def _set_xh_vect(self, spin_line, col, spin, spin_id1=None, spin_id2=None, verbosity=1):
"""Set the unit vectors.
@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 spin: The spin container.
@type spin: SpinContainer instance
@keyword spin_id1: The ID string of the first spin.
@type spin_id1: str
@keyword spin_id2: The ID string of the second spin.
@type spin_id2: str
@keyword verbosity: A variable specifying the amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
"""
# Get the interatomic data container.
interatom = return_interatom(spin_id1=spin_id1, spin_id2=spin_id2)
if interatom == None:
raise RelaxError("No interatomic interaction between spins '%s' and '%s' could be found." (spin_id1, spin_id2))
# The vector.
vector = eval(spin_line[col['xh_vect']])
if vector:
# numpy array format.
try:
vector = array(vector, float64)
except:
raise RelaxError("The XH unit vector " + spin_line[col['xh_vect']] + " is invalid.")
# Set the vector.
interatom.vector = vector
def return_error(self, data_id):
"""Return the RDC or PCS error structure.
@param data_id: The data set as yielded by the base_data_loop() generator method.
@type data_id: list of str
@return: The array of RDC or PCS error values.
@rtype: list of float
"""
# Initialise the MC data structure.
mc_errors = []
# The RDC data.
if data_id[0] == 'rdc':
# Unpack the set.
data_type, spin_hash1, spin_hash2, align_id = data_id
# Get the interatomic data container.
interatom = return_interatom(spin_hash1=spin_hash1,
spin_hash2=spin_hash2)
# Do errors exist?
if not hasattr(interatom, 'rdc_err'):
raise RelaxError(
"The RDC errors are missing for interatomic data container between spins '%s' and '%s'."
% (spin_id1, spin_id2))
# Handle missing data.
if align_id not in interatom.rdc_err:
mc_errors.append(None)
# Append the data.
else:
mc_errors.append(interatom.rdc_err[align_id])
# The PCS data.
elif data_id[0] == 'pcs':
# Unpack the set.
data_type, spin_id, align_id = data_id
# Get the spin container.
spin = return_spin(spin_id=spin_id)
# Do errors exist?
if not hasattr(spin, 'pcs_err'):
raise RelaxError("The PCS errors are missing for spin '%s'." %
spin_id)
# Handle missing data.
if align_id not in spin.pcs_err:
mc_errors.append(None)
# Append the data.
else:
mc_errors.append(spin.pcs_err[align_id])
# Return the errors.
return mc_errors
def create_mc_data(self, data_id=None):
"""Create the Monte Carlo data by back calculating the RDCs or PCSs.
@keyword data_id: The data set as yielded by the base_data_loop() generator method.
@type data_id: list of str
@return: The Monte Carlo simulation data.
@rtype: list of floats
"""
# Initialise the MC data structure.
mc_data = []
# The RDC data.
if data_id[0] == 'rdc':
# Unpack the set.
data_type, spin_hash1, spin_hash2, align_id = data_id
# Get the interatomic data container.
interatom = return_interatom(spin_hash1=spin_hash1,
spin_hash2=spin_hash2)
# Does back-calculated data exist?
if not hasattr(interatom, 'rdc_bc'):
self.calculate()
# The data.
if not hasattr(interatom,
'rdc_bc') or align_id not in interatom.rdc_bc:
data = None
else:
data = interatom.rdc_bc[align_id]
# Append the data.
mc_data.append(data)
# The PCS data.
elif data_id[0] == 'pcs':
# Unpack the set.
data_type, spin_id, align_id = data_id
# Get the spin container.
spin = return_spin(spin_id=spin_id)
# Does back-calculated data exist?
if not hasattr(spin, 'pcs_bc'):
self.calculate()
# The data.
if not hasattr(spin, 'pcs_bc') or align_id not in spin.pcs_bc:
data = None
else:
data = spin.pcs_bc[align_id]
# Append the data.
mc_data.append(data)
# Return the data.
return mc_data
def create_mc_data(self, data_id=None):
"""Create the Monte Carlo data by back calculating the RDCs or PCSs.
@keyword data_id: The data set as yielded by the base_data_loop() generator method.
@type data_id: list of str
@return: The Monte Carlo simulation data.
@rtype: list of floats
"""
# Initialise the MC data structure.
mc_data = []
# The RDC data.
if data_id[0] == 'rdc':
# Unpack the set.
data_type, spin_id1, spin_id2, align_id = data_id
# Get the interatomic data container.
interatom = return_interatom(spin_id1, spin_id2)
# Does back-calculated data exist?
if not hasattr(interatom, 'rdc_bc'):
self.calculate()
# The data.
if not hasattr(interatom, 'rdc_bc') or align_id not in interatom.rdc_bc:
data = None
else:
data = interatom.rdc_bc[align_id]
# Append the data.
mc_data.append(data)
# The PCS data.
elif data_id[0] == 'pcs':
# Unpack the set.
data_type, spin_id, align_id = data_id
# Get the spin container.
spin = return_spin(spin_id)
# Does back-calculated data exist?
if not hasattr(spin, 'pcs_bc'):
self.calculate()
# The data.
if not hasattr(spin, 'pcs_bc') or align_id not in spin.pcs_bc:
data = None
else:
data = spin.pcs_bc[align_id]
# Append the data.
mc_data.append(data)
# Return the data.
return mc_data
def return_error(self, data_id):
"""Return the RDC or PCS error structure.
@param data_id: The data set as yielded by the base_data_loop() generator method.
@type data_id: list of str
@return: The array of RDC or PCS error values.
