petab.lint

Integrity checks and tests for specific features used

Functions

assert_all_parameters_present_in_parameter_df(…) Ensure all required parameters are contained in the parameter table with no additional ones
assert_measured_observables_defined(…) Check if all observables in the measurement table have been defined in the observable table
assert_measurement_conditions_present_in_condition_table(…) Ensure that all entries from measurement_df.simulationConditionId and measurement_df.preequilibrationConditionId are present in condition_df.index.
assert_model_parameters_in_condition_or_parameter_table(…) Model parameters that are targets of AssignmentRule must not be present in parameter table or in condition table columns.
assert_no_leading_trailing_whitespace(…) Check that there is no trailing whitespace in elements of Iterable
assert_noise_distributions_valid(observable_df) Ensure that noise distributions and transformations for observables are valid.
assert_parameter_bounds_are_numeric(parameter_df) Check if all entries in the lowerBound and upperBound columns of the parameter table are numeric.
assert_parameter_estimate_is_boolean(…) Check if all entries in the estimate column of the parameter table are 0 or 1.
assert_parameter_id_is_string(parameter_df) Check if all entries in the parameterId column of the parameter table are string and not empty.
assert_parameter_prior_parameters_are_valid(…) Check that the prior parameters are valid.
assert_parameter_prior_type_is_valid(…) Check that valid prior types have been selected
assert_parameter_scale_is_valid(parameter_df) Check if all entries in the parameterScale column of the parameter table are ‘lin’ for linear, ‘log’ for natural logarithm or ‘log10’ for base 10 logarithm.
assert_unique_observable_ids(observable_df) Check if the observableId column of the observable table is unique.
assert_unique_parameter_ids(parameter_df) Check if the parameterId column of the parameter table is unique.
check_condition_df(df, sbml_model) Run sanity checks on PEtab condition table
check_ids(ids, kind) Check IDs are valid
check_measurement_df(df, observable_df) Run sanity checks on PEtab measurement table
check_observable_df(observable_df) Check validity of observable table
check_parameter_bounds(parameter_df) Check if all entries in the lowerBound are smaller than upperBound column in the parameter table and that bounds are positive for parameterScale log|log10.
check_parameter_df(df, sbml_model, …) Run sanity checks on PEtab parameter table
condition_table_is_parameter_free(condition_df) Check if all entries in the condition table are numeric (no parameter IDs)
get_non_unique(values)
is_valid_identifier(x) Check whether x is a valid identifier
lint_problem(problem) Run PEtab validation on problem
measurement_table_has_observable_parameter_numeric_overrides(…) Are there any numbers to override observable parameters?
measurement_table_has_timepoint_specific_mappings(…) Are there time-point or replicate specific parameter assignments in the measurement table.
petab.lint._check_df(df: pandas.core.frame.DataFrame, req_cols: Iterable[T_co], name: str) → None

Check if given columns are present in DataFrame

Parameters:
  • df – Dataframe to check
  • req_cols – Column names which have to be present
  • name – Name of the DataFrame to be included in error message
Raises:

AssertionError – if a column is missing

petab.lint.assert_all_parameters_present_in_parameter_df(parameter_df: pandas.core.frame.DataFrame, sbml_model: libsbml.Model, observable_df: pandas.core.frame.DataFrame, measurement_df: pandas.core.frame.DataFrame, condition_df: pandas.core.frame.DataFrame) → None

Ensure all required parameters are contained in the parameter table with no additional ones

Parameters:
  • parameter_df – PEtab parameter DataFrame
  • sbml_model – PEtab SBML Model
  • observable_df – PEtab observable table
  • measurement_df – PEtab measurement table
  • condition_df – PEtab condition table
Raises:

AssertionError – in case of problems

petab.lint.assert_measured_observables_defined(measurement_df: pandas.core.frame.DataFrame, observable_df: pandas.core.frame.DataFrame) → None

Check if all observables in the measurement table have been defined in the observable table

Parameters:
  • measurement_df – PEtab measurement table
  • observable_df – PEtab observable table
Raises:

AssertionError – in case of problems

petab.lint.assert_measurement_conditions_present_in_condition_table(measurement_df: pandas.core.frame.DataFrame, condition_df: pandas.core.frame.DataFrame) → None

Ensure that all entries from measurement_df.simulationConditionId and measurement_df.preequilibrationConditionId are present in condition_df.index.

Parameters:
  • measurement_df – PEtab measurement table
  • condition_df – PEtab condition table
Raises:

AssertionError – in case of problems

petab.lint.assert_model_parameters_in_condition_or_parameter_table(sbml_model: libsbml.Model, condition_df: pandas.core.frame.DataFrame, parameter_df: pandas.core.frame.DataFrame) → None

Model parameters that are targets of AssignmentRule must not be present in parameter table or in condition table columns. Other parameters must only be present in either in parameter table or condition table columns. Check that.

Parameters:
  • parameter_df – PEtab parameter DataFrame
  • sbml_model – PEtab SBML Model
  • condition_df – PEtab condition table
Raises:

AssertionError – in case of problems

petab.lint.assert_no_leading_trailing_whitespace(names_list: Iterable[str], name: str) → None

Check that there is no trailing whitespace in elements of Iterable

Parameters:
  • names_list – strings to check for whitespace
  • name – name of names_list for error messages
Raises:

AssertionError – if there is trailing whitespace

petab.lint.assert_noise_distributions_valid(observable_df: pandas.core.frame.DataFrame) → None

Ensure that noise distributions and transformations for observables are valid.

Parameters:observable_df – PEtab observable table
Raises:AssertionError – in case of problems
petab.lint.assert_parameter_bounds_are_numeric(parameter_df: pandas.core.frame.DataFrame) → None

Check if all entries in the lowerBound and upperBound columns of the parameter table are numeric.

