def software(name=None, version=None, url=None, vendor_name=None, cite_ids=None, tasks=None):
"""Select by name the software used in the analysis.
@param name: The name of the software program.
@type name: str
@keyword version: The program version.
@type version: None or str
@keyword url: The program's URL.
@type url: None or str
@keyword vendor_name: The name of the company or person behind the program.
@type vendor_name: str
@keyword cite_ids: The citation ID numbers.
@type cite_ids: None or str
@keyword tasks: The tasks performed by the program.
@type tasks: list of str
"""
# Test if the current pipe exists.
check_pipe()
# Set up the experimental info data container, if needed.
if not hasattr(cdp, 'exp_info'):
cdp.exp_info = ExpInfo()
# Place the data in the container.
cdp.exp_info.software_setup(name=name, version=version, url=url, vendor_name=vendor_name, cite_ids=cite_ids, tasks=tasks)
def bmrb_write_citations(star):
"""Generate the Citations saveframe records.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
"""
# Test if the current pipe exists.
check_pipe()
# Loop over the citations.
if hasattr(cdp, 'exp_info') and hasattr(cdp.exp_info, 'citations'):
for citations in cdp.exp_info.citations:
# Rearrange the author list.
author_given_name = []
author_family_name = []
author_first_init = []
author_mid_init = []
author_family_title = []
for i in range(len(citations.authors)):
author_given_name.append(citations.authors[i][0])
author_family_name.append(citations.authors[i][1])
author_first_init.append(citations.authors[i][2])
author_mid_init.append(citations.authors[i][3])
author_family_title.append(None)
# Add the citation.
star.citations.add(citation_label=citations.cite_id, author_given_name=author_given_name, author_family_name=author_family_name, author_first_init=author_first_init, author_mid_init=author_mid_init, author_family_title=author_family_title, doi=citations.doi, pubmed_id=citations.pubmed_id, full_citation=citations.full_citation, title=citations.title, status=citations.status, type=citations.type, journal_abbrev=citations.journal_abbrev, journal_full=citations.journal_full, volume=citations.volume, issue=citations.issue, page_first=citations.page_first, page_last=citations.page_last, year=citations.year)
def bmrb_write_software(star):
"""Generate the Software saveframe records.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
@return: A list BMRB software IDs and a list of software labels.
@rtype: tuple of list of int and list of str
"""
# Test if the current pipe exists.
check_pipe()
# Loop over the software.
software_ids = []
software_labels = []
if hasattr(cdp, 'exp_info') and hasattr(cdp.exp_info, 'software'):
for software in cdp.exp_info.software:
# Get the citation ID numbers.
cite_id_nums = []
for cite in software.cite_ids:
cite_id_nums.append(cdp.exp_info.get_cite_id_num(cite))
# The program info.
id = star.software.add(name=software.software_name, version=software.version, vendor_name=software.vendor_name, vendor_eaddress=software.url, task=software.tasks, cite_ids=cite_id_nums)
# Append the software info.
software_ids.append(id)
software_labels.append(software.software_name)
# relax cannot be the only program used!
else:
raise RelaxError("relax cannot be the only program used in the analysis - spectral analysis programs, etc. must also have been used. Please use the relevant BMRB user functions to specify these.")
# Return the software info.
return software_ids, software_labels
def select_model(model=None, spin_id=None):
"""Function for the selection of a preset model-free model.
@param model: The name of the model.
@type model: str
@param spin_id: The spin identification string.
@type spin_id: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the pipe type is 'mf'.
function_type = pipes.get_type()
if function_type != "mf":
raise RelaxFuncSetupError(specific_analyses.get_string(function_type))
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Obtain the model info.
equation, params = model_map(model)
# Set up the model.
model_setup(model, equation, params, spin_id)
def sobol_setup(max_num=200, oversample=100):
"""Oversampling setup for the quasi-random Sobol' sequence used for numerical PCS integration.
@keyword max_num: The maximum number of integration points N.
@type max_num: int
@keyword oversample: The oversampling factor Ov used for the N * Ov * 10**M, where M is the number of dimensions or torsion-tilt angles for the system.
@type oversample: int
"""
# Test if the current data pipe exists.
check_pipe()
# Throw a warning to the user if not enough points are being used.
if max_num < 200:
warn(
RelaxWarning(
"To obtain reliable results in a frame order analysis, the maximum number of integration points should be greater than 200."
))
# Store the values.
cdp.sobol_max_points = max_num
cdp.sobol_oversample = oversample
# Count the number of Sobol' points for the current model.
count_sobol_points()
def write(ri_id=None, file=None, dir=None, bc=False, force=False):
"""Write relaxation data to a file.
@keyword ri_id: The relaxation data ID string.
@type ri_id: str
@keyword file: The name of the file to create.
@type file: str
@keyword dir: The directory to write to.
@type dir: str or None
@keyword bc: A flag which if True will cause the back calculated relaxation data to be written.
@type bc: bool
@keyword force: A flag which if True will cause any pre-existing file to be overwritten.
@type force: bool
"""
# Test if the current pipe exists.
check_pipe()
# Test if the sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Create the file name if none is given.
if file == None:
file = ri_id + ".out"
# Write the data.
value.write(param=ri_id, file=file, dir=dir, bc=bc, force=force, return_value=return_value, return_data_desc=return_data_desc)
def peak_intensity_type(ri_id=None, type=None):
"""Set the type of intensity measured for the peaks.
@keyword ri_id: The relaxation data ID string.
@type ri_id: str
@keyword type: The peak intensity type, one of 'height' or 'volume'.
@type type: str
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Check the values, and warn if not in the list.
valid = ['height', 'volume']
if type not in valid:
raise RelaxError("The '%s' peak intensity type is unknown. Please select one of %s." % (type, valid))
# Set up the experimental info data container, if needed.
if not hasattr(cdp, 'exp_info'):
cdp.exp_info = ExpInfo()
# Store the type.
cdp.exp_info.setup_peak_intensity_type(ri_id, type)
def type(ri_id=None, ri_type=None):
"""Set or reset the frequency associated with the ID.
@param ri_id: The relaxation data ID string.
@type ri_id: str
@param ri_type: The relaxation data type, ie 'R1', 'R2', or 'NOE'.
@type ri_type: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if sequence data exists.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Check if the type is valid.
if ri_type not in VALID_TYPES:
raise RelaxError("The relaxation data type '%s' must be one of %s." % (ri_type, VALID_TYPES))
# Initialise if needed.
if not hasattr(cdp, 'ri_type'):
cdp.ri_type = {}
# Set the type.
cdp.ri_type[ri_id] = ri_type
def temp_control(ri_id=None, method=None):
"""Set the temperature control method.
@keyword ri_id: The relaxation data ID string.
@type ri_id: str
@keyword method: The temperature control method.
@type method: str
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Check the values, and warn if not in the list.
valid = ['single scan interleaving', 'temperature compensation block', 'single scan interleaving and temperature compensation block', 'single fid interleaving', 'single experiment interleaving', 'no temperature control applied']
if method not in valid:
raise RelaxError("The '%s' method is unknown. Please select one of %s." % (method, valid))
# Set up the experimental info data container, if needed.
if not hasattr(cdp, 'exp_info'):
cdp.exp_info = ExpInfo()
# Store the method.
cdp.exp_info.temp_control_setup(ri_id, method)
def select_model(model=None):
"""Select the N-state model type.
