def return_attached_protons(spin_id=None):
"""Return a list of all proton spin containers attached to the given spin.
@keyword spin_id: The spin ID string.
@type spin_id: str
@return: The list of proton spin containers attached to the given spin.
@rtype: list of SpinContainer instances
"""
# Initialise.
spin_list = []
# Get all interatomic data containers.
interatoms = return_interatom_list(spin_id)
# No containers.
if not len(interatoms):
return spin_list
# Loop over the containers.
for i in range(len(interatoms)):
# Get the attached spin.
if interatoms[i].spin_id1 == spin_id:
attached = return_spin(interatoms[i].spin_id2)
else:
attached = return_spin(interatoms[i].spin_id1)
# Is it a proton?
if (hasattr(attached, 'element') and attached.element == 'H') or attached.name == 'H':
spin_list.append(attached)
# Return the list.
return spin_list
def return_attached_protons(spin_hash=None):
"""Return a list of all proton spin containers attached to the given spin.
@keyword spin_hash: The unique spin hash.
@type spin_hash: str
@return: The list of proton spin containers attached to the given spin.
@rtype: list of SpinContainer instances
"""
# Initialise.
spin_list = []
# Get all interatomic data containers.
interatoms = return_interatom_list(spin_hash=spin_hash)
# No containers.
if not len(interatoms):
return spin_list
# Loop over the containers.
for i in range(len(interatoms)):
# Get the attached spin.
if interatoms[i]._spin_hash1 == spin_hash:
attached = return_spin(spin_hash=interatoms[i]._spin_hash2)
else:
attached = return_spin(spin_hash=interatoms[i]._spin_hash1)
# Is it a proton?
if (hasattr(attached, 'element') and attached.element == 'H') or attached.name == 'H':
spin_list.append(attached)
# Return the list.
return spin_list
def attach_protons():
"""Attach a single proton to all heteronuclei."""
# Loop over all spins.
mol_names = []
res_nums = []
res_names = []
for spin, mol_name, res_num, res_name, spin_id in spin_loop(full_info=True, return_id=True):
# The spin is already a proton.
if hasattr(spin, 'element') and spin.element == 'H':
continue
# Get the interatomic data container.
interatoms = return_interatom_list(spin_hash=spin._hash)
proton_found = False
if len(interatoms):
for i in range(len(interatoms)):
# Get the attached spin.
spin_attached = return_spin(spin_hash=interatoms[i]._spin_hash1)
if id(spin_attached) == id(spin):
spin_attached = return_spin(spin_hash=interatoms[i]._spin_hash2)
# Is it a proton?
if hasattr(spin_attached, 'element') and spin_attached.element == 'H' or spin.name == 'H':
proton_found = True
break
# Attached proton found.
if proton_found:
continue
# Store the sequence info.
mol_names.append(mol_name)
res_nums.append(res_num)
res_names.append(res_name)
# Create all protons (this must be done out of the spin loop, as it affects the looping!).
ids = []
for i in range(len(mol_names)):
# Create the spin container.
spin = create_spin(spin_name='H', res_name=res_names[i], res_num=res_nums[i], mol_name=mol_names[i])[0]
ids.append(generate_spin_id(mol_name=mol_names[i], res_num=res_nums[i], res_name=res_names[i], spin_name='H'))
print("Creating the spins %s." % ids)
# Set the element and spin type.
set_spin_element(spin_id='@H', element='H')
set_spin_isotope(spin_id='@H', isotope='1H')
def attach_protons():
"""Attach a single proton to all heteronuclei."""
# Loop over all spins.
mol_names = []
res_nums = []
res_names = []
for spin, mol_name, res_num, res_name, spin_id in spin_loop(full_info=True, return_id=True):
# The spin is already a proton.
if hasattr(spin, 'element') and spin.element == 'H':
continue
# Get the interatomic data container.
interatoms = return_interatom_list(spin_id)
proton_found = False
if len(interatoms):
for i in range(len(interatoms)):
# Get the attached spin.
spin_attached = return_spin(interatoms[i].spin_id1)
if id(spin_attached) == id(spin):
spin_attached = return_spin(interatoms[i].spin_id2)
# Is it a proton?
if hasattr(spin_attached, 'element') and spin_attached.element == 'H' or spin.name == 'H':
proton_found = True
break
# Attached proton found.
if proton_found:
continue
# Store the sequence info.
mol_names.append(mol_name)
res_nums.append(res_num)
res_names.append(res_name)
# Create all protons (this must be done out of the spin loop, as it affects the looping!).
ids = []
for i in range(len(mol_names)):
# Create the spin container.
spin = create_spin(spin_name='H', res_name=res_names[i], res_num=res_nums[i], mol_name=mol_names[i])
ids.append(generate_spin_id(mol_name=mol_names[i], res_num=res_nums[i], res_name=res_names[i], spin_name='H'))
print("Creating the spins %s." % ids)
# Set the element and spin type.
set_spin_element(spin_id='@H', element='H')
set_spin_isotope(spin_id='@H', isotope='1H')
def create_mfpar(file,
spin=None,
spin_id=None,
res_num=None,
atom1=None,
atom2=None):
"""Create the Modelfree4 input file 'mfpar'.
@param file: The writable file object.
@type file: file object
@keyword spin: The spin container.
@type spin: SpinContainer instance
@keyword spin_id: The spin identification string.
@type spin_id str
@keyword res_num: The residue number from the PDB file corresponding to the spin.
@type res_num: int
@keyword atom1: The name of the heteronucleus in the PDB file.
@type atom1: str
@keyword atom2: The name of the proton in the PDB file.
@type atom2: str
"""
# Get the interatomic data containers.
interatoms = return_interatom_list(spin_hash=spin._hash)
if len(interatoms) == 0:
raise RelaxNoInteratomError
elif len(interatoms) > 1:
raise RelaxError(
"Only one interatomic data container, hence dipole-dipole interaction, is supported per spin."
)
# Spin title.
file.write("\nspin " + spin_id + "\n")
file.write('%-14s' % "constants")
file.write('%-6i' % res_num)
file.write('%-7s' % spin.isotope)
file.write('%-8.4f' %
(periodic_table.gyromagnetic_ratio(spin.isotope) / 1e7))
file.write('%-8.3f' % (interatoms[0].r * 1e10))
file.write('%-8.3f\n' % (spin.csa * 1e6))
file.write('%-10s' % "vector")
file.write('%-4s' % atom1)
file.write('%-4s\n' % atom2)
def create_mfpar(file, spin=None, spin_id=None, res_num=None, atom1=None, atom2=None):
"""Create the Modelfree4 input file 'mfpar'.
@param file: The writable file object.
@type file: file object
@keyword spin: The spin container.
@type spin: SpinContainer instance
@keyword spin_id: The spin identification string.
@type spin_id str
@keyword res_num: The residue number from the PDB file corresponding to the spin.
@type res_num: int
@keyword atom1: The name of the heteronucleus in the PDB file.
@type atom1: str
@keyword atom2: The name of the proton in the PDB file.
@type atom2: str
"""
# Get the interatomic data containers.
interatoms = return_interatom_list(spin_id)
if len(interatoms) == 0:
raise RelaxNoInteratomError
elif len(interatoms) > 1:
raise RelaxError("Only one interatomic data container, hence dipole-dipole interaction, is supported per spin.")
# Spin title.
file.write("\nspin " + spin_id + "\n")
file.write('%-14s' % "constants")
file.write('%-6i' % res_num)
file.write('%-7s' % spin.isotope)
file.write('%-8.4f' % (return_gyromagnetic_ratio(spin.isotope) / 1e7))
file.write('%-8.3f' % (interatoms[0].r * 1e10))
file.write('%-8.3f\n' % (spin.csa * 1e6))
file.write('%-10s' % "vector")
file.write('%-4s' % atom1)
file.write('%-4s\n' % atom2)
def bmrb_write(self, file_path, version=None):
"""Write the model-free results to a BMRB NMR-STAR v3.1 formatted file.
@param file_path: The full file path.
@type file_path: str
@keyword version: The BMRB NMR-STAR dictionary format to output to.
@type version: str
"""
# Alias the current data pipe.
cdp = pipes.get_pipe()
# Initialise the NMR-STAR data object.
star = bmrblib.create_nmr_star('relax_model_free_results', file_path, version)
# Global minimisation stats.
global_chi2 = None
if hasattr(cdp, 'chi2'):
global_chi2 = cdp.chi2
# Rex frq.
rex_frq = cdp.spectrometer_frq[cdp.ri_ids[0]]
# Initialise the spin specific data lists.
mol_name_list = []
res_num_list = []
res_name_list = []
atom_name_list = []
csa_list = []
r_list = []
isotope_list = []
element_list = []
local_tm_list = []
s2_list = []
s2f_list = []
s2s_list = []
te_list = []
tf_list = []
ts_list = []
rex_list = []
local_tm_err_list = []
s2_err_list = []
s2f_err_list = []
s2s_err_list = []
te_err_list = []
tf_err_list = []
ts_err_list = []
rex_err_list = []
chi2_list = []
model_list = []
# Store the spin specific data in lists for later use.
for spin, mol_name, res_num, res_name, spin_id in spin_loop(full_info=True, return_id=True):
# Skip the protons.
if spin.name == 'H' or (hasattr(spin, 'element') and spin.element == 'H'):
warn(RelaxWarning("Skipping the proton spin '%s'." % spin_id))
continue
# Check the data for None (not allowed in BMRB!).
if res_num == None:
raise RelaxError("For the BMRB, the residue of spin '%s' must be numbered." % spin_id)
if res_name == None:
raise RelaxError("For the BMRB, the residue of spin '%s' must be named." % spin_id)
if spin.name == None:
raise RelaxError("For the BMRB, the spin '%s' must be named." % spin_id)
if not hasattr(spin, 'isotope') or spin.isotope == None:
raise RelaxError("For the BMRB, the spin isotope type of '%s' must be specified." % spin_id)
if not hasattr(spin, 'element') or spin.element == None:
raise RelaxError("For the BMRB, the spin element type of '%s' must be specified. Please use the spin user function for setting the element type." % spin_id)
# The molecule/residue/spin info.
mol_name_list.append(mol_name)
res_num_list.append(res_num)
res_name_list.append(res_name)
atom_name_list.append(spin.name)
# CSA values.
if hasattr(spin, 'csa'):
csa_list.append(spin.csa * 1e6) # In ppm.
else:
csa_list.append(None)
# Interatomic distances.
interatoms = return_interatom_list(spin_id)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# Add the interatomic distance.
if hasattr(interatoms[i], 'r'):
r_list.append(interatoms[i].r)
else:
r_list.append(None)
# Stop adding.
break
# The nuclear isotope.
if hasattr(spin, 'isotope'):
isotope_list.append(int(spin.isotope.strip(string.ascii_letters)))
else:
isotope_list.append(None)
# The element.
if hasattr(spin, 'element'):
element_list.append(spin.element)
else:
element_list.append(None)
# Diffusion tensor.
if hasattr(spin, 'local_tm'):
local_tm_list.append(spin.local_tm)
else:
local_tm_list.append(None)
if hasattr(spin, 'local_tm_err'):
local_tm_err_list.append(spin.local_tm_err)
else:
local_tm_err_list.append(None)
# Model-free parameter values.
s2_list.append(spin.s2)
s2f_list.append(spin.s2f)
s2s_list.append(spin.s2s)
te_list.append(spin.te)
tf_list.append(spin.tf)
ts_list.append(spin.ts)
if spin.rex == None:
rex_list.append(None)
else:
rex_list.append(spin.rex / (2.0*pi*rex_frq**2))
# Model-free parameter errors.
params = ['s2', 's2f', 's2s', 'te', 'tf', 'ts', 'rex']
for param in params:
# The error list.
err_list = locals()[param+'_err_list']
# Append the error.
if hasattr(spin, param+'_err'):
# The value.
val = getattr(spin, param+'_err')
# Scaling.
if param == 'rex' and val != None:
val = val / (2.0*pi*rex_frq**2)
# Append.
err_list.append(val)
# Or None.
else:
err_list.append(None)
# Opt stats.
chi2_list.append(spin.chi2)
# Model-free model.
model_list.append(self._to_bmrb_model(spin.model))
# Convert the molecule names into the entity IDs.
entity_ids = zeros(len(mol_name_list), int32)
mol_names = get_molecule_names()
for i in range(len(mol_name_list)):
for j in range(len(mol_names)):
if mol_name_list[i] == mol_names[j]:
entity_ids[i] = j+1
# Create Supergroup 2 : The citations.
######################################
# Generate the citations saveframe.
exp_info.bmrb_write_citations(star)
# Create Supergroup 3 : The molecular assembly saveframes.
##########################################################
# Generate the entity saveframe.
mol_res_spin.bmrb_write_entity(star)
# Create Supergroup 4: The experimental descriptions saveframes.
#################################################################
# Generate the method saveframes.
exp_info.bmrb_write_methods(star)
# Generate the software saveframe.
software_ids, software_labels = exp_info.bmrb_write_software(star)
# Create Supergroup 5 : The NMR parameters saveframes.
######################################################
# Generate the CSA saveframe.
star.chem_shift_anisotropy.add(entity_ids=entity_ids, res_nums=res_num_list, res_names=res_name_list, atom_names=atom_name_list, atom_types=element_list, isotope=isotope_list, csa=csa_list)
# Create Supergroup 6 : The kinetic data saveframes.
####################################################
# Generate the relaxation data saveframes.
relax_data.bmrb_write(star)
# Create Supergroup 7 : The thermodynamics saveframes.
######################################################
# Get the relax software id.
for i in range(len(software_ids)):
if software_labels[i] == 'relax':
software_id = software_ids[i]
# Generate the model-free data saveframe.
star.model_free.add(global_chi2=global_chi2, software_ids=[software_id], software_labels=['relax'], entity_ids=entity_ids, res_nums=res_num_list, res_names=res_name_list, atom_names=atom_name_list, atom_types=element_list, isotope=isotope_list, bond_length=r_list, local_tc=local_tm_list, s2=s2_list, s2f=s2f_list, s2s=s2s_list, te=te_list, tf=tf_list, ts=ts_list, rex=rex_list, local_tc_err=local_tm_err_list, s2_err=s2_err_list, s2f_err=s2f_err_list, s2s_err=s2s_err_list, te_err=te_err_list, tf_err=tf_err_list, ts_err=ts_err_list, rex_err=rex_err_list, rex_frq=rex_frq, chi2=chi2_list, model_fit=model_list)
# Create Supergroup 8 : The structure determination saveframes.
###############################################################
# Generate the diffusion tensor saveframes.
diffusion_tensor.bmrb_write(star)
# Write the contents to the STAR formatted file.
star.write()
def minimise_data_setup(data_store, min_algor, num_data_sets, min_options, spin=None, sim_index=None):
"""Set up all the data required for minimisation.
