def test_complete_collapse_two_dims_using_moments_kernel(self):
self.kernel = aggregation_kernels['moments']
data1 = make_from_cube(make_5x3_lon_lat_2d_cube_with_missing_data())
data1.var_name = 'var1'
data2 = make_from_cube(make_5x3_lon_lat_2d_cube_with_missing_data())
data2.var_name = 'var2'
data2.data += 10
data = GriddedDataList([data1, data2])
grid = {'x': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), False),
'y': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), False)}
agg = Aggregator(data, grid)
output = agg.aggregate_gridded(self.kernel)
expect_mean = numpy.array(7.75)
expect_stddev = numpy.array(numpy.sqrt(244.25 / 11))
expect_count = numpy.array(12)
assert isinstance(output, GriddedDataList)
assert len(output) == 6
mean_1, mean_2, stddev_1, stddev_2, count_1, count_2 = output
assert mean_1.var_name == 'var1'
assert stddev_1.var_name == 'var1_std_dev'
assert count_1.var_name == 'var1_num_points'
assert mean_2.var_name == 'var2'
assert stddev_2.var_name == 'var2_std_dev'
assert count_2.var_name == 'var2_num_points'
# Latitude area weighting means these aren't quite right so increase the rtol.
assert numpy.allclose(mean_1.data, expect_mean, 1e-3)
assert numpy.allclose(mean_2.data, expect_mean + 10, 1e-3)
assert numpy.allclose(stddev_1.data, expect_stddev)
assert numpy.allclose(stddev_2.data, expect_stddev)
assert numpy.allclose(count_1.data, expect_count)
assert numpy.allclose(count_2.data, expect_count)
def test_aggregating_to_length_one_with_explicit_bounds_get_correct_bounds(self):
data = make_regular_2d_ungridded_data()
grid = {'x': AggregationGrid(-180, 180, 360, False), 'y': AggregationGrid(-90, 90, 10, False),}
agg = Aggregator(data, grid)
output = agg.aggregate_ungridded(self.kernel)
lon = output.coord('longitude')
assert_arrays_equal(lon.bounds, [[-180, 180]])
def test_partial_aggregation_over_more_than_one_dim_on_multidimensional_coord(self):
from cis.data_io.gridded_data import GriddedDataList, make_from_cube
data1 = make_from_cube(make_mock_cube(time_dim_length=7, hybrid_pr_len=5))
data2 = make_from_cube(make_mock_cube(time_dim_length=7, hybrid_pr_len=5, data_offset=1))
datalist = GriddedDataList([data1, data2])
grid = {'t': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), True),
'x': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), False)}
agg = Aggregator(datalist, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result_data = numpy.array([[51.0, 52.0, 53.0, 54.0, 55.0],
[156.0, 157.0, 158.0, 159.0, 160.0],
[261.0, 262.0, 263.0, 264.0, 265.0],
[366.0, 367.0, 368.0, 369.0, 370.0],
[471.0, 472.0, 473.0, 474.0, 475.0]], dtype=np.float)
multidim_coord_points = numpy.array([1000000., 3100000., 5200000., 7300000., 9400000.], dtype=np.float)
assert_arrays_almost_equal(cube_out[0].data, result_data)
assert_arrays_almost_equal(cube_out[1].data, result_data+1)
