"""Create a base class for :class:`.Variable` and :class:`.Constraint`."""
import math
from abc import ABC
from dataclasses import dataclass
from typing import Self
import numpy as np
import pandas as pd
from lightwin.util.dicts_output import markdown
[docs]
@dataclass
class DesignSpaceParameter(ABC):
"""Hold a single variable or constraint.
Parameters
----------
name :
Name of the parameter. Must be compatible with the
:meth:`.SimulationOutput.get` method, and be in
:data:`.IMPLEMENTED_VARIABLES` or :data:`.IMPLEMENTED_CONSTRAINTS`.
element_name :
Name of the element concerned by the parameter.
limits :
Lower and upper bound for the variable. ``np.nan`` deactivates a bound.
"""
name: str
element_name: str
limits: tuple[float, float]
[docs]
@classmethod
def from_floats(
cls,
name: str,
element_name: str,
x_min: float,
x_max: float,
x_0: float = np.nan,
) -> Self:
"""Initialize object with ``x_min``, ``x_max`` instead of ``limits``.
Parameters
----------
name :
Name of the parameter. Must be compatible with the
:meth:`.SimulationOutput.get` method, and be in
:data:`.IMPLEMENTED_VARIABLES` or :data:`.IMPLEMENTED_CONSTRAINTS`.
element_name :
Name of the element concerned by the parameter.
x_min :
Lower limit. ``np.nan`` to deactivate lower bound.
x_max :
Upper limit. ``np.nan`` to deactivate lower bound.
Returns
-------
A DesignSpaceParameter with limits = (x_min, x_max).
"""
return cls(name, element_name, (x_min, x_max))
[docs]
@classmethod
def from_pd_series(
cls, name: str, element_name: str, pd_series: pd.Series
) -> Self:
"""Init object from a pd series (file import)."""
x_min = pd_series.loc[f"{name}: x_min"]
x_max = pd_series.loc[f"{name}: x_max"]
return cls.from_floats(name, element_name, x_min, x_max)
[docs]
def __post_init__(self):
"""Convert values in deg for output if it is angle."""
self._to_deg = False
self._to_numpy = False
@property
def x_min(self) -> float:
"""Return lower variable/constraint bound."""
return self.limits[0]
@property
def x_max(self) -> float:
"""Return upper variable/constraint bound."""
return self.limits[1]
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def change_limits(
self, x_min: float | None = None, x_max: float | None = None
) -> None:
"""Change the limits after creation of the object."""
self.limits = (
x_min if x_min is not None else self.x_min,
x_max if x_max is not None else self.x_max,
)
@property
def _fmt_x_min(self) -> float:
"""Lower limit in deg if it is has ``'phi'`` in it's name."""
if "phi" in self.name:
return math.degrees(self.x_min)
return self.x_min
@property
def _fmt_x_max(self) -> float:
"""Lower limit in deg if it is has ``'phi'`` in it's name."""
if "phi" in self.name:
return math.degrees(self.x_max)
return self.x_max
@property
def _fmt_x_0(self) -> float:
"""Initial value but with a better output."""
assert hasattr(self, "x_0"), (
"This design space parameter has no "
"attribute x_0. Maybe you took a Contraint for a Variable?"
)
x_0 = getattr(self, "x_0")
if "phi" in self.name:
return math.degrees(x_0)
return x_0
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def __str__(self) -> str:
"""Output parameter name and limits."""
out = f"{markdown[self.name]:25} | {self.element_name:15} | "
if hasattr(self, "x_0"):
out += f"{self._fmt_x_0:>8.3f} | "
else:
out += " | "
out += f"{self._fmt_x_min:>9.3f} | {self._fmt_x_max:>9.3f}"
return out
[docs]
def to_dict(
self,
*to_get: str,
missing_value: float | None = None,
prepend_parameter_name: bool = False,
) -> dict[str, float | None | tuple[float, float] | str]:
"""Convert important data to dict to convert it later in pandas df."""
out = {
attribute: getattr(self, attribute, missing_value)
for attribute in to_get
}
if not prepend_parameter_name:
return out
return {f"{self.name}: {key}": value for key, value in out.items()}