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(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 assemble_param_vector(spin=None,
spin_id=None,
sim_index=None,
model_type=None):
"""Assemble the model-free parameter vector (as numpy array).
If the spin argument is supplied, then the spin_id argument will be ignored.
@keyword spin: The spin data container.
@type spin: SpinContainer instance
@keyword spin_id: The spin identification string.
@type spin_id: str
@keyword sim_index: The optional MC simulation index.
@type sim_index: int
@keyword model_type: The optional model type, one of 'all', 'diff', 'mf', or 'local_tm'.
@type model_type: str or None
@return: An array of the parameter values of the model-free model.
@rtype: numpy array
"""
# Initialise.
param_vector = []
# Determine the model type.
if not model_type:
model_type = determine_model_type()
# Diffusion tensor parameters.
if model_type == 'diff' or model_type == 'all':
# Normal parameters.
if sim_index == None:
# Spherical diffusion.
if cdp.diff_tensor.type == 'sphere':
if hasattr(cdp.diff_tensor, 'tm'):
param_vector.append(cdp.diff_tensor.tm)
else:
param_vector.append(None)
# Spheroidal diffusion.
elif cdp.diff_tensor.type == 'spheroid':
if hasattr(cdp.diff_tensor, 'tm'):
param_vector.append(cdp.diff_tensor.tm)
param_vector.append(cdp.diff_tensor.Da)
param_vector.append(cdp.diff_tensor.theta)
param_vector.append(cdp.diff_tensor.phi)
else:
param_vector += [None, None, None, None]
# Ellipsoidal diffusion.
elif cdp.diff_tensor.type == 'ellipsoid':
if hasattr(cdp.diff_tensor, 'tm'):
param_vector.append(cdp.diff_tensor.tm)
param_vector.append(cdp.diff_tensor.Da)
param_vector.append(cdp.diff_tensor.Dr)
param_vector.append(cdp.diff_tensor.alpha)
param_vector.append(cdp.diff_tensor.beta)
param_vector.append(cdp.diff_tensor.gamma)
else:
param_vector += [None, None, None, None, None, None]
# Monte Carlo diffusion tensor parameters.
else:
# Spherical diffusion.
if cdp.diff_tensor.type == 'sphere':
param_vector.append(cdp.diff_tensor.tm_sim[sim_index])
# Spheroidal diffusion.
elif cdp.diff_tensor.type == 'spheroid':
param_vector.append(cdp.diff_tensor.tm_sim[sim_index])
param_vector.append(cdp.diff_tensor.Da_sim[sim_index])
param_vector.append(cdp.diff_tensor.theta_sim[sim_index])
param_vector.append(cdp.diff_tensor.phi_sim[sim_index])
# Ellipsoidal diffusion.
elif cdp.diff_tensor.type == 'ellipsoid':
param_vector.append(cdp.diff_tensor.tm_sim[sim_index])
param_vector.append(cdp.diff_tensor.Da_sim[sim_index])
param_vector.append(cdp.diff_tensor.Dr_sim[sim_index])
param_vector.append(cdp.diff_tensor.alpha_sim[sim_index])
param_vector.append(cdp.diff_tensor.beta_sim[sim_index])
param_vector.append(cdp.diff_tensor.gamma_sim[sim_index])
# Model-free parameters (spin specific parameters).
if model_type != 'diff':
# The loop.
if spin:
loop = [spin]
else:
loop = spin_loop(spin_id)
# Loop over the spins.
for spin in loop:
# Skip deselected spins.
if not spin.select:
continue
# Skip spins with no parameters.
if not hasattr(spin, 'params'):
continue
# Loop over the model-free parameters.
for i in range(len(spin.params)):
# local tm.
if spin.params[i] == 'local_tm':
if sim_index == None:
param_vector.append(spin.local_tm)
else:
param_vector.append(spin.local_tm_sim[sim_index])
# S2.
elif spin.params[i] == 's2':
if sim_index == None:
param_vector.append(spin.s2)
else:
param_vector.append(spin.s2_sim[sim_index])
