move some functions
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@ -364,7 +364,7 @@ def generalize_S(
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g = (z0 - r0) / (z0 + r0)
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D = numpy.diag((1 - g) / numpy.abs(1 - g.conj()) * numpy.sqrt(1 - numpy.abs(g * g.conj())))
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G = numpy.diag(g)
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U = numpy.eye(S.shape[0])
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U = numpy.eye(S.shape[-1]).reshape((S.ndim - 2) * (1,) + (S.shape[-2], S.shape[-1]))
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S_gen = pinv(D.conj()) @ (S - G.conj()) @ pinv(U - G @ S) @ D
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return S_gen
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@ -375,7 +375,7 @@ def change_R0(
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r2: float,
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) -> NDArray[numpy.complex128]:
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g = (r2 - r1) / (r2 + r1)
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U = numpy.eye(S.shape[0])
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U = numpy.eye(S.shape[-1]).reshape((S.ndim - 2) * (1,) + (S.shape[-2], S.shape[-1]))
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G = U * g
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S_r2 = (S - G) @ pinv(U - G @ S)
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return S_r2
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200
spconv.py
200
spconv.py
@ -1,6 +1,8 @@
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import scipy
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import numpy
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from numpy import sqrt, real, abs
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from numpy.typing import ArrayLike, NDArray
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from numpy.linalg import pinv
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from numpy import sqrt, real, abs, pi
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def diag(twod):
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@ -29,7 +31,9 @@ def change_of_zref(
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# A = inv(G0') @ G0 @ inv(I - rho*) (diagonal)
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# rho = (Z0' - Z0) @ inv(Z0' + Z0) (diagonal)
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I = numpy.eye(SL.shape[-1])[None, :, :]
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I = numpy.zeros_like(SL)
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numpy.einsum('...jj->...j', I)[...] = 1
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zref_old = numpy.array(zref_old, copy=False)
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zref_new = numpy.array(zref_new, copy=False)
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@ -51,10 +55,14 @@ def embedding(
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SL, # (nf, ni, ni)
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):
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# Reveyrand, doi:10.1109/INMMIC.2018.8430023
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I = numpy.eye(SL.shape[-1])[None, :, :]
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I = numpy.zeros_like(SL)
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numpy.einsum('...jj->...j', I)[...] = 1
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S_tot = See + Sei @ pinv(I - SL @ Sii) @ SL @ Sie
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return S_tot
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def deembedding(
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Sei, # (nf, ne, ni)
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Sie, # (nf, ni, ne)
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@ -73,14 +81,188 @@ def thru_with_Zref_change(
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zref0, # (nf,)
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zref1, # (nf,)
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):
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nf = zref0.shape[0]
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s = numpy.empty((nf, 2, 2), dtype=complex)
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s[:, 0, 0] = zref1 - zref0
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s[:, 0, 1] = 2 * sqrt(zref0 * zref1)
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s[:, 1, 0] = s[:, 0, 1]
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s[:, 1, 1] = -s[:, 0, 0]
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s = numpy.empty(tuple(zref0.shape) + (2, 2), dtype=complex)
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s[..., 0, 0] = zref1 - zref0
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s[..., 0, 1] = 2 * sqrt(zref0 * zref1)
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s[..., 1, 0] = s[..., 0, 1]
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s[..., 1, 1] = -s[..., 0, 0]
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s /= zref0 + zref1
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return s
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def propagation_matrix(mode_neffs: ArrayLike, wavelength: float, distance: float):
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eigenv = numpy.array(mode_neffs, copy=False) * 2 * pi / wavelength
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prop_diag = numpy.diag(numpy.exp(distance * 1j * numpy.hstack((eigenv, eigenv))))
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prop_matrix = numpy.roll(prop_diag, len(eigenv), axis=0)
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return prop_matrix
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def connect_s(
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A: NDArray[numpy.complex128],
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k: int,
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B: NDArray[numpy.complex128],
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l: int,
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) -> NDArray[numpy.complex128]:
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"""
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TODO
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freq x ... x n x n
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Based on skrf implementation
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Connect two n-port networks' s-matrices together.
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Specifically, connect port `k` on network `A` to port `l` on network
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`B`. The resultant network has nports = (A.rank + B.rank-2); first
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(A.rank - 1) ports are from `A`, remainder are from B.
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Assumes same reference impedance for both `k` and `l`; may need to
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connect an "impedance mismatch" thru element first!
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Args:
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A: S-parameter matrix of `A`, shape is fxnxn
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k: port index on `A` (port indices start from 0)
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B: S-parameter matrix of `B`, shape is fxnxn
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l: port index on `B`
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Returns:
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new S-parameter matrix
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"""
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if k > A.shape[-1] - 1 or l > B.shape[-1] - 1:
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raise ValueError("port indices are out of range")
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#C = scipy.sparse.block_diag((A, B), dtype=complex)
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#return innerconnect_s(C, k, A.shape[0] + l)
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nA = A.shape[-1]
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nB = B.shape[-1]
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nC = nA + nB - 2
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assert numpy.array_equal(A.shape[:-2], B.shape[:-2])
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ll = slice(l, l + 1)
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kk = slice(k, k + 1)
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denom = 1 - A[..., kk, kk] * B[..., ll, ll]
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Anew = A + A[..., kk, :] * B[..., ll, ll] * A[..., :, kk] / denom
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Bnew = A[..., kk, :] * B[..., :, ll] / denom
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Anew = numpy.delete(Anew, (k,), 1)
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Anew = numpy.delete(Anew, (k,), 2)
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Bnew = numpy.delete(Bnew, (l,), 1)
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Bnew = numpy.delete(Bnew, (l,), 2)
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dtype = (A[0, 0] * B[0, 0]).dtype
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C = numpy.zeros(tuple(A.shape[:-2]) + (nC, nC), dtype=dtype)
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C[..., :nA - 1, :nA - 1] = Anew
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C[..., nA - 1:, nA - 1:] = Bnew
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return C
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def innerconnect_s(
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S: NDArray[numpy.complex128],
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k: int,
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l: int,
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) -> NDArray[numpy.complex128]:
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"""
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TODO
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freq x ... x n x n
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Based on skrf implementation
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Connect two ports of a single n-port network's s-matrix.