@rtype: list of float
"""
# Initialise the MC data structure.
mc_errors = []
# The RDC data.
if data_id[0] == 'rdc':
# Unpack the set.
data_type, spin_id1, spin_id2, align_id = data_id
# Get the interatomic data container.
interatom = return_interatom(spin_id1, spin_id2)
# Do errors exist?
if not hasattr(interatom, 'rdc_err'):
raise RelaxError("The RDC errors are missing for interatomic data container between spins '%s' and '%s'." % (spin_id1, spin_id2))
# Handle missing data.
if align_id not in interatom.rdc_err:
mc_errors.append(None)
# Append the data.
else:
mc_errors.append(interatom.rdc_err[align_id])
# The PCS data.
elif data_id[0] == 'pcs':
# Unpack the set.
data_type, spin_id, align_id = data_id
# Get the spin container.
spin = return_spin(spin_id)
# Do errors exist?
if not hasattr(spin, 'pcs_err'):
raise RelaxError("The PCS errors are missing for spin '%s'." % spin_id)
# Handle missing data.
if align_id not in spin.pcs_err:
mc_errors.append(None)
# Append the data.
else:
mc_errors.append(spin.pcs_err[align_id])
# Return the errors.
return mc_errors
def set_xh_vect(spin_line,
col,
spin,
spin_id1=None,
spin_id2=None,
spin_hash1=None,
spin_hash2=None,
verbosity=1):
"""Set the unit vectors.
@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 spin: The spin container.
@type spin: SpinContainer instance
@keyword spin_id1: The ID string of the first spin.
@type spin_id1: str
@keyword spin_id2: The ID string of the second spin.
@type spin_id2: str
@keyword spin_hash1: The unique hash of the first spin.
@type spin_hash1: str
@keyword spin_hash2: The unique hash of the second spin.
@type spin_hash2: str
@keyword verbosity: A variable specifying the amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
"""
# Get the interatomic data container.
interatom = return_interatom(spin_hash1=spin_hash1, spin_hash2=spin_hash2)
if interatom == None:
raise RelaxError(
"No interatomic interaction between spins '%s' and '%s' could be found." (
spin_id1, spin_id2))
# The vector.
vector = eval(spin_line[col['xh_vect']])
if vector:
# numpy array format.
try:
vector = array(vector, float64)
except:
raise RelaxError("The XH unit vector " +
spin_line[col['xh_vect']] + " is invalid.")
# Set the vector.
interatom.vector = vector
def sim_pack_data(self, data_id, sim_data):
"""Pack the Monte Carlo simulation data.
@param data_id: The data set as yielded by the base_data_loop() generator method.
@type data_id: list of str
@param sim_data: The Monte Carlo simulation data.
@type sim_data: list of float
"""
# The RDC data.
if data_id[0] == 'rdc':
# Unpack the set.
data_type, spin_hash1, spin_hash2, align_id = data_id
# Get the interatomic data container.
interatom = return_interatom(spin_hash1=spin_hash1,
spin_hash2=spin_hash2)
# Initialise.
if not hasattr(interatom, 'rdc_sim'):
interatom.rdc_sim = {}
# Store the data structure.
interatom.rdc_sim[align_id] = []
for i in range(cdp.sim_number):
interatom.rdc_sim[align_id].append(sim_data[i][0])
# The PCS data.
elif data_id[0] == 'pcs':
# Unpack the set.
data_type, spin_id, align_id = data_id
# Get the spin container.
spin = return_spin(spin_id=spin_id)
# Initialise.
if not hasattr(spin, 'pcs_sim'):
spin.pcs_sim = {}
# Store the data structure.
spin.pcs_sim[data_id[2]] = []
for i in range(cdp.sim_number):
spin.pcs_sim[data_id[2]].append(sim_data[i][0])
def setup_pseudoatom_rdc():
"""Make sure that the interatom system is properly set up for pseudo-atoms and RDCs.
Interatomic data containers between the non-pseudo-atom and the pseudo-atom members will be deselected.
"""
# Loop over all interatomic data containers.
for interatom in interatomic_loop():
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# Checks.
flag1 = is_pseudoatom(spin1)
flag2 = is_pseudoatom(spin2)
# No pseudo-atoms, so do nothing.
if not (flag1 or flag2):
continue
# Both are pseudo-atoms.
if flag1 and flag2:
warn(RelaxWarning("Support for both spins being in a dipole pair being pseudo-atoms is not implemented yet, deselecting the interatomic data container for the spin pair '%s' and '%s'." % (interatom.spin_id1, interatom.spin_id2)))
interatom.select = False
# Alias the pseudo and normal atoms.
pseudospin = spin1
base_spin_id = interatom.spin_id2
pseudospin_id = interatom.spin_id1
if flag2:
pseudospin = spin2
base_spin_id = interatom.spin_id1
pseudospin_id = interatom.spin_id2
# Loop over the atoms of the pseudo-atom.
for spin, spin_id in pseudoatom_loop(pseudospin, return_id=True):
# Get the corresponding interatomic data container.
pseudo_interatom = return_interatom(spin_id1=spin_id, spin_id2=base_spin_id)
# Deselect if needed.
if pseudo_interatom.select:
warn(RelaxWarning("Deselecting the interatomic data container for the spin pair '%s' and '%s' as it is part of the pseudo-atom system of the spin pair '%s' and '%s'." % (pseudo_interatom.spin_id1, pseudo_interatom.spin_id2, base_spin_id, pseudospin_id)))
pseudo_interatom.select = False
def sim_pack_data(self, data_id, sim_data):
"""Pack the Monte Carlo simulation data.
@param data_id: The data set as yielded by the base_data_loop() generator method.
@type data_id: list of str
@param sim_data: The Monte Carlo simulation data.
@type sim_data: list of float
"""
# The RDC data.
if data_id[0] == 'rdc':
# Unpack the set.
data_type, spin_id1, spin_id2, align_id = data_id
# Get the interatomic data container.
interatom = return_interatom(spin_id1, spin_id2)
# Initialise.
if not hasattr(interatom, 'rdc_sim'):
interatom.rdc_sim = {}
# Store the data structure.
interatom.rdc_sim[align_id] = []
for i in range(cdp.sim_number):
interatom.rdc_sim[align_id].append(sim_data[i][0])
# The PCS data.
elif data_id[0] == 'pcs':
# Unpack the set.
data_type, spin_id, align_id = data_id
# Get the spin container.
spin = return_spin(spin_id)
# Initialise.
if not hasattr(spin, 'pcs_sim'):
