def _check(self, data, dtype=None, shape=None):
data = np.asarray(data, dtype=dtype)
if shape is not None:
data = data.reshape(shape)
array = biggus.NumpyArrayAdapter(data)
result = count(array, axis=0).ndarray()
expected = np.ones(data.shape[1:]) * data.shape[0]
np.testing.assert_array_equal(result, expected)
def _check(self, source):
array = biggus.NumpyArrayAdapter(np.arange(2, dtype=source))
agg = count(array, axis=0)
self.assertEqual(agg.dtype, np.dtype('i'))
def test_multiple(self):
array = biggus.NumpyArrayAdapter(np.arange(12).reshape(3, 4))
with self.assertRaises(biggus.AxisSupportError):
count(array, axis=(0, 1))
def test_too_small(self):
with self.assertRaises(ValueError):
count(self.array, axis=-2)
def test_too_large(self):
with self.assertRaises(ValueError):
count(self.array, axis=1)
def test_none(self):
with self.assertRaises(biggus.AxisSupportError):
count(self.array)
def _check(self, data):
array = biggus.NumpyArrayAdapter(data)
result = count(array, axis=0).masked_array()
expected = ma.asarray(ma.count(data, axis=0))
np.testing.assert_array_equal(result.filled(), expected)
np.testing.assert_array_equal(result.mask, expected.mask)