@param model: The N-state model type. Can be one of '2-domain', 'population', or 'fixed'.
@type model: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the model name exists.
if not model in ['2-domain', 'population', 'fixed']:
raise RelaxError("The model name " + repr(model) + " is invalid.")
# Test if the model is setup.
if hasattr(cdp, 'model'):
warn(RelaxWarning("The N-state model has already been set up. Switching from model '%s' to '%s'." % (cdp.model, model)))
# Set the model
cdp.model = model
# Initialise the list of model parameters.
cdp.params = []
# Update the model.
update_model()
def temp_calibration(ri_id=None, method=None):
"""Set the temperature calibration method.
@keyword ri_id: The relaxation data type, ie 'R1', 'R2', or 'NOE'.
@type ri_id: str
@keyword method: The temperature calibration method.
@type method: str
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Check the values, and warn if not in the list.
valid = ['methanol', 'monoethylene glycol', 'no calibration applied']
if method not in valid:
warn(RelaxWarning("The '%s' method is unknown. Please try to use one of %s." % (method, valid)))
# Set up the experimental info data container, if needed.
if not hasattr(cdp, 'exp_info'):
cdp.exp_info = ExpInfo()
# Store the method.
cdp.exp_info.temp_calibration_setup(ri_id, method)
def type(ri_id=None, ri_type=None):
"""Set or reset the frequency associated with the ID.
@param ri_id: The relaxation data ID string.
@type ri_id: str
@param ri_type: The relaxation data type, ie 'R1', 'R2', or 'NOE'.
@type ri_type: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if sequence data exists.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Check if the type is valid.
if ri_type not in VALID_TYPES:
raise RelaxError("The relaxation data type '%s' must be one of %s." %
(ri_type, VALID_TYPES))
# Initialise if needed.
if not hasattr(cdp, 'ri_type'):
cdp.ri_type = {}
# Set the type.
cdp.ri_type[ri_id] = ri_type
def cartoon():
"""Apply the PyMOL cartoon style and colour by secondary structure."""
# Test if the current data pipe exists.
check_pipe()
# Test for the structure.
if not hasattr(cdp, 'structure'):
raise RelaxNoPdbError
# Loop over the PDB files.
open_files = []
for model in cdp.structure.structural_data:
for mol in model.mol:
# Identifier.
pdb_file = mol.file_name
if mol.file_path:
pdb_file = mol.file_path + sep + pdb_file
id = file_root(pdb_file)
# Already loaded.
if pdb_file in open_files:
continue
# Add to the open file list.
open_files.append(pdb_file)
# Hide everything.
pymol_obj.exec_cmd("cmd.hide('everything'," + repr(id) + ")")
# Show the cartoon style.
pymol_obj.exec_cmd("cmd.show('cartoon'," + repr(id) + ")")
# Colour by secondary structure.
pymol_obj.exec_cmd("util.cbss(" + repr(id) + ", 'red', 'yellow', 'green')")
def set_temperature(id=None, temp=None):
"""Set the experimental temperature.
@keyword id: The experiment ID string (allowing for multiple experiments per data pipe).
@type id: str
@keyword temp: The temperature in Kelvin.
@type temp: float
"""
# Test if the current data pipe exists.
check_pipe()
# Set up the dictionary data structure if it doesn't exist yet.
if not hasattr(cdp, 'temperature'):
cdp.temperature = {}
# Convert to a float.
temp = float(temp)
# Test the temperature has not already been set.
if id in cdp.temperature and cdp.temperature[id] != temp:
raise RelaxError("The temperature for the experiment '%s' has already been set to %s K." % (id, cdp.temperature[id]))
# Set the temperature.
cdp.temperature[id] = temp
def exists_data(pipe=None):
"""Determine if any interatomic data exists.
@keyword pipe: The data pipe in which the interatomic data will be checked for.
@type pipe: str
@return: The answer to the question about the existence of data.
@rtype: bool
"""
# The current data pipe.
if pipe == None:
pipe = pipes.cdp_name()
# Test the data pipe.
check_pipe(pipe)
# Get the data pipe.
dp = pipes.get_pipe(pipe)
# The interatomic data structure is empty.
if dp.interatomic.is_empty():
return False
# Otherwise.
return True
def delete_frequencies(id=None):
"""Delete the spectrometer frequency corresponding to the experiment ID.
@keyword id: The experiment ID string.
@type id: str
"""
# Checks.
check_pipe()
check_frequency(id=id)
# Delete the frequency.
frq = cdp.spectrometer_frq[id]
del cdp.spectrometer_frq[id]
# Update the structures as needed.
if frq in cdp.spectrometer_frq_list and frq not in list(cdp.spectrometer_frq.values()):
cdp.spectrometer_frq_list.pop(cdp.spectrometer_frq_list.index(frq))
cdp.spectrometer_frq_count = len(cdp.spectrometer_frq_list)
# Cleanup.
if len(cdp.spectrometer_frq) == 0:
del cdp.spectrometer_frq
del cdp.spectrometer_frq_list
del cdp.spectrometer_frq_count
def temp_calibration(ri_id=None, method=None):
"""Set the temperature calibration method.
@keyword ri_id: The relaxation data type, ie 'R1', 'R2', or 'NOE'.
@type ri_id: str
@keyword method: The temperature calibration method.
@type method: str
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Check the values, and warn if not in the list.
valid = ['methanol', 'monoethylene glycol', 'no calibration applied']
if method not in valid:
warn(
RelaxWarning(
"The '%s' method is unknown. Please try to use one of %s." %
(method, valid)))
# Set up the experimental info data container, if needed.
if not hasattr(cdp, 'exp_info'):
cdp.exp_info = ExpInfo()
# Store the method.
cdp.exp_info.temp_calibration_setup(ri_id, method)
def exists_data(pipe=None):
"""Determine if any interatomic data exists.
@keyword pipe: The data pipe in which the interatomic data will be checked for.
@type pipe: str
@return: The answer to the question about the existence of data.
@rtype: bool
"""
# The current data pipe.
if pipe == None:
pipe = pipes.cdp_name()
# Test the data pipe.
check_pipe(pipe)
# Get the data pipe.
dp = pipes.get_pipe(pipe)
# The interatomic data structure is empty.
if dp.interatomic.is_empty():
return False
# Otherwise.
return True
def ref_domain(ref=None):
"""Set the reference domain for the '2-domain' N-state model.
@param ref: The reference domain.
@type ref: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the model is setup.
if not hasattr(cdp, 'model'):
raise RelaxNoModelError('N-state')
# Test that the correct model is set.
if cdp.model != '2-domain':
raise RelaxError(
"Setting the reference domain is only possible for the '2-domain' N-state model."
)
# Test if the reference domain exists.
exists = False
for tensor_cont in cdp.align_tensors:
if tensor_cont.domain == ref:
exists = True
if not exists:
raise RelaxError(
"The reference domain cannot be found within any of the loaded tensors."
)
# Set the reference domain.
cdp.ref_domain = ref
# Update the model.
update_model()
def sobol_setup(max_num=200, oversample=100):
"""Oversampling setup for the quasi-random Sobol' sequence used for numerical PCS integration.
