"""A-matrix construction"""
import numpy
from scipy.linalg import block_diag, eig, inv, solve
from math import pi
from at.lattice import AtError
__all__ = ['a_matrix', 'amat', 'jmat', 'jmatswap',
'get_tunes_damp', 'get_mode_matrices', 'symplectify']
_i2 = numpy.array([[-1.j, -1.], [1., 1.j]])
# Prepare symplectic identity matrix
_j2 = numpy.array([[0., 1.], [-1., 0.]])
_jm = [_j2, block_diag(_j2, _j2), block_diag(_j2, _j2, _j2)]
_jmswap = [_j2, block_diag(_j2, _j2), block_diag(_j2, _j2, _j2.T)]
_vxyz = [_i2, block_diag(_i2, _i2), block_diag(_i2, _i2, _i2)]
_submat = [slice(0, 2), slice(2, 4), slice(4, 6)]
[docs]def jmat(ind: int):
"""antisymetric block diagonal matrix [[0, 1][-1, 0]]
Parameters:
ind: Matrix dimension: 1, 2 or 3
Returns:
S: Block diagonal matrix, (2, 2) or (4, 4) or (6, 6)
"""
return _jm[ind - 1]
[docs]def jmatswap(ind: int):
"""Modified antisymetric block diagonal matrix to deal with the swap of the
longitudinal coordinates
"""
return _jmswap[ind - 1]
# noinspection PyPep8Naming
[docs]def a_matrix(M):
r"""Find the :math:`\mathbf{A}` matrix from one turn map :math:`\mathbf{M}`
:math:`\mathbf{A}` represents a change of referential which converts the
one-turn transfer matrix :math:`\mathbf{M}` into a set of rotations:
.. math:: \mathbf{M} = \mathbf{A} \cdot \mathbf{R} \cdot \mathbf{A}^{-1}
with :math:`\mathbf{R}` A block-diagonal matrix:
.. math::
\mathbf{R}=\begin{pmatrix}rot_1 & 0 & 0 \\ 0 & rot_2 & 0 \\ 0 & 0 &
rot_3\end{pmatrix} \text{, and } rot_i = \begin{pmatrix}\cos{\mu_i} &
\sin{\mu_i} \\ -\sin{\mu_i} & cos{\mu_i}\end{pmatrix}
With radiation damping, the diagonal blocks are instead damped rotations:
.. math::
rot_i = \begin{pmatrix}\exp{(-\alpha_i}) & 0 \\ 0 & \exp{(-\alpha_i)}
\end{pmatrix} \cdot \begin{pmatrix}\cos{\mu_i} & \sin{\mu_i} \\
-\sin{\mu_i} & cos{\mu_i}\end{pmatrix}
The order of diagonal blocks it set so that it is close to the order of
x,y,z.
Parameters:
M: (m, m) transfer matrix for 1 turn
m, the dimension of :math:`\mathbf{M}`, may be 2 (single plane),
4 (betatron motion) or 6 (full motion)
Returns:
A: (m, m) A-matrix
eigval: (m/2,) Vector of Eigen values of T
References:
**[1]** Etienne Forest, Phys. Rev. E 58, 2481 – Published 1 August 1998
"""
nv = M.shape[0]
dms = int(nv / 2)
jmt = jmatswap(dms)
select = numpy.arange(0, nv, 2)
rbase = numpy.stack((select, select), axis=1).flatten()
# noinspection PyTupleAssignmentBalance
lmbd, vv = eig(M)
# Compute the norms
vp = vv.conj().T @ jmt
n = -0.5j * numpy.sum(vp.T * vv, axis=0)
if any(abs(n) < 1.0E-12):
raise AtError('Unstable ring')
# Move positive before negatives
order = rbase + (n < 0)
vv = vv[:, order]
n = n[order]
lmbd = lmbd[order]
# Normalize vectors
vn = vv / numpy.sqrt(abs(n)).reshape((1, nv))
# find the vectors that project most onto x,y,z, and reorder
# nn will have structure
# n1x n1y n1z
# n2x n2y n2z
# n3x n3y n3z
nn = 0.5 * abs(numpy.sqrt(-1.j * vn.conj().T @ jmt @ _vxyz[dms - 1]))
rows = list(select)
order = []
for ixz in select:
ind = numpy.argmax(nn[rows, ixz])
order.append(rows[ind])
del rows[ind]
v_ordered = vn[:, order]
lmbd = lmbd[order]
aa = numpy.vstack((numpy.real(v_ordered), numpy.imag(v_ordered))).reshape(
(nv, nv), order='F')
return aa, lmbd
# noinspection PyPep8Naming
[docs]def amat(M):
"""Find the A matrix from one turn map matrix T
Provided for backward compatibility, see :py:func:`a_matrix`
Parameters:
M: (m, m) transfer matrix for 1 turn
Returns:
A: (m, m) A-matrix
"""
aa, _ = a_matrix(M)
return aa
# noinspection PyPep8Naming
[docs]def symplectify(M):
"""Makes A matrix more symplectic
following the Healy algorithm described by MacKay
Parameters:
M: Almost symplectic matrix
Returns:
MS: Symplectic matrix
References:
**[1]** `W.W.MacKay, Comment on Healy's symplectification algorithm,
Proceedings of EPAC 2006
<https://accelconf.web.cern.ch/e06/PAPERS/WEPCH152.PDF>`_
"""
nv = M.shape[0]
S = jmat(nv // 2)
I = numpy.identity(nv)
V = S @ (I - M) @ inv(I + M)
