def deembedleft(e, X):
"""deembedleft deembeds S-matrix e from S-matrix X from the left
e, and S should all be on the four-matrix form:
Frequency is first index, measurement sweep is second index and port
indices are third and fourth index.
"""
X, e = make_same_dims(X, e)
neworder = tuple([x for x in X.dims if not isinstance(x, _DimMatrix)])
X = X.reorder_dimensions(*neworder)
e = e.reorder_dimensions(*neworder)
klass, dims = X.__class__, X.dims
# X, e = X.view(type=ndarray), X.view(type=ndarray)
denom = (e[..., 0, 1] * e[..., 1, 0] - e[..., 0, 0] * e[..., 1, 1] +
e[..., 1, 1] * X[..., 0, 0])
maxshape = tuple(max(x) for x in zip(X.shape, e.shape))
res = X.__class__(np.zeros(maxshape, X.dtype), dims=X.dims)
e11 = (X[..., 0, 0] - e[..., 0, 0])
e12 = (X[..., 0, 1] * e[..., 1, 0])
e21 = (X[..., 1, 0] * e[..., 0, 1])
e22 = (-X[..., 0, 1] * X[..., 1, 0] * e[..., 1, 1])
res[..., 0, 0] = e11
res[..., 0, 1] = e12
res[..., 1, 0] = e21
res[..., 1, 1] = e22
res = res / denom
res[..., 1, 1] = res[..., 1, 1] + X[..., 1, 1]
res = klass(res, dims=dims)
return res
def deembedleft(e, X):
"""deembedleft deembeds S-matrix e from S-matrix X from the left
e, and S should all be on the four-matrix form:
Frequency is first index, measurement sweep is second index and port
indices are third and fourth index.
"""
X, e = make_same_dims(X, e)
neworder = tuple([x for x in X.dims if not isinstance(x, _DimMatrix)])
X = X.reorder_dimensions(*neworder)
e = e.reorder_dimensions(*neworder)
klass, dims = X.__class__, X.dims
# X, e = X.view(type=ndarray), X.view(type=ndarray)
denom = e[..., 0, 1] * e[..., 1, 0] - e[..., 0, 0] * e[..., 1, 1] + e[..., 1, 1] * X[..., 0, 0]
maxshape = tuple(max(x) for x in zip(X.shape, e.shape))
res = X.__class__(np.zeros(maxshape, X.dtype), dims=X.dims)
e11 = X[..., 0, 0] - e[..., 0, 0]
e12 = X[..., 0, 1] * e[..., 1, 0]
e21 = X[..., 1, 0] * e[..., 0, 1]
e22 = -X[..., 0, 1] * X[..., 1, 0] * e[..., 1, 1]
res[..., 0, 0] = e11
res[..., 0, 1] = e12
res[..., 1, 0] = e21
res[..., 1, 1] = e22
res = res / denom
res[..., 1, 1] = res[..., 1, 1] + X[..., 1, 1]
res = klass(res, dims=dims)
return res
def cascadeS(S1, S2, *rest):
"""Cascade arrays containing S-parameters.
"""
S1, S2 = make_same_dims(S1, S2)
neworder = tuple([x for x in S1.dims if not isinstance(x, _DimMatrix)])
S1 = S1.reorder_dimensions(*neworder)
S2 = S2.reorder_dimensions(*neworder)
denom = (1 - S1[..., 1, 1] * S2[..., 0, 0])
s1det = det(S1)
s2det = det(S2)
maxshape = tuple(max(x) for x in zip(S1.shape, S2.shape))
res = S1.__class__(np.zeros(maxshape, S1.dtype), dims=S1.dims)
res[..., 0, 0] = S1[..., 0, 0] - S2[..., 0, 0] * s1det
res[..., 0, 1] = S1[..., 0, 1] * S2[..., 0, 1]
res[..., 1, 0] = S1[..., 1, 0] * S2[..., 1, 0]
res[..., 1, 1] = S2[..., 1, 1] - S1[..., 1, 1] * s2det
res = res / denom
res = S1.__class__(res, dims=S1.dims)
return res
def cascadeS(S1, S2, *rest):
"""Cascade arrays containing S-parameters.
"""
S1, S2 = make_same_dims(S1, S2)
neworder = tuple([x for x in S1.dims if not isinstance(x, _DimMatrix)])
S1 = S1.reorder_dimensions(*neworder)
S2 = S2.reorder_dimensions(*neworder)
denom = 1 - S1[..., 1, 1] * S2[..., 0, 0]
s1det = det(S1)
s2det = det(S2)
maxshape = tuple(max(x) for x in zip(S1.shape, S2.shape))
res = S1.__class__(np.zeros(maxshape, S1.dtype), dims=S1.dims)
res[..., 0, 0] = S1[..., 0, 0] - S2[..., 0, 0] * s1det
res[..., 0, 1] = S1[..., 0, 1] * S2[..., 0, 1]
res[..., 1, 0] = S1[..., 1, 0] * S2[..., 1, 0]
res[..., 1, 1] = S2[..., 1, 1] - S1[..., 1, 1] * s2det
res = res / denom
res = S1.__class__(res, dims=S1.dims)
return res
