def test_inheritance(self):
import libcellml
from libcellml import Validator
# Test inheritance
x = Validator()
self.assertIsInstance(x, libcellml.logger.Logger)
# Test access to inherited methods
self.assertIsNone(x.issue(0))
self.assertIsNone(x.issue(-1))
self.assertEqual(x.issueCount(), 0)
x.addIssue(libcellml.Issue())
self.assertEqual(x.issueCount(), 1)
# 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')
# Check again.
validator.validateModel(model)
print_model(model, True)
# end 1
print('----------------------------')
print(' STEP 2: Validate the model')
print('----------------------------')
# 2.a
# Create a Validator and pass the model into it.
validator = Validator()
validator.validateModel(model)
# 2.b
# Check the number of issues returned from the validator.
num_validation_issues = validator.issueCount()
print('The validator has found {} issues!'.format(num_validation_issues))
# 2.c
# Retrieve the issues, and print their description, url, reference, and
# type of item stored to the terminal. The type of stored item is
# available as an enum, which can be turned into a string for output using
# the utility function, getItemTypeFromEnum(type).
for e in range(0, num_validation_issues):
issue = validator.issue(e)
reference = issue.referenceHeading()
print(' Validator issue[{}]:'.format(e))
print(' Description: {}'.format(issue.description()))
print(' Type of item stored: {}'.format(
cellmlElementTypeAsString(issue.item().type())))
print(' URL: {}'.format(issue.url()))
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)
# Repeat the validation and analysis above on the flattened model.
validator.validateModel(flat_model)