Parameters:parameter_df – PEtab parameter DataFrame
Raises:AssertionError – in case of problems
petab.lint.assert_parameter_estimate_is_boolean(parameter_df: pandas.core.frame.DataFrame) → None

Check if all entries in the estimate column of the parameter table are 0 or 1.

Parameters:parameter_df – PEtab parameter DataFrame
Raises:AssertionError – in case of problems
petab.lint.assert_parameter_id_is_string(parameter_df: pandas.core.frame.DataFrame) → None

Check if all entries in the parameterId column of the parameter table are string and not empty.

Parameters:parameter_df – PEtab parameter DataFrame
Raises:AssertionError – in case of problems
petab.lint.assert_parameter_prior_parameters_are_valid(parameter_df: pandas.core.frame.DataFrame) → None

Check that the prior parameters are valid.

Parameters:parameter_df – PEtab parameter table
Raises:AssertionError in case of invalide prior parameters
petab.lint.assert_parameter_prior_type_is_valid(parameter_df: pandas.core.frame.DataFrame) → None

Check that valid prior types have been selected

Parameters:parameter_df – PEtab parameter table
Raises:AssertionError in case of invalid prior
petab.lint.assert_parameter_scale_is_valid(parameter_df: pandas.core.frame.DataFrame) → None

Check if all entries in the parameterScale column of the parameter table are ‘lin’ for linear, ‘log’ for natural logarithm or ‘log10’ for base 10 logarithm.

Parameters:parameter_df – PEtab parameter DataFrame
Raises:AssertionError – in case of problems
petab.lint.assert_unique_observable_ids(observable_df: pandas.core.frame.DataFrame) → None

Check if the observableId column of the observable table is unique.

Parameters:observable_df – PEtab observable DataFrame
Raises:AssertionError – in case of problems
petab.lint.assert_unique_parameter_ids(parameter_df: pandas.core.frame.DataFrame) → None

Check if the parameterId column of the parameter table is unique.

Parameters:parameter_df – PEtab parameter DataFrame
Raises:AssertionError – in case of problems
petab.lint.check_condition_df(df: pandas.core.frame.DataFrame, sbml_model: Optional[libsbml.Model]) → None

Run sanity checks on PEtab condition table

Parameters:
  • df – PEtab condition DataFrame
  • sbml_model – SBML Model for additional checking of parameter IDs
Raises:

AssertionError – in case of problems

petab.lint.check_ids(ids: Iterable[str], kind: str = '') → None

Check IDs are valid

Parameters:
  • ids – Iterable of IDs to check
  • kind – Kind of IDs, for more informative error message
Raises:

ValueError - in case of invalid IDs

petab.lint.check_measurement_df(df: pandas.core.frame.DataFrame, observable_df: Optional[pandas.core.frame.DataFrame] = None) → None

Run sanity checks on PEtab measurement table

Parameters:
  • df – PEtab measurement DataFrame
  • observable_df – PEtab observable DataFrame for checking if measurements are compatible with observable transformations.
Raises:

AssertionError, ValueError – in case of problems

petab.lint.check_observable_df(observable_df: pandas.core.frame.DataFrame) → None

Check validity of observable table

Parameters:observable_df – PEtab observable DataFrame
Raises:AssertionError – in case of problems
petab.lint.check_parameter_bounds(parameter_df: pandas.core.frame.DataFrame) → None

Check if all entries in the lowerBound are smaller than upperBound column in the parameter table and that bounds are positive for parameterScale log|log10.

Parameters:parameter_df – PEtab parameter DataFrame
Raises:AssertionError – in case of problems
petab.lint.check_parameter_df(df: pandas.core.frame.DataFrame, sbml_model: Optional[libsbml.Model] = None, observable_df: Optional[pandas.core.frame.DataFrame] = None, measurement_df: Optional[pandas.core.frame.DataFrame] = None, condition_df: Optional[pandas.core.frame.DataFrame] = None) → None

Run sanity checks on PEtab parameter table

Parameters:
  • df – PEtab condition DataFrame
  • sbml_model – SBML Model for additional checking of parameter IDs
  • observable_df – PEtab observable table for additional checks
  • measurement_df – PEtab measurement table for additional checks
  • condition_df – PEtab condition table for additional checks
Raises:

AssertionError – in case of problems

petab.lint.condition_table_is_parameter_free(condition_df: pandas.core.frame.DataFrame) → bool

Check if all entries in the condition table are numeric (no parameter IDs)

Parameters:condition_df – PEtab condition table
Returns:True if there are no parameter overrides in the condition table, False otherwise.
petab.lint.is_valid_identifier(x: str) → bool

Check whether x is a valid identifier

Check whether x is a valid identifier for conditions, parameters, observables… . Identifiers may contain upper and lower case letters, digits and underscores, but must not start with a digit.

Parameters:x – string to check
Returns:True if valid, False otherwise
petab.lint.lint_problem(problem: petab.problem.Problem) → bool

Run PEtab validation on problem

Parameters:problem – PEtab problem to check
Returns:True is errors occurred, False otherwise
petab.lint.measurement_table_has_observable_parameter_numeric_overrides(measurement_df: pandas.core.frame.DataFrame) → bool

Are there any numbers to override observable parameters?

Parameters:measurement_df – PEtab measurement table
Returns:True if there any numbers to override observable parameters, False otherwise.
petab.lint.measurement_table_has_timepoint_specific_mappings(measurement_df: pandas.core.frame.DataFrame) → bool

Are there time-point or replicate specific parameter assignments in the measurement table.

Parameters:measurement_df – PEtab measurement table
Returns:True if there are time-point or replicate specific parameter assignments in the measurement table, False otherwise.