# S2f.
elif spin.params[i] == 's2f':
if sim_index == None:
param_vector.append(spin.s2f)
else:
param_vector.append(spin.s2f_sim[sim_index])
# S2s.
elif spin.params[i] == 's2s':
if sim_index == None:
param_vector.append(spin.s2s)
else:
param_vector.append(spin.s2s_sim[sim_index])
# te.
elif spin.params[i] == 'te':
if sim_index == None:
param_vector.append(spin.te)
else:
param_vector.append(spin.te_sim[sim_index])
# tf.
elif spin.params[i] == 'tf':
if sim_index == None:
param_vector.append(spin.tf)
else:
param_vector.append(spin.tf_sim[sim_index])
# ts.
elif spin.params[i] == 'ts':
if sim_index == None:
param_vector.append(spin.ts)
else:
param_vector.append(spin.ts_sim[sim_index])
# Rex.
elif spin.params[i] == 'rex':
if sim_index == None:
param_vector.append(spin.rex)
else:
param_vector.append(spin.rex_sim[sim_index])
# r.
elif spin.params[i] == 'r':
if sim_index == None:
param_vector.append(spin.r)
else:
param_vector.append(spin.r_sim[sim_index])
# CSA.
elif spin.params[i] == 'csa':
if sim_index == None:
param_vector.append(spin.csa)
else:
param_vector.append(spin.csa_sim[sim_index])
# Unknown parameter.
else:
raise RelaxError("Unknown parameter.")
# Return a numpy array.
return array(param_vector, float64)
def extract(dir, spin_id=None):
"""Extract the Modelfree4 results out of the 'mfout' file.
@param dir: The directory containing the 'mfout' file.
@type dir: str or None
@keyword spin_id: The spin identification string.
@type spin_id: str or None
"""
# Test if sequence data is loaded.
if not exists_mol_res_spin_data():
raise RelaxNoSequenceError
# Check for the diffusion tensor.
if not hasattr(cdp, 'diff_tensor'):
raise RelaxNoTensorError('diffusion')
# The directory.
if dir == None:
dir = pipes.cdp_name()
if not access(dir, F_OK):
raise RelaxDirError('Modelfree4', dir)
# Test if the file exists.
if not access(dir + sep + 'mfout', F_OK):
raise RelaxFileError('Modelfree4', dir + sep + 'mfout')
# Determine the parameter set.
model_type = determine_model_type()
# Open the file.
mfout_file = open(dir + sep + 'mfout', 'r')
mfout_lines = mfout_file.readlines()
mfout_file.close()
# Get the section line positions of the mfout file.
global_chi2_pos, diff_pos, s2_pos, s2f_pos, s2s_pos, te_pos, rex_pos, chi2_pos = line_positions(
mfout_lines)
# Find out if simulations were carried out.
sims = 0
for i in range(len(mfout_lines)):
if search('_iterations', mfout_lines[i]):
row = mfout_lines[i].split()
sims = int(row[1])
# Global data.
if model_type in ['all', 'diff']:
# Global chi-squared.
row = mfout_lines[global_chi2_pos].split()
cdp.chi2 = float(row[1])
# Spherical diffusion tensor.
if cdp.diff_tensor.type == 'sphere':
# Split the lines.
tm_row = mfout_lines[diff_pos].split()
# Set the params.
cdp.diff_tensor.set(param='tm', value=float(tm_row[2]))
# Spheroid diffusion tensor.
else:
# Split the lines.
tm_row = mfout_lines[diff_pos].split()
dratio_row = mfout_lines[diff_pos + 1].split()
theta_row = mfout_lines[diff_pos + 2].split()
phi_row = mfout_lines[diff_pos + 3].split()
# Set the params.
diffusion_tensor.set([
float(tm_row[2]),
float(dratio_row[2]),
float(theta_row[2]) * 2.0 * pi / 360.0,
float(phi_row[2]) * 2.0 * pi / 360.0
], ['tm', 'Dratio', 'theta', 'phi'])
