def wizard_page_after_cpmg_setup(self):
"""Set the page after the CPMG setup page.
This should either be 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
"""
# R1rho experiments exists.
if 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_cpmg_setup(self):
"""Set the page after the CPMG setup page.
This should either be 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
"""
# R1rho experiments exists.
if 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)