@param data_store: A data storage container.
@type data_store: class instance
@param min_algor: The minimisation algorithm to use.
@type min_algor: str
@param num_data_sets: The number of data sets.
@type num_data_sets: int
@param min_options: The minimisation options array.
@type min_options: list
@keyword spin: The spin data container.
@type spin: SpinContainer instance
@keyword sim_index: The optional MC simulation index.
@type sim_index: int
@return: An insane tuple. The full tuple is (ri_data, ri_data_err, equations, param_types, param_values, r, csa, num_frq, frq, num_ri, remap_table, noe_r1_table, ri_types, num_params, xh_unit_vectors, diff_type, diff_params)
@rtype: tuple
"""
# Initialise the data structures for the model-free function.
data_store.ri_data = []
data_store.ri_data_err = []
data_store.equations = []
data_store.param_types = []
data_store.param_values = None
data_store.r = []
data_store.csa = []
data_store.num_frq = []
data_store.frq = []
data_store.num_ri = []
data_store.remap_table = []
data_store.noe_r1_table = []
data_store.ri_types = []
data_store.gx = []
data_store.gh = []
data_store.num_params = []
data_store.xh_unit_vectors = []
if data_store.model_type == "local_tm":
data_store.mf_params = []
elif data_store.model_type == "diff":
data_store.param_values = []
# Set up the data for the back_calc function.
if min_algor == "back_calc":
# The spin data.
data_store.ri_data = [0.0]
data_store.ri_data_err = [0.000001]
data_store.equations = [spin.equation]
data_store.param_types = [spin.params]
data_store.csa = [spin.csa]
data_store.num_frq = [1]
data_store.frq = [[min_options[3]]]
data_store.num_ri = [1]
data_store.remap_table = [[0]]
data_store.noe_r1_table = [[None]]
data_store.ri_types = [[min_options[2]]]
data_store.gx = [periodic_table.gyromagnetic_ratio(spin.isotope)]
# The interatomic data.
interatoms = return_interatom_list(data_store.spin_id)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if data_store.spin_id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id2)
# The data.
data_store.r = [interatoms[i].r]
data_store.gh = [periodic_table.gyromagnetic_ratio(spin2.isotope)]
if data_store.model_type != "local_tm" and cdp.diff_tensor.type != "sphere":
data_store.xh_unit_vectors = [interatoms[i].vector]
else:
data_store.xh_unit_vectors = [None]
# Count the number of model-free parameters for the spin index.
data_store.num_params = [len(spin.params)]
# Loop over the number of data sets.
for j in range(num_data_sets):
# Set the spin index and get the spin, if not already set.
if data_store.model_type == "diff" or data_store.model_type == "all":
spin_index = j
spin, data_store.spin_id = return_spin_from_index(global_index=spin_index, return_spin_id=True)
# Skip deselected spins.
if not spin.select:
continue
# Skip spins where there is no data or errors.
if not hasattr(spin, "ri_data") or not hasattr(spin, "ri_data_err"):
continue
# Make sure that the errors are strictly positive numbers.
for ri_id in cdp.ri_ids:
# Skip missing data.
if not ri_id in spin.ri_data_err:
continue
# Alias.
err = spin.ri_data_err[ri_id]
# Checks.
if err != None and err == 0.0:
raise RelaxError(
"Zero error for spin '%s' for the relaxation data ID '%s', minimisation not possible."
% (data_store.spin_id, ri_id)
)
elif err != None and err < 0.0:
raise RelaxError(
"Negative error of %s for spin '%s' for the relaxation data ID '%s', minimisation not possible."
% (err, data_store.spin_id, ri_id)
)
# The relaxation data optimisation structures.
data = relax_data_opt_structs(spin, sim_index=sim_index)
# Append the data.
data_store.ri_data.append(data[0])
data_store.ri_data_err.append(data[1])
data_store.num_frq.append(data[2])
data_store.num_ri.append(data[3])
data_store.ri_types.append(data[4])
data_store.frq.append(data[5])
data_store.remap_table.append(data[6])
data_store.noe_r1_table.append(data[7])
if sim_index == None or data_store.model_type == "diff":
data_store.csa.append(spin.csa)
else:
data_store.csa.append(spin.csa_sim[sim_index])
# Repackage the spin data.
data_store.equations.append(spin.equation)
data_store.param_types.append(spin.params)
data_store.gx.append(periodic_table.gyromagnetic_ratio(spin.isotope))
# Repackage the interatomic data.
interatoms = return_interatom_list(data_store.spin_id)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if data_store.spin_id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id2)
# The data.
data_store.gh.append(periodic_table.gyromagnetic_ratio(spin2.isotope))
if sim_index == None or data_store.model_type == "diff" or not hasattr(interatoms[i], "r_sim"):
data_store.r.append(interatoms[i].r)
else:
data_store.r.append(interatoms[i].r_sim[sim_index])
# Vectors.
if data_store.model_type != "local_tm" and cdp.diff_tensor.type != "sphere":
# Check that this is a single vector!
if lib.arg_check.is_num_list(interatoms[i].vector[0], raise_error=False):
raise RelaxMultiVectorError(data_store.spin_id)
# Store the vector.
data_store.xh_unit_vectors.append(interatoms[i].vector)
# No vector.
else:
data_store.xh_unit_vectors.append(None)
# Stop - only one mechanism is current supported.
break
# Model-free parameter values.
if data_store.model_type == "local_tm":
pass
# Count the number of model-free parameters for the spin index.
data_store.num_params.append(len(spin.params))
# Repackage the parameter values for minimising just the diffusion tensor parameters.
if data_store.model_type == "diff":
data_store.param_values.append(assemble_param_vector(model_type="mf"))
# Convert to numpy arrays.
for k in range(len(data_store.ri_data)):
data_store.ri_data[k] = array(data_store.ri_data[k], float64)
data_store.ri_data_err[k] = array(data_store.ri_data_err[k], float64)
# Diffusion tensor type.
if data_store.model_type == "local_tm":
data_store.diff_type = "sphere"
else:
data_store.diff_type = cdp.diff_tensor.type
# Package the diffusion tensor parameters.
data_store.diff_params = None
if data_store.model_type == "mf":
# Spherical diffusion.
if data_store.diff_type == "sphere":
data_store.diff_params = [cdp.diff_tensor.tm]
# Spheroidal diffusion.
elif data_store.diff_type == "spheroid":
data_store.diff_params = [
cdp.diff_tensor.tm,
cdp.diff_tensor.Da,
cdp.diff_tensor.theta,
cdp.diff_tensor.phi,
]
# Ellipsoidal diffusion.
elif data_store.diff_type == "ellipsoid":
data_store.diff_params = [
cdp.diff_tensor.tm,
cdp.diff_tensor.Da,
cdp.diff_tensor.Dr,
cdp.diff_tensor.alpha,
cdp.diff_tensor.beta,
cdp.diff_tensor.gamma,
]
elif min_algor == "back_calc" and data_store.model_type == "local_tm":
# Spherical diffusion.
data_store.diff_params = [spin.local_tm]
def bmrb_write(star):
"""Generate the relaxation data saveframes for the NMR-STAR dictionary object.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
"""
# Get the current data pipe.
cdp = pipes.get_pipe()
# Initialise the spin specific data lists.
mol_name_list = []
res_num_list = []
res_name_list = []
atom_name_list = []
isotope_list = []
element_list = []
attached_atom_name_list = []
attached_isotope_list = []
attached_element_list = []
ri_data_list = []
ri_data_err_list = []
for i in range(len(cdp.ri_ids)):
ri_data_list.append([])
ri_data_err_list.append([])
# Relax data labels.
labels = cdp.ri_ids
exp_label = []
spectro_ids = []
spectro_labels = []
# Store the spin specific data in lists for later use.
for spin, mol_name, res_num, res_name, spin_id in spin_loop(
full_info=True, return_id=True):
# Skip spins with no relaxation data.
if not hasattr(spin, 'ri_data'):
continue
# Check the data for None (not allowed in BMRB!).
if res_num == None:
raise RelaxError(
"For the BMRB, the residue of spin '%s' must be numbered." %
spin_id)
if res_name == None:
raise RelaxError(
"For the BMRB, the residue of spin '%s' must be named." %
spin_id)
if spin.name == None:
raise RelaxError("For the BMRB, the spin '%s' must be named." %
spin_id)
if spin.isotope == None:
raise RelaxError(
"For the BMRB, the spin isotope type of '%s' must be specified."
% spin_id)
# The molecule/residue/spin info.
mol_name_list.append(mol_name)
res_num_list.append(str(res_num))
res_name_list.append(str(res_name))
atom_name_list.append(str(spin.name))
# Interatomic info.
interatoms = return_interatom_list(spin_hash=spin._hash)
if len(interatoms) == 0:
raise RelaxError(
"No interatomic interactions are defined for the spin '%s'." %
spin_id)
if len(interatoms) > 1:
raise RelaxError(
"The BMRB only handles a signal interatomic interaction for the spin '%s'."
% spin_id)
# Get the attached spin.
spin_attached = return_spin(spin_hash=interatoms[0]._spin_hash1)
if id(spin_attached) == id(spin):
spin_attached = return_spin(spin_hash=interatoms[0]._spin_hash2)
# The attached atom info.
if hasattr(spin_attached, 'name'):
attached_atom_name_list.append(str(spin_attached.name))
else:
attached_atom_name_list.append(None)
if hasattr(spin_attached, 'isotope'):
attached_element_list.append(
element_from_isotope(spin_attached.isotope))
attached_isotope_list.append(
str(number_from_isotope(spin_attached.isotope)))
else:
attached_element_list.append(None)
attached_isotope_list.append(None)
# The relaxation data.
used_index = -ones(len(cdp.ri_ids))
for i in range(len(cdp.ri_ids)):
# Data exists.
if cdp.ri_ids[i] in spin.ri_data:
ri_data_list[i].append(str(spin.ri_data[cdp.ri_ids[i]]))
ri_data_err_list[i].append(str(
spin.ri_data_err[cdp.ri_ids[i]]))
else:
ri_data_list[i].append(None)
ri_data_err_list[i].append(None)
# Other info.
isotope_list.append(int(spin.isotope.strip(string.ascii_letters)))
element_list.append(spin.element)
# Convert the molecule names into the entity IDs.
entity_ids = zeros(len(mol_name_list), int32)
mol_names = get_molecule_names()
for i in range(len(mol_name_list)):
for j in range(len(mol_names)):
if mol_name_list[i] == mol_names[j]:
entity_ids[i] = j + 1
# Check the temperature control methods.
if not hasattr(cdp, 'exp_info') or not hasattr(cdp.exp_info,
'temp_calibration'):
raise RelaxError(
"The temperature calibration methods have not been specified.")
if not hasattr(cdp, 'exp_info') or not hasattr(cdp.exp_info,
'temp_control'):
raise RelaxError(
"The temperature control methods have not been specified.")
# Check the peak intensity type.
if not hasattr(cdp, 'exp_info') or not hasattr(cdp.exp_info,
'peak_intensity_type'):
raise RelaxError(
"The peak intensity types measured for the relaxation data have not been specified."
)
# Loop over the relaxation data.
for i in range(len(cdp.ri_ids)):
# Alias.
ri_id = cdp.ri_ids[i]
ri_type = cdp.ri_type[ri_id]
# Convert to MHz.
frq = cdp.spectrometer_frq[ri_id] * 1e-6
# Get the temperature control methods.
temp_calib = cdp.exp_info.temp_calibration[ri_id]
temp_control = cdp.exp_info.temp_control[ri_id]
# Get the peak intensity type.
peak_intensity_type = cdp.exp_info.peak_intensity_type[ri_id]
# Check.
if not temp_calib:
raise RelaxError(
"The temperature calibration method for the '%s' relaxation data ID string has not been specified."
