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v2: Remove parameter scale #408

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Merged
merged 1 commit into from
Jul 22, 2025
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v2: Remove parameter scale #408

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3 changes: 0 additions & 3 deletions petab/v2/C.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,8 +69,6 @@
PARAMETER_ID = "parameterId"
#: Parameter name column in the parameter table
PARAMETER_NAME = "parameterName"
#: Parameter scale column in the parameter table
PARAMETER_SCALE = "parameterScale"
#: Lower bound column in the parameter table
LOWER_BOUND = "lowerBound"
#: Upper bound column in the parameter table
Expand All @@ -87,7 +85,6 @@
#: Mandatory columns of parameter table
PARAMETER_DF_REQUIRED_COLS = [
PARAMETER_ID,
PARAMETER_SCALE,
LOWER_BOUND,
UPPER_BOUND,
ESTIMATE,
Expand Down
126 changes: 3 additions & 123 deletions petab/v2/problem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,6 @@

from ..v1 import (
parameter_mapping,
parameters,
validate_yaml_syntax,
yaml,
)
Expand Down Expand Up @@ -522,15 +521,6 @@ def get_optimization_parameters(self) -> list[str]:
"""
return [p.id for p in self.parameters if p.estimate]

def get_optimization_parameter_scales(self) -> dict[str, str]:
"""
Return list of optimization parameter scaling strings.

See :py:func:`petab.parameters.get_optimization_parameters`.
"""
# TODO: to be removed in v2?
return parameters.get_optimization_parameter_scaling(self.parameter_df)

def get_observable_ids(self) -> list[str]:
"""
Returns dictionary of observable ids.
Expand Down Expand Up @@ -595,9 +585,7 @@ def x_fixed_ids(self) -> list[str]:
"""Parameter table parameter IDs, for fixed parameters."""
return self.get_x_ids(free=False)

def get_x_nominal(
self, free: bool = True, fixed: bool = True, scaled: bool = False
) -> list:
def get_x_nominal(self, free: bool = True, fixed: bool = True) -> list:
"""Generic function to get parameter nominal values.

Parameters
Expand All @@ -607,9 +595,6 @@ def get_x_nominal(
fixed:
Whether to return fixed parameters, i.e. parameters not to
estimate.
scaled:
Whether to scale the values according to the parameter scale,
or return them on linear scale.

Returns
-------
Expand All @@ -620,10 +605,6 @@ def get_x_nominal(
for p in self.parameters
]

if scaled:
v = list(
parameters.map_scale(v, self.parameter_df[PARAMETER_SCALE])
)
return self._apply_mask(v, free=free, fixed=fixed)

@property
Expand All @@ -641,28 +622,7 @@ def x_nominal_fixed(self) -> list:
"""Parameter table nominal values, for fixed parameters."""
return self.get_x_nominal(free=False)

@property
def x_nominal_scaled(self) -> list:
"""Parameter table nominal values with applied parameter scaling"""
return self.get_x_nominal(scaled=True)

@property
def x_nominal_free_scaled(self) -> list:
"""Parameter table nominal values with applied parameter scaling,
for free parameters.
"""
return self.get_x_nominal(fixed=False, scaled=True)

@property
def x_nominal_fixed_scaled(self) -> list:
"""Parameter table nominal values with applied parameter scaling,
for fixed parameters.
"""
return self.get_x_nominal(free=False, scaled=True)

def get_lb(
self, free: bool = True, fixed: bool = True, scaled: bool = False
):
def get_lb(self, free: bool = True, fixed: bool = True):
"""Generic function to get lower parameter bounds.

Parameters
Expand All @@ -672,34 +632,20 @@ def get_lb(
fixed:
Whether to return fixed parameters, i.e. parameters not to
estimate.
scaled:
Whether to scale the values according to the parameter scale,
or return them on linear scale.

Returns
-------
The lower parameter bounds.
"""
v = [p.lb if p.lb is not None else nan for p in self.parameters]
if scaled:
v = list(
parameters.map_scale(v, self.parameter_df[PARAMETER_SCALE])
)
return self._apply_mask(v, free=free, fixed=fixed)

@property
def lb(self) -> list:
"""Parameter table lower bounds."""
return self.get_lb()

@property
def lb_scaled(self) -> list:
"""Parameter table lower bounds with applied parameter scaling"""
return self.get_lb(scaled=True)

def get_ub(
self, free: bool = True, fixed: bool = True, scaled: bool = False
):
def get_ub(self, free: bool = True, fixed: bool = True):
"""Generic function to get upper parameter bounds.

Parameters
Expand All @@ -709,31 +655,19 @@ def get_ub(
fixed:
Whether to return fixed parameters, i.e. parameters not to
estimate.
scaled:
Whether to scale the values according to the parameter scale,
or return them on linear scale.

Returns
-------
The upper parameter bounds.
"""
v = [p.ub if p.ub is not None else nan for p in self.parameters]
if scaled:
v = list(
parameters.map_scale(v, self.parameter_df[PARAMETER_SCALE])
)
return self._apply_mask(v, free=free, fixed=fixed)

@property
def ub(self) -> list:
"""Parameter table upper bounds"""
return self.get_ub()

@property
def ub_scaled(self) -> list:
"""Parameter table upper bounds with applied parameter scaling"""
return self.get_ub(scaled=True)

@property
def x_free_indices(self) -> list[int]:
"""Parameter table estimated parameter indices."""
Expand Down Expand Up @@ -790,56 +724,6 @@ def sample_parameter_startpoints_dict(
)
]

# TODO: remove in v2?
def unscale_parameters(
self,
x_dict: dict[str, float],
) -> dict[str, float]:
"""Unscale parameter values.

Parameters
----------
x_dict:
Keys are parameter IDs in the PEtab problem, values are scaled
parameter values.

Returns
-------
The unscaled parameter values.
"""
return {
parameter_id: parameters.unscale(
parameter_value,
self.parameter_df[PARAMETER_SCALE][parameter_id],
)
for parameter_id, parameter_value in x_dict.items()
}

# TODO: remove in v2?
def scale_parameters(
self,
x_dict: dict[str, float],
) -> dict[str, float]:
"""Scale parameter values.

Parameters
----------
x_dict:
Keys are parameter IDs in the PEtab problem, values are unscaled
parameter values.

Returns
-------
The scaled parameter values.
"""
return {
parameter_id: parameters.scale(
parameter_value,
self.parameter_df[PARAMETER_SCALE][parameter_id],
)
for parameter_id, parameter_value in x_dict.items()
}

@property
def n_estimated(self) -> int:
"""The number of estimated parameters."""
Expand Down Expand Up @@ -986,7 +870,6 @@ def add_parameter(
id_: str,
estimate: bool | str = True,
nominal_value: Number | None = None,
scale: str = None,
lb: Number = None,
ub: Number = None,
prior_dist: str = None,
Expand All @@ -1002,7 +885,6 @@ def add_parameter(
id_: The parameter id
estimate: Whether the parameter is estimated
nominal_value: The nominal value of the parameter
scale: The parameter scale
lb: The lower bound of the parameter
ub: The upper bound of the parameter
prior_dist: The type of the prior distribution
Expand All @@ -1016,8 +898,6 @@ def add_parameter(
record[ESTIMATE] = estimate
if nominal_value is not None:
record[NOMINAL_VALUE] = nominal_value
if scale is not None:
record[PARAMETER_SCALE] = scale
if lb is not None:
record[LOWER_BOUND] = lb
if ub is not None:
Expand Down
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