import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
from iwopy import Problem
import foxes.constants as FC
[docs]
class GeomRegGrid(Problem):
"""
A regular grid within a boundary geometry.
This optimization problem does not involve
wind farms.
Attributes
----------
boundary: foxes.utils.geom2d.AreaGeometry
The boundary geometry
n_turbines: int
The number of turbines in the layout
min_dist: float
The minimal distance between points
max_dist: float
The maximal distance between points
D: float
The diameter of circle fully within boundary
:group: opt.problems.layout.geom_layouts
"""
[docs]
def __init__(
self,
boundary,
n_turbines,
min_dist,
max_dist=None,
D=None,
):
"""
Constructor.
Parameters
----------
boundary: foxes.utils.geom2d.AreaGeometry
The boundary geometry
n_turbines: int
The number of turbines in the layout
min_dist: float
The minimal distance between points
max_dist: float, optional
The maximal distance between points
D: float, optional
The diameter of circle fully within boundary
"""
super().__init__(name="geom_reg_grid")
self.boundary = boundary
self.n_turbines = n_turbines
self.min_dist = float(min_dist)
self.max_dist = float(max_dist) if max_dist is not None else max_dist
self.D = D
self._SX = "sx"
self._SY = "sy"
self._DX = "dx"
self._DY = "dy"
self._ALPHA = "alpha"
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def initialize(self, verbosity=1):
"""
Initialize the object.
Parameters
----------
verbosity: int
The verbosity level, 0 = silent
"""
super().initialize(verbosity)
pmin = self.boundary.p_min()
pmax = self.boundary.p_max()
self._pc = 0.5 * (pmin + pmax)
self._diag = np.linalg.norm(pmax - pmin)
self.max_dist = self._diag if self.max_dist is None else self.max_dist
self._nrow = (
int(np.maximum(self._diag / self.min_dist, np.sqrt(self.n_turbines) + 0.5))
+ 3
)
if verbosity > 0:
print(f"Grid data:")
print(f" pmin = {pmin}")
print(f" pmax = {pmax}")
print(f" min dist = {self.min_dist}")
print(f" max dist = {self.max_dist}")
print(f" n row max = {self._nrow}")
print(f" n max = {self._nrow**2}")
self.apply_individual(self.initial_values_int(), self.initial_values_float())
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def var_names_float(self):
"""
The names of float variables.
Returns
-------
names: list of str
The names of the float variables
"""
return list(np.array([self._SX, self._SY, self._DX, self._DY, self._ALPHA]))
[docs]
def initial_values_float(self):
"""
The initial values of the float variables.
Returns
-------
values: numpy.ndarray
Initial float values, shape: (n_vars_float,)
"""
vals = np.zeros(5, dtype=FC.DTYPE)
vals[2:4] = self.min_dist
return vals
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def min_values_float(self):
"""
The minimal values of the float variables.
Use -numpy.inf for unbounded.
Returns
-------
values: numpy.ndarray
Minimal float values, shape: (n_vars_float,)
"""
vals = np.zeros(5, dtype=FC.DTYPE)
vals[:2] = -0.5
vals[2:4] = self.min_dist
return vals
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def max_values_float(self):
"""
The maximal values of the float variables.
Use numpy.inf for unbounded.
Returns
-------
values: numpy.ndarray
Maximal float values, shape: (n_vars_float,)
"""
vals = np.zeros(5, dtype=FC.DTYPE)
vals[:2] = 0.5
vals[2:4] = self.max_dist
vals[4] = 90.0
return vals
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def apply_individual(self, vars_int, vars_float):
"""
Apply new variables to the problem.
Parameters
----------
vars_int: np.array
The integer variable values, shape: (n_vars_int,)
vars_float: np.array
The float variable values, shape: (n_vars_float,)
Returns
-------
problem_results: Any
The results of the variable application
to the problem
"""
sx, sy, dx, dy, alpha = vars_float
a = np.deg2rad(alpha)
nax = np.stack([np.cos(a), np.sin(a)], axis=-1)
nay = np.stack([-np.sin(a), np.cos(a)], axis=-1)
pts = (
self._pc[None, None, :]
+ (np.arange(self._nrow)[:, None, None] - (self._nrow - 1) / 2 + sx)
* dx
* nax[None, None, :]
+ (np.arange(self._nrow)[None, :, None] - (self._nrow - 1) / 2 + sy)
* dy
* nay[None, None, :]
)
pts = pts.reshape(self._nrow**2, 2)
if self.D is None:
valid = self.boundary.points_inside(pts)
else:
valid = self.boundary.points_inside(pts) & (
self.boundary.points_distance(pts) >= self.D / 2
)
nvl = np.sum(valid)
if nvl >= self.n_turbines:
return pts[valid][: self.n_turbines], np.ones(self.n_turbines, dtype=bool)
else:
qts = np.append(pts[valid], pts[~valid][: (self.n_turbines - nvl)], axis=0)
vld = np.zeros(self.n_turbines, dtype=bool)
vld[:nvl] = True
return qts, vld
[docs]
def apply_population(self, vars_int, vars_float):
"""
Apply new variables to the problem,
for a whole population.