% ri_id)
if not temp_control:
raise RelaxError(
"The temperature control method for the '%s' relaxation data ID string has not been specified."
% ri_id)
# Add the relaxation data.
star.relaxation.add(data_type=ri_type,
frq=frq,
entity_ids=entity_ids,
res_nums=res_num_list,
res_names=res_name_list,
atom_names=atom_name_list,
atom_types=element_list,
isotope=isotope_list,
entity_ids_2=entity_ids,
res_nums_2=res_num_list,
res_names_2=res_name_list,
atom_names_2=attached_atom_name_list,
atom_types_2=attached_element_list,
isotope_2=attached_isotope_list,
data=ri_data_list[i],
errors=ri_data_err_list[i],
temp_calibration=temp_calib,
temp_control=temp_control,
peak_intensity_type=peak_intensity_type)
# The experimental label.
if ri_type == 'NOE':
exp_name = 'steady-state NOE'
else:
exp_name = ri_type
exp_label.append("%s MHz %s" % (frq, exp_name))
# Spectrometer info.
frq_num = 1
for frq in loop_frequencies():
if frq == cdp.spectrometer_frq[ri_id]:
break
frq_num += 1
spectro_ids.append(frq_num)
spectro_labels.append("$spectrometer_%s" % spectro_ids[-1])
# Add the spectrometer info.
num = 1
for frq in loop_frequencies():
star.nmr_spectrometer.add(name="$spectrometer_%s" % num,
manufacturer=None,
model=None,
frq=int(frq / 1e6))
num += 1
# Add the experiment saveframe.
star.experiment.add(name=exp_label,
spectrometer_ids=spectro_ids,
spectrometer_labels=spectro_labels)
def calculate(self, spin_id=None, scaling_matrix=None, verbosity=1, sim_index=None):
"""Calculation of the consistency functions.
@keyword spin_id: The spin identification string.
@type spin_id: None or str
@keyword scaling_matrix: The per-model list of diagonal and square scaling matrices.
@type scaling_matrix: list of numpy rank-2, float64 array or list of None
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword sim_index: The optional MC simulation index.
@type sim_index: None or int
"""
# Test if the frequency has been set.
if not hasattr(cdp, 'ct_frq') or not isinstance(cdp.ct_frq, float):
raise RelaxError("The frequency has not been set up.")
# Test if the sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the spin data has been set.
for spin, id in spin_loop(spin_id, return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Test if the nuclear isotope type has been set.
if not hasattr(spin, 'isotope'):
raise RelaxSpinTypeError
# Test if the CSA value has been set.
if not hasattr(spin, 'csa') or spin.csa == None:
raise RelaxNoValueError("CSA")
# Test if the angle Theta has been set.
if not hasattr(spin, 'orientation') or spin.orientation == None:
raise RelaxNoValueError("angle Theta")
# Test if the correlation time has been set.
if not hasattr(spin, 'tc') or spin.tc == None:
raise RelaxNoValueError("correlation time")
# Test the interatomic data.
interatoms = return_interatom_list(spin_hash=spin._hash)
for interatom in interatoms:
# No relaxation mechanism.
if not interatom.dipole_pair:
continue
# The interacting spin.
if id != interatom.spin_id1:
spin_id2 = interatom.spin_id1
else:
spin_id2 = interatom.spin_id2
spin2 = return_spin(spin_id=spin_id2)
# Test if the nuclear isotope type has been set.
if not hasattr(spin2, 'isotope'):
raise RelaxSpinTypeError
# Test if the interatomic distance has been set.
if not hasattr(interatom, 'r') or interatom.r == None:
raise RelaxNoValueError("interatomic distance", spin_id=spin_id, spin_id2=spin_id2)
# Frequency index.
if cdp.ct_frq not in list(cdp.spectrometer_frq.values()):
raise RelaxError("No relaxation data corresponding to the frequency %s has been loaded." % cdp.ct_frq)
# Consistency testing.
for spin, id in spin_loop(spin_id, return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Set the r1, r2, and NOE to None.
r1 = None
r2 = None
noe = None
# Get the R1, R2, and NOE values corresponding to the set frequency.
for ri_id in cdp.ri_ids:
# The frequency does not match.
if cdp.spectrometer_frq[ri_id] != cdp.ct_frq:
continue
# R1.
if cdp.ri_type[ri_id] == 'R1':
if sim_index == None:
r1 = spin.ri_data[ri_id]
else:
r1 = spin.ri_data_sim[ri_id][sim_index]
# R2.
if cdp.ri_type[ri_id] == 'R2':
if sim_index == None:
r2 = spin.ri_data[ri_id]
else:
r2 = spin.ri_data_sim[ri_id][sim_index]
# NOE.
if cdp.ri_type[ri_id] == 'NOE':
if sim_index == None:
noe = spin.ri_data[ri_id]
else:
noe = spin.ri_data_sim[ri_id][sim_index]
# Skip the spin if not all of the three value exist.
if r1 == None or r2 == None or noe == None:
continue
# Loop over the interatomic data.
interatoms = return_interatom_list(spin_hash=spin._hash)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id=spin_id2)
# Gyromagnetic ratios.
gx = periodic_table.gyromagnetic_ratio(spin.isotope)
gh = periodic_table.gyromagnetic_ratio(spin2.isotope)
# The interatomic distance.
r = interatoms[i].r
# Initialise the function to calculate.
ct = Consistency(frq=cdp.ct_frq, gx=gx, gh=gh, mu0=mu0, h_bar=h_bar)
# Calculate the consistency tests values.
j0, f_eta, f_r2 = ct.func(orientation=spin.orientation, tc=spin.tc, r=r, csa=spin.csa, r1=r1, r2=r2, noe=noe)
# Consistency tests values.
if sim_index == None:
spin.j0 = j0
spin.f_eta = f_eta
spin.f_r2 = f_r2
# Monte Carlo simulated consistency tests values.
else:
# Initialise the simulation data structures.
self.data_init(id, sim=1)
if spin.j0_sim == None:
spin.j0_sim = []
spin.f_eta_sim = []
spin.f_r2_sim = []
# Consistency tests values.
spin.j0_sim.append(j0)
spin.f_eta_sim.append(f_eta)
spin.f_r2_sim.append(f_r2)
def overfit_deselect(self, data_check=True, verbose=True):
"""Deselect spins which have insufficient data to support calculation.
@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
"""
# Print out.
if verbose:
print("\nOver-fit spin deselection:")
# Test the sequence data exists.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Loop over spin data.
deselect_flag = False
spin_count = 0
for spin, spin_id in spin_loop(return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# The interatomic data.
interatoms = return_interatom_list(spin_hash=spin._hash)
# Loop over the interatomic data.
dipole_relax = False
for i in range(len(interatoms)):
# No dipolar relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if spin_id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id=spin_id2)
# Dipolar relaxation flag.
dipole_relax = True
# No relaxation mechanism.
if not dipole_relax or not hasattr(spin, 'csa') or spin.csa == None:
warn(RelaxDeselectWarning(spin_id, 'an absence of relaxation mechanisms'))
spin.select = False
deselect_flag = True
continue
# Data checks.
if data_check:
# The number of relaxation data points (and for infinite data).
data_points = 0
inf_data = False
if hasattr(cdp, 'ri_ids') and hasattr(spin, 'ri_data'):
for id in cdp.ri_ids:
if id in spin.ri_data and spin.ri_data[id] != None:
data_points += 1
# Infinite data!
if isInf(spin.ri_data[id]):
inf_data = True
# Infinite data.
if inf_data:
warn(RelaxDeselectWarning(spin_id, 'infinite relaxation data'))
spin.select = False
deselect_flag = True
continue
# Relaxation data must exist!
if not hasattr(spin, 'ri_data'):
warn(RelaxDeselectWarning(spin_id, 'missing relaxation data'))
spin.select = False
deselect_flag = True
continue
# Require 3 or more relaxation data points.
if data_points < 3:
warn(RelaxDeselectWarning(spin_id, 'insufficient relaxation data, 3 or more data points are required'))
spin.select = False
deselect_flag = True
continue
# Increment the spin number.
spin_count += 1
# No spins selected, so fail hard to prevent the user from going any further.
if spin_count == 0:
warn(RelaxWarning("No spins are selected therefore the optimisation or calculation cannot proceed."))
# Final printout.
if verbose and not deselect_flag:
print("No spins have been deselected.")
def minimise_data_setup(data_store,
min_algor,
num_data_sets,
min_options,
spin=None,
sim_index=None):
"""Set up all the data required for minimisation.
@param data_store: A data storage container.
@type data_store: class instance
@param min_algor: The minimisation algorithm to use.
@type min_algor: str
@param num_data_sets: The number of data sets.
@type num_data_sets: int
@param min_options: The minimisation options array.
@type min_options: list
@keyword spin: The spin data container.
@type spin: SpinContainer instance
@keyword sim_index: The optional MC simulation index.
@type sim_index: int
@return: An insane tuple. The full tuple is (ri_data, ri_data_err, equations, param_types, param_values, r, csa, num_frq, frq, num_ri, remap_table, noe_r1_table, ri_types, num_params, xh_unit_vectors, diff_type, diff_params)
@rtype: tuple
"""
# Initialise the data structures for the model-free function.
data_store.ri_data = []
data_store.ri_data_err = []
data_store.equations = []
data_store.param_types = []
data_store.param_values = None
data_store.r = []
data_store.csa = []
data_store.num_frq = []
data_store.frq = []
data_store.num_ri = []
data_store.remap_table = []
data_store.noe_r1_table = []
data_store.ri_types = []
data_store.gx = []
data_store.gh = []
data_store.num_params = []
data_store.xh_unit_vectors = []
if data_store.model_type == 'local_tm':
data_store.mf_params = []
elif data_store.model_type == 'diff':
data_store.param_values = []
# Set up the data for the back_calc function.
if min_algor == 'back_calc':
# The spin data.
data_store.ri_data = [0.0]
data_store.ri_data_err = [0.000001]
data_store.equations = [spin.equation]
data_store.param_types = [spin.params]
data_store.csa = [spin.csa]
data_store.num_frq = [1]
data_store.frq = [[min_options[3]]]
data_store.num_ri = [1]
data_store.remap_table = [[0]]
data_store.noe_r1_table = [[None]]
data_store.ri_types = [[min_options[2]]]
data_store.gx = [periodic_table.gyromagnetic_ratio(spin.isotope)]
# The interatomic data.
interatoms = return_interatom_list(spin_hash=spin._hash)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if data_store.spin_id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id=spin_id2)
# The data.
data_store.r = [interatoms[i].r]
data_store.gh = [periodic_table.gyromagnetic_ratio(spin2.isotope)]
if data_store.model_type != 'local_tm' and cdp.diff_tensor.type != 'sphere':
data_store.xh_unit_vectors = [interatoms[i].vector]
else:
data_store.xh_unit_vectors = [None]
# Count the number of model-free parameters for the spin index.
data_store.num_params = [len(spin.params)]
# Loop over the number of data sets.
for j in range(num_data_sets):
# Set the spin index and get the spin, if not already set.
if data_store.model_type == 'diff' or data_store.model_type == 'all':
spin_index = j
spin, data_store.spin_id = return_spin_from_index(
global_index=spin_index, return_spin_id=True)
# Skip deselected spins.
if not spin.select:
continue
# Skip spins where there is no data or errors.
if not hasattr(spin, 'ri_data') or not hasattr(spin, 'ri_data_err'):
continue
# Make sure that the errors are strictly positive numbers.
for ri_id in cdp.ri_ids:
# Skip missing data.
if not ri_id in spin.ri_data_err:
continue
# Alias.
err = spin.ri_data_err[ri_id]
# Checks.
if err != None and err == 0.0:
raise RelaxError(
"Zero error for spin '%s' for the relaxation data ID '%s', minimisation not possible."
% (data_store.spin_id, ri_id))
elif err != None and err < 0.0:
raise RelaxError(
"Negative error of %s for spin '%s' for the relaxation data ID '%s', minimisation not possible."
