def assemble_data(self):
"""Assemble the data required for the auto-analysis.
See the docstring for auto_analyses.relax_fit for details. All data is taken from the relax data store, so data upload from the GUI to there must have been previously performed.
@return: A container with all the data required for the auto-analysis.
@rtype: class instance, list of str
"""
# The data container.
data = Container()
missing = []
# The pipe name and bundle.
data.pipe_name = self.data.pipe_name
data.pipe_bundle = self.data.pipe_bundle
# The frequency.
frq = gui_to_str(self.field_nmr_frq.GetValue())
if frq == None:
missing.append('NMR frequency')
# File root.
data.file_root = '%s.%s' % (self.analysis_type, frq)
# Check if sequence data is loaded
if not exists_mol_res_spin_data():
missing.append("Sequence data")
# Spectral data.
if not hasattr(cdp, 'spectrum_ids') or len(cdp.spectrum_ids) < 3:
missing.append("Spectral data")
# Increment size.
data.inc = gui_to_int(self.grid_inc.GetValue())
# The number of Monte Carlo simulations to be used for error analysis at the end of the analysis.
data.mc_sim_num = gui_to_int(self.mc_sim_num.GetValue())
# Results directory.
data.save_dir = self.data.save_dir
# Return the container and list of missing data.
return data, missing
def assemble_data(self):
"""Assemble the data required for the Auto_noe class.
@return: A container with all the data required for the auto-analysis.
@rtype: class instance, list of str
"""
# The data container.
data = Container()
missing = []
# The pipe name and bundle.
data.pipe_name = self.data.pipe_name
data.pipe_bundle = self.data.pipe_bundle
# The frequency.
frq = gui_to_str(self.field_nmr_frq.GetValue())
if frq == None:
missing.append('NMR frequency')
# Filename.
data.file_root = 'noe.%s' % frq
# Results directory.
data.save_dir = self.data.save_dir
# Check if sequence data is loaded
if not exists_mol_res_spin_data():
missing.append("Sequence data")
# Spectral data.
if not hasattr(cdp, 'spectrum_ids') or len(cdp.spectrum_ids) < 2:
missing.append("Spectral data")
# Return the container and list of missing data.
return data, missing
def assemble_data(self):
"""Assemble the data required for the auto-analysis.
See the docstring for auto_analyses.dauvernge_protocol for details. All data is taken from the relax data store, so data upload from the GUI to there must have been previously performed.
@return: A container with all the data required for the auto-analysis.
@rtype: class instance, list of str
"""
# The data container.
data = Container()
missing = []
# The pipe name and bundle.
data.pipe_name = self.data.pipe_name
data.pipe_bundle = self.data.pipe_bundle
# The model-free models (do not change these unless absolutely necessary).
data.local_tm_models = self.local_tm_model_field.GetValue()
data.mf_models = self.mf_model_field.GetValue()
# Automatic looping over all rounds until convergence (must be a boolean value of True or False).
data.conv_loop = True
# Increment size.
data.inc = gui_to_int(self.grid_inc.GetValue())
if hasattr(self.data, 'diff_tensor_grid_inc'):
data.diff_tensor_grid_inc = self.data.diff_tensor_grid_inc
else:
data.diff_tensor_grid_inc = {
'sphere': 11,
'prolate': 11,
'oblate': 11,
'ellipsoid': 6
}
# The number of Monte Carlo simulations to be used for error analysis at the end of the analysis.
data.mc_sim_num = gui_to_int(self.mc_sim_num.GetValue())
# Number of maximum iterations.
data.max_iter = self.data.max_iter
# Results directory.
data.save_dir = self.data.save_dir
# Check if sequence data is loaded
if not exists_mol_res_spin_data():
missing.append("Sequence data")
# Relaxation data.
if not hasattr(cdp, 'ri_ids') or len(cdp.ri_ids) == 0:
missing.append("Relaxation data")
# Insufficient data.
if hasattr(cdp, 'ri_ids') and len(cdp.ri_ids) <= 3:
missing.append(
"Insufficient relaxation data, 4 or more data sets are essential for the execution of the dauvergne_protocol auto-analysis. Check that you have entered data for a least two spectrometer fields."
