def test_basic_weights(self):
"""Test that the function returns the correct triangular weights in a
simple case"""
width = 3
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(15)
midpoint = 5
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
expected_weights = np.array([
0.0,
0.0,
0.0,
0.11111111,
0.22222222,
0.33333333,
0.22222222,
0.11111111,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
self.assertArrayAlmostEqual(weights, expected_weights)
def test_unevenly_spaced_coord(self):
"""Test the case where the input coordinate is not equally spaced.
This represents the case where the data changes to 3 hourly. In this
case the weights are assigned according to the value in the
coordinate."""
width = 5
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(10)
coord_vals = np.append(coord_vals, [12, 15, 18, 21, 24])
midpoint = 8
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
expected_weights = np.array([
0.0,
0.0,
0.0,
0.0,
0.05,
0.1,
0.15,
0.2,
0.25,
0.2,
0.05,
0.0,
0.0,
0.0,
0.0,
])
self.assertArrayAlmostEqual(weights, expected_weights)
def test_non_integer_width(self):
"""Test when the width of the triangle does not fall on a grid point.
This only affects the slope of the triangle slightly."""
width = 3.5
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(15)
midpoint = 5
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
expected_weights = np.array([
0.0,
0.0,
0.04,
0.12,
0.2,
0.28,
0.2,
0.12,
0.04,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
self.assertArrayAlmostEqual(weights, expected_weights)
def test_non_integer_midpoint(self):
"""Test the case where the midpoint of the triangle is not a point in
the input coordinate.
In this case we do not sample the peak of the triangle."""
width = 2
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(15)
midpoint = 3.5
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
expected_weights = np.array([
0.0,
0.0,
0.125,
0.375,
0.375,
0.125,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
self.assertArrayAlmostEqual(weights, expected_weights)
def test_large_width(self):
"""Test the case where the width of the triangle is larger than the
coordinate input.
In this case all the weights are non-zero but still form the
shape of a triangle."""
width = 10
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(15)
midpoint = 5
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
expected_weights = np.array([
0.055556,
0.066667,
0.077778,
0.088889,
0.1,
0.111111,
0.1,
0.088889,
0.077778,
0.066667,
0.055556,
0.044444,
0.033333,
0.022222,
0.011111,
])
self.assertArrayAlmostEqual(weights, expected_weights)
def test_midpoint_at_edge(self):
"""Test that the correct triangular weights are returned for a case
where the midpoint is close to the end of the input coordinate.
In this case the triangle is cut off at the end of the coordinate"""
width = 3
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(15)
midpoint = 1
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
expected_weights = np.array([
0.25,
0.375,
0.25,
0.125,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
])
self.assertArrayAlmostEqual(weights, expected_weights)
def test_basic(self):
"""Test that the function returns a numpy array.
Also check that the length of the weights is correct and they add
up to 1.0"""
width = 3
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(15)
midpoint = 5
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
self.assertIsInstance(weights, np.ndarray)
self.assertEqual(len(weights), len(coord_vals))
self.assertEqual(weights.sum(), 1.0)
def test_basic(self):
"""Test that the function returns a numpy array.
Also check that the length of the weights is correct and they add
up to 1.0"""
width = 3
triangular_weights_instance = ChooseDefaultWeightsTriangular(width)
coord_vals = np.arange(15)
midpoint = 5
weights = triangular_weights_instance.triangular_weights(
coord_vals, midpoint, width)
self.assertIsInstance(weights, np.ndarray)
self.assertEqual(len(weights), len(coord_vals))
# Using float32s for weights can give 1.0000001 here - good enough.
self.assertAlmostEqual(weights.sum(), 1.0, places=6)