def sync_ds(self, upload=False):
"""Synchronise the analysis frame and the relax data store, both ways.
This method allows the frame information to be uploaded into the relax data store, or for the information in the relax data store to be downloaded by the frame.
@keyword upload: A flag which if True will cause the frame to send data to the relax data store. If False, data will be downloaded from the relax data store to update the frame.
@type upload: bool
"""
# The numeric solution only flag.
if upload:
self.data.numeric_only = self.numeric_only.GetValue()
elif hasattr(self.data, 'numeric_only'):
self.numeric_only.SetValue(bool(self.data.numeric_only))
# The grid incs.
if upload:
self.data.grid_inc = gui_to_int(self.grid_inc.GetValue())
elif hasattr(self.data, 'grid_inc'):
self.grid_inc.SetValue(int(self.data.grid_inc))
# The MC sim number.
if upload:
self.data.mc_sim_num = gui_to_int(self.mc_sim_num.GetValue())
elif hasattr(self.data, 'mc_sim_num'):
self.mc_sim_num.SetValue(int(self.data.mc_sim_num))
# The All model MC sim flag.
if upload:
self.data.mc_sim_all_models = self.mc_sim_all_models.GetValue()
elif hasattr(self.data, 'mc_sim_all_models'):
self.mc_sim_all_models.SetValue(bool(self.data.mc_sim_all_models))
# The insignificance level.
if upload:
self.data.insignificance = self.insignificance.GetValue()
try:
self.data.insignificance = gui_to_float(self.data.insignificance)
except:
pass
elif hasattr(self.data, 'insignificance'):
self.insignificance.SetValue(float_to_gui(self.data.insignificance))
# The results directory.
if upload:
self.data.save_dir = gui_to_str(self.field_results_dir.GetValue())
else:
self.field_results_dir.SetValue(str_to_gui(self.data.save_dir))
# The previous run results directory.
if upload:
self.data.pre_run_dir = gui_to_str(self.field_pre_run_dir.GetValue())
elif hasattr(self.data, 'pre_run_dir'):
self.field_pre_run_dir.SetValue(str_to_gui(self.data.pre_run_dir))
# The models to use.
if upload:
self.data.disp_models = self.model_field.GetValue()
else:
self.model_field.set_value(self.data.disp_models)
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
def sync_ds(self, upload=False):
"""Synchronise the analysis frame and the relax data store, both ways.
This method allows the frame information to be uploaded into the relax data store, or for the information in the relax data store to be downloaded by the frame.
@keyword upload: A flag which if True will cause the frame to send data to the relax data store. If False, data will be downloaded from the relax data store to update the frame.
@type upload: bool
"""
# The R1 parameter fitting flag.
if upload:
self.data.r1_fit = self.r1_fit.GetValue()
elif hasattr(self.data, 'r1_fit'):
self.r1_fit.SetValue(bool(self.data.r1_fit))
# The numeric solution only flag.
if upload:
self.data.numeric_only = self.numeric_only.GetValue()
elif hasattr(self.data, 'numeric_only'):
self.numeric_only.SetValue(bool(self.data.numeric_only))
# The grid incs.
if upload:
self.data.grid_inc = gui_to_int(self.grid_inc.GetValue())
elif hasattr(self.data, 'grid_inc'):
if self.data.grid_inc == None:
self.data.grid_inc = default_grid_inc
self.grid_inc.SetValue(int(self.data.grid_inc))
# The MC sim number.
if upload:
self.data.mc_sim_num = gui_to_int(self.mc_sim_num.GetValue())
elif hasattr(self.data, 'mc_sim_num'):
if self.data.mc_sim_num == None:
self.data.mc_sim_num = default_mc_sim_num
self.mc_sim_num.SetValue(int(self.data.mc_sim_num))
# The EXP MC sim number.
if upload:
self.data.exp_mc_sim_num = gui_to_int(self.exp_mc_sim_num.GetValue())
elif hasattr(self.data, 'exp_mc_sim_num'):
if self.data.exp_mc_sim_num == None:
self.data.exp_mc_sim_num = default_exp_mc_sim_num
self.exp_mc_sim_num.SetValue(int(self.data.exp_mc_sim_num))
# The All model MC sim flag.
if upload:
self.data.mc_sim_all_models = self.mc_sim_all_models.GetValue()
elif hasattr(self.data, 'mc_sim_all_models'):
self.mc_sim_all_models.SetValue(bool(self.data.mc_sim_all_models))
# The insignificance level.
if upload:
self.data.insignificance = self.insignificance.GetValue()
try:
self.data.insignificance = gui_to_float(self.data.insignificance)
except:
pass
elif hasattr(self.data, 'insignificance'):
self.insignificance.SetValue(float_to_gui(self.data.insignificance))
# The results directory.
if upload:
self.data.save_dir = gui_to_str(self.field_results_dir.GetValue())
else:
self.field_results_dir.SetValue(str_to_gui(self.data.save_dir))
# The previous run results directory.
if upload:
self.data.pre_run_dir = gui_to_str(self.field_pre_run_dir.GetValue())
elif hasattr(self.data, 'pre_run_dir'):
self.field_pre_run_dir.SetValue(str_to_gui(self.data.pre_run_dir))
# The models to use.
if upload:
self.data.disp_models = self.model_field.GetValue()
else:
self.model_field.set_value(self.data.disp_models)
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