spin.pcs_sim = {}
# Store the data structure.
spin.pcs_sim[data_id[2]] = []
for i in range(cdp.sim_number):
spin.pcs_sim[data_id[2]].append(sim_data[i][0])
if file[:3] == 'pcs':
# Load the original file.
pcs.read(align_id=file, file='%s.txt'%file, dir='../free_rotor/', mol_name_col=1, res_num_col=2, res_name_col=3, spin_num_col=4, spin_name_col=5, data_col=6, error_col=7)
# Delete the data.
for spin_id in missing_pcs[file]:
# Get the container.
spin = return_spin(spin_id)
# Delete the data.
del spin.pcs[file]
# Write data to file.
pcs.write(align_id=file, file='%s.txt'%file, dir='.', force=True)
# RDC data.
elif file[:3] == 'rdc':
# Load the original file.
rdc.read(align_id=file, file='%s.txt'%file, dir='../free_rotor/', data_col=3, error_col=4)
# Delete the data.
for spin_id1, spin_id2 in missing_rdc[file]:
# Get the container.
interatom = return_interatom(spin_id1, spin_id2)
# Delete the data.
del interatom.rdc[file]
# Write data to file.
rdc.write(align_id=file, file='%s.txt'%file, dir='.', force=True)
def read(file=None, dir=None, file_data=None, spin_id1_col=None, spin_id2_col=None, data_col=None, error_col=None, sign_col=None, sep=None):
"""Read the J coupling data from file.
@keyword file: The name of the file to open.
@type file: str
@keyword dir: The directory containing the file (defaults to the current directory if None).
@type dir: str or None
@keyword file_data: An alternative to opening a file, if the data already exists in the correct format. The format is a list of lists where the first index corresponds to the row and the second the column.
@type file_data: list of lists
@keyword spin_id1_col: The column containing the spin ID strings of the first spin.
@type spin_id1_col: int
@keyword spin_id2_col: The column containing the spin ID strings of the second spin.
@type spin_id2_col: int
@keyword data_col: The column containing the J coupling data in Hz.
@type data_col: int or None
@keyword error_col: The column containing the J coupling errors.
@type error_col: int or None
@keyword sign_col: The optional column containing the sign of the J coupling.
@type sign_col: int or None
@keyword sep: The column separator which, if None, defaults to whitespace.
@type sep: str or None
"""
# Check the pipe setup.
check_pipe_setup(sequence=True)
# Either the data or error column must be supplied.
if data_col == None and error_col == None:
raise RelaxError("One of either the data or error column must be supplied.")
# Extract the data from the file, and remove comments and blank lines.
file_data = extract_data(file, dir, sep=sep)
file_data = strip(file_data, comments=True)
# Loop over the J coupling data.
data = []
for line in file_data:
# Invalid columns.
if spin_id1_col > len(line):
warn(RelaxWarning("The data %s is invalid, no first spin ID column can be found." % line))
continue
if spin_id2_col > len(line):
warn(RelaxWarning("The data %s is invalid, no second spin ID column can be found." % line))
continue
if data_col and data_col > len(line):
warn(RelaxWarning("The data %s is invalid, no data column can be found." % line))
continue
if error_col and error_col > len(line):
warn(RelaxWarning("The data %s is invalid, no error column can be found." % line))
continue
if sign_col and sign_col > len(line):
warn(RelaxWarning("The data %s is invalid, no sign column can be found." % line))
continue
# Unpack.
spin_id1 = line[spin_id1_col-1]
spin_id2 = line[spin_id2_col-1]
value = None
if data_col:
value = line[data_col-1]
error = None
if error_col:
error = line[error_col-1]
sign = None
if sign_col:
sign = line[sign_col-1]
# Convert the spin IDs.
if spin_id1[0] in ["\"", "\'"]:
spin_id1 = eval(spin_id1)
if spin_id2[0] in ["\"", "\'"]:
spin_id2 = eval(spin_id2)
# Convert and check the value.
if value == 'None':
value = None
if value != None:
try:
value = float(value)
except ValueError:
warn(RelaxWarning("The J coupling value of the line %s is invalid." % line))
continue
# The sign data.
if sign == 'None':
sign = None
if sign != None:
try:
sign = float(sign)
except ValueError:
warn(RelaxWarning("The J coupling sign of the line %s is invalid." % line))
continue
if sign not in [1.0, -1.0]:
warn(RelaxWarning("The J coupling sign of the line %s is invalid." % line))
continue
# Convert and check the error.
if error == 'None':
error = None
if error != None:
try:
error = float(error)
except ValueError:
warn(RelaxWarning("The error value of the line %s is invalid." % line))
continue
# Get the spins.
spin1 = return_spin(spin_id=spin_id1)
spin2 = return_spin(spin_id=spin_id2)
# Check the spin IDs.
if not spin1:
warn(RelaxWarning("The spin ID '%s' cannot be found in the current data pipe, skipping the data %s." % (spin_id1, line)))
continue
if not spin2:
warn(RelaxWarning("The spin ID '%s' cannot be found in the current data pipe, skipping the data %s." % (spin_id2, line)))
continue
# Test the error value (cannot be 0.0).
if error == 0.0:
raise RelaxError("An invalid error value of zero has been encountered.")
# Get the interatomic data container.
interatom = return_interatom(spin_hash1=spin1._hash, spin_hash2=spin2._hash)
# Create the container if needed.
if interatom == None:
interatom = create_interatom(spin_id1=spin_id1, spin_id2=spin_id2)
# Add the data.
if data_col:
# Sign conversion.
if sign != None:
value = value * sign
# Add the value.
interatom.j_coupling = value
# Add the error.
if error_col:
interatom.j_coupling_err = error
# Append the data for printout.
data.append([spin_id1, spin_id2])
if is_float(value):
data[-1].append("%20.15f" % value)
else:
data[-1].append("%20s" % value)
if is_float(error):
data[-1].append("%20.15f" % error)
else:
data[-1].append("%20s" % error)
# No data, so fail hard!
if not len(data):
raise RelaxError("No J coupling data could be extracted.")
# Print out.
print("The following J coupling have been loaded into the relax data store:\n")
write_data(out=sys.stdout, headings=["Spin_ID1", "Spin_ID2", "Value", "Error"], data=data)
def read(file=None, dir=None, file_data=None, spin_id1_col=None, spin_id2_col=None, data_col=None, error_col=None, sign_col=None, sep=None):
"""Read the J coupling data from file.
