def wizard_page_after_relax_time(self):
"""Set the page after the relaxation time period page.
This should either be the CPMG page if CPMG experiment types exist, the R1rho page if R1rho experiment types exist, or terminate the wizard.
@return: The index of the next page, which is the current page index plus one.
@rtype: int
"""
# CPMG experiments exists.
if has_cpmg_exp_type():
return self.page_indices['cpmg_setup']
# R1rho experiments exists.
elif has_r1rho_exp_type():
return self.page_indices['spin_lock_field']
# Nothing left, so run off the end.
else:
return self._num_pages + 1
def wizard_page_after_relax_time(self):
"""Set the page after the relaxation time period page.
This should either be the CPMG page if CPMG experiment types exist, the R1rho page if R1rho experiment types exist, or terminate the wizard.
@return: The index of the next page, which is the current page index plus one.
@rtype: int
"""
# CPMG experiments exists.
if has_cpmg_exp_type():
return self.page_indices['cpmg_setup']
# R1rho experiments exists.
elif has_r1rho_exp_type():
return self.page_indices['spin_lock_field']
# Nothing left, so run off the end.
else:
return self._num_pages + 1
def write_results(self, path=None, model=None):
"""Create a set of results, text and Grace files for the current data pipe.
@keyword path: The directory to place the files into.
@type path: str
"""
# Printout.
section(file=sys.stdout, text="Results writing", prespace=2)
# If this is the final model selection round, check which models have been tested.
if model == None:
models_tested = []
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
spin_model = spin.model
# Add to list, if not in already.
if spin_model not in models_tested:
models_tested.append(spin_model)
else:
models_tested = None
# Special for R2eff model.
if model == MODEL_R2EFF:
# The R2eff parameter.
self.interpreter.value.write(param='r2eff',
file='r2eff.out',
dir=path,
force=True)
self.interpreter.grace.write(x_data_type='res_num',
y_data_type='r2eff',
file='r2eff.agr',
dir=path,
force=True)
# Exponential curves.
if has_exponential_exp_type():
self.interpreter.relax_disp.plot_exp_curves(
file='intensities.agr', dir=path,
force=True) # Average peak intensities.
self.interpreter.relax_disp.plot_exp_curves(
file='intensities_norm.agr',
dir=path,
force=True,
norm=True) # Average peak intensities (normalised).
# The I0 parameter.
self.interpreter.value.write(param='i0',
file='i0.out',
dir=path,
force=True)
self.interpreter.grace.write(x_data_type='res_num',
y_data_type='i0',
file='i0.agr',
dir=path,
force=True)
# Dispersion curves.
self.interpreter.relax_disp.plot_disp_curves(dir=path, force=True)
self.interpreter.relax_disp.write_disp_curves(dir=path, force=True)
# The selected models for the final run.
if model == None:
self.interpreter.value.write(param='model',
file='model.out',
dir=path,
force=True)
# For CPMG models.
if has_cpmg_exp_type():
# The R20 parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='r2',
file_name_ini='r20')
# The R20A and R20B parameters.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='r2a',
file_name_ini='r20a')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='r2b',
file_name_ini='r20b')
# For R1ho models.
if has_r1rho_exp_type():
# The R1 parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='r1')
# The R1rho prime parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='r2',
file_name_ini='r1rho_prime')
# Plot specific R1rho graphs.
if model in [None] + MODEL_LIST_R1RHO:
self.interpreter.relax_disp.plot_disp_curves(
dir=path, x_axis=X_AXIS_THETA, force=True)
self.interpreter.relax_disp.plot_disp_curves(
dir=path,
y_axis=Y_AXIS_R2_R1RHO,
x_axis=X_AXIS_W_EFF,
force=True)
self.interpreter.relax_disp.plot_disp_curves(
dir=path,
y_axis=Y_AXIS_R2_EFF,
x_axis=X_AXIS_THETA,
interpolate=INTERPOLATE_OFFSET,
force=True)
# The calculation of theta and w_eff parameter in R1rho experiments.
if model in MODEL_LIST_R1RHO_FULL:
self.interpreter.value.write(param='theta',
file='theta.out',
dir=path,
force=True)
self.interpreter.value.write(param='w_eff',
file='w_eff.out',
dir=path,
force=True)
# The pA and pB parameters.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='pA')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='pB')
# The pC parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='pC')
# The phi_ex parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='phi_ex')
# The phi_ex_B nd phi_ex_C parameters.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='phi_ex_B')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='phi_ex_C')
# The dw parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dw')
# The dw_AB, dw_BC and dw_AC parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dw_AB')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dw_BC')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dw_AC')
# The dwH parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dwH')
# The dwH_AB, dwH_BC and dwH_AC parameter.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dwH_AB')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dwH_BC')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='dwH_AC')
# The k_AB, kex and tex parameters.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='k_AB')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='kex')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='tex')
# The kex_AB, kex_BC, kex_AC parameters.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='kex_AB')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='kex_BC')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='kex_AC')
# The kB and kC parameters.
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='kB')
self.write_results_test(path=path,
model=model,
models_tested=models_tested,
param='kC')
# Minimisation statistics.
if not (model == MODEL_R2EFF and has_fixed_time_exp_type()):
self.interpreter.value.write(param='chi2',
file='chi2.out',
dir=path,
force=True)
self.interpreter.grace.write(y_data_type='chi2',
file='chi2.agr',
dir=path,
force=True)
# Finally save the results. This is last to allow the continuation of an interrupted analysis while ensuring that all results files have been created.
self.interpreter.results.write(file='results', dir=path, force=True)
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 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 write_results(self, path=None, model=None):
"""Create a set of results, text and Grace files for the current data pipe.
