def test_mtest():
data = np.array([
[0.58429, 0.88333],
[1.14892, 2.22854],
[2.87128, 3.06369],
[1.07677, 1.49836],
[2.96969, 1.51748],
])
h, mu, ci = pycircstat.tests.mtest(data, [np.pi/2., np.pi], xi=.2, axis=0)
out1 = np.array([0.76976, 0.50149])
assert_allclose(pycircstat.mean_ci_limits(data, ci=0.8, axis=0),
out1, rtol=1e-4)
assert_true(np.all(h == [False, True]))
h, mu, ci = pycircstat.tests.mtest(data, np.pi/2., xi=.2, axis=1)
out2 = np.array([0.17081, 0.72910, 0.10911, 0.24385, 0.95426])
assert_allclose(pycircstat.mean_ci_limits(data, ci=0.8, axis=1),
out2, rtol=1e-4)
assert_true(np.all(h == [True, False, True, True, False]))
out3 = np.array([1.0577, 2.4170])
h, mu, ci = pycircstat.tests.mtest(data, np.pi/2., xi=.05, axis=None)
assert_allclose(mu + pycircstat.mean_ci_limits(data, ci=0.95, axis=None),
out3[1], rtol=1e-4)
assert_allclose(mu - pycircstat.mean_ci_limits(data, ci=0.95, axis=None),
out3[0], rtol=1e-4)
assert_true(~h)
assert_allclose(mu, 1.737335083370)
def test_mean_ci_limits():
data = np.array([
[0.58429, 0.88333],
[1.14892, 2.22854],
[2.87128, 3.06369],
[1.07677, 1.49836],
[2.96969, 1.51748],
])
out1 = np.array([0.76976, 0.50149])
out2 = np.array([0.17081, 0.72910, 0.10911, 0.24385, 0.95426])
assert_allclose(pycircstat.mean_ci_limits(data, ci=0.8, axis=0),
out1, rtol=1e-4)
assert_allclose(pycircstat.mean_ci_limits(data, ci=0.8, axis=1),
out2, rtol=1e-4)
def test_mean_ci_limits():
data = np.array([
[0.58429, 0.88333],
[1.14892, 2.22854],
[2.87128, 3.06369],
[1.07677, 1.49836],
[2.96969, 1.51748],
])
out1 = np.array([0.76976, 0.50149])
out2 = np.array([0.17081, 0.72910, 0.10911, 0.24385, 0.95426])
assert_allclose(pycircstat.mean_ci_limits(data, ci=0.8, axis=0),
out1, rtol=1e-4)
assert_allclose(pycircstat.mean_ci_limits(data, ci=0.8, axis=1),
out2, rtol=1e-4)