def test_new_med_ndwi():
medndwi = NormalisedDifferenceStats('green',
'nir',
'ndwi',
stats=['median'])
arr = np.random.uniform(low=-1, high=1, size=(5, 100, 100))
data_array_1 = xr.DataArray(arr,
dims=('time', 'y', 'x'),
coords={'time': list(range(5))},
attrs={'crs': 'Fake CRS'})
arr = np.random.uniform(low=-1, high=1, size=(5, 100, 100))
data_array_2 = xr.DataArray(arr,
dims=('time', 'y', 'x'),
coords={'time': list(range(5))},
attrs={'crs': 'Fake CRS'})
dataset = xr.Dataset(data_vars={
'green': data_array_1,
'nir': data_array_2
},
attrs={'crs': 'Fake CRS'})
result = medndwi.compute(dataset)
assert isinstance(result, xr.Dataset)
assert 'crs' in result.attrs
assert 'ndwi_median' in result.data_vars
def test_normalised_difference_stats(dataset, output_name):
var1, var2 = list(dataset.data_vars)
ndstat = NormalisedDifferenceStats(var1, var2, output_name)
result = ndstat.compute(dataset)
assert isinstance(result, xr.Dataset)
assert 'time' not in result.dims
assert dataset.crs == result.crs
expected_output_varnames = set(f'{output_name}_{stat_name}' for stat_name in ndstat.stats)
assert set(result.data_vars) == expected_output_varnames
# Check the measurements() function raises an error on bad input_measurements
with pytest.raises(StatsConfigurationError):
invalid_names = [Measurement(name='foo', **FAKE_MEASUREMENT_INFO)]
ndstat.measurements(invalid_names)
# Check the measurements() function returns something reasonable
input_measurements = [Measurement(name=name, **FAKE_MEASUREMENT_INFO) for name in (var1, var2)]
output_measurements = ndstat.measurements(input_measurements)
measurement_names = set(m.name for m in output_measurements)
assert expected_output_varnames == measurement_names