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)