@keyword file: The name of the file to open.
@type file: str
@keyword dir: The directory containing the file (defaults to the current directory if None).
@type dir: str or None
@keyword file_data: An alternative to opening a file, if the data already exists in the correct format. The format is a list of lists where the first index corresponds to the row and the second the column.
@type file_data: list of lists
@keyword spin_id1_col: The column containing the spin ID strings of the first spin.
@type spin_id1_col: int
@keyword spin_id2_col: The column containing the spin ID strings of the second spin.
@type spin_id2_col: int
@keyword data_col: The column containing the J coupling data in Hz.
@type data_col: int or None
@keyword error_col: The column containing the J coupling errors.
@type error_col: int or None
@keyword sign_col: The optional column containing the sign of the J coupling.
@type sign_col: int or None
@keyword sep: The column separator which, if None, defaults to whitespace.
@type sep: str or None
"""
# Check the pipe setup.
check_pipe_setup(sequence=True)
# Either the data or error column must be supplied.
if data_col == None and error_col == None:
raise RelaxError("One of either the data or error column must be supplied.")
# Extract the data from the file, and remove comments and blank lines.
file_data = extract_data(file, dir, sep=sep)
file_data = strip(file_data, comments=True)
# Loop over the J coupling data.
data = []
for line in file_data:
# Invalid columns.
if spin_id1_col > len(line):
warn(RelaxWarning("The data %s is invalid, no first spin ID column can be found." % line))
continue
if spin_id2_col > len(line):
warn(RelaxWarning("The data %s is invalid, no second spin ID column can be found." % line))
continue
if data_col and data_col > len(line):
warn(RelaxWarning("The data %s is invalid, no data column can be found." % line))
continue
if error_col and error_col > len(line):
warn(RelaxWarning("The data %s is invalid, no error column can be found." % line))
continue
if sign_col and sign_col > len(line):
warn(RelaxWarning("The data %s is invalid, no sign column can be found." % line))
continue
# Unpack.
spin_id1 = line[spin_id1_col-1]
spin_id2 = line[spin_id2_col-1]
value = None
if data_col:
value = line[data_col-1]
error = None
if error_col:
error = line[error_col-1]
sign = None
if sign_col:
sign = line[sign_col-1]
# Convert the spin IDs.
if spin_id1[0] in ["\"", "\'"]:
spin_id1 = eval(spin_id1)
if spin_id2[0] in ["\"", "\'"]:
spin_id2 = eval(spin_id2)
# Convert and check the value.
if value == 'None':
value = None
if value != None:
try:
value = float(value)
except ValueError:
warn(RelaxWarning("The J coupling value of the line %s is invalid." % line))
continue
# The sign data.
if sign == 'None':
sign = None
if sign != None:
try:
sign = float(sign)
except ValueError:
warn(RelaxWarning("The J coupling sign of the line %s is invalid." % line))
continue
if sign not in [1.0, -1.0]:
warn(RelaxWarning("The J coupling sign of the line %s is invalid." % line))
continue
# Convert and check the error.
if error == 'None':
error = None
if error != None:
try:
error = float(error)
except ValueError:
warn(RelaxWarning("The error value of the line %s is invalid." % line))
continue
# Get the spins.
spin1 = return_spin(spin_id1)
spin2 = return_spin(spin_id2)
# Check the spin IDs.
if not spin1:
warn(RelaxWarning("The spin ID '%s' cannot be found in the current data pipe, skipping the data %s." % (spin_id1, line)))
continue
if not spin2:
warn(RelaxWarning("The spin ID '%s' cannot be found in the current data pipe, skipping the data %s." % (spin_id2, line)))
continue
# Test the error value (cannot be 0.0).
if error == 0.0:
raise RelaxError("An invalid error value of zero has been encountered.")
# Get the interatomic data container.
interatom = return_interatom(spin_id1, spin_id2)
# Create the container if needed.
if interatom == None:
interatom = create_interatom(spin_id1=spin_id1, spin_id2=spin_id2)
# Add the data.
if data_col:
# Sign conversion.
if sign != None:
value = value * sign
# Add the value.
interatom.j_coupling = value
# Add the error.
if error_col:
interatom.j_coupling_err = error
# Append the data for printout.
data.append([spin_id1, spin_id2])
if is_float(value):
data[-1].append("%20.15f" % value)
else:
data[-1].append("%20s" % value)
if is_float(error):
data[-1].append("%20.15f" % error)
else:
data[-1].append("%20s" % error)
# No data, so fail hard!
if not len(data):
raise RelaxError("No J coupling data could be extracted.")
# Print out.
print("The following J coupling have been loaded into the relax data store:\n")
write_data(out=sys.stdout, headings=["Spin_ID1", "Spin_ID2", "Value", "Error"], data=data)
spin = return_spin(spin_id=spin_id)
# Delete the data.
del spin.pcs[file]
# Write data to file.
pcs.write(align_id=file, file='%s.txt' % file, dir='.', force=True)
# RDC data.
elif file[:3] == 'rdc':
# Load the original file.
rdc.read(align_id=file,
file='%s.txt' % file,
dir='../free_rotor/',
data_col=3,
error_col=4)
# Delete the data.
for spin_id1, spin_id2 in missing_rdc[file]:
# Get the container.
spin1 = return_spin(spin_id=spin_id1)
spin2 = return_spin(spin_id=spin_id2)
interatom = return_interatom(spin_hash1=spin1._hash,
spin_hash1=spin2._hash)
# Delete the data.
del interatom.rdc[file]
# Write data to file.
rdc.write(align_id=file, file='%s.txt' % file, dir='.', force=True)
def bmrb_read(star, sample_conditions=None):
"""Read the relaxation data from the NMR-STAR dictionary object.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
@keyword sample_conditions: The sample condition label to read. Only one sample condition can be read per data pipe.