@keyword path: The directory to place the files into.
@type path: str
"""
# Printout.
section(file=sys.stdout, text="Results writing", prespace=2)
# If this is the final model selection round, check which models have been tested.
if model == None:
models_tested = []
for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
spin_model = spin.model
# Add to list, if not in already.
if spin_model not in models_tested:
models_tested.append(spin_model)
else:
models_tested = None
# Special for R2eff model.
if model == MODEL_R2EFF:
# The R2eff parameter.
self.interpreter.value.write(param='r2eff', file='r2eff.out', dir=path, force=True)
self.interpreter.grace.write(x_data_type='res_num', y_data_type='r2eff', file='r2eff.agr', dir=path, force=True)
# Exponential curves.
if has_exponential_exp_type():
self.interpreter.relax_disp.plot_exp_curves(file='intensities.agr', dir=path, force=True) # Average peak intensities.
self.interpreter.relax_disp.plot_exp_curves(file='intensities_norm.agr', dir=path, force=True, norm=True) # Average peak intensities (normalised).
# The I0 parameter.
self.interpreter.value.write(param='i0', file='i0.out', dir=path, force=True)
self.interpreter.grace.write(x_data_type='res_num', y_data_type='i0', file='i0.agr', dir=path, force=True)
# Dispersion curves.
self.interpreter.relax_disp.plot_disp_curves(dir=path, force=True)
self.interpreter.relax_disp.write_disp_curves(dir=path, force=True)
# The selected models for the final run.
if model == None:
self.interpreter.value.write(param='model', file='model.out', dir=path, force=True)
# For CPMG models.
if has_cpmg_exp_type():
# The R20 parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='r2', file_name_ini='r20')
# The R20A and R20B parameters.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='r2a', file_name_ini='r20a')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='r2b', file_name_ini='r20b')
# For R1ho models.
if has_r1rho_exp_type():
# The R1 parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='r1')
# The R1rho prime parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='r2', file_name_ini='r1rho_prime')
# Plot specific R1rho graphs.
if model in [None] + MODEL_LIST_R1RHO:
self.interpreter.relax_disp.plot_disp_curves(dir=path, x_axis=X_AXIS_THETA, force=True)
self.interpreter.relax_disp.plot_disp_curves(dir=path, y_axis=Y_AXIS_R2_R1RHO, x_axis=X_AXIS_W_EFF, force=True)
self.interpreter.relax_disp.plot_disp_curves(dir=path, y_axis=Y_AXIS_R2_EFF, x_axis=X_AXIS_THETA, interpolate=INTERPOLATE_OFFSET, force=True)
# The calculation of theta and w_eff parameter in R1rho experiments.
if model in MODEL_LIST_R1RHO_FULL:
self.interpreter.value.write(param='theta', file='theta.out', dir=path, force=True)
self.interpreter.value.write(param='w_eff', file='w_eff.out', dir=path, force=True)
# The pA and pB parameters.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='pA')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='pB')
# The pC parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='pC')
# The phi_ex parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='phi_ex')
# The phi_ex_B nd phi_ex_C parameters.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='phi_ex_B')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='phi_ex_C')
# The dw parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dw')
# The dw_AB, dw_BC and dw_AC parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dw_AB')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dw_BC')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dw_AC')
# The dwH parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dwH')
# The dwH_AB, dwH_BC and dwH_AC parameter.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dwH_AB')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dwH_BC')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='dwH_AC')
# The k_AB, kex and tex parameters.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='k_AB')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='kex')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='tex')
# The kex_AB, kex_BC, kex_AC parameters.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='kex_AB')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='kex_BC')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='kex_AC')
# The kB and kC parameters.
self.write_results_test(path=path, model=model, models_tested=models_tested, param='kB')
self.write_results_test(path=path, model=model, models_tested=models_tested, param='kC')
# Minimisation statistics.
if not (model == MODEL_R2EFF and has_fixed_time_exp_type()):
self.interpreter.value.write(param='chi2', file='chi2.out', dir=path, force=True)
self.interpreter.grace.write(y_data_type='chi2', file='chi2.agr', dir=path, force=True)
# Finally save the results. This is last to allow the continuation of an interrupted analysis while ensuring that all results files have been created.
self.interpreter.results.write(file='results', dir=path, force=True)