# V should be almost symmetric. Replace with symmetrized version.
W = (V + V.T) / 2
# Now reconstruct M from W
SW = S @ W
MS = (I + SW) @ inv(I - SW)
return MS
# noinspection PyPep8Naming
[docs]def get_mode_matrices(A):
"""Derives the R-matrices from the A-matrix
Parameters:
A: A-matrix
Returns:
R:
References:
**[1]** Andrzej Wolski, Phys. Rev. ST Accel. Beams 9, 024001 –
Published 3 February 2006
"""
def mul2(slc):
return A[:, slc] @ tt[slc, slc]
dms = A.shape[0] // 2
# Rk = A * Ik * A.T Only for symplectic
# modelist = [numpy.dot(A[:, sl], A.T[sl, :]) for sl in _submat[:dms]]
# Rk = A * S * Ik * inv(A) * S.T Even for non-symplectic
ss = jmat(dms)
tt = jmatswap(dms)
a_s = numpy.concatenate([mul2(slc) for slc in _submat[:dms]], axis=1)
inva = solve(A, ss.T)
modelist = [a_s[:, sl] @ inva[sl, :] for sl in _submat[:dms]]
return numpy.stack(modelist, axis=0)
# noinspection PyPep8Naming
[docs]def get_tunes_damp(M, R=None):
r"""Computes the mode emittances, tunes and damping times
Parameters:
M: (m, m) transfer matrix for 1 turn
R: (m, m) beam matrix (optional), allows computing the mode
emittances
m, the dimension of :math:`\mathbf{M}`, may be 2 (single plane),
4 (betatron motion) or 6 (full motion)
Returns:
V: record array with the following fields:
**tunes** (m/2,) tunes of the m/2 normal modes
**damping_rates** (m/2,) damping rates of the m/2 normal modes
**mode_matrices** (m/2, m, m) the R-matrices of the m/2 normal
modes
**mode_emittances** Only if R is specified: (m/2,) emittance of each
of the m/2 normal modes
"""
nv = M.shape[0]
dms = int(nv / 2)
A, vps = a_matrix(M)
tunes = numpy.mod(numpy.angle(vps) / 2.0 / pi, 1.0)
damping_rates = -numpy.log(numpy.absolute(vps))
if R is None:
return numpy.rec.fromarrays(
(numpy.array(tunes), numpy.array(damping_rates),
numpy.array(get_mode_matrices(A))),
dtype=[('tunes', numpy.float64, (dms,)),
('damping_rates', numpy.float64, (dms,)),
('mode_matrices', numpy.float64, (dms, nv, nv))]
)
else:
jmt = jmat(dms)
rdiag = numpy.diag(A.T @ jmt @ R @ jmt @ A)
mode_emit = -0.5 * (rdiag[0:nv:2] + rdiag[1:nv:2])
return numpy.rec.fromarrays(
(numpy.array(tunes), numpy.array(damping_rates),
numpy.array(get_mode_matrices(A)), mode_emit),
dtype=[('tunes', numpy.float64, (dms,)),
('damping_rates', numpy.float64, (dms,)),
('mode_matrices', numpy.float64, (dms, nv, nv)),
('mode_emittances', numpy.float64, (dms,))]
)