Esempio n. 1
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def test_cressman_point(test_data):
    r"""Test Cressman 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([30, 30], r=r)

    dists = dist_2(30, 30, xp[indices], yp[indices])
    values = z[indices]

    truth = 1.05499444404

    value = cressman_point(dists, values, r)

    assert_almost_equal(truth, value)
Esempio n. 2
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def test_cressman_point(test_data):
    r"""Test Cressman 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([30, 30], r=r)

    dists = dist_2(30, 30, xp[indices], yp[indices])
    values = z[indices]

    truth = 1.05499444404

    value = cressman_point(dists, values, r)

    assert_almost_equal(truth, value)
Esempio n. 3
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#
# The variable ``indices`` represents the index of each matched coordinate within the
# cKDTree's ``data`` list.
grid_points = np.array(list(zip(sim_gridx, sim_gridy)))

radius = 40
obs_tree = cKDTree(list(zip(xp, yp)))
indices = obs_tree.query_ball_point(grid_points, r=radius)

###########################################
# For grid 0, we will use Cressman to interpolate its value.
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]]

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.
#
# The variable ``indices`` represents the index of each matched coordinate within the
# cKDTree's ``data`` list.
grid_points = np.array(list(zip(sim_gridx, sim_gridy)))

radius = 40
obs_tree = cKDTree(list(zip(xp, yp)))
indices = obs_tree.query_ball_point(grid_points, r=radius)

###########################################
# For grid 0, we will use Cressman to interpolate its value.
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)

###########################################