def test_single_point_gives_single_value_in_masked_cell_using_kernel_and_con_missing_for_masked_true_binned_only(
self):
from cis.collocation.col_implementations import max
con = BinnedCubeCellOnlyConstraint()
kernel = max()
single_point_results_in_single_value_in_masked_cell_using_kernel_and_con_missing_for_masked_true(con, kernel)
def test_max_kernel_with_dataset_in_two_dimensions_with_missing_values(self):
self.kernel = max()
grid = {'x': slice(-7.5, 7.5, 5), 'y': slice(-12.5, 12.5, 12.5)}
data = make_regular_2d_ungridded_data_with_missing_values()
cube_out = data.aggregate(how=self.kernel, **grid)
result = numpy.ma.array([[4.0, 2.0, 6.0],
[10.0, 14.0, 15.0]],
mask=[[0, 0, 0],
[0, 0, 0]], fill_value=float('nan'))
compare_masked_arrays(cube_out.data, result)
def test_max_kernel_with_dataset_in_two_dimensions_with_missing_values(
self):
self.kernel = max()
grid = {'x': slice(-7.5, 7.5, 5), 'y': slice(-12.5, 12.5, 12.5)}
data = make_regular_2d_ungridded_data_with_missing_values()
cube_out = data.aggregate(how=self.kernel, **grid)
result = numpy.ma.array([[4.0, 2.0, 6.0], [10.0, 14.0, 15.0]],
mask=[[0, 0, 0], [0, 0, 0]],
fill_value=float('nan'))
compare_masked_arrays(cube_out.data, result)
def test_max_kernel_with_dataset_in_two_dimensions_with_missing_values(self):
self.kernel = max()
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([[4.0, 2.0, 6.0],
[10.0, 14.0, 15.0]],
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))