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Add missing RFNetwork examples and update VectorFitting import syntax
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‎docs/source/examples/rf_network_oneport.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "34829d58-0d57-4e51-a072-85ba31ddae69",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"# RF Network - One Port"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a17a7d0b-3409-42db-9c73-928f1343e349",
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"metadata": {},
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"source": [
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"This is an example of a simulation of a Radio Frequency (RF) network with PathSim."
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]
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},
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{
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"cell_type": "raw",
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"id": "f7785b45-d223-4b21-9199-776db9bf9a47",
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"metadata": {
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"editable": true,
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"raw_mimetype": "text/x-rst",
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"In this example, we are using the block :class:`.RFNetwork` provided in PathSim to create the state-space model of an N-port RF network. This block uses the `scikit-rf <https://scikit-rf.readthedocs.io/en/latest/>`__ package to convert the frequency domain data into a state-space model. This conversion is performed using a `Vector Fitting <https://scikit-rf.readthedocs.io/en/latest/tutorials/VectorFitting.html>`__ method for fitting a rational function (model) to a set of frequency‐domain.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c9b536c4-9450-4b39-b719-a1f85f6f32b7",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"Let's first make all the necessary import"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "489bad7b-a5a3-4a12-b57d-8464e02a220c",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Apply PathSim docs matplotlib style for consistent, theme-friendly figures\n",
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"plt.style.use('../pathsim_docs.mplstyle')\n",
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"\n",
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"from pathsim import Simulation, Connection\n",
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"from pathsim.blocks import Spectrum, GaussianPulseSource\n",
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"from pathsim.solvers import RKBS32\n",
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"\n",
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"from pathsim_rf import RFNetwork # requires the scikit-rf package to be installed"
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]
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},
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{
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"cell_type": "raw",
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"id": "143be8a6-51b1-45ad-ba2b-5bc06011ad07",
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"metadata": {
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"editable": true,
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"raw_mimetype": "text/restructuredtext",
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"The block :class:`.RFNetwork` takes as input either a `Touchstone file <https://en.wikipedia.org/wiki/Touchstone_file>`__ (.sNp file) or a scikit-rf `Network <https://scikit-rf.readthedocs.io/en/latest/api/network.html>`__. An N-port network has N inputs and N outputs.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d89dffe4-7717-44b5-aaf2-3c618a3fd282",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The RF-Network block is created from a scikit-rf Network object example.\n",
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"# Here we use a frequency measurement example of a 1-port RF network (included in scikit-rf)\n",
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"import skrf as rf # for the example\n",
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"rfntwk = RFNetwork(rf.data.ring_slot_meas)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7dec1f58-7924-442d-9e91-d1719c31b947",
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"metadata": {},
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"source": [
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"Under the hood, scikit-rf performs a Vector Fitting of the frequency data and creates a PathSim State-Space model."
