def test_z_mc4():
"""test properties of z_mc, is STD correct, is 2*STD correct"""
N = 3
z_arr = np.random.uniform(size=N) * 1.5 + 0.4
z_sigma = np.random.uniform(size=N) * 0.1 + 0.1
x = np.arange(0.1, 3.01, 0.01)
z_mc_std = np.zeros(N)
z_mc_mean = np.zeros(N)
z_mc_95 = np.zeros(N)
for i in range(N):
pdf = mlab.normpdf(x, z_arr[i], z_sigma[i])
z_mc = np.array([pval.get_mc(pdf, x) for j in range(100000)])
z_mc_std[i] = np.std(z_mc)
z_mc_mean[i] = np.mean(z_mc)
z_mc_95[i] = pval.sigma_95(z_mc)
dx = (x[1] - x[0]) / 2.0
print 'diff sig', z_sigma - z_mc_std, dx
print 'diff mean', z_arr - z_mc_mean, dx
print 'diff 2xsig', z_mc_95 - z_mc_std * 2, dx
for i in range(N):
np.testing.assert_almost_equal(z_sigma[i] - z_mc_std[i], 0, decimal=3)
np.testing.assert_almost_equal(z_arr[i] - z_mc_mean[i], 0, decimal=3)
np.testing.assert_almost_equal(2 * z_sigma[i] - z_mc_95[i],
0,
decimal=2)
def test_sigma_95_axis():
""" test sigma_95 = 2xstd dev for gauss for N-d array"""
arr = np.zeros((5e5, 10))
sigs = np.arange(10) * 0.1 + 0.5
for i in range(10):
arr[:, i] = np.random.normal(size=5e5) * sigs[i] + sigs[i]
sig95 = pval.sigma_95(arr, axis=0)
print sig95
print sigs * 2.0
np.testing.assert_almost_equal(sigs * 2.0, sig95, decimal=2)
def test_sigma95_2():
"""test sigma95 to high intermediate level of accuracy"""
arr1 = np.arange(1000)
np.random.shuffle(arr1)
print 2 * pval.sigma_95(arr1)
np.testing.assert_equal(np.abs(2 * pval.sigma_95(arr1) - 953) < 1, True)
def test_sigma95_3():
"""test sigma95 to high level of accuracy"""
arr2 = np.arange(1e4) * 1.0
np.random.shuffle(arr2)
print 2 * pval.sigma_95(arr2)
np.testing.assert_equal(np.abs(2 * pval.sigma_95(arr2) - 9540) < 1, True)
def test_sigma95_1():
"""test sigma95 to high level of accuracy"""
arr1 = np.random.normal(size=1e7)
np.testing.assert_equal(
np.abs(pval.sigma_95(arr1) - 2 * np.std(arr1)) < 0.01, True)