def loadModel(filename):
dm = DataManager() # Initialize the DataManager and call it dm
dm.set_overwrite(
True) # tell the DataManager to overwrite files with the same name
#print("Reading file")
manager = dm.get_model(filename)
return manager
def read_map_and_model(file_name_1, file_name_2):
'''
Identify which file is map and which is model, read in and
create map_model_manager
'''
map_file_name = None
model_file_name = None
for f in [file_name_1, file_name_2]:
for ending in ['.ccp4', '.mrc', '.map']:
if f.endswith(ending):
map_file_name = f
for ending in ['.pdb', '.cif']:
if f.endswith(ending):
model_file_name = f
if not map_file_name or not model_file_name:
raise Sorry("Unable to identify map and model from %s and %s" %
(file_name_1, file_name_2))
from iotbx.data_manager import DataManager
from iotbx.map_model_manager import map_model_manager
dm = DataManager()
dm.process_real_map_file(map_file_name)
mm = dm.get_real_map(map_file_name)
dm.process_model_file(model_file_name)
model = dm.get_model(model_file_name)
mam = map_model_manager(model=model, map_manager=mm)
return mam
def read_model(filename):
from iotbx.data_manager import DataManager
dm = DataManager()
dm.set_overwrite(True)
dm.process_model_file(filename)
model = dm.get_model(filename)
model.add_crystal_symmetry_if_necessary()
return model
def run(args, log=sys.stdout):
print("-"*79, file=log)
print(legend, file=log)
print("-"*79, file=log)
inputs = mmtbx.utils.process_command_line_args(args = args,
master_params = master_params(),
suppress_symmetry_related_errors = True)
params = inputs.params.extract()
# model
broadcast(m="Input PDB:", log=log)
file_names = inputs.pdb_file_names
if(len(file_names) != 1): raise Sorry("PDB file has to given.")
from iotbx.data_manager import DataManager
dm = DataManager()
dm.set_overwrite(True)
model = dm.get_model(file_names[0])
# map
broadcast(m="Input map:", log=log)
if(inputs.ccp4_map is None): raise Sorry("Map file has to given.")
from iotbx.map_model_manager import map_model_manager
mam = map_model_manager(model = model, map_manager = inputs.ccp4_map,
wrapping = params.wrapping,
ignore_symmetry_conflicts = params.ignore_symmetry_conflicts)
mam.model().setup_scattering_dictionaries(
scattering_table=params.scattering_table)
mam.model().get_xray_structure().show_summary(f=log, prefix=" ")
inputs.ccp4_map.show_summary(prefix=" ")
# estimate resolution
d_min = params.resolution
if(d_min is None):
raise Sorry("Map resolution must be given.")
print(" d_min: %6.4f"%d_min, file=log)
#
result_obj = compdiff(
map_data_obs = mam.map_manager().map_data(), # NOTE this will always wrap map
xrs = mam.model().get_xray_structure(),
d_min = d_min,
vector_map = False)
output_map_manager=mam.map_manager().customized_copy(
map_data=result_obj.map_result)
dm.write_real_map_file(output_map_manager, "map_model_difference_1.ccp4")
#
result_obj = compdiff(
map_data_obs = mam.map_manager().map_data(),
xrs = mam.model().get_xray_structure(),
d_min = d_min,
vector_map = True)
output_map_manager=mam.map_manager().customized_copy(
map_data=result_obj.map_result)
dm.write_real_map_file(output_map_manager, "map_model_difference_2.ccp4")
def test_01(method='model_sharpen', expected_results=None):
# Source data
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data', 'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data', 'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
# Read in data
dm = DataManager(['ncs_spec', 'model', 'real_map', 'phil'])
dm.set_overwrite(True)
map_file = data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file = data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file = data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
mmm = map_model_manager(model=model,
map_manager_1=mm.deep_copy(),
map_manager_2=mm.deep_copy(),
ncs_object=ncs,
wrapping=False)
mmm.add_map_manager_by_id(map_id='external_map',
map_manager=mmm.map_manager().deep_copy())
mmm.set_resolution(3)
mmm.set_log(sys.stdout)
dc = mmm.deep_copy()
sharpen_method = getattr(mmm, method)
# sharpen by method (can be model_sharpen, half_map_sharpen or
# external_sharpen)
mmm = dc.deep_copy()
sharpen_method(anisotropic_sharpen=False, n_bins=10)
assert mmm.map_model_cc() > 0.9
sharpen_method(anisotropic_sharpen=False, n_bins=10, local_sharpen=True)
assert mmm.map_model_cc() > 0.9
sharpen_method(anisotropic_sharpen=True, n_bins=10)
assert mmm.map_model_cc() > 0.9
sharpen_method(anisotropic_sharpen=True,
n_bins=10,
local_sharpen=True,
n_boxes=1)
assert mmm.map_model_cc() > 0.9
def loadModel(filename):
dm = DataManager() # Initialize the DataManager and call it dm
dm.set_overwrite(
True) # tell the DataManager to overwrite files with the same name
#print("Reading file")
model = dm.get_model(filename)
#print("Processing model")
#model.process_input_model(make_restraints=True)
# removed because Restraints Manager will not operate
# on unfamiliar residues KPB 6/10/2021
return model
def exercise_ss_creation_crash():
pdb_str = """
CRYST1 145.350 135.090 157.320 90.00 90.00 90.00 P 1
SCALE1 0.006880 0.000000 0.000000 0.00000
SCALE2 0.000000 0.007402 0.000000 0.00000
SCALE3 0.000000 0.000000 0.006356 0.00000
ATOM 1 N ASN A 1 47.095 160.279 31.220 1.00 30.00 N
ATOM 2 CA ASN A 1 65.985 120.233 34.727 1.00 30.00 C
ATOM 3 C ASN A 1 56.657 138.700 33.374 1.00 30.00 C
ATOM 4 O ASN A 1 56.353 138.977 34.561 1.00 30.00 O
ATOM 5 CB ASN A 1 65.238 120.133 36.068 1.00 30.00 C
ATOM 6 CG ASN A 1 66.087 119.360 37.057 1.00 30.00 C
ATOM 7 OD1 ASN A 1 65.746 118.217 37.441 1.00 30.00 O
ATOM 8 ND2 ASN A 1 67.240 119.920 37.395 1.00 30.00 N
ATOM 9 N ASN A 2 56.939 137.441 33.021 1.00 30.00 N
ATOM 10 CA ASN A 2 67.135 117.384 35.354 1.00 30.00 C
ATOM 11 C ASN A 2 74.935 104.398 35.546 1.00 30.00 C
ATOM 12 O ASN A 2 74.423 104.166 34.444 1.00 30.00 O
ATOM 13 CB ASN A 2 65.828 116.703 35.809 1.00 30.00 C
ATOM 14 CG ASN A 2 66.092 115.518 36.718 1.00 30.00 C
ATOM 15 OD1 ASN A 2 66.641 114.515 36.266 1.00 30.00 O
ATOM 16 ND2 ASN A 2 65.744 115.556 38.000 1.00 30.00 N
ATOM 17 N ASN A 3 76.102 103.886 35.920 1.00 30.00 N
ATOM 18 CA ASN A 3 68.960 115.076 35.163 1.00 30.00 C
ATOM 19 C ASN A 3 86.047 90.376 35.591 1.00 30.00 C
ATOM 20 O ASN A 3 87.134 90.903 35.535 1.00 30.00 O
ATOM 21 CB ASN A 3 70.251 115.882 34.903 1.00 30.00 C
ATOM 22 CG ASN A 3 71.023 116.208 36.192 1.00 30.00 C
ATOM 23 OD1 ASN A 3 70.637 117.096 36.957 1.00 30.00 O
ATOM 24 ND2 ASN A 3 72.106 115.481 36.436 1.00 30.00 N
ATOM 25 OXT ASN A 3 85.912 89.104 36.045 1.00 30.00 O
TER
END
"""
with open("exercise_ss_creation_crash_model.pdb","w") as fo:
fo.write(pdb_str)
from iotbx.data_manager import DataManager
dm=DataManager()
params = mmtbx.model.manager.get_default_pdb_interpretation_params()
params.pdb_interpretation.secondary_structure.enabled=True
model = dm.get_model('exercise_ss_creation_crash_model.pdb')
model.set_pdb_interpretation_params(params)
model.process_input_model(make_restraints=True)
def run(prefix="tst_00_mmtbx_building_ligands"):
# Ligand file
with open("%s.pdb"%prefix,"w") as fo:
fo.write(pdb_str)
# Read map and model
from iotbx.data_manager import DataManager
dm = DataManager()
map_file = libtbx.env.find_in_repositories(
relative_path="mmtbx/building/ligands/tst_00_mmtbx_building_ligands.map",
test=os.path.isfile)
mm = dm.get_real_map(map_file)
model = dm.get_model("%s.pdb"%prefix)
model.set_crystal_symmetry(mm.crystal_symmetry())
model.process(make_restraints=True)
# Create map_model_manager
mmm = iotbx.map_model_manager.map_model_manager(map_manager=mm, model=model)
# Build ligand
o = ligands.lifi.run(map_model_manager = mmm, d_min = 2.5)
def read_map_and_model(file_name_1,
file_name_2,
regression_directory=None,
prefix=None):
'''
Identify which file is map and which is model, read in and
create map_model_manager
If regression_directory is specified, look there for these files, assuming
prefix of $PHENIX/modules/phenix_regression/
'''
if regression_directory and not prefix:
import libtbx.load_env
prefix = libtbx.env.under_dist(module_name="phenix_regression",
path=regression_directory,
test=os.path.isdir)
if prefix:
file_name_1 = os.path.join(prefix, file_name_1)
file_name_2 = os.path.join(prefix, file_name_2)
map_file_name = None
model_file_name = None
for f in [file_name_1, file_name_2]:
for ending in ['.ccp4', '.mrc', '.map']:
if f.endswith(ending):
map_file_name = f
for ending in ['.pdb', '.cif']:
if f.endswith(ending):
model_file_name = f
if not map_file_name or not model_file_name:
raise Sorry("Unable to guess map and model from %s and %s" %
(file_name_1, file_name_2))
from iotbx.data_manager import DataManager
from iotbx.map_model_manager import map_model_manager
dm = DataManager()
dm.process_real_map_file(map_file_name)
mm = dm.get_real_map(map_file_name)
dm.process_model_file(model_file_name)
model = dm.get_model(model_file_name)
mam = map_model_manager(model=model, map_manager=mm)
return mam
def main():
dm = DataManager()
dm.process_model_str('testing', model_1yjp)
model = dm.get_model()
rc = model.restraints_as_geo(force=True)
rc = check_geo(rc)
assert rc == count_1yjp, check_diff(rc, count_1yjp)
dm = DataManager()
dm.process_model_str('testing', model_1yjp_with_waters)
model = dm.get_model()
rc = model.restraints_as_geo(force=True)
rc = check_geo(rc)
assert rc == count_1yjp_with_waters, rc
params = model.get_default_pdb_interpretation_params()
edits_1yjp = params.geometry_restraints.edits
edits_1yjp.bond[0].action = 'add'
edits_1yjp.bond[0].atom_selection_1 = 'resname HOH and resid 10 and name O'
edits_1yjp.bond[
0].atom_selection_2 = 'resname ASN and resid 2 and name ND2'
edits_1yjp.bond[0].distance_ideal = 2.1
edits_1yjp.bond[0].sigma = 0.1
model.set_pdb_interpretation_params(params)
rc = model.restraints_as_geo(force=True)
rc = check_geo(rc)
current = count_1yjp_with_waters.copy()
current['User supplied restraints'] = 1
current['Nonbonded interactions'] = 1176
assert rc == current, check_diff(rc, current)
edits_1yjp.angle[0].action = 'add'
edits_1yjp.angle[
0].atom_selection_1 = 'resname HOH and resid 10 and name O'
edits_1yjp.angle[
0].atom_selection_2 = 'resname ASN and resid 2 and name ND2'
edits_1yjp.angle[
0].atom_selection_3 = 'resname ASN and resid 2 and name CG'
edits_1yjp.angle[0].angle_ideal = 21.9
edits_1yjp.angle[0].sigma = 1.1
model.set_pdb_interpretation_params(params)
rc = model.restraints_as_geo(force=True)
rc = check_geo(rc)
current = count_1yjp_with_waters.copy()
current['User supplied restraints'] = 1
current['User supplied angle restraints'] = 1
current['Nonbonded interactions'] = 1176
assert rc == current, check_diff(rc, current)
edits_1yjp.dihedral[0].action = 'add'
edits_1yjp.dihedral[
0].atom_selection_1 = 'resname HOH and resid 10 and name O'
edits_1yjp.dihedral[
0].atom_selection_2 = 'resname ASN and resid 2 and name ND2'
edits_1yjp.dihedral[
0].atom_selection_3 = 'resname ASN and resid 2 and name CG'
edits_1yjp.dihedral[
0].atom_selection_4 = 'resname ASN and resid 2 and name CB'
edits_1yjp.dihedral[0].angle_ideal = 121.9
edits_1yjp.dihedral[0].sigma = 1.12
edits_1yjp.dihedral[0].periodicity = 10
model.set_pdb_interpretation_params(params)
rc = model.restraints_as_geo(force=True)
rc = check_geo(rc)
current = count_1yjp_with_waters.copy()
current['User supplied restraints'] = 1
current['User supplied angle restraints'] = 1
current['User supplied torsion angle restraints'] = 1
#current[' sinusoidal'] = 16
current['Nonbonded interactions'] = 1176
assert rc == current, check_diff(rc, current)
print('OK')
def RunProbeTests(inFileName):
#========================================================================
# Call the test functions for the libraries we test.
ret = probeext.DotSpheres_test()
assert len(ret) == 0, "DotSpheres_test() failed: " + ret
ret = probeext.SpatialQuery_test()
assert len(ret) == 0, "SpatialQuery_test() failed: " + ret
ret = probeext.Scoring_test()
assert len(ret) == 0, "Scoring_test() failed: " + ret
AtomTypes.Test()
Helpers.Test()
