def test_large(self): # Source data: 3 MB array = self._small_array() target = _WriteCounter(array.shape) with mock.patch('biggus._init.MAX_CHUNK_SIZE', 4096): biggus.save([array], [target]) self.assertTrue(target.all_written())
def test(self): # If we switch evaluation engine, does it get used? array = _Aggregation(biggus.ConstantArray(3, 2), None, None, None, None, {}) return_value = (mock.sentinel.result,) engine = mock.Mock(**{"ndarrays.return_value": return_value}) with mock.patch("biggus.engine", engine): result = array.ndarray() self.assertIs(result, mock.sentinel.result)
def test(self): # If we switch evaluation engine, does it get used? array = biggus._Aggregation(biggus.ConstantArray(3, 2), None, None, None, None, {}) return_value = (mock.sentinel.result,) engine = mock.Mock(**{'ndarrays.return_value': return_value}) with mock.patch('biggus.engine', engine): result = array.ndarray() self.assertIs(result, mock.sentinel.result)
def _test_flow(self, axis): data = np.arange(3 * 4 * 5, dtype='f4').reshape(3, 4, 5) array = biggus.NumpyArrayAdapter(data) mean = biggus.mean(array, axis=axis) # Artificially constrain the chunk size to eight bytes to # ensure biggus is stepping across axes in the correct # order. with mock.patch('biggus._init.MAX_CHUNK_SIZE', 8): op_result, = biggus.ndarrays([mean]) np_result = np.mean(data, axis=axis) np.testing.assert_array_almost_equal(op_result, np_result)
def test_updates_all_override_name(self): with mock.patch('biggus._init.__all__', []) as tmp_all: _ufunc_wrapper(np.exp, 'foo') self.assertEqual(tmp_all, ['foo'])
def test_updates_all_default_name(self): with mock.patch('biggus._init.__all__', []) as tmp_all: _ufunc_wrapper(np.exp) self.assertEqual(tmp_all, ['exp'])