def return_rdc_data(sim_index=None):
"""Set up the data structures for optimisation using RDCs as base data sets.
@keyword sim_index: The index of the simulation to optimise. This should be None if normal optimisation is desired.
@type sim_index: None or int
@return: The assembled data structures for using RDCs as the base data for optimisation. These include:
- rdc, the RDC values.
- rdc_err, the RDC errors.
- rdc_weight, the RDC weights.
- vectors, the interatomic vectors (pseudo-atom dependent).
- rdc_const, the dipolar constants (pseudo-atom dependent).
- absolute, the absolute value flags (as 1's and 0's).
- T_flags, the flags for T = J+D type data (as 1's and 0's).
- j_couplings, the J coupling values if the RDC data type is set to T = J+D.
- pseudo_flags, the list of flags indicating if the interatomic data contains a pseudo-atom (as 1's and 0's).
@rtype: tuple of (numpy rank-2 float64 array, numpy rank-2 float64 array, numpy rank-2 float64 array, list of numpy rank-3 float64 arrays, list of lists of floats, numpy rank-2 int32 array, numpy rank-2 int32 array, numpy rank-2 float64 array, numpy rank-1 int32 array)
"""
# Sort out pseudo-atoms first. This only needs to be called once.
setup_pseudoatom_rdc()
# Initialise.
rdc = []
rdc_err = []
rdc_weight = []
unit_vect = []
rdc_const = []
absolute = []
T_flags = []
j_couplings = []
pseudo_flags = []
# The unit vectors, RDC constants, and J couplings.
for interatom in interatomic_loop():
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# RDC checks.
if not check_rdcs(interatom):
continue
# Gyromagnetic ratios.
g1 = return_gyromagnetic_ratio(spin1.isotope)
g2 = return_gyromagnetic_ratio(spin2.isotope)
# Pseudo atoms.
if is_pseudoatom(spin1) and is_pseudoatom(spin2):
raise RelaxError("Support for both spins being in a dipole pair being pseudo-atoms is not implemented yet.")
if is_pseudoatom(spin1) or is_pseudoatom(spin2):
# Set the flag.
pseudo_flags.append(1)
# Alias the pseudo and normal atoms.
if is_pseudoatom(spin1):
pseudospin = spin1
base_spin = spin2
pseudospin_id = interatom.spin_id1
base_spin_id = interatom.spin_id2
else:
pseudospin = spin2
base_spin = spin1
pseudospin_id = interatom.spin_id2
base_spin_id = interatom.spin_id1
# Loop over the atoms of the pseudo-atom, storing the data.
pseudo_unit_vect = []
pseudo_rdc_const = []
for spin, spin_id in pseudoatom_loop(pseudospin, return_id=True):
# Get the corresponding interatomic data container.
pseudo_interatom = return_interatom(spin_id1=spin_id, spin_id2=base_spin_id)
# Check.
if pseudo_interatom == None:
raise RelaxError("The interatomic data container between the spins '%s' and '%s' for the pseudo-atom '%s' is not defined." % (base_spin_id, spin_id, pseudospin_id))
# Add the vectors.
if is_float(interatom.vector[0]):
pseudo_unit_vect.append([pseudo_interatom.vector])
else:
pseudo_unit_vect.append(pseudo_interatom.vector)
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
pseudo_rdc_const.append(3.0/(2.0*pi) * dipolar_constant(g1, g2, pseudo_interatom.r))
# Reorder the unit vectors so that the structure and pseudo-atom dimensions are swapped.
pseudo_unit_vect = transpose(array(pseudo_unit_vect, float64), (1, 0, 2))
# Block append the pseudo-data.
unit_vect.append(pseudo_unit_vect)
rdc_const.append(pseudo_rdc_const)
# Normal atom.
else:
# Set the flag.
pseudo_flags.append(0)
# Add the vectors.
if is_float(interatom.vector[0]):
unit_vect.append([interatom.vector])
else:
unit_vect.append(interatom.vector)
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
rdc_const.append(3.0/(2.0*pi) * dipolar_constant(g1, g2, interatom.r))
# Store the measured J coupling.
if opt_uses_j_couplings():
j_couplings.append(interatom.j_coupling)
# Fix the unit vector data structure.
num = None
for rdc_index in range(len(unit_vect)):
# Convert to numpy structures.
unit_vect[rdc_index] = array(unit_vect[rdc_index], float64)
# Number of vectors.
if num == None:
if unit_vect[rdc_index] != None:
num = len(unit_vect[rdc_index])
continue
# Check.
if unit_vect[rdc_index] != None and len(unit_vect[rdc_index]) != num:
raise RelaxError("The number of interatomic vectors for all no match:\n%s" % unit_vect[rdc_index])
# Missing unit vectors.
if num == None:
raise RelaxError("No interatomic vectors could be found.")
