def test_validate_model(self):
import libcellml
from libcellml import Validator
# void validateModel(const ModelPtr &model)
v = Validator()
v.validateModel(libcellml.Model())
# 2.c
# Use the importer to create a flattened version of the model.
flatModel = importer.flattenModel(model)
# end 2
print('----------------------------------------------------------')
print(' STEP 3: Validate and analyse the flattened model ')
print('----------------------------------------------------------')
# 3.a
# Create a Validator instance, pass in the flattened model, and check that
# there are no issues raised.
validator = Validator()
validator.validateModel(flatModel)
print_issues(validator)
# 3.b
# Create an Analyser instance, pass in the flattened model, and check that
# there are no issues raised.
analyser = Analyser()
analyser.analyseModel(flatModel)
print_issues(analyser)
# end 3
print('----------------------------------------------------------')
print(' STEP 4: Generate code and output ')
print('----------------------------------------------------------')
# Resolve the imports.
importer.resolveImports(model, '')
# Check for issues.
print('The importer found {} issues.'.format(importer.issueCount()))
for i in range(0, importer.issueCount()):
issue = importer.issue(i)
print(issue.description())
# Flatten the model.
model = importer.flattenModel(model)
# STEP 3
# Create an Validator instance and pass the model to it for processing.
validator = Validator()
validator.validateModel(model)
# Print any issues to the terminal.
print('The validator found {} issues.'.format(validator.issueCount()))
for i in range(0, validator.issueCount()):
issue = validator.issue(i)
print(issue.description())
# STEP 4
# Fix the validation errors.
# Add units to the variable 'b' in component 'validationErrors'.
model.component('validationErrors').variable('b').setUnits('dimensionless')
# Change the name of the variable 'iShouldBeNamed_c' to be 'c'.
model.component('validationErrors').variable('iShouldBeNamed_c').setName('c')
model = parser.parseModel(read_file.read())
# 1.d
# Print the parsed model to the terminal for viewing.
print_model(model, False)
# end 1
print('----------------------------------------------------------')
print(' STEP 2: Validate the parsed model ')
print('----------------------------------------------------------')
# 2.a
# Create a Validator item and validate the model.
validator = Validator()
validator.validateModel(model)
# end 2.a
# Each Validator issue contains:
# - a description of the problem
# - the reference heading in the normative specification which affects this issue
# - a URL at which the informative specification notes can be found
# - an std.any item storing the CellML element most relevant to the issue and
# - a level indication.
# 2.b
# Retrieve any issues from the validator and print their descriptions and help URL to the terminal.
# If you're already familiar with these you can use the print_issues function instead.
print('The validator found {} issues.'.format(validator.issueCount()))
for i in range(0, validator.issueCount()):
read_file1 = open("../resources/tutorial8_MembraneModel.cellml", "r")
# 1.b Create a temporary model for the membrane
membrane_model = parser.parseModel(read_file1.read())
membrane_model.setName("membraneModel")
# 1.b Extract the membrane component from the parsed model and add it
# to the combined model. Note that the membrane component's parent
# must be removed before adding it to the model.
membrane = membrane_model.component("membrane")
membrane.removeParent()
model.addComponent(membrane)
# 1.c Validate the combined model. We expect to see errors from:
# - missing units, as we have only added the component so far
validator.validateModel(model)
print_errors_to_terminal(validator)
# 1.d Import the units from the membraneModel into the combined model
for u in range(0, membrane_model.unitsCount()):
model.addUnits(membrane_model.units(u))
# 1.e No errors expected this time :)
validator.validateModel(model)
print_errors_to_terminal(validator)
print("\n-----------------------------------------------")
print(" STEP 2: Read the sodium channel")
print("-----------------------------------------------")
# 2.a Read the model created in Tutorial 7 representing the sodium channel.
print('The importer found {} issues.'.format(importer.issueCount()))
for i in range(0,importer.issueCount()):
print(importer.issue(i).description())
print()
# STEP 3
# The analysis tools - the Validator and Analyser - will read only the submitted
# model they do not look into any of the imported items, so they can't check them.
# In order to retain the import structure but be able to use the diagnostic tools,
# we can create a flattened copy of the model for testing. This can be used to
# identify mistakes in the unflattened model too.
# Create a Validator and Analyser and submit the original, unflattened model.
# We don't expect either of these to report any issues.
validator = Validator()
validator.validateModel(original_model)
print('Investigating the original model:')
print(' - the validator found {} issues'.format(validator.issueCount()))
for i in range(0, validator.issueCount()):
print(' - {}'.format(validator.issue(i).description()))
analyser = Analyser()
analyser.analyseModel(original_model)
print(' - the analyser found {} issues'.format(analyser.issueCount()))
for i in range(0, analyser.issueCount()):
print(' - {}'.format(analyser.issue(i).description()))
print()
# Create a flattened version for diagnostics.
flat_model = importer.flattenModel(original_model)