def generate_delaunay(variables, num=10, **kwds):
"""
Generate a Delaunay triangulation of the D-dimensional
bounded variable domain given a list of D variables.
Requires numpy and scipy.
Args:
variables: A list of variables, each having a finite
upper and lower bound.
num (int): The number of grid points to generate for
each variable (default=10).
**kwds: All additional keywords are passed to the
scipy.spatial.Delaunay constructor.
Returns:
A scipy.spatial.Delaunay object.
"""
linegrids = []
for v in variables:
if v.has_lb() and v.has_ub():
linegrids.append(numpy.linspace(v.lb, v.ub, num))
else:
raise ValueError("Variable %s does not have a "
"finite lower and upper bound.")
# generates a meshgrid and then flattens and transposes
# the meshgrid into an (npoints, D) shaped array of
# coordinates
points = numpy.vstack(numpy.meshgrid(*linegrids)).\
reshape(len(variables),-1).T
return scipy.spatial.Delaunay(points, **kwds)
def _get_XYgrid(x, y, ncells):
xlin = np.linspace(
min(x) - abs(max(x) - min(x)) / 2,
max(x) + abs(max(x) - min(x)) / 2, ncells)
ylin = np.linspace(
min(y) - abs(max(y) - min(y)) / 2,
max(y) + abs(max(y) - min(y)) / 2, ncells)
X, Y = np.meshgrid(xlin, ylin)
return X, Y