# Update None entries.
for i in range(len(unit_vect)):
if unit_vect[i] == None:
unit_vect[i] = [[None, None, None]]*num
# The RDC data.
for i in range(len(cdp.align_ids)):
# Alias the ID.
align_id = cdp.align_ids[i]
# Skip non-optimised data.
if not opt_uses_align_data(align_id):
continue
# Append empty arrays to the RDC structures.
rdc.append([])
rdc_err.append([])
rdc_weight.append([])
absolute.append([])
T_flags.append([])
# Interatom loop.
for interatom in interatomic_loop():
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# RDC checks.
if not check_rdcs(interatom):
continue
# T-type data.
if align_id in interatom.rdc_data_types and interatom.rdc_data_types[align_id] == 'T':
T_flags[-1].append(True)
else:
T_flags[-1].append(False)
# Check for J couplings if the RDC data type is T = J+D.
if T_flags[-1][-1] and not hasattr(interatom, 'j_coupling'):
continue
# Defaults of None.
value = None
error = None
# Normal set up.
if align_id in interatom.rdc.keys():
# The RDC.
if sim_index != None:
value = interatom.rdc_sim[align_id][sim_index]
else:
value = interatom.rdc[align_id]
# The error.
if hasattr(interatom, 'rdc_err') and align_id in interatom.rdc_err.keys():
# T values.
if T_flags[-1][-1]:
error = sqrt(interatom.rdc_err[align_id]**2 + interatom.j_coupling_err**2)
# D values.
else:
error = interatom.rdc_err[align_id]
# Append the RDCs to the list.
rdc[-1].append(value)
# Append the RDC errors.
rdc_err[-1].append(error)
# Append the weight.
if hasattr(interatom, 'rdc_weight') and align_id in interatom.rdc_weight.keys():
rdc_weight[-1].append(interatom.rdc_weight[align_id])
else:
rdc_weight[-1].append(1.0)
# Append the absolute value flag.
if hasattr(interatom, 'absolute_rdc') and align_id in interatom.absolute_rdc.keys():
absolute[-1].append(interatom.absolute_rdc[align_id])
else:
absolute[-1].append(False)
# Convert to numpy objects.
rdc = array(rdc, float64)
rdc_err = array(rdc_err, float64)
rdc_weight = array(rdc_weight, float64)
absolute = array(absolute, int32)
T_flags = array(T_flags, int32)
if not opt_uses_j_couplings():
j_couplings = None
pseudo_flags = array(pseudo_flags, int32)
# Return the data structures.
return rdc, rdc_err, rdc_weight, unit_vect, rdc_const, absolute, T_flags, j_couplings, pseudo_flags
def back_calc(align_id=None):
"""Back calculate the RDC from the alignment tensor and unit bond vectors.
@keyword align_id: The alignment tensor ID string.
@type align_id: str
"""
# Check the pipe setup.
check_pipe_setup(rdc_id=align_id, sequence=True, N=True, tensors=True)
# Convert the align IDs to an array, or take all IDs.
if align_id:
align_ids = [align_id]
else:
align_ids = cdp.align_ids
# Add the ID to the RDC IDs, if needed.
for align_id in align_ids:
# Init.
if not hasattr(cdp, 'rdc_ids'):
cdp.rdc_ids = []
# Add the ID.
if align_id not in cdp.rdc_ids:
cdp.rdc_ids.append(align_id)
# The weights.
weights = ones(cdp.N, float64) / cdp.N
# Unit vector data structure init.
unit_vect = zeros((cdp.N, 3), float64)
# Loop over the interatomic data.
count = 0
for interatom in interatomic_loop():
# Skip containers with no interatomic vectors.
if not hasattr(interatom, 'vector'):
continue
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# Checks.
if not hasattr(spin1, 'isotope'):
raise RelaxSpinTypeError(interatom.spin_id1)
if not hasattr(spin2, 'isotope'):
raise RelaxSpinTypeError(interatom.spin_id2)
# Single vector.
if is_float(interatom.vector[0]):
vectors = [interatom.vector]
else:
vectors = interatom.vector
# Gyromagnetic ratios.
g1 = return_gyromagnetic_ratio(spin1.isotope)
g2 = return_gyromagnetic_ratio(spin2.isotope)
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
dj = 3.0/(2.0*pi) * dipolar_constant(g1, g2, interatom.r)
# Unit vectors.
for c in range(cdp.N):
unit_vect[c] = vectors[c] / norm(vectors[c])