assert_arrays_almost_equal(cube_out[0].coord('surface_air_pressure').points, multidim_coord_points)
assert_arrays_almost_equal(cube_out[1].coord('surface_air_pressure').points, multidim_coord_points)
def test_GIVEN_single_variable_WHEN_aggregate_THEN_DataWriter_called_correctly(self):
variables = 'var_name'
filenames = 'filename'
output_file = 'output.hdf'
kernel = 'mean'
grid = None
input_data = GriddedDataList([make_from_cube(make_square_5x3_2d_cube())])
output_data = make_from_cube(make_square_5x3_2d_cube() + 1)
mock_data_reader = DataReader()
mock_data_reader.read_data_list = MagicMock(return_value=input_data)
mock_data_writer = DataWriter()
mock_data_writer.write_data = Mock()
mock_aggregator = Aggregator(None, None)
mock_aggregator.aggregate_gridded = MagicMock(return_value=output_data) # Return the modified data array
aggregate = Aggregate(grid, output_file, data_reader=mock_data_reader, data_writer=mock_data_writer)
aggregate._create_aggregator = MagicMock(return_value=mock_aggregator)
aggregate.aggregate(variables, filenames, None, kernel)
assert_that(mock_data_writer.write_data.call_count, is_(1))
written_data = mock_data_writer.write_data.call_args[0][0]
written_filename = mock_data_writer.write_data.call_args[0][1]
assert_that(written_data.data.tolist(), is_([[2, 3, 4], [5, 6, 7], [8, 9, 10], [11, 12, 13], [14, 15, 16]]))
assert_that(written_filename, is_(output_file))
def test_collapsed_coords_get_output_as_length_1(self):
data = make_regular_2d_ungridded_data()
grid = {'x': AggregationGrid(0, 360, 10, False)}
agg = Aggregator(data, grid)
output = agg.aggregate_ungridded(self.kernel)
lat = output.coord('latitude')
assert_that(lat.points, is_([0]))
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': AggregationGrid(-12.5, 12.5, 15, False), 'x': AggregationGrid(-7.5, 7.5, 10, False)}
agg = Aggregator(data, grid)
output = agg.aggregate_ungridded(self.kernel)
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': AggregationGrid(-7.5, 7.5, 5, False), 'y': AggregationGrid(-12.5, 12.5, 5, False)}
datalist = UngriddedDataList([make_regular_2d_ungridded_data_with_missing_values(),
make_regular_2d_ungridded_data_with_missing_values()])
agg = Aggregator(datalist, grid)
cube_out = agg.aggregate_ungridded(self.kernel)
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('inf'))
print cube_out[0].data.fill_value
assert len(cube_out) == 2
assert numpy.array_equal(numpy.ma.filled(cube_out[0].data), numpy.ma.filled(result))
assert numpy.array_equal(numpy.ma.filled(cube_out[1].data), numpy.ma.filled(result))
def test_collapsed_coords_get_max_min_bounds(self):
data = make_regular_2d_ungridded_data()
grid = {'y': AggregationGrid(-90, 90, 10, False)}
agg = Aggregator(data, grid)
output = agg.aggregate_ungridded(self.kernel)
lon = output.coord('longitude')
assert_arrays_equal(lon.bounds, [[-5, 5]])
def test_calc_new_dims(self):