@keyword max_num: The maximum number of integration points N.
@type max_num: int
@keyword oversample: The oversampling factor Ov used for the N * Ov * 10**M, where M is the number of dimensions or torsion-tilt angles for the system.
@type oversample: int
"""
# Test if the current data pipe exists.
check_pipe()
# Throw a warning to the user if not enough points are being used.
if max_num < 200:
warn(
RelaxWarning(
"To obtain reliable results in a frame order analysis, the maximum number of integration points should be greater than 200."
)
)
# Store the values.
cdp.sobol_max_points = max_num
cdp.sobol_oversample = oversample
# Count the number of Sobol' points for the current model.
count_sobol_points()
def delete():
"""Delete all the model-free data."""
# Test if the current pipe exists.
check_pipe()
# Test if the pipe type is set to 'mf'.
function_type = pipes.get_type()
if function_type != "mf":
raise RelaxFuncSetupError(specific_analyses.setup.get_string(function_type))
# Test if the sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Get all data structure names.
names = api_model_free.data_names(scope="spin")
# Loop over the spins.
for spin in spin_loop():
# Loop through the data structure names.
for name in names:
# Skip the data structure if it does not exist.
if not hasattr(spin, name):
continue
# Delete the data.
delattr(spin, name)
def peak_intensity_type(ri_id=None, type=None):
"""Set the type of intensity measured for the peaks.
@keyword ri_id: The relaxation data ID string.
@type ri_id: str
@keyword type: The peak intensity type, one of 'height' or 'volume'.
@type type: str
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if data exists.
if not hasattr(cdp, 'ri_ids') or ri_id not in cdp.ri_ids:
raise RelaxNoRiError(ri_id)
# Check the values, and warn if not in the list.
valid = ['height', 'volume']
if type not in valid:
raise RelaxError(
"The '%s' peak intensity type is unknown. Please select one of %s."
% (type, valid))
# Set up the experimental info data container, if needed.
if not hasattr(cdp, 'exp_info'):
cdp.exp_info = ExpInfo()
# Store the type.
cdp.exp_info.setup_peak_intensity_type(ri_id, type)
def delete():
"""Delete all the model-free data."""
# Test if the current pipe exists.
check_pipe()
# Test if the pipe type is set to 'mf'.
function_type = pipes.get_type()
if function_type != 'mf':
raise RelaxFuncSetupError(
specific_analyses.setup.get_string(function_type))
# Test if the sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Get all data structure names.
names = api_model_free.data_names(scope='spin')
# Loop over the spins.
for spin in spin_loop():
# Loop through the data structure names.
for name in names:
# Skip the data structure if it does not exist.
if not hasattr(spin, name):
continue
# Delete the data.
delattr(spin, name)
def select_model(model=None):
"""Select the Frame Order model.
@param model: The Frame Order model. This can be one of 'pseudo-ellipse', 'pseudo-ellipse, torsionless', 'pseudo-ellipse, free rotor', 'iso cone', 'iso cone, torsionless', 'iso cone, free rotor', 'rotor', 'rigid', 'free rotor', 'double rotor'.
@type model: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the model name exists.
if not model in MODEL_LIST:
raise RelaxError(
"The model name '%s' is invalid, it must be one of %s." %
(model, MODEL_LIST))
# Set the model
cdp.model = model
# Initialise the list of model parameters.
cdp.params = []
# Set the integration method if needed.
if not hasattr(cdp, 'quad_int'):
# Scipy quadratic numerical integration.
if cdp.model in []:
cdp.quad_int = True
# Quasi-random numerical integration.
else:
cdp.quad_int = False
# Update the model.
update_model()
def select_model(model=None, spin_id=None):
"""Function for the selection of a preset model-free model.
@param model: The name of the model.
@type model: str
@param spin_id: The spin identification string.
@type spin_id: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the pipe type is 'mf'.
function_type = pipes.get_type()
if function_type != 'mf':
raise RelaxFuncSetupError(specific_analyses.get_string(function_type))
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Obtain the model info.
equation, params = model_map(model)
# Set up the model.
model_setup(model, equation, params, spin_id)
def select_model(model=None):
"""Select the Frame Order model.
@param model: The Frame Order model. This can be one of 'pseudo-ellipse', 'pseudo-ellipse, torsionless', 'pseudo-ellipse, free rotor', 'iso cone', 'iso cone, torsionless', 'iso cone, free rotor', 'rotor', 'rigid', 'free rotor', 'double rotor'.
@type model: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the model name exists.
if not model in MODEL_LIST:
raise RelaxError("The model name '%s' is invalid, it must be one of %s." % (model, MODEL_LIST))
# Set the model
cdp.model = model
# Initialise the list of model parameters.
cdp.params = []
# Set the integration method if needed.
if not hasattr(cdp, 'quad_int'):
# Scipy quadratic numerical integration.
if cdp.model in []:
cdp.quad_int = True
# Quasi-random numerical integration.
else:
cdp.quad_int = False
# Update the model.
update_model()
def cartoon():
"""Apply the PyMOL cartoon style and colour by secondary structure."""
# Test if the current data pipe exists.
check_pipe()
# Test for the structure.
if not hasattr(cdp, 'structure'):
raise RelaxNoPdbError
# Loop over the PDB files.
open_files = []
for model in cdp.structure.structural_data:
for mol in model.mol:
# Identifier.
pdb_file = mol.file_name
if mol.file_path:
pdb_file = mol.file_path + sep + pdb_file
id = file_root(pdb_file)
# Already loaded.
if pdb_file in open_files:
continue
# Add to the open file list.
open_files.append(pdb_file)
# Hide everything.
pymol_obj.exec_cmd("cmd.hide('everything'," + repr(id) + ")")
# Show the cartoon style.
pymol_obj.exec_cmd("cmd.show('cartoon'," + repr(id) + ")")
# Colour by secondary structure.
pymol_obj.exec_cmd("util.cbss(" + repr(id) + ", 'red', 'yellow', 'green')")
def baseplane_rmsd(error=0.0, spectrum_id=None, spin_id=None):
"""Set the peak intensity errors, as defined as the baseplane RMSD.
@param error: The peak intensity error value defined as the RMSD of the base plane
noise.
@type error: float
@keyword spectrum_id: The spectrum id.
@type spectrum_id: str
@param spin_id: The spin identification string.
@type spin_id: str
"""
# Data checks.
check_pipe()
check_mol_res_spin_data()
check_spectrum_id(spectrum_id)
# The scaling by NC_proc.
if hasattr(cdp, 'ncproc') and spectrum_id in cdp.ncproc:
scale = 1.0 / 2**cdp.ncproc[spectrum_id]
else:
scale = 1.0
# Loop over the spins.
for spin in spin_loop(spin_id):
# Skip deselected spins.
if not spin.select:
continue
# Initialise or update the baseplane_rmsd data structure as necessary.
if not hasattr(spin, 'baseplane_rmsd'):
spin.baseplane_rmsd = {}
# Set the error.
spin.baseplane_rmsd[spectrum_id] = float(error) * scale
def ref_domain(ref=None):
"""Set the reference domain for the '2-domain' N-state model.
