def test_validate_model(self):
import libcellml
from libcellml import Validator
# void validateModel(const ModelPtr &model)
v = Validator()
v.validateModel(libcellml.Model())
def test_create_destroy(self):
from libcellml import Validator
x = Validator()
del (x)
y = Validator()
z = Validator(y)
del (y, z)
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)
def test_inheritance(self):
import libcellml
from libcellml import Validator
# Test inheritance
x = Validator()
self.assertIsInstance(x, libcellml.Logger)
# Test access to inherited methods
self.assertIsNone(x.getError(0))
self.assertIsNone(x.getError(-1))
self.assertEqual(x.errorCount(), 0)
x.addError(libcellml.Error())
self.assertEqual(x.errorCount(), 1)
print_issues(importer)
# 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'.
def test_create_destroy(self):
from libcellml import Validator
x = Validator()
del x
model = parser.parseModel(content)
# 1.c
# Use the print_model utility function to display the contents of the
# parsed model in the terminal.
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()
' <ci>E_K</ci>\n'\
' </apply>\n'\
' </apply>\n'\
' </apply>\n'
k_channel_equations.setMath(math_header)
k_channel_equations.appendMath(equation_iK)
k_channel_equations.appendMath(math_footer)
# 2.c
# Once the mathematics has been added to the component, and the component to the
# model, we can make use of the diagnostic messages within the Validator class
# to tell us what else needs to be done.
# Create a Validator instance, and pass it your model for processing using the
# validateModel function.
validator = Validator()
validator.validateModel(model)
# end 2.c
# Calling the validator does not return anything: we have to go looking for issues
# that it found during processing. When a problem is found, an Issue item is created
# containing:
# - a description string explaining the problem
# - a URL at which more information is available
# - an std.any item relevant to the problem, if available
# - a level indicator and
# - a cause indicator relevant to the stored item.
# We can use these issues as we need to. The simplest way is to print the descriptions
# to the terminal.
Tutorial 7 assumes that you are already comfortable with:
- the concept of component hierarchy and encapsulation (Tutorial 5)
- the use of the API to create all of the entities in a model (Tutorial 3)
- the content MathML2 markup for mathematical equations (Tutorial 4)
- serialisation and printing of a model to a CellML file (Tutorial 1)
"""
from libcellml import Component, Model, Printer, Units, Validator, Variable
from utilities.tutorial_utilities import print_errors_to_terminal
if __name__ == "__main__":
# 0 Setup stuff that is used throughout
validator = Validator()
model = Model()
model.setName("Tutorial7_SodiumChannelModel")
math_header = '<math xmlns="http://www.w3.org/1998/Math/MathML" xmlns:cellml="http://www.cellml.org/cellml/2.0#">'
math_footer = '</math>'
print("-----------------------------------------------")
print(" STEP 1: Creating the sodium channel")
print("-----------------------------------------------")
# 1.a Create the component instance, name it, and add to the model
sodium_channel = Component()
sodium_channel.setName("sodiumChannel")
model.addComponent(sodium_channel)
# ---------------------------------------------------------------------------
# STEP 2: Print the contents of the model to the terminal so that we can read it more easily. This step makes
# use of the utilities in the 'tutorial_utilities.py' file
print("-----------------------------------------------")
print(" Printing the parsed model ")
print("-----------------------------------------------")
print_model_to_terminal(model)
# ---------------------------------------------------------------------------
# STEP 3: Check that the model meets the CellML2.0 specifications using the Validator
#
# 2.a Create a Validator and pass the model into it
print("-----------------------------------------------")
print(" Validating the parsed model")
print("-----------------------------------------------")
validator = Validator()
validator.validateModel(model)
# 2.b Check whether there were errors returned from the validator
number_of_validation_errors = validator.errorCount()
if number_of_validation_errors != 0:
print("The validator has found {n} errors!".format(
n=number_of_validation_errors))
# 2.c Retrieve the errors, and print their description and specification reference to the terminal
for e in range(0, number_of_validation_errors):
validator_error = validator.error(e)
specification_heading = validator_error.specificationHeading()
the model which can be solved in Python or C. By the time you have worked
through Tutorial 6 you will be able to:
- use the Generator functionality to create models in Python or C format
- use the simple solver provided to run the created models.
Tutorial 6 assumes that you are already comfortable with:
- file manipulation and summarising using the utility functions
"""
from libcellml import Model, Parser, Validator
if __name__ == "__main__":
# 0 Create a new model instance representing the combined model and name it.
model = Model()
model.setName("Tutorial8_HHModel")
validator = Validator()
parser = Parser()
print("-----------------------------------------------")
print(" STEP 1: Read the membrane component")
print("-----------------------------------------------")
# 1.a Read the model provided for you in the "Tutorial8_MembraneModel.cellml"
# file in the resources folder.
# 1.b Create a temporary model for the membrane
# 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 cleared before adding it to the model.
# Use the parser to deserialise the contents of the string you've read and return the model.
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()))
Tutorial 6 assumes that you are already comfortable with:
- file manipulation and summarising using the utility functions
"""
from libcellml import Component, Model, Parser, Printer, Validator, Variable
from utilities.tutorial_utilities import print_errors_to_terminal, print_encapsulation_structure_to_terminal, \
switch_units_in_maths, insert_into_mathml_string
if __name__ == "__main__":
# 0.a Create a new model instance representing the combined model and name it.
model = Model()
model.setName("Tutorial8_HHModel")
validator = Validator()
parser = Parser()
print("-----------------------------------------------")
print(" STEP 1: Read the membrane component")
print("-----------------------------------------------")
# 1.a Read the model provided for you in the "Tutorial8_MembraneModel.cellml"
# file in the resources folder.
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
# Check for issues.
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.
