discretize.operators.InnerProducts.closest_points_index

InnerProducts.closest_points_index(locations, grid_loc='CC', discard=False)

Find the indicies for the nearest grid location for a set of points.

Parameters:

locations(n, dim) numpy.ndarray

Points to query.

grid_loc{‘CC’, ‘N’, ‘Fx’, ‘Fy’, ‘Fz’, ‘Ex’, ‘Ex’, ‘Ey’, ‘Ez’}

Specifies the grid on which points are being moved to.

discardbool, optional

Whether to discard the intenally created scipy.spatial.KDTree.

Returns:

(n ) numpy.ndarray of int

Vector of length n containing the indicies for the closest respective cell center, node, face or edge.

Examples

Here we define a set of random (x, y) locations and find the closest cell centers and nodes on a mesh.

>>> from discretize import TensorMesh
>>> from discretize.utils import closest_points_index
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> h = 2*np.ones(5)
>>> mesh = TensorMesh([h, h], x0='00')

Define some random locations, grid cell centers and grid nodes,

>>> xy_random = np.random.uniform(0, 10, size=(4,2))
>>> xy_centers = mesh.cell_centers
>>> xy_nodes = mesh.nodes

Find indicies of closest cell centers and nodes,

>>> ind_centers = mesh.closest_points_index(xy_random, 'cell_centers')
>>> ind_nodes = mesh.closest_points_index(xy_random, 'nodes')

Plot closest cell centers and nodes

>>> fig = plt.figure(figsize=(5, 5))
>>> ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
>>> mesh.plot_grid(ax=ax)
>>> ax.scatter(xy_random[:, 0], xy_random[:, 1], 50, 'k')
>>> ax.scatter(xy_centers[ind_centers, 0], xy_centers[ind_centers, 1], 50, 'r')
>>> ax.scatter(xy_nodes[ind_nodes, 0], xy_nodes[ind_nodes, 1], 50, 'b')
>>> plt.show()

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