def exercise_convert_to_isotropic():
pdb_str = """
ATOM 104 N SER A 8 14.526 19.060 18.223 1.00 5.10 N
ANISOU 104 N SER A 8 500 632 808 -107 58 104 N
ATOM 105 CA SER A 8 14.099 17.792 18.758 1.00 5.95 C
"""
cs = crystal.symmetry((5.01, 5.01, 5.47, 90, 90, 120), "P 62 2 2")
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
model = mmtbx.model.manager(model_input=pdb_inp, crystal_symmetry=cs)
#
m = model.deep_copy()
m.convert_to_isotropic()
assert approx_equal(m.get_hierarchy().atoms().extract_b(),
[5.105875, 5.95])
assert approx_equal(m.get_hierarchy().atoms().extract_uij(),
[(-1.0, -1.0, -1.0, -1.0, -1.0, -1.0),
(-1.0, -1.0, -1.0, -1.0, -1.0, -1.0)])
#
m = model.deep_copy()
m.convert_to_isotropic(selection=flex.bool([False, True]))
assert approx_equal(m.get_hierarchy().atoms().extract_b(),
model.get_hierarchy().atoms().extract_b())
assert approx_equal(m.get_hierarchy().atoms().extract_uij(),
model.get_hierarchy().atoms().extract_uij())
#
m1 = model.deep_copy()
m2 = model.deep_copy()
m1.convert_to_isotropic(selection=flex.bool([True, False]))
m2.convert_to_isotropic()
assert approx_equal(m1.get_hierarchy().atoms().extract_b(),
m2.get_hierarchy().atoms().extract_b())
assert approx_equal(m1.get_hierarchy().atoms().extract_uij(),
m2.get_hierarchy().atoms().extract_uij())
def exercise_set_b_iso():
pdb_str = """
ATOM 104 N SER A 8 14.526 19.060 18.223 1.00 5.10 N
ANISOU 104 N SER A 8 500 632 808 -107 58 104 N
ATOM 105 CA SER A 8 14.099 17.792 18.758 1.00 5.95 C
"""
cs = crystal.symmetry((5.01, 5.01, 5.47, 90, 90, 120), "P 62 2 2")
uc = cs.unit_cell()
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
model = mmtbx.model.manager(model_input=pdb_inp, crystal_symmetry=cs)
#
m = model.deep_copy()
bs = flex.double([10, 20])
m.set_b_iso(values=bs)
assert approx_equal(m.get_hierarchy().atoms().extract_b(), [-1.0, 20.0])
us = m.get_hierarchy().atoms().extract_uij()
assert approx_equal(us[0], model.get_hierarchy().atoms().extract_uij()[0])
assert approx_equal(us[1], (-1.0, -1.0, -1.0, -1.0, -1.0, -1.0))
u_iso_1 = m.get_xray_structure().scatterers().extract_u_iso()
b_iso_2 = m.get_hierarchy().atoms().extract_b()
assert approx_equal(u_iso_1[0], -1)
assert approx_equal(b_iso_2[0], -1)
assert approx_equal(adptbx.u_as_b(u_iso_1[1]), 20)
assert approx_equal(b_iso_2[1], 20)
#
m = model.deep_copy()
m.convert_to_isotropic()
bs = flex.double([10, 20])
sel = flex.bool([True, False])
m.set_b_iso(values=bs, selection=sel)
assert approx_equal(m.get_b_iso(), [10, 5.95])
def exercise_3():
"""
Make sure model can do deep_copy() straight away.
"""
inp = iotbx.pdb.input(lines=pdb_str_1, source_info=None)
model = mmtbx.model.manager(model_input=inp)
new_model = model.deep_copy()
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
