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Oct 2, 2025
Merged

Misc petab.v2 fixes/cleanup #445

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82 changes: 21 additions & 61 deletions petab/v2/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -992,16 +992,13 @@ def _validate(self) -> Self:
"Estimated parameter must have lower and upper bounds set"
)

# TODO: also if not estimated?
if (
self.estimate
and self.lb is not None
and self.ub is not None
and self.lb >= self.ub
):
raise ValueError("Lower bound must be less than upper bound.")
if self.lb is not None and self.ub is not None and self.lb > self.ub:
raise ValueError(
"Lower bound must be less than or equal to upper bound."
)

# TODO priorType, priorParameters
# NOTE: priorType and priorParameters are currently checked in
# `CheckPriorDistribution`

return self

Expand Down Expand Up @@ -1294,50 +1291,6 @@ def from_yaml(
mapping_tables=mapping_tables,
)

@staticmethod
def from_dfs(
model: Model = None,
condition_df: pd.DataFrame = None,
experiment_df: pd.DataFrame = None,
measurement_df: pd.DataFrame = None,
parameter_df: pd.DataFrame = None,
observable_df: pd.DataFrame = None,
mapping_df: pd.DataFrame = None,
config: ProblemConfig = None,
):
"""
Construct a PEtab problem from dataframes.

Parameters:
condition_df: PEtab condition table
experiment_df: PEtab experiment table
measurement_df: PEtab measurement table
parameter_df: PEtab parameter table
observable_df: PEtab observable table
mapping_df: PEtab mapping table
model: The underlying model
config: The PEtab problem configuration
"""
# TODO: do we really need this?

observable_table = ObservableTable.from_df(observable_df)
condition_table = ConditionTable.from_df(condition_df)
experiment_table = ExperimentTable.from_df(experiment_df)
measurement_table = MeasurementTable.from_df(measurement_df)
mapping_table = MappingTable.from_df(mapping_df)
parameter_table = ParameterTable.from_df(parameter_df)

return Problem(
models=[model],
condition_tables=[condition_table],
experiment_tables=[experiment_table],
observable_tables=[observable_table],
measurement_tables=[measurement_table],
parameter_tables=[parameter_table],
mapping_tables=[mapping_table],
config=config,
)

@staticmethod
def from_combine(filename: Path | str) -> Problem:
"""Read PEtab COMBINE archive (http://co.mbine.org/documents/archive).
Expand Down Expand Up @@ -2235,6 +2188,7 @@ def model_dump(self, **kwargs) -> dict[str, Any]:
'experiment_files': [],
'extensions': {},
'format_version': '2.0.0',
'id': None,
'mapping_files': [],
'measurement_files': [],
'model_files': {},
Expand Down Expand Up @@ -2343,19 +2297,25 @@ class ProblemConfig(BaseModel):
#: The problem ID.
id: str | None = None

#: The path to the parameter file, relative to ``base_path``.
# TODO https://github.com/PEtab-dev/PEtab/pull/641:
# rename to parameter_files in yaml for consistency with other files?
# always a list?
parameter_files: list[AnyUrl | Path] = Field(
default=[], alias=C.PARAMETER_FILES
)

#: The paths to the parameter tables.
# Absolute or relative to `base_path`.
parameter_files: list[AnyUrl | Path] = []
#: The model IDs and files used by the problem (`id->ModelFile`).
model_files: dict[str, ModelFile] | None = {}
#: The paths to the measurement tables.
# Absolute or relative to `base_path`.
measurement_files: list[AnyUrl | Path] = []
#: The paths to the condition tables.
# Absolute or relative to `base_path`.
condition_files: list[AnyUrl | Path] = []
#: The paths to the experiment tables.
# Absolute or relative to `base_path`.
experiment_files: list[AnyUrl | Path] = []
#: The paths to the observable tables.
# Absolute or relative to `base_path`.
observable_files: list[AnyUrl | Path] = []
#: The paths to the mapping tables.
# Absolute or relative to `base_path`.
mapping_files: list[AnyUrl | Path] = []

#: Extensions used by the problem.
Expand Down
72 changes: 38 additions & 34 deletions petab/v2/lint.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -992,11 +992,6 @@ def append_overrides(overrides):
append_overrides(m.observable_parameters)
append_overrides(m.noise_parameters)

# TODO remove `observable_ids` when
# `get_output_parameters` is updated for PEtab v2/v1.1, where
# observable IDs are allowed in observable formulae
observable_ids = {o.id for o in problem.observables}

# Add output parameters except for placeholders
for formula_type, placeholder_sources in (
(
Expand All @@ -1021,9 +1016,7 @@ def append_overrides(overrides):
**placeholder_sources,
)
parameter_ids.update(
p
for p in output_parameters
if p not in placeholders and p not in observable_ids
p for p in output_parameters if p not in placeholders
)

# Add condition table parametric overrides unless already defined in the
Expand All @@ -1048,44 +1041,55 @@ def get_output_parameters(
) -> list[str]:
"""Get output parameters

Returns IDs of parameters used in observable and noise formulas that are
not defined in the model.
Returns IDs of symbols used in observable and noise formulas that are
not observables and that are not defined in the model.

Arguments:
problem: The PEtab problem
observables: Include parameters from observableFormulas
noise: Include parameters from noiseFormulas

Returns:
List of output parameter IDs
List of output parameter IDs, including any placeholder parameters.
"""
formulas = []
# collect free symbols from observable and noise formulas,
# skipping observable IDs
candidates = set()
if observables:
formulas.extend(o.formula for o in problem.observables)
candidates |= {
str_sym
for o in problem.observables
if o.formula is not None
for sym in o.formula.free_symbols
if (str_sym := str(sym)) != o.id
}
if noise:
formulas.extend(o.noise_formula for o in problem.observables)
output_parameters = OrderedDict()
candidates |= {
str_sym
for o in problem.observables
if o.noise_formula is not None
for sym in o.noise_formula.free_symbols
if (str_sym := str(sym)) != o.id
}

for formula in formulas:
free_syms = sorted(
formula.free_symbols,
key=lambda symbol: symbol.name,
)
for free_sym in free_syms:
sym = str(free_sym)
if problem.model.symbol_allowed_in_observable_formula(sym):
continue
output_parameters = OrderedDict()

# does it map to a model entity?
for mapping in problem.mappings:
if mapping.petab_id == sym and mapping.model_id is not None:
if problem.model.symbol_allowed_in_observable_formula(
mapping.model_id
):
break
else:
# no mapping to a model entity, so it is an output parameter
output_parameters[sym] = None
# filter out symbols that are defined in the model or mapped to
# such symbols
for candidate in sorted(candidates):
if problem.model.symbol_allowed_in_observable_formula(candidate):
continue

# does it map to a model entity?
for mapping in problem.mappings:
if mapping.petab_id == candidate and mapping.model_id is not None:
if problem.model.symbol_allowed_in_observable_formula(
mapping.model_id
):
break
else:
# no mapping to a model entity, so it is an output parameter
output_parameters[candidate] = None

return list(output_parameters.keys())

Expand Down