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Specifically, connect port `k` to port `l` on `S`. This results in
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a (n-2)-port network.
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Assumes same reference impedance for both `k` and `l`; may need to
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connect an "impedance mismatch" thru element first!
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Args:
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S: S-parameter matrix of `S`, shape is fxnxn
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k: port index on `S` (port indices start from 0)
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l: port index on `S`
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Returns:
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new S-parameter matrix
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Notes:
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- Compton, R.C., "Perspectives in microwave circuit analysis",
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doi:10.1109/MWSCAS.1989.101955
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- Filipsson, G., "A New General Computer Algorithm for S-Matrix Calculation
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of Interconnected Multiports",
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doi:10.1109/EUMA.1981.332972
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"""
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if k > S.shape[-1] - 1 or l > S.shape[-1] - 1:
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raise ValueError("port indices are out of range")
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ll = slice(l, l + 1)
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kk = slice(k, k + 1)
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mkl = 1 - S[..., kk, ll]
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mlk = 1 - S[..., ll, kk]
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C = S + (
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S[..., kk, :] * S[..., :, l] * mlk
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+ S[..., ll, :] * S[..., :, k] * mkl
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+ S[..., kk, :] * S[..., l, l] * S[..., :, kk]
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+ S[..., ll, :] * S[..., k, k] * S[..., :, ll]
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) / (
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mlk * mkl - S[..., kk, kk] * S[..., ll, ll]
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)
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# remove connected ports
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C = numpy.delete(C, (k, l), 1)
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C = numpy.delete(C, (k, l), 2)
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return C
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def s2abcd(
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S: NDArray[numpy.complex128],
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z0: NDArray[numpy.complex128],
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) -> NDArray[numpy.complex128]:
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assert numpy.array_equal(S.shape[:2] == (2, 2))
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Z1, Z2 = z0
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cross = S[0, 1] * S[1, 0]
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T = numpy.empty_like(S, dtype=complex)
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T[0, 0, :] = (Z1.conj() + S[0, 0] * Z1) * (1 - S[1, 1]) + cross * Z1 # A numerator
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T[0, 1, :] = (Z1.conj() + S[0, 0] * Z1) * (Z1.conj() + S[1, 1] * Z2) - cross * Z1 * Z2 # B numerator
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T[1, 0, :] = (1 - S[0, 0]) * (1 - S[1, 1]) - cross # C numerator
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T[1, 1, :] = (1 - S[0, 0]) * (Z2.conj() + S[1, 1] * Z2) + cross * Z2 # D numerator
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det = 2 * S[1, 0] * numpy.sqrt(Z1.real * Z2.real)
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T /= det
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return T
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def generalize_S(
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S: NDArray[numpy.complex128],
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r0: float,
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z0: NDArray[numpy.complex128],
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) -> NDArray[numpy.complex128]:
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g = (z0 - r0) / (z0 + r0)
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D = numpy.diag((1 - g) / numpy.abs(1 - g.conj()) * numpy.sqrt(1 - numpy.abs(g * g.conj())))
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G = numpy.diag(g)
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U = numpy.eye(S.shape[-1]).reshape((S.ndim - 2) * (1,) + (S.shape[-2], S.shape[-1]))
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S_gen = pinv(D.conj()) @ (S - G.conj()) @ pinv(U - G @ S) @ D
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return S_gen
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def change_R0(
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S: NDArray[numpy.complex128],
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r1: float,
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r2: float,
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) -> NDArray[numpy.complex128]:
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g = (r2 - r1) / (r2 + r1)
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U = numpy.eye(S.shape[-1]).reshape((S.ndim - 2) * (1,) + (S.shape[-2], S.shape[-1]))
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G = U * g
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S_r2 = (S - G) @ pinv(U - G @ S)
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return S_r2
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# Zc = numpy.sqrt(B / C)
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# gamma = numpy.arccosh(A) / L_TL
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# n_eff = -1j * gamma * c_light / (2 * pi * f)
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# n_eff_grp = n_eff + f * diff(n_eff) / diff(f)
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# attenuation = (1 - S[0, 0] * S[0, 0].conj()) / (S[1, 0] * S[1, 0].conj())
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# R = numpy.real(gamma * Zc)
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# C = numpy.real(gamma / Zc)
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# L = numpy.imag(gamma * Zc) / (-1j * 2 * pi * f)
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# G = numpy.imag(gamma / Zc) / (-1j * 2 * pi * f)
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