def average_position(structure=None, models=None, sim=None):
"""Shift the given structural object to the average position.
@keyword structure: The structural object to operate on.
@type structure: lib.structure.internal.object.Internal instance
@keyword models: The list of models to shift.
@type models: list of int
@keyword sim: A flag which if True will use the Monte Carlo simulation results. In this case, the model list should be set to the simulation indices plus 1 and the structural object should have one model per simulation already set up.
@type sim: bool
"""
# The selection object.
selection = structure.selection(atom_id=domain_moving())
# Loop over each model.
for i in range(len(models)):
# First rotate the moving domain to the average position.
R = zeros((3, 3), float64)
if hasattr(cdp, 'ave_pos_alpha'):
if sim:
euler_to_R_zyz(cdp.ave_pos_alpha_sim[i],
cdp.ave_pos_beta_sim[i],
cdp.ave_pos_gamma_sim[i], R)
else:
euler_to_R_zyz(cdp.ave_pos_alpha, cdp.ave_pos_beta,
cdp.ave_pos_gamma, R)
else:
if sim:
euler_to_R_zyz(0.0, cdp.ave_pos_beta_sim[i],
cdp.ave_pos_gamma_sim[i], R)
else:
euler_to_R_zyz(0.0, cdp.ave_pos_beta, cdp.ave_pos_gamma, R)
origin = pipe_centre_of_mass(atom_id=domain_moving(),
verbosity=0,
missing_error=False)
structure.rotate(R=R,
origin=origin,
model=models[i],
selection=selection)
# Then translate the moving domain.
if sim:
T = [
cdp.ave_pos_x_sim[i], cdp.ave_pos_y_sim[i],
cdp.ave_pos_z_sim[i]
]
else:
T = [cdp.ave_pos_x, cdp.ave_pos_y, cdp.ave_pos_z]
structure.translate(T=T, model=models[i], selection=selection)
def base_data_loop(self):
"""Generator method for looping over the base data - RDCs and PCSs.
This loop yields the following:
- The RDC identification data for the interatomic data container and alignment.
- The PCS identification data for the spin data container and alignment.
@return: The base data type ('rdc' or 'pcs'), the spin or interatomic data container information (either one or two spin hashes), and the alignment ID string.
@rtype: list of str
"""
# Loop over the interatomic data containers for the moving domain (for the RDC data).
for interatom in interatomic_loop(selection1=domain_moving()):
# Skip deselected containers.
if not interatom.select:
continue
# No RDC, so skip.
if not hasattr(interatom, 'rdc'):
continue
# Loop over the alignment IDs.
for align_id in cdp.rdc_ids:
# Yield the info set.
if align_id in interatom.rdc and interatom.rdc[
align_id] != None:
yield [
'rdc', interatom._spin_hash1, interatom._spin_hash2,
align_id
]
# Loop over the spin containers for the moving domain (for the PCS data).
for spin, spin_id in spin_loop(selection=domain_moving(),
return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# No PCS, so skip.
if not hasattr(spin, 'pcs'):
continue
# Loop over the alignment IDs.
for align_id in cdp.pcs_ids:
# Yield the info set.
if align_id in spin.pcs and spin.pcs[align_id] != None:
yield ['pcs', spin_id, align_id]
def overfit_deselect(self, data_check=True, verbose=True):
"""Deselect spins which have insufficient data to support minimisation.
@keyword data_check: A flag to signal if the presence of base data is to be checked for.
@type data_check: bool
@keyword verbose: A flag which if True will allow printouts.
@type verbose: bool
"""
# Nothing to do.
if not data_check:
return
# Loop over spin data, checking for PCS data.
ids = []
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
if not hasattr(spin, 'pcs'):
spin.select = False
ids.append(spin_id)
if verbose and len(ids):
warn(RelaxWarning("No PCS data is present, deselecting the spins %s." % ids))
# Loop over the interatomic data containers, checking for RDC data.
ids = []
for interatom in interatomic_loop(selection1=domain_moving()):
if not hasattr(interatom, 'rdc'):
interatom.select = False
ids.append("%s - %s" % (interatom.spin_id1, interatom.spin_id2))
if verbose and len(ids):
warn(RelaxWarning("No RDC data is present, deselecting the interatomic data containers between spin pairs %s." % ids))
def base_data_loop(self):
"""Generator method for looping over the base data - RDCs and PCSs.
