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p is never above 1 

1e-4 is quite low, usually we are far above. 1e-3 from https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html
is not too far away.
This commit is contained in:
Martin Diehl 2020-09-20 19:13:54 +02:00
parent 5b0b0de6b4
commit 5895e74029
1 changed files with 4 additions and 4 deletions
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8
python/tests/test_Rotation.py
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@ -908,13 +908,13 @@ class TestRotation:
@pytest.mark.parametrize('N',[1000,10000,100000]) @pytest.mark.parametrize('N',[1000,10000,100000])
def test_spherical_component(self,N,sigma): def test_spherical_component(self,N,sigma):
c = Rotation.from_random() c = Rotation.from_random()
o = Rotation.from_spherical_component(c,sigma,N,seed=N+sigma) o = Rotation.from_spherical_component(c,sigma,N)
_, angles = c.misorientation(o).as_axis_angle(pair=True,degrees=True) _, angles = c.misorientation(o).as_axis_angle(pair=True,degrees=True)
angles[::2] *= -1 # flip angle for every second to symmetrize distribution angles[::2] *= -1 # flip angle for every second to symmetrize distribution
p = stats.normaltest(angles)[1] p = stats.normaltest(angles)[1]
sigma_out = np.std(angles) sigma_out = np.std(angles)
assert (.9 < sigma/sigma_out < 1.1) and p > 1, f'{sigma/sigma_out},{p}' assert (.9 < sigma/sigma_out < 1.1) and p > 1e-4, f'{sigma/sigma_out},{p}'
@pytest.mark.parametrize('sigma',[5,10,15,20]) @pytest.mark.parametrize('sigma',[5,10,15,20])
@ -929,10 +929,10 @@ class TestRotation:
ax = np.append(np.cross(f_in_C,f_in_S), - np.arccos(np.dot(f_in_C,f_in_S))) ax = np.append(np.cross(f_in_C,f_in_S), - np.arccos(np.dot(f_in_C,f_in_S)))
n = Rotation.from_axis_angle(ax if ax[3] > 0.0 else ax*-1.0 ,normalize=True) # rotation to align fiber axis in crystal and sample system n = Rotation.from_axis_angle(ax if ax[3] > 0.0 else ax*-1.0 ,normalize=True) # rotation to align fiber axis in crystal and sample system
o = Rotation.from_fiber_component(alpha,beta,np.radians(sigma),N,False,seed=N+sigma) o = Rotation.from_fiber_component(alpha,beta,np.radians(sigma),N,False)
angles = np.arccos(np.clip(np.dot(o@np.broadcast_to(f_in_S,(N,3)),n@f_in_S),-1,1)) angles = np.arccos(np.clip(np.dot(o@np.broadcast_to(f_in_S,(N,3)),n@f_in_S),-1,1))
dist = np.array(angles) * (np.random.randint(0,2,N)*2-1) dist = np.array(angles) * (np.random.randint(0,2,N)*2-1)
p = stats.normaltest(dist)[1] p = stats.normaltest(dist)[1]
sigma_out = np.degrees(np.std(dist)) sigma_out = np.degrees(np.std(dist))
assert (.9 < sigma/sigma_out < 1.1) and p > 0.001, f'{sigma/sigma_out},{p}' assert (.9 < sigma/sigma_out < 1.1) and p > 1.e-4, f'{sigma/sigma_out},{p}'