% (err, data_store.spin_id, ri_id))
# The relaxation data optimisation structures.
data = relax_data_opt_structs(spin, sim_index=sim_index)
# Append the data.
data_store.ri_data.append(data[0])
data_store.ri_data_err.append(data[1])
data_store.num_frq.append(data[2])
data_store.num_ri.append(data[3])
data_store.ri_types.append(data[4])
data_store.frq.append(data[5])
data_store.remap_table.append(data[6])
data_store.noe_r1_table.append(data[7])
if sim_index == None or data_store.model_type == 'diff':
data_store.csa.append(spin.csa)
else:
data_store.csa.append(spin.csa_sim[sim_index])
# Repackage the spin data.
data_store.equations.append(spin.equation)
data_store.param_types.append(spin.params)
data_store.gx.append(periodic_table.gyromagnetic_ratio(spin.isotope))
# Repackage the interatomic data.
interatoms = return_interatom_list(spin_hash=spin._hash)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if data_store.spin_id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id=spin_id2)
# The data.
data_store.gh.append(
periodic_table.gyromagnetic_ratio(spin2.isotope))
if sim_index == None or data_store.model_type == 'diff' or not hasattr(
interatoms[i], 'r_sim'):
data_store.r.append(interatoms[i].r)
else:
data_store.r.append(interatoms[i].r_sim[sim_index])
# Vectors.
if data_store.model_type != 'local_tm' and cdp.diff_tensor.type != 'sphere':
# Check that this is a single vector!
if lib.arg_check.is_num_list(interatoms[i].vector[0],
raise_error=False):
raise RelaxMultiVectorError(data_store.spin_id)
# Store the vector.
data_store.xh_unit_vectors.append(interatoms[i].vector)
# No vector.
else:
data_store.xh_unit_vectors.append(None)
# Stop - only one mechanism is current supported.
break
# Model-free parameter values.
if data_store.model_type == 'local_tm':
pass
# Count the number of model-free parameters for the spin index.
data_store.num_params.append(len(spin.params))
# Repackage the parameter values for minimising just the diffusion tensor parameters.
if data_store.model_type == 'diff':
data_store.param_values.append(
assemble_param_vector(model_type='mf'))
# Convert to numpy arrays.
for k in range(len(data_store.ri_data)):
data_store.ri_data[k] = array(data_store.ri_data[k], float64)
data_store.ri_data_err[k] = array(data_store.ri_data_err[k], float64)
# Diffusion tensor type.
if data_store.model_type == 'local_tm':
data_store.diff_type = 'sphere'
else:
data_store.diff_type = cdp.diff_tensor.type
# Package the diffusion tensor parameters.
data_store.diff_params = None
if data_store.model_type == 'mf':
# Spherical diffusion.
if data_store.diff_type == 'sphere':
data_store.diff_params = [cdp.diff_tensor.tm]
# Spheroidal diffusion.
elif data_store.diff_type == 'spheroid':
data_store.diff_params = [
cdp.diff_tensor.tm, cdp.diff_tensor.Da, cdp.diff_tensor.theta,
cdp.diff_tensor.phi
]
# Ellipsoidal diffusion.
elif data_store.diff_type == 'ellipsoid':
data_store.diff_params = [
cdp.diff_tensor.tm, cdp.diff_tensor.Da, cdp.diff_tensor.Dr,
cdp.diff_tensor.alpha, cdp.diff_tensor.beta,
cdp.diff_tensor.gamma
]
elif min_algor == 'back_calc' and data_store.model_type == 'local_tm':
# Spherical diffusion.
data_store.diff_params = [spin.local_tm]
def calculate(self, spin_id=None, verbosity=1, sim_index=None):
"""Calculation of the consistency functions.
@keyword spin_id: The spin identification string.
@type spin_id: None or str
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword sim_index: The optional MC simulation index.
@type sim_index: None or int
"""
# Test if the frequency has been set.
if not hasattr(cdp, 'ct_frq') or not isinstance(cdp.ct_frq, float):
raise RelaxError("The frequency has not been set up.")
# Test if the sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the spin data has been set.
for spin, id in spin_loop(spin_id, return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Test if the nuclear isotope type has been set.
if not hasattr(spin, 'isotope'):
raise RelaxSpinTypeError
# Test if the CSA value has been set.
if not hasattr(spin, 'csa') or spin.csa == None:
raise RelaxNoValueError("CSA")
# Test if the angle Theta has been set.
if not hasattr(spin, 'orientation') or spin.orientation == None:
raise RelaxNoValueError("angle Theta")
# Test if the correlation time has been set.
if not hasattr(spin, 'tc') or spin.tc == None:
raise RelaxNoValueError("correlation time")
# Test the interatomic data.
interatoms = return_interatom_list(id)
for interatom in interatoms:
# No relaxation mechanism.
if not interatom.dipole_pair:
continue
# The interacting spin.
if id != interatom.spin_id1:
spin_id2 = interatom.spin_id1
else:
spin_id2 = interatom.spin_id2
spin2 = return_spin(spin_id2)
# Test if the nuclear isotope type has been set.
if not hasattr(spin2, 'isotope'):
raise RelaxSpinTypeError
# Test if the interatomic distance has been set.
if not hasattr(interatom, 'r') or interatom.r == None:
raise RelaxNoValueError("interatomic distance", spin_id=spin_id, spin_id2=spin_id2)
# Frequency index.
if cdp.ct_frq not in cdp.spectrometer_frq.values():
raise RelaxError("No relaxation data corresponding to the frequency %s has been loaded." % cdp.ct_frq)
# Consistency testing.
for spin, id in spin_loop(spin_id, return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Set the r1, r2, and NOE to None.
r1 = None
r2 = None
noe = None
# Get the R1, R2, and NOE values corresponding to the set frequency.
for ri_id in cdp.ri_ids:
# The frequency does not match.
if cdp.spectrometer_frq[ri_id] != cdp.ct_frq:
continue
# R1.
if cdp.ri_type[ri_id] == 'R1':
if sim_index == None:
r1 = spin.ri_data[ri_id]
else:
r1 = spin.ri_data_sim[ri_id][sim_index]
# R2.
if cdp.ri_type[ri_id] == 'R2':
if sim_index == None:
r2 = spin.ri_data[ri_id]
else:
r2 = spin.ri_data_sim[ri_id][sim_index]
# NOE.
if cdp.ri_type[ri_id] == 'NOE':
if sim_index == None:
noe = spin.ri_data[ri_id]
else:
noe = spin.ri_data_sim[ri_id][sim_index]
# Skip the spin if not all of the three value exist.
if r1 == None or r2 == None or noe == None:
continue
# Loop over the interatomic data.
interatoms = return_interatom_list(id)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id2)
# Gyromagnetic ratios.
gx = return_gyromagnetic_ratio(spin.isotope)
gh = return_gyromagnetic_ratio(spin2.isotope)
# The interatomic distance.
r = interatoms[i].r
# Initialise the function to calculate.
self.ct = Consistency(frq=cdp.ct_frq, gx=gx, gh=gh, mu0=mu0, h_bar=h_bar)
# Calculate the consistency tests values.
j0, f_eta, f_r2 = self.ct.func(orientation=spin.orientation, tc=spin.tc, r=r, csa=spin.csa, r1=r1, r2=r2, noe=noe)
# Consistency tests values.
if sim_index == None:
spin.j0 = j0
spin.f_eta = f_eta
spin.f_r2 = f_r2
# Monte Carlo simulated consistency tests values.
else:
# Initialise the simulation data structures.
self.data_init(spin, sim=1)
if spin.j0_sim == None:
spin.j0_sim = []
spin.f_eta_sim = []
spin.f_r2_sim = []
# Consistency tests values.
spin.j0_sim.append(j0)
spin.f_eta_sim.append(f_eta)
spin.f_r2_sim.append(f_r2)
def _load_model_free_data(self, spin_line, col, data_set, spin, spin_id, verbosity=1):
"""Read the model-free data for the spin.
@param spin_line: The line of data for a single spin.
@type spin_line: list of str
@param col: The column indices.
@type col: dict of int
@param data_set: The data set type, one of 'value', 'error', or 'sim_xxx' (where xxx is
a number).
@type data_set: str
@param spin: The spin container.
@type spin: SpinContainer instance
@param spin_id: The spin identification string.
@type spin_id: str
@keyword verbosity: A variable specifying the amount of information to print. The higher
the value, the greater the verbosity.
@type verbosity: int
"""
# Set up the model-free models.
if data_set == 'value':
# Get the model-free model.
model = spin_line[col['model']]
if model == 'None':
model = None
# Get the model-free equation.
equation = spin_line[col['eqi']]
if equation == 'None':
equation = None
# Get the model-free parameters.
params = eval(spin_line[col['params']])
# Loop over and convert the parameters.
if params:
for i in range(len(params)):
# Fix for the 1.2 relax versions whereby the parameter 'tm' was renamed to 'local_tm' (which occurred in version 1.2.5).
if params[i] == 'tm':
params[i] = 'local_tm'
# Lower case conversion.
params[i] = params[i].lower()
# Set up the model-free model.
self._model_setup(model=model, equation=equation, params=params, spin_id=spin_id)
# The model type.
model_type = spin_line[col['param_set']]
# Get the interatomic data container.
interatom = return_interatom_list(spin_id)[0]
# Values.
if data_set == 'value':
# S2.
try:
spin.s2 = float(spin_line[col['s2']]) * self.return_conversion_factor('s2')
except ValueError:
spin.s2 = None
# S2f.
try:
spin.s2f = float(spin_line[col['s2f']]) * self.return_conversion_factor('s2f')
except ValueError:
spin.s2f = None
# S2s.
try:
spin.s2s = float(spin_line[col['s2s']]) * self.return_conversion_factor('s2s')
except ValueError:
spin.s2s = None
# Local tm.
try:
spin.local_tm = float(spin_line[col['local_tm']]) * self.return_conversion_factor('local_tm')
except ValueError:
spin.local_tm = None
# te.
try:
spin.te = float(spin_line[col['te']]) * self.return_conversion_factor('te')
except ValueError:
spin.te = None
# tf.
try:
spin.tf = float(spin_line[col['tf']]) * self.return_conversion_factor('tf')
except ValueError:
spin.tf = None
# ts.
try:
spin.ts = float(spin_line[col['ts']]) * self.return_conversion_factor('ts')
except ValueError:
spin.ts = None
# Rex.
try:
spin.rex = float(spin_line[col['rex']]) * self.return_conversion_factor('rex')
except ValueError:
spin.rex = None
# CSA.
try:
spin.csa = float(spin_line[col['csa']]) * self.return_conversion_factor('csa')
except ValueError:
spin.csa = None
# Minimisation details (global minimisation results).
if model_type == 'diff' or model_type == 'all':
cdp.chi2 = eval(spin_line[col['chi2']])
cdp.iter = eval(spin_line[col['iter']])
cdp.f_count = eval(spin_line[col['f_count']])
cdp.g_count = eval(spin_line[col['g_count']])
cdp.h_count = eval(spin_line[col['h_count']])
if spin_line[col['warn']] == 'None':
cdp.warning = None
else:
cdp.warning = spin_line[col['warn']].replace('_', ' ')
# Minimisation details (individual residue results).
else:
spin.chi2 = eval(spin_line[col['chi2']])
spin.iter = eval(spin_line[col['iter']])
spin.f_count = eval(spin_line[col['f_count']])
spin.g_count = eval(spin_line[col['g_count']])
spin.h_count = eval(spin_line[col['h_count']])
if spin_line[col['warn']] == 'None':
spin.warning = None
else:
spin.warning = spin_line[col['warn']].replace('_', ' ')
# Interatomic distances.
try:
interatom.r = float(spin_line[col['r']]) * 1e-10
except ValueError:
interatom.r = None
# Errors.
if data_set == 'error':
# S2.
try:
spin.s2_err = float(spin_line[col['s2']]) * self.return_conversion_factor('s2')
except ValueError:
spin.s2_err = None
# S2f.
try:
spin.s2f_err = float(spin_line[col['s2f']]) * self.return_conversion_factor('s2f')
except ValueError:
spin.s2f_err = None
# S2s.
try:
spin.s2s_err = float(spin_line[col['s2s']]) * self.return_conversion_factor('s2s')
except ValueError:
spin.s2s_err = None
# Local tm.
try:
spin.local_tm_err = float(spin_line[col['local_tm']]) * self.return_conversion_factor('local_tm')
except ValueError:
spin.local_tm_err = None
# te.
try:
spin.te_err = float(spin_line[col['te']]) * self.return_conversion_factor('te')
except ValueError:
spin.te_err = None
# tf.
try:
spin.tf_err = float(spin_line[col['tf']]) * self.return_conversion_factor('tf')
except ValueError:
spin.tf_err = None
# ts.
try:
spin.ts_err = float(spin_line[col['ts']]) * self.return_conversion_factor('ts')
except ValueError:
spin.ts_err = None
# Rex.
try:
spin.rex_err = float(spin_line[col['rex']]) * self.return_conversion_factor('rex')
except ValueError:
spin.rex_err = None
# CSA.
try:
spin.csa_err = float(spin_line[col['csa']]) * self.return_conversion_factor('csa')
except ValueError:
spin.csa_err = None
# Construct the simulation data structures.
if data_set == 'sim_0':
# Get the parameter object names.
param_names = self.data_names(set='params', scope='spin')
# Get the minimisation statistic object names.
min_names = self.data_names(set='min', scope='spin')
# Loop over all the parameter names.
for object_name in param_names:
# Name for the simulation object.
sim_object_name = object_name + '_sim'
# Create the simulation object.
setattr(spin, sim_object_name, [])
# Loop over all the minimisation object names.
for object_name in min_names:
# Name for the simulation object.
sim_object_name = object_name + '_sim'
# Create the simulation object.
if model_type == 'diff' or model_type == 'all':
setattr(cdp, sim_object_name, [])
object = getattr(cdp, sim_object_name)
object = []
else:
setattr(spin, sim_object_name, [])
# Simulations.
if data_set != 'value' and data_set != 'error':
# S2.
try:
spin.s2_sim.append(float(spin_line[col['s2']]) * self.return_conversion_factor('s2'))
except ValueError:
spin.s2_sim.append(None)
# S2f.
try:
spin.s2f_sim.append(float(spin_line[col['s2f']]) * self.return_conversion_factor('s2f'))
except ValueError:
spin.s2f_sim.append(None)