This loop yields the following:
- The RDC identification data for the interatomic data container and alignment.
- The PCS identification data for the spin data container and alignment.
@return: The base data type ('rdc' or 'pcs'), the spin or interatomic data container information (either one or two spin IDs), and the alignment ID string.
@rtype: list of str
"""
# Loop over the interatomic data containers for the moving domain (for the RDC data).
for interatom in interatomic_loop(selection1=domain_moving()):
# Skip deselected containers.
if not interatom.select:
continue
# No RDC, so skip.
if not hasattr(interatom, 'rdc'):
continue
# Loop over the alignment IDs.
for align_id in cdp.rdc_ids:
# Yield the info set.
if align_id in interatom.rdc and interatom.rdc[align_id] != None:
yield ['rdc', interatom.spin_id1, interatom.spin_id2, align_id]
# Loop over the spin containers for the moving domain (for the PCS data).
for spin, spin_id in spin_loop(selection=domain_moving(), return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# No PCS, so skip.
if not hasattr(spin, 'pcs'):
continue
# Loop over the alignment IDs.
for align_id in cdp.pcs_ids:
# Yield the info set.
if align_id in spin.pcs and spin.pcs[align_id] != None:
yield ['pcs', spin_id, align_id]
def average_position(structure=None, models=None, sim=None):
"""Shift the given structural object to the average position.
@keyword structure: The structural object to operate on.
@type structure: lib.structure.internal.object.Internal instance
@keyword models: The list of models to shift.
@type models: list of int
@keyword sim: A flag which if True will use the Monte Carlo simulation results. In this case, the model list should be set to the simulation indices plus 1 and the structural object should have one model per simulation already set up.
@type sim: bool
"""
# The selection object.
selection = structure.selection(atom_id=domain_moving())
# Loop over each model.
for i in range(len(models)):
# First rotate the moving domain to the average position.
R = zeros((3, 3), float64)
if hasattr(cdp, 'ave_pos_alpha'):
if sim:
euler_to_R_zyz(cdp.ave_pos_alpha_sim[i], cdp.ave_pos_beta_sim[i], cdp.ave_pos_gamma_sim[i], R)
else:
euler_to_R_zyz(cdp.ave_pos_alpha, cdp.ave_pos_beta, cdp.ave_pos_gamma, R)
else:
if sim:
euler_to_R_zyz(0.0, cdp.ave_pos_beta_sim[i], cdp.ave_pos_gamma_sim[i], R)
else:
euler_to_R_zyz(0.0, cdp.ave_pos_beta, cdp.ave_pos_gamma, R)
origin = pipe_centre_of_mass(atom_id=domain_moving(), verbosity=0, missing_error=False)
structure.rotate(R=R, origin=origin, model=models[i], selection=selection)
# Then translate the moving domain.
if sim:
T = [cdp.ave_pos_x_sim[i], cdp.ave_pos_y_sim[i], cdp.ave_pos_z_sim[i]]
else:
T = [cdp.ave_pos_x, cdp.ave_pos_y, cdp.ave_pos_z]
structure.translate(T=T, model=models[i], selection=selection)
def overfit_deselect(self, data_check=True, verbose=True):
"""Deselect spins which have insufficient data to support minimisation.
@keyword data_check: A flag to signal if the presence of base data is to be checked for.
@type data_check: bool
@keyword verbose: A flag which if True will allow printouts.
@type verbose: bool
"""
# Nothing to do.
if not data_check:
return
# Loop over spin data, checking for PCS data.
ids = []
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
if not hasattr(spin, 'pcs'):
spin.select = False
ids.append(spin_id)
if verbose and len(ids):
warn(
RelaxWarning(
"No PCS data is present, deselecting the spins %s." % ids))
# Loop over the interatomic data containers, checking for RDC data.
ids = []
for interatom in interatomic_loop(selection1=domain_moving()):
if not hasattr(interatom, 'rdc'):
interatom.select = False
ids.append("%s - %s" %
(interatom.spin_id1, interatom.spin_id2))
if verbose and len(ids):
warn(
RelaxWarning(
"No RDC data is present, deselecting the interatomic data containers between spin pairs %s."
% ids))
def distribute(file="distribution.pdb.bz2", dir=None, atom_id=None, total=1000, max_rotations=100000, model=1, force=True):
"""Create a uniform distribution of structures for the frame order motions.