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]
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},
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{
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"cell_type": "raw",
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"id": "be668818-88e6-4648-8267-d11966f9cdda",
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"metadata": {
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"editable": true,
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"raw_mimetype": "text/restructuredtext",
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"In the following, we use a gaussian pulse to simulate the impulse response of the RF block. A spectrum analyzer (:class:`Spectrum`) is used to display the frequency response of the response. This frequency response is then compared to the original frequency data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e1308665-3910-44f5-8d63-a7a45881b388",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Gaussian pulse simulating an impulse response\n",
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"# Note that the scikit-rf Network object is passed as the 'network' parameter of the block,\n",
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"# which is convenient to access its frequency data.\n",
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"src = GaussianPulseSource(f_max=rfntwk.network.frequency.stop)\n",
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"\n",
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"# Spectrum analyser setup with the start and stop frequencies of the RF network\n",
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"spc = Spectrum(\n",
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" freq=rfntwk.network.f,\n",
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" labels=[\"pulse\", \"response\"]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67f427af-deb1-417c-b602-2d454fac9489",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# create the system connections and simulation setup\n",
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"sim = Simulation(\n",
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" blocks=[src, rfntwk, spc],\n",
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" connections=[\n",
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" Connection(src, rfntwk, spc[0]),\n",
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" Connection(rfntwk, spc[1])\n",
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" ],\n",
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" tolerance_lte_abs=1e-16, # this is due to the super tiny states\n",
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" tolerance_lte_rel=1e-5, # so error control is dominated by the relative truncation error\n",
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" Solver=RKBS32,\n",
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")\n",
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"\n",
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"sim.run(1e-9)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d659c9e6-560d-4a40-a11d-cc00faf00e7d",
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"metadata": {},
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"source": [
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"Below, we compare the PathSim's frequency response to the original measurement data and to their Vector Fitted model calculated with scikit-rf. The Vector Fitted model and the PathSim's frequency response are in perfect agreement:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b2d1f099-c806-4c11-b588-ed5ab1ba12fc",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# model frequency response H(f) recovered from the spectrum block\n",
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"freq, (G_pulse, G_filt) = spc.read()\n",
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"H_filt_sim = G_filt / G_pulse\n",
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"\n",
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"# plot the original S11 data, the vector-fitted model and the recovered frequency response\n",
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"fig, ax = plt.subplots()\n",
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"ax.plot(rfntwk.network.f/1e9, abs(rfntwk.network.s[:, 0, 0]), '.', label=\"S11 measurements\", alpha=0.5)\n",
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"ax.plot(freq/1e9, abs(rfntwk.vf.get_model_response(0, 0, freqs=freq)), lw=2, label=\"scikit-rf vector-fitting model\")\n",
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"ax.plot(freq/1e9, abs(H_filt_sim), '--', lw=2, label=\"pathsim impulse response\")\n",
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"ax.set_xlabel(\"Frequency [GHz]\")\n",
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"ax.set_ylabel(\"S11 magnitude\")\n",
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"ax.legend()\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "pathsim-rf",
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"language": "python",
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"name": "pathsim-rf"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎src/pathsim_rf/network.py‎

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from pathlib import Path
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import skrf as rf
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from skrf.vectorFitting import VectorFitting
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from pathsim.blocks.lti import StateSpace
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scikit-rf RF Network object, or file to load information from.
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Supported formats are touchstone file V1 (.s?p) or V2 (.ts).
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auto_fit : bool
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If True (default), use ``skrf.VectorFitting.auto_fit`` for automatic
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pole selection. If False, use ``skrf.VectorFitting.vector_fit``.
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If True (default), use ``skrf.vectorFitting.VectorFitting.auto_fit`` for automatic
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pole selection. If False, use ``skrf.vectorFitting.VectorFitting.vector_fit``.
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**kwargs
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Additional keyword arguments forwarded to the vector fitting function.
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# select the vector fitting function from scikit-rf
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vf_fun_name = "auto_fit" if auto_fit else "vector_fit"
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vf_fun = getattr(rf.VectorFitting, vf_fun_name)
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vf_fun = getattr(VectorFitting, vf_fun_name)
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# filter kwargs for the selected vf function
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vf_fun_keys = signature(vf_fun).parameters
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vf_kwargs = {k: v for k, v in kwargs.items() if k in vf_fun_keys}
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# apply vector fitting
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vf = rf.VectorFitting(ntwk)
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vf = VectorFitting(ntwk)
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getattr(vf, vf_fun_name)(**vf_kwargs)
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A, B, C, D, _ = vf._get_ABCDE()
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s : :py:class:`~numpy.ndarray`
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Complex-valued S-matrices (fxNxN) calculated at frequencies ``freqs``.
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"""
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return rf.VectorFitting._get_s_from_ABCDE(
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return VectorFitting._get_s_from_ABCDE(
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freqs, self.A, self.B, self.C, self.D, 0
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)

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