# S2s.
try:
spin.s2s_sim.append(float(spin_line[col['s2s']]) * self.return_conversion_factor('s2s'))
except ValueError:
spin.s2s_sim.append(None)
# Local tm.
try:
spin.local_tm_sim.append(float(spin_line[col['local_tm']]) * self.return_conversion_factor('local_tm'))
except ValueError:
spin.local_tm_sim.append(None)
# te.
try:
spin.te_sim.append(float(spin_line[col['te']]) * self.return_conversion_factor('te'))
except ValueError:
spin.te_sim.append(None)
# tf.
try:
spin.tf_sim.append(float(spin_line[col['tf']]) * self.return_conversion_factor('tf'))
except ValueError:
spin.tf_sim.append(None)
# ts.
try:
spin.ts_sim.append(float(spin_line[col['ts']]) * self.return_conversion_factor('ts'))
except ValueError:
spin.ts_sim.append(None)
# Rex.
try:
spin.rex_sim.append(float(spin_line[col['rex']]) * self.return_conversion_factor('rex'))
except ValueError:
spin.rex_sim.append(None)
# CSA.
try:
spin.csa_sim.append(float(spin_line[col['csa']]) * self.return_conversion_factor('csa'))
except ValueError:
spin.csa_sim.append(None)
# Minimisation details (global minimisation results).
if model_type == 'diff' or model_type == 'all':
# The simulation index.
index = int(data_set.split('_')[1])
# Already loaded.
if len(cdp.chi2_sim) == index + 1:
return
# Set the values.
cdp.chi2_sim.append(eval(spin_line[col['chi2']]))
cdp.iter_sim.append(eval(spin_line[col['iter']]))
cdp.f_count_sim.append(eval(spin_line[col['f_count']]))
cdp.g_count_sim.append(eval(spin_line[col['g_count']]))
cdp.h_count_sim.append(eval(spin_line[col['h_count']]))
if spin_line[col['warn']] == 'None':
cdp.warning_sim.append(None)
else:
cdp.warning_sim.append(spin_line[col['warn']].replace('_', ' '))
# Minimisation details (individual residue results).
else:
spin.chi2_sim.append(eval(spin_line[col['chi2']]))
spin.iter_sim.append(eval(spin_line[col['iter']]))
spin.f_count_sim.append(eval(spin_line[col['f_count']]))
spin.g_count_sim.append(eval(spin_line[col['g_count']]))
spin.h_count_sim.append(eval(spin_line[col['h_count']]))
if spin_line[col['warn']] == 'None':
spin.warning_sim.append(None)
else:
spin.warning_sim.append(spin_line[col['warn']].replace('_', ' '))
def load_model_free_data(spin_line, col, data_set, spin, spin_id, verbosity=1):
"""Read the model-free data for the spin.
@param spin_line: The line of data for a single spin.
@type spin_line: list of str
@param col: The column indices.
@type col: dict of int
@param data_set: The data set type, one of 'value', 'error', or 'sim_xxx' (where xxx is
a number).
@type data_set: str
@param spin: The spin container.
@type spin: SpinContainer instance
@param spin_id: The spin identification string.
@type spin_id: str
@keyword verbosity: A variable specifying the amount of information to print. The higher
the value, the greater the verbosity.
@type verbosity: int
"""
# Set up the model-free models.
if data_set == 'value':
# Get the model-free model.
model = spin_line[col['model']]
if model == 'None':
model = None
# Get the model-free equation.
equation = spin_line[col['eqi']]
if equation == 'None':
equation = None
# Get the model-free parameters.
params = eval(spin_line[col['params']])
# Loop over and convert the parameters.
if params:
for i in range(len(params)):
# Fix for the 1.2 relax versions whereby the parameter 'tm' was renamed to 'local_tm' (which occurred in version 1.2.5).
if params[i] == 'tm':
params[i] = 'local_tm'
# Lower case conversion.
params[i] = params[i].lower()
# Set up the model-free model.
model_setup(model=model,
equation=equation,
params=params,
spin_id=spin_id)
# The model type.
model_type = spin_line[col['param_set']]
# Get the interatomic data container.
spin = return_spin(spin_id)
interatom = return_interatom_list(spin_hash=spin._hash)[0]
# Values.
if data_set == 'value':
# S2.
try:
spin.s2 = float(spin_line[
col['s2']]) * api_model_free.return_conversion_factor('s2')
except ValueError:
spin.s2 = None
# S2f.
try:
spin.s2f = float(spin_line[
col['s2f']]) * api_model_free.return_conversion_factor('s2f')
except ValueError:
spin.s2f = None
# S2s.
try:
spin.s2s = float(spin_line[
col['s2s']]) * api_model_free.return_conversion_factor('s2s')
except ValueError:
spin.s2s = None
# Local tm.
try:
spin.local_tm = float(
spin_line[col['local_tm']]
) * api_model_free.return_conversion_factor('local_tm')
except ValueError:
spin.local_tm = None
# te.
try:
spin.te = float(spin_line[
col['te']]) * api_model_free.return_conversion_factor('te')
except ValueError:
spin.te = None
# tf.
try:
spin.tf = float(spin_line[
col['tf']]) * api_model_free.return_conversion_factor('tf')
except ValueError:
spin.tf = None
# ts.
try:
spin.ts = float(spin_line[
col['ts']]) * api_model_free.return_conversion_factor('ts')
except ValueError:
spin.ts = None
# Rex.
try:
spin.rex = float(spin_line[
col['rex']]) * api_model_free.return_conversion_factor('rex')
except ValueError:
spin.rex = None
# CSA.
try:
spin.csa = float(spin_line[
col['csa']]) * api_model_free.return_conversion_factor('csa')
except ValueError:
spin.csa = None
# Minimisation details (global minimisation results).
if model_type == 'diff' or model_type == 'all':
cdp.chi2 = eval(spin_line[col['chi2']])
cdp.iter = eval(spin_line[col['iter']])
cdp.f_count = eval(spin_line[col['f_count']])
cdp.g_count = eval(spin_line[col['g_count']])
cdp.h_count = eval(spin_line[col['h_count']])
if spin_line[col['warn']] == 'None':
cdp.warning = None
else:
cdp.warning = spin_line[col['warn']].replace('_', ' ')
# Minimisation details (individual residue results).
else:
spin.chi2 = eval(spin_line[col['chi2']])
spin.iter = eval(spin_line[col['iter']])
spin.f_count = eval(spin_line[col['f_count']])
spin.g_count = eval(spin_line[col['g_count']])
spin.h_count = eval(spin_line[col['h_count']])
if spin_line[col['warn']] == 'None':
spin.warning = None
else:
spin.warning = spin_line[col['warn']].replace('_', ' ')
# Interatomic distances.
try:
interatom.r = float(spin_line[col['r']]) * 1e-10
except ValueError:
interatom.r = None
# Errors.
if data_set == 'error':
# S2.
try:
spin.s2_err = float(spin_line[
col['s2']]) * api_model_free.return_conversion_factor('s2')
except ValueError:
spin.s2_err = None
# S2f.
try:
spin.s2f_err = float(spin_line[
col['s2f']]) * api_model_free.return_conversion_factor('s2f')
except ValueError:
spin.s2f_err = None
# S2s.
try:
spin.s2s_err = float(spin_line[
col['s2s']]) * api_model_free.return_conversion_factor('s2s')
except ValueError:
spin.s2s_err = None
# Local tm.
try:
spin.local_tm_err = float(
spin_line[col['local_tm']]
) * api_model_free.return_conversion_factor('local_tm')
except ValueError:
spin.local_tm_err = None
# te.
try:
spin.te_err = float(spin_line[
col['te']]) * api_model_free.return_conversion_factor('te')
except ValueError:
spin.te_err = None
# tf.
try:
spin.tf_err = float(spin_line[
col['tf']]) * api_model_free.return_conversion_factor('tf')
except ValueError:
spin.tf_err = None
# ts.
try:
spin.ts_err = float(spin_line[
col['ts']]) * api_model_free.return_conversion_factor('ts')
except ValueError:
spin.ts_err = None
# Rex.
try:
spin.rex_err = float(spin_line[
col['rex']]) * api_model_free.return_conversion_factor('rex')
except ValueError:
spin.rex_err = None
# CSA.
try:
spin.csa_err = float(spin_line[
col['csa']]) * api_model_free.return_conversion_factor('csa')
except ValueError:
spin.csa_err = None
# Construct the simulation data structures.
if data_set == 'sim_0':
# Get the parameter object names.
param_names = api_model_free.data_names(set='params', scope='spin')
# Get the minimisation statistic object names.
min_names = api_model_free.data_names(set='min', scope='spin')
# Loop over all the parameter names.
for object_name in param_names:
# Name for the simulation object.
sim_object_name = object_name + '_sim'
# Create the simulation object.
setattr(spin, sim_object_name, [])
# Loop over all the minimisation object names.
for object_name in min_names:
# Name for the simulation object.
sim_object_name = object_name + '_sim'
# Create the simulation object.
if model_type == 'diff' or model_type == 'all':
setattr(cdp, sim_object_name, [])
object = getattr(cdp, sim_object_name)
object = []
else:
setattr(spin, sim_object_name, [])
# Simulations.
if data_set != 'value' and data_set != 'error':
# S2.
try:
spin.s2_sim.append(
float(spin_line[col['s2']]) *
api_model_free.return_conversion_factor('s2'))
except ValueError:
spin.s2_sim.append(None)
# S2f.
try:
spin.s2f_sim.append(
float(spin_line[col['s2f']]) *
api_model_free.return_conversion_factor('s2f'))
except ValueError:
spin.s2f_sim.append(None)
# S2s.
try:
spin.s2s_sim.append(
float(spin_line[col['s2s']]) *
api_model_free.return_conversion_factor('s2s'))
except ValueError:
spin.s2s_sim.append(None)
# Local tm.
try:
spin.local_tm_sim.append(
float(spin_line[col['local_tm']]) *
api_model_free.return_conversion_factor('local_tm'))
except ValueError:
spin.local_tm_sim.append(None)
# te.
try:
spin.te_sim.append(
float(spin_line[col['te']]) *
api_model_free.return_conversion_factor('te'))
except ValueError:
spin.te_sim.append(None)
# tf.
try:
spin.tf_sim.append(
float(spin_line[col['tf']]) *
api_model_free.return_conversion_factor('tf'))
except ValueError:
spin.tf_sim.append(None)
# ts.
try:
spin.ts_sim.append(
float(spin_line[col['ts']]) *
api_model_free.return_conversion_factor('ts'))
except ValueError:
spin.ts_sim.append(None)
# Rex.
try:
spin.rex_sim.append(
float(spin_line[col['rex']]) *
api_model_free.return_conversion_factor('rex'))
except ValueError:
spin.rex_sim.append(None)
# CSA.
try:
spin.csa_sim.append(
float(spin_line[col['csa']]) *
api_model_free.return_conversion_factor('csa'))
except ValueError:
spin.csa_sim.append(None)
# Minimisation details (global minimisation results).
if model_type == 'diff' or model_type == 'all':
# The simulation index.
index = int(data_set.split('_')[1])
# Already loaded.
if len(cdp.chi2_sim) == index + 1:
return
# Set the values.
cdp.chi2_sim.append(eval(spin_line[col['chi2']]))
cdp.iter_sim.append(eval(spin_line[col['iter']]))
cdp.f_count_sim.append(eval(spin_line[col['f_count']]))
cdp.g_count_sim.append(eval(spin_line[col['g_count']]))
cdp.h_count_sim.append(eval(spin_line[col['h_count']]))
if spin_line[col['warn']] == 'None':
cdp.warning_sim.append(None)
else:
cdp.warning_sim.append(spin_line[col['warn']].replace(
'_', ' '))
# Minimisation details (individual residue results).
else:
spin.chi2_sim.append(eval(spin_line[col['chi2']]))
spin.iter_sim.append(eval(spin_line[col['iter']]))
spin.f_count_sim.append(eval(spin_line[col['f_count']]))
spin.g_count_sim.append(eval(spin_line[col['g_count']]))
spin.h_count_sim.append(eval(spin_line[col['h_count']]))
if spin_line[col['warn']] == 'None':
spin.warning_sim.append(None)
else:
spin.warning_sim.append(spin_line[col['warn']].replace(
'_', ' '))
def test_angles(self):
"""The user function angles()."""