# Initialise if necessary.
if not hasattr(interatom, 'rdc_bc'):
interatom.rdc_bc = {}
# Calculate the RDCs.
for id in align_ids:
# The signed value.
interatom.rdc_bc[id] = ave_rdc_tensor(dj, unit_vect, cdp.N, cdp.align_tensors[get_tensor_index(align_id=id)].A, weights=weights)
# T values.
if hasattr(interatom, 'rdc_data_types') and align_id in interatom.rdc_data_types and interatom.rdc_data_types[align_id] == 'T':
if not hasattr(interatom, 'j_coupling'):
raise RelaxNoJError
interatom.rdc_bc[id] += interatom.j_coupling
# The absolute value.
if hasattr(interatom, 'absolute_rdc') and id in interatom.absolute_rdc.keys() and interatom.absolute_rdc[id]:
interatom.rdc_bc[id] = abs(interatom.rdc_bc[id])
# Increment the counter.
count += 1
# No RDCs calculated.
if not count:
warn(RelaxWarning("No RDCs have been back calculated, probably due to missing bond vector information."))
def q_factors(spin_id=None):
"""Calculate the Q-factors for the RDC data.
@keyword spin_id: The spin ID string used to restrict the Q-factor calculation to a subset of all spins.
@type spin_id: None or str
"""
# Check the pipe setup.
check_pipe_setup(sequence=True)
# No RDCs, so no Q factors can be calculated.
if not hasattr(cdp, 'rdc_ids') or not len(cdp.rdc_ids):
warn(RelaxWarning("No RDC data exists, Q factors cannot be calculated."))
return
# Q-factor dictonaries.
cdp.q_factors_rdc = {}
cdp.q_factors_rdc_norm2 = {}
# Loop over the alignments.
for align_id in cdp.rdc_ids:
# Init.
D2_sum = 0.0
sse = 0.0
# Interatomic data loop.
dj = None
N = 0
interatom_count = 0
rdc_data = False
rdc_bc_data = False
norm2_flag = True
for interatom in interatomic_loop():
# Increment the counter.
interatom_count += 1
# Data checks.
if hasattr(interatom, 'rdc') and align_id in interatom.rdc:
rdc_data = True
if hasattr(interatom, 'rdc_bc') and align_id in interatom.rdc_bc:
rdc_bc_data = True
j_flag = False
if hasattr(interatom, 'rdc_data_types') and align_id in interatom.rdc_data_types and interatom.rdc_data_types[align_id] == 'T':
j_flag = True
if not hasattr(interatom, 'j_coupling'):
raise RelaxNoJError
# Skip containers without RDC data.
if not hasattr(interatom, 'rdc') or not hasattr(interatom, 'rdc_bc') or not align_id in interatom.rdc or interatom.rdc[align_id] == None or not align_id in interatom.rdc_bc or interatom.rdc_bc[align_id] == None:
continue
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# Sum of squares.
sse = sse + (interatom.rdc[align_id] - interatom.rdc_bc[align_id])**2
# Sum the RDCs squared (for one type of normalisation).
if j_flag:
D2_sum = D2_sum + (interatom.rdc[align_id] - interatom.j_coupling)**2
else:
D2_sum = D2_sum + interatom.rdc[align_id]**2
# Gyromagnetic ratios.
g1 = return_gyromagnetic_ratio(spin1.isotope)
g2 = return_gyromagnetic_ratio(spin2.isotope)
# Skip the 2Da^2(4 + 3R)/5 normalised Q factor if pseudo-atoms are present.
if norm2_flag and (is_pseudoatom(spin1) or is_pseudoatom(spin2)):
warn(RelaxWarning("Pseudo-atoms are present, skipping the Q factor normalised with 2Da^2(4 + 3R)/5."))
norm2_flag = False
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
if norm2_flag:
dj_new = 3.0/(2.0*pi) * dipolar_constant(g1, g2, interatom.r)
if dj != None and dj_new != dj:
warn(RelaxWarning("The dipolar constant is not the same for all RDCs, skipping the Q factor normalised with 2Da^2(4 + 3R)/5."))
norm2_flag = False
else:
dj = dj_new
# Increment the number of data sets.
N = N + 1
# Warnings (and then exit).
if not interatom_count:
warn(RelaxWarning("No interatomic data containers have been used in the calculation, skipping the RDC Q factor calculation."))