@keyword file: The PDB file for storing the frame order motional distribution. The compression is determined automatically by the file extensions '*.pdb', '*.pdb.gz', and '*.pdb.bz2'.
@type file: str
@keyword dir: The directory name to place the file into.
@type dir: str or None
@keyword atom_id: The atom identification string to allow the distribution to be a subset of all atoms.
@type atom_id: None or str
@keyword total: The total number of states/model/structures in the distribution.
@type total: int
@keyword max_rotations: The maximum number of rotations to generate the distribution from. This prevents an execution for an infinite amount of time when a frame order amplitude parameter is close to zero so that the subset of all rotations within the distribution is close to zero.
@type max_rotations: int
@keyword model: Only one model from an analysed ensemble of structures can be used for the distribution, as the corresponding PDB file consists of one model per state.
@type model: int
@keyword force: A flag which, if set to True, will overwrite the any pre-existing file.
@type force: bool
"""
# Printout.
print("Uniform distribution of structures representing the frame order motions.")
# Check the total.
if total > 9999:
raise RelaxError("A maximum of 9999 models is allowed in the PDB format.")
# Checks.
check_pipe()
check_model()
check_domain()
check_parameters()
check_pivot()
# Skip the rigid model.
if cdp.model == MODEL_RIGID:
print("Skipping the rigid model.")
return
# Open the output file.
file = open_write_file(file_name=file, dir=dir, force=force)
# The parameter values.
values = assemble_param_vector()
params = {}
i = 0
for name in cdp.params:
params[name] = values[i]
i += 1
# The structure.
structure = deepcopy(cdp.structure)
if structure.num_models() > 1:
structure.collapse_ensemble(model_num=model)
# The pivot points.
num_states = 1
if cdp.model == MODEL_DOUBLE_ROTOR:
num_states = 2
pivot = zeros((num_states, 3), float64)
for i in range(num_states):
pivot[i] = generate_pivot(order=i+1, pdb_limit=True)
# Shift to the average position.
average_position(structure=structure, models=[None])
# The motional eigenframe.
frame = generate_axis_system()
# Only work with a subset.
if atom_id:
# The inverted selection.
selection = structure.selection(atom_id=atom_id, inv=True)
# Delete the data.
structure.delete(selection=selection, verbosity=0)
# Create the distribution.
uniform_distribution(file=file, model=cdp.model, structure=structure, parameters=params, eigenframe=frame, pivot=pivot, atom_id=domain_moving(), total=total, max_rotations=max_rotations)
# Close the file.
file.close()
def simulate(file="simulation.pdb.bz2", dir=None, step_size=2.0, snapshot=10, total=1000, model=1, force=True):
"""Pseudo-Brownian dynamics simulation of the frame order motions.
@keyword file: The PDB file for storing the frame order pseudo-Brownian dynamics simulation. The compression is determined automatically by the file extensions '*.pdb', '*.pdb.gz', and '*.pdb.bz2'.
@type file: str
@keyword dir: The directory name to place the file into.
@type dir: str or None
@keyword step_size: The rotation will be of a random direction but with this fixed angle. The value is in degrees.
@type step_size: float
@keyword snapshot: The number of steps in the simulation when snapshots will be taken.
@type snapshot: int
@keyword total: The total number of snapshots to take before stopping the simulation.
@type total: int
@keyword model: Only one model from an analysed ensemble of structures can be used for the pseudo-Brownian simulation, as the simulation and corresponding PDB file consists of one model per simulation.
@type model: int
@keyword force: A flag which, if set to True, will overwrite the any pre-existing file.
@type force: bool
"""
# Printout.
print("Pseudo-Brownian dynamics simulation of the frame order motions.")