# Cube dims [0, 1, 2, 3]
# Coord dims [0, 1, 2]
# Dims to collapse ^
# Untouched dims [0, 2]
# New dims [0, 1]
# Local dims [1]
# Mirrors: test_partial_aggregation_over_multidimensional_coord_along_middle_of_cube
eq_(Aggregator._calc_new_dims([0, 1, 2], {1}), [0, 1])
# Cube dims [0, 1, 2, 3]
# Coord dims [0, 1, 2]
# Dims to collapse ^
# Untouched dims [0, 1]
# New dims [0, 1]
# Local dims [2]
# Mirrors: test_partial_aggregation_over_multidimensional_coord
eq_(Aggregator._calc_new_dims([0, 1, 2], {2}), [0, 1])
# Cube dims [0, 1, 2, 3]
# Coord dims [0, ., 1, 2]
# Coord dims [0, 2, 3]
# Dims to collapse ^
# Untouched dims [1, 2]
# New dims [1, 2]
# Local dims [0]
# Mirrors: test_netCDF_gridded_hybrid_height_partial
eq_(Aggregator._calc_new_dims([0, 2, 3], {0}), [1, 2])
# Test collapsing multiple dimensions
eq_(Aggregator._calc_new_dims([0, 1, 2], {0, 1}), [0])
# Test collapsing multiple dimensions, some of which aren't on the coordinate
eq_(Aggregator._calc_new_dims([0, 1, 2], {2, 3}), [0, 1])
def test_aggregating_coord_to_length_one_with_explicit_bounds_gets_output_as_length_one(self):
data = make_regular_2d_ungridded_data()
grid = {'x': AggregationGrid(-180, 180, 360, False), 'y': AggregationGrid(-90, 90, 10, False),}
agg = Aggregator(data, grid)
output = agg.aggregate_ungridded(self.kernel)
lon = output.coord('longitude')
assert_that(lon.points, is_([0]))
def test_complete_collapse_one_dim_using_moments_kernel(self):
self.kernel = aggregation_kernels['moments']
data1 = make_from_cube(make_5x3_lon_lat_2d_cube_with_missing_data())
data1.var_name = 'var1'
data2 = make_from_cube(make_5x3_lon_lat_2d_cube_with_missing_data())
data2.var_name = 'var2'
data2.data += 10
data = GriddedDataList([data1, data2])
grid = {'x': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), False)}
agg = Aggregator(data, grid)
output = agg.aggregate_gridded(self.kernel)
expect_mean = numpy.array([[5.5, 8.75, 9]])
expect_stddev = numpy.array([numpy.sqrt(15), numpy.sqrt(26.25), numpy.sqrt(30)])
expect_count = numpy.array([[4, 4, 4]])
assert isinstance(output, GriddedDataList)
assert len(output) == 6
mean_1, mean_2, stddev_1, stddev_2, count_1, count_2 = output
assert mean_1.var_name == 'var1'
assert stddev_1.var_name == 'var1_std_dev'
assert count_1.var_name == 'var1_num_points'
assert mean_2.var_name == 'var2'
assert stddev_2.var_name == 'var2_std_dev'
assert count_2.var_name == 'var2_num_points'
assert_arrays_almost_equal(mean_1.data, expect_mean)
assert_arrays_almost_equal(mean_2.data, expect_mean + 10)
assert_arrays_almost_equal(stddev_1.data, expect_stddev)
assert_arrays_almost_equal(stddev_2.data, expect_stddev)
assert_arrays_almost_equal(count_1.data, expect_count)
assert_arrays_almost_equal(count_2.data, expect_count)
def test_partial_aggregation_over_multidimensional_coord_along_middle_of_cube(self):
from cis.data_io.gridded_data import GriddedData
