def test_fails_sum__not_equal_to_one(self):
"""Test it fails if sum of input weights not equal to one. """
weights_in = np.array([3.0, 2.0, 1.0])
missing_weights = np.ones(3)
msg = ('Sum of weights must be 1.0')
with self.assertRaisesRegex(ValueError, msg):
WeightsUtilities.redistribute_weights(weights_in, missing_weights)
def test_fails_less_points_in_coord(self):
"""Test fails if less points in coord than in cube. """
exp_coord_vals = self.exp_coord_vals[0]
msg = ('The cube coordinate has more points ' 'than requested coord, ')
with self.assertRaisesRegex(ValueError, msg):
WeightsUtilities.process_coord(self.cube, self.coordinate,
exp_coord_vals)
def test_fails_if_can_not_convert_units(self):
"""Test fails if it can not convert units """
units = 'mm'
msg = ('Failed to convert coord units ')
with self.assertRaisesRegex(ValueError, msg):
WeightsUtilities.process_coord(self.cube, self.coordinate,
self.wrong_coord_vals, units)
def test_fails_mismatch_array_sizes(self):
"""Test it fails if weights and missing_weights not the same size."""
weights_in = np.array([0.7, 0.2, 0.1])
missing_weights = np.ones(2)
msg = ('Arrays weights and forecast_present not the same size')
with self.assertRaisesRegex(ValueError, msg):
WeightsUtilities.redistribute_weights(weights_in, missing_weights)
def test_fails_weight_less_than_zero(self):
"""Test it fails if weight less than zero. """
weights_in = np.array([-0.1, 1.1])
missing_weights = np.ones(2)
msg = ('Weights should be positive or at least one > 0.0')
with self.assertRaisesRegex(ValueError, msg):
WeightsUtilities.redistribute_weights(weights_in, missing_weights)
def test_all_missing(self):
"""Test it raises the correct error when none of the expected
coordinate values are present on the cube."""
weights_in = np.array([0.6, 0.3, 0.1])
missing_weights = np.zeros(3)
msg = 'None of the expected forecasts were found.'
with self.assertRaisesRegexp(ValueError, msg):
WeightsUtilities.redistribute_weights(weights_in, missing_weights)
def test_basic(self):
"""Test process_coord returns num and array of missing_weights. """
(result_num_of_weights,
result_missing) = WeightsUtilities.process_coord(
self.cube, self.coordinate, self.exp_coord_vals)
self.assertIsInstance(result_num_of_weights, int)
self.assertIsInstance(result_missing, np.ndarray)
def test_basic(self):
"""Test that the function returns an array of weights. """
weights_in = np.array([0.6, 0.3, 0.1])
missing_weights = np.ones(3)
result = WeightsUtilities.redistribute_weights(weights_in,
missing_weights)
self.assertIsInstance(result, np.ndarray)
def test_returns_correct_values_2darray_zero_weights(self):
"""Test normalizing along the columns of the array when there are
zeros in the input array."""
weights_in = np.array([[6.0, 3.0, 0.0], [0.0, 1.0, 3.0]])
result = WeightsUtilities.normalise_weights(weights_in, axis=0)
expected_result = np.array([[1.0, 0.75, 0.0], [0.0, 0.25, 1.0]])
self.assertArrayAlmostEqual(result, expected_result)
def test_returns_correct_values_2darray_axis1(self):
"""Test normalizing along the rows of the array."""
weights_in = np.array([[6.0, 3.0, 1.0],
[4.0, 1.0, 3.0]])
result = WeightsUtilities.normalise_weights(weights_in, axis=1)
expected_result = np.array([[0.6, 0.3, 0.1],
[0.5, 0.125, 0.375]])
self.assertArrayAlmostEqual(result, expected_result)
def test_no_points_set_in_coord(self):
"""Test returns num in coordinate if no points set in coord. """
expected_num = len(self.cube_coord.points)
expected_array = np.ones(expected_num)
(result_num_of_weights,
result_missing) = WeightsUtilities.process_coord(self.cube,
self.coordinate)
self.assertAlmostEquals(result_num_of_weights, expected_num)
self.assertArrayAlmostEqual(result_missing, expected_array)
def test_finds_missing_points(self):
"""Test correct values are returned for case where not all expected
coordinate values are present in cube."""
