def test_calc_kappa(test_coords):
    r"""Test calculate kappa parameter function."""
    x, y = test_coords

    spacing = np.mean((cdist(list(zip(x, y)), list(zip(x, y)))))

    value = calc_kappa(spacing)

    truth = 5762.6872048

    assert_almost_equal(truth, value, decimal=6)
示例#2
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def test_calc_kappa(test_coords):
    r"""Test calculate kappa parameter function."""
    x, y = test_coords

    spacing = np.mean((cdist(list(zip(x, y)),
                             list(zip(x, y)))))

    value = calc_kappa(spacing)

    truth = 5762.6872048

    assert_almost_equal(truth, value, decimal=6)
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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)
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x1, y1 = obs_tree.data[indices[0]].T
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]]

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')