# JASCIS-126
self.cube = make_mock_cube(time_dim_length=7, hybrid_pr_len=5)
grid = {'x': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), False)}
agg = Aggregator(GriddedData.make_from_cube(self.cube), grid)
cube_out = agg.aggregate_gridded(self.kernel)
result_data = numpy.array([[[36.0, 37.0, 38.0, 39.0, 40.0],
[41.0, 42.0, 43.0, 44.0, 45.0],
[46.0, 47.0, 48.0, 49.0, 50.0],
[51.0, 52.0, 53.0, 54.0, 55.0],
[56.0, 57.0, 58.0, 59.0, 60.0],
[61.0, 62.0, 63.0, 64.0, 65.0],
[66.0, 67.0, 68.0, 69.0, 70.0]],
[[141.0, 142.0, 143.0, 144.0, 145.0],
[146.0, 147.0, 148.0, 149.0, 150.0],
[151.0, 152.0, 153.0, 154.0, 155.0],
[156.0, 157.0, 158.0, 159.0, 160.0],
[161.0, 162.0, 163.0, 164.0, 165.0],
[166.0, 167.0, 168.0, 169.0, 170.0],
[171.0, 172.0, 173.0, 174.0, 175.0]],
[[246.0, 247.0, 248.0, 249.0, 250.0],
[251.0, 252.0, 253.0, 254.0, 255.0],
[256.0, 257.0, 258.0, 259.0, 260.0],
[261.0, 262.0, 263.0, 264.0, 265.0],
[266.0, 267.0, 268.0, 269.0, 270.0],
[271.0, 272.0, 273.0, 274.0, 275.0],
[276.0, 277.0, 278.0, 279.0, 280.0]],
[[351.0, 352.0, 353.0, 354.0, 355.0],
[356.0, 357.0, 358.0, 359.0, 360.0],
[361.0, 362.0, 363.0, 364.0, 365.0],
[366.0, 367.0, 368.0, 369.0, 370.0],
[371.0, 372.0, 373.0, 374.0, 375.0],
[376.0, 377.0, 378.0, 379.0, 380.0],
[381.0, 382.0, 383.0, 384.0, 385.0]],
[[456.0, 457.0, 458.0, 459.0, 460.0],
[461.0, 462.0, 463.0, 464.0, 465.0],
[466.0, 467.0, 468.0, 469.0, 470.0],
[471.0, 472.0, 473.0, 474.0, 475.0],
[476.0, 477.0, 478.0, 479.0, 480.0],
[481.0, 482.0, 483.0, 484.0, 485.0],
[486.0, 487.0, 488.0, 489.0, 490.0]]], dtype=np.float)
multidim_coord_points = numpy.array([[700000., 800000., 900000., 1000000., 1100000., 1200000., 1300000.],
[2800000., 2900000., 3000000., 3100000., 3200000., 3300000., 3400000.],
[4900000., 5000000., 5100000., 5200000., 5300000., 5400000., 5500000.],
[7000000., 7100000., 7200000., 7300000., 7400000., 7500000., 7600000.],
[9100000., 9200000., 9300000., 9400000., 9500000., 9600000., 9700000.]],
dtype=np.float)
assert_arrays_almost_equal(cube_out[0].data, result_data)
assert_arrays_almost_equal(cube_out[0].coord('surface_air_pressure').points, multidim_coord_points)
def test_GIVEN_grid_contains_single_points_WHEN_collapse_THEN_stddev_undefined(self):
grid = {'y': AggregationGrid(-10, 10, float('Nan'), False)}
cube = mock.make_mock_cube(2, 2)
cube.data = numpy.ma.masked_invalid([[float('Nan'), 1], [float('Nan'), float('Nan')]])
kernel = aggregation_kernels['moments']
agg = Aggregator(cube, grid)
result = agg.aggregate_gridded(kernel)
assert_that(result[1].data.mask.all())
def test_collapsing_everything_returns_a_single_value(self):
grid = {'x': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False),
'y': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result = numpy.array(8.0)
assert numpy.array_equal(result, cube_out[0].data)
def test_aggregating_to_same_grid_raises_NotImplementedError(self):
