def __init__(self,
initial_value: np.ndarray,
coarse_x: np.ndarray,
coarse_y: np.ndarray,
fine_x: np.ndarray,
fine_y: np.ndarray,
symmetry: np.ndarray = np.array([0, 0]),
periodicity: np.ndarray = np.array([0, 0]),
periods: np.ndarray = np.array([0, 0]),
lower_bound: Union[float, List[float]] = 0,
upper_bound: Union[float, List[float]] = 1,
bounds: List[float] = None) -> None:
self.x_z = fine_x
self.y_z = fine_y
self.x_p = coarse_x
self.y_p = coarse_y
self.beta = 1 / 3 # relaxation factor of the fabrication constraint
self.k = 4 # factor in the exponential in the sigmoid function used to discretize
self.geometry_matrix, self.reverse_geometry_matrix = cubic_utils.make_geometry_matrix_cubic(
(len(coarse_x), len(coarse_y)), symmetry, periodicity, periods)
# correct the initial value
if isinstance(initial_value, (float, int, complex)):
self.vector = initial_value * np.ones(
self.geometry_matrix.shape[1])
elif len(initial_value) == self.reverse_geometry_matrix.shape[0]:
self.vector = initial_value
elif len(initial_value) == self.reverse_geometry_matrix.shape[1]:
self.vector = self.reverse_geometry_matrix @ initial_value
else:
raise ValueError('Invalid initial value')
# Make the interpolation matrix.
#periodicity_phi2f = np.logical_and(periodicity, np.logical_not(periods))
phi2f, _, _ = cubic_utils.CubicMatrices(self.x_z, self.y_z, self.x_p,
self.y_p, periodicity)
self.vec2f = phi2f @ self.geometry_matrix
# Set bounds
if bounds:
self.lower_bound = bounds[0]
self.upper_bound = bounds[1]
else:
self.lower_bound = lower_bound
self.upper_bound = upper_bound
def __init__(self,
initial_value: np.ndarray,
coarse_x: np.ndarray,
coarse_y: np.ndarray,
fine_x: np.ndarray,
fine_y: np.ndarray,
symmetry: np.ndarray = np.array([0, 0]),
periodicity: np.ndarray = np.array([0, 0]),
periods: np.ndarray = np.array([0, 0]),
lower_bound: Union[float, List[float]] = -np.inf,
upper_bound: Union[float, List[float]] = np.inf,
bounds: List[float] = None,
scale: float = 1.75) -> None:
self.x_z = fine_x
self.y_z = fine_y
self.x_p = coarse_x
self.y_p = coarse_y
self.beta = 1 / 3 # relaxation factor of the fabrication constraint
self.k = 4 # factor in the exponential in the sigmoid function used to discretize
self.scale_deriv = scale
self.fine_x_grid, self.fine_y_grid = np.meshgrid(
fine_x, fine_y, indexing='ij')
self.geometry_matrix, self.reverse_geometry_matrix = cubic_utils.make_geometry_matrix_hermite(
(len(coarse_x), len(coarse_y)), symmetry, periodicity, periods)
# correct the initial value
self.derivative_matrix = cubic_utils.idxdydxy_matrix(
coarse_x,
coarse_y,
deriv_scaling=np.array([
1, scale * np.diff(fine_x).mean(),
scale ** 2 * np.diff(fine_x).mean() ** 2
]))
# correct the initial value
if isinstance(initial_value, (float, int, complex)):
self.vector = initial_value * np.ones(self.geometry_matrix.shape[1])
elif len(initial_value) == self.geometry_matrix.shape[1]:
self.vector = initial_value
elif len(initial_value) == self.geometry_matrix.shape[0]:
self.vector = self.reverse_geometry_matrix @ initial_value
elif len(initial_value) == self.derivative_matrix.shape[1]:
self.vector = self.reverse_geometry_matrix @ \
self.derivative_matrix @ initial_value
# TODO vcruysse: account for the following cases
# elif len(initial_value) == symmetry_matrix.shape[1]*4:
# elif len(initial_value) == symmetry_matrix.shape[1]:
# elif len(initial_value) == periodic_matrix_n.shape[0]*4:
# elif len(initial_value) == periodic_matrix_n.shape[0]:
else:
raise ValueError('Invalid initial value')
# Make the interpolation matrix.
phi2f, _, _ = cubic_utils.CubicMatrices(
fine_x,
fine_y,
coarse_x,
coarse_y,
periodicity,
derivatives=True,
deriv_scaling=np.array([
1, scale * np.diff(fine_x).mean(),
scale ** 2 * np.diff(fine_x).mean() ** 2
]))
self.vec2f = phi2f @ self.geometry_matrix
# Set bounds
if bounds:
self.lower_bound = bounds[0]
self.upper_bound = bounds[1]
else:
self.lower_bound = lower_bound
self.upper_bound = upper_bound