T-Resonator Model Interactive.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ITER Window test-bed T-Resonator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import skrf as rf\n",
    "from skrf.media import Coaxial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the DUT S-parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DUT0=rf.Network('S-parameters/Tresonator_window_front_window.s2p')\n",
    "DUT0.plot_s_db(m=1, n=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the T-resonator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tresonator():\n",
    "    def __init__(self, L_CEA=100, L_DUT=200, f=None):\n",
    "        self.Z_short_DUT = 1e-5\n",
    "        self.Z_short_CEA = 2e-2\n",
    "        self.L_CEA = L_CEA\n",
    "        self.L_DUT = L_DUT\n",
    "\n",
    "        self.DUT = rf.Network('S-parameters/Tresonator_window_front_window.s2p')\n",
    "\n",
    "        # the frequency range is the one of the DUT\n",
    "        if f:\n",
    "            self.frequency = rf.Frequency(f, f, npoints=1, unit='MHz')\n",
    "        else:    \n",
    "            self.frequency = rf.Frequency(start=50, stop=60, npoints=200, unit='MHz')\n",
    "        self.DUT = self.DUT.interpolate(self.frequency)\n",
    "\n",
    "        # electrical conductivities\n",
    "        self.conductivity_SS = 1/69e-8 # 1.45e6 in Wikipedia\n",
    "        self.conductivity_Cu = 1/1.682e-8 # 5.8e7 # Annealed copper.  5.96e7 for pure Cu in Wikipedia\n",
    "        self.conductivity_Cu = 34578645  # Copper 200deg \n",
    "        self.conductivity_Ag = 1/1.59e-8 #6.3e7 # Wikipedia\n",
    "        self.conductivity_Al = 1/2.65e-8 #3.8e7 # Wikipedia\n",
    "\n",
    "    @property\n",
    "    def circuit(self):\n",
    "        Tee = Coaxial(frequency=self.frequency, Dint=0.140,  Dout=0.230, epsilon_r=1, sigma=self.conductivity_Ag).tee(name='Tee')\n",
    "\n",
    "        # Equivalent DUT\n",
    "#         self.DUT = Coaxial(frequency=self.frequency, Dint=0.140, Dout=0.196, epsilon_r=1, sigma=self.conductivity_Cu).line()\n",
    "        \n",
    "        # CEA Branch\n",
    "        C0 = Coaxial(frequency=self.frequency, Dint=0.140,  Dout=0.230, epsilon_r=1, sigma=self.conductivity_Ag).line(200, unit='mm', name='C0')  # T (1/2)\n",
    "        C1 = Coaxial(frequency=self.frequency, Dint=0.130,  Dout=0.294, epsilon_r=1, sigma=self.conductivity_Ag).line(180, unit='mm', name='C1')\n",
    "        C2 = Coaxial(frequency=self.frequency, Dint=0.140,  Dout=0.230, epsilon_r=1, sigma=self.conductivity_Al).line(2000, unit='mm', name='C2')\n",
    "        _C3 = Coaxial(frequency=self.frequency, Dint=0.