# Checks.
check_pipe()
check_model()
check_domain()
check_parameters()
check_pivot()
# Skip the rigid model.
if cdp.model == MODEL_RIGID:
print("Skipping the rigid model.")
return
# Open the output file.
file = open_write_file(file_name=file, dir=dir, force=force)
# The parameter values.
values = assemble_param_vector()
params = {}
i = 0
for name in cdp.params:
params[name] = values[i]
i += 1
# The structure.
structure = deepcopy(cdp.structure)
if structure.num_models() > 1:
structure.collapse_ensemble(model_num=model)
# The pivot points.
num_states = 1
if cdp.model == MODEL_DOUBLE_ROTOR:
num_states = 2
pivot = zeros((num_states, 3), float64)
for i in range(num_states):
pivot[i] = generate_pivot(order=i+1, pdb_limit=True)
# Shift to the average position.
average_position(structure=structure, models=[None])
# The motional eigenframe.
frame = generate_axis_system()
# Create the distribution.
brownian(file=file, model=cdp.model, structure=structure, parameters=params, eigenframe=frame, pivot=pivot, atom_id=domain_moving(), step_size=step_size, snapshot=snapshot, total=total)
# Close the file.
file.close()
def simulate(file="simulation.pdb.bz2",
dir=None,
step_size=2.0,
snapshot=10,
total=1000,
model=1,
force=True):
"""Pseudo-Brownian dynamics simulation of the frame order motions.
@keyword file: The PDB file for storing the frame order pseudo-Brownian dynamics simulation. The compression is determined automatically by the file extensions '*.pdb', '*.pdb.gz', and '*.pdb.bz2'.
@type file: str
@keyword dir: The directory name to place the file into.
@type dir: str or None
@keyword step_size: The rotation will be of a random direction but with this fixed angle. The value is in degrees.
@type step_size: float
@keyword snapshot: The number of steps in the simulation when snapshots will be taken.
@type snapshot: int
@keyword total: The total number of snapshots to take before stopping the simulation.
@type total: int
@keyword model: Only one model from an analysed ensemble of structures can be used for the pseudo-Brownian simulation, as the simulation and corresponding PDB file consists of one model per simulation.
@type model: int
@keyword force: A flag which, if set to True, will overwrite the any pre-existing file.
@type force: bool
"""
# Printout.
print("Pseudo-Brownian dynamics simulation of the frame order motions.")
# Checks.
check_pipe()
check_model()
check_domain()
check_parameters()
check_pivot()
# Skip the rigid model.
if cdp.model == MODEL_RIGID:
print("Skipping the rigid model.")
return
# Open the output file.
file = open_write_file(file_name=file, dir=dir, force=force)
# The parameter values.
values = assemble_param_vector()
params = {}
i = 0
for name in cdp.params:
params[name] = values[i]
i += 1
# The structure.
structure = deepcopy(cdp.structure)
if structure.num_models() > 1:
structure.collapse_ensemble(model_num=model)
# The pivot points.
num_states = 1
if cdp.model == MODEL_DOUBLE_ROTOR:
num_states = 2
pivot = zeros((num_states, 3), float64)
for i in range(num_states):
pivot[i] = generate_pivot(order=i + 1, pdb_limit=True)
# Shift to the average position.
average_position(structure=structure, models=[None])
# The motional eigenframe.
frame = generate_axis_system()
# Create the distribution.
brownian(file=file,
model=cdp.model,
structure=structure,
parameters=params,
eigenframe=frame,
pivot=pivot,
atom_id=domain_moving(),
step_size=step_size,
snapshot=snapshot,
total=total)
# Close the file.
file.close()
def distribute(file="distribution.pdb.bz2",
dir=None,
atom_id=None,
total=1000,
max_rotations=100000,
model=1,
force=True):
"""Create a uniform distribution of structures for the frame order motions.
@keyword file: The PDB file for storing the frame order motional distribution. The compression is determined automatically by the file extensions '*.pdb', '*.pdb.gz', and '*.pdb.bz2'.
@type file: str
@keyword dir: The directory name to place the file into.
@type dir: str or None
@keyword atom_id: The atom identification string to allow the distribution to be a subset of all atoms.
@type atom_id: None or str
@keyword total: The total number of states/model/structures in the distribution.
@type total: int
@keyword max_rotations: The maximum number of rotations to generate the distribution from. This prevents an execution for an infinite amount of time when a frame order amplitude parameter is close to zero so that the subset of all rotations within the distribution is close to zero.
@type max_rotations: int
@keyword model: Only one model from an analysed ensemble of structures can be used for the distribution, as the corresponding PDB file consists of one model per state.
@type model: int
@keyword force: A flag which, if set to True, will overwrite the any pre-existing file.
@type force: bool
"""
# Printout.
print(
"Uniform distribution of structures representing the frame order motions."
