def single_point_results_in_single_value_in_cell_with_pressure_with_cube_with_pressure(con, kernel):
sample_cube = make_square_5x3_2d_cube_with_pressure()
data_point = make_dummy_ungridded_data_single_point(0.5, 0.5, 1.2, pressure=1.0)
col = GeneralGriddedCollocator(fill_value=-999.9)
out_cube = col.collocate(points=sample_cube, data=data_point, constraint=con, kernel=kernel)[0]
expected_result = numpy.array([[[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9]],
[[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9]],
[[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, 1.2, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9]],
[[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9]],
[[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9, -999.9, -999.9, -999.9, -999.9]]])
print(out_cube.data.filled())
assert_arrays_equal(out_cube.data.filled(), expected_result)
def single_masked_point_results_in_single_value_in_cell_using_kernel_and_con(con, kernel):
sample_cube = make_mock_cube()
data_point = make_dummy_ungridded_data_single_point(0.5, 0.5, 1.2, mask=True)
col = GeneralGriddedCollocator(fill_value=-999.9)
out_cube = col.collocate(points=sample_cube, data=data_point, constraint=con, kernel=kernel)[0]
expected_result = numpy.array([[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9]])
assert_arrays_equal(out_cube.data.filled(), expected_result)
def single_point_on_grid_corner_is_counted_once(con, kernel):
sample_cube = make_mock_cube()
data_point = make_dummy_ungridded_data_single_point(10, 5, 1.2)
col = GeneralGriddedCollocator(fill_value=-999.9)
out_cube = col.collocate(points=sample_cube, data=data_point, constraint=con, kernel=kernel)[0]
expected_result = numpy.array([[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, 1.2]])
assert_arrays_equal(out_cube.data.filled(), expected_result)
def single_point_outside_grid_and_one_inside_excludes_outside_using_binned_only(con, kernel):
sample_cube = make_mock_cube()
data_point = make_dummy_ungridded_data_single_point([0.5, 99], [0.5, 99], [1.2, 5])
col = GeneralGriddedCollocator(fill_value=-999.9)
out_cube = col.collocate(points=sample_cube, data=data_point, constraint=con, kernel=kernel)[0]
expected_result = numpy.array([[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, 1.2, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9]])
assert_arrays_equal(out_cube.data.filled(), expected_result)
def multiple_points_inside_grid_and_outside(con, kernel):
sample_cube = make_mock_cube()
data_point = make_dummy_ungridded_data_single_point([0.5, 99, 0.6, 3.0, -9], [0.5, 99, 0.6, 0.5, -3],
[1.2, 5, 3.4, 5, 8])
col = GeneralGriddedCollocator(fill_value=-999.9)
out_cube = col.collocate(points=sample_cube, data=data_point, constraint=con, kernel=kernel)[0]
expected_result = numpy.array([[8, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, 2.3, -999.9],
[-999.9, 5, -999.9],
[-999.9, -999.9, -999.9]])
assert_arrays_equal(out_cube.data.filled(), expected_result)
def test_longitude_0_360_one_degree(self):
x = np.arange(0.5, 360.5, 1)
y = np.array([50.5, 51.5])
values = np.arange(len(y) * len(x)).reshape((len(y), len(x)))
latitude = DimCoord(y, standard_name='latitude', units='degrees')
longitude = DimCoord(x, standard_name='longitude', units='degrees')
data = make_from_cube(Cube(values, dim_coords_and_dims=[(latitude, 0), (longitude, 1)]))
out_values, out_x, out_y= Generic2DPlot._cube_manipulation(data, longitude.contiguous_bounds(),
latitude.contiguous_bounds())
x_bounds = np.arange(0, 361, 1)
y_bounds = np.array([50, 51, 52])
assert_arrays_equal(out_x, x_bounds)
assert_arrays_equal(out_y, y_bounds)
def test_longitude_0_360_one_degree(self):
x = np.arange(0.5, 360.5, 1)
y = np.array([50.5, 51.5])
values = np.arange(len(y) * len(x)).reshape((len(y), len(x)))
latitude = DimCoord(y, standard_name='latitude', units='degrees')
longitude = DimCoord(x, standard_name='longitude', units='degrees')
data = make_from_cube(
Cube(values, dim_coords_and_dims=[(latitude, 0), (longitude, 1)]))
out_values, out_x, out_y = Generic2DPlot._cube_manipulation(
data, longitude.contiguous_bounds(), latitude.contiguous_bounds())
x_bounds = np.arange(0, 361, 1)
y_bounds = np.array([50, 51, 52])
assert_arrays_equal(out_x, x_bounds)
assert_arrays_equal(out_y, y_bounds)
def can_collocate_list_of_data(constraint, kernel):
col = GeneralGriddedCollocator()
sample = make_square_5x3_2d_cube()
data1 = make_regular_2d_ungridded_data(10, -10, 10, 6, -5, 5, 0)
data2 = make_regular_2d_ungridded_data(10, -10, 10, 6, -5, 5, 10)
output = col.collocate(sample, UngriddedDataList([data1, data2]), constraint, kernel)
assert len(output) == 2
expected_data2 = numpy.array([[14.5, 16.5, 18.5],
[26.5, 28.5, 30.5],
[38.5, 40.5, 42.5],
[50.5, 52.5, 54.5],
[62.5, 64.5, 66.5]])
expected_data1 = expected_data2 - 10
assert_arrays_equal(output[0].data, expected_data1)
assert_arrays_equal(output[1].data, expected_data2)
def single_point_results_in_single_value_in_masked_cell_using_kernel_and_con_missing_for_masked_false(con, kernel):
mask = [[False, False, False],
[False, False, False],
[False, True, False],
[False, False, False],
[False, False, False]]
sample_cube = make_mock_cube(mask=mask)
data_point = make_dummy_ungridded_data_single_point(0.5, 0.5, 1.2)
col = GeneralGriddedCollocator(fill_value=-999.9, missing_data_for_missing_sample=False)
out_cube = col.collocate(points=sample_cube, data=data_point, constraint=con, kernel=kernel)[0]
expected_result = numpy.array([[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, 1.2, -999.9],
[-999.9, -999.9, -999.9],
[-999.9, -999.9, -999.9]])
assert_arrays_equal(out_cube.data.filled(), expected_result)
def test_longitude_0_360(self):
x = np.arange(10, 370, 20)
y = np.array([50.5, 51.5])
values = np.arange(len(y) * len(x)).reshape((len(y), len(x)))
latitude = DimCoord(y, standard_name='latitude', units='degrees')
longitude = DimCoord(x, standard_name='longitude', units='degrees')
data = make_from_cube(Cube(values, dim_coords_and_dims=[(latitude, 0), (longitude, 1)]))
out_x, out_y, out_values = make_color_mesh_cells(data, self.plot_args)
x_bounds = np.arange(0, 380, 20)
y_bounds = np.array([50, 51, 52])
expected_x, expected_y = np.meshgrid(x_bounds, y_bounds)
assert_arrays_equal(out_x, expected_x)
assert_arrays_equal(out_y, expected_y)