return
if not rdc_data:
warn(RelaxWarning("No RDC data can be found for the alignment ID '%s', skipping the RDC Q factor calculation for this alignment." % align_id))
continue
if not rdc_bc_data:
warn(RelaxWarning("No back-calculated RDC data can be found for the alignment ID '%s', skipping the RDC Q factor calculation for this alignment." % align_id))
continue
# Normalisation factor of 2Da^2(4 + 3R)/5.
if norm2_flag:
D = dj * cdp.align_tensors[cdp.align_ids.index(align_id)].A_diag
Da = 1.0/3.0 * (D[2, 2] - (D[0, 0]+D[1, 1])/2.0)
Dr = 1.0/3.0 * (D[0, 0] - D[1, 1])
if Da == 0:
R = nan
else:
R = Dr / Da
norm = 2.0 * (Da)**2 * (4.0 + 3.0*R**2)/5.0
if Da == 0.0:
norm = 1e-15
# The Q-factor for the alignment.
cdp.q_factors_rdc[align_id] = sqrt(sse / N / norm)
else:
cdp.q_factors_rdc[align_id] = 0.0
# The second Q-factor definition.
cdp.q_factors_rdc_norm2[align_id] = sqrt(sse / D2_sum)
# The total Q-factor.
cdp.q_rdc = 0.0
cdp.q_rdc_norm2 = 0.0
for id in cdp.q_factors_rdc:
cdp.q_rdc = cdp.q_rdc + cdp.q_factors_rdc[id]**2
for id in cdp.q_factors_rdc_norm2:
cdp.q_rdc_norm2 = cdp.q_rdc_norm2 + cdp.q_factors_rdc_norm2[id]**2
cdp.q_rdc = sqrt(cdp.q_rdc / len(cdp.q_factors_rdc))
cdp.q_rdc_norm2 = sqrt(cdp.q_rdc_norm2 / len(cdp.q_factors_rdc_norm2))
def rdc_analysis(self):
"""Perform the RDC part of the analysis."""
# Redirect STDOUT to a log file.
if self.log:
sys.stdout = open(self.results_dir+sep+"logs" + sep + "RDC_%s_analysis.log" % self.rdc_name, 'w')
# The dipolar constant.
d = 0.0
if self.bond_length != None:
d = 3.0 / (2.0*pi) * dipolar_constant(g13C, g1H, self.bond_length)
# Create a directory for the save files.
dir = self.results_dir + sep + "RDC_%s_results" % self.rdc_name
mkdir_nofail(dir=dir)
# Loop over the configurations.
for config in self.configs:
# Print out.
print("\n"*10 + "# Set up for config " + config + " #" + "\n")
# Open the results files.
out = open(self.results_dir+sep+"Q_factors_" + config, 'w')
out_sorted = open(self.results_dir+sep+"Q_factors_" + config + "_sorted", 'w')
out.write("%-20s%20s%20s\n" % ("# Ensemble", "RDC_Q_factor(pales)", "RDC_Q_factor(standard)"))
out_sorted.write("%-20s%20s\n" % ("# Ensemble", "RDC_Q_factor(pales)"))
# Create the data pipe.
self.interpreter.pipe.create("rdc_analysis_%s" % config, "N-state")
# Read the first structure.
self.interpreter.structure.read_pdb("ensembles_superimposed" + sep + config + "0.pdb", dir=self.results_dir, set_mol_name=config, set_model_num=list(range(1, self.num_models+1)))
# Load all spins as the sequence.
self.interpreter.structure.load_spins(ave_pos=False)
# Create the pseudo-atoms.
for i in range(len(self.pseudo)):
self.interpreter.spin.create_pseudo(spin_name=self.pseudo[i][0], members=self.pseudo[i][1], averaging="linear")
self.interpreter.sequence.display()
# Read the RDC data.
self.interpreter.rdc.read(align_id=self.rdc_file, file=self.rdc_file, spin_id1_col=self.rdc_spin_id1_col, spin_id2_col=self.rdc_spin_id2_col, data_col=self.rdc_data_col, error_col=self.rdc_error_col)
# Define the magnetic dipole-dipole relaxation interaction.
if self.bond_length != None:
self.interpreter.interatom.set_dist(spin_id1='@C*', spin_id2='@H*', ave_dist=self.bond_length)
self.interpreter.interatom.set_dist(spin_id1='@C*', spin_id2='@Q*', ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(file=self.bond_length_file, spin_id1_col=1, spin_id2_col=2, data_col=3)
# Set the nuclear isotope.
self.interpreter.spin.isotope(isotope='13C', spin_id='@C*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@H*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@Q*')
# Set up the model.
self.interpreter.n_state_model.select_model(model="fixed")
# Print out.
print("\n"*2 + "# Set up complete #" + "\n"*10)
# Loop over each ensemble.
q_factors = []
for ens in range(self.num_ens):
