def test_aggregation_two_dims_using_moments_kernel(self):
self.kernel = moments()
data1 = make_regular_2d_ungridded_data_with_missing_values()
data2 = make_regular_2d_ungridded_data_with_missing_values()
data2.metadata._name = 'snow'
data2._data += 10
data = UngriddedDataList([data1, data2])
grid = {'y': slice(-12.5, 12.5, 15), 'x': slice(-7.5, 7.5, 10)}
output = data.aggregate(how=self.kernel, **grid)
expect_mean = numpy.array([[4.4, 4.5], [35.0 / 3, 13.5]])
expect_stddev = numpy.array([[numpy.sqrt(9.3), numpy.sqrt(4.5)],
[numpy.sqrt(13.0 / 3), numpy.sqrt(4.5)]])
expect_count = numpy.array([[5, 2], [3, 2]])
assert isinstance(output, GriddedDataList)
assert len(output) == 6
mean_1, stddev_1, count_1, mean_2, stddev_2, count_2 = output
assert mean_1.var_name == 'rain'
assert stddev_1.var_name == 'rain_std_dev'
assert count_1.var_name == 'rain_num_points'
assert mean_2.var_name == 'snow'
assert stddev_2.var_name == 'snow_std_dev'
assert count_2.var_name == 'snow_num_points'
assert_arrays_almost_equal(mean_1.data, expect_mean)
assert_arrays_almost_equal(stddev_1.data, expect_stddev)
assert_arrays_almost_equal(count_1.data, expect_count)
assert_arrays_almost_equal(mean_2.data, expect_mean + 10)
assert_arrays_almost_equal(stddev_2.data, expect_stddev)
assert_arrays_almost_equal(count_2.data, expect_count)
def test_aggregating_list_of_datasets_over_two_dims_with_diff_masks(self):
grid = {'x': slice(-7.5, 7.5, 5), 'y': slice(-12.5, 12.5, 5)}
var_0 = make_regular_2d_ungridded_data_with_missing_values()
var_1 = make_regular_2d_ungridded_data_with_missing_values()
var_1.data.mask = 1
datalist = UngriddedDataList([var_0, var_1])
cube_out = datalist.aggregate(how=self.kernel, **grid)
result_0 = numpy.ma.array(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
[10.0, 11.0, 12.0], [13.0, 14.0, 15.0]],
mask=[[0, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 0], [1, 0, 0]],
fill_value=float('nan'))
result_1 = numpy.ma.array(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
[10.0, 11.0, 12.0], [13.0, 14.0, 15.0]],
mask=[[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]],
fill_value=float('nan'))
print(cube_out[0].data.fill_value)
assert len(cube_out) == 2
compare_masked_arrays(cube_out[0].data, result_0)
compare_masked_arrays(cube_out[1].data, result_1)
def test_aggregation_two_dims_using_moments_kernel(self):
self.kernel = moments()
data1 = make_regular_2d_ungridded_data_with_missing_values()
data2 = make_regular_2d_ungridded_data_with_missing_values()
data2.metadata._name = 'snow'
data2._data += 10
data = UngriddedDataList([data1, data2])
grid = {'y': slice(-12.5, 12.5, 15), 'x': slice(-7.5, 7.5, 10)}
output = data.aggregate(how=self.kernel, **grid)
expect_mean = numpy.array([[4.4, 4.5], [35.0 / 3, 13.5]])
expect_stddev = numpy.array([[numpy.sqrt(9.3),
numpy.sqrt(4.5)],
[numpy.sqrt(13.0 / 3),
numpy.sqrt(4.5)]])
expect_count = numpy.array([[5, 2], [3, 2]])
assert isinstance(output, GriddedDataList)
assert len(output) == 6
mean_1, stddev_1, count_1, mean_2, stddev_2, count_2 = output
assert mean_1.var_name == 'rain'
assert stddev_1.var_name == 'rain_std_dev'
assert count_1.var_name == 'rain_num_points'
assert mean_2.var_name == 'snow'
assert stddev_2.var_name == 'snow_std_dev'
assert count_2.var_name == 'snow_num_points'
assert_arrays_almost_equal(mean_1.data, expect_mean)
assert_arrays_almost_equal(stddev_1.data, expect_stddev)
assert_arrays_almost_equal(count_1.data, expect_count)
assert_arrays_almost_equal(mean_2.data, expect_mean + 10)
assert_arrays_almost_equal(stddev_2.data, expect_stddev)
assert_arrays_almost_equal(count_2.data, expect_count)
def test_aggregating_list_of_datasets_over_two_dims(self):
grid = {'x': slice(-7.5, 7.5, 5), 'y': slice(-12.5, 12.5, 5)}
datalist = UngriddedDataList([make_regular_2d_ungridded_data_with_missing_values(),
make_regular_2d_ungridded_data_with_missing_values()])
cube_out = datalist.aggregate(how=self.kernel, **grid)
result = numpy.ma.array([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0],
[10.0, 11.0, 12.0],
[13.0, 14.0, 15.0]],
mask=[[0, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0],
[1, 0, 0]], fill_value=float('nan'))
assert len(cube_out) == 2
compare_masked_arrays(cube_out[0].data, result)
compare_masked_arrays(cube_out[1].data, result)