@param ref: The reference domain.
@type ref: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the model is setup.
if not hasattr(cdp, 'model'):
raise RelaxNoModelError('N-state')
# Test that the correct model is set.
if cdp.model != '2-domain':
raise RelaxError("Setting the reference domain is only possible for the '2-domain' N-state model.")
# Test if the reference domain exists.
exists = False
for tensor_cont in cdp.align_tensors:
if tensor_cont.domain == ref:
exists = True
if not exists:
raise RelaxError("The reference domain cannot be found within any of the loaded tensors.")
# Set the reference domain.
cdp.ref_domain = ref
# Update the model.
update_model()
def bmrb_write_methods(star):
"""Generate the Software saveframe records.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
@return: A list BMRB software IDs and a list of software labels.
@rtype: tuple of list of int and list of str
"""
# Test if the current pipe exists.
check_pipe()
# The scripts.
if hasattr(cdp, 'exp_info') and hasattr(cdp.exp_info, 'scripts'):
for script in cdp.exp_info.scripts:
# Get the citation ID numbers.
cite_id_nums = []
if script.cite_ids:
for cite in script.cite_ids:
cite_id_nums.append(cdp.exp_info.get_cite_id_num(cite))
# The name.
name = script.file + " relax script"
# The method info.
star.method.add(name=name, details=None, cite_ids=cite_id_nums, file_name=script.file, file_text=script.text)
def bmrb_write_methods(star):
"""Generate the Software saveframe records.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
@return: A list BMRB software IDs and a list of software labels.
@rtype: tuple of list of int and list of str
"""
# Test if the current pipe exists.
check_pipe()
# The scripts.
if hasattr(cdp, 'exp_info') and hasattr(cdp.exp_info, 'scripts'):
for script in cdp.exp_info.scripts:
# Get the citation ID numbers.
cite_id_nums = []
if script.cite_ids:
for cite in script.cite_ids:
cite_id_nums.append(cdp.exp_info.get_cite_id_num(cite))
# The name.
name = script.file + " relax script"
# The method info.
star.method.add(name=name, details=None, cite_ids=cite_id_nums, file_name=script.file, file_text=script.text)
def select_model(model=None):
"""Select the N-state model type.
@param model: The N-state model type. Can be one of '2-domain', 'population', or 'fixed'.
@type model: str
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the model name exists.
if not model in ['2-domain', 'population', 'fixed']:
raise RelaxError("The model name " + repr(model) + " is invalid.")
# Test if the model is setup.
if hasattr(cdp, 'model'):
warn(
RelaxWarning(
"The N-state model has already been set up. Switching from model '%s' to '%s'."
% (cdp.model, model)))
# Set the model
cdp.model = model
# Initialise the list of model parameters.
cdp.params = []
# Update the model.
update_model()
def bmrb_write_software(star):
"""Generate the Software saveframe records.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
@return: A list BMRB software IDs and a list of software labels.
@rtype: tuple of list of int and list of str
"""
# Test if the current pipe exists.
check_pipe()
# Loop over the software.
software_ids = []
software_labels = []
if hasattr(cdp, 'exp_info') and hasattr(cdp.exp_info, 'software'):
for software in cdp.exp_info.software:
# Get the citation ID numbers.
cite_id_nums = []
for cite in software.cite_ids:
cite_id_nums.append(cdp.exp_info.get_cite_id_num(cite))
# The program info.
id = star.software.add(name=software.software_name, version=software.version, vendor_name=software.vendor_name, vendor_eaddress=software.url, task=software.tasks, cite_ids=cite_id_nums)
# Append the software info.
software_ids.append(id)
software_labels.append(software.software_name)
# relax cannot be the only program used!
else:
raise RelaxError("relax cannot be the only program used in the analysis - spectral analysis programs, etc. must also have been used. Please use the relevant BMRB user functions to specify these.")
# Return the software info.
return software_ids, software_labels
def baseplane_rmsd(error=0.0, spectrum_id=None, spin_id=None):
"""Set the peak intensity errors, as defined as the baseplane RMSD.
@param error: The peak intensity error value defined as the RMSD of the base plane
noise.
@type error: float
@keyword spectrum_id: The spectrum id.
@type spectrum_id: str
@param spin_id: The spin identification string.
@type spin_id: str
"""
# Data checks.
check_pipe()
check_mol_res_spin_data()
check_spectrum_id(spectrum_id)
# The scaling by NC_proc.
if hasattr(cdp, 'ncproc') and spectrum_id in cdp.ncproc:
scale = 1.0 / 2**cdp.ncproc[spectrum_id]
else:
scale = 1.0
# Loop over the spins.
for spin in spin_loop(spin_id):
# Skip deselected spins.
if not spin.select:
continue
# Initialise or update the baseplane_rmsd data structure as necessary.
if not hasattr(spin, 'baseplane_rmsd'):
spin.baseplane_rmsd = {}
# Set the error.
spin.baseplane_rmsd[spectrum_id] = float(error) * scale
def software(name=None, version=None, url=None, vendor_name=None, cite_ids=None, tasks=None):
"""Select by name the software used in the analysis.
@param name: The name of the software program.
@type name: str
@keyword version: The program version.
@type version: None or str
@keyword url: The program's URL.
@type url: None or str
@keyword vendor_name: The name of the company or person behind the program.
@type vendor_name: str
@keyword cite_ids: The citation ID numbers.
@type cite_ids: None or str
@keyword tasks: The tasks performed by the program.
@type tasks: list of str
"""
# Test if the current pipe exists.
check_pipe()
# Set up the experimental info data container, if needed.
if not hasattr(cdp, 'exp_info'):
cdp.exp_info = ExpInfo()
# Place the data in the container.
cdp.exp_info.software_setup(name=name, version=version, url=url, vendor_name=vendor_name, cite_ids=cite_ids, tasks=tasks)
def angle_diff_frame():
"""Function for calculating the angle defining the XH vector in the diffusion frame."""
# Test if the current data pipe exists.
check_pipe()
# Test if the PDB file has been loaded.
if not hasattr(cdp, 'structure'):
raise RelaxNoPdbError
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the diffusion tensor data is loaded.
if not hasattr(cdp, 'diff_tensor'):
raise RelaxNoTensorError('diffusion')
# Sphere.
if cdp.diff_tensor.type == 'sphere':
return
# Spheroid.
elif cdp.diff_tensor.type == 'spheroid':
spheroid_frame()
# Ellipsoid.
elif cdp.diff_tensor.type == 'ellipsoid':
raise RelaxError("No coded yet.")
def bmrb_write_citations(star):
"""Generate the Citations saveframe records.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
"""
# Test if the current pipe exists.
check_pipe()
# Loop over the citations.
if hasattr(cdp, 'exp_info') and hasattr(cdp.exp_info, 'citations'):
for citations in cdp.exp_info.citations:
# Rearrange the author list.
author_given_name = []
author_family_name = []
author_first_init = []
author_mid_init = []
author_family_title = []
for i in range(len(citations.authors)):
author_given_name.append(citations.authors[i][0])
author_family_name.append(citations.authors[i][1])
author_first_init.append(citations.authors[i][2])
author_mid_init.append(citations.authors[i][3])
author_family_title.append(None)
# Add the citation.
star.citations.add(citation_label=citations.cite_id, author_given_name=author_given_name, author_family_name=author_family_name, author_first_init=author_first_init, author_mid_init=author_mid_init, author_family_title=author_family_title, doi=citations.doi, pubmed_id=citations.pubmed_id, full_citation=citations.full_citation, title=citations.title, status=citations.status, type=citations.type, journal_abbrev=citations.journal_abbrev, journal_full=citations.journal_full, volume=citations.volume, issue=citations.issue, page_first=citations.page_first, page_last=citations.page_last, year=citations.year)
def display():
"""Displaying the results/contents of the current data pipe."""
