def petab_problem(minimal_sbml_model): # pylint: disable=W0621
"""Test petab problem."""
# create test model
document, model = minimal_sbml_model
p = model.createParameter()
p.setId('fixedParameter1')
p.setName('FixedParameter1')
p = model.createParameter()
p.setId('observable_1')
p.setName('Observable 1')
measurement_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1', 'obs2'],
OBSERVABLE_PARAMETERS: ['', 'p1;p2'],
NOISE_PARAMETERS: ['p3;p4', 'p5']
})
condition_df = pd.DataFrame(data={
CONDITION_ID: ['condition1', 'condition2'],
CONDITION_NAME: ['', 'Condition 2'],
'fixedParameter1': [1.0, 2.0]
}).set_index(CONDITION_ID)
parameter_df = pd.DataFrame(data={
PARAMETER_ID: ['dynamicParameter1', 'dynamicParameter2'],
PARAMETER_NAME: ['', '...'],
}).set_index(PARAMETER_ID)
observable_df = pd.DataFrame(data={
OBSERVABLE_ID: ['observable_1'],
OBSERVABLE_NAME: ['julius'],
OBSERVABLE_FORMULA: ['observable_1'],
NOISE_FORMULA: [1],
}).set_index(OBSERVABLE_ID)
with tempfile.TemporaryDirectory() as temp_dir:
sbml_file_name = Path(temp_dir, "model.xml")
libsbml.writeSBMLToFile(document, str(sbml_file_name))
measurement_file_name = Path(temp_dir, "measurements.tsv")
petab.write_measurement_df(measurement_df, measurement_file_name)
condition_file_name = Path(temp_dir, "conditions.tsv")
petab.write_condition_df(condition_df, condition_file_name)
parameter_file_name = Path(temp_dir, "parameters.tsv")
petab.write_parameter_df(parameter_df, parameter_file_name)
observable_file_name = Path(temp_dir, "observables.tsv")
petab.write_observable_df(observable_df, observable_file_name)
yield petab.Problem.from_files(
sbml_file=sbml_file_name,
measurement_file=measurement_file_name,
condition_file=condition_file_name,
parameter_file=parameter_file_name,
observable_files=observable_file_name)
def to_petab_files(
timecourse: Timecourse,
condition_location: TYPE_PATH,
timecourse_location: TYPE_PATH,
) -> pd.DataFrame:
dfs = to_petab_dataframes(timecourse)
petab.write_condition_df(dfs[CONDITION], str(condition_location))
write_timecourse_df(dfs[TIMECOURSE], str(timecourse_location))
def test_write_condition_df():
"""Test conditions.write_condition_df."""
condition_df = pd.DataFrame(
data={
CONDITION_ID: ['condition1', 'condition2'],
CONDITION_NAME: ['Condition 1', 'Condition 2'],
'fixedParameter1': [1.0, 2.0]
}).set_index(CONDITION_ID)
with tempfile.NamedTemporaryFile(mode='w', delete=True) as fh:
file_name = fh.name
petab.write_condition_df(condition_df, file_name)
re_df = petab.get_condition_df(file_name)
assert (condition_df == re_df).all().all()
def test_write_condition_df():
"""Test conditions.write_condition_df."""
condition_df = pd.DataFrame(
data={
CONDITION_ID: ['condition1', 'condition2'],
CONDITION_NAME: ['Condition 1', 'Condition 2'],
'fixedParameter1': [1.0, 2.0]
}).set_index(CONDITION_ID)
with tempfile.TemporaryDirectory() as temp_dir:
file_name = Path(temp_dir) / "conditions.tsv"
petab.write_condition_df(condition_df, file_name)
re_df = petab.get_condition_df(file_name)
assert (condition_df == re_df).all().all()
def test_combine_archive(minimal_sbml_model):
"""Test `create_combine_archive` and `Problem.from_combine`"""
# Create test files
document, _ = minimal_sbml_model
# Create tables with arbitrary content
measurement_df = pd.DataFrame(
data={
OBSERVABLE_ID: ['obs1', 'obs2'],
OBSERVABLE_PARAMETERS: ['', 'p1;p2'],
NOISE_PARAMETERS: ['p3;p4', 'p5']
})
condition_df = pd.DataFrame(
data={
CONDITION_ID: ['condition1', 'condition2'],
CONDITION_NAME: ['', 'Condition 2'],
'fixedParameter1': [1.0, 2.0]
})
condition_df.set_index(CONDITION_ID, inplace=True)
parameter_df = pd.DataFrame(
data={
