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# Copyright Cartopy Contributors
#
# This file is part of Cartopy and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
import cartopy.vector_transform as vec_trans
import cartopy.crs as ccrs
def _sample_plate_carree_coordinates():
x = np.array([-10, 0, 10, -9, 0, 9])
y = np.array([10, 10, 10, 5, 5, 5])
return x, y
def _sample_plate_carree_scalar_field():
return np.array([2, 4, 2, 1.2, 3, 1.2])
def _sample_plate_carree_vector_field():
u = np.array([2, 4, 2, 1.2, 3, 1.2])
v = np.array([5.5, 4, 5.5, 1.2, .3, 1.2])
return u, v
class Test_interpolate_to_grid:
@classmethod
def setup_class(cls):
cls.x, cls.y = _sample_plate_carree_coordinates()
cls.s = _sample_plate_carree_scalar_field()
def test_data_extent(self):
# Interpolation to a grid with extents of the input data.
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
x_grid, y_grid, s_grid = vec_trans._interpolate_to_grid(
5, 3, self.x, self.y, self.s)
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(s_grid, expected_s_grid)
def test_explicit_extent(self):
# Interpolation to a grid with explicit extents.
expected_x_grid = np.array([[-5., 0., 5., 10.],
[-5., 0., 5., 10.]])
expected_y_grid = np.array([[7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10]])
expected_s_grid = np.array([[2.5, 3.5, 2.5, np.nan],
[3., 4., 3., 2.]])
extent = (-5, 10, 7.5, 10)
x_grid, y_grid, s_grid = vec_trans._interpolate_to_grid(
4, 2, self.x, self.y, self.s, target_extent=extent)
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(s_grid, expected_s_grid)
def test_multiple_fields(self):
# Interpolation of multiple fields in one go.
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
x_grid, y_grid, s_grid1, s_grid2, s_grid3 = \
vec_trans._interpolate_to_grid(5, 3, self.x, self.y,
self.s, self.s, self.s)
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(s_grid1, expected_s_grid)
assert_array_almost_equal(s_grid2, expected_s_grid)
assert_array_almost_equal(s_grid3, expected_s_grid)
class Test_vector_scalar_to_grid:
@classmethod
def setup_class(cls):
cls.x, cls.y = _sample_plate_carree_coordinates()
cls.u, cls.v = _sample_plate_carree_vector_field()
cls.s = _sample_plate_carree_scalar_field()
def test_no_transform(self):
# Transform and regrid vector (with no projection transform).
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan],
[np.nan, 2.675, 2.15, 2.675, np.nan],
[5.5, 4.75, 4., 4.75, 5.5]])
src_crs = target_crs = ccrs.PlateCarree()
x_grid, y_grid, u_grid, v_grid = vec_trans.vector_scalar_to_grid(
src_crs, target_crs, (5, 3), self.x, self.y, self.u, self.v)
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(u_grid, expected_u_grid)
assert_array_almost_equal(v_grid, expected_v_grid)
def test_with_transform(self):
# Transform and regrid vector.
target_crs = ccrs.PlateCarree()
src_crs = ccrs.NorthPolarStereo()
input_coords = [src_crs.transform_point(xp, yp, target_crs)
for xp, yp in zip(self.x, self.y)]
x_nps = np.array([ic[0] for ic in input_coords])
y_nps = np.array([ic[1] for ic in input_coords])
u_nps, v_nps = src_crs.transform_vectors(target_crs, self.x, self.y,
self.u, self.v)
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, 2.3838, 3.5025, 2.6152, np.nan],
[2, 3.0043, 4, 2.9022, 2]])
expected_v_grid = np.array([[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, 2.6527, 2.1904, 2.4192, np.nan],
[5.5, 4.6483, 4, 4.47, 5.5]])
x_grid, y_grid, u_grid, v_grid = vec_trans.vector_scalar_to_grid(
src_crs, target_crs, (5, 3), x_nps, y_nps, u_nps, v_nps)
assert_array_almost_equal(x_grid, expected_x_grid)
assert_array_almost_equal(y_grid, expected_y_grid)
# Vector transforms are somewhat approximate, so we are more lenient
# with the returned values since we have transformed twice.
assert_array_almost_equal(u_grid, expected_u_grid, decimal=4)
assert_array_almost_equal(v_grid, expected_v_grid, decimal=4)
def test_with_scalar_field(self):
# Transform and regrid vector (with no projection transform) with an
# additional scalar field.
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan],
[np.nan, 2.675, 2.15, 2.675, np.nan],
[5.5, 4.75, 4., 4.75, 5.5]])
expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
src_crs = target_crs = ccrs.PlateCarree()
x_grid, y_grid, u_grid, v_grid, s_grid = \
vec_trans.vector_scalar_to_grid(src_crs, target_crs, (5, 3),
self.x, self.y,
self.u, self.v, self.s)
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(u_grid, expected_u_grid)
assert_array_almost_equal(v_grid, expected_v_grid)
assert_array_almost_equal(s_grid, expected_s_grid)
def test_with_scalar_field_non_ndarray_data(self):
# Transform and regrid vector (with no projection transform) with an
# additional scalar field wich is not a ndarray.
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan],
[np.nan, 2.675, 2.15, 2.675, np.nan],
[5.5, 4.75, 4., 4.75, 5.5]])
expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
src_crs = target_crs = ccrs.PlateCarree()
x_grid, y_grid, u_grid, v_grid, s_grid = \
vec_trans.vector_scalar_to_grid(src_crs, target_crs, (5, 3),
list(self.x), list(self.y),
list(self.u), list(self.v),
list(self.s))
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(u_grid, expected_u_grid)
assert_array_almost_equal(v_grid, expected_v_grid)
assert_array_almost_equal(s_grid, expected_s_grid)
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