def test_valid_mask():
xx = np.zeros((4, 8), dtype='float32')
mm = valid_mask(xx, 0)
assert mm.dtype == 'bool'
assert mm.shape == xx.shape
assert not mm.all()
assert not mm.any()
mm = valid_mask(xx, 13)
assert mm.dtype == 'bool'
assert mm.shape == xx.shape
assert mm.all()
mm = valid_mask(xx, None)
assert mm.dtype == 'bool'
assert mm.shape == xx.shape
assert mm.all()
mm = valid_mask(xx, np.nan)
assert mm.dtype == 'bool'
assert mm.shape == xx.shape
assert mm.all()
xx[0, 0] = np.nan
mm = valid_mask(xx, np.nan)
assert not mm[0, 0]
assert mm.sum() == (4*8-1)
def valid_data_mask(data):
"""
Returns bool arrays where the data is not `nodata`
:param Dataset or DataArray data:
:return: Dataset or DataArray
"""
if isinstance(data, Dataset):
return data.apply(valid_data_mask)
if not isinstance(data, DataArray):
raise TypeError('valid_data_mask not supported for type {}'.format(type(data)))
nodata = data.attrs.get('nodata', None)
return xarray.apply_ufunc(lambda xx: valid_mask(xx, nodata), data, dask='parallelized',
output_dtypes=[numpy.bool])