def angles_to_dcms(rotations, sequence=(2, 1, 0)):
"""Builds an euler angle rotation matrix
Assumptions:
N/A
Source:
N/A
Inputs:
rotations [radians] [r1s r2s r3s], column array of rotations
sequence [-] (2,1,0) (default), (2,1,2), etc.. a combination of three column indices
Outputs:
transform [-] 3-dimensional array with direction cosine matrices
patterned along dimension zero
Properties Used:
N/A
"""
# transform map
Ts = {0: T0, 1: T1, 2: T2}
# a bunch of eyes
transform = new_tensor(rotations[:, 0])
# build the tranform
for dim in sequence[::-1]:
angs = rotations[:, dim]
transform = orientation_product(transform, Ts[dim](angs))
# done!
return transform
def angles_to_dcms(rotations,sequence=(2,1,0)):
""" transform = angles_to_dcms([r1s,r2s,r3s],seq)
builds euler angle rotation matrix
Inputs:
rotations = [r1s r2s r3s], column array of rotations
sequence = (2,1,0) (default)
(2,1,2)
etc...
a combination of three column indeces
Outputs:
transform = 3-dimensional array with direction cosine matricies
patterned along dimension zero
"""
# transform map
Ts = { 0:T0, 1:T1, 2:T2 }
# a bunch of eyes
transform = new_tensor(rotations[:,0])
# build the tranform
for dim in sequence[::-1]:
angs = rotations[:,dim]
transform = orientation_product( transform, Ts[dim](angs) )
# done!
return transform
def angles_to_dcms(rotations,sequence=(2,1,0)):
"""Builds an euler angle rotation matrix
Assumptions:
N/A
Source:
N/A
Inputs:
rotations [radians] [r1s r2s r3s], column array of rotations
sequence [-] (2,1,0) (default), (2,1,2), etc.. a combination of three column indices
Outputs:
transform [-] 3-dimensional array with direction cosine matrices
patterned along dimension zero
Properties Used:
N/A
"""
# transform map
Ts = { 0:T0, 1:T1, 2:T2 }
# a bunch of eyes
transform = new_tensor(rotations[:,0])
# build the tranform
for dim in sequence[::-1]:
angs = rotations[:,dim]
transform = orientation_product( transform, Ts[dim](angs) )
# done!
return transform
Fx = np.linspace(0, 10, n_t)
Fy = np.linspace(0, 10, n_t)
Fz = np.linspace(0, 10, n_t)
F = np.array([Fx, Fy, Fz]).T
print rotations
print F
print '\n'
T = angles_to_dcms(rotations, [2, 1, 0])
print T
print '\n'
F2 = orientation_product(T, F)
F2_expected = np.array([[0., 0., 0.], [4.17578046, 0.99539256, 0.56749556],
[7.9402314, -1.50584339, -3.11209913],
[8.04794057, -6.95068339, -7.46114288],
[7.04074369, -13.25444263, -8.64567399]])
print F2
print '\n'
print 'should be nearly zero:'
print np.sum(F2 - F2_expected)
print '\n'
Tt = orientation_transpose(T)
F3 = orientation_product(Tt, F2)
Fx = np.linspace(0,10,n_t)
Fy = np.linspace(0,10,n_t)
Fz = np.linspace(0,10,n_t)
F = np.array([Fx,Fy,Fz]).T
print rotations
print F
print '\n'
T = angles_to_dcms(rotations,[2,1,0])
print T
print '\n'
F2 = orientation_product(T,F)
F2_expected = np.array(
[[ 0. , 0. , 0. ],
[ 4.17578046, 0.99539256, 0.56749556],
[ 7.9402314 , -1.50584339, -3.11209913],
[ 8.04794057, -6.95068339, -7.46114288],
[ 7.04074369, -13.25444263, -8.64567399]]
)
print F2
print '\n'
print 'should be nearly zero:'
print np.sum(F2-F2_expected)
print '\n'