# Execute the script.
self.script_exec(status.install_path + sep+'test_suite'+sep+'system_tests'+sep+'scripts'+sep+'angles.py')
# Res info.
res_name = ['GLY', 'PRO', 'LEU', 'GLY', 'SER', 'MET', 'ASP', 'SER', 'PRO', 'PRO', 'GLU', 'GLY']
spin_num = [1, 10, 24, 43, 50, 61, 78, 90, 101, 115, 129, 144]
spin_name = ['N']*12
attached_atoms = [None, None, 'H', 'H', 'H', 'H', 'H', 'H', None, None, 'H', 'H']
xh_vects = [
None,
None,
[0.408991870425, -0.805744582632, 0.428370537602],
[-0.114123686687, -0.989411605119, -0.0896686109685],
[-0.0162975723187, -0.975817142584, 0.217980029763],
[-0.255934111969, -0.960517663248, -0.109103386377],
[0.922628022844, 0.38092966093, 0.0604162634271],
[0.926402811426, 0.281593806116, 0.249965516299],
None,
None,
[0.820296708196, 0.570330671495, -0.0428513205774],
[-0.223383112106, -0.034680483158, -0.974113571055]
]
alpha = [None, None, 2.8102691247870459, 2.6063738282640672, 2.9263088853837358, 2.5181004004450211, 1.3361463581932049, 1.5031623128368377, None, None, 1.0968465542222101, 1.1932423104331247]
# Molecule checks.
self.assertEqual(len(cdp.mol), 1)
self.assertEqual(cdp.mol[0].name, 'Ap4Aase_res1-12_mol1')
self.assertEqual(len(cdp.mol[0].res), 12)
# Checks for the first 12 residues.
for i in range(12):
# Check the residue and spin info.
self.assertEqual(cdp.mol[0].res[i].num, i+1)
self.assertEqual(cdp.mol[0].res[i].name, res_name[i])
self.assertEqual(cdp.mol[0].res[i].spin[0].num, spin_num[i])
self.assertEqual(cdp.mol[0].res[i].spin[0].name, spin_name[i])
# Get the interatomic container.
interatoms = return_interatom_list(cdp.mol[0].res[i].spin[0]._spin_ids[0])
# Check the containers.
self.assert_(len(interatoms) <= 1)
# No interatomic container.
if not interatoms:
# The spin info.
self.assertEqual(len(cdp.mol[0].res[i].spin), 1)
# Check the interatomic info.
else:
# The spin info.
self.assertEqual(len(cdp.mol[0].res[i].spin), 2)
self.assertEqual(cdp.mol[0].res[i].spin[1].name, attached_atoms[i])
# The vector.
for j in range(3):
self.assertAlmostEqual(interatoms[0].vector[j], xh_vects[i][j])
# Check the alpha angles.
self.assertAlmostEqual(interatoms[0].alpha, alpha[i])
def bmrb_write(star):
"""Generate the relaxation data saveframes for the NMR-STAR dictionary object.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
"""
# Get the current data pipe.
cdp = pipes.get_pipe()
# Initialise the spin specific data lists.
mol_name_list = []
res_num_list = []
res_name_list = []
atom_name_list = []
isotope_list = []
element_list = []
attached_atom_name_list = []
attached_isotope_list = []
attached_element_list = []
ri_data_list = []
ri_data_err_list = []
for i in range(len(cdp.ri_ids)):
ri_data_list.append([])
ri_data_err_list.append([])
# Relax data labels.
labels = cdp.ri_ids
exp_label = []
spectro_ids = []
spectro_labels = []
# Store the spin specific data in lists for later use.
for spin, mol_name, res_num, res_name, spin_id in spin_loop(full_info=True, return_id=True):
# Skip spins with no relaxation data.
if not hasattr(spin, 'ri_data'):
continue
# Check the data for None (not allowed in BMRB!).
if res_num == None:
raise RelaxError("For the BMRB, the residue of spin '%s' must be numbered." % spin_id)
if res_name == None:
raise RelaxError("For the BMRB, the residue of spin '%s' must be named." % spin_id)
if spin.name == None:
raise RelaxError("For the BMRB, the spin '%s' must be named." % spin_id)
if spin.isotope == None:
raise RelaxError("For the BMRB, the spin isotope type of '%s' must be specified." % spin_id)
# The molecule/residue/spin info.
mol_name_list.append(mol_name)
res_num_list.append(str(res_num))
res_name_list.append(str(res_name))
atom_name_list.append(str(spin.name))
# Interatomic info.
interatoms = return_interatom_list(spin_id)
if len(interatoms) == 0:
raise RelaxError("No interatomic interactions are defined for the spin '%s'." % spin_id)
if len(interatoms) > 1:
raise RelaxError("The BMRB only handles a signal interatomic interaction for the spin '%s'." % spin_id)
# Get the attached spin.
spin_attached = return_spin(interatoms[0].spin_id1)
if id(spin_attached) == id(spin):
spin_attached = return_spin(interatoms[0].spin_id2)
# The attached atom info.
if hasattr(spin_attached, 'name'):
attached_atom_name_list.append(str(spin_attached.name))
else:
attached_atom_name_list.append(None)
if hasattr(spin_attached, 'isotope'):
attached_element_list.append(element_from_isotope(spin_attached.isotope))
attached_isotope_list.append(str(number_from_isotope(spin_attached.isotope)))
else:
attached_element_list.append(None)
attached_isotope_list.append(None)
# The relaxation data.
used_index = -ones(len(cdp.ri_ids))
for i in range(len(cdp.ri_ids)):
# Data exists.
if cdp.ri_ids[i] in list(spin.ri_data.keys()):
ri_data_list[i].append(str(spin.ri_data[cdp.ri_ids[i]]))
ri_data_err_list[i].append(str(spin.ri_data_err[cdp.ri_ids[i]]))
else:
ri_data_list[i].append(None)
ri_data_err_list[i].append(None)
# Other info.
isotope_list.append(int(spin.isotope.strip(string.ascii_letters)))
element_list.append(spin.element)
# Convert the molecule names into the entity IDs.
entity_ids = zeros(len(mol_name_list), int32)
mol_names = get_molecule_names()
for i in range(len(mol_name_list)):
for j in range(len(mol_names)):
if mol_name_list[i] == mol_names[j]:
entity_ids[i] = j+1
# Check the temperature control methods.
if not hasattr(cdp, 'exp_info') or not hasattr(cdp.exp_info, 'temp_calibration'):
raise RelaxError("The temperature calibration methods have not been specified.")
if not hasattr(cdp, 'exp_info') or not hasattr(cdp.exp_info, 'temp_control'):
raise RelaxError("The temperature control methods have not been specified.")
# Check the peak intensity type.
if not hasattr(cdp, 'exp_info') or not hasattr(cdp.exp_info, 'peak_intensity_type'):
raise RelaxError("The peak intensity types measured for the relaxation data have not been specified.")
# Loop over the relaxation data.
for i in range(len(cdp.ri_ids)):
# Alias.
ri_id = cdp.ri_ids[i]
ri_type = cdp.ri_type[ri_id]
# Convert to MHz.
frq = cdp.spectrometer_frq[ri_id] * 1e-6
# Get the temperature control methods.
temp_calib = cdp.exp_info.temp_calibration[ri_id]
temp_control = cdp.exp_info.temp_control[ri_id]
# Get the peak intensity type.
peak_intensity_type = cdp.exp_info.peak_intensity_type[ri_id]
# Check.
if not temp_calib:
raise RelaxError("The temperature calibration method for the '%s' relaxation data ID string has not been specified." % ri_id)
if not temp_control:
raise RelaxError("The temperature control method for the '%s' relaxation data ID string has not been specified." % ri_id)
# Add the relaxation data.
star.relaxation.add(data_type=ri_type, frq=frq, entity_ids=entity_ids, res_nums=res_num_list, res_names=res_name_list, atom_names=atom_name_list, atom_types=element_list, isotope=isotope_list, entity_ids_2=entity_ids, res_nums_2=res_num_list, res_names_2=res_name_list, atom_names_2=attached_atom_name_list, atom_types_2=attached_element_list, isotope_2=attached_isotope_list, data=ri_data_list[i], errors=ri_data_err_list[i], temp_calibration=temp_calib, temp_control=temp_control, peak_intensity_type=peak_intensity_type)
# The experimental label.
if ri_type == 'NOE':
exp_name = 'steady-state NOE'
else:
exp_name = ri_type
exp_label.append("%s MHz %s" % (frq, exp_name))
# Spectrometer info.
frq_num = 1
for frq in loop_frequencies():
if frq == cdp.spectrometer_frq[ri_id]:
break
frq_num += 1
spectro_ids.append(frq_num)
spectro_labels.append("$spectrometer_%s" % spectro_ids[-1])
# Add the spectrometer info.
num = 1
for frq in loop_frequencies():
star.nmr_spectrometer.add(name="$spectrometer_%s" % num, manufacturer=None, model=None, frq=int(frq/1e6))
num += 1
# Add the experiment saveframe.
star.experiment.add(name=exp_label, spectrometer_ids=spectro_ids, spectrometer_labels=spectro_labels)
def create_script(file, model_type, algor):
"""Create the Dasha script file.
@param file: The opened file descriptor.
@type file: file object
@param model_type: The model-free model type.
@type model_type: str
@param algor: The optimisation algorithm to use. This can be the Levenberg-Marquardt algorithm 'LM' or the Newton-Raphson algorithm 'NR'.
@type algor: str
"""
# Delete all data.
file.write("# Delete all data.\n")
file.write("del 1 10000\n")
# Nucleus type.
file.write("\n# Nucleus type.\n")
nucleus = None
for spin in spin_loop():
# Skip protons.
if spin.isotope == '1H':
continue
# Can only handle one spin type.
if nucleus and spin.isotope != nucleus:
raise RelaxError(
"The nuclei '%s' and '%s' do not match, relax can only handle one nucleus type in Dasha."
% (nucleus, spin.isotope))
# Set the nucleus.
if not nucleus:
nucleus = spin.isotope
# Convert the name and write it.
if nucleus == '15N':
nucleus = 'N15'
elif nucleus == '13C':
nucleus = 'C13'
else:
raise RelaxError("Cannot handle the nucleus type '%s' within Dasha." %
nucleus)
file.write("set nucl %s\n" % nucleus)
# Number of frequencies.
file.write("\n# Number of frequencies.\n")
file.write("set n_freq %s\n" % cdp.spectrometer_frq_count)
# Frequency values.
file.write("\n# Frequency values.\n")
count = 1
for frq in loop_frequencies():
file.write("set H1_freq %s %s\n" % (frq / 1e6, count))
count += 1
# Set the diffusion tensor.
file.write("\n# Set the diffusion tensor.\n")
if model_type != 'local_tm':
# Sphere.
if cdp.diff_tensor.type == 'sphere':
file.write("set tr %s\n" % (cdp.diff_tensor.tm / 1e-9))
# Spheroid.
elif cdp.diff_tensor.type == 'spheroid':
file.write('set tr %s\n' % (cdp.diff_tensor.tm / 1e-9))
# Ellipsoid.
elif cdp.diff_tensor.type == 'ellipsoid':
# Get the eigenvales.
Dx, Dy, Dz = diffusion_tensor.return_eigenvalues()
# Geometric parameters.
file.write("set tr %s\n" % (cdp.diff_tensor.tm / 1e-9))
file.write("set D1/D3 %s\n" % (Dx / Dz))
file.write("set D2/D3 %s\n" % (Dy / Dz))
# Orientational parameters.
file.write("set alfa %s\n" % (cdp.diff_tensor.alpha /
(2.0 * pi) * 360.0))
file.write("set betta %s\n" % (cdp.diff_tensor.beta /
(2.0 * pi) * 360.0))
file.write("set gamma %s\n" % (cdp.diff_tensor.gamma /
(2.0 * pi) * 360.0))
# Reading the relaxation data.
file.write("\n# Reading the relaxation data.\n")
file.write("echo Reading the relaxation data.\n")
noe_index = 1
r1_index = 1
r2_index = 1
for ri_id in cdp.ri_ids:
# NOE.
if cdp.ri_type[ri_id] == 'NOE':
# Data set number.
number = noe_index
# Data type.
data_type = 'noe'
# Increment the data set index.
noe_index = noe_index + 1
# R1.
elif cdp.ri_type[ri_id] == 'R1':
# Data set number.
number = r1_index
# Data type.
data_type = '1/T1'
# Increment the data set index.
r1_index = r1_index + 1
# R2.
elif cdp.ri_type[ri_id] == 'R2':
# Data set number.
number = r2_index
# Data type.
data_type = '1/T2'
# Increment the data set index.
r2_index = r2_index + 1
# Set the data type.
if number == 1:
file.write("\nread < %s\n" % data_type)
else:
file.write("\nread < %s %s\n" % (data_type, number))
# The relaxation data.
for residue in residue_loop():
# Alias the spin.
spin = residue.spin[0]
# Skip deselected spins.
if not spin.select:
continue
# Skip and deselect spins for which relaxation data is missing.
if len(spin.ri_data) != len(
cdp.ri_ids) or spin.ri_data[ri_id] == None:
spin.select = False
continue
# Data and errors.
file.write(
"%s %s %s\n" %
(residue.num, spin.ri_data[ri_id], spin.ri_data_err[ri_id]))
# Terminate the reading.
file.write("exit\n")
# Individual residue optimisation.
if model_type == 'mf':
# Loop over the residues.
for residue in residue_loop():
# Alias the spin.
spin = residue.spin[0]
# Skip deselected spins.
if not spin.select:
continue
# Get the interatomic data containers.
interatoms = return_interatom_list(spin_hash=spin._hash)
if len(interatoms) == 0:
raise RelaxNoInteratomError
elif len(interatoms) > 1:
raise RelaxError(
"Only one interatomic data container, hence dipole-dipole interaction, is supported per spin."