# Test if the current data pipe exists.
check_pipe()
# Write the results.
ds.to_xml(sys.stdout)
def display():
"""Displaying the results/contents of the current data pipe."""
# Test if the current data pipe exists.
check_pipe()
# Write the results.
ds.to_xml(sys.stdout)
def read_spins(file=None, dir=None, dim=1, spin_id_col=None, mol_name_col=None, res_num_col=None, res_name_col=None, spin_num_col=None, spin_name_col=None, sep=None, spin_id=None, verbose=True):
"""Read the peak intensity data.
@keyword file: The name of the file containing the peak intensities.
@type file: str
@keyword dir: The directory where the file is located.
@type dir: str
@keyword dim: The dimension of the peak list to associate the data with.
@type dim: int
@keyword spin_id_col: The column containing the spin ID strings (used by the generic intensity file format). If supplied, the mol_name_col, res_name_col, res_num_col, spin_name_col, and spin_num_col arguments must be none.
@type spin_id_col: int or None
@keyword mol_name_col: The column containing the molecule name information (used by the generic intensity file format). If supplied, spin_id_col must be None.
@type mol_name_col: int or None
@keyword res_name_col: The column containing the residue name information (used by the generic intensity file format). If supplied, spin_id_col must be None.
@type res_name_col: int or None
@keyword res_num_col: The column containing the residue number information (used by the generic intensity file format). If supplied, spin_id_col must be None.
@type res_num_col: int or None
@keyword spin_name_col: The column containing the spin name information (used by the generic intensity file format). If supplied, spin_id_col must be None.
@type spin_name_col: int or None
@keyword spin_num_col: The column containing the spin number information (used by the generic intensity file format). If supplied, spin_id_col must be None.
@type spin_num_col: int or None
@keyword sep: The column separator which, if None, defaults to whitespace.
@type sep: str or None
@keyword spin_id: The spin ID string used to restrict data loading to a subset of all spins. If 'auto' is provided for a NMRPipe seriesTab formatted file, the ID's are auto generated in form of Z_Ai.
@type spin_id: None or str
@keyword verbose: A flag which if True will cause all relaxation data loaded to be printed out.
@type verbose: bool
"""
# Data checks.
check_pipe()
# Check the file name.
if file == None:
raise RelaxError("The file name must be supplied.")
# Read the peak list data.
peak_list = read_peak_list(file=file, dir=dir, spin_id_col=spin_id_col, mol_name_col=mol_name_col, res_num_col=res_num_col, res_name_col=res_name_col, spin_num_col=spin_num_col, spin_name_col=spin_name_col, sep=sep, spin_id=spin_id)
# Loop over the peak_list.
created_spins = []
for assign in peak_list:
mol_name = assign.mol_names[dim-1]
res_num = assign.res_nums[dim-1]
res_name = assign.res_names[dim-1]
spin_num = assign.spin_nums[dim-1]
spin_name = assign.spin_names[dim-1]
# Generate the spin_id.
spin_id = generate_spin_id_unique(mol_name=mol_name, res_num=res_num, res_name=res_name, spin_name=spin_name)
# Check if the spin already exist.
if return_spin(spin_id=spin_id) == None:
# Create the spin if not exist.
create_spin(spin_num=spin_num, spin_name=spin_name, res_num=res_num, res_name=res_name, mol_name=mol_name)
# Test that data exists.
check_mol_res_spin_data()
def pdb_model(ave_pos="ave_pos",
rep="frame_order",
dir=None,
compress_type=0,
size=30.0,
inc=36,
model=1,
force=False):
"""Create 3 different PDB files for representing the frame order dynamics of the system.
@keyword ave_pos: The file root for the average molecule structure.
@type ave_pos: str or None
@keyword rep: The file root of the PDB representation of the frame order dynamics to create.
@type rep: str or None
@keyword dist: The file root which will contain multiple models spanning the full dynamics distribution of the frame order model.
@type dist: str or None
@keyword dir: The name of the directory to place the PDB file into.
@type dir: str
@keyword compress_type: The compression type. The integer values correspond to the compression type: 0, no compression; 1, Bzip2 compression; 2, Gzip compression.
@type compress_type: int
@keyword size: The size of the geometric object in Angstroms.
@type size: float
@keyword inc: The number of increments for the filling of the cone objects.
@type inc: int
@keyword model: Only one model from an analysed ensemble can be used for the PDB representation of the Monte Carlo simulations, as these consists of one model per simulation.
@type model: int
@keyword force: Flag which if set to True will cause any pre-existing file to be overwritten.
@type force: bool
"""
# Check that at least one PDB file name is given.
if not ave_pos and not rep and not dist:
raise RelaxError("Minimally one PDB file name must be supplied.")
# Test if the current data pipe exists.
check_pipe()
# Create the average position structure.
if ave_pos:
create_ave_pos(file=ave_pos,
dir=dir,
compress_type=compress_type,
model=model,
force=force)
# Nothing more to do for the rigid model.
if cdp.model == MODEL_RIGID:
return
# Create the geometric representation.
if rep:
create_geometric_rep(file=rep,
dir=dir,
compress_type=compress_type,
size=size,
inc=inc,
force=force)
def calc(verbosity=1):
"""Function for calculating the function value.
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
"""
# Test if the current data pipe exists.
check_pipe()
# Reset the minimisation statistics.
reset_min_stats(verbosity=verbosity)
# The specific analysis API object.
api = return_api()
# Deselect spins lacking data:
api.overfit_deselect()
# Create the scaling matrix.
scaling_matrix = assemble_scaling_matrix()
# Get the Processor box singleton (it contains the Processor instance) and alias the Processor.
processor_box = Processor_box()
processor = processor_box.processor
# Monte Carlo simulation calculation.
if hasattr(cdp, 'sim_state') and cdp.sim_state == 1:
# Loop over the simulations.
for i in range(cdp.sim_number):
# Status.
if status.current_analysis:
status.auto_analysis[status.current_analysis].mc_number = i
else:
status.mc_number = i
# Calculation.
api.calculate(verbosity=verbosity - 1,
sim_index=i,
scaling_matrix=scaling_matrix)
# Print out.
if verbosity and not processor.is_queued():
print("Simulation " + repr(i + 1))
# Unset the status.
if status.current_analysis:
status.auto_analysis[status.current_analysis].mc_number = None
else:
status.mc_number = None
# Minimisation.
else:
api.calculate(verbosity=verbosity, scaling_matrix=scaling_matrix)
# Execute any queued commands.
processor.run_queue()
def replicated(spectrum_ids=None):
"""Set which spectra are replicates.
@keyword spectrum_ids: A list of spectrum ids corresponding to replicated spectra.
