def test_dimension_mismatch(self):
gradient = 0
values = np.zeros([3, 2])
with self.assertRaises(Exception) as e:
output = gridpp.simple_gradient(grid, points, values, gradient)
with self.assertRaises(Exception) as e:
output = gridpp.simple_gradient(grid, grid, values, gradient)
def test_point_to_point_3d(self):
values = np.zeros([2, 3, 3])
values[:, 0, 0] = 4
values[:, 1, 1] = 3
gradient = 0
output = gridpp.simple_gradient(grid, points, values, gradient)
np.testing.assert_array_almost_equal(output, [[4, 3], [4, 3]])
gradient = 1
output = gridpp.simple_gradient(grid, points, values, gradient)
np.testing.assert_array_almost_equal(output, [[9, -2], [9, -2]])
def test_point_to_point(self):
values = np.zeros([3, 3])
values[0, 0] = 4
values[1, 1] = 3
# Point 0: Move up 5 meters
# Point 1: Move down 5 meters
gradient = 0
output = gridpp.simple_gradient(grid, points, values, gradient)
np.testing.assert_array_almost_equal(output, [4, 3])
gradient = 1
output = gridpp.simple_gradient(grid, points, values, gradient)
np.testing.assert_array_almost_equal(output, [9, -2])
def test_no_point_elev(self):
"""Check that output is missing when points do not have elevation defined"""
points0 = gridpp.Points([-1, 0.9], [-1, 0.9])
values = np.reshape(np.arange(9), [3, 3])
for gradient in [0, 1]:
output = gridpp.simple_gradient(grid, points0, values, gradient)
np.testing.assert_array_almost_equal(output, [np.nan, np.nan])
def test_no_grid_elev(self):
"""Check that output is missing when grid does not have elevation defined"""
grid0 = gridpp.Grid(lats, lons)
values = np.reshape(np.arange(9), [3, 3])
for gradient in [0, 1]:
output = gridpp.simple_gradient(grid0, points, values, gradient)
np.testing.assert_array_almost_equal(output, [np.nan, np.nan])
def test_grid_to_grid(self):
# Output grid has one row and column outside domain
values = np.zeros([3, 3])
values[0, 0] = 4
values[1, 1] = 3
lats0, lons0 = np.meshgrid([-0.1, 0.1, 1.1], [-0.1, 0.1, 1.1])
elevs0 = np.reshape(np.arange(9), [3, 3])
grid0 = gridpp.Grid(lats0, lons0, elevs0)
gradient = 0
output = gridpp.simple_gradient(grid, grid0, values, gradient)
np.testing.assert_array_almost_equal(output,
[[4, 4, 0], [4, 4, 0], [0, 0, 3]])
gradient = 1
output = gridpp.simple_gradient(grid, grid0, values, gradient)
# Elevation changes:
# -10 -> 0, -10 -> 1, 0 -> 2
# -10 -> 3, -10 -> 4, 0 -> 5
# 0 -> 6, 0 -> 7, 10 -> 8
np.testing.assert_array_almost_equal(
output, [[14, 15, 2], [17, 18, 5], [6, 7, 1]])
def test_3d(self):
values = np.zeros([2, 3, 3])
values[:, 0, 0] = 4
values[:, 1, 1] = 3
lats0, lons0 = np.meshgrid([-0.1, 0.1, 1.1], [-0.1, 0.1, 1.1])
elevs0 = np.reshape(np.arange(9), [3, 3])
grid0 = gridpp.Grid(lats0, lons0, elevs0)
gradient = 1
output = gridpp.simple_gradient(grid, grid0, values, gradient)
expected = [[14, 15, 2], [17, 18, 5], [6, 7, 1]]
expected = [expected, expected]
np.testing.assert_array_almost_equal(output, expected)
def test_missing_values(self):
values = np.zeros([3, 3])
values[1, 1] = np.nan
gradient = 1
output = gridpp.simple_gradient(grid, points, values, gradient)
np.testing.assert_array_almost_equal(output, [5, np.nan])
index_control = 0
blats_2500m = file.variables['latitude'][:]
blons_2500m = file.variables['longitude'][:]
belevs_2500m = file.variables['surface_geopotential'][0, 0, index_control, :, :] / 9.81
bgrid_2500m = gridpp.Grid(blats_2500m, blons_2500m, belevs_2500m)
background_2500m = file.variables['air_temperature_2m'][0, 0, index_control, :, :]
with netCDF4.Dataset('template.nc', 'r') as file:
blats = file.variables['latitude'][:]
blons = file.variables['longitude'][:]
belevs = file.variables['altitude'][:]
bgrid = gridpp.Grid(blats, blons, belevs)
# Downscaling
gradient = -0.0065
background = gridpp.simple_gradient(bgrid_2500m, bgrid, background_2500m, gradient)
points = gridpp.Points(obs_lats[index_valid_obs], obs_lons[index_valid_obs], obs_elevs[index_valid_obs])
pbackground = gridpp.bilinear(bgrid, points, background)
variance_ratios = np.full(points.size(), 0.1)
h = 100000
v = 200
structure = gridpp.BarnesStructure(h, v)
max_points = 50
analysis = gridpp.optimal_interpolation(bgrid, background, points,
obs[index_valid_obs], variance_ratios, pbackground, structure, max_points)