# Test that a plot doesn't fail.
map = Heatmap([data], self.plot_args)
map.plot()
def single_moments(constraint, kernel):
col = GeneralGriddedCollocator()
sample = make_square_5x3_2d_cube()
data = make_regular_2d_ungridded_data(10, -9.9, 9.9, 6, -4.9, 4.9, 10)
output = col.collocate(sample, data, constraint, kernel)
expected_data = numpy.array([[14.5, 16.5, 18.5],
[26.5, 28.5, 30.5],
[38.5, 40.5, 42.5],
[50.5, 52.5, 54.5],
[62.5, 64.5, 66.5]])
expected_stddev = numpy.ones((5, 3)) * 3.5118845842842465
expected_num = numpy.ones((5, 3)) * 4
assert len(output) == 3
assert isinstance(output, GriddedDataList)
assert output[0].var_name == 'rain'
assert output[1].var_name == 'rain_std_dev'
assert output[2].var_name == 'rain_num_points'
assert_arrays_equal(output[0].data, expected_data)
assert numpy.allclose(output[1].data, expected_stddev)
assert numpy.array_equal(output[2].data, expected_num)
def test_lat_lon_decreasing_no_bounds(self):
x = np.array([0.5, -0.5])
y = np.array([51.5, 50.5])
values = np.array([[1, 2], [3, 4]])
latitude = DimCoord(y, standard_name='latitude', units='degrees')
longitude = DimCoord(x, standard_name='longitude', units='degrees')
data = make_from_cube(Cube(values, dim_coords_and_dims=[(latitude, 0), (longitude, 1)]))
out_values, out_x, out_y= Generic2DPlot._cube_manipulation(data, longitude.contiguous_bounds(),
latitude.contiguous_bounds())
expected_x = np.array([1, 0, -1])
expected_y = np.array([52, 51, 50])
expected_v = np.array([[1, 2],
[3, 4]])
assert_arrays_equal(out_x, expected_x)
assert_arrays_equal(out_y, expected_y)
assert_arrays_equal(out_values, expected_v)
def test_lat_lon_increasing_no_bounds(self):
x = np.array([0.5, 1.5])
y = np.array([50.5, 51.5])
values = np.array([[1, 2], [3, 4]])
latitude = DimCoord(y, standard_name='latitude', units='degrees')
longitude = DimCoord(x, standard_name='longitude', units='degrees')
data = make_from_cube(
Cube(values, dim_coords_and_dims=[(latitude, 0), (longitude, 1)]))
out_values, out_x, out_y = Generic2DPlot._cube_manipulation(
data, longitude.contiguous_bounds(), latitude.contiguous_bounds())
expected_x = np.array([0, 1, 2])
expected_y = np.array([50, 51, 52])
expected_v = np.array([[1, 2], [3, 4]])
assert_arrays_equal(out_x, expected_x)
assert_arrays_equal(out_y, expected_y)
assert_arrays_equal(out_values, expected_v)
def test_lat_lon_decreasing_no_bounds(self):
x = np.array([0.5, -0.5])
y = np.array([51.5, 50.5])
values = np.array([[1, 2], [3, 4]])
latitude = DimCoord(y, standard_name='latitude', units='degrees')
longitude = DimCoord(x, standard_name='longitude', units='degrees')
data = make_from_cube(Cube(values, dim_coords_and_dims=[(latitude, 0), (longitude, 1)]))
out_x, out_y, out_values = make_color_mesh_cells(data, self.plot_args)
expected_x = np.array([[1, 0, -1],
[1, 0, -1],
[1, 0, -1]])
expected_y = np.array([[52, 52, 52],
[51, 51, 51],
[50, 50, 50]])
expected_v = np.array([[1, 2],
[3, 4]])
assert_arrays_equal(out_x, expected_x)
assert_arrays_equal(out_y, expected_y)
assert_arrays_equal(out_values, expected_v)
# Test that a plot doesn't fail.
map = Heatmap([data], self.plot_args)
map.plot()
