def test_nn_point(test_data):
r"""Test find natural neighbors for a point interpolation function."""
xp, yp, z = test_data
tri = Delaunay(list(zip(xp, yp)))
sim_gridx = [30]
sim_gridy = [30]
members, tri_info = find_natural_neighbors(tri,
list(zip(sim_gridx, sim_gridy)))
val = nn_point(xp, yp, z, [sim_gridx[0], sim_gridy[0]],
tri, members[0], tri_info)
truth = 1.009
assert_almost_equal(truth, val, 3)
def test_nn_point(test_data):
r"""Test find natural neighbors for a point interpolation function."""
xp, yp, z = test_data
tri = Delaunay(list(zip(xp, yp)))
sim_gridx = [30]
sim_gridy = [30]
members, tri_info = find_natural_neighbors(tri,
list(zip(sim_gridx, sim_gridy)))
val = nn_point(xp, yp, z, [sim_gridx[0], sim_gridy[0]], tri, members[0],
tri_info)
truth = 1.009
assert_almost_equal(truth, val, 3)
delaunay_plot_2d(tri, ax=ax)
for i, zval in enumerate(zp):
ax.annotate('{} F'.format(zval), xy=(pts[i, 0] + 2, pts[i, 1]))
sim_gridx = [30., 60.]
sim_gridy = [30., 60.]
ax.plot(sim_gridx, sim_gridy, '+', markersize=10)
ax.set_aspect('equal', 'datalim')
ax.set_title('Triangulation of observations and test grid cell '
'natural neighbor interpolation values')
members, tri_info = triangles.find_natural_neighbors(tri, list(zip(sim_gridx, sim_gridy)))
val = nn_point(xp, yp, zp, (sim_gridx[0], sim_gridy[0]), tri, members[0], tri_info)
ax.annotate('grid 0: {:.3f}'.format(val), xy=(sim_gridx[0] + 2, sim_gridy[0]))
val = nn_point(xp, yp, zp, (sim_gridx[1], sim_gridy[1]), tri, members[1], tri_info)
ax.annotate('grid 1: {:.3f}'.format(val), xy=(sim_gridx[1] + 2, sim_gridy[1]))
###########################################
# Using the circumcenter and circumcircle radius information from
# :func:`metpy.gridding.triangles.find_natural_neighbors`, we can visually
# examine the results to see if they are correct.
def draw_circle(ax, x, y, r, m, label):
th = np.linspace(0, 2 * np.pi, 100)
nx = x + r * np.cos(th)
ny = y + r * np.sin(th)
ax.plot(nx, ny, m, label=label)
delaunay_plot_2d(tri)
for i, zval in enumerate(zp):
plt.annotate('{} F'.format(zval), xy=(pts[i, 0] + 2, pts[i, 1]))
sim_gridx = [30., 60.]
sim_gridy = [30., 60.]
plt.plot(sim_gridx, sim_gridy, '+', markersize=10)
plt.axes().set_aspect('equal', 'datalim')
plt.title('Triangulation of observations and test grid cell '
'natural neighbor interpolation values')
members, tri_info = triangles.find_natural_neighbors(tri, list(zip(sim_gridx, sim_gridy)))
val = nn_point(xp, yp, zp, (sim_gridx[0], sim_gridy[0]), tri, members[0], tri_info)
plt.annotate('grid 0: {:.3f}'.format(val), xy=(sim_gridx[0] + 2, sim_gridy[0]))
val = nn_point(xp, yp, zp, (sim_gridx[1], sim_gridy[1]), tri, members[1], tri_info)
plt.annotate('grid 1: {:.3f}'.format(val), xy=(sim_gridx[1] + 2, sim_gridy[1]))
###########################################
# Using the circumcenter and circumcircle radius information from
# :func:`metpy.gridding.triangles.find_natural_neighbors`, we can visually
# examine the results to see if they are correct.
def draw_circle(x, y, r, m, label):
nx = x + r * np.cos(np.deg2rad(list(range(360))))
ny = y + r * np.sin(np.deg2rad(list(range(360))))
plt.plot(nx, ny, m, label=label)