# Print out the ensemble to both the log and screen.
if self.log:
sys.stdout.write(config + repr(ens) + "\n")
sys.stderr.write(config + repr(ens) + "\n")
# Delete the old structures.
self.interpreter.structure.delete()
# Read the ensemble.
self.interpreter.structure.read_pdb("ensembles_superimposed" + sep + config + repr(ens) + ".pdb", dir=self.results_dir, set_mol_name=config, set_model_num=list(range(1, self.num_models+1)))
# Get the positional information, then load the CH vectors.
self.interpreter.structure.get_pos(ave_pos=False)
if self.bond_length != None:
self.interpreter.interatom.set_dist(spin_id1='@C*', spin_id2='@H*', ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(file=self.bond_length_file, spin_id1_col=1, spin_id2_col=2, data_col=3)
self.interpreter.interatom.unit_vectors(ave=False)
# Minimisation.
#grid_search(inc=4)
self.interpreter.minimise("simplex", constraints=False)
# Store and write out the Q-factors.
q_factors.append([cdp.q_rdc, ens])
out.write("%-20i%20.15f%20.15f\n" % (ens, cdp.q_rdc, cdp.q_rdc_norm2))
# Calculate the alignment tensor in Hz, and store it for reference.
cdp.align_tensor_Hz = d * cdp.align_tensors[0].A
cdp.align_tensor_Hz_5D = d * cdp.align_tensors[0].A_5D
# Save the state.
self.interpreter.results.write(file="%s_results_%s" % (config, ens), dir=dir, force=True)
# Sort the NOE violations.
q_factors.sort()
# Write the data.
for i in range(len(q_factors)):
out_sorted.write("%-20i%20.15f\n" % (q_factors[i][1], q_factors[i][0]))
# Load the spins. self._execute_uf(uf_name='structure.load_spins', spin_id='@N', ave_pos=False) self._execute_uf(uf_name='structure.load_spins', spin_id='@H', ave_pos=False) # Define the magnetic dipole-dipole relaxation interaction. self._execute_uf(uf_name='interatom.define', spin_id1='@N', spin_id2='@H', direct_bond=True) self._execute_uf(uf_name='interatom.set_dist', spin_id1='@N', spin_id2='@H', ave_dist=NH_BOND_LENGTH_RDC) self._execute_uf(uf_name='interatom.unit_vectors', ave=True) # Set the nuclear isotope. self._execute_uf(uf_name='spin.isotope', isotope='15N', spin_id='@N') self._execute_uf(uf_name='spin.isotope', isotope='1H', spin_id='@H') # The dipolar constant. const = 3.0 / (2.0*pi) * dipolar_constant(g15N, g1H, NH_BOND_LENGTH_RDC) # The tensor. tensor = 'A' self._execute_uf(uf_name='align_tensor.init', tensor=tensor, params=(4.724/const, 11.856/const, 0, 0, 0), align_id=tensor, param_types=2) # The temperature. self._execute_uf(uf_name='spectrometer.temperature', id=tensor, temp=298) # The frequency. self._execute_uf(uf_name='spectrometer.frequency', id=tensor, frq=900.0 * 1e6) # One state model. self._execute_uf(uf_name='n_state_model.select_model', model='fixed') self._execute_uf(uf_name='n_state_model.number_of_states', N=1)
# Load the spins.
self._execute_uf(uf_name='structure.load_spins', spin_id='@N', ave_pos=False)
self._execute_uf(uf_name='structure.load_spins', spin_id='@H', ave_pos=False)
# Define the magnetic dipole-dipole relaxation interaction.
self._execute_uf(uf_name='interatom.define', spin_id1='@N', spin_id2='@H', direct_bond=True)
self._execute_uf(uf_name='interatom.set_dist', spin_id1='@N', spin_id2='@H', ave_dist=NH_BOND_LENGTH_RDC)
self._execute_uf(uf_name='interatom.unit_vectors', ave=True)
# Set the nuclear isotope.
self._execute_uf(uf_name='spin.isotope', isotope='15N', spin_id='@N')
self._execute_uf(uf_name='spin.isotope', isotope='1H', spin_id='@H')
# The dipolar constant.
const = 3.0 / (2.0*pi) * dipolar_constant(periodic_table.gyromagnetic_ratio('15N'), periodic_table.gyromagnetic_ratio('1H'), NH_BOND_LENGTH_RDC)
# The tensor.
tensor = 'A'
self._execute_uf(uf_name='align_tensor.init', tensor=tensor, params=(4.724/const, 11.856/const, 0, 0, 0), align_id=tensor, param_types=2)
# The temperature.
self._execute_uf(uf_name='spectrometer.temperature', id=tensor, temp=298)
# The frequency.
self._execute_uf(uf_name='spectrometer.frequency', id=tensor, frq=900.0 * 1e6)
# One state model.
self._execute_uf(uf_name='n_state_model.select_model', model='fixed')
self._execute_uf(uf_name='n_state_model.number_of_states', N=1)
def rdc_analysis(self):
"""Perform the RDC part of the analysis."""