#========================================================================
# Now ensure that we can use the C++-wrapped classes as intended to make sure
# that the wrapping code or parameters have not changed.
#========================================================================
# Make sure we can get at the DotSphere objects and their methods
cache = probeext.DotSphereCache(10)
sphere1 = cache.get_sphere(1)
dots = sphere1.dots()
#========================================================================
# Make sure we can fill in an ExtraAtomInfoList and pass it to scoring
# Generate an example data model with a small molecule in it
if inFileName is not None and len(inFileName) > 0:
# Read a model from a file using the DataManager
dm = DataManager()
dm.process_model_file(inFileName)
model = dm.get_model(inFileName)
else:
# Generate a small-molecule model using the map model manager
mmm = map_model_manager(
) # get an initialized instance of the map_model_manager
mmm.generate_map(
) # get a model from a generated small library model and calculate a map for it
model = mmm.model() # get the model
# Fix up bogus unit cell when it occurs by checking crystal symmetry.
cs = model.crystal_symmetry()
if (cs is None) or (cs.unit_cell() is None):
model = shift_and_box_model(model=model)
# Get the list of all atoms in the model
atoms = model.get_atoms()
# Get the bonding information we'll need to exclude our bonded neighbors.
try:
p = mmtbx.model.manager.get_default_pdb_interpretation_params()
model.process(make_restraints=True,
pdb_interpretation_params=p) # make restraints
geometry = model.get_restraints_manager().geometry
sites_cart = model.get_sites_cart() # cartesian coordinates
bond_proxies_simple, asu = \
geometry.get_all_bond_proxies(sites_cart = sites_cart)
except Exception as e:
raise Exception("Could not get bonding information for input file: " +
str(e))
bondedNeighbors = Helpers.getBondedNeighborLists(atoms,
bond_proxies_simple)
# Traverse the hierarchy and look up the extra data to be filled in.
ret = Helpers.getExtraAtomInfo(model)
extra = ret.extraAtomInfo
# Construct a SpatialQuery and fill in the atoms. Ensure that we can make a
# query within 1000 Angstroms of the origin.
sq = probeext.SpatialQuery(atoms)
nb = sq.neighbors((0, 0, 0), 0, 1000)
# Construct a DotScorer object.
# Find the radius of each atom in the structure and construct dot spheres for
# them. Find the atoms that are bonded to them and add them to an excluded list.
# Then compute the score for each of them and report the summed score over the
# whole molecule the way that Reduce will.
ds = probeext.DotScorer(extra)
total = 0
badBumpTotal = 0
for a in atoms:
rad = extra.getMappingFor(a).vdwRadius
assert rad > 0, "Invalid radius for atom look-up: " + a.name + " rad = " + str(
rad)
sphere = cache.get_sphere(rad)
# Excluded atoms that are bonded to me or to one of my neightbors.
# It has the side effect of excluding myself if I have any neighbors.
# Construct as a set to avoid duplicates.
exclude = set()
for n in bondedNeighbors[a]:
exclude.add(n)
for n2 in bondedNeighbors[n]:
exclude.add(n2)
exclude = list(exclude)
dots = sphere.dots()
res = ds.score_dots(a, 1.0, sq, rad * 3, 0.25, exclude, sphere.dots(),
sphere.density(), False)
total += res.totalScore()
if res.hasBadBump:
badBumpTotal += 1
# Test calling the single-dot checking code as will be used by Probe to make sure
# all of the Python linkage is working
dotOffset = [1, 0, 0]
check = ds.check_dot(atoms[0], dotOffset, 1, atoms, [atoms[0]])
overlapType = check.overlapType
# Test calling the interaction_type method to be sure Python linkage is working
interactionType = ds.interaction_type(check.overlapType, check.gap)
def test_modified_residues():
# modified amino acid example from 1e0z
seq_str1 = """\
>pdb|1e0z|A
PTVEYLNYETLDDQGWDMDDDDLFEKAADAGLDGEDYGTMEVAEGEYILEAAEAQGYDWPFSCRAGACANCASIVKEG
EIDMDMQQILSDEEVEEKDVRLTCIGSPAADEVKIVYNAKHLDYLQNRVI
"""
pdb_str1 = """\
HETATM 1707 OH ALY A 118 9.253 -11.285 16.293 1.00 0.00 O
HETATM 1708 CH ALY A 118 8.541 -10.835 15.417 1.00 0.00 C
HETATM 1709 CH3 ALY A 118 8.924 -9.562 14.659 1.00 0.00 C
HETATM 1710 NZ ALY A 118 7.420 -11.417 15.087 1.00 50.00 N
HETATM 1711 CE ALY A 118 7.573 -12.895 15.202 1.00 50.00 C
HETATM 1712 CD ALY A 118 6.273 -13.470 14.634 1.00 12.50 C
HETATM 1713 CG ALY A 118 5.506 -14.188 15.746 1.00 12.50 C
HETATM 1714 CB ALY A 118 6.259 -15.458 16.148 1.00 12.50 C
HETATM 1715 CA ALY A 118 5.885 -15.846 17.580 1.00 12.50 C
HETATM 1716 N ALY A 118 4.407 -15.666 17.660 1.00 12.50 N
HETATM 1717 C ALY A 118 6.576 -14.924 18.588 1.00 12.50 C
HETATM 1718 O ALY A 118 7.674 -15.190 19.036 1.00 12.50 O
HETATM 1719 HH31 ALY A 118 9.665 -9.016 15.224 1.00 0.00 H
HETATM 1720 HH32 ALY A 118 8.047 -8.945 14.525 1.00 0.00 H
HETATM 1721 HH33 ALY A 118 9.329 -9.826 13.694 1.00 0.00 H
HETATM 1722 HE3 ALY A 118 7.690 -13.181 16.237 1.00 0.00 H
HETATM 1723 HE2 ALY A 118 8.419 -13.232 14.620 1.00 0.00 H
HETATM 1724 HD3 ALY A 118 6.503 -14.171 13.845 1.00 0.00 H
HETATM 1725 HD2 ALY A 118 5.667 -12.668 14.239 1.00 0.00 H
HETATM 1726 HG3 ALY A 118 4.520 -14.451 15.392 1.00 0.00 H
HETATM 1727 HG2 ALY A 118 5.419 -13.535 16.602 1.00 0.00 H
HETATM 1728 HB3 ALY A 118 7.323 -15.279 16.091 1.00 0.00 H
HETATM 1729 HB2 ALY A 118 5.994 -16.261 15.477 1.00 0.00 H
HETATM 1730 HCA ALY A 118 6.150 -16.875 17.765 1.00 0.00 H
HETATM 1731 H ALY A 118 3.878 -15.580 16.839 1.00 99.00 H
"""
# modified nucleic acid example from 4eec
seq_str2 = """\
>4EEC_1|Chains A,B|StaL|Streptomyces toyocaensis (55952)
MGSSHHHHHHSSGLVPRGSMCWIASYPKAGGHWLRCMLTSYVTGEPVETWPGIQAGVPHLEGLLRDGEAPSADPDEQV
LLATHFTADRPVLRFYRESTAKVVCLIRNPRDAMLSLMRMKGIPPEDVEACRKIAETFIADEGFSSVRIWAGEGSWPE
NIRSWTDSVHESFPNAAVLAVRYEDLRKDPEGELWKVVDFLELGGRDGVADAVANCTLERMREMEERSKLLGLETTGL
MTRGGKQLPFVGKGGQRKSLKFMGDDIEKAYADLLHGETDFAHYARLYGYAE
>4EEC_2|Chain C|desulfo-A47934|Streptomyces toyocaensis (55952)
GXXGXXX
"""
pdb_str2 = """\
HETATM 3866 P1 A3P A 301 -32.928 0.112 -1.515 1.00 60.90 P
HETATM 3867 O1P A3P A 301 -32.567 0.971 -2.721 1.00 58.74 O
HETATM 3868 O2P A3P A 301 -33.836 -1.041 -1.926 1.00 60.18 O
HETATM 3869 O3P A3P A 301 -33.351 0.858 -0.247 1.00 59.07 O
HETATM 3870 P2 A3P A 301 -26.843 -0.504 2.943 1.00 61.59 P
HETATM 3871 O4P A3P A 301 -25.377 -0.388 2.609 1.00 57.50 O
HETATM 3872 O5P A3P A 301 -27.164 -1.892 3.467 1.00 62.98 O
HETATM 3873 O6P A3P A 301 -27.445 0.681 3.730 1.00 62.21 O
HETATM 3874 O5' A3P A 301 -27.694 -0.432 1.563 1.00 60.14 O
HETATM 3875 C5' A3P A 301 -29.109 -0.472 1.636 1.00 57.46 C
HETATM 3876 C4' A3P A 301 -29.649 -0.859 0.288 1.00 58.10 C
HETATM 3877 O4' A3P A 301 -29.334 -2.235 0.029 1.00 59.74 O
HETATM 3878 C3' A3P A 301 -31.164 -0.780 0.284 1.00 58.44 C
HETATM 3879 O3' A3P A 301 -31.539 -0.556 -1.069 1.00 59.37 O
HETATM 3880 C2' A3P A 301 -31.538 -2.197 0.626 1.00 58.35 C
HETATM 3881 O2' A3P A 301 -32.890 -2.542 0.343 1.00 58.12 O
HETATM 3882 C1' A3P A 301 -30.561 -2.939 -0.264 1.00 58.48 C
HETATM 3883 N9 A3P A 301 -30.576 -4.401 0.059 1.00 58.16 N
HETATM 3884 C8 A3P A 301 -30.559 -4.926 1.312 1.00 57.04 C
HETATM 3885 N7 A3P A 301 -30.604 -6.285 1.255 1.00 57.35 N
HETATM 3886 C5 A3P A 301 -30.645 -6.664 -0.042 1.00 55.60 C
HETATM 3887 C6 A3P A 301 -30.683 -7.957 -0.767 1.00 54.29 C
HETATM 3888 N6 A3P A 301 -30.686 -9.129 -0.066 1.00 54.08 N
HETATM 3889 N1 A3P A 301 -30.706 -7.913 -2.130 1.00 53.41 N
HETATM 3890 C2 A3P A 301 -30.703 -6.743 -2.815 1.00 52.74 C
HETATM 3891 N3 A3P A 301 -30.659 -5.530 -2.220 1.00 53.74 N
HETATM 3892 C4 A3P A 301 -30.631 -5.420 -0.849 1.00 56.63 C
"""
for pdb_str, seq_str in [(pdb_str1, seq_str1), (pdb_str2, seq_str2)]:
seq_file = tempfile.NamedTemporaryFile(suffix='.fasta')
seq_file.write(seq_str)
seq_file.flush()
model_file = tempfile.NamedTemporaryFile(suffix='.pdb')
model_file.write(pdb_str)
model_file.flush()
dm = DataManager()
dm.process_model_file(model_file.name)
dm.process_sequence_file(seq_file.name)
model = dm.get_model()
seq = dm.get_sequence()
model.set_sequences(seq)
model_file.close()
seq_file.close()
def test_01():
# Source data (map and model)
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data', 'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data', 'non_zero_origin_model.pdb')
# Read in map data with data_manager
dm = DataManager(['real_map'])
dm.set_overwrite(True)
# Next step uses map_manager to do the actual reading
dm.process_real_map_file(data_ccp4)
mm = dm.get_real_map()
# Shift the origin of the map; starts at (100,100,100)
print(mm.map_data().origin())
assert mm.map_data().origin() == (100, 100, 100)
assert mm.origin_shift_grid_units == (0, 0, 0)
mm.shift_origin()
assert mm.map_data().origin() == (0, 0, 0)
assert mm.origin_shift_grid_units == (100, 100, 100)
mm.show_summary()
# test cc_to_other_map
assert mm.cc_to_other_map_manager(mm) == 1
# test writing and reading file
dm.write_real_map_file(mm,
filename='test_map_manager.ccp4',
overwrite=True)
dm.process_real_map_file('test_map_manager.ccp4')
new_mm = dm.get_real_map('test_map_manager.ccp4')
os.remove('test_map_manager.ccp4')
new_mm.shift_origin()
# Check whether gridding and crystal_symmetry are similar for mm, new_mm
assert new_mm.is_similar(mm)
assert approx_equal(new_mm.map_data()[3125], mm.map_data()[3125])