@type sample_conditions: None or str
"""
# Get the relaxation data.
for data in star.relaxation.loop():
# Sample conditions do not match (remove the $ sign).
if 'sample_cond_list_label' in data and sample_conditions and data[
'sample_cond_list_label'].replace('$',
'') != sample_conditions:
continue
# Create the labels.
ri_type = data['data_type']
frq = float(data['frq']) * 1e6
# Round the label to the nearest factor of 10.
frq_label = create_frq_label(float(data['frq']) * 1e6)
# The ID string.
ri_id = "%s_%s" % (ri_type, frq_label)
# The number of spins.
N = bmrb.num_spins(data)
# No data in the saveframe.
if N == 0:
continue
# The molecule names.
mol_names = bmrb.molecule_names(data, N)
# Generate the sequence if needed.
bmrb.generate_sequence(N,
spin_names=data['atom_names'],
res_nums=data['res_nums'],
res_names=data['res_names'],
mol_names=mol_names,
isotopes=data['isotope'],
elements=data['atom_types'])
# The attached protons.
if 'atom_names_2' in data:
# Generate the proton spins.
bmrb.generate_sequence(N,
spin_names=data['atom_names_2'],
res_nums=data['res_nums'],
res_names=data['res_names'],
mol_names=mol_names,
isotopes=data['isotope_2'],
elements=data['atom_types_2'])
# Define the dipolar interaction.
for i in range(len(data['atom_names'])):
# The spin IDs.
spin_id1 = generate_spin_id_unique(
spin_name=data['atom_names'][i],
res_num=data['res_nums'][i],
res_name=data['res_names'][i],
mol_name=mol_names[i])
spin_id2 = generate_spin_id_unique(
spin_name=data['atom_names_2'][i],
res_num=data['res_nums'][i],
res_name=data['res_names'][i],
mol_name=mol_names[i])
# Check if the container exists.
spin1 = return_spin(spin_id=spin_id1)
spin2 = return_spin(spin_id=spin_id2)
if return_interatom(spin_hash1=spin1._hash,
spin_hash2=spin2._hash):
continue
# Define.
define_dipole_pair(spin_id1=spin_id1,
spin_id2=spin_id2,
spin1=spin1,
spin2=spin2,
verbose=False)
# The data and error.
vals = data['data']
errors = data['errors']
if vals == None:
vals = [None] * N
if errors == None:
errors = [None] * N
# Data transformation.
if vals != None and 'units' in data:
# Scaling.
if data['units'] == 'ms':
# Loop over the data.
for i in range(N):
# The value.
if vals[i] != None:
vals[i] = vals[i] / 1000
# The error.
if errors[i] != None:
errors[i] = errors[i] / 1000
# Invert.
if data['units'] in ['s', 'ms']:
# Loop over the data.
for i in range(len(vals)):
# The value.
if vals[i] != None:
vals[i] = 1.0 / vals[i]
# The error.
if vals[i] != None and errors[i] != None:
errors[i] = errors[i] * vals[i]**2
# Pack the data.
pack_data(ri_id,
ri_type,
frq,
vals,
errors,
mol_names=mol_names,
res_nums=data['res_nums'],
res_names=data['res_names'],
spin_nums=None,
spin_names=data['atom_names'],
gen_seq=True,
verbose=False)
# Store the temperature calibration and control.
if data['temp_calibration']:
temp_calibration(ri_id=ri_id, method=data['temp_calibration'])
if data['temp_control']:
temp_control(ri_id=ri_id, method=data['temp_control'])
# Peak intensity type.
if data['peak_intensity_type']:
peak_intensity_type(ri_id=ri_id, type=data['peak_intensity_type'])
def return_rdc_data(sim_index=None):
"""Set up the data structures for optimisation using RDCs as base data sets.
@keyword sim_index: The index of the simulation to optimise. This should be None if normal optimisation is desired.
@type sim_index: None or int
@return: The assembled data structures for using RDCs as the base data for optimisation. These include:
- rdc, the RDC values.
- rdc_err, the RDC errors.
- rdc_weight, the RDC weights.
- vectors, the interatomic vectors (pseudo-atom dependent).
- rdc_const, the dipolar constants (pseudo-atom dependent).
- absolute, the absolute value flags (as 1's and 0's).
- T_flags, the flags for T = J+D type data (as 1's and 0's).
- j_couplings, the J coupling values if the RDC data type is set to T = J+D.
- pseudo_flags, the list of flags indicating if the interatomic data contains a pseudo-atom (as 1's and 0's).
@rtype: tuple of (numpy rank-2 float64 array, numpy rank-2 float64 array, numpy rank-2 float64 array, list of numpy rank-3 float64 arrays, list of lists of floats, numpy rank-2 int32 array, numpy rank-2 int32 array, numpy rank-2 float64 array, numpy rank-1 int32 array)
"""
# Sort out pseudo-atoms first. This only needs to be called once.
setup_pseudoatom_rdc()
# Initialise.
rdc = []
rdc_err = []
rdc_weight = []
unit_vect = []
rdc_const = []
absolute = []
T_flags = []
j_couplings = []
pseudo_flags = []
# The unit vectors, RDC constants, and J couplings.
for interatom in interatomic_loop():
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# RDC checks.
if not check_rdcs(interatom):
continue
# Gyromagnetic ratios.
g1 = return_gyromagnetic_ratio(spin1.isotope)
g2 = return_gyromagnetic_ratio(spin2.isotope)
# Pseudo atoms.
if is_pseudoatom(spin1) and is_pseudoatom(spin2):
raise RelaxError("Support for both spins being in a dipole pair being pseudo-atoms is not implemented yet.")