# Redirect STDOUT to a log file.
if self.log:
sys.stdout = open(
self.results_dir + sep + "logs" + sep +
"RDC_%s_analysis.log" % self.rdc_name, 'w')
# The dipolar constant.
d = 0.0
if self.bond_length != None:
d = 3.0 / (2.0 * pi) * dipolar_constant(
periodic_table.gyromagnetic_ratio('13C'),
periodic_table.gyromagnetic_ratio('1H'), self.bond_length)
# Create a directory for the save files.
dir = self.results_dir + sep + "RDC_%s_results" % self.rdc_name
mkdir_nofail(dir=dir)
# Loop over the configurations.
for config in self.configs:
# Print out.
print("\n" * 10 + "# Set up for config " + config + " #" + "\n")
# Open the results files.
out = open(self.results_dir + sep + "Q_factors_" + config, 'w')
out_sorted = open(
self.results_dir + sep + "Q_factors_" + config + "_sorted",
'w')
out.write("%-20s%20s%20s\n" % ("# Ensemble", "RDC_Q_factor(pales)",
"RDC_Q_factor(standard)"))
out_sorted.write("%-20s%20s\n" %
("# Ensemble", "RDC_Q_factor(pales)"))
# Create the data pipe.
self.interpreter.pipe.create("rdc_analysis_%s" % config, "N-state")
# Read the first structure.
self.interpreter.structure.read_pdb(
"ensembles_superimposed" + sep + config + "0.pdb",
dir=self.results_dir,
set_mol_name=config,
set_model_num=list(range(1, self.num_models + 1)))
# Load all spins as the sequence.
self.interpreter.structure.load_spins(ave_pos=False)
# Create the pseudo-atoms.
for i in range(len(self.pseudo)):
self.interpreter.spin.create_pseudo(
spin_name=self.pseudo[i][0],
members=self.pseudo[i][1],
averaging="linear")
self.interpreter.sequence.display()
# Read the RDC data.
self.interpreter.rdc.read(align_id=self.rdc_file,
file=self.rdc_file,
spin_id1_col=self.rdc_spin_id1_col,
spin_id2_col=self.rdc_spin_id2_col,
data_col=self.rdc_data_col,
error_col=self.rdc_error_col)
# Define the magnetic dipole-dipole relaxation interaction.
if self.bond_length != None:
self.interpreter.interatom.set_dist(spin_id1='@C*',
spin_id2='@H*',
ave_dist=self.bond_length)
self.interpreter.interatom.set_dist(spin_id1='@C*',
spin_id2='@Q*',
ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(
file=self.bond_length_file,
spin_id1_col=1,
spin_id2_col=2,
data_col=3)
# Set the nuclear isotope.
self.interpreter.spin.isotope(isotope='13C', spin_id='@C*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@H*')
self.interpreter.spin.isotope(isotope='1H', spin_id='@Q*')
# Set up the model.
self.interpreter.n_state_model.select_model(model="fixed")
# Print out.
print("\n" * 2 + "# Set up complete #" + "\n" * 10)
# Loop over each ensemble.
q_factors = []
for ens in range(self.num_ens):
# Print out the ensemble to both the log and screen.
if self.log:
sys.stdout.write(config + repr(ens) + "\n")
sys.stderr.write(config + repr(ens) + "\n")
# Delete the old structures.
self.interpreter.structure.delete()
# Read the ensemble.
self.interpreter.structure.read_pdb(
"ensembles_superimposed" + sep + config + repr(ens) +
".pdb",
dir=self.results_dir,
set_mol_name=config,
set_model_num=list(range(1, self.num_models + 1)))
# Get the positional information, then load the CH vectors.
self.interpreter.structure.get_pos(ave_pos=False)
if self.bond_length != None:
self.interpreter.interatom.set_dist(
spin_id1='@C*',
spin_id2='@H*',
ave_dist=self.bond_length)
else:
self.interpreter.interatom.read_dist(
file=self.bond_length_file,
spin_id1_col=1,
spin_id2_col=2,
data_col=3)
self.interpreter.interatom.unit_vectors(ave=False)
