def computeNz(N):
Nx, Ny = N[:, 0], N[:, 1]
Nxy_sq = l2NormVectors(Nx) + l2NormVectors(Ny)
Nz_sq = 1.0 - Nxy_sq
Nz_sq = np.clip(Nz_sq, 0.0, 1.0)
Nz = np.sqrt(Nz_sq)
Nz = np.clip(Nz, 0.0, 1.0)
N_new = np.array(N)
N_new[:, 2] = Nz
return N_new
def NxyToNz(N_32F):
N = N_32F.reshape(-1, 3)
Nx, Ny = N[:, 0], N[:, 1]
Nxy_sq = l2NormVectors(Nx) + l2NormVectors(Ny)
Nz_sq = 1.0 - Nxy_sq
Nz_sq = np.clip(Nz_sq, 0.0, 1.0)
Nz = np.sqrt(Nz_sq)
Nz = np.clip(Nz, 0.0, 1.0)
N_new = np.array(N)
N_new[:, 2] = Nz
return N_new.reshape(N_32F.shape)
def NxyToNz(N_32F):
N = N_32F.reshape(-1, 3)
Nx, Ny = N[:, 0], N[:, 1]
Nxy_sq = l2NormVectors(Nx) + l2NormVectors(Ny)
Nz_sq = 1.0 - Nxy_sq
Nz_sq = np.clip(Nz_sq, 0.0, 1.0)
Nz = np.sqrt(Nz_sq)
Nz = np.clip(Nz, 0.0, 1.0)
N_new = np.array(N)
N_new[:, 2] = Nz
return N_new.reshape(N_32F.shape)