# test writing and reading file without shifting origin
dm = DataManager(['real_map'])
dm.set_overwrite(True)
dm.process_real_map_file(data_ccp4)
mm = dm.get_real_map()
mm.show_summary()
dm.write_real_map_file(mm,
filename='test_map_manager.ccp4',
overwrite=True)
new_mm = map_manager('test_map_manager.ccp4')
assert (new_mm.is_similar(mm))
new_mm.shift_origin()
assert (not new_mm.is_similar(mm))
# get map_data
dm = DataManager(['real_map'])
dm.set_overwrite(True)
dm.process_real_map_file(data_ccp4)
mm = dm.get_real_map()
mm.shift_origin()
map_data = mm.map_data()
assert approx_equal(map_data[15, 10, 19], 0.38, eps=0.01)
# get crystal_symmetry
cs = mm.crystal_symmetry()
assert approx_equal(cs.unit_cell().parameters()[0], 22.41, eps=0.01)
# and full cell symmetry
full_cs = mm.unit_cell_crystal_symmetry()
assert approx_equal(full_cs.unit_cell().parameters()[0],
149.4066,
eps=0.01)
# write map directly:
mm.write_map('test_direct.ccp4')
# read back directly
new_mm = map_manager('test_direct.ccp4')
assert (not new_mm.is_similar(mm))
new_mm.shift_origin()
assert mm.is_similar(new_mm)
assert approx_equal(new_mm.map_data()[3125], mm.map_data()[3125])
# deep_copy
new_mm = mm.deep_copy()
assert new_mm.is_similar(mm)
assert approx_equal(new_mm.map_data()[3125], mm.map_data()[3125])
# deep_copy a map without shifting origin
# Make a DataManager that can write a map coeffs file too
dm = DataManager(['miller_array', 'real_map'])
dm.set_overwrite(True)
dm.process_real_map_file(data_ccp4)
omm = dm.get_real_map()
omm.show_summary()
new_omm = omm.deep_copy()
assert new_omm.is_similar(omm)
assert (not new_omm.is_similar(mm))
# customized_copy
new_mm = mm.customized_copy(map_data=mm.map_data().deep_copy())
assert new_mm.is_similar(mm)
# Initialize with parameters
mm_para = map_manager(
unit_cell_grid=mm.unit_cell_grid,
unit_cell_crystal_symmetry=mm.unit_cell_crystal_symmetry(),
origin_shift_grid_units=mm.origin_shift_grid_units,
map_data=mm.map_data(),
wrapping=False)
assert mm_para.is_similar(mm)
# Adjust origin and gridding:
mm_read = map_manager(data_ccp4)
mm_read.shift_origin()
mm.show_summary()
mm_read.show_summary()
mm_read.set_original_origin_and_gridding((10, 10, 10),
gridding=(100, 100, 100))
mm_read.show_summary()
assert (not mm_read.is_similar(mm))
assert (mm_read.origin_is_zero())
# Set program name
mm_read.set_program_name('test program')
assert mm_read.program_name == 'test program'
# Set limitation
mm_read.add_limitation('map_is_sharpened')
assert mm_read.limitations == ['map_is_sharpened']
# Add a label
mm_read.add_label('TEST LABEL')
assert mm_read.labels[0] == 'TEST LABEL'
mm_read.write_map('map_with_labels.mrc')
new_mm = map_manager('map_with_labels.mrc')
assert 'TEST LABEL' in new_mm.labels
assert new_mm.is_in_limitations('map_is_sharpened')
assert new_mm.labels[0].find('test program') > -1
# change the cell dimensions
mm_read = map_manager(data_ccp4)
mm_read.shift_origin()
assert mm_read.is_similar(mm)
assert approx_equal(mm_read.pixel_sizes(), (0.7470, 0.7231, 0.7374),
eps=0.001)
from cctbx import crystal
new_uc_params = list(
mm_read.unit_cell_crystal_symmetry().unit_cell().parameters())
new_uc_params[0] += 10
new_cs = crystal.symmetry(new_uc_params, 1)
mm_read.set_unit_cell_crystal_symmetry(new_cs)
assert not mm_read.crystal_symmetry().is_similar_symmetry(
mm.crystal_symmetry())
assert not mm_read.is_similar(mm)
mm_read.show_summary()
assert approx_equal(mm_read.pixel_sizes(), (0.7970, 0.7231, 0.7374),
eps=0.001)
# Read a map directly
mm_read = map_manager(data_ccp4)
mm_read.shift_origin()
assert mm_read.is_similar(mm)
# Set log
import sys
mm.set_log(sys.stdout)
# Add map_data
new_mm = mm_read.customized_copy(map_data=mm.map_data().deep_copy())
assert new_mm.is_similar(mm)
# replace data
new_mm.set_map_data(map_data=mm.map_data().deep_copy())
assert new_mm.is_similar(mm)
# create a full-sized map from this one
mm_full_size = mm_read.deep_copy().as_full_size_map()
assert not mm_full_size.is_similar(mm_read)
print(mm_full_size.map_data().origin(), mm_read.map_data().origin())
print(mm_full_size.map_data().all(), mm_read.map_data().all())
# Apply a mask to edges of a map
assert approx_equal(new_mm.map_data().as_1d().min_max_mean().max,
mm.map_data().as_1d().min_max_mean().max)
assert approx_equal((new_mm.map_data()[0], mm.map_data()[0]), (0.0, 0.0))
new_mm.create_mask_around_edges(soft_mask_radius=3)
new_mm.soft_mask(soft_mask_radius=3)
assert approx_equal(new_mm.map_data().as_1d().min_max_mean().max,
mm.map_data().as_1d().min_max_mean().max)
new_mm.apply_mask(set_outside_to_mean_inside=True)
assert approx_equal((new_mm.map_data()[0], mm.map_data()[0]),
(0.0116267086024, 0.0))
dm.process_real_map_file('test_map_manager.ccp4')
new_mm = dm.get_real_map('test_map_manager.ccp4')
new_mm.show_summary()
assert (not new_mm.is_similar(mm))
new_mm.shift_origin()
new_mm.show_summary()
assert new_mm.is_similar(mm)
os.remove('test_map_manager.ccp4')
# Check origin_shifts
print(new_mm.origin_shift_grid_units)
print(new_mm.shift_cart())
assert approx_equal(new_mm.origin_shift_grid_units, (100, 100, 100))
assert approx_equal(
new_mm.shift_cart(),
(-74.70333099365234, -72.30750274658205, -73.7437515258789))
# Convert to map coeffs, write out, read back, convert back to map
map_coeffs = mm.map_as_fourier_coefficients(d_min=3)
mtz_dataset = map_coeffs.as_mtz_dataset(column_root_label='F')
mtz_object = mtz_dataset.mtz_object()
dm.write_miller_array_file(mtz_object, filename="map_coeffs.mtz")
# Note these Fourier coeffs correspond to working map (not original position)
array_labels = dm.get_miller_array_labels("map_coeffs.mtz")
labels = array_labels[0]
dm.get_reflection_file_server(filenames=["map_coeffs.mtz"],
labels=[labels])
miller_arrays = dm.get_miller_arrays()
new_map_coeffs = miller_arrays[0]
mm_from_map_coeffs = mm.fourier_coefficients_as_map_manager(
map_coeffs=new_map_coeffs)
assert mm_from_map_coeffs.is_similar(mm)
# Find map symmetry in a map
data_d7 = os.path.join(data_dir, 'data', 'D7.ccp4')
dm = DataManager(['real_map', 'model'])
dm.process_real_map_file(data_d7)
dm.process_model_file(data_pdb)
mm = dm.get_real_map(data_d7)
model = dm.get_model(data_pdb)
mm.shift_origin()
mm.set_original_origin_and_gridding(original_origin=(0, 0, 0))
# Box it so it is not so easy to find symmetry
from cctbx.maptbx.box import with_bounds
box = with_bounds(mm, lower_bounds=(2, 2, 2), upper_bounds=(43, 43, 43))
new_mm = box.map_manager()
new_mm.find_map_symmetry(symmetry='d7',
min_ncs_cc=0.8,
include_helical_symmetry=False)
ncs_obj = new_mm.ncs_object()
assert ncs_obj is not None
print("NCS: ", new_mm.ncs_object().as_ncs_spec_string())
another_mm = map_manager(
unit_cell_grid=new_mm.unit_cell_grid,
unit_cell_crystal_symmetry=new_mm.unit_cell_crystal_symmetry(),
origin_shift_grid_units=new_mm.origin_shift_grid_units,
map_data=new_mm.map_data(),
ncs_object=ncs_obj,
wrapping=False)
assert another_mm.is_similar(new_mm)
assert ncs_obj.is_similar_ncs_object(another_mm.ncs_object())
assert new_mm.is_similar(another_mm)
# Adjust model and ncs symmetry to match this map
assert model.shift_cart() is None
new_mm.set_model_symmetries_and_shift_cart_to_match_map(model)
assert approx_equal(
model.shift_cart(),
(-0.888888888888889, -0.8888888888888891, -0.888888888888889))
assert new_mm.is_compatible_ncs_object(ncs_obj)
ncs_obj.set_shift_cart((0, 0, 0))
assert not new_mm.is_compatible_ncs_object(ncs_obj)
new_mm.set_ncs_object_shift_cart_to_match_map(ncs_obj)
new_mm.set_ncs_object(ncs_obj)
assert new_mm.is_compatible_ncs_object(new_mm.ncs_object())
new_mm.show_summary()
new_mm.shift_origin(desired_origin=(11, 1, 1))
print(new_mm.shift_cart(), new_mm.ncs_object().shift_cart())
assert new_mm.is_compatible_ncs_object(new_mm.ncs_object())
new_mm.shift_origin()
assert new_mm.is_compatible_ncs_object(new_mm.ncs_object())
# filter a map
dm = DataManager()
mm = dm.get_real_map(data_d7)
low_pass_filtered = mm.deep_copy()
low_pass_filtered.resolution_filter(d_min=2.5)
high_pass_filtered = mm.deep_copy()
high_pass_filtered.resolution_filter(d_max=2.5)
gaussian = mm.deep_copy()
gaussian.gaussian_filter(smoothing_radius=1)
binary = mm.deep_copy()
binary.binary_filter(threshold=0.5)
assert approx_equal(
(mm.map_data().as_1d()[1073],
low_pass_filtered.map_data().as_1d()[1073],
high_pass_filtered.map_data().as_1d()[1073],
gaussian.map_data().as_1d()[1073], binary.map_data().as_1d()[1073]),
(0.0171344596893, 0.0227163900537, -0.0072717454565, 0.0149086679298,
0.0))
info = mm.get_density_along_line((5, 5, 5), (10, 10, 10))
assert approx_equal([info.along_density_values[4]] +
list(info.along_sites[4]),
[-0.562231123447, 8.0, 8.0, 8.0])
from iotbx.map_model_manager import map_model_manager
extra_map_manager_id_list = [
"low_pass_filtered", "high_pass_filtered", "gaussian", "binary"
]
expected_cc = [
0.999920243317, 0.0129365545729, 0.971491994253, 0.733986499746
]
mam = map_model_manager(
map_manager=mm,
extra_map_manager_list=[
low_pass_filtered, high_pass_filtered, gaussian, binary
],
extra_map_manager_id_list=extra_map_manager_id_list,
)
for other_id, cc in zip(extra_map_manager_id_list, expected_cc):
assert approx_equal(
cc, mam.map_map_cc(map_id='map_manager', other_map_id=other_id))