PARAMETER_ID: ['dynamicParameter1', 'dynamicParameter2'],
PARAMETER_NAME: ['', '...'],
})
parameter_df.set_index(PARAMETER_ID, inplace=True)
observable_df = pd.DataFrame(
data={
OBSERVABLE_ID: ['observable_1'],
OBSERVABLE_FORMULA: ['observable_1'],
NOISE_FORMULA: [1],
})
observable_df.set_index(OBSERVABLE_ID, inplace=True)
sbml_file_name = 'model.xml'
measurement_file_name = 'measurements.tsv'
condition_file_name = 'conditions.tsv'
parameter_file_name = 'parameters.tsv'
observable_file_name = 'observables.tsv'
yaml_file_name = 'test.yaml'
yaml_config = {
FORMAT_VERSION:
petab.__format_version__,
PARAMETER_FILE:
parameter_file_name,
PROBLEMS: [{
SBML_FILES: [sbml_file_name],
MEASUREMENT_FILES: [measurement_file_name],
CONDITION_FILES: [condition_file_name],
OBSERVABLE_FILES: [observable_file_name]
}]
}
with tempfile.TemporaryDirectory(prefix='petab_test_combine_archive') \
as tempdir:
# Write test data
petab.write_sbml(document, os.path.join(tempdir, sbml_file_name))
petab.write_measurement_df(
measurement_df, os.path.join(tempdir, measurement_file_name))
petab.write_parameter_df(parameter_df,
os.path.join(tempdir, parameter_file_name))
petab.write_observable_df(observable_df,
os.path.join(tempdir, observable_file_name))
petab.write_condition_df(condition_df,
os.path.join(tempdir, condition_file_name))
petab.write_yaml(yaml_config, os.path.join(tempdir, yaml_file_name))
archive_file_name = os.path.join(tempdir, 'test.omex')
# Create COMBINE archive
petab.create_combine_archive(os.path.join(tempdir, yaml_file_name),
archive_file_name,
family_name="Tester")
# Read COMBINE archive
problem = petab.Problem.from_combine(archive_file_name)
assert problem.parameter_df is not None
assert problem.condition_df is not None
assert problem.measurement_df is not None
assert problem.observable_df is not None
PARAMETER_NAME: parameter_dict0['name'],
PARAMETER_SCALE: LIN,
NOMINAL_VALUE: 1,
ESTIMATE: 0,
}
else:
raise NotImplementedError(parameter_dict0['id'])
parameter_dicts.append(parameter_dict)
## Noise
parameter_dicts.append({
PARAMETER_ID: noise,
PARAMETER_NAME: noise,
PARAMETER_SCALE: LOG10,
LOWER_BOUND: '1e-12',
UPPER_BOUND: '1e3',
NOMINAL_VALUE: 0.1,
ESTIMATE: 1,
})
condition_df = petab.get_condition_df(
pd.DataFrame({CONDITION_ID: [condition_id]}))
observable_df = petab.get_observable_df(pd.DataFrame(observable_dicts))
measurement_df = petab.get_measurement_df(pd.DataFrame(measurement_dicts))
parameter_df = petab.get_parameter_df(pd.DataFrame(parameter_dicts))
petab.write_condition_df(condition_df, 'output/petab/conditions.tsv')
petab.write_observable_df(observable_df, 'output/petab/observables.tsv')
petab.write_measurement_df(measurement_df, 'output/petab/measurements.tsv')
petab.write_parameter_df(parameter_df, 'output/petab/parameters.tsv')
shutil.copy('input/petab_problem.yaml', 'output/petab/petab_problem.yaml')
OBSERVABLE_ID: ['obs_a'],
OBSERVABLE_FORMULA: ['A'],
NOISE_FORMULA: [sigma]
}).set_index([OBSERVABLE_ID])
parameter_df = pd.DataFrame(
data={
PARAMETER_ID: ['k1', 'k2'],
PARAMETER_SCALE: [LOG] * 2,
LOWER_BOUND: [1e-5] * 2,
UPPER_BOUND: [1e5] * 2,
NOMINAL_VALUE: [k1, k2],
ESTIMATE: [1, 1],
}).set_index(PARAMETER_ID)
petab.write_condition_df(condition_df, "conditions.tsv")
petab.write_measurement_df(measurement_df, "measurements.tsv")
petab.write_observable_df(observable_df, "observables.tsv")
petab.write_parameter_df(parameter_df, "parameters.tsv")
yaml_config = {
FORMAT_VERSION:
1,
PARAMETER_FILE:
"parameters.tsv",
PROBLEMS: [{
SBML_FILES: ["model_conversion_reaction.xml"],
CONDITION_FILES: ["conditions.tsv"],
MEASUREMENT_FILES: ["measurements.tsv"],
OBSERVABLE_FILES: ["observables.tsv"]
}]