# Loop over the sequence.
pos = 0
for spin, mol_name, res_num, res_name in spin_loop(spin_id,
full_info=True):
# Skip deselected residues.
if not spin.select:
continue
# Get the residue number from the mfout file.
mfout_res_num = int(mfout_lines[s2_pos + pos].split()[0])
# Skip the spin if the residue doesn't match.
if mfout_res_num != res_num:
continue
# Test that the model has been set (needed to differentiate between te and ts).
if not hasattr(spin, 'model'):
raise RelaxNoModelError
# Get the S2 data.
if 's2' in spin.params:
spin.s2, spin.s2_err = get_mf_data(mfout_lines, s2_pos + pos)
# Get the S2f data.
if 's2f' in spin.params or 's2s' in spin.params:
spin.s2f, spin.s2f_err = get_mf_data(mfout_lines, s2f_pos + pos)
# Get the S2s data.
if 's2f' in spin.params or 's2s' in spin.params:
spin.s2s, spin.s2s_err = get_mf_data(mfout_lines, s2s_pos + pos)
# Get the te data.
if 'te' in spin.params:
spin.te, spin.te_err = get_mf_data(mfout_lines, te_pos + pos)
spin.te = spin.te / 1e12
spin.te_err = spin.te_err / 1e12
# Get the ts data.
if 'ts' in spin.params:
spin.ts, spin.ts_err = get_mf_data(mfout_lines, te_pos + pos)
spin.ts = spin.ts / 1e12
spin.ts_err = spin.ts_err / 1e12
# Get the Rex data.
if 'rex' in spin.params:
spin.rex, spin.rex_err = get_mf_data(mfout_lines, rex_pos + pos)
spin.rex = spin.rex / (2.0 * pi *
cdp.spectrometer_frq[cdp.ri_ids[0]])**2
spin.rex_err = spin.rex_err / (
2.0 * pi * cdp.spectrometer_frq[cdp.ri_ids[0]])**2
# Get the chi-squared data.
if not sims:
row = mfout_lines[chi2_pos + pos].split()
spin.chi2 = float(row[1])
else:
# The mfout chi2 position (with no sims) plus 2 (for the extra XML) plus the residue position times 22 (because of the simulated SSE rows).
row = mfout_lines[chi2_pos + 2 + 22 * pos].split()
spin.chi2 = float(row[1])
# Increment the residue position.
pos = pos + 1
def assemble_param_vector(spin=None, spin_id=None, sim_index=None, model_type=None):
"""Assemble the model-free parameter vector (as numpy array).
If the spin argument is supplied, then the spin_id argument will be ignored.
@keyword spin: The spin data container.
@type spin: SpinContainer instance
@keyword spin_id: The spin identification string.
@type spin_id: str
@keyword sim_index: The optional MC simulation index.
@type sim_index: int
@keyword model_type: The optional model type, one of 'all', 'diff', 'mf', or 'local_tm'.
@type model_type: str or None
@return: An array of the parameter values of the model-free model.
@rtype: numpy array
"""
# Initialise.
param_vector = []
# Determine the model type.
if not model_type:
model_type = determine_model_type()
# Diffusion tensor parameters.
if model_type == "diff" or model_type == "all":
# Normal parameters.
if sim_index == None:
# Spherical diffusion.
if cdp.diff_tensor.type == "sphere":
if hasattr(cdp.diff_tensor, "tm"):
param_vector.append(cdp.diff_tensor.tm)
else:
param_vector.append(None)
# Spheroidal diffusion.
elif cdp.diff_tensor.type == "spheroid":
if hasattr(cdp.diff_tensor, "tm"):
param_vector.append(cdp.diff_tensor.tm)
param_vector.append(cdp.diff_tensor.Da)
param_vector.append(cdp.diff_tensor.theta)
param_vector.append(cdp.diff_tensor.phi)
else:
param_vector += [None, None, None, None]
