Le = zeros((Nc,4))
La = zeros(Nc)
## Find transformation to take alternative orientations to Corisco
## reference frame.
tt = solve_transformation(Lq, Lr)
Lr_hat = zeros((Nc,4))
for n in range(Nc):
Lr_hat[n] = (tt * Quat(Lq[n])).q
dd = [{"set": s, "frame": fr}
for s in ["set-apa09.json", "set-apa08.json"]
for fr in range(24)]
#print zip(dd, Lr_hat)
tref = [dict(meta.items() + [("ori_ref", ori.tolist())])
for meta, ori in zip(dd, Lr_hat)]
savetxt('tardiff_apastundistorted_ref_transformed.dat', Lr_hat)
# with open('transformed_reference_apaundistorted.dat', 'w') as fp:
# json.dump(tref, fp, indent=4)
## Calculate errors between each estimated orientation and
## transformed reference.
for n in range(Nc):
Le[n] = ( Quat(Lr_hat[n]) / Quat(Lr[n]) ).canonical().q
La[n] = Quat(Le[n]).angle()
ion()
fix_reference_quaternions(Lr)
Le = zeros((Nc, 4))
La = zeros(Nc)
## Find transformation to take alternative orientations to Corisco
## reference frame.
tt = solve_transformation(Lq, Lr)
Lr_hat = zeros((Nc, 4))
for n in range(Nc):
Lr_hat[n] = (tt * Quat(Lq[n])).q
dd = [{"set": s, "frame": fr} for s in ["set-apa09.json", "set-apa08.json"] for fr in range(24)]
# print zip(dd, Lr_hat)
tref = [dict(meta.items() + [("ori_ref", ori.tolist())]) for meta, ori in zip(dd, Lr_hat)]
with open("transformed_reference_apa.dat", "w") as fp:
json.dump(tref, fp, indent=4)
## Calculate errors between each estimated orientation and
## transformed reference.
for n in range(Nc):
Le[n] = (Quat(Lr_hat[n]) / Quat(Lr[n])).canonical().q
La[n] = Quat(Le[n]).angle()
ion()
figure(4, figsize=(8 * 0.8, 6 * 0.8))
title(u"Distribuição acumulada de erros")
xlabel("Erro [graus]")