# Partial collapse of gridded data not supported (see JASCIS-148).
grid = {'y': AggregationGrid(-12.5, 12.5, 5, False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
assert numpy.array_equal(self.cube.data, cube_out.data)
assert numpy.array_equal(self.cube.coords('latitude')[0].points, cube_out.coords('latitude')[0].points)
assert numpy.array_equal(self.cube.coords('longitude')[0].points, cube_out.coords('longitude')[0].points)
def test_can_name_variables_by_variable_name(self):
grid = {'lon': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False),
'lat': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result = numpy.array(8.0)
assert numpy.array_equal(result, cube_out[0].data)
def test_aggregating_using_std_dev_kernel_returns_sample_standard_deviation(self):
self.kernel = iris.analysis.STD_DEV
grid = {'y': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result = numpy.array([numpy.sqrt(22.5), numpy.sqrt(22.5), numpy.sqrt(22.5)])
assert numpy.array_equal(result, cube_out[0].data)
assert numpy.array_equal(self.cube.coords('longitude')[0].points, cube_out.coords('longitude')[0].points)
def test_collapsing_coordinate_takes_start_end_but_ignores_them(self):
grid = {'x': AggregationGrid(0, 5, float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result = numpy.array([2, 5, 8, 11, 14])
# There is a small deviation to the weighting correction applied by Iris when completely collapsing
assert_arrays_almost_equal(result, cube_out[0].data)
assert numpy.array_equal(self.cube.coords('latitude')[0].points, cube_out.coords('latitude')[0].points)
def test_aggregating_single_point_in_one_dimension(self):
grid = {'y': AggregationGrid(-12.5, 12.5, 5, False)}
data = make_dummy_ungridded_data_single_point()
agg = Aggregator(data, grid)
cube_out = agg.aggregate_ungridded(self.kernel)
result = numpy.ma.array([[0], [0], [1.0], [0], [0]], mask=[[1], [1], [0], [1], [1]], fill_value=float('inf'))
assert_arrays_equal(numpy.ma.filled(cube_out[0].data), numpy.ma.filled(result))
def test_aggregating_using_min_kernel_returns_minimums(self):
self.kernel = iris.analysis.MIN
grid = {'x': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False),
'y': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result = numpy.array(1)
assert numpy.array_equal(result, cube_out[0].data)
def test_aggregation_on_three_dimensional_grid_with_time(self):
self.cube = make_mock_cube(time_dim_length=7)
grid = {'t': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), True),
'x': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False),
'y': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result_data = numpy.array(53)
assert_arrays_almost_equal(result_data, cube_out[0].data)
def test_aggregation_over_multidimensional_coord(self):
self.cube = make_mock_cube(time_dim_length=7, hybrid_pr_len=5)
grid = {'t': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), True),
'x': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False),
'y': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False),
'air_pressure': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result_data = numpy.array(263)
assert_arrays_almost_equal(cube_out[0].data, result_data)
def test_collapsing_everything_returns_a_single_value_with_missing_values(self):
self.cube = make_5x3_lon_lat_2d_cube_with_missing_data()
grid = {'x': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False),
'y': AggregationGrid(float('Nan'), float('Nan'), float('NaN'), False)}
agg = Aggregator(self.cube, grid)
cube_out = agg.aggregate_gridded(self.kernel)
# result = numpy.array(8.1538461538461533)
result = numpy.array(numpy.mean(self.cube.data))
assert numpy.allclose(result, cube_out[0].data, rtol=1e-2)
def test_mean_kernel_with_dataset_in_two_dimensions_with_missing_values(self):
grid = {'x': AggregationGrid(-7.5, 7.5, 5, False), 'y': AggregationGrid(-12.5, 12.5, 12.5, False)}
data = make_regular_2d_ungridded_data_with_missing_values()
agg = Aggregator(data, grid)
cube_out = agg.aggregate_ungridded(self.kernel)
