import numpy as np
from foxes_opt.core.farm_opt_problem import FarmOptProblem
from foxes.config import config
import foxes.variables as FV
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class FarmLayoutOptProblem(FarmOptProblem):
"""
The turbine positioning optimization problem
"""
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def var_names_float(self) -> list[str]:
"""
The names of float variables.
Returns
-------
names
The names of the float variables
"""
vrs = []
for ti in self.sel_turbines:
vrs += [self.tvar(FV.X, ti), self.tvar(FV.Y, ti)]
return vrs
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def initial_values_float(self) -> np.ndarray:
"""
The initial values of the float variables.
Returns
-------
values
Initial float values, shape: (n_vars_float,)
"""
out = np.zeros((self.n_sel_turbines, 2), dtype=config.dtype_double)
for i, ti in enumerate(self.sel_turbines):
out[i] = self.farm.turbines[ti].xy
return out.reshape(self.n_sel_turbines * 2)
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def min_values_float(self) -> np.ndarray:
"""
The minimal values of the float variables.
Use -numpy.inf for unbounded.
Returns
-------
values
Minimal float values, shape: (n_vars_float,)
"""
b = self.farm.boundary
assert b is not None, f"Problem '{self.name}': Missing wind farm boundary."
out = np.zeros((self.n_sel_turbines, 2), dtype=config.dtype_double)
out[:] = b.p_min()[None, :]
return out.reshape(self.n_sel_turbines * 2)
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def max_values_float(self) -> np.ndarray:
"""
The maximal values of the float variables.
Use numpy.inf for unbounded.
Returns
-------
values
Maximal float values, shape: (n_vars_float,)
"""
b = self.farm.boundary
assert b is not None, f"Problem '{self.name}': Missing wind farm boundary."
out = np.zeros((self.n_sel_turbines, 2), dtype=config.dtype_double)
out[:] = b.p_max()[None, :]
return out.reshape(self.n_sel_turbines * 2)
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def update_problem_individual(
self, vars_int: np.ndarray, vars_float: np.ndarray
) -> None:
"""
Update the algo and other data using
the latest optimization variables.
This function is called before running the farm
calculation.
Parameters
----------
vars_int
The integer variable values, shape: (n_vars_int,)
vars_float
The float variable values, shape: (n_vars_float,)
"""
super().update_problem_individual(vars_int, vars_float)
xy = vars_float.reshape(self.n_sel_turbines, 2)
for i, ti in enumerate(self.sel_turbines):
t = self.algo.farm.turbines[ti]
t.xy = xy[i]
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def update_problem_population(
self, vars_int: np.ndarray, vars_float: np.ndarray
) -> None:
"""
Update the algo and other data using
the latest optimization variables.
This function is called before running the farm
calculation.
Parameters
----------
vars_int
The integer variable values, shape: (n_pop, n_vars_int,)
vars_float
The float variable values, shape: (n_pop, n_vars_float,)
"""
super().update_problem_population(vars_int, vars_float)
n_pop: int = len(vars_float)
n_ostates = self._org_n_states
assert n_ostates is not None
n_states = n_pop * n_ostates
xy = vars_float.reshape(n_pop, self.n_sel_turbines, 2)
sxy = np.zeros(
(n_ostates, n_pop, self.n_sel_turbines, 2), dtype=vars_float.dtype
)
sxy[:] = xy[None, :, :, :]
sxy = sxy.reshape(n_states, self.n_sel_turbines, 2)
del xy
for i, ti in enumerate(self.sel_turbines):
t = self.algo.farm.turbines[ti]
t.xy = sxy[:, i]