def test_get_empty_like(self):
kwargs = dict(name='tas', alias='tas2', units='celsius', meta={'foo': 5}, uid=5, data='foo', did=5)
value = np.array([1, 2, 3, 4, 5])
value = np.ma.array(value, mask=[False, True, False, True, False])
kwargs['value'] = value
kwargs['attrs'] = OrderedDict(foo=5)
var = Variable(**kwargs)
for shape in [None, (2, 2)]:
new_var = var.get_empty_like(shape=shape)
self.assertDictEqual(new_var.attrs, kwargs['attrs'])
new_var.attrs['hi'] = 'wow'
self.assertNotEqual(new_var.attrs, kwargs['attrs'])
self.assertEqual(new_var.uid, var.uid)
if shape is None:
actual = np.ma.array(np.zeros(5), dtype=var.dtype, fill_value=var.fill_value, mask=value.mask)
else:
actual = np.ma.array(np.zeros((2, 2)), dtype=var.dtype, fill_value=var.fill_value, mask=False)
self.assertNumpyAll(actual, new_var.value)
# the meta dictionary should be deepcopied
new_var.meta['hi'] = 'there'
self.assertDictEqual(var.meta, {'foo': 5})