)
# Check the total.
if total > 9999:
raise RelaxError(
"A maximum of 9999 models is allowed in the PDB format.")
# Checks.
check_pipe()
check_model()
check_domain()
check_parameters()
check_pivot()
# Skip the rigid model.
if cdp.model == MODEL_RIGID:
print("Skipping the rigid model.")
return
# Open the output file.
file = open_write_file(file_name=file, dir=dir, force=force)
# The parameter values.
values = assemble_param_vector()
params = {}
i = 0
for name in cdp.params:
params[name] = values[i]
i += 1
# The structure.
structure = deepcopy(cdp.structure)
if structure.num_models() > 1:
structure.collapse_ensemble(model_num=model)
# The pivot points.
num_states = 1
if cdp.model == MODEL_DOUBLE_ROTOR:
num_states = 2
pivot = zeros((num_states, 3), float64)
for i in range(num_states):
pivot[i] = generate_pivot(order=i + 1, pdb_limit=True)
# Shift to the average position.
average_position(structure=structure, models=[None])
# The motional eigenframe.
frame = generate_axis_system()
# Only work with a subset.
if atom_id:
# The inverted selection.
selection = structure.selection(atom_id=atom_id, inv=True)
# Delete the data.
structure.delete(selection=selection, verbosity=0)
# Create the distribution.
uniform_distribution(file=file,
model=cdp.model,
structure=structure,
parameters=params,
eigenframe=frame,
pivot=pivot,
atom_id=domain_moving(),
total=total,
max_rotations=max_rotations)
# Close the file.
file.close()
def decompose(root="decomposed", dir=None, atom_id=None, model=1, force=True):
"""Structural representation of the individual frame order motional components.
@keyword root: The file root for the PDB files created. Each motional component will be represented by a different PDB file appended with '_mode1.pdb', '_mode2.pdb', '_mode3.pdb', etc.
@type root: str
@keyword dir: The directory name to place the file into.
@type dir: str or None
@keyword atom_id: The atom identification string to allow the decomposition to be applied to subset of all atoms.
@type atom_id: None or str
@keyword model: Only one model from an analysed ensemble of structures can be used for the decomposition, as the corresponding PDB file consists of one model per state.
@type model: int
@keyword force: A flag which, if set to True, will overwrite the any pre-existing file.
@type force: bool
"""
# Printout.
print(
"PDB representation of the individual components of the frame order motions."
)
# Checks.
check_pipe()
check_model()
check_domain()
check_parameters()
check_pivot()
# Skip any unsupported models.
unsupported = [MODEL_RIGID, MODEL_DOUBLE_ROTOR]
if cdp.model in unsupported:
print("Skipping the unsupported '%s' model." % cdp.model)
return
# Initialise the angle vector (cone opening angle 1, cone opening angle 2, torsion angle).
angles = zeros(3, float64)
# Cone opening.
if cdp.model in MODEL_LIST_ISO_CONE:
angles[0] = angles[1] = cdp.cone_theta
elif cdp.model in MODEL_LIST_PSEUDO_ELLIPSE:
angles[0] = cdp.cone_theta_y
angles[1] = cdp.cone_theta_x
# Non-zero torsion angle.
if cdp.model in MODEL_LIST_FREE_ROTORS:
angles[2] = pi
elif cdp.model in MODEL_LIST_RESTRICTED_TORSION:
angles[2] = cdp.cone_sigma_max
# The motional eigenframe.
frame = generate_axis_system()
# Mode ordering from largest to smallest.
indices = argsort(angles)
angles = angles[indices[::-1]]
frame = transpose(transpose(frame)[indices[::-1]])
# The pivot point.
pivot = generate_pivot(order=1, pdb_limit=True)
# Loop over each mode.
for i in range(3):
# Skip modes with no motion.
if angles[i] < 1e-7:
continue
# Open the output file.
file_name = "%s_mode%i.pdb" % (root, i + 1)
file = open_write_file(file_name=file_name, dir=dir, force=force)
# The structure.
structure = deepcopy(cdp.structure)
if structure.num_models() > 1:
structure.collapse_ensemble(model_num=model)
# Shift to the average position.
average_position(structure=structure, models=[None])
# Create the representation.
mode_distribution(file=file,
structure=structure,
axis=frame[:, i],
angle=angles[i],
pivot=pivot,
atom_id=domain_moving())
# Close the file.
file.close()