# Minimisation.
#minimise.grid_search(inc=4)
self.interpreter.minimise.execute("simplex", constraints=False)
# Store and write out the Q factors.
q_factors.append([cdp.q_rdc_norm_squared_sum, ens])
out.write("%-20i%20.15f%20.15f\n" %
(ens, cdp.q_rdc_norm_squared_sum,
cdp.q_rdc_norm_squared_sum))
# Calculate the alignment tensor in Hz, and store it for reference.
cdp.align_tensor_Hz = d * cdp.align_tensors[0].A
cdp.align_tensor_Hz_5D = d * cdp.align_tensors[0].A_5D
# Save the state.
self.interpreter.results.write(file="%s_results_%s" %
(config, ens),
dir=dir,
force=True)
# Sort the NOE violations.
q_factors.sort()
# Write the data.
for i in range(len(q_factors)):
out_sorted.write("%-20i%20.15f\n" %
(q_factors[i][1], q_factors[i][0]))
def _calculate_rdc(self):
"""Calculate the averaged RDC for all states."""
# Open the output files.
if self.ROT_FILE:
rot_file = open_write_file('rotations', dir=self.save_path, compress_type=1, force=True)
# Printout.
sys.stdout.write("\n\nRotating %s states for the RDC:\n\n" % locale.format("%d", self.N**self.MODES, grouping=True))
# Turn off the relax interpreter echoing to allow the progress meter to be shown correctly.
self.interpreter.off()
# Set up some data structures for faster calculations.
interatoms = []
vectors = []
d = []
for interatom in interatomic_loop():
# Nothing to do.
if not hasattr(interatom, 'vector'):
continue
# Initialise the RDC structure (as a 1D numpy.float128 array for speed and minimising truncation artifacts).
interatom.rdc = {}
for tag in self._tensors:
interatom.rdc[tag] = zeros(1, float128)
# Pack the interatomic containers and vectors.
interatoms.append(interatom)
vectors.append(interatom.vector)
# Get the spins.
spin1 = return_spin(spin_id=interatom.spin_id1)
spin2 = return_spin(spin_id=interatom.spin_id2)
# Gyromagnetic ratios.
g1 = periodic_table.gyromagnetic_ratio(spin1.isotope)
g2 = periodic_table.gyromagnetic_ratio(spin2.isotope)
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
d.append(3.0/(2.0*pi) * dipolar_constant(g1, g2, interatom.r))
# Repackage the data for speed.
vectors = transpose(array(vectors, float64))
d = array(d, float64)
num_interatoms = len(vectors)
# Store the alignment tensors.
A = []
for i in range(len(self._tensors)):
A.append(cdp.align_tensors[i].A)
# Loop over each position.
for global_index, mode_indices in self._state_loop():
# The progress meter.
self._progress(global_index)
# Total rotation matrix (for construction of the frame order matrix).
total_R = eye(3)
# Data initialisation.
new_vect = vectors
# Loop over each motional mode.
for motion_index in range(self.MODES):
# Generate the distribution specific rotation.
self.rotation(mode_indices[motion_index], motion_index=motion_index)
# Rotate the NH vector.
new_vect = dot(self.R, new_vect)
# Decompose the rotation into Euler angles and store them.
if self.ROT_FILE:
a, b, g = R_to_euler_zyz(self.R)
rot_file.write('Mode %i: %10.7f %10.7f %10.7f\n' % (motion_index, a, b, g))
# Contribution to the total rotation.
total_R = dot(self.R, total_R)
# Loop over each alignment.
for i in range(len(self._tensors)):
# Calculate the RDC as quickly as possible.
rdcs = d * tensordot(transpose(new_vect), tensordot(A[i], new_vect, axes=1), axes=1)
# Store the values.
for j in range(len(interatoms)):
interatoms[j].rdc[self._tensors[i]][0] += rdcs[j, j]
# The frame order matrix component.
self.daeg += kron_prod(total_R, total_R)
# Print out.
sys.stdout.write('\n\n')
# Frame order matrix averaging.
self.daeg = self.daeg / self.N**self.MODES
# Write out the frame order matrix.
file = open(self.save_path+sep+'frame_order_matrix', 'w')
print_frame_order_2nd_degree(self.daeg, file=file, places=8)
# Reactive the interpreter echoing.
self.interpreter.on()
# Average the RDC and write the data.
for tag in self._tensors:
# Average.
for interatom in interatomic_loop():
interatom.rdc[tag] = interatom.rdc[tag][0] / self.N**self.MODES
# Save.
self.interpreter.rdc.write(align_id=tag, file='rdc_%s.txt'%tag, dir=self.save_path, force=True)
def _calculate_rdc(self):
"""Calculate the averaged RDC for all states."""
