Add RFNetwork block migrated from pathsim core

Author: milanofthe

pyproject.toml

@@ -29,6 +29,7 @@ dependencies = [
     "pathsim>0.13",
     "numpy>=1.15",
     "scipy>=1.2",
+    "scikit-rf>=0.30",
 ]
 
 [project.optional-dependencies]

src/pathsim_rf/__init__.py

@@ -11,3 +11,5 @@
     __version__ = "unknown"
 
 __all__ = ["__version__"]
+
+from .network import *

src/pathsim_rf/network.py

@@ -0,0 +1,87 @@
+#########################################################################################
+##
+##                                  RF Network Block
+##
+#########################################################################################
+
+# IMPORTS ===============================================================================
+
+from __future__ import annotations
+
+import numpy as np
+
+from inspect import signature
+from pathlib import Path
+
+import skrf as rf
+
+from pathsim.blocks.lti import StateSpace
+
+# BLOCKS ================================================================================
+
+class RFNetwork(StateSpace):
+    """N-port RF network linear time invariant (LTI) MIMO state-space model.
+
+    Uses Vector Fitting for rational approximation of the frequency response
+    using poles and residues. The resulting approximation has guaranteed stable
+    poles that are real or come in complex conjugate pairs.
+
+    Assumes N inputs and N outputs, where N is the number of ports of the
+    RF network.
+
+    Parameters
+    ----------
+    ntwk : :py:class:`~skrf.network.Network`, str, or Path
+        scikit-rf RF Network object, or file to load information from.
+        Supported formats are touchstone file V1 (.s?p) or V2 (.ts).
+    auto_fit : bool
+        If True (default), use ``skrf.VectorFitting.auto_fit`` for automatic
+        pole selection. If False, use ``skrf.VectorFitting.vector_fit``.
+    **kwargs
+        Additional keyword arguments forwarded to the vector fitting function.
+
+    References
+    ----------
+    .. [skrf] scikit-rf webpage https://scikit-rf.org/
+    """
+
+    def __init__(self, ntwk: rf.Network | str | Path, auto_fit: bool = True, **kwargs):
+
+        if isinstance(ntwk, (Path, str)):
+            ntwk = rf.Network(ntwk)
+
+        # select the vector fitting function from scikit-rf
+        vf_fun_name = "auto_fit" if auto_fit else "vector_fit"
+        vf_fun = getattr(rf.VectorFitting, vf_fun_name)
+
+        # filter kwargs for the selected vf function
+        vf_fun_keys = signature(vf_fun).parameters
+        vf_kwargs = {k: v for k, v in kwargs.items() if k in vf_fun_keys}
+
+        # apply vector fitting
+        vf = rf.VectorFitting(ntwk)
+        getattr(vf, vf_fun_name)(**vf_kwargs)
+        A, B, C, D, _ = vf._get_ABCDE()
+
+        # keep a copy of the network and VF
+        self.network = ntwk
+        self.vf = vf
+
+        super().__init__(A, B, C, D)
+
+    def s(self, freqs: np.ndarray) -> np.ndarray:
+        """S-matrix of the vector fitted N-port model from its state-space representation.
+
+        Parameters
+        ----------
+        freqs : :py:class:`~numpy.ndarray`
+            Frequencies (in Hz) at which to calculate the S-matrices.
+
+        Returns
+        -------
+        s : :py:class:`~numpy.ndarray`
+            Complex-valued S-matrices (fxNxN) calculated at frequencies ``freqs``.
+        """
+        return rf.VectorFitting._get_s_from_ABCDE(
+            freqs, self.A, self.B, self.C, self.D, 0
+        )