def create_model_from_file(path_to_pdb_file): from iotbx.data_manager import DataManager # Load in the DataManager dm = DataManager() # Initialize the DataManager and call it dm model = dm.get_model(path_to_pdb_file) return model
def test_01():
# Source data
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data',
'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data',
'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
# DataManager
dm = DataManager(['ncs_spec','model', 'real_map', 'phil'])
dm.set_overwrite(True)
# Read in map and model and ncs
map_file=data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file=data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file=data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
ncs_dc = ncs.deep_copy()
mmmn = match_map_model_ncs()
mmmn.add_map_manager(mm)
mmmn.add_model(model)
mmmn.add_ncs_object(ncs)
# Save it
mmmn_dc=mmmn.deep_copy()
# Make sure we can add an ncs object that is either shifted or not
mmmn_dcdc=mmmn.deep_copy()
new_mmmn = match_map_model_ncs()
new_mmmn.add_map_manager(mmmn_dcdc.map_manager())
new_mmmn.add_model(mmmn_dcdc.model())
new_mmmn.add_ncs_object(mmmn_dcdc.ncs_object())
assert new_mmmn.ncs_object().shift_cart() == new_mmmn.map_manager().shift_cart()
mmmn_dcdc=mmmn.deep_copy()
new_mmmn = match_map_model_ncs()
new_mmmn.add_map_manager(mmmn_dcdc.map_manager())
new_mmmn.add_model(mmmn_dcdc.model())
new_mmmn.add_ncs_object(ncs_dc)
assert new_mmmn.ncs_object().shift_cart() == new_mmmn.map_manager().shift_cart()
original_ncs=mmmn.ncs_object()
assert approx_equal((24.0528, 11.5833, 20.0004),
tuple(original_ncs.ncs_groups()[0].translations_orth()[-1]),
eps=0.1)
assert tuple(mmmn._map_manager.origin_shift_grid_units) == (0,0,0)
# Shift origin to (0,0,0)
mmmn=mmmn_dc.deep_copy() # fresh version of match_map_model_ncs
mmmn.shift_origin()
new_ncs=mmmn.ncs_object()
assert tuple(mmmn._map_manager.origin_shift_grid_units) == (100,100,100)
mmmn.write_model('s.pdb')
mmmn.write_map('s.mrc')
shifted_ncs=mmmn.ncs_object()
assert approx_equal((-153.758, -74.044, -127.487),
tuple(shifted_ncs.ncs_groups()[0].translations_orth()[-1]),eps=0.1)
# Shift a model and shift it back
mmmn=mmmn_dc.deep_copy() # fresh version of match_map_model_ncs
model=mmmn.model()
shifted_model=mmmn.shift_model_to_match_working_map(model=model)
model_in_original_position=mmmn.shift_model_to_match_original_map(
model=shifted_model)
assert (approx_equal(model.get_sites_cart(), # not a copy
shifted_model.get_sites_cart()))
assert approx_equal(model.get_sites_cart(),
model_in_original_position.get_sites_cart())
# Generate a map and model
import sys
mmm=map_model_manager(log=sys.stdout)
mmm.generate_map()
model=mmm.model()
mm=mmm.map_manager()
assert approx_equal(
model.get_sites_cart()[0], (14.476, 10.57, 8.34) ,eps=0.01)
assert approx_equal(mm.map_data()[10,10,10],-0.0195,eps=0.001)
# Save it
mmm_dc=mmm.deep_copy()
# Check on wrapping
assert not mm.wrapping() # this one should not wrap because it is zero at edges
# Make a new one with no buffer so it is not zero at edges
mmm=map_model_manager()
mmm.generate_map(box_cushion=0)
mm=mmm.map_manager()
# check its compatibility with wrapping
assert mm.is_consistent_with_wrapping()
mmm.show_summary()
# now box it
sel=mmm.model().selection("resseq 221:221")
new_model=mmm.model().deep_copy().select(sel)
new_mmm=map_model_manager(model=new_model,map_manager=mm.deep_copy())
new_mmm.box_all_maps_around_model_and_shift_origin()
new_mm=new_mmm.map_manager()
assert not new_mm.wrapping()
assert not new_mm.is_consistent_with_wrapping()
# now box it with selection
new_mmm_1=map_model_manager(
model=mmm.model().deep_copy(),map_manager=mm.deep_copy())
new_mmm_1.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1=new_mmm_1.map_manager()
assert not new_mm_1.wrapping()
assert not new_mm_1.is_consistent_with_wrapping()
assert new_mm_1.map_data().all()== new_mm.map_data().all()
# create map_model_manager with just half-maps
mm1=mm.deep_copy()
mm2=mm.deep_copy()
map_data=mm2.map_data()
map_data+=1.
new_mmm=map_model_manager(model=mmm.model().deep_copy(),
map_manager_1=mm1,
map_manager_2=mm2)
assert new_mmm._map_dict.get('map_manager') is None # should not be any yet
assert approx_equal(new_mmm.map_manager().map_data()[232],
mm.deep_copy().map_data()[232]+0.5)
assert new_mmm._map_dict.get('map_manager') is not None # now should be there
# generate map data from a model
mm1=mm.deep_copy()
mm2=mm.deep_copy()
new_mmm=map_model_manager(model=mmm.model().deep_copy(), map_manager=mm1)
mmm.generate_map(model=mmm.model())
mm=mmm.map_manager()
mmm.show_summary()
def generate_model(file_name=None,
n_residues=None,
start_res=None,
b_iso=30,
box_cushion=5,
space_group_number=1,
output_model_file_name=None,
shake=None,
random_seed=None,
log=sys.stdout):
'''
generate_model: Simple utility for generating a model for testing purposes.
This function typically accessed and tested through map_model_manager
Summary
-------
Generate a model from a user-specified file or from some examples available
in the cctbx. Cut out specified number of residues, shift to place on
positive side of origin, optionally set b values to b_iso,
place in box with buffering of box_cushion on all
edges, optionally randomly shift (shake) atomic positions by rms of shake A,
and write out to output_model_file_name and return model object.
Parameters:
file_name (string, None): File containing model (PDB, CIF format)
n_residues (int, 10): Number of residues to include
start_res (int, None): Starting residue number
b_iso (float, 30): B-value (ADP) to use for all atoms
box_cushion (float, 5): Buffer (A) around model
space_group_number (int, 1): Space group to use
output_model_file_name (string, None): File for output model
shake (float, None): RMS variation to add (A) in shake
random_seed (int, None): Random seed for shake
Returns:
model.manager object (model) in a box defined by a crystal_symmetry object
'''
# Get the parameters
space_group_number = int(space_group_number)
if n_residues is not None:
n_residues = int(n_residues)
box_cushion = float(box_cushion)
if start_res:
start_res = int(start_res)
if shake:
shake = float(shake)
if random_seed:
random_seed = int(random_seed)
import random
random.seed(random_seed)
random_seed = random.randint(1, 714717)
flex.set_random_seed(random_seed)
# Choose file with coordinates
if not file_name:
if not n_residues:
n_residues = 10 # default
import libtbx.load_env
iotbx_regression = os.path.join(
libtbx.env.find_in_repositories("iotbx"), 'regression')
if n_residues < 25:
file_name = os.path.join(iotbx_regression, 'secondary_structure',
'5a63_chainBp.pdb') # starts at 219
if not start_res: start_res = 219
elif n_residues < 167:
file_name = os.path.join(iotbx_regression, 'secondary_structure',
'3jd6_noh.pdb') # starts at 58
if not start_res: start_res = 58
else:
file_name = os.path.join(iotbx_regression, 'secondary_structure',
'4a7h_chainC.pdb') # starts at 9
if not start_res: start_res = 9
else: # have file_name
if start_res is None:
start_res = 1
if not n_residues:
n_residues = 100000 # a big number
# Read in coordinates and cut out the part of the model we want
from iotbx.data_manager import DataManager
dm = DataManager(['model'])
dm.process_model_file(file_name)
model = dm.get_model(file_name)
if not model.crystal_symmetry() or not model.crystal_symmetry().unit_cell(
):
from cctbx.maptbx.box import shift_and_box_model
model = shift_and_box_model(model=model, box_cushion=box_cushion)
selection = model.selection('resseq %s:%s' %
(start_res, start_res + n_residues - 1))
model = model.select(selection)
# shift the model and return it with new crystal_symmetry
from cctbx.maptbx.box import shift_and_box_model
model = shift_and_box_model(model=model, box_cushion=box_cushion)
if b_iso is not None:
b_values = flex.double(model.get_sites_cart().size(), b_iso)
ph = model.get_hierarchy()
ph.atoms().set_b(b_values)
# Optionally shake model
if shake:
model = shake_model(model, shake=shake)
if output_model_file_name:
f = open(output_model_file_name, 'w')
print("%s" % (model.model_as_pdb()), file=f)
f.close()
print("Writing model with %s residues and b_iso=%s from %s to %s" %
(n_residues, b_iso, file_name, output_model_file_name),
file=log)
else:
print("Generated model with %s residues and b_iso=%s from %s " %
(n_residues, b_iso, file_name),
file=log)
return model
def test_model_and_restraint():
# from 3tpj
model_str = '''
CRYST1 104.428 128.690 76.662 90.00 90.00 90.00 C 2 2 21
ATOM 5877 O URE A 403 -37.796 -38.296 5.693 1.00 15.43 O
ATOM 5878 C URE A 403 -36.624 -38.509 5.800 1.00 20.53 C
ATOM 5879 N2 URE A 403 -36.191 -39.836 6.120 1.00 27.82 N
ATOM 5880 N1 URE A 403 -35.679 -37.450 5.644 1.00 21.36 N
ATOM 5881 HN11 URE A 403 -34.792 -37.617 5.732 1.00 25.63 H
ATOM 5882 HN12 URE A 403 -35.965 -36.613 5.445 1.00 25.63 H
ATOM 5883 HN21 URE A 403 -35.307 -40.015 6.211 1.00 33.38 H
ATOM 5884 HN22 URE A 403 -36.801 -40.499 6.221 1.00 33.38 H
'''
restraint_str = '''
#
data_comp_list
loop_
_chem_comp.id
_chem_comp.three_letter_code
_chem_comp.name
_chem_comp.group
_chem_comp.number_atoms_all
_chem_comp.number_atoms_nh
_chem_comp.desc_level
URE URE Unknown ligand 8 4 .