)
# Interatomic data containers.
if not hasattr(cdp, 'interatomic') or len(cdp.interatomic) == 0:
missing.append(
"Interatomic data (for the dipole-dipole interaction)")
# Get the mode.
mode = gui_to_str(self.mode.GetValue())
# Solve for all global models.
if mode == 'Fully automated':
# The global model list.
data.global_models = [
'local_tm', 'sphere', 'prolate', 'oblate', 'ellipsoid', 'final'
]
# Any global model selected.
else:
data.global_models = [mode]
# Check for vectors.
vector_check = False
if 'prolate' in data.global_models or 'oblate' in data.global_models or 'ellipsoid' in data.global_models:
vector_check = True
# Spin variables.
for spin, spin_id in spin_loop(return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# The message skeleton.
msg = "Spin '%s' - %s (try the %s user function)." % (spin_id,
"%s", "%s")
# Test if the nuclear isotope type has been set.
if not hasattr(spin, 'isotope') or spin.isotope == None:
missing.append(msg % ("nuclear isotope data", "spin.isotope"))
# Test if the CSA value has been set for the heteronuclei.
if (hasattr(spin, 'isotope') and spin.isotope
in ['15N', '13C']) and (not hasattr(spin, 'csa')
or spin.csa == None):
missing.append(msg % ("CSA data", "value.set"))
# Interatomic data container variables.
for interatom in interatomic_loop():
# Get the spin containers.
spin1 = return_spin(spin_hash=interatom._spin_hash1)
spin2 = return_spin(spin_hash=interatom._spin_hash2)
# Skip deselected spins.
if not spin1.select:
continue
if not spin2.select:
continue
# The message skeleton.
msg = "Spin pair '%s' and '%s' - %s (try the %s user function)." % (
interatom.spin_id1, interatom.spin_id2, "%s", "%s")
# Test if the interatomic distance has been set.
if not hasattr(interatom, 'r') or interatom.r == None:
missing.append(msg % ("bond length data", "value.set"))
# Test if the unit vectors have been loaded.
if vector_check and (not hasattr(interatom, 'vector')
or interatom.vector is None):
missing.append(msg %
("unit vectors", "interatom.unit_vectors"))
# Return the container and list of missing data.
return data, missing
def assemble_data(self):
"""Assemble the data required for the Auto_noe class.
@return: A container with all the data required for the auto-analysis, the missing list, and a list of models that don't match the experiment types.
@rtype: class instance, list of str, list of str
"""
# The data container.
data = Container()
missing = []
model_mismatch = []
# The pipe name and bundle.
data.pipe_name = self.data.pipe_name
data.pipe_bundle = self.data.pipe_bundle
# Results directories.
data.save_dir = self.data.save_dir
data.pre_run_dir = gui_to_str(self.field_pre_run_dir.GetValue())
# Check if sequence data is loaded
if not exists_mol_res_spin_data():
missing.append("Sequence data")
# Spin variables.
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
# The message skeleton.
msg = "Spin '%s' - %s (try the %s user function)." % (spin_id, "%s", "%s")
# Test if the nuclear isotope type has been set.
if not hasattr(spin, 'isotope') or spin.isotope == None:
missing.append(msg % ("nuclear isotope data", "spin.isotope"))
# Spectral data.
if not hasattr(cdp, 'spectrum_ids') or len(cdp.spectrum_ids) < 2:
missing.append("Spectral data")
# The dispersion models.
data.models = self.model_field.GetValue()
# Invalid models.
for model in data.models:
# Invalid CPMG models.
if model != MODEL_NOREX and model in MODEL_LIST_CPMG and not has_cpmg_exp_type():
model_mismatch.append([model, 'CPMG'])
# Invalid R1rho models.
if model != MODEL_NOREX and model in MODEL_LIST_R1RHO and not has_r1rho_exp_type():
model_mismatch.append([model, 'R1rho'])
# The R1 parameter fitting flag.
data.r1_fit = self.r1_fit.GetValue()
# The numeric only solution.
data.numeric_only = self.numeric_only.GetValue()
# Increment size.
data.inc = gui_to_int(self.grid_inc.GetValue())
# The number of Monte Carlo simulations to be used for error analysis at the end of the analysis.
data.mc_sim_num = gui_to_int(self.mc_sim_num.GetValue())
data.exp_mc_sim_num = gui_to_int(self.exp_mc_sim_num.GetValue())
data.mc_sim_all_models = self.mc_sim_all_models.GetValue()
# The insignificance level.
data.insignificance = self.insignificance.GetValue()
try:
data.insignificance = gui_to_float(data.insignificance)
except:
missing.append("The insignificance level must be a number.")
# Optimisation precision.
data.opt_func_tol = self.opt_func_tol
data.opt_max_iterations = self.opt_max_iterations
# Return the container, the list of missing data, and any models that don't match the experiment types.
return data, missing, model_mismatch