@type spectrum_ids: list of str
"""
# Test if the current pipe exists.
check_pipe()
# Test for None.
if spectrum_ids == None:
warn(RelaxWarning("The spectrum ID list cannot be None."))
return
# Test if spectra have been loaded.
if not hasattr(cdp, 'spectrum_ids'):
raise RelaxError(
"No spectra have been loaded therefore replicates cannot be specified."
)
# Test the spectrum id strings.
for spectrum_id in spectrum_ids:
check_spectrum_id(spectrum_id)
# Test for more than one element.
if len(spectrum_ids) == 1:
warn(
RelaxWarning(
"The number of spectrum IDs in the list %s must be greater than one."
% spectrum_ids))
return
# Initialise.
if not hasattr(cdp, 'replicates'):
cdp.replicates = []
# Check if the spectrum IDs are already in the list.
found = False
for i in range(len(cdp.replicates)):
# Loop over all elements of the first.
for j in range(len(spectrum_ids)):
if spectrum_ids[j] in cdp.replicates[i]:
found = True
# One of the spectrum IDs already have a replicate specified.
if found:
# Add the remaining replicates to the list and quit this function.
for j in range(len(spectrum_ids)):
if spectrum_ids[j] not in cdp.replicates[i]:
cdp.replicates[i].append(spectrum_ids[j])
# Nothing more to do.
return
# A new set of replicates.
cdp.replicates.append(spectrum_ids)
def create(algor='LM', dir=None, force=False):
"""Create the Dasha script file 'dasha_script' for controlling the program.
@keyword algor: The optimisation algorithm to use. This can be the Levenberg-Marquardt algorithm 'LM' or the Newton-Raphson algorithm 'NR'.
@type algor: str
@keyword dir: The optional directory to place the script into.
@type dir: str or None
@keyword force: A flag which if True will cause any pre-existing file to be overwritten.
@type force: bool
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Determine the parameter set.
model_type = determine_model_type()
# Test if diffusion tensor data for the data_pipe exists.
if model_type != 'local_tm' and not hasattr(cdp, 'diff_tensor'):
raise RelaxNoTensorError('diffusion')
# Test if the PDB file has been loaded (for the spheroid and ellipsoid).
if model_type != 'local_tm' and cdp.diff_tensor.type != 'sphere' and not hasattr(
cdp, 'structure'):
raise RelaxNoPdbError
# Test the optimisation algorithm.
if algor not in ['LM', 'NR']:
raise RelaxError(
"The Dasha optimisation algorithm '%s' is unknown, it should either be 'LM' or 'NR'."
% algor)
# Deselect certain spins.
__deselect_spins()
# Directory creation.
if dir == None:
dir = pipes.cdp_name()
mkdir_nofail(dir, verbosity=0)
# Calculate the angle alpha of the XH vector in the spheroid diffusion frame.
if cdp.diff_tensor.type == 'spheroid':
angles.spheroid_frame()
# Calculate the angles theta and phi of the XH vector in the ellipsoid diffusion frame.
elif cdp.diff_tensor.type == 'ellipsoid':
angles.ellipsoid_frame()
# The 'dasha_script' file.
script = open_write_file(file_name='dasha_script', dir=dir, force=force)
create_script(script, model_type, algor)
script.close()
def create_rotor_pdb(file=None, dir=None, rotor_angle=None, axis=None, axis_pt=True, centre=None, span=2e-9, blade_length=5e-10, force=False, staggered=False):
"""Create a PDB representation of a rotor motional model.
@keyword file: The name of the PDB file to create.
@type file: str
@keyword dir: The name of the directory to place the PDB file into.
@type dir: str
@keyword rotor_angle: The angle of the rotor motion in degrees.
@type rotor_angle: float
@keyword axis: The vector defining the rotor axis.
@type axis: numpy rank-1, 3D array
@keyword axis_pt: A point lying anywhere on the rotor axis. This is used to define the position of the axis in 3D space.
@type axis_pt: numpy rank-1, 3D array
@keyword centre: The central point of the representation. If this point is not on the rotor axis, then the closest point on the axis will be used for the centre.
@type centre: numpy rank-1, 3D array
@keyword span: The distance from the central point to the rotor blades (meters).
@type span: float
@keyword blade_length: The length of the representative rotor blades.
@type blade_length: float
@keyword force: A flag which if set will overwrite any pre-existing file.
@type force: bool
@keyword staggered: A flag which if True will cause the rotor blades to be staggered. This is used to avoid blade overlap.
@type staggered: bool
"""
# Test if the current pipe exists.
check_pipe()
# Convert the angle to radians.
rotor_angle = rotor_angle / 360.0 * 2.0 * pi
# Create the structural object.
structure = Internal()
# Generate the rotor object.
rotor(structure=structure, rotor_angle=rotor_angle, axis=axis, axis_pt=axis_pt, centre=centre, span=span, blade_length=blade_length, staggered=staggered)
# Print out.
print("\nGenerating the PDB file.")
# Open the PDB file for writing.
tensor_pdb_file = open_write_file(file, dir, force=force)
# Write the data.
structure.write_pdb(tensor_pdb_file)
# Close the file.
tensor_pdb_file.close()
# Add the file to the results file list.
if not hasattr(cdp, 'result_files'):
cdp.result_files = []
if dir == None:
dir = getcwd()
cdp.result_files.append(['rotor_pdb', 'Rotor PDB', get_file_path(file, dir)])
status.observers.result_file.notify()
def check_pipe_setup(pipe=None, pcs_id=None, sequence=False, N=False, tensors=False, pcs=False, paramag_centre=False):
"""Check that the current data pipe has been setup sufficiently.
@keyword pipe: The data pipe to check, defaulting to the current pipe.
@type pipe: None or str
@keyword pcs_id: The PCS ID string to check for in cdp.pcs_ids.
@type pcs_id: None or str
@keyword sequence: A flag which when True will invoke the sequence data check.
@type sequence: bool
@keyword N: A flag which if True will check that cdp.N is set.
@type N: bool
@keyword tensors: A flag which if True will check that alignment tensors exist.
@type tensors: bool
@keyword pcs: A flag which if True will check that PCSs exist.
@type pcs: bool
@keyword paramag_centre: A flag which if True will check that the paramagnetic centre has been set.
@type paramag_centre: bool
"""
# The data pipe.
if pipe == None:
pipe = pipes.cdp_name()
# Get the data pipe.
dp = pipes.get_pipe(pipe)
# Test if the current data pipe exists.
check_pipe(pipe)
# Test if sequence data exists.
if sequence and not exists_mol_res_spin_data(pipe):
raise RelaxNoSequenceError(pipe)
# Check for dp.N.
if N and not hasattr(dp, 'N'):
raise RelaxError("The number of states N has not been set.")