#
data_comp_URE
#
loop_
_chem_comp_atom.comp_id
_chem_comp_atom.atom_id
_chem_comp_atom.type_symbol
_chem_comp_atom.type_energy
_chem_comp_atom.partial_charge
_chem_comp_atom.x
_chem_comp_atom.y
_chem_comp_atom.z
URE C C C . 0.4968 -0.0000 -0.0000
URE O O O . 1.7184 -0.0000 -0.0000
URE N1 N NH2 . -0.2180 -0.0000 1.2381
URE N2 N NH2 . -0.2180 0.0000 -1.2381
URE HN11 H HNH2 . 0.2355 -0.0000 2.0237
URE HN12 H HNH2 . -1.1251 0.0000 1.2382
URE HN21 H HNH2 . 0.2355 0.0000 -2.0237
URE HN22 H HNH2 . -1.1251 -0.0000 -1.2382
#
loop_
_chem_comp_bond.comp_id
_chem_comp_bond.atom_id_1
_chem_comp_bond.atom_id_2
_chem_comp_bond.type
_chem_comp_bond.value_dist
_chem_comp_bond.value_dist_esd
URE C O double 1.222 0.020
URE C N1 single 1.430 0.020
URE C N2 single 1.430 0.020
URE N1 HN11 single 0.907 0.020
URE N1 HN12 single 0.907 0.020
URE N2 HN21 single 0.907 0.020
URE N2 HN22 single 0.907 0.020
#
loop_
_chem_comp_angle.comp_id
_chem_comp_angle.atom_id_1
_chem_comp_angle.atom_id_2
_chem_comp_angle.atom_id_3
_chem_comp_angle.value_angle
_chem_comp_angle.value_angle_esd
URE N2 C N1 120.00 3.000
URE N2 C O 120.00 3.000
URE N1 C O 120.00 3.000
URE HN12 N1 HN11 120.00 3.000
URE HN12 N1 C 120.00 3.000
URE HN11 N1 C 120.00 3.000
URE HN22 N2 HN21 120.00 3.000
URE HN22 N2 C 120.00 3.000
URE HN21 N2 C 120.00 3.000
#
loop_
_chem_comp_tor.comp_id
_chem_comp_tor.id
_chem_comp_tor.atom_id_1
_chem_comp_tor.atom_id_2
_chem_comp_tor.atom_id_3
_chem_comp_tor.atom_id_4
_chem_comp_tor.value_angle
_chem_comp_tor.value_angle_esd
_chem_comp_tor.period
URE CONST_01 HN11 N1 C O 0.00 0.0 0
URE CONST_02 HN12 N1 C O 180.00 0.0 0
URE CONST_03 HN21 N2 C O -0.00 0.0 0
URE CONST_04 HN22 N2 C O 180.00 0.0 0
URE CONST_05 HN21 N2 C N1 180.00 0.0 0
URE CONST_06 HN22 N2 C N1 -0.00 0.0 0
URE CONST_07 HN11 N1 C N2 -180.00 0.0 0
URE CONST_08 HN12 N1 C N2 -0.00 0.0 0
#
loop_
_chem_comp_plane_atom.comp_id
_chem_comp_plane_atom.plane_id
_chem_comp_plane_atom.atom_id
_chem_comp_plane_atom.dist_esd
URE plan-1 C 0.020
URE plan-1 O 0.020
URE plan-1 N1 0.020
URE plan-1 N2 0.020
URE plan-1 HN11 0.020
URE plan-1 HN12 0.020
URE plan-1 HN21 0.020
URE plan-1 HN22 0.020
'''
model_filename = 'ure.pdb'
restraint_filename = 'ure.cif'
dm = DataManager(['model', 'restraint'])
dm.write_model_file(model_str, filename=model_filename, overwrite=True)
dm.write_restraint_file(restraint_str,
filename=restraint_filename,
overwrite=True)
# fails because no restraints are loaded
dm.process_model_file(model_filename)
model = dm.get_model()
try:
model.get_restraints_manager()
except Sorry:
pass
# automatically add restraints
dm.process_restraint_file(restraint_filename)
model = dm.get_model()
model.get_restraints_manager()
os.remove(model_filename)
os.remove(restraint_filename)
def tst_01(log=sys.stdout):
# Check calculations of conversion between rmsd, lddt , and B values
print("\nChecking conversions between rmsd, lddt and B-values", file=log)
for maximum_rmsd, minimum_lddt, target_b in [
(1.5, None, 59.2175263686),
(None, 0.7, 59.2175263686),
(1.5, 0.7, 59.2175263686),
(1.0, None, 26.3189006083),
(None, 0.5, 293.306328196),
]:
print()
cutoff_b = get_cutoff_b_value(maximum_rmsd, minimum_lddt, log=log)
print("maximum_rmsd: %s min lddt %s Cutoff B: %.2f" %
(maximum_rmsd, minimum_lddt, cutoff_b),
file=log)
assert approx_equal(cutoff_b, target_b)
# Read in alphafold model and get LDDT from B-value field
print("\nReading in alphafold model with lddt values in B-value field",
file=log)
dm = DataManager()
dm.set_overwrite(True)
m = dm.get_model(model_file)
pae_m = dm.get_model(pae_model_file)
pae_matrix = parse_pae_file(pae_file)
lddt_values = m.get_hierarchy().atoms().extract_b().deep_copy()
print("\nLDDT mean:", lddt_values.min_max_mean().mean)
assert approx_equal(lddt_values.min_max_mean().mean, 82.5931111111)
# Multiply lddt_values by 0.01 (fractional)
fractional_lddt = lddt_values * 0.01
# Convert lddt to b
b_values = get_b_values_from_lddt(lddt_values)
print("B-value mean:", b_values.min_max_mean().mean)
assert approx_equal(b_values.min_max_mean().mean, 24.7254093338)
# Convert b to lddt
lddt = get_lddt_from_b(b_values)
assert approx_equal(lddt, fractional_lddt)
lddt = get_lddt_from_b(b_values, input_lddt_is_fractional=False)
assert approx_equal(lddt, lddt_values)
# Convert lddt to rmsd
rmsd_values = get_rmsd_from_lddt(lddt_values)
print("RMSD mean:", rmsd_values.min_max_mean().mean)
assert approx_equal(rmsd_values.min_max_mean().mean, 0.93559254135)
# use process_predicted_model to convert lddt or rmsd to B return with
# mark_atoms_to_ignore_with_occ_zero
print(
"\nConverting lddt to B values and using mark_atoms_to_ignore_with_occ_zero",
file=log)
params.process_predicted_model.maximum_fraction_close = 0.5
params.process_predicted_model.b_value_field_is = 'lddt'
params.process_predicted_model.remove_low_confidence_residues = True
params.process_predicted_model.maximum_rmsd = 1.5
params.process_predicted_model.split_model_by_compact_regions = True
params.process_predicted_model.maximum_domains = 3
model_info = process_predicted_model(m,
params,
mark_atoms_to_keep_with_occ_one=True)
models = model_info.model_list
for mm, n1, n2 in zip(models, [84, 88], [88, 84]):
model_occ_values = mm.get_hierarchy().atoms().extract_occ()
assert model_occ_values.count(1) == n1
assert model_occ_values.count(0) == n2
# use process_predicted_model to convert lddt or rmsd to B
print("\nConverting lddt to B values", file=log)
params.process_predicted_model.maximum_fraction_close = 0.5
params.process_predicted_model.b_value_field_is = 'lddt'
params.process_predicted_model.remove_low_confidence_residues = False
params.process_predicted_model.split_model_by_compact_regions = False
params.process_predicted_model.input_lddt_is_fractional = None
model_info = process_predicted_model(m, params)
model = model_info.model
model_b_values = model.get_hierarchy().atoms().extract_b()
assert approx_equal(b_values, model_b_values,
eps=0.02) # come back rounded
print("\nConverting fractional lddt to B values", file=log)
ph = model.get_hierarchy().deep_copy()
ph.atoms().set_b(fractional_lddt)
test_model = model.as_map_model_manager().model_from_hierarchy(
ph, return_as_model=True)
params.process_predicted_model.maximum_fraction_close = 0.5
params.process_predicted_model.b_value_field_is = 'lddt'
params.process_predicted_model.remove_low_confidence_residues = False
params.process_predicted_model.split_model_by_compact_regions = False
params.process_predicted_model.input_lddt_is_fractional = None
model_info = process_predicted_model(test_model, params)
model = model_info.model
model_b_values = model.get_hierarchy().atoms().extract_b()
assert approx_equal(b_values, model_b_values,
eps=3) # come back very rounded
ph = model.get_hierarchy().deep_copy()
ph.atoms().set_b(rmsd_values)
test_model = model.as_map_model_manager().model_from_hierarchy(
ph, return_as_model=True)
print("\nConverting rmsd to B values", file=log)
params.process_predicted_model.maximum_fraction_close = 0.5
params.process_predicted_model.b_value_field_is = 'rmsd'
params.process_predicted_model.remove_low_confidence_residues = False
params.process_predicted_model.split_model_by_compact_regions = False
params.process_predicted_model.input_lddt_is_fractional = None
model_info = process_predicted_model(test_model, params)
model = model_info.model
model_b_values = model.get_hierarchy().atoms().extract_b()
assert approx_equal(b_values, model_b_values, eps=0.5) # come back rounded
print("B-values > 59: %s of %s" %
((model_b_values > 59).count(True), model_b_values.size()),
file=log)
print("\nConverting rmsd to B values and selecting rmsd < 1.5", file=log)
params.process_predicted_model.maximum_fraction_close = 0.5
params.process_predicted_model.b_value_field_is = 'rmsd'
params.process_predicted_model.remove_low_confidence_residues = True
params.process_predicted_model.maximum_rmsd = 1.5
params.process_predicted_model.split_model_by_compact_regions = False
params.process_predicted_model.input_lddt_is_fractional = None
model_info = process_predicted_model(test_model, params)
model = model_info.model
print("Residues before: %s After: %s " % (
test_model.get_hierarchy().overall_counts().n_residues,
model.get_hierarchy().overall_counts().n_residues,
),
file=log)
# Check splitting model into domains
print("\nSplitting model into domains", file=log)
model_info = split_model_into_compact_units(model,
maximum_fraction_close=0.5,
log=log)
chainid_list = model_info.chainid_list
print("Segments found: %s" % (" ".join(chainid_list)), file=log)
assert len(chainid_list) == 2
# Check processing and splitting model into domains
print("\nProcessing and splitting model into domains", file=log)
params.process_predicted_model.maximum_fraction_close = 0.5
params.process_predicted_model.b_value_field_is = 'lddt'
params.process_predicted_model.remove_low_confidence_residues = True
params.process_predicted_model.maximum_rmsd = 1.5
params.process_predicted_model.split_model_by_compact_regions = True
params.process_predicted_model.maximum_domains = 3
model_info = process_predicted_model(m, params, log=log)
chainid_list = model_info.chainid_list
print("Segments found: %s" % (" ".join(chainid_list)), file=log)
assert len(chainid_list) == 2
mmm = model_info.model.as_map_model_manager()
mmm.write_model('model_with_groupings.pdb')
residue_count = []
expected_residue_count = [84, 88]
for chainid in chainid_list:
selection_string = "chain %s" % (chainid)
ph = model_info.model.get_hierarchy()
asc1 = ph.atom_selection_cache()
sel = asc1.selection(selection_string)
m1 = model_info.model.select(sel)
n = m1.get_hierarchy().overall_counts().n_residues
print("Residues in %s: %s" % (selection_string, n), file=log)
residue_count.append(n)
assert expected_residue_count == residue_count
# Now process and use pae model and pae model file
print("\nProcessing and splitting model into domains with pae", file=log)
params.process_predicted_model.maximum_fraction_close = 0.5
params.process_predicted_model.b_value_field_is = 'lddt'
params.process_predicted_model.remove_low_confidence_residues = True
params.process_predicted_model.maximum_rmsd = 0.7
params.process_predicted_model.split_model_by_compact_regions = True
params.process_predicted_model.maximum_domains = 3
params.process_predicted_model.pae_power = 2
model_info = process_predicted_model(pae_m,
params,
pae_matrix=pae_matrix,
log=log)
print("libtbx.python process_predicted_model.py input.pdb output.pdb")
else:
input_file_name = args[0]
output_file_name = args[1]
if len(args) > 2:
p.b_value_field_is = args[2]
else:
p.b_value_field_is = 'lddt'
if len(args) > 3:
p.domain_size = float(args[3])
else:
p.domain_size = 15
from iotbx.data_manager import DataManager
dm = DataManager()
dm.set_overwrite(True)
m = dm.get_model(input_file_name)
p.remove_low_confidence_residues = True
p.maximum_rmsd = 1.5
p.split_model_by_compact_regions = True
print("\nProcessing and splitting model into domains")
model_info = process_predicted_model(m, params)
chainid_list = model_info.chainid_list
print("Segments found: %s" % (" ".join(chainid_list)))
mmm = model_info.model.as_map_model_manager()
mmm.write_model(output_file_name)
for chainid in chainid_list:
selection_string = "chain %s" % (chainid)
def test_01():
# Source data
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data', 'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data', 'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
# Read in data
dm = DataManager(['ncs_spec', 'model', 'real_map', 'phil'])
dm.set_overwrite(True)
map_file = data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file = data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file = data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
mmm = map_model_manager(model=model,
map_manager_1=mm.deep_copy(),
map_manager_2=mm.deep_copy(),
ncs_object=ncs,
wrapping=False)
mmm.add_map_manager_by_id(map_id='external_map',
map_manager=mmm.map_manager().deep_copy())
mmm.set_resolution(3)
mmm.set_log(sys.stdout)
dc = mmm.deep_copy()
# Model sharpening
mmm = dc.deep_copy()
tls_info = mmm.tls_from_map(
n_bins=10,
model_id='model',
map_id='map_manager',
iterations=1,
)
tlso = tls_info.tlso_list[0]
print("t:", tlso.t)
print("l:", tlso.l)
print("s:", tlso.s)
print("origin:", tlso.origin)
assert approx_equal(
tlso.t,
(1.1665511122614693, 1.2026392186971397, 1.1654187623738737,
-0.08474662045683597, -0.02260930304525043, 0.06492095346560478))
assert approx_equal(tlso.l,
(-0.002162154945537812, -0.0023776908642138776,
0.0009748174775374614, -5.9732257180723945e-05,
-0.0001342760165428358, -9.055411066345411e-05))
assert approx_equal(
tlso.s,
(3.409944886438518e-08, 6.0542707156228405e-09, -8.938076172958137e-09,
4.8771411705994806e-09, -2.6247834187732072e-08,
4.605012474599143e-09, 6.090471572948155e-10, 2.1790753409285795e-09,
-7.851614684653208e-09))
assert approx_equal(
tlso.origin,
(-64.70331931297399, -62.30573551948903, -63.743687240164604))
print("TLS: ", tlso.t, tlso.l, tlso.s, tlso.origin)
ncs_groups = ncs_obj.ncs_groups()
assert (len(ncs_groups) == 1)
ncs_group = ncs_groups[0]