)
# Comment.
file.write("\n\n\n# Residue %s\n\n" % residue.num)
# Echo.
file.write("echo Optimisation of residue %s\n" % residue.num)
# Select the spin.
file.write("\n# Select the residue.\n")
file.write("set cres %s\n" % residue.num)
# The angle alpha of the XH vector in the spheroid diffusion frame.
if cdp.diff_tensor.type == 'spheroid':
file.write("set teta %s\n" % spin.alpha)
# The angles theta and phi of the XH vector in the ellipsoid diffusion frame.
elif cdp.diff_tensor.type == 'ellipsoid':
file.write(
"\n# Setting the spherical angles of the XH vector in the ellipsoid diffusion frame.\n"
)
file.write("set teta %s\n" % spin.theta)
file.write("set fi %s\n" % spin.phi)
# The 'jmode'.
if 'ts' in spin.params:
jmode = 3
elif 'te' in spin.params:
jmode = 2
elif 's2' in spin.params:
jmode = 1
# Chemical exchange.
if 'rex' in spin.params:
exch = True
else:
exch = False
# Anisotropic diffusion.
if cdp.diff_tensor.type == 'sphere':
anis = False
else:
anis = True
# Axial symmetry.
if cdp.diff_tensor.type == 'spheroid':
sym = True
else:
sym = False
# Set the jmode.
file.write("\n# Set the jmode.\n")
file.write("set def jmode %s" % jmode)
if exch:
file.write(" exch")
if anis:
file.write(" anis")
if sym:
file.write(" sym")
file.write("\n")
# Parameter default values.
file.write("\n# Parameter default values.\n")
file.write("reset jmode %s\n" % residue.num)
# Bond length.
file.write("\n# Bond length.\n")
file.write("set r_hx %s\n" % (interatoms[0].r / 1e-10))
# CSA value.
file.write("\n# CSA value.\n")
file.write("set csa %s\n" % (spin.csa / 1e-6))
# Fix the tf parameter if it isn't in the model.
if not 'tf' in spin.params and jmode == 3:
file.write("\n# Fix the tf parameter.\n")
file.write("fix tf 0\n")
# Optimisation of all residues.
file.write("\n\n\n# Optimisation of all residues.\n")
if algor == 'LM':
file.write("lmin %s %s" %
(first_residue_num(), last_residue_num()))
elif algor == 'NR':
file.write("min %s %s" % (first_residue_num(), last_residue_num()))
# Show the results.
file.write("\n# Show the results.\n")
file.write("echo\n")
file.write("show all\n")
# Write the results.
file.write("\n# Write the results.\n")
file.write("write s2.out S\n")
file.write("write s2f.out Sf\n")
file.write("write s2s.out Ss\n")
file.write("write te.out te\n")
file.write("write tf.out tf\n")
file.write("write ts.out ts\n")
file.write("write rex.out rex\n")
file.write("write chi2.out F\n")
else:
raise RelaxError(
"Optimisation of the parameter set '%s' currently not supported." %
model_type)
def create_script(file, model_type, algor):
"""Create the Dasha script file.
@param file: The opened file descriptor.
@type file: file object
@param model_type: The model-free model type.
@type model_type: str
@param algor: The optimisation algorithm to use. This can be the Levenberg-Marquardt algorithm 'LM' or the Newton-Raphson algorithm 'NR'.
@type algor: str
"""
# Delete all data.
file.write("# Delete all data.\n")
file.write("del 1 10000\n")
# Nucleus type.
file.write("\n# Nucleus type.\n")
nucleus = None
for spin in spin_loop():
# Skip protons.
if spin.isotope == '1H':
continue
# Can only handle one spin type.
if nucleus and spin.isotope != nucleus:
raise RelaxError("The nuclei '%s' and '%s' do not match, relax can only handle one nucleus type in Dasha." % (nucleus, spin.isotope))
# Set the nucleus.
if not nucleus:
nucleus = spin.isotope
# Convert the name and write it.
if nucleus == '15N':
nucleus = 'N15'
elif nucleus == '13C':
nucleus = 'C13'
else:
raise RelaxError("Cannot handle the nucleus type '%s' within Dasha." % nucleus)
file.write("set nucl %s\n" % nucleus)
# Number of frequencies.
file.write("\n# Number of frequencies.\n")
file.write("set n_freq %s\n" % cdp.spectrometer_frq_count)
# Frequency values.
file.write("\n# Frequency values.\n")
count = 1
for frq in loop_frequencies():
file.write("set H1_freq %s %s\n" % (frq / 1e6, count))
count += 1
# Set the diffusion tensor.
file.write("\n# Set the diffusion tensor.\n")
if model_type != 'local_tm':
# Sphere.
if cdp.diff_tensor.type == 'sphere':
file.write("set tr %s\n" % (cdp.diff_tensor.tm / 1e-9))
# Spheroid.
elif cdp.diff_tensor.type == 'spheroid':
file.write('set tr %s\n' % (cdp.diff_tensor.tm / 1e-9))
# Ellipsoid.
elif cdp.diff_tensor.type == 'ellipsoid':
# Get the eigenvales.
Dx, Dy, Dz = diffusion_tensor.return_eigenvalues()
# Geometric parameters.
file.write("set tr %s\n" % (cdp.diff_tensor.tm / 1e-9))
file.write("set D1/D3 %s\n" % (Dx / Dz))
file.write("set D2/D3 %s\n" % (Dy / Dz))
# Orientational parameters.
file.write("set alfa %s\n" % (cdp.diff_tensor.alpha / (2.0 * pi) * 360.0))
file.write("set betta %s\n" % (cdp.diff_tensor.beta / (2.0 * pi) * 360.0))
file.write("set gamma %s\n" % (cdp.diff_tensor.gamma / (2.0 * pi) * 360.0))
# Reading the relaxation data.
file.write("\n# Reading the relaxation data.\n")
file.write("echo Reading the relaxation data.\n")
noe_index = 1
r1_index = 1
r2_index = 1
for ri_id in cdp.ri_ids:
# NOE.
if cdp.ri_type[ri_id] == 'NOE':
# Data set number.
number = noe_index
# Data type.
data_type = 'noe'
# Increment the data set index.
noe_index = noe_index + 1
# R1.
elif cdp.ri_type[ri_id] == 'R1':
# Data set number.
number = r1_index
# Data type.
data_type = '1/T1'
# Increment the data set index.
r1_index = r1_index + 1
# R2.
elif cdp.ri_type[ri_id] == 'R2':
# Data set number.
number = r2_index
# Data type.
data_type = '1/T2'
# Increment the data set index.
r2_index = r2_index + 1
# Set the data type.
if number == 1:
file.write("\nread < %s\n" % data_type)
else:
file.write("\nread < %s %s\n" % (data_type, number))
# The relaxation data.
for residue in residue_loop():
# Alias the spin.
spin = residue.spin[0]
# Skip deselected spins.
if not spin.select:
continue
# Skip and deselect spins for which relaxation data is missing.
if len(spin.ri_data) != len(cdp.ri_ids) or spin.ri_data[ri_id] == None:
spin.select = False
continue
# Data and errors.
file.write("%s %s %s\n" % (residue.num, spin.ri_data[ri_id], spin.ri_data_err[ri_id]))
# Terminate the reading.
file.write("exit\n")
# Individual residue optimisation.
if model_type == 'mf':
# Loop over the residues.
for residue in residue_loop():
# Alias the spin.
spin = residue.spin[0]
# Skip deselected spins.
if not spin.select:
continue
# Get the interatomic data containers.
interatoms = return_interatom_list(spin._spin_ids[0])
if len(interatoms) == 0:
raise RelaxNoInteratomError
elif len(interatoms) > 1:
raise RelaxError("Only one interatomic data container, hence dipole-dipole interaction, is supported per spin.")
# Comment.
file.write("\n\n\n# Residue %s\n\n" % residue.num)
# Echo.
file.write("echo Optimisation of residue %s\n" % residue.num)
# Select the spin.
file.write("\n# Select the residue.\n")
file.write("set cres %s\n" % residue.num)
# The angle alpha of the XH vector in the spheroid diffusion frame.
if cdp.diff_tensor.type == 'spheroid':
file.write("set teta %s\n" % spin.alpha)
# The angles theta and phi of the XH vector in the ellipsoid diffusion frame.
elif cdp.diff_tensor.type == 'ellipsoid':
file.write("\n# Setting the spherical angles of the XH vector in the ellipsoid diffusion frame.\n")
file.write("set teta %s\n" % spin.theta)
file.write("set fi %s\n" % spin.phi)
# The 'jmode'.
if 'ts' in spin.params:
jmode = 3
elif 'te' in spin.params:
jmode = 2
elif 's2' in spin.params:
jmode = 1
# Chemical exchange.
if 'rex' in spin.params:
exch = True
else:
exch = False
# Anisotropic diffusion.
if cdp.diff_tensor.type == 'sphere':
anis = False
else:
anis = True
# Axial symmetry.
if cdp.diff_tensor.type == 'spheroid':
sym = True
else:
sym = False
# Set the jmode.
file.write("\n# Set the jmode.\n")
file.write("set def jmode %s" % jmode)
if exch:
file.write(" exch")
if anis:
file.write(" anis")
if sym:
file.write(" sym")
file.write("\n")
# Parameter default values.
file.write("\n# Parameter default values.\n")
file.write("reset jmode %s\n" % residue.num)
# Bond length.
file.write("\n# Bond length.\n")
file.write("set r_hx %s\n" % (interatoms[0].r / 1e-10))
# CSA value.
file.write("\n# CSA value.\n")
file.write("set csa %s\n" % (spin.csa / 1e-6))
# Fix the tf parameter if it isn't in the model.
if not 'tf' in spin.params and jmode == 3:
file.write("\n# Fix the tf parameter.\n")
file.write("fix tf 0\n")
# Optimisation of all residues.
file.write("\n\n\n# Optimisation of all residues.\n")
if algor == 'LM':
file.write("lmin %s %s" % (first_residue_num(), last_residue_num()))
elif algor == 'NR':
file.write("min %s %s" % (first_residue_num(), last_residue_num()))
# Show the results.
file.write("\n# Show the results.\n")
file.write("echo\n")
file.write("show all\n")
# Write the results.
file.write("\n# Write the results.\n")
file.write("write s2.out S\n")
file.write("write s2f.out Sf\n")
file.write("write s2s.out Ss\n")
file.write("write te.out te\n")
file.write("write tf.out tf\n")
file.write("write ts.out ts\n")
file.write("write rex.out rex\n")
file.write("write chi2.out F\n")
else:
raise RelaxError("Optimisation of the parameter set '%s' currently not supported." % model_type)
def calculate(self, spin_id=None, scaling_matrix=None, verbosity=1, sim_index=None):
"""Calculation of the spectral density values.
@keyword spin_id: The spin identification string.
@type spin_id: None or str
@keyword scaling_matrix: The per-model list of diagonal and square scaling matrices.
@type scaling_matrix: list of numpy rank-2, float64 array or list of None
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword sim_index: The optional MC simulation index.
@type sim_index: None or int
"""
# Test if the frequency has been set.
if not hasattr(cdp, 'jw_frq') or not isinstance(cdp.jw_frq, float):
raise RelaxError("The frequency has not been set up.")
# Test if the sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the spin data has been set.
for spin, id in spin_loop(spin_id, return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Test if the nuclear isotope type has been set.
if not hasattr(spin, 'isotope'):
raise RelaxSpinTypeError
# Test if the CSA value has been set.
if not hasattr(spin, 'csa') or spin.csa == None:
raise RelaxNoValueError("CSA")
# Test the interatomic data.
interatoms = return_interatom_list(id)
for interatom in interatoms:
# No relaxation mechanism.
if not interatom.dipole_pair:
continue
# The interacting spin.
if id != interatom.spin_id1:
spin_id2 = interatom.spin_id1
else:
spin_id2 = interatom.spin_id2
spin2 = return_spin(spin_id2)
# Test if the nuclear isotope type has been set.
if not hasattr(spin2, 'isotope'):
raise RelaxSpinTypeError
# Test if the interatomic distance has been set.
if not hasattr(interatom, 'r') or interatom.r == None:
raise RelaxNoValueError("interatomic distance", spin_id=spin_id, spin_id2=spin_id2)
# Frequency index.
if cdp.jw_frq not in list(cdp.spectrometer_frq.values()):
raise RelaxError("No relaxation data corresponding to the frequency " + repr(cdp.jw_frq) + " has been loaded.")