# Check that alignment tensors are present.
if tensors and (not hasattr(dp, 'align_tensors') or len(dp.align_tensors) == 0):
raise RelaxNoTensorError('alignment')
# Test for the alignment ID.
if pcs_id and (not hasattr(dp, 'align_ids') or pcs_id not in dp.align_ids):
raise RelaxNoAlignError(pcs_id, pipe)
# Test if PCS data exists.
if pcs and not hasattr(dp, 'align_ids'):
raise RelaxNoAlignError()
if pcs and not hasattr(dp, 'pcs_ids'):
raise RelaxNoPCSError()
elif pcs and pcs_id and pcs_id not in dp.pcs_ids:
raise RelaxNoPCSError(pcs_id)
# Test if the paramagnetic centre is set.
if paramag_centre and not hasattr(cdp, 'paramagnetic_centre'):
raise RelaxError("The paramagnetic centre has not been defined.")
def cpmgfit_input(dir=None, binary='cpmgfit', spin_id=None, force=False):
"""Create the CPMGFit input files.
@keyword dir: The optional directory to place the files into. If None, then the files will be placed into a directory named after the dispersion model.
@type dir: str or None
@keyword binary: The name of the CPMGFit binary file. This can include the path to the binary.
@type binary: str
@keyword spin_id: The spin ID string to restrict the file creation to.
@type spin_id: str
@keyword force: A flag which if True will cause all pre-existing files to be overwritten.
@type force: bool
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the experiment type has been set.
if not hasattr(cdp, 'exp_type'):
raise RelaxError("The relaxation dispersion experiment type has not been specified.")
# Test if the model has been set.
if not hasattr(cdp, 'model_type'):
raise RelaxError("The relaxation dispersion model has not been specified.")
# Directory creation.
if dir != None:
mkdir_nofail(dir, verbosity=0)
# The 'run.sh' script.
batch = open_write_file('batch_run.sh', dir, force)
batch.write("#! /bin/sh\n\n")
# Generate the input files for each spin.
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
# Translate the model.
function = translate_model(spin.model)
# Create the input file.
file_in = create_spin_input(function=function, spin=spin, spin_id=spin_id, dir=dir)
# The output file name.
file_out = spin_file_name(spin_id=spin_id, output=True)
# Add the file to the batch script.
batch.write("%s -grid -xmgr -f %s | tee %s\n" % (binary, file_in, file_out))
# Close the batch script, then make it executable.
batch.close()
if dir:
chmod(dir + sep + 'batch_run.sh', S_IRWXU|S_IRGRP|S_IROTH)
else:
chmod('batch_run.sh', S_IRWXU|S_IRGRP|S_IROTH)
def cpmgfit_input(dir=None, binary='cpmgfit', spin_id=None, force=False):
"""Create the CPMGFit input files.
@keyword dir: The optional directory to place the files into. If None, then the files will be placed into a directory named after the dispersion model.
@type dir: str or None
@keyword binary: The name of the CPMGFit binary file. This can include the path to the binary.
@type binary: str
@keyword spin_id: The spin ID string to restrict the file creation to.
@type spin_id: str
@keyword force: A flag which if True will cause all pre-existing files to be overwritten.
@type force: bool
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Test if the experiment type has been set.
if not hasattr(cdp, 'exp_type'):
raise RelaxError("The relaxation dispersion experiment type has not been specified.")
# Test if the model has been set.
if not hasattr(cdp, 'model_type'):
raise RelaxError("The relaxation dispersion model has not been specified.")
# Directory creation.
if dir != None:
mkdir_nofail(dir, verbosity=0)
# The 'run.sh' script.
batch = open_write_file('batch_run.sh', dir, force)
batch.write("#! /bin/sh\n\n")
# Generate the input files for each spin.
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
# Translate the model.
function = translate_model(spin.model)
# Create the input file.
file_in = create_spin_input(function=function, spin=spin, spin_id=spin_id, dir=dir)
# The output file name.
file_out = spin_file_name(spin_id=spin_id, output=True)
# Add the file to the batch script.
batch.write("%s -grid -xmgr -f %s | tee %s\n" % (binary, file_in, file_out))
# Close the batch script, then make it executable.
batch.close()
if dir:
chmod(dir + sep + 'batch_run.sh', S_IRWXU|S_IRGRP|S_IROTH)
else:
chmod('batch_run.sh', S_IRWXU|S_IRGRP|S_IROTH)
def calc(verbosity=1):
"""Function for calculating the function value.
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
"""
# Test if the current data pipe exists.
check_pipe()
# Reset the minimisation statistics.
reset_min_stats(verbosity=verbosity)
# The specific analysis API object.
api = return_api()
# Deselect spins lacking data:
api.overfit_deselect()
# Create the scaling matrix.
scaling_matrix = assemble_scaling_matrix()
# Get the Processor box singleton (it contains the Processor instance) and alias the Processor.
processor_box = Processor_box()
processor = processor_box.processor
# Monte Carlo simulation calculation.
if hasattr(cdp, 'sim_state') and cdp.sim_state == 1:
# Loop over the simulations.
for i in range(cdp.sim_number):
# Status.
if status.current_analysis:
status.auto_analysis[status.current_analysis].mc_number = i
else:
status.mc_number = i
# Calculation.
api.calculate(verbosity=verbosity-1, sim_index=i, scaling_matrix=scaling_matrix)
# Print out.
if verbosity and not processor.is_queued():
print("Simulation " + repr(i+1))
# Unset the status.
if status.current_analysis:
status.auto_analysis[status.current_analysis].mc_number = None
else:
status.mc_number = None
# Minimisation.
else:
api.calculate(verbosity=verbosity, scaling_matrix=scaling_matrix)
# Execute any queued commands.
processor.run_queue()
def remove_tm(spin_id=None):
"""Remove local tm from the set of model-free parameters for the given spins.
@param spin_id: The spin identification string.
@type spin_id: str or None
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the pipe type is 'mf'.
function_type = pipes.get_type()
if function_type != 'mf':
raise RelaxFuncSetupError(specific_analyses.get_string(function_type))
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Loop over the spins.
for spin in spin_loop(spin_id):
# Skip deselected spins.
if not spin.select:
continue
# Test if a local tm parameter exists.
if not hasattr(spin, 'params') or not 'local_tm' in spin.params:
continue
# Remove tm.
spin.params.remove('local_tm')
# Model name.
if match('^tm', spin.model):
spin.model = spin.model[1:]
# Delete the local tm variable.
del spin.local_tm
# Set all the minimisation stats to None.
spin.chi2 = None
spin.iter = None
spin.f_count = None
spin.g_count = None
spin.h_count = None
spin.warning = None
# Set the global minimisation stats to None.
cdp.chi2 = None
cdp.iter = None
cdp.f_count = None
cdp.g_count = None
cdp.h_count = None
cdp.warning = None
def remove_tm(spin_id=None):
"""Remove local tm from the set of model-free parameters for the given spins.
@param spin_id: The spin identification string.
@type spin_id: str or None
"""
# Test if the current data pipe exists.
check_pipe()
# Test if the pipe type is 'mf'.
function_type = pipes.get_type()
if function_type != "mf":
raise RelaxFuncSetupError(specific_analyses.get_string(function_type))
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Loop over the spins.
for spin in spin_loop(spin_id):
# Skip deselected spins.
if not spin.select:
continue
# Test if a local tm parameter exists.
if not hasattr(spin, "params") or not "local_tm" in spin.params:
continue
# Remove tm.
spin.params.remove("local_tm")
# Model name.
if match("^tm", spin.model):
spin.model = spin.model[1:]
# Delete the local tm variable.
del spin.local_tm
# Set all the minimisation stats to None.
spin.chi2 = None
spin.iter = None
spin.f_count = None
spin.g_count = None
spin.h_count = None
spin.warning = None
# Set the global minimisation stats to None.
cdp.chi2 = None
cdp.iter = None
cdp.f_count = None
cdp.g_count = None
cdp.h_count = None
cdp.warning = None
def create(algor='LM', dir=None, force=False):
"""Create the Dasha script file 'dasha_script' for controlling the program.