# or if reading from file...
# ncs_object = ncs()
# ncs_object.read_ncs("../MapSymmetry_4/symmetry_from_map.ncs_spec")
# this already exists
print(ncs_group.format_for_biomt())
# Build cif model from ncs_obj
from iotbx import cif
model = dm.get_model()
h = model.get_hierarchy()
chains = [c.id for c in h.chains()]
n_oper = ncs_group.n_ncs_oper()
# start cif building
builder = cif.builders.cif_model_builder()
builder.add_data_block("assembly_information")
# add pdbx_struct_assembly loop
headers = [
'_pdbx_struct_assembly.id', '_pdbx_struct_assembly.details',
'_pdbx_struct_assembly.method_details',
'_pdbx_struct_assembly.oligomeric_details',
'_pdbx_struct_assembly.oligomeric_count'
def test_01():
# Source data
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data', 'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data', 'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
# DataManager
dm = DataManager(['ncs_spec', 'model', 'real_map', 'phil'])
dm.set_overwrite(True)
# Read in map and model and ncs
map_file = data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file = data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file = data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
ncs_dc = ncs.deep_copy()
mmmn = match_map_model_ncs()
mmmn.add_map_manager(mm)
mmmn.add_model(model)
mmmn.add_ncs_object(ncs)
# Save it
mmmn_dc = mmmn.deep_copy()
# Make sure we can add an ncs object that is either shifted or not
mmmn_dcdc = mmmn.deep_copy()
new_mmmn = match_map_model_ncs()
new_mmmn.add_map_manager(mmmn_dcdc.map_manager())
new_mmmn.add_model(mmmn_dcdc.model())
new_mmmn.add_ncs_object(mmmn_dcdc.ncs_object())
assert new_mmmn.ncs_object().shift_cart() == new_mmmn.map_manager(
).shift_cart()
mmmn_dcdc = mmmn.deep_copy()
new_mmmn = match_map_model_ncs()
new_mmmn.add_map_manager(mmmn_dcdc.map_manager())
new_mmmn.add_model(mmmn_dcdc.model())
new_mmmn.add_ncs_object(ncs_dc)
assert new_mmmn.ncs_object().shift_cart() == new_mmmn.map_manager(
).shift_cart()
original_ncs = mmmn.ncs_object()
assert approx_equal(
(24.0528, 11.5833, 20.0004),
tuple(original_ncs.ncs_groups()[0].translations_orth()[-1]),
eps=0.1)
assert tuple(mmmn._map_manager.origin_shift_grid_units) == (0, 0, 0)
# Shift origin to (0,0,0)
mmmn = mmmn_dc.deep_copy() # fresh version of match_map_model_ncs
mmmn.shift_origin()
new_ncs = mmmn.ncs_object()
assert tuple(mmmn._map_manager.origin_shift_grid_units) == (100, 100, 100)
mmmn.write_model('s.pdb')
mmmn.write_map('s.mrc')
shifted_ncs = mmmn.ncs_object()
assert approx_equal(
(-153.758, -74.044, -127.487),
tuple(shifted_ncs.ncs_groups()[0].translations_orth()[-1]),
eps=0.1)
# Shift a model and shift it back
mmmn = mmmn_dc.deep_copy() # fresh version of match_map_model_ncs
model = mmmn.model()
shifted_model = mmmn.shift_model_to_match_working_map(model=model)
model_in_original_position = mmmn.shift_model_to_match_original_map(
model=shifted_model)
assert (approx_equal(
model.get_sites_cart(), # not a copy
shifted_model.get_sites_cart()))
assert approx_equal(model.get_sites_cart(),
model_in_original_position.get_sites_cart())
# test data_manager map_model_manager
generated_mmm = dm.get_map_model_manager()
print(generated_mmm)
assert (isinstance(generated_mmm, map_model_manager))
# Generate a map and model
import sys
mmm = map_model_manager(log=sys.stdout)
mmm.generate_map()
model = mmm.model()
mm = mmm.map_manager()
assert approx_equal(model.get_sites_cart()[0], (14.476, 10.57, 8.34),
eps=0.01)
assert approx_equal(mm.map_data()[10, 10, 10], -0.0506, eps=0.001)
# Save it
mmm_dc = mmm.deep_copy()
# Create model from sites
mmm_sites = mmm_dc.deep_copy()
from scitbx.array_family import flex
sites_cart = flex.vec3_double()
sites_cart.append((3, 4, 5))
mmm_sites.model_from_sites_cart(sites_cart=sites_cart,
model_id='new_model')
assert mmm_sites.get_model_by_id('new_model').get_sites_cart()[0] == (3, 4,
5)
# Set crystal_symmetry and unit_cell_crystal_symmetry and shift_cart
# Box and shift the map_model_manager so we have new coordinate system
mmm_sites.box_all_maps_around_model_and_shift_origin()
new_model = mmm_sites.get_model_by_id('new_model')
assert approx_equal(
(3.747033333333334, 4.723075000000001, 5.0),
mmm_sites.get_model_by_id('new_model').get_sites_cart()[0])
# arbitrarily set unit_cell crystal symmetry of model to
# match crystal_symmetry. First have to set shift_cart to None
new_model.set_shift_cart(shift_cart=None)
new_model.set_unit_cell_crystal_symmetry_and_shift_cart()
assert new_model.crystal_symmetry() != mmm_sites.crystal_symmetry()
# now set crystal symmetries and shift cart of model to match the manager
mmm_sites.set_model_symmetries_and_shift_cart_to_match_map(new_model)
assert new_model.crystal_symmetry().is_similar_symmetry(
mmm_sites.crystal_symmetry())
assert new_model.unit_cell_crystal_symmetry().is_similar_symmetry(
mmm_sites.unit_cell_crystal_symmetry())
assert new_model.shift_cart() == mmm_sites.shift_cart()
# Import hierarchy into a model and set symmetries and shift to match
mmm_sites.model_from_hierarchy(hierarchy=mmm_sites.model().get_hierarchy(),
model_id='model_from_hierarchy')
assert mmm_sites.get_model_by_id('model_from_hierarchy').model_as_pdb() \
== mmm_sites.get_model_by_id('model').model_as_pdb()
# Check on wrapping
assert not mm.wrapping(
) # this one should not wrap because it is zero at edges
# Make a new one with no buffer so it is not zero at edges
mmm = map_model_manager()
mmm.generate_map(box_cushion=0)
mm = mmm.map_manager()
# check its compatibility with wrapping
assert mm.is_consistent_with_wrapping()
mmm.show_summary()
# now box it
sel = mmm.model().selection("resseq 221:221")
new_model = mmm.model().deep_copy().select(sel)
new_mmm = map_model_manager(model=new_model, map_manager=mm.deep_copy())
new_mmm.box_all_maps_around_model_and_shift_origin()
new_mm = new_mmm.map_manager()
assert not new_mm.wrapping()
assert not new_mm.is_consistent_with_wrapping()
# now box it with selection
new_mmm_1 = map_model_manager(model=mmm.model().deep_copy(),
map_manager=mm.deep_copy())
new_mmm_1.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1 = new_mmm_1.map_manager()
assert not new_mm_1.wrapping()
assert not new_mm_1.is_consistent_with_wrapping()
assert new_mm_1.map_data().all() == new_mm.map_data().all()
# create map_model_manager with just half-maps
mm1 = mm.deep_copy()
mm2 = mm.deep_copy()
map_data = mm2.map_data()
map_data += 1.
new_mmm = map_model_manager(model=mmm.model().deep_copy(),
map_manager_1=mm1,
map_manager_2=mm2)
assert new_mmm._map_dict.get(
'map_manager') is None # should not be any yet
assert approx_equal(new_mmm.map_manager().map_data()[232],
mm.deep_copy().map_data()[232] + 0.5)
assert new_mmm._map_dict.get(
'map_manager') is not None # now should be there
# generate map data from a model
mm1 = mm.deep_copy()
mm2 = mm.deep_copy()
new_mmm = map_model_manager(model=mmm.model().deep_copy(), map_manager=mm1)
mmm.generate_map(model=mmm.model())
mm = mmm.map_manager()
mmm.show_summary()
# check get_map_model_manager function
dm = DataManager(['model'])
assert not hasattr(dm, 'get_map_model_manager')
dm = DataManager(['real_map'])
assert not hasattr(dm, 'get_map_model_manager')
dm = DataManager(['sequence'])
assert not hasattr(dm, 'get_map_model_manager')
dm = DataManager(['model', 'real_map'])
assert hasattr(dm, 'get_map_model_manager')
# usage
dm.get_map_model_manager(model_file=data_pdb, map_files=data_ccp4)
dm.get_map_model_manager(model_file=data_pdb, map_files=[data_ccp4])
dm.get_map_model_manager(model_file=data_pdb,
map_files=[data_ccp4, data_ccp4, data_ccp4])
dm.get_map_model_manager(model_file=data_pdb,
map_files=data_ccp4,
ignore_symmetry_conflicts=True)
# errors
try:
dm.get_map_model_manager(model_file=data_pdb,
map_files=data_ccp4,
from_phil=True)
except Sorry as e:
assert 'from_phil is set to True' in str(e)
try:
dm.get_map_model_manager(model_file=data_pdb,
map_files=data_ccp4,
abc=123)
except TypeError as e:
assert 'unexpected keyword argument' in str(e)
try:
dm.get_map_model_manager(model_file=data_pdb,
map_files=[data_ccp4, data_ccp4])
except Sorry as e:
assert '1 full map and 2 half maps' in str(e)
# PHIL
class test_program(ProgramTemplate):
master_phil_str = '''
include scope iotbx.map_model_manager.map_model_phil_str
'''
working_phil_str = '''
map_model {
full_map = %s
half_map = %s
half_map = s.mrc
model = %s
}
''' % (data_ccp4, data_ccp4, data_pdb)
master_phil = parse(test_program.master_phil_str, process_includes=True)
working_phil = master_phil.fetch(parse(working_phil_str))
tp = test_program(dm, working_phil.extract())
try:
dm.get_map_model_manager(from_phil=True)
except Exception as e:
assert 'ignore_symmetry_conflicts' in str(e)
try:
dm.get_map_model_manager(from_phil=True,
ignore_symmetry_conflicts=True)
except AssertionError:
pass
def test_model_datatype():
import mmtbx.monomer_library.server
try:
mon_lib_srv = mmtbx.monomer_library.server.server()
except mmtbx.monomer_library.server.MonomerLibraryServerError:
print(
"Can not initialize monomer_library, skipping test_model_datatype."
