def test_subtitle(self):
"""Test of the lib.text.sectioning.subtitle() function."""
# Write out the subtitle.
file = DummyFileObject()
subtitle(file=file, text='Test subtitle')
# Read the results.
lines = file.readlines()
print("Formatted subtitle lines: %s" % lines)
# Check the title.
real_lines = [
'\n',
'-----------------\n',
'- Test subtitle -\n',
'-----------------\n',
'\n',
]
self.assertEqual(len(lines), len(real_lines))
for i in range(len(lines)):
self.assertEqual(lines[i], real_lines[i])
def run(self):
"""Execute the auto-analysis."""
# Peak intensity error analysis.
if MODEL_R2EFF in self.models:
self.error_analysis()
# R1 parameter fitting.
if self.r1_fit:
subtitle(file=sys.stdout,
text="R1 parameter optimisation activation",
prespace=3)
self.interpreter.relax_disp.r1_fit(fit=self.r1_fit)
else:
# No print out.
self.interpreter.relax_disp.r1_fit(fit=self.r1_fit)
# Loop over the models.
self.model_pipes = []
for model in self.models:
# Printout.
subtitle(file=sys.stdout,
text="The '%s' model" % model,
prespace=3)
# The results directory path.
model_path = model.replace(" ", "_")
path = self.results_dir + sep + model_path
# The name of the data pipe for the model.
model_pipe = self.name_pipe(model)
if self.is_model_for_selection(model):
self.model_pipes.append(model_pipe)
# Check that results do not already exist - i.e. a previous run was interrupted.
path1 = path + sep + 'results'
path2 = path1 + '.bz2'
path3 = path1 + '.gz'
if access(path1, F_OK) or access(path2, F_OK) or access(
path2, F_OK):
# Printout.
print(
"Detected the presence of results files for the '%s' model - loading these instead of performing optimisation for a second time."
% model)
# Create a data pipe and switch to it.
self.interpreter.pipe.create(pipe_name=model_pipe,
pipe_type='relax_disp',
bundle=self.pipe_bundle)
self.interpreter.pipe.switch(model_pipe)
# Load the results.
self.interpreter.results.read(file='results', dir=path)
# Jump to the next model.
continue
# Create the data pipe by copying the base pipe, then switching to it.
self.interpreter.pipe.copy(pipe_from=self.pipe_name,
pipe_to=model_pipe,
bundle_to=self.pipe_bundle)
self.interpreter.pipe.switch(model_pipe)
# Select the model.
self.interpreter.relax_disp.select_model(model)
# Copy the R2eff values from the R2eff model data pipe.
if model != MODEL_R2EFF and MODEL_R2EFF in self.models:
self.interpreter.value.copy(
pipe_from=self.name_pipe(MODEL_R2EFF),
pipe_to=model_pipe,
param='r2eff')
# Calculate the R2eff values for the fixed relaxation time period data types.
if model == MODEL_R2EFF and not has_exponential_exp_type():
self.interpreter.minimise.calculate()
# Optimise the model.
else:
self.optimise(model=model, model_path=model_path)
# Write out the results.
self.write_results(path=path, model=model)
# The final model selection data pipe.
if len(self.models) >= 2:
# Printout.
section(file=sys.stdout, text="Final results", prespace=2)
# Perform model selection.
self.interpreter.model_selection(
method=self.modsel,
modsel_pipe=self.name_pipe('final'),
bundle=self.pipe_bundle,
pipes=self.model_pipes)
# Final Monte Carlo simulations only.
if not self.mc_sim_all_models:
self.interpreter.monte_carlo.setup(number=self.mc_sim_num)
self.interpreter.monte_carlo.create_data()
self.interpreter.monte_carlo.initial_values()
self.interpreter.minimise.execute(
'simplex',
func_tol=self.opt_func_tol,
max_iter=self.opt_max_iterations,
constraints=True)
if self.eliminate:
self.interpreter.eliminate()
self.interpreter.monte_carlo.error_analysis()
# Writing out the final results.
self.write_results(path=self.results_dir + sep + 'final')
# No model selection.
else:
warn(
RelaxWarning(
"Model selection in the dispersion auto-analysis has been skipped as only %s models have been optimised."
% len(self.model_pipes)))
# Finally save the program state.
self.interpreter.state.save(state='final_state',
dir=self.results_dir,
force=True)
def run(self):
"""Execute the auto-analysis."""
# Peak intensity error analysis.
if MODEL_R2EFF in self.models:
self.error_analysis()
# Loop over the models.
self.model_pipes = []
for model in self.models:
# Printout.
subtitle(file=sys.stdout, text="The '%s' model" % model, prespace=3)
# The results directory path.
path = self.results_dir+sep+model
# The name of the data pipe for the model.
model_pipe = model
if self.is_model_for_selection(model):
self.model_pipes.append(model_pipe)
# Check that results do not already exist - i.e. a previous run was interrupted.
path1 = path + sep + 'results'
path2 = path1 + '.bz2'
path3 = path1 + '.gz'
if access(path1, F_OK) or access(path2, F_OK) or access(path2, F_OK):
# Printout.
print("Detected the presence of results files for the '%s' model - loading these instead of performing optimisation for a second time." % model)
# Create a data pipe and switch to it.
self.interpreter.pipe.create(pipe_name=model_pipe, pipe_type='relax_disp', bundle=self.pipe_bundle)
self.interpreter.pipe.switch(model_pipe)
# Load the results.
self.interpreter.results.read(file='results', dir=path)
# Jump to the next model.
continue
# Create the data pipe by copying the base pipe, then switching to it.
self.interpreter.pipe.copy(pipe_from=self.pipe_name, pipe_to=model_pipe, bundle_to=self.pipe_bundle)
self.interpreter.pipe.switch(model_pipe)
# Select the model.
self.interpreter.relax_disp.select_model(model)
# Copy the R2eff values from the R2eff model data pipe.
if model != MODEL_R2EFF and MODEL_R2EFF in self.models:
self.interpreter.value.copy(pipe_from=MODEL_R2EFF, pipe_to=model, param='r2eff')
# Calculate the R2eff values for the fixed relaxation time period data types.
if model == MODEL_R2EFF and not has_exponential_exp_type():
self.interpreter.calc()
# Optimise the model.
else:
self.optimise(model=model)
# Write out the results.
self.write_results(path=path, model=model)
# The final model selection data pipe.
if len(self.models) >= 2:
# Printout.
section(file=sys.stdout, text="Final results", prespace=2)
# Perform model selection.
self.interpreter.model_selection(method=self.modsel, modsel_pipe='final', bundle=self.pipe_bundle, pipes=self.model_pipes)
# Final Monte Carlo simulations only.
if not self.mc_sim_all_models:
self.interpreter.monte_carlo.setup(number=self.mc_sim_num)
self.interpreter.monte_carlo.create_data()
self.interpreter.monte_carlo.initial_values()
self.interpreter.minimise('simplex', func_tol=self.opt_func_tol, max_iter=self.opt_max_iterations, constraints=True)
if self.eliminate:
self.interpreter.eliminate()
self.interpreter.monte_carlo.error_analysis()
# Writing out the final results.
self.write_results(path=self.results_dir+sep+'final')
# No model selection.
else:
warn(RelaxWarning("Model selection in the dispersion auto-analysis has been skipped as only %s models have been optimised." % len(self.model_pipes)))
# Finally save the program state.
self.interpreter.state.save(state='final_state', dir=self.results_dir, force=True)