if is_pseudoatom(spin1) or is_pseudoatom(spin2):
# Set the flag.
pseudo_flags.append(1)
# Alias the pseudo and normal atoms.
if is_pseudoatom(spin1):
pseudospin = spin1
base_spin = spin2
pseudospin_id = interatom.spin_id1
base_spin_id = interatom.spin_id2
else:
pseudospin = spin2
base_spin = spin1
pseudospin_id = interatom.spin_id2
base_spin_id = interatom.spin_id1
# Loop over the atoms of the pseudo-atom, storing the data.
pseudo_unit_vect = []
pseudo_rdc_const = []
for spin, spin_id in pseudoatom_loop(pseudospin, return_id=True):
# Get the corresponding interatomic data container.
pseudo_interatom = return_interatom(spin_id1=spin_id, spin_id2=base_spin_id)
# Check.
if pseudo_interatom == None:
raise RelaxError("The interatomic data container between the spins '%s' and '%s' for the pseudo-atom '%s' is not defined." % (base_spin_id, spin_id, pseudospin_id))
# Add the vectors.
if is_float(interatom.vector[0]):
pseudo_unit_vect.append([pseudo_interatom.vector])
else:
pseudo_unit_vect.append(pseudo_interatom.vector)
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
pseudo_rdc_const.append(3.0/(2.0*pi) * dipolar_constant(g1, g2, pseudo_interatom.r))
# Reorder the unit vectors so that the structure and pseudo-atom dimensions are swapped.
pseudo_unit_vect = transpose(array(pseudo_unit_vect, float64), (1, 0, 2))
# Block append the pseudo-data.
unit_vect.append(pseudo_unit_vect)
rdc_const.append(pseudo_rdc_const)
# Normal atom.
else:
# Set the flag.
pseudo_flags.append(0)
# Add the vectors.
if is_float(interatom.vector[0]):
unit_vect.append([interatom.vector])
else:
unit_vect.append(interatom.vector)
# Calculate the RDC dipolar constant (in Hertz, and the 3 comes from the alignment tensor), and append it to the list.
rdc_const.append(3.0/(2.0*pi) * dipolar_constant(g1, g2, interatom.r))
# Store the measured J coupling.
if opt_uses_j_couplings():
j_couplings.append(interatom.j_coupling)
# Fix the unit vector data structure.
num = None
for rdc_index in range(len(unit_vect)):
# Convert to numpy structures.
unit_vect[rdc_index] = array(unit_vect[rdc_index], float64)
# Number of vectors.
if num == None:
if unit_vect[rdc_index] != None:
num = len(unit_vect[rdc_index])
continue
# Check.
if unit_vect[rdc_index] != None and len(unit_vect[rdc_index]) != num:
raise RelaxError("The number of interatomic vectors for all no match:\n%s" % unit_vect[rdc_index])
# Missing unit vectors.
if num == None:
raise RelaxError("No interatomic vectors could be found.")
# Update None entries.
for i in range(len(unit_vect)):
if unit_vect[i] == None:
unit_vect[i] = [[None, None, None]]*num
# The RDC data.
for i in range(len(cdp.align_ids)):
# Alias the ID.
align_id = cdp.align_ids[i]
# Skip non-optimised data.
if not opt_uses_align_data(align_id):
continue
# Append empty arrays to the RDC structures.
rdc.append([])
rdc_err.append([])
rdc_weight.append([])
absolute.append([])
T_flags.append([])
# Interatom loop.
for interatom in interatomic_loop():
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# RDC checks.
if not check_rdcs(interatom):
continue
# T-type data.
if align_id in interatom.rdc_data_types and interatom.rdc_data_types[align_id] == 'T':
T_flags[-1].append(True)
else:
T_flags[-1].append(False)
# Check for J couplings if the RDC data type is T = J+D.
if T_flags[-1][-1] and not hasattr(interatom, 'j_coupling'):
continue
# Defaults of None.
value = None
error = None
# Normal set up.
if align_id in interatom.rdc.keys():
# The RDC.
if sim_index != None:
value = interatom.rdc_sim[align_id][sim_index]
else:
value = interatom.rdc[align_id]
# The error.
if hasattr(interatom, 'rdc_err') and align_id in interatom.rdc_err.keys():
# T values.
if T_flags[-1][-1]:
error = sqrt(interatom.rdc_err[align_id]**2 + interatom.j_coupling_err**2)
# D values.
else:
error = interatom.rdc_err[align_id]
# Append the RDCs to the list.
rdc[-1].append(value)
# Append the RDC errors.
rdc_err[-1].append(error)
# Append the weight.
if hasattr(interatom, 'rdc_weight') and align_id in interatom.rdc_weight.keys():
rdc_weight[-1].append(interatom.rdc_weight[align_id])
else:
rdc_weight[-1].append(1.0)
# Append the absolute value flag.
if hasattr(interatom, 'absolute_rdc') and align_id in interatom.absolute_rdc.keys():
absolute[-1].append(interatom.absolute_rdc[align_id])
else:
absolute[-1].append(False)
# Convert to numpy objects.
rdc = array(rdc, float64)
rdc_err = array(rdc_err, float64)
rdc_weight = array(rdc_weight, float64)
absolute = array(absolute, int32)
T_flags = array(T_flags, int32)
if not opt_uses_j_couplings():
j_couplings = None
pseudo_flags = array(pseudo_flags, int32)
# Return the data structures.
return rdc, rdc_err, rdc_weight, unit_vect, rdc_const, absolute, T_flags, j_couplings, pseudo_flags
def read(align_id=None, file=None, dir=None, file_data=None, data_type='D', spin_id1_col=None, spin_id2_col=None, data_col=None, error_col=None, sep=None, neg_g_corr=False, absolute=False):
"""Read the RDC data from file.
@keyword align_id: The alignment tensor ID string.
@type align_id: str
@keyword file: The name of the file to open.
@type file: str
@keyword dir: The directory containing the file (defaults to the current directory if None).
@type dir: str or None
@keyword file_data: An alternative to opening a file, if the data already exists in the correct format. The format is a list of lists where the first index corresponds to the row and the second the column.
@type file_data: list of lists
@keyword data_type: A string which is set to 'D' means that the splitting in the aligned sample was assumed to be J + D, or if set to '2D' then the splitting was taken as J + 2D. If set to 'T', then the data will be marked as being J+D values.
@keyword spin_id1_col: The column containing the spin ID strings of the first spin.
@type spin_id1_col: int
@keyword spin_id2_col: The column containing the spin ID strings of the second spin.
@type spin_id2_col: int
@keyword data_col: The column containing the RDC data in Hz.
@type data_col: int or None
@keyword error_col: The column containing the RDC errors.