)
return
# 1yjp
model_str = '''
CRYST1 21.937 4.866 23.477 90.00 107.08 90.00 P 1 21 1 2
ORIGX1 1.000000 0.000000 0.000000 0.00000
ORIGX2 0.000000 1.000000 0.000000 0.00000
ORIGX3 0.000000 0.000000 1.000000 0.00000
SCALE1 0.045585 0.000000 0.014006 0.00000
SCALE2 0.000000 0.205508 0.000000 0.00000
SCALE3 0.000000 0.000000 0.044560 0.00000
ATOM 1 N GLY A 1 -9.009 4.612 6.102 1.00 16.77 N
ATOM 2 CA GLY A 1 -9.052 4.207 4.651 1.00 16.57 C
ATOM 3 C GLY A 1 -8.015 3.140 4.419 1.00 16.16 C
ATOM 4 O GLY A 1 -7.523 2.521 5.381 1.00 16.78 O
ATOM 5 N ASN A 2 -7.656 2.923 3.155 1.00 15.02 N
ATOM 6 CA ASN A 2 -6.522 2.038 2.831 1.00 14.10 C
ATOM 7 C ASN A 2 -5.241 2.537 3.427 1.00 13.13 C
ATOM 8 O ASN A 2 -4.978 3.742 3.426 1.00 11.91 O
ATOM 9 CB ASN A 2 -6.346 1.881 1.341 1.00 15.38 C
ATOM 10 CG ASN A 2 -7.584 1.342 0.692 1.00 14.08 C
ATOM 11 OD1 ASN A 2 -8.025 0.227 1.016 1.00 17.46 O
ATOM 12 ND2 ASN A 2 -8.204 2.155 -0.169 1.00 11.72 N
ATOM 13 N ASN A 3 -4.438 1.590 3.905 1.00 12.26 N
ATOM 14 CA ASN A 3 -3.193 1.904 4.589 1.00 11.74 C
ATOM 15 C ASN A 3 -1.955 1.332 3.895 1.00 11.10 C
ATOM 16 O ASN A 3 -1.872 0.119 3.648 1.00 10.42 O
ATOM 17 CB ASN A 3 -3.259 1.378 6.042 1.00 12.15 C
ATOM 18 CG ASN A 3 -2.006 1.739 6.861 1.00 12.82 C
ATOM 19 OD1 ASN A 3 -1.702 2.925 7.072 1.00 15.05 O
ATOM 20 ND2 ASN A 3 -1.271 0.715 7.306 1.00 13.48 N
ATOM 21 N GLN A 4 -1.005 2.228 3.598 1.00 10.29 N
ATOM 22 CA GLN A 4 0.384 1.888 3.199 1.00 10.53 C
ATOM 23 C GLN A 4 1.435 2.606 4.088 1.00 10.24 C
ATOM 24 O GLN A 4 1.547 3.843 4.115 1.00 8.86 O
ATOM 25 CB GLN A 4 0.656 2.148 1.711 1.00 9.80 C
ATOM 26 CG GLN A 4 1.944 1.458 1.213 1.00 10.25 C
ATOM 27 CD GLN A 4 2.504 2.044 -0.089 1.00 12.43 C
ATOM 28 OE1 GLN A 4 2.744 3.268 -0.190 1.00 14.62 O
ATOM 29 NE2 GLN A 4 2.750 1.161 -1.091 1.00 9.05 N
ATOM 30 N GLN A 5 2.154 1.821 4.871 1.00 10.38 N
ATOM 31 CA GLN A 5 3.270 2.361 5.640 1.00 11.39 C
ATOM 32 C GLN A 5 4.594 1.768 5.172 1.00 11.52 C
ATOM 33 O GLN A 5 4.768 0.546 5.054 1.00 12.05 O
ATOM 34 CB GLN A 5 3.056 2.183 7.147 1.00 11.96 C
ATOM 35 CG GLN A 5 1.829 2.950 7.647 1.00 10.81 C
ATOM 36 CD GLN A 5 1.344 2.414 8.954 1.00 13.10 C
ATOM 37 OE1 GLN A 5 0.774 1.325 9.002 1.00 10.65 O
ATOM 38 NE2 GLN A 5 1.549 3.187 10.039 1.00 12.30 N
ATOM 39 N ASN A 6 5.514 2.664 4.856 1.00 11.99 N
ATOM 40 CA ASN A 6 6.831 2.310 4.318 1.00 12.30 C
ATOM 41 C ASN A 6 7.854 2.761 5.324 1.00 13.40 C
ATOM 42 O ASN A 6 8.219 3.943 5.374 1.00 13.92 O
ATOM 43 CB ASN A 6 7.065 3.016 2.993 1.00 12.13 C
ATOM 44 CG ASN A 6 5.961 2.735 2.003 1.00 12.77 C
ATOM 45 OD1 ASN A 6 5.798 1.604 1.551 1.00 14.27 O
ATOM 46 ND2 ASN A 6 5.195 3.747 1.679 1.00 10.07 N
ATOM 47 N TYR A 7 8.292 1.817 6.147 1.00 14.70 N
ATOM 48 CA TYR A 7 9.159 2.144 7.299 1.00 15.18 C
ATOM 49 C TYR A 7 10.603 2.331 6.885 1.00 15.91 C
ATOM 50 O TYR A 7 11.041 1.811 5.855 1.00 15.76 O
ATOM 51 CB TYR A 7 9.061 1.065 8.369 1.00 15.35 C
ATOM 52 CG TYR A 7 7.665 0.929 8.902 1.00 14.45 C
ATOM 53 CD1 TYR A 7 6.771 0.021 8.327 1.00 15.68 C
ATOM 54 CD2 TYR A 7 7.210 1.756 9.920 1.00 14.80 C
ATOM 55 CE1 TYR A 7 5.480 -0.094 8.796 1.00 13.46 C
ATOM 56 CE2 TYR A 7 5.904 1.649 10.416 1.00 14.33 C
ATOM 57 CZ TYR A 7 5.047 0.729 9.831 1.00 15.09 C
ATOM 58 OH TYR A 7 3.766 0.589 10.291 1.00 14.39 O
ATOM 59 OXT TYR A 7 11.358 2.999 7.612 1.00 17.49 O
TER 60 TYR A 7
HETATM 61 O HOH A 8 -6.471 5.227 7.124 1.00 22.62 O
HETATM 62 O HOH A 9 10.431 1.858 3.216 1.00 19.71 O
HETATM 63 O HOH A 10 -11.286 1.756 -1.468 1.00 17.08 O
HETATM 64 O HOH A 11 11.808 4.179 9.970 1.00 23.99 O
HETATM 65 O HOH A 12 13.605 1.327 9.198 1.00 26.17 O
HETATM 66 O HOH A 13 -2.749 3.429 10.024 1.00 39.15 O
HETATM 67 O HOH A 14 -1.500 0.682 10.967 1.00 43.49 O
MASTER 238 0 0 0 0 0 0 6 66 1 0 1
END
'''
# test reading/writing PDB
test_filename = 'test_model.pdb'
test_output_filename = 'test_model_output.pdb'
test_eff = 'model.eff'
dm = DataManager(['model'])
dm.process_model_str(test_filename, model_str)
dm.write_model_file(model_str,
filename=test_output_filename,
overwrite=True)
m = dm.get_model(test_output_filename)
assert test_output_filename in dm.get_model_names()
dm.write_model_file(m, overwrite=True)
pdb_filename = 'cctbx_program.pdb'
assert os.path.exists(pdb_filename)
dm.process_model_file(pdb_filename)
assert not dm.get_model(pdb_filename).input_model_format_cif()
dm.write_model_file(m, test_filename, overwrite=True)
# test reading PDB writing CIF
test_filename = 'test_model.pdb'
test_output_filename = 'test_model.cif'
dm = DataManager(['model'])
dm.process_model_str(test_filename, model_str)
m = dm.get_model(test_filename)
dm.write_model_file(m,
filename=test_output_filename,
format='cif',
overwrite=True)
m = dm.get_model(test_output_filename)
assert test_output_filename in dm.get_model_names()
dm.write_model_file(m, overwrite=True)
cif_filename = 'cctbx_program.cif'
assert os.path.exists(cif_filename)
dm.process_model_file(cif_filename)
assert dm.get_model(cif_filename).input_model_format_cif()
# test type
assert dm.get_model_type() == 'x_ray'
dm.set_model_type(test_filename, 'neutron')
assert dm.get_model_type() == 'neutron'
phil_scope = dm.export_phil_scope()
extract = phil_scope.extract()
assert extract.data_manager.model[0].type == 'neutron'
with open(test_eff, 'w') as f:
f.write(phil_scope.as_str())
new_phil_scope = iotbx.phil.parse(file_name=test_eff)
new_dm = DataManager(['model'])
new_dm.load_phil_scope(new_phil_scope)
assert new_dm.get_model_type(test_filename) == 'neutron'
new_dm = DataManager(['model'])
try:
new_dm.set_default_model_type('nonsense')
except Sorry:
pass
new_dm.set_default_model_type('electron')
new_dm.process_model_file(test_filename)
assert new_dm.get_model_type() == 'electron'
assert len(new_dm.get_model_names()) == 1
assert len(new_dm.get_model_names(model_type='electron')) == 1
assert len(new_dm.get_model_names(model_type='neutron')) == 0
os.remove(test_eff)
os.remove(test_filename)
# test reading/writing CIF
test_filename = 'test_model_datatype.cif'
dm.write_model_file(dm.get_model().model_as_mmcif(),
filename=test_filename,
overwrite=True)
dm.process_model_file(test_filename)
os.remove(test_filename)
assert test_filename in dm.get_model_names()
m = dm.get_model(test_filename)
dm.write_model_file(m, overwrite=True)
cif_filename = 'cctbx_program.cif'
assert os.path.exists(cif_filename)
dm.process_model_file(cif_filename)
assert dm.get_model(cif_filename).input_model_format_cif()
os.remove(pdb_filename)
os.remove(cif_filename)
# test pdb_interpretation
extract = mmtbx.model.manager.get_default_pdb_interpretation_params()
extract.pdb_interpretation.use_neutron_distances = True
dm.update_pdb_interpretation_for_model(test_filename, extract)
assert dm.get_model(test_filename).restraints_manager is None
def test_01():
# Source data
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data',
'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data',
'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
# Read in data
dm = DataManager(['ncs_spec','model', 'real_map', 'phil'])
dm.set_overwrite(True)
map_file=data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file=data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file=data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
mmm=map_model_manager(
model = model,
map_manager_1 = mm.deep_copy(),
map_manager_2 = mm.deep_copy(),
ncs_object = ncs,
wrapping = False)
mmm.add_map_manager_by_id(
map_id='external_map',map_manager=mmm.map_manager().deep_copy())
mmm.set_resolution(3)
mmm.set_log(sys.stdout)
dc = mmm.deep_copy()
# Model sharpening
mmm = dc.deep_copy()
tls_info=mmm.tls_from_map(n_bins=10,
model_id = 'model',
map_id = 'map_manager',
iterations = 1,
)
tlso = tls_info.tlso_list[0]
print ("t:", tlso.t)
print ("l:", tlso.l)
print ("s:", tlso.s)
print ("origin:", tlso.origin)
assert approx_equal(tlso.t,
(0.7920199173476214, 0.742281514408794, 0.7103008342583756, -0.05199687072329786, 0.04301326317889638, -0.0032498605215769945))
assert approx_equal(tlso.l,
(-0.0004103603606922303, -0.00042929108338180655, 0.0001519656028327732, -2.8489076942333132e-06, -6.23198622708519e-05, 3.694504506269563e-05))
assert approx_equal(tlso.s,
(5.562000065270741e-09, -5.108813278707348e-10, -4.132731326301999e-09, 7.925572910527853e-10, -2.7018794222798323e-09, 1.0742616538181975e-09, 7.501166703517675e-10, -1.0661760561475498e-09, -2.860120638319051e-09))
assert approx_equal(tlso.origin,
(-64.703319312974, -62.30575036040377, -63.74368724016462))
print("TLS: ",tlso.t,tlso.l,tlso.s,tlso.origin)
def test_data_manager():
a = DataManager(['model'])
a.add_model('a', 'b')
a.add_model('c', 'd')
assert a.get_model() == 'b'
assert a.get_model('a') == 'b'
assert a.get_model('c') == 'd'
assert a.get_model_names() == ['a', 'c']
assert a.has_models()
assert a.has_models(exact_count=True, expected_n=2)
assert not a.has_models(expected_n=3, raise_sorry=False)
# exporting phil
working_phil = a.export_phil_scope()
assert len(working_phil.extract().data_manager.model) == 2
# data tracking
try:
a.has_models(expected_n=3, raise_sorry=True)
except Sorry:
pass
try:
a.has_models(exact_count=True, raise_sorry=True)
except Sorry:
pass
a.set_default_model('c')
assert a.get_model() == 'd'
assert a.get_model_names() == ['a', 'c'
] or a.get_model_names() == ['c', 'a']
a.remove_model('c')
try:
a.get_model()
except Sorry:
pass
try:
a.get_model('missing')
except Sorry:
pass
try:
a.set_default_model('missing')
except Sorry:
pass
a = DataManager(datatypes=['sequence', 'phil'])
assert a.get_sequence_names() == []
assert not hasattr(a, 'get_model')
# phil functions
test_phil_str = '''
data_manager {
phil_files = data_manager_test.eff
}
'''
with open('data_manager_test.eff', 'w') as f:
f.write(test_phil_str)
# loading file with get function
assert len(a.get_phil_names()) == 0
p = a.get_phil('data_manager_test.eff')
assert type(p) == libtbx.phil.scope
assert 'data_manager_test.eff' in a.get_phil_names()
# loading file with phil
a = DataManager(datatypes=['phil'])
test_phil = iotbx.phil.parse(test_phil_str)
a.load_phil_scope(test_phil)
assert 'data_manager_test.eff' in a.get_phil_names()
assert a.get_default_phil_name() == 'data_manager_test.eff'
os.remove('data_manager_test.eff')
# writing
a = DataManager(datatypes=['model', 'phil', 'sequence'])
a.add_model('a', 'b')
a.add_phil('c', 'd')
a.add_sequence('e', 'f')
a.write_model_file(a.get_model(), filename='a.dat', overwrite=True)
a.write_phil_file(a.get_phil(), filename='c.dat', overwrite=True)
a.write_sequence_file(a.get_sequence(), filename='e.dat', overwrite=True)
with open('a.dat', 'r') as f:
lines = f.readlines()
assert lines[0] == 'b'
os.remove('a.dat')
os.remove('c.dat')
os.remove('e.dat')
def test_01():
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data', 'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data', 'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
dm = DataManager(['ncs_spec', 'model', 'real_map', 'phil'])
dm.set_overwrite(True)
# Read in map and model
map_file = data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file = data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file = data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
mmm = map_model_manager()
mmm.add_map_manager(mm)
mmm.add_model(model)
mmm.add_ncs_object(ncs)
original_ncs = mmm.ncs_object()
assert approx_equal(
(24.0528, 11.5833, 20.0004),
tuple(original_ncs.ncs_groups()[0].translations_orth()[-1]),
eps=0.1)
assert tuple(mmm._map_manager.origin_shift_grid_units) == (0, 0, 0)
# Shift origin to (0,0,0)
mmm.shift_origin()
assert tuple(mmm._map_manager.origin_shift_grid_units) == (100, 100, 100)
mmm.write_model('s.pdb')
mmm.write_map('s.mrc')
shifted_ncs = mmm.ncs_object()
assert approx_equal(
(-153.758, -74.044, -127.487),
tuple(shifted_ncs.ncs_groups()[0].translations_orth()[-1]),
eps=0.1)
# Shift a model and shift it back
model = mmm.model()
shifted_model = mmm.shift_model_to_match_working_map(model=model)
model_in_original_position = mmm.shift_model_to_match_original_map(
model=shifted_model)
assert (not approx_equal(
model.get_sites_cart(), shifted_model.get_sites_cart(), out=None))
assert approx_equal(model.get_sites_cart(),
model_in_original_position.get_sites_cart())
# Generate a map and model
mmm.generate_map()
model = mmm.model()
mm = mmm.map_manager()
assert approx_equal(model.get_sites_cart()[0], (14.476, 10.57, 8.34),
eps=0.01)
assert approx_equal(mm.map_data()[10, 10, 10], -0.0195, eps=0.001)
def Test(inFileName = None):
