def test_axis(self):
x = np.array([[355, 5, 2, 359, 10, 350], [351, 7, 4, 352, 9, 349],
[357, 9, 8, 358, 4, 356]])
M1 = circ_average(x, high=360)
M2 = circ_average(x.ravel(), high=360)
np.testing.assert_allclose(M1, M2, rtol=1e-14)
M1 = circ_average(x, high=360, axis=1)
M2 = [circ_average(x[i], high=360) for i in range(x.shape[0])]
np.testing.assert_allclose(M1, M2, rtol=1e-14)
M1 = circ_average(x, high=360, axis=0)
M2 = [circ_average(x[:, i], high=360) for i in range(x.shape[1])]
np.testing.assert_allclose(M1, M2, rtol=1e-14)
def test_range(self):
m = circ_average(np.arange(0, 2, 0.1), np.pi, -np.pi)
assert m < np.pi
assert m > -np.pi
def test_scalar(self):
x = 1.
M1 = x
M2 = circ_average(x)
np.testing.assert_allclose(M2, M1, rtol=1e-5)
def test_empty(self):
assert np.isnan(circ_average([]))
def test_array_like(self):
x = [355, 5, 2, 359, 10, 350]
np.testing.assert_allclose(circ_average(x, high=360),
0.167690146,
rtol=1e-7)
def test_small(self):
x = np.array([20, 21, 22, 18, 19, 20.5, 19.2])
expected = x.mean()
assert circ_average(x, high=360) == pytest.approx(expected)
def test_high_360(self):
x = np.array([355, 5, 2, 359, 10, 350])
expected = 0.167690146
assert circ_average(x, high=360) == pytest.approx(expected)
def test_no_weights(self):
expected = 3.136593
assert circ_average([-3.14, 3.13]) == pytest.approx(expected)