def test_inconsistent_mask_along_leading_dim(self):
"""Test an inconsistently masked array raises an error."""
mask = np.array([[[True, False, False, True],
[True, False, True, True]],
[[True, False, True, True],
[True, False, True, True]],
[[True, False, True, True],
[True, False, True, False]]])
masked_data_array = np.ma.MaskedArray(self.data_array, mask)
expected_message = "The mask on the input array is not the same"
with self.assertRaisesRegex(ValueError, expected_message):
flatten_ignoring_masked_data(
masked_data_array, preserve_leading_dimension=True)
def test_1D_input(self):
"""Test input array is unchanged when input in 1D"""
data_array = self.data_array.flatten()
expected_result = data_array.copy()
result = flatten_ignoring_masked_data(data_array)
self.assertArrayAlmostEqual(result, expected_result)
self.assertEqual(result.dtype, np.float32)
def test_basic_not_masked_preserver_leading_dim(self):
"""Test a basic unmasked array, with preserve_leading_dimension"""
result = flatten_ignoring_masked_data(
self.data_array, preserve_leading_dimension=True)
self.assertArrayAlmostEqual(
result, self.expected_result_preserve_leading_dim)
self.assertEqual(result.dtype, np.float32)
def test_all_masked(self):
"""Test empty array is returned when all points are masked."""
mask = np.ones((3, 2, 4)) * True
masked_data_array = np.ma.MaskedArray(self.data_array, mask)
expected_result = np.array([], dtype=np.float32)
result = flatten_ignoring_masked_data(masked_data_array)
self.assertArrayAlmostEqual(result, expected_result)
self.assertEqual(result.dtype, np.float32)
def test_basic_masked(self):
"""Test a basic masked array"""
masked_data_array = np.ma.MaskedArray(self.data_array, self.mask)
expected_result = np.array([1., 5., 9., 13., 17., 21.],
dtype=np.float32)
result = flatten_ignoring_masked_data(masked_data_array)
self.assertArrayAlmostEqual(result, expected_result)
self.assertEqual(result.dtype, np.float32)
def test_4D_input_not_masked_preserve_leading_dim(self):
"""Test input array is unchanged when input in 4D.
This should give the same answer as the corresponding 3D array."""
data_array = self.data_array.reshape(3, 2, 2, 2)
result = flatten_ignoring_masked_data(
data_array, preserve_leading_dimension=True)
self.assertArrayAlmostEqual(
result, self.expected_result_preserve_leading_dim)
self.assertEqual(result.dtype, np.float32)
def test_basic_masked_preserver_leading_dim(self):
"""Test a basic masked array, with preserve_leading_dimension"""
masked_data_array = np.ma.MaskedArray(self.data_array, self.mask)
expected_result = np.array([[1., 5.],
[9., 13.],
[17., 21.]],
dtype=np.float32)
result = flatten_ignoring_masked_data(
masked_data_array, preserve_leading_dimension=True)
self.assertArrayAlmostEqual(result, expected_result)
self.assertEqual(result.dtype, np.float32)
def test_basic_not_masked(self):
"""Test a basic unmasked array"""
expected_result = np.arange(0, 24, 1, dtype=np.float32)
result = flatten_ignoring_masked_data(self.data_array)
self.assertArrayAlmostEqual(result, expected_result)
self.assertEqual(result.dtype, np.float32)
