q_grid = eu2quat(bungeAngs).T
""" use sklearn KDTree for reduction of points for query (euclidean) """
qgrid_pos = np.copy(q_grid)
qgrid_pos[qgrid_pos[:,0] < 0] *= -1
tree = KDTree(qgrid_pos)
#rotation around path in space
phi = np.linspace(0,2*np.pi,365)
rad = np.sqrt( 2 * ( 1 - np.cos(0.5*theta) ) )
euc_rad = np.sqrt( 4 * np.sin(0.25*theta)**2 )
# q_path = calcFibre(hkls,xyz_pf,q_grid,phi,rad,tree,euc_rad)
symOps = genSymOps('m-3m')
symOps = np.unique(np.swapaxes(symOps,2,0),axis=0)
""" sym ops """
proper = np.where( np.linalg.det(symOps) == 1 ) #proper orthogonal
quatSymOps = quat.from_matrix(symOps[proper])
quatSymOps = np.tile(quatSymOps[:,:,np.newaxis],(1,1,len(phi)))
quatSymOps = quatSymOps.transpose((0,2,1))
""" create small grid centered around [001] """
polar_stepN = 9
polar_step = np.deg2rad(5) / (polar_stepN-1)
polar = np.arange(0,polar_stepN) * polar_step
testSym = symOri(test.as_matrix(), cs, ss)
#try transpose
testSym = testSym.transpose((0, 2, 1))
# convert to euler
testSym_eu = om2eu(testSym)
# pick fundamental zone
fz = (testSym_eu[:, 0] <= 2 * np.pi) & (testSym_eu[:, 1] <= np.pi / 2) & (
testSym_eu[:, 2] <= np.pi / 2)
fz_idx = np.nonzero(fz)
g_fz = testSym[fz_idx[0], :, :]
# generate crystal sym ops
crysSymOps = genSymOps(cs)
smplSymOps = genSymOps(ss)
# create Nx3 array of grid points
eu_grid = np.array(
[od.phi1cen.flatten(),
od.Phicen.flatten(),
od.phi2cen.flatten()]).T
g_grid = eu2om(eu_grid, out='mdarray_2')
g_grid = g_grid.transpose((2, 0, 1))
trace = {}
misori = {}
mo_cell = []
for gi, g in enumerate(g_fz):
""" symmetry after """
fibre_e = {}
fibre_q = {}
weights = {}
hkls = normalize(hkls)
""" search for unique hkls to save time """
hkls_loop, uni_hkls_idx, hkls_loop_idx = np.unique(hkls,
axis=0,
return_inverse=True,
return_index=True)
symHKL_loop = symmetrise(crystalSym, hkls_loop)
symHKL_loop = normalize(symHKL_loop)
symOps = genSymOps(crystalSym)
symOps = np.unique(np.swapaxes(symOps, 2, 0), axis=0)
""" only use proper rotations """
""" complicated, simplify? """
proper = np.where(np.linalg.det(symOps) == 1) #proper orthogonal
quatSymOps = quat.from_matrix(symOps[proper])
quatSymOps = np.tile(quatSymOps[:, :, np.newaxis], (1, 1, len(omega)))
quatSymOps = quatSymOps.transpose((2, 0, 1))
""" gen quats from bunge grid """
bungeAngs = np.zeros((np.product(od.phi1cen.shape), 3))
for ii, i in enumerate(np.ndindex(od.phi1cen.shape)):
bungeAngs[ii, :] = np.array((od.phi1cen[i], od.Phicen[i], od.phi2cen[i]))