@type error_col: int or None
@keyword sep: The column separator which, if None, defaults to whitespace.
@type sep: str or None
@keyword neg_g_corr: A flag which is used to correct for the negative gyromagnetic ratio of 15N. If True, a sign inversion will be applied to all RDC values to be loaded.
@type neg_g_corr: bool
@keyword absolute: A flag which if True indicates that the RDCs to load are signless. All RDCs will then be converted to positive values.
@type absolute: bool
"""
# Check the pipe setup.
check_pipe_setup(sequence=True)
# Either the data or error column must be supplied.
if data_col == None and error_col == None:
raise RelaxError("One of either the data or error column must be supplied.")
# Check the data types.
rdc_types = ['D', '2D', 'T']
if data_type not in rdc_types:
raise RelaxError("The RDC data type '%s' must be one of %s." % (data_type, rdc_types))
# Spin specific data.
#####################
# Extract the data from the file, and remove comments and blank lines.
file_data = extract_data(file, dir, sep=sep)
file_data = strip(file_data, comments=True)
# Loop over the RDC data.
data = []
for line in file_data:
# Invalid columns.
if spin_id1_col > len(line):
warn(RelaxWarning("The data %s is invalid, no first spin ID column can be found." % line))
continue
if spin_id2_col > len(line):
warn(RelaxWarning("The data %s is invalid, no second spin ID column can be found." % line))
continue
if data_col and data_col > len(line):
warn(RelaxWarning("The data %s is invalid, no data column can be found." % line))
continue
if error_col and error_col > len(line):
warn(RelaxWarning("The data %s is invalid, no error column can be found." % line))
continue
# Unpack.
spin_id1 = line[spin_id1_col-1]
spin_id2 = line[spin_id2_col-1]
value = None
if data_col:
value = line[data_col-1]
error = None
if error_col:
error = line[error_col-1]
# Convert the spin IDs.
if spin_id1[0] in ["\"", "\'"]:
spin_id1 = eval(spin_id1)
if spin_id2[0] in ["\"", "\'"]:
spin_id2 = eval(spin_id2)
# Convert and check the value.
if value == 'None':
value = None
if value != None:
try:
value = float(value)
except ValueError:
warn(RelaxWarning("The RDC value of the line %s is invalid." % line))
continue
# Convert and check the error.
if error == 'None':
error = None
if error != None:
try:
error = float(error)
except ValueError:
warn(RelaxWarning("The error value of the line %s is invalid." % line))
continue
# Get the spins.
spin1 = return_spin(spin_id1)
spin2 = return_spin(spin_id2)
# Check the spin IDs.
if not spin1:
warn(RelaxWarning("The spin ID '%s' cannot be found in the current data pipe, skipping the data %s." % (spin_id1, line)))
continue
if not spin2:
warn(RelaxWarning("The spin ID '%s' cannot be found in the current data pipe, skipping the data %s." % (spin_id2, line)))
continue
# Get the interatomic data container.
interatom = return_interatom(spin_id1, spin_id2)
# Create the container if needed.
if interatom == None:
interatom = create_interatom(spin_id1=spin_id1, spin_id2=spin_id2)
# Test the error value (a value of 0.0 will cause the interatomic container to be deselected).
if error == 0.0:
interatom.select = False
warn(RelaxWarning("An error value of zero has been encountered, deselecting the interatomic container between spin '%s' and '%s'." % (spin_id1, spin_id2)))
continue
# Store the data type as global data (need for the conversion of RDC data).
if not hasattr(interatom, 'rdc_data_types'):
interatom.rdc_data_types = {}
if not align_id in interatom.rdc_data_types:
interatom.rdc_data_types[align_id] = data_type
# Convert and add the data.
if data_col:
# Data conversion.
value = convert(value, data_type, align_id, to_intern=True)
# Correction for the negative gyromagnetic ratio of 15N.
if neg_g_corr and value != None:
value = -value
# Absolute values.
if absolute:
# Force the value to be positive.
value = abs(value)
# Initialise.
if not hasattr(interatom, 'rdc'):
interatom.rdc = {}
# Add the value.
interatom.rdc[align_id] = value
# Store the absolute value flag.
if not hasattr(interatom, 'absolute_rdc'):
interatom.absolute_rdc = {}
interatom.absolute_rdc[align_id] = absolute
# Convert and add the error.
if error_col:
# Data conversion.
error = convert(error, data_type, align_id, to_intern=True)
# Initialise.
if not hasattr(interatom, 'rdc_err'):
interatom.rdc_err = {}
# Append the error.
interatom.rdc_err[align_id] = error
# Append the data for printout.
data.append([spin_id1, spin_id2])
if is_float(value):
data[-1].append("%20.15f" % value)
else:
data[-1].append("%20s" % value)
if is_float(error):
data[-1].append("%20.15f" % error)
else:
data[-1].append("%20s" % error)
# No data, so fail hard!
if not len(data):
raise RelaxError("No RDC data could be extracted.")
# Print out.
print("The following RDCs have been loaded into the relax data store:\n")
write_data(out=sys.stdout, headings=["Spin_ID1", "Spin_ID2", "Value", "Error"], data=data)
# Initialise some global structures.
if not hasattr(cdp, 'align_ids'):
cdp.align_ids = []
if not hasattr(cdp, 'rdc_ids'):
cdp.rdc_ids = []
# Add the RDC id string.
if align_id not in cdp.align_ids:
cdp.align_ids.append(align_id)
if align_id not in cdp.rdc_ids:
cdp.rdc_ids.append(align_id)
def check_rdcs(interatom):
"""Check if all data required for calculating the RDC is present.
@param interatom: The interatomic data container.
@type interatom: InteratomContainer instance
@return: True if all data required for calculating the RDC is present, False otherwise.
