def test_dist_2():
r"""Test squared distance function."""
x0 = 0
y0 = 0
x1 = 10
y1 = 10
truth = 200
dist2 = dist_2(x0, y0, x1, y1)
assert_almost_equal(truth, dist2)
def test_dist_2():
r"""Test squared distance function."""
x0 = 0
y0 = 0
x1 = 10
y1 = 10
truth = 200
dist2 = dist_2(x0, y0, x1, y1)
assert_almost_equal(truth, dist2)
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_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)
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)
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)
########################################### # Set up a cKDTree object and query all the observations within "radius" of each grid point. # # 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)
########################################### # Set up a cKDTree object and query all of the observations within "radius" of each grid point. # # 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)