result = numpy.ma.array([[2.5, 2.0, 4.5],
[8.5, 11.0, 13.5]],
mask=[[0, 0, 0],
[0, 0, 0]], fill_value=float('inf'))
assert_arrays_equal(numpy.ma.filled(cube_out[0].data), numpy.ma.filled(result))
def test_can_name_variables_by_standard_name(self):
"""
Note that this is also the variable name for many ungridded cases
"""
grid = {'latitude': AggregationGrid(-12.5, 12.5, 5, False)}
data = make_dummy_ungridded_data_single_point()
agg = Aggregator(data, grid)
cube_out = agg.aggregate_ungridded(self.kernel)
result = numpy.ma.array([[0], [0], [1.0], [0], [0]], mask=[[1], [1], [0], [1], [1]], fill_value=float('inf'))
assert_arrays_equal(numpy.ma.filled(cube_out[0].data), numpy.ma.filled(result))
def test_GIVEN_gridded_data_WHEN_full_collapse_THEN_calculations_correct(self):
grid = {'y': AggregationGrid(-10, 10, float('Nan'), False)}
cube = mock.make_mock_cube()
kernel = aggregation_kernels['moments']
agg = Aggregator(cube, grid)
result = agg.aggregate_gridded(kernel)
expected_means = numpy.array([7, 8, 9])
expected_std_dev = numpy.array(3 * [numpy.sqrt(22.5)])
expected_no = numpy.array([5, 5, 5])
assert_that(len(result), is_(3))
assert_that(numpy.allclose(result[0].data, expected_means))
assert_that(numpy.allclose(result[1].data, expected_std_dev))
assert_that(numpy.array_equal(result[2].data, expected_no))
def test_GIVEN_ungridded_data_WHEN_collapse_THEN_calculations_correct(self):
grid = {'y': AggregationGrid(-12.5, 12.5, 12.5, False)}
data = mock.make_regular_2d_ungridded_data()
kernel_class = get_kernel('moments')
kernel = kernel_class()
agg = Aggregator(data, grid)
result = agg.aggregate_ungridded(kernel)
expected_means = numpy.array([3.5, 11])
expected_std_dev = numpy.array([numpy.sqrt(3.5), numpy.sqrt(7.5)])
expected_no = numpy.array([6, 9])
assert_that(len(result), is_(3))
assert_arrays_almost_equal(result[0].data.flatten(), expected_means)
assert_arrays_almost_equal(result[1].data.flatten(), expected_std_dev)
assert_that(numpy.array_equal(result[2].data.flatten(), expected_no))
def test_aggregating_single_point_in_one_dimension_upper_bound_edge_case(self):
"""
Test to document the behaviour of aggregation when a point on a cell's upper bound is taken to be in that cell
or out of it. If the point is on the upper bound it is NOT taken to be part of that cell.
"""
grid = {'y': AggregationGrid(-12.5, 12.5, 5, False)}
data = make_dummy_ungridded_data_single_point(lat=2.50000)
agg = Aggregator(data, grid)
cube_out = agg.aggregate_ungridded(self.kernel)
result = numpy.ma.array([[0], [0], [0.0], [1.0], [0]], mask=[[1], [1], [1], [0], [1]], fill_value=float('inf'))
assert_arrays_equal(numpy.ma.filled(cube_out[0].data), numpy.ma.filled(result))
def test_stddev_kernel_with_dataset_in_two_dimensions_with_missing_values(self):
self.kernel = stddev()
grid = {'x': AggregationGrid(-7.5, 7.5, 5, False), 'y': AggregationGrid(-12.5, 12.5, 12.5, False)}
data = make_regular_2d_ungridded_data_with_missing_values()
agg = Aggregator(data, grid)
cube_out = agg.aggregate_ungridded(self.kernel)
result = numpy.ma.array([[numpy.sqrt(4.5), float('NaN'), numpy.sqrt(4.5)],
[numpy.sqrt(4.5), 3.0, numpy.sqrt(4.5)]],
mask=[[0, 1, 0],
[0, 0, 0]], fill_value=float('inf'))
assert_arrays_equal(numpy.ma.filled(cube_out[0].data), numpy.ma.filled(result))
def test_aggregate_mean(self):
from cis.data_io.gridded_data import GriddedDataList, make_from_cube
data1 = make_from_cube(make_mock_cube())
data2 = make_from_cube(make_mock_cube(data_offset=1))
datalist = GriddedDataList([data1, data2])
grid = {'y': AggregationGrid(float('Nan'), float('Nan'), float('Nan'), False)}
agg = Aggregator(datalist, grid)
cube_out = agg.aggregate_gridded(self.kernel)
result1 = numpy.array([7, 8, 9])
result2 = result1 + 1
assert isinstance(cube_out, GriddedDataList)
# There is a small deviation to the weighting correction applied by Iris when completely collapsing
assert_arrays_almost_equal(result1, cube_out[0].data)
assert_arrays_almost_equal(result2, cube_out[1].data)