@rtype: bool
"""
# Skip deselected interatomic data containers.
if not interatom.select:
return False
# Only use interatomic data containers with RDC data.
if not hasattr(interatom, 'rdc'):
return False
# Only use interatomic data containers with RDC and J coupling data.
if opt_uses_j_couplings() and not hasattr(interatom, 'j_coupling'):
return False
# Get the spins.
spin1 = return_spin(interatom.spin_id1)
spin2 = return_spin(interatom.spin_id2)
# Spin information checks.
if not hasattr(spin1, 'isotope'):
warn(RelaxSpinTypeWarning(interatom.spin_id1))
return False
if not hasattr(spin2, 'isotope'):
warn(RelaxSpinTypeWarning(interatom.spin_id2))
return False
if is_pseudoatom(spin1) and is_pseudoatom(spin2):
warn(RelaxWarning("Support for both spins being in a dipole pair being pseudo-atoms is not implemented yet."))
return False
# Pseudo-atom checks.
if is_pseudoatom(spin1) or is_pseudoatom(spin2):
# Alias the pseudo and normal atoms.
pseudospin = spin1
base_spin_id = interatom.spin_id2
pseudospin_id = interatom.spin_id1
if is_pseudoatom(spin2):
pseudospin = spin2
base_spin_id = interatom.spin_id1
pseudospin_id = interatom.spin_id2
# Loop over the atoms of the pseudo-atom.
for spin, spin_id in pseudoatom_loop(pseudospin, return_id=True):
# Get the corresponding interatomic data container.
pseudo_interatom = return_interatom(spin_id1=spin_id, spin_id2=base_spin_id)
# Unit vector check.
if not hasattr(pseudo_interatom, 'vector'):
warn(RelaxWarning("The interatomic vector is missing for the spin pair '%s' and '%s' of the pseudo-atom '%s', skipping the RDC for the spin pair '%s' and '%s'." % (pseudo_interatom.spin_id1, pseudo_interatom.spin_id2, pseudospin_id, base_spin_id, pseudospin_id)))
return False
# Check.
if not hasattr(pseudo_interatom, 'r'):
warn(RelaxWarning("The averaged interatomic distance between spins '%s' and '%s' for the pseudo-atom '%s' has not been set yet." % (spin_id, base_spin_id, pseudospin_id)))
return False
# Normal atoms checks.
else:
# Unit vector check.
if not hasattr(interatom, 'vector'):
warn(RelaxWarning("The interatomic vector is missing, skipping the spin pair '%s' and '%s'." % (interatom.spin_id1, interatom.spin_id2)))
return False
# Distance information check.
if not hasattr(interatom, 'r'):
warn(RelaxWarning("The averaged interatomic distance between spins '%s' and '%s' has not been set yet." % (interatom.spin_id1, interatom.spin_id2)))
return False
# Everything is ok.
return True
def bmrb_read(star, sample_conditions=None):
"""Read the relaxation data from the NMR-STAR dictionary object.
@param star: The NMR-STAR dictionary object.
@type star: NMR_STAR instance
@keyword sample_conditions: The sample condition label to read. Only one sample condition can be read per data pipe.
@type sample_conditions: None or str
"""
# Get the relaxation data.
for data in star.relaxation.loop():
# Store the keys.
keys = list(data.keys())
# Sample conditions do not match (remove the $ sign).
if 'sample_cond_list_label' in keys and sample_conditions and data['sample_cond_list_label'].replace('$', '') != sample_conditions:
continue
# Create the labels.
ri_type = data['data_type']
frq = float(data['frq']) * 1e6
# Round the label to the nearest factor of 10.
frq_label = create_frq_label(float(data['frq']) * 1e6)
# The ID string.
ri_id = "%s_%s" % (ri_type, frq_label)
# The number of spins.
N = bmrb.num_spins(data)
# No data in the saveframe.
if N == 0:
continue
# The molecule names.
mol_names = bmrb.molecule_names(data, N)
# Generate the sequence if needed.
bmrb.generate_sequence(N, spin_names=data['atom_names'], res_nums=data['res_nums'], res_names=data['res_names'], mol_names=mol_names, isotopes=data['isotope'], elements=data['atom_types'])
# The attached protons.
if 'atom_names_2' in data:
# Generate the proton spins.
bmrb.generate_sequence(N, spin_names=data['atom_names_2'], res_nums=data['res_nums'], res_names=data['res_names'], mol_names=mol_names, isotopes=data['isotope_2'], elements=data['atom_types_2'])
# Define the dipolar interaction.
for i in range(len(data['atom_names'])):
# The spin IDs.
spin_id1 = generate_spin_id_unique(spin_name=data['atom_names'][i], res_num=data['res_nums'][i], res_name=data['res_names'][i], mol_name=mol_names[i])
spin_id2 = generate_spin_id_unique(spin_name=data['atom_names_2'][i], res_num=data['res_nums'][i], res_name=data['res_names'][i], mol_name=mol_names[i])
# Check if the container exists.
if return_interatom(spin_id1=spin_id1, spin_id2=spin_id2):
continue
# Define.
define(spin_id1=spin_id1, spin_id2=spin_id2, verbose=False)
# The data and error.
vals = data['data']
errors = data['errors']
if vals == None:
vals = [None] * N
if errors == None:
errors = [None] * N
# Data transformation.
if vals != None and 'units' in keys:
# Scaling.
if data['units'] == 'ms':
# Loop over the data.
for i in range(N):
# The value.
if vals[i] != None:
vals[i] = vals[i] / 1000
# The error.
if errors[i] != None:
errors[i] = errors[i] / 1000
# Invert.
if data['units'] in ['s', 'ms']:
# Loop over the data.
for i in range(len(vals)):
# The value.
if vals[i] != None:
vals[i] = 1.0 / vals[i]
# The error.
if vals[i] != None and errors[i] != None:
errors[i] = errors[i] * vals[i]**2
# Pack the data.
pack_data(ri_id, ri_type, frq, vals, errors, mol_names=mol_names, res_nums=data['res_nums'], res_names=data['res_names'], spin_nums=None, spin_names=data['atom_names'], gen_seq=True, verbose=False)
# Store the temperature calibration and control.
if data['temp_calibration']:
temp_calibration(ri_id=ri_id, method=data['temp_calibration'])
if data['temp_control']:
temp_control(ri_id=ri_id, method=data['temp_control'])
# Peak intensity type.
if data['peak_intensity_type']:
peak_intensity_type(ri_id=ri_id, type=data['peak_intensity_type'])