@keyword algor: The optimisation algorithm to use. This can be the Levenberg-Marquardt algorithm 'LM' or the Newton-Raphson algorithm 'NR'.
@type algor: str
@keyword dir: The optional directory to place the script into.
@type dir: str or None
@keyword force: A flag which if True will cause any pre-existing file to be overwritten.
@type force: bool
"""
# Test if the current pipe exists.
check_pipe()
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Determine the parameter set.
model_type = determine_model_type()
# Test if diffusion tensor data for the data_pipe exists.
if model_type != 'local_tm' and not hasattr(cdp, 'diff_tensor'):
raise RelaxNoTensorError('diffusion')
# Test if the PDB file has been loaded (for the spheroid and ellipsoid).
if model_type != 'local_tm' and cdp.diff_tensor.type != 'sphere' and not hasattr(cdp, 'structure'):
raise RelaxNoPdbError
# Test the optimisation algorithm.
if algor not in ['LM', 'NR']:
raise RelaxError("The Dasha optimisation algorithm '%s' is unknown, it should either be 'LM' or 'NR'." % algor)
# Deselect certain spins.
__deselect_spins()
# Directory creation.
if dir == None:
dir = pipes.cdp_name()
mkdir_nofail(dir, verbosity=0)
# Calculate the angle alpha of the XH vector in the spheroid diffusion frame.
if cdp.diff_tensor.type == 'spheroid':
angles.spheroid_frame()
# Calculate the angles theta and phi of the XH vector in the ellipsoid diffusion frame.
elif cdp.diff_tensor.type == 'ellipsoid':
angles.ellipsoid_frame()
# The 'dasha_script' file.
script = open_write_file(file_name='dasha_script', dir=dir, force=force)
create_script(script, model_type, algor)
script.close()
def quad_int(flag=False):
"""Turn the high precision Scipy quadratic numerical integration on or off.
@keyword flag: The flag which if True will perform high precision numerical integration via the scipy.integrate quad(), dblquad() and tplquad() integration methods rather than the rough quasi-random numerical integration.
@type flag: bool
"""
# Test if the current data pipe exists.
check_pipe()
# Store the flag.
cdp.quad_int = flag
def monte_carlo_on():
"""Turn simulations on."""
# Test if the current data pipe exists.
check_pipe()
# Test if simulations have been set up.
if not hasattr(cdp, 'sim_state'):
raise RelaxError("Monte Carlo simulations have not been set up.")
# Turn simulations on.
cdp.sim_state = True
def quad_int(flag=False):
"""Turn the high precision Scipy quadratic numerical integration on or off.
@keyword flag: The flag which if True will perform high precision numerical integration via the scipy.integrate quad(), dblquad() and tplquad() integration methods rather than the rough quasi-random numerical integration.
@type flag: bool
"""
# Test if the current data pipe exists.
check_pipe()
# Store the flag.
cdp.quad_int = flag
def grid_zoom(level=0):
"""Store the grid zoom level.
@keyword level: The zoom level.
@type level: int
"""
# Test if the current data pipe exists.
check_pipe()
# Store the values.
cdp.grid_zoom_level = level
def get_frequency(id=None):
"""Return the frequency corresponding to the given ID.
@param id: The experiment ID string.
@type id: str
@return: The spectrometer proton frequency in Hertz for the given ID.
@rtype: float
"""
# Checks.
check_pipe()
check_frequency(id=id)
# Return the frequency in Hz.
return cdp.spectrometer_frq[id]
def monte_carlo_initial_values():
"""Set the initial simulation parameter values."""
# Test if the current data pipe exists.
check_pipe()
# Test if simulations have been set up.
if not hasattr(cdp, 'sim_state'):
raise RelaxError("Monte Carlo simulations have not been set up.")
# The specific analysis API object.
api = return_api()
# Set the initial parameter values.
api.sim_init_values()
def delete(spectrum_id=None):
"""Delete spectral data corresponding to the spectrum ID.
@keyword spectrum_id: The spectrum ID string.
@type spectrum_id: str
"""
# Data checks.
check_pipe()
check_mol_res_spin_data()
check_spectrum_id(spectrum_id)
# Remove the ID.
cdp.spectrum_ids.pop(cdp.spectrum_ids.index(spectrum_id))
# The ncproc parameter.
if hasattr(cdp, 'ncproc') and spectrum_id in cdp.ncproc:
del cdp.ncproc[spectrum_id]
# Replicates.
if hasattr(cdp, 'replicates'):
# Loop over the replicates.
for i in range(len(cdp.replicates)):
# The spectrum is replicated.
if spectrum_id in cdp.replicates[i]:
# Duplicate.
if len(cdp.replicates[i]) == 2:
cdp.replicates.pop(i)
# More than two spectra:
else:
cdp.replicates[i].pop(cdp.replicates[i].index(spectrum_id))
# No need to check further.
break
# Errors.
if hasattr(cdp, 'sigma_I') and spectrum_id in cdp.sigma_I:
del cdp.sigma_I[spectrum_id]
if hasattr(cdp, 'var_I') and spectrum_id in cdp.var_I:
del cdp.var_I[spectrum_id]
# Loop over the spins.
for spin in spin_loop():
# Intensity data.
if hasattr(spin,
'peak_intensity') and spectrum_id in spin.peak_intensity:
del spin.peak_intensity[spectrum_id]
def pivot(pivot=None, order=1, fix=False):
"""Set the pivot point for the 2 body motion.
@keyword pivot: The pivot point of the two bodies (domains, etc.) in the structural coordinate system.
@type pivot: list of num
@keyword order: The ordinal number of the pivot point. The value of 1 is for the first pivot point, the value of 2 for the second pivot point, and so on.
@type order: int
@keyword fix: A flag specifying if the pivot point should be fixed during optimisation.
@type fix: bool
"""
# Test if the current data pipe exists.
check_pipe()
# Store the fixed flag.
cdp.pivot_fixed = fix
# No pivot given, so update the model if needed and quit.
if pivot is None:
if hasattr(cdp, 'model'):
update_model()
return
# Convert the pivot to a numpy array.
pivot = array(pivot, float64)
# Check the pivot validity.
is_float_array(pivot, name='pivot point', size=3)
# Store the pivot point and fixed flag.
if order == 1:
cdp.pivot_x = pivot[0]
cdp.pivot_y = pivot[1]
cdp.pivot_z = pivot[2]
else:
# The variable names.
name_x = 'pivot_x_%i' % order
name_y = 'pivot_y_%i' % order
name_z = 'pivot_z_%i' % order
# Store the variables.
setattr(cdp, name_x, pivot[0])
setattr(cdp, name_y, pivot[1])
setattr(cdp, name_z, pivot[2])
# Update the model.
if hasattr(cdp, 'model'):
update_model()