#========================================================================
# Make sure we can fill in mmtbx.probe.ExtraAtomInfoList info.
# Generate an example data model with a small molecule in it unless we
# were given a file name on the command line.
if inFileName is not None and len(inFileName) > 0:
# Read a model from a file using the DataManager
dm = DataManager()
dm.process_model_file(inFileName)
model = dm.get_model(inFileName)
else:
# Generate a small-molecule model using the map model manager
mmm=map_model_manager() # get an initialized instance of the map_model_manager
mmm.generate_map() # get a model from a generated small library model and calculate a map for it
model = mmm.model() # get the model
# Fill in an ExtraAtomInfoList with an entry for each atom in the hierarchy.
# We first find the largest i_seq sequence number in the model and reserve that
# many entries so we will always be able to fill in the entry for an atom.
atoms = model.get_atoms()
maxI = atoms[0].i_seq
for a in atoms:
if a.i_seq > maxI:
maxI = a.i_seq
extra = []
for i in range(maxI+1):
extra.append(probe.ExtraAtomInfo())
# Traverse the hierarchy and look up the extra data to be filled in.
# Get a list of all the atoms in the chain while we're at it
at = AtomTypes()
ph = model.get_hierarchy()
for m in ph.models():
for chain in m.chains():
for rg in chain.residue_groups():
for ag in rg.atom_groups():
for a in ag.atoms():
ei, warn = at.FindProbeExtraAtomInfo(a)
extra[a.i_seq] = ei
# User code should test for and print warnings
#if len(warn) > 0:
# print(warn)
#========================================================================
# Find an Oxygen atom and ask for its radii with explicit Hydrogen, implicit Hydrogen,
# and Nuclear radii.
o = None
ph = model.get_hierarchy()
for a in ph.models()[0].atoms():
if a.element.strip() == 'O':
o = a
assert o is not None, "AtomTypes.Test(): Could not find Oxygen (internal test failure)"
explicitH = AtomTypes(useNeutronDistances = False,
useImplicitHydrogenDistances = False).FindProbeExtraAtomInfo(o)[0].vdwRadius
implicitH = AtomTypes(useNeutronDistances = False,
useImplicitHydrogenDistances = True).FindProbeExtraAtomInfo(o)[0].vdwRadius
neutronH = AtomTypes(useNeutronDistances = True,
useImplicitHydrogenDistances = False).FindProbeExtraAtomInfo(o)[0].vdwRadius
assert explicitH != implicitH, "AtomTypes.Test(): Implicit and explicit Oxygen radii did not differ as expected"
#========================================================================
# Check MaximumVDWRadius, calling it twice to make sure both the cached and non-cached
# results work.
for i in range(2):
assert at.MaximumVDWRadius() == 2.5, "AtomTypes.Test(): Unexpected MaximumVDWRadius(): got "+str(MaximumVDWRadius())+", expected 2.5"
#========================================================================
# Check IsAromatic() to ensure it gives results when expected and not when not.
aromaticChecks = [
['PHE', 'CE2', True],
[' U', 'HN3', True],
['ASN', 'O', False]
]
for a in aromaticChecks:
assert IsAromatic(a[0],a[1]) == a[2], "AtomTypes.Test(): {} {} not marked as aromatic {}".format(a[0],a[1],a[2])
def test_01():
# Source data
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data', 'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data', 'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
# Read in data
dm = DataManager(['ncs_spec', 'model', 'real_map', 'phil'])
dm.set_overwrite(True)
map_file = data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file = data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file = data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
mmm = map_model_manager(model=model,
map_manager_1=mm.deep_copy(),
map_manager_2=mm.deep_copy(),
ncs_object=ncs,
wrapping=False)
mmm.add_map_manager_by_id(map_id='external_map',
map_manager=mmm.map_manager().deep_copy())
mmm.set_resolution(3)
mmm.set_log(sys.stdout)
dc = mmm.deep_copy()
# Model sharpening
mmm = dc.deep_copy()
tls_info = mmm.tls_from_map(
n_bins=10,
model_id='model',
map_id='map_manager',
iterations=1,
)
tlso = tls_info.tlso_list[0]
print("t:", tlso.t)
print("l:", tlso.l)
print("s:", tlso.s)
print("origin:", tlso.origin)
assert approx_equal(
tlso.t,
(0.6518723599417712, 0.6807846368236251, 0.6161941485135081,
-0.04791178588048965, -0.0014794039180157132, 0.032774655367019095))
assert approx_equal(tlso.l,
(-0.00020092054127279798, -9.557441568256003e-05,
-1.711699526358822e-05, -4.8893794663274e-05,
-2.026091444762368e-05, -2.194054393244713e-05))
assert approx_equal(
tlso.s,
(1.3393493443427932e-09, 3.660975429526433e-10, -6.07051859483934e-10,
4.1665859321329886e-10, -1.35856164931005e-09, 5.409502867516901e-10,
-5.4005830782848e-10, -1.2017586805521653e-09,
1.9212302485258283e-11))
assert approx_equal(
tlso.origin,
(-64.70331931297407, -62.305747062422725, -63.74368724016457))
print("TLS: ", tlso.t, tlso.l, tlso.s, tlso.origin)
def test_01():
# Source data
data_dir = os.path.dirname(os.path.abspath(__file__))
data_ccp4 = os.path.join(data_dir, 'data', 'non_zero_origin_map.ccp4')
data_pdb = os.path.join(data_dir, 'data', 'non_zero_origin_model.pdb')
data_ncs_spec = os.path.join(data_dir, 'data',
'non_zero_origin_ncs_spec.ncs_spec')
# Read in data
dm = DataManager(['ncs_spec', 'model', 'real_map', 'phil'])
dm.set_overwrite(True)
map_file = data_ccp4
dm.process_real_map_file(map_file)
mm = dm.get_real_map(map_file)
model_file = data_pdb
dm.process_model_file(model_file)
model = dm.get_model(model_file)
ncs_file = data_ncs_spec
dm.process_ncs_spec_file(ncs_file)
ncs = dm.get_ncs_spec(ncs_file)
mmm = map_model_manager(model=model,
map_manager_1=mm.deep_copy(),
map_manager_2=mm.deep_copy(),
ncs_object=ncs,
wrapping=False)
mmm.add_map_manager_by_id(map_id='external_map',
map_manager=mmm.map_manager().deep_copy())
mmm.set_resolution(3)
mmm.set_log(sys.stdout)
dc = mmm.deep_copy()
# Model sharpening
mmm = dc.deep_copy()
tls_info = mmm.tls_from_map(
n_bins=10,
model_id='model',
map_id='map_manager',
iterations=1,
)
tlso = tls_info.tlso_list[0]
print("t:", tlso.t)
print("l:", tlso.l)
print("s:", tlso.s)
print("origin:", tlso.origin)
assert approx_equal(
tlso.t,
(1.180418902258779, 1.1747521845606608, 1.178996799712174,
-0.08474662674769494, -0.022609295693646402, 0.0649209491344932))
assert approx_equal(
tlso.l,
(-0.002159404807991249, -0.002107964765763024, 0.0008301439376854558,
-5.973347993775719e-05, -0.000134276871934738, -9.05515898670584e-05))
assert approx_equal(
tlso.s,
(2.9348223335616302e-08, 5.52441087256425e-09, -5.382459681103171e-09,
4.3530347434547015e-09, -2.3559464233595e-08, 4.217968590464982e-09,
-4.380707049750269e-09, 1.9232725033868253e-09,
-5.788759082799497e-09))
assert approx_equal(
tlso.origin,
(-64.70331931297399, -62.30573551948903, -63.743687240164604))
print("TLS: ", tlso.t, tlso.l, tlso.s, tlso.origin)
def run_comparison(fileName1, fileName2, thresh, verbosity = 10):
# Read the the models from each file.
dm = DataManager()
dm.set_overwrite(True)
if verbosity >= 1:
print('Reading model from',fileName1)
dm.process_model_file(fileName1)
m1 = dm.get_model(fileName1)
if verbosity >= 1:
print('Reading model from',fileName2)
dm.process_model_file(fileName2)
m2 = dm.get_model(fileName2)
# Compare the two models and report on any differences.
# Differences in numbers of models, chains, or residues are fatal.
# Differences in atoms are what is reported.
models1 = m1.get_hierarchy().models()
models2 = m2.get_hierarchy().models()
if len(models1) != len(models2):
print('Error: Different number of models:',len(models1),'vs.',len(models2))
sys.exit(2)
for i in range(len(models1)):
if verbosity >= 2:
print('Model',models1[i].id)
chains1 = models1[i].chains()
chains2 = models2[i].chains()
if len(chains1) != len(chains2):
print('Error: Different number of chains:',len(chains1),'vs.',len(chains2),'in model',models1[i].id)
sys.exit(2)
for j in range(len(chains1)):
if verbosity >= 3:
print(' Chain',chains1[j].id)
r1 = chains1[j].residue_groups()
r2 = chains2[j].residue_groups()
if len(r1) != len(r2):
print('Error: Different number of residues:',len(r1),'vs.',len(r2),'in chain',chains1[j].id)
sys.exit(2)
for k in range(len(r1)):
if verbosity >= 4:
print(' Residue group',r1[k].resseq)
ag1 = r1[k].atom_groups()
ag2 = r2[k].atom_groups()
if len(ag1) != len(ag2):
print('Error: Different number of atom groups:',len(ag1),'vs.',len(ag2),'in residue group',r1[k].resseq)
sys.exit(2)
for m in range(len(ag1)):
if verbosity >= 5:
print(' Atom group',ag1[m].resname)
as1 = ag1[m].atoms()
as2 = ag2[m].atoms()
# Atom names are unique within residues, so we can use that to
# match atoms in one file from those in the other and print out
# missing atoms and differences in positions.
# We only check differences in positions for matches in the first
# list because they will be duplicated by a check for the second.
for a1 in as1:
match = ag2[m].get_atom(a1.name.strip())
if match is None:
print(ag1[m].resname+' '+chains1[j].id+r1[k].resseq,'Only in first:',a1.name.strip())
continue
dist = a1.distance(match)
if dist > thresh:
print(ag1[m].resname+' '+chains1[j].id+r1[k].resseq,'Distance between',a1.name.strip(),"{:.2f}".format(dist))
for a2 in as2:
match = ag1[m].get_atom(a2.name.strip())
if match is None:
print(ag1[m].resname+' '+chains1[j].id+r1[k].resseq,'Only in second:',a2.name.strip())