# Reduced spectral density mapping.
for spin, id in spin_loop(spin_id, return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Set the r1, r2, and NOE to None.
r1 = None
r2 = None
noe = None
# Get the R1, R2, and NOE values corresponding to the set frequency.
for ri_id in cdp.ri_ids:
# The frequency does not match.
if cdp.spectrometer_frq[ri_id] != cdp.jw_frq:
continue
# R1.
if cdp.ri_type[ri_id] == 'R1':
if sim_index == None:
r1 = spin.ri_data[ri_id]
else:
r1 = spin.ri_data_sim[ri_id][sim_index]
# R2.
if cdp.ri_type[ri_id] == 'R2':
if sim_index == None:
r2 = spin.ri_data[ri_id]
else:
r2 = spin.ri_data_sim[ri_id][sim_index]
# NOE.
if cdp.ri_type[ri_id] == 'NOE':
if sim_index == None:
noe = spin.ri_data[ri_id]
else:
noe = spin.ri_data_sim[ri_id][sim_index]
# Skip the spin if not all of the three value exist.
if r1 == None or r2 == None or noe == None:
continue
# Loop over the interatomic data.
interatoms = return_interatom_list(id)
for i in range(len(interatoms)):
# No relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id2)
# Gyromagnetic ratios.
gx = periodic_table.gyromagnetic_ratio(spin.isotope)
gh = periodic_table.gyromagnetic_ratio(spin2.isotope)
# The interatomic distance.
r = interatoms[i].r
# Initialise the function to calculate.
jw = Mapping(frq=cdp.jw_frq, gx=gx, gh=gh, mu0=mu0, h_bar=h_bar)
# Calculate the spectral density values.
j0, jwx, jwh = jw.func(r=r, csa=spin.csa, r1=r1, r2=r2, noe=noe)
# Reduced spectral density values.
if sim_index == None:
spin.j0 = j0
spin.jwx = jwx
spin.jwh = jwh
# Monte Carlo simulated reduced spectral density values.
else:
# Initialise the simulation data structures.
self.data_init(id, sim=1)
if spin.j0_sim == None:
spin.j0_sim = []
spin.jwx_sim = []
spin.jwh_sim = []
# Reduced spectral density values.
spin.j0_sim.append(j0)
spin.jwx_sim.append(jwx)
spin.jwh_sim.append(jwh)
def overfit_deselect(self, data_check=True, verbose=True):
"""Deselect spins which have insufficient data to support calculation.
@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
"""
# Print out.
if verbose:
print("\nOver-fit spin deselection:")
# Test the sequence data exists.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Loop over spin data.
deselect_flag = False
spin_count = 0
for spin, spin_id in spin_loop(return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# The interatomic data.
interatoms = return_interatom_list(spin_id)
# Loop over the interatomic data.
dipole_relax = False
for i in range(len(interatoms)):
# No dipolar relaxation mechanism.
if not interatoms[i].dipole_pair:
continue
# The surrounding spins.
if spin_id != interatoms[i].spin_id1:
spin_id2 = interatoms[i].spin_id1
else:
spin_id2 = interatoms[i].spin_id2
spin2 = return_spin(spin_id2)
# Dipolar relaxation flag.
dipole_relax = True
# No relaxation mechanism.
if not dipole_relax or not hasattr(spin, 'csa') or spin.csa == None:
warn(RelaxDeselectWarning(spin_id, 'an absence of relaxation mechanisms'))
spin.select = False
deselect_flag = True
continue
# Data checks.
if data_check:
# The number of relaxation data points (and for infinite data).
data_points = 0
inf_data = False
if hasattr(cdp, 'ri_ids') and hasattr(spin, 'ri_data'):
for id in cdp.ri_ids:
if id in spin.ri_data and spin.ri_data[id] != None:
data_points += 1
# Infinite data!
if isInf(spin.ri_data[id]):
inf_data = True
# Infinite data.
if inf_data:
warn(RelaxDeselectWarning(spin_id, 'infinite relaxation data'))
spin.select = False
deselect_flag = True
continue
# Relaxation data must exist!
if not hasattr(spin, 'ri_data'):
warn(RelaxDeselectWarning(spin_id, 'missing relaxation data'))
spin.select = False
deselect_flag = True
continue
# Require 3 or more relaxation data points.
if data_points < 3:
warn(RelaxDeselectWarning(spin_id, 'insufficient relaxation data, 3 or more data points are required'))
spin.select = False
deselect_flag = True
continue
# Increment the spin number.
spin_count += 1
# No spins selected, so fail hard to prevent the user from going any further.
if spin_count == 0:
warn(RelaxWarning("No spins are selected therefore the optimisation or calculation cannot proceed."))
# Final printout.
if verbose and not deselect_flag:
print("No spins have been deselected.")
def load_model_free_data(spin_line, col, data_set, spin, spin_id, verbosity=1):
"""Read the model-free data for the spin.
@param spin_line: The line of data for a single spin.
@type spin_line: list of str
@param col: The column indices.
@type col: dict of int
@param data_set: The data set type, one of 'value', 'error', or 'sim_xxx' (where xxx is
a number).
@type data_set: str
@param spin: The spin container.
@type spin: SpinContainer instance
@param spin_id: The spin identification string.
@type spin_id: str
@keyword verbosity: A variable specifying the amount of information to print. The higher
the value, the greater the verbosity.
@type verbosity: int
"""
# Set up the model-free models.
if data_set == "value":
# Get the model-free model.
model = spin_line[col["model"]]
if model == "None":
model = None
# Get the model-free equation.
equation = spin_line[col["eqi"]]
if equation == "None":
equation = None
# Get the model-free parameters.
params = eval(spin_line[col["params"]])
# Loop over and convert the parameters.
if params:
for i in range(len(params)):
# Fix for the 1.2 relax versions whereby the parameter 'tm' was renamed to 'local_tm' (which occurred in version 1.2.5).
if params[i] == "tm":
params[i] = "local_tm"
# Lower case conversion.
params[i] = params[i].lower()
# Set up the model-free model.
model_setup(model=model, equation=equation, params=params, spin_id=spin_id)
# The model type.
model_type = spin_line[col["param_set"]]
# Get the interatomic data container.
interatom = return_interatom_list(spin_id)[0]
# Values.
if data_set == "value":
# S2.
try:
spin.s2 = float(spin_line[col["s2"]]) * api_model_free.return_conversion_factor("s2")
except ValueError:
spin.s2 = None
# S2f.
try:
spin.s2f = float(spin_line[col["s2f"]]) * api_model_free.return_conversion_factor("s2f")
except ValueError:
spin.s2f = None
# S2s.
try:
spin.s2s = float(spin_line[col["s2s"]]) * api_model_free.return_conversion_factor("s2s")
except ValueError:
spin.s2s = None
# Local tm.
try:
spin.local_tm = float(spin_line[col["local_tm"]]) * api_model_free.return_conversion_factor("local_tm")
except ValueError:
spin.local_tm = None
# te.
try:
spin.te = float(spin_line[col["te"]]) * api_model_free.return_conversion_factor("te")
except ValueError:
spin.te = None
# tf.
try:
spin.tf = float(spin_line[col["tf"]]) * api_model_free.return_conversion_factor("tf")
except ValueError:
spin.tf = None
# ts.
try:
spin.ts = float(spin_line[col["ts"]]) * api_model_free.return_conversion_factor("ts")
except ValueError:
spin.ts = None
# Rex.
try:
spin.rex = float(spin_line[col["rex"]]) * api_model_free.return_conversion_factor("rex")
except ValueError:
spin.rex = None
# CSA.
try:
spin.csa = float(spin_line[col["csa"]]) * api_model_free.return_conversion_factor("csa")
except ValueError:
spin.csa = None
# Minimisation details (global minimisation results).
if model_type == "diff" or model_type == "all":
cdp.chi2 = eval(spin_line[col["chi2"]])
cdp.iter = eval(spin_line[col["iter"]])
cdp.f_count = eval(spin_line[col["f_count"]])
cdp.g_count = eval(spin_line[col["g_count"]])
cdp.h_count = eval(spin_line[col["h_count"]])
if spin_line[col["warn"]] == "None":
cdp.warning = None
else:
cdp.warning = spin_line[col["warn"]].replace("_", " ")
# Minimisation details (individual residue results).
else:
spin.chi2 = eval(spin_line[col["chi2"]])
spin.iter = eval(spin_line[col["iter"]])
spin.f_count = eval(spin_line[col["f_count"]])
spin.g_count = eval(spin_line[col["g_count"]])
spin.h_count = eval(spin_line[col["h_count"]])
if spin_line[col["warn"]] == "None":
spin.warning = None
else:
spin.warning = spin_line[col["warn"]].replace("_", " ")
# Interatomic distances.
try:
interatom.r = float(spin_line[col["r"]]) * 1e-10
except ValueError:
interatom.r = None
# Errors.
if data_set == "error":
# S2.
try:
spin.s2_err = float(spin_line[col["s2"]]) * api_model_free.return_conversion_factor("s2")
except ValueError:
spin.s2_err = None
# S2f.
try:
spin.s2f_err = float(spin_line[col["s2f"]]) * api_model_free.return_conversion_factor("s2f")
except ValueError:
spin.s2f_err = None
# S2s.
try:
spin.s2s_err = float(spin_line[col["s2s"]]) * api_model_free.return_conversion_factor("s2s")
except ValueError:
spin.s2s_err = None
# Local tm.
try:
spin.local_tm_err = float(spin_line[col["local_tm"]]) * api_model_free.return_conversion_factor("local_tm")
except ValueError:
spin.local_tm_err = None
# te.
try:
spin.te_err = float(spin_line[col["te"]]) * api_model_free.return_conversion_factor("te")
except ValueError:
spin.te_err = None
# tf.
try:
spin.tf_err = float(spin_line[col["tf"]]) * api_model_free.return_conversion_factor("tf")
except ValueError:
spin.tf_err = None
# ts.
try:
spin.ts_err = float(spin_line[col["ts"]]) * api_model_free.return_conversion_factor("ts")
except ValueError:
spin.ts_err = None
# Rex.
try:
spin.rex_err = float(spin_line[col["rex"]]) * api_model_free.return_conversion_factor("rex")
except ValueError:
spin.rex_err = None
# CSA.
try:
spin.csa_err = float(spin_line[col["csa"]]) * api_model_free.return_conversion_factor("csa")
except ValueError:
spin.csa_err = None
# Construct the simulation data structures.
if data_set == "sim_0":
# Get the parameter object names.
param_names = api_model_free.data_names(set="params", scope="spin")
# Get the minimisation statistic object names.
min_names = api_model_free.data_names(set="min", scope="spin")
# Loop over all the parameter names.
for object_name in param_names:
# Name for the simulation object.
sim_object_name = object_name + "_sim"
# Create the simulation object.
setattr(spin, sim_object_name, [])
# Loop over all the minimisation object names.
for object_name in min_names:
# Name for the simulation object.
sim_object_name = object_name + "_sim"
# Create the simulation object.
if model_type == "diff" or model_type == "all":
setattr(cdp, sim_object_name, [])
object = getattr(cdp, sim_object_name)
object = []
else:
setattr(spin, sim_object_name, [])
# Simulations.
if data_set != "value" and data_set != "error":
# S2.
try:
spin.s2_sim.append(float(spin_line[col["s2"]]) * api_model_free.return_conversion_factor("s2"))
except ValueError:
spin.s2_sim.append(None)
# S2f.
try:
spin.s2f_sim.append(float(spin_line[col["s2f"]]) * api_model_free.return_conversion_factor("s2f"))
except ValueError:
spin.s2f_sim.append(None)
# S2s.
try:
spin.s2s_sim.append(float(spin_line[col["s2s"]]) * api_model_free.return_conversion_factor("s2s"))
except ValueError:
spin.s2s_sim.append(None)
# Local tm.
try:
spin.local_tm_sim.append(
float(spin_line[col["local_tm"]]) * api_model_free.return_conversion_factor("local_tm")
)
except ValueError:
spin.local_tm_sim.append(None)
# te.
try:
spin.te_sim.append(float(spin_line[col["te"]]) * api_model_free.return_conversion_factor("te"))
except ValueError:
spin.te_sim.append(None)
# tf.
try:
spin.tf_sim.append(float(spin_line[col["tf"]]) * api_model_free.return_conversion_factor("tf"))
except ValueError:
spin.tf_sim.append(None)
# ts.
try:
spin.ts_sim.append(float(spin_line[col["ts"]]) * api_model_free.return_conversion_factor("ts"))
except ValueError:
spin.ts_sim.append(None)
# Rex.
try:
spin.rex_sim.append(float(spin_line[col["rex"]]) * api_model_free.return_conversion_factor("rex"))
except ValueError:
spin.rex_sim.append(None)
# CSA.
try:
spin.csa_sim.append(float(spin_line[col["csa"]]) * api_model_free.return_conversion_factor("csa"))
except ValueError:
spin.csa_sim.append(None)
# Minimisation details (global minimisation results).
if model_type == "diff" or model_type == "all":
# The simulation index.
index = int(data_set.split("_")[1])
# Already loaded.
if len(cdp.chi2_sim) == index + 1:
return
# Set the values.
cdp.chi2_sim.append(eval(spin_line[col["chi2"]]))
cdp.iter_sim.append(eval(spin_line[col["iter"]]))
cdp.f_count_sim.append(eval(spin_line[col["f_count"]]))
cdp.g_count_sim.append(eval(spin_line[col["g_count"]]))
cdp.h_count_sim.append(eval(spin_line[col["h_count"]]))
if spin_line[col["warn"]] == "None":
cdp.warning_sim.append(None)
else:
cdp.warning_sim.append(spin_line[col["warn"]].replace("_", " "))
# Minimisation details (individual residue results).
else:
spin.chi2_sim.append(eval(spin_line[col["chi2"]]))
spin.iter_sim.append(eval(spin_line[col["iter"]]))
spin.f_count_sim.append(eval(spin_line[col["f_count"]]))
spin.g_count_sim.append(eval(spin_line[col["g_count"]]))
spin.h_count_sim.append(eval(spin_line[col["h_count"]]))
if spin_line[col["warn"]] == "None":
spin.warning_sim.append(None)
else:
spin.warning_sim.append(spin_line[col["warn"]].replace("_", " "))
