failed ', msg = msg)
# base = map_and_model.input(
# map_manager = map_inp,
# map_manager_1 = map_inp_1,
# map_manager_2 = map_inp_2,
# model = model,
# box = True)
checked = map_model_manager(
map_manager = map_inp,
map_manager_1 = map_inp_1,
map_manager_2 = map_inp_2,
model = model,
wrapping = None)
checked.box_all_maps_around_model_and_shift_origin()
params = validation_cryoem.master_params().extract()
params.resolution = self.resolution
params.slim = False
if (p == ''):
print('\tGet map parameters...', file=self.logger)
self.get_map_parameters(base = checked)
print('\tRun Mtriage...', file=self.logger)
try:
o = validation_cryoem.validation(
model = checked.model(),
map_data = checked.map_data(),
map_data_1 = checked.map_data_1(),
map_data_2 = checked.map_data_2(),
params = params)
except Exception as e:
# success = False
self.json_data['success_statistics'] = False
msg = traceback.format_exc()
print(msg, file=self.logger)
self.write_log(step = 'Statistics ', msg = msg)
self.save_json()
continue
print('\tStore info in pkl...', file=self.logger)
o_dict = self.fill_dictionary(o)
if (p == ''):
self.json_data['o'] = o_dict
sc = o.model.get_sites_cart()
elif (p == 'r_'):
self.json_data['o_refined'] = o_dict
r_sc = o.model.get_sites_cart()
if o is not None:
if o.model_vs_data is not None:
if o.model_vs_data.cc is not None:
if o.model_vs_data.cc.cc_per_chain is not None:
if (p == ''):
self.pickle_data.cc_per_chain = o.model_vs_data.cc.cc_per_chain
if (p == 'r_'):
self.pickle_data.r_cc_per_chain = o.model_vs_data.cc.cc_per_chain
if o.model_vs_data.cc.cc_per_residue is not None:
if (p == ''):
self.pickle_data.cc_per_residue = o.model_vs_data.cc.cc_per_residue
if (p == 'r_'):
self.pickle_data.r_cc_per_residue = o.model_vs_data.cc.cc_per_residue
if o.model_stats.geometry.ramachandran.ramalyze is not None:
rl = o.model_stats.geometry.ramachandran.ramalyze
if (p == ''): self.pickle_data.ramalyze = rl
if (p == 'r_'): self.pickle_data.r_ramalyze = rl
# values for the curve go up to very high reso, get highest reso limit
# if o is not None:
# reso_list = [self.json_data['d_pdb'],self.json_data['d_cif'],
# self.json_data['d_map'], self.json_data['o']['d99'],
# self.json_data['o']['d_model'], self.json_data['o']['d_fsc']]
# else:
# reso_list = [self.json_data['d_pdb'],self.json_data['d_cif'],
# self.json_data['d_map']]
# min_reso = min([elem for elem in reso_list if elem is not None])
# self.pickle_data.min_reso = min_reso
min_reso = self.resolution
self.pickle_data.min_reso = self.resolution
# apply some arbitrary buffer
min_reso = min_reso - 0.5
if o is not None:
masked = o.data_stats.masked
if (masked.fsc_curve_model is not None):
if ((masked.fsc_curve_model.d_inv is not None) and
(masked.fsc_curve_model.fsc is not None)):
if (p == ''):
self.pickle_data.d_inv = masked.fsc_curve_model.d_inv
self.pickle_data.fsc_model = masked.fsc_curve_model.fsc
if (p == 'r_'):
self.pickle_data.r_d_inv = masked.fsc_curve_model.d_inv
self.pickle_data.r_fsc_model = masked.fsc_curve_model.fsc
if (p == ''):
if (masked.fsc_curve is not None):
if (masked.fsc_curve.fsc is not None):
if ((masked.fsc_curve.fsc.d_inv is not None) and
(masked.fsc_curve.fsc.fsc is not None)):
self.pickle_data.d_inv_half = masked.fsc_curve.fsc.d_inv
self.pickle_data.fsc_half = masked.fsc_curve.fsc.fsc
if o is not None:
print("\nd99 :", o.data_stats.masked.d99)
print("d_model :", o.data_stats.masked.d_model)
print("d_fsc :", o.data_stats.masked.d_fsc)
print("ramachandran.outliers:", o.model_stats.geometry.ramachandran.outliers)
print("cc_mask :", o.model_vs_data.cc.cc_mask)
print("cc_box :", o.model_vs_data.cc.cc_box)
if sc is not None and r_sc is not None:
self.json_data['rmsd'] = sc.rms_difference(r_sc)
if self.json_data['success_statistics'] is not False:
self.json_data['success_statistics'] = success
def rama_z_score(self): return rama_z.rama_z(models = [self.model], log = null_out()).get_result()