# Open the output files.
if self.ROT_FILE:
rot_file = open_write_file('rotations', dir=self.save_path, compress_type=1, force=True)
# Printout.
sys.stdout.write("\n\nRotating %s states for the RDC:\n\n" % locale.format("%d", self.N**self.MODES, grouping=True))
# Turn off the relax interpreter echoing to allow the progress meter to be shown correctly.
self.interpreter.off()
# Set up some data structures for faster calculations.
interatoms = []
vectors = []
d = []
for interatom in interatomic_loop():
# Nothing to do.
if not hasattr(interatom, 'vector'):
continue
# Initialise the RDC structure (as a 1D numpy.float128 array for speed and minimising truncation artifacts).
interatom.rdc = {}
for tag in self._tensors:
interatom.rdc[tag] = zeros(1, float128)
# Pack the interatomic containers and vectors.
interatoms.append(interatom)
vectors.append(interatom.vector)
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# Gyromagnetic ratios.
g1 = periodic_table.gyromagnetic_ratio(spin1.isotope)
g2 = periodic_table.gyromagnetic_ratio(spin2.isotope)
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
d.append(3.0/(2.0*pi) * dipolar_constant(g1, g2, interatom.r))
# Repackage the data for speed.
vectors = transpose(array(vectors, float64))
d = array(d, float64)
num_interatoms = len(vectors)
# Store the alignment tensors.
A = []
for i in range(len(self._tensors)):
A.append(cdp.align_tensors[i].A)
# Loop over each position.
for global_index, mode_indices in self._state_loop():
# The progress meter.
self._progress(global_index)
# Total rotation matrix (for construction of the frame order matrix).
total_R = eye(3)
# Data initialisation.
new_vect = vectors
# Loop over each motional mode.
for motion_index in range(self.MODES):
# Generate the distribution specific rotation.
self.rotation(mode_indices[motion_index], motion_index=motion_index)
# Rotate the NH vector.
new_vect = dot(self.R, new_vect)
# Decompose the rotation into Euler angles and store them.
if self.ROT_FILE:
a, b, g = R_to_euler_zyz(self.R)
rot_file.write('Mode %i: %10.7f %10.7f %10.7f\n' % (motion_index, a, b, g))
# Contribution to the total rotation.
total_R = dot(self.R, total_R)
# Loop over each alignment.
for i in range(len(self._tensors)):
# Calculate the RDC as quickly as possible.
rdcs = d * tensordot(transpose(new_vect), tensordot(A[i], new_vect, axes=1), axes=1)
# Store the values.
for j in range(len(interatoms)):
interatoms[j].rdc[self._tensors[i]][0] += rdcs[j, j]
# The frame order matrix component.
self.daeg += kron_prod(total_R, total_R)
# Print out.
sys.stdout.write('\n\n')
# Frame order matrix averaging.
self.daeg = self.daeg / self.N**self.MODES
# Write out the frame order matrix.
file = open(self.save_path+sep+'frame_order_matrix', 'w')
print_frame_order_2nd_degree(self.daeg, file=file, places=8)
# Reactive the interpreter echoing.
self.interpreter.on()
# Average the RDC and write the data.
for tag in self._tensors:
# Average.
for interatom in interatomic_loop():
interatom.rdc[tag] = interatom.rdc[tag][0] / self.N**self.MODES
# Save.
self.interpreter.rdc.write(align_id=tag, file='rdc_%s.txt'%tag, dir=self.save_path, force=True)
# Path of the files.
str_path = status.install_path + sep+'test_suite'+sep+'shared_data'+sep+'structures'
# The data pipe.
self._execute_uf(uf_name='pipe.create', pipe_name='pcs_back_calc', pipe_type='N-state')
# Load the structures.
self._execute_uf(uf_name='structure.read_pdb', file='trunc_ubi_pcs.pdb', dir=str_path)
# Load the proton spins.
self._execute_uf(uf_name='structure.load_spins', spin_id='@H')
# The dipolar constant.
const = 3.0 / (2.0*pi) * dipolar_constant(periodic_table.gyromagnetic_ratio('15N'), periodic_table.gyromagnetic_ratio('1H'), NH_BOND_LENGTH_RDC)
# The tensor.
tensor = 'A'
align_id = tensor
self._execute_uf(uf_name='align_tensor.init', tensor=tensor, params=(4.724/const, 11.856/const, 0, 0, 0), align_id=align_id, param_types=2)
# The temperature.
self._execute_uf(uf_name='spectrometer.temperature', id=align_id, temp=298)
# The frequency.
self._execute_uf(uf_name='spectrometer.frequency', id=align_id, frq=900.0 * 1e6)
# One state model.
self._execute_uf(uf_name='n_state_model.select_model', model='fixed')
self._execute_uf(uf_name='n_state_model.number_of_states', N=1)