expected_num = 3
expected_array = np.ones(expected_num)
expected_array[0] = 0.0
(result_num_of_weights,
result_missing) = WeightsUtilities.process_coord(
self.cube, self.coordinate, self.wrong_coord_vals)
self.assertAlmostEqual(result_num_of_weights, expected_num)
self.assertArrayAlmostEqual(result_missing, expected_array)
def test_returns_correct_values_evenly(self):
"""Test it returns the correct values, method is evenly."""
weights_in = np.array([0.41957573, 0.25174544,
0.15104726, 0.09062836,
0.05437701, 0.03262621])
missing_weights = np.ones(6)
missing_weights[2] = 0.0
result = WeightsUtilities.redistribute_weights(weights_in,
missing_weights)
expected_result = np.array([0.44978518, 0.28195489,
0.12083781,
0.08458647, 0.06283566])
self.assertArrayAlmostEqual(result, expected_result)
def test_returns_correct_values_proportional(self):
"""Test it returns the correct values, method is proportional."""
weights_in = np.array([0.41957573, 0.25174544,
0.15104726, 0.09062836,
0.05437701, 0.03262621])
missing_weights = np.ones(6)
missing_weights[2] = 0.0
result = WeightsUtilities.redistribute_weights(weights_in,
missing_weights,
method='proportional')
expected_result = np.array([0.49422742, 0.29653645,
0.10675312,
0.06405187, 0.03843112])
self.assertArrayAlmostEqual(result, expected_result)
def renormalize_weights(self, nbhood_cube):
"""
Renormalize the weights taking into account where there are NaNs in the
result from neighbourhood.
The weights corresponding to NaNs in the result from neighbourhooding
with a mask are set to zero and then the weights are renormalized along
the axis corresponding to the coordinate we want to collapse.
Args:
nbhood_cube (iris.cube.Cube):
The cube that has been through masked neighbourhood processing
and has the dimension we wish to collapse. Must have the same
dimensions of the cube.
"""
# If the weights are masked we want to retain the mask.
condition = np.isnan(nbhood_cube.data)
if ma.is_masked(self.weights.data):
condition = condition & ~self.weights.data.mask
self.weights.data[condition] = 0.0
axis = nbhood_cube.coord_dims(self.coord_masked)
self.weights.data = WeightsUtilities.normalise_weights(
self.weights.data, axis=axis)
def test_basic(self):
"""Test that the __repr__ returns the expected string."""
result = str(WeightsUtilities())
msg = '<WeightsUtilities>'
self.assertEqual(result, msg)
def test_fails_sum_equals_zero(self):
"""Test it fails if sum of input weights is zero. """
weights_in = np.array([0.0, 0.0, 0.0])
msg = ('Sum of weights must be > 0.0')
with self.assertRaisesRegexp(ValueError, msg):
WeightsUtilities.normalise_weights(weights_in)
def test_basic(self):
"""Test that the function returns an array of weights. """
weights_in = np.array([1.0, 2.0, 3.0])
result = WeightsUtilities.normalise_weights(weights_in)
self.assertIsInstance(result, np.ndarray)
def test_fails_coord_not_in_cube(self):
""" Test process_cord fails if coord not in cube """
msg = ('The coord for this plugin must be '
'an existing coordinate in the input cube.')
with self.assertRaisesRegexp(ValueError, msg):
WeightsUtilities.process_coord(self.cube, 'not_in_cube', '0')
def test_array_sum_equals_one(self):
"""Test that the resulting weights add up to one. """
weights_in = np.array([1.0, 2.0, 3.0])
result = WeightsUtilities.normalise_weights(weights_in)
self.assertAlmostEquals(result.sum(), 1.0)
def test_fails_weight_less_than_zero(self):
"""Test it fails if weight less than zero. """
weights_in = np.array([-1.0, 0.1])
msg = ('Weights must be positive')
with self.assertRaisesRegexp(ValueError, msg):
WeightsUtilities.normalise_weights(weights_in)
def test_returns_correct_values(self):
"""Test it returns the correct values. """
weights_in = np.array([6.0, 3.0, 1.0])
result = WeightsUtilities.normalise_weights(weights_in)
expected_result = np.array([0.6, 0.3, 0.1])
self.assertArrayAlmostEqual(result, expected_result)
