def test_barnes_point(test_data):
r"""Test Barnes interpolation for a point function."""
xp, yp, z = test_data
r = 40
obs_tree = cKDTree(list(zip(xp, yp)))
indices = obs_tree.query_ball_point([60, 60], r=r)
dists = dist_2(60, 60, xp[indices], yp[indices])
values = z[indices]
assert_almost_equal(barnes_point(dists, values, 5762.7), 4.0871824)
def test_barnes_point(test_data):
r"""Test Barnes interpolation for a point function."""
xp, yp, z = test_data
r = 40
obs_tree = cKDTree(list(zip(xp, yp)))
indices = obs_tree.query_ball_point([60, 60], r=r)
dists = dist_2(60, 60, xp[indices], yp[indices])
values = z[indices]
truth = 4.08718241061
ave_spacing = np.mean((cdist(list(zip(xp, yp)), list(zip(xp, yp)))))
kappa = calc_kappa(ave_spacing)
value = barnes_point(dists, values, kappa)
assert_almost_equal(truth, value)
def test_barnes_point(test_data):
r"""Test Barnes interpolation for a point function."""
xp, yp, z = test_data
r = 40
obs_tree = cKDTree(list(zip(xp, yp)))
indices = obs_tree.query_ball_point([60, 60], r=r)
dists = dist_2(60, 60, xp[indices], yp[indices])
values = z[indices]
truth = 4.08718241061
ave_spacing = np.mean((cdist(list(zip(xp, yp)), list(zip(xp, yp)))))
kappa = calc_kappa(ave_spacing)
value = barnes_point(dists, values, kappa)
assert_almost_equal(truth, value)
cress_dist = dist_2(sim_gridx[0], sim_gridy[0], x1, y1)
cress_obs = zp[indices[0]]
cress_val = cressman_point(cress_dist, cress_obs, radius)
###########################################
# For grid 1, we will use barnes to interpolate its value.
#
# We need to calculate kappa--the average distance between observations over the domain.
x2, y2 = obs_tree.data[indices[1]].T
barnes_dist = dist_2(sim_gridx[1], sim_gridy[1], x2, y2)
barnes_obs = zp[indices[1]]
kappa = calc_kappa(average_spacing(list(zip(xp, yp))))
barnes_val = barnes_point(barnes_dist, barnes_obs, kappa)
###########################################
# Plot all of the affiliated information and interpolation values.
fig, ax = plt.subplots(1, 1, figsize=(15, 10))
for i, zval in enumerate(zp):
ax.plot(pts[i, 0], pts[i, 1], '.')
ax.annotate(str(zval) + ' F', xy=(pts[i, 0] + 2, pts[i, 1]))
ax.plot(sim_gridx, sim_gridy, '+', markersize=10)
ax.plot(x1, y1, 'ko', fillstyle='none', markersize=10, label='grid 0 matches')
ax.plot(x2, y2, 'ks', fillstyle='none', markersize=10, label='grid 1 matches')
draw_circle(ax,
sim_gridx[0],
cress_obs = zp[indices[0]]
cress_val = cressman_point(cress_dist, cress_obs, radius)
###########################################
# For grid 1, we will use barnes to interpolate its value.
#
# We need to calculate kappa--the average distance between observations over the domain.
x2, y2 = obs_tree.data[indices[1]].T
barnes_dist = dist_2(sim_gridx[1], sim_gridy[1], x2, y2)
barnes_obs = zp[indices[1]]
ave_spacing = np.mean((cdist(list(zip(xp, yp)), list(zip(xp, yp)))))
kappa = calc_kappa(ave_spacing)
barnes_val = barnes_point(barnes_dist, barnes_obs, kappa)
###########################################
# Plot all of the affiliated information and interpolation values.
fig, ax = plt.subplots(1, 1, figsize=(15, 10))
for i, zval in enumerate(zp):
ax.plot(pts[i, 0], pts[i, 1], '.')
ax.annotate(str(zval) + ' F', xy=(pts[i, 0] + 2, pts[i, 1]))
ax.plot(sim_gridx, sim_gridy, '+', markersize=10)
ax.plot(x1, y1, 'ko', fillstyle='none', markersize=10, label='grid 0 matches')
ax.plot(x2, y2, 'ks', fillstyle='none', markersize=10, label='grid 1 matches')
draw_circle(ax, sim_gridx[0], sim_gridy[0], m='k-', r=radius, label='grid 0 radius')
draw_circle(ax, sim_gridx[1], sim_gridy[1], m='b-', r=radius, label='grid 1 radius')