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streamlined fiber/spherical component sampling

This commit is contained in:
Philip Eisenlohr 2020-09-15 19:12:30 -04:00
parent c6be6fe87f
commit ed006d1a89
1 changed files with 6 additions and 7 deletions
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13
python/damask/_rotation.py
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@ -665,12 +665,11 @@ class Rotation:
""" """
rng = np.random.default_rng(seed) rng = np.random.default_rng(seed)
sigma = np.radians(sigma) if degrees else sigma sigma = np.radians(sigma) if degrees else sigma
u,Theta = (rng.random((N,2)) * 2.0 * np.array([1,np.pi]) - np.array([1.0, 0])).T u,Theta = (rng.random((N,2)) * 2.0 * np.array([1,np.pi]) - np.array([1.0, 0])).T
omega = rng.normal(scale=sigma,size=N) omega = abs(rng.normal(scale=sigma,size=N))
p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta), p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta),
np.sqrt(1-u**2)*np.sin(Theta), np.sqrt(1-u**2)*np.sin(Theta),
u, omega]) u, omega])
p[p[:,3]<0] *= -1
return Rotation.from_axis_angle(p) @ center return Rotation.from_axis_angle(p) @ center
@ -704,16 +703,16 @@ class Rotation:
d_lab = np.array([np.sin( beta_[0])*np.cos( beta_[1]), np.sin( beta_[0])*np.sin( beta_[1]), np.cos( beta_[0])]) d_lab = np.array([np.sin( beta_[0])*np.cos( beta_[1]), np.sin( beta_[0])*np.sin( beta_[1]), np.cos( beta_[0])])
ax_align = np.append(np.cross(d_lab,d_cr), np.arccos(np.dot(d_lab,d_cr))) ax_align = np.append(np.cross(d_lab,d_cr), np.arccos(np.dot(d_lab,d_cr)))
if np.isclose(ax_align[3],0.0): ax_align[:3] = np.array([1,0,0]) if np.isclose(ax_align[3],0.0): ax_align[:3] = np.array([1,0,0])
R_align = Rotation.from_axis_angle(ax_align if ax_align[3] > 0.0 else -ax_align,normalize=True) # rotation to align fiber axis in crystal and sample system R_align = Rotation.from_axis_angle(ax_align if ax_align[3] > 0.0 else -ax_align,normalize=True) # rotate fiber axis from sample to crystal frame
u,Theta,b = (np.random.random((N,3)) * 2 * np.array([1,np.pi,np.pi]) - np.array([1,0,np.pi])).T u,Theta,b = (rng.random((N,3)) * np.array([2,2*np.pi,np.pi]) - np.array([1,0,0])).T
omega = abs(np.random.normal(scale=sigma_,size=N)) omega = abs(rng.normal(scale=sigma_,size=N))
p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta), p = np.column_stack([np.sqrt(1-u**2)*np.cos(Theta),
np.sqrt(1-u**2)*np.sin(Theta), np.sqrt(1-u**2)*np.sin(Theta),
u, omega]) u, omega])
p[:,:3] = np.einsum('ij,...j->...i',np.eye(3)-np.outer(d_lab,d_lab),p[:,:3]) p[:,:3] = np.einsum('ij,...j->...i',np.eye(3)-np.outer(d_lab,d_lab),p[:,:3])
f = np.column_stack((np.broadcast_to(d_lab,(N,3)),b)) f = np.column_stack((np.broadcast_to(d_lab,(N,3)),b))
f[f[:,3]<0] *= -1. f[::2,:3] *= -1 # flip half the rotation axes to negative sense
return R_align.broadcast_to(N) \ return R_align.broadcast_to(N) \
@ Rotation.from_axis_angle(p,normalize=True) \ @ Rotation.from_axis_angle(p,normalize=True) \
@ Rotation.from_axis_angle(f) @ Rotation.from_axis_angle(f)