Parameters
----------
vars_int: np.array
The integer variable values, shape: (n_pop, n_vars_int)
vars_float: np.array
The float variable values, shape: (n_pop, n_vars_float)
Returns
-------
problem_results: Any
The results of the variable application
to the problem
"""
n_pop = vars_float.shape[0]
sx = vars_float[:, 0]
sy = vars_float[:, 1]
dx = vars_float[:, 2]
dy = vars_float[:, 3]
alpha = vars_float[:, 4]
a = np.deg2rad(alpha)
nax = np.stack([np.cos(a), np.sin(a)], axis=-1)
nay = np.stack([-np.sin(a), np.cos(a)], axis=-1)
pts = (
self._pc[None, None, None, :]
+ (
np.arange(self._nrow)[None, :, None, None]
- (self._nrow - 1) / 2
+ sx[:, None, None, None]
)
* dx[:, None, None, None]
* nax[:, None, None, :]
+ (
np.arange(self._nrow)[None, None, :, None]
- (self._nrow - 1) / 2
+ sy[:, None, None, None]
)
* dy[:, None, None, None]
* nay[:, None, None, :]
)
pts = pts.reshape(n_pop * self._nrow**2, 2)
if self.D is None:
valid = self.boundary.points_inside(pts)
else:
valid = self.boundary.points_inside(pts) & (
self.boundary.points_distance(pts) >= self.D / 2
)
valid = valid.reshape(n_pop, self._nrow**2)
pts = pts.reshape(n_pop, self._nrow**2, 2)
nvl = np.sum(valid, axis=1)
qts = np.zeros((n_pop, self.n_turbines, 2), dtype=FC.DTYPE)
vld = np.zeros((n_pop, self.n_turbines), dtype=bool)
for pi in range(n_pop):
if nvl[pi] >= self.n_turbines:
qts[pi] = pts[pi, valid[pi]][: self.n_turbines]
vld[pi] = np.ones(self.n_turbines, dtype=bool)
else:
qts[pi] = np.append(
pts[pi, valid[pi]],
pts[pi, ~valid[pi]][: (self.n_turbines - nvl[pi])],
axis=0,
)
vld[pi, : nvl[pi]] = True
return qts, vld
[docs]
def get_fig(
self, xy=None, valid=None, ax=None, title=None, true_circle=True, **bargs
):
"""
Return plotly figure axis.
Parameters
----------
xy: numpy.ndarary, optional
The xy coordinate array, shape: (n_points, 2)
valid: numpy.ndarray, optional
Boolean array of validity, shape: (n_points,)
ax: pyplot.Axis, optional
The figure axis
title: str, optional
The figure title
true_circle: bool
Draw points as circles with diameter self.D
bars: dict, optional
The boundary plot arguments
Returns
-------
ax: pyplot.Axis
The figure axis
"""
if ax is None:
__, ax = plt.subplots()
hbargs = {"fill_mode": "inside_lightgray"}
hbargs.update(bargs)
self.boundary.add_to_figure(ax, **hbargs)
if xy is not None:
if valid is not None:
xy = xy[valid]
if not true_circle or self.D is None:
ax.scatter(xy[:, 0], xy[:, 1], color="orange")
else:
for x, y in xy:
ax.add_patch(
plt.Circle((x, y), self.D / 2, color="blue", fill=True)
)
ax.set_aspect("equal", adjustable="box")
ax.set_xlabel("x [m]")
ax.set_ylabel("y [m]")
if title is None:
if xy is None:
title = f"Optimization area"
else:
l = len(xy) if xy is not None else 0
dists = cdist(xy, xy)
np.fill_diagonal(dists, 1e20)
title = f"N = {l}, min_dist = {np.min(dists):.1f} m"
ax.set_title(title)
return ax