# Ellipsoidal diffusion.
elif cdp.diff_tensor.type == "ellipsoid":
if hasattr(cdp.diff_tensor, "tm"):
param_vector.append(cdp.diff_tensor.tm)
param_vector.append(cdp.diff_tensor.Da)
param_vector.append(cdp.diff_tensor.Dr)
param_vector.append(cdp.diff_tensor.alpha)
param_vector.append(cdp.diff_tensor.beta)
param_vector.append(cdp.diff_tensor.gamma)
else:
param_vector += [None, None, None, None, None, None]
# Monte Carlo diffusion tensor parameters.
else:
# Spherical diffusion.
if cdp.diff_tensor.type == "sphere":
param_vector.append(cdp.diff_tensor.tm_sim[sim_index])
# Spheroidal diffusion.
elif cdp.diff_tensor.type == "spheroid":
param_vector.append(cdp.diff_tensor.tm_sim[sim_index])
param_vector.append(cdp.diff_tensor.Da_sim[sim_index])
param_vector.append(cdp.diff_tensor.theta_sim[sim_index])
param_vector.append(cdp.diff_tensor.phi_sim[sim_index])
# Ellipsoidal diffusion.
elif cdp.diff_tensor.type == "ellipsoid":
param_vector.append(cdp.diff_tensor.tm_sim[sim_index])
param_vector.append(cdp.diff_tensor.Da_sim[sim_index])
param_vector.append(cdp.diff_tensor.Dr_sim[sim_index])
param_vector.append(cdp.diff_tensor.alpha_sim[sim_index])
param_vector.append(cdp.diff_tensor.beta_sim[sim_index])
param_vector.append(cdp.diff_tensor.gamma_sim[sim_index])
# Model-free parameters (spin specific parameters).
if model_type != "diff":
# The loop.
if spin:
loop = [spin]
else:
loop = spin_loop(spin_id)
# Loop over the spins.
for spin in loop:
# Skip deselected spins.
if not spin.select:
continue
# Skip spins with no parameters.
if not hasattr(spin, "params"):
continue
# Loop over the model-free parameters.
for i in range(len(spin.params)):
# local tm.
if spin.params[i] == "local_tm":
if sim_index == None:
param_vector.append(spin.local_tm)
else:
param_vector.append(spin.local_tm_sim[sim_index])
# S2.
elif spin.params[i] == "s2":
if sim_index == None:
param_vector.append(spin.s2)
else:
param_vector.append(spin.s2_sim[sim_index])
# S2f.
elif spin.params[i] == "s2f":
if sim_index == None:
param_vector.append(spin.s2f)
else:
param_vector.append(spin.s2f_sim[sim_index])
# S2s.
elif spin.params[i] == "s2s":
if sim_index == None:
param_vector.append(spin.s2s)
else:
param_vector.append(spin.s2s_sim[sim_index])
# te.
elif spin.params[i] == "te":
if sim_index == None:
param_vector.append(spin.te)
else:
param_vector.append(spin.te_sim[sim_index])
# tf.
elif spin.params[i] == "tf":
if sim_index == None:
param_vector.append(spin.tf)
else:
param_vector.append(spin.tf_sim[sim_index])
# ts.
elif spin.params[i] == "ts":
if sim_index == None:
param_vector.append(spin.ts)
else:
param_vector.append(spin.ts_sim[sim_index])
# Rex.
elif spin.params[i] == "rex":
if sim_index == None:
param_vector.append(spin.rex)
else:
param_vector.append(spin.rex_sim[sim_index])
# r.
elif spin.params[i] == "r":
if sim_index == None:
param_vector.append(spin.r)
else:
param_vector.append(spin.r_sim[sim_index])
# CSA.
elif spin.params[i] == "csa":
if sim_index == None:
param_vector.append(spin.csa)
else:
param_vector.append(spin.csa_sim[sim_index])
# Unknown parameter.
else:
raise RelaxError("Unknown parameter.")
# Return a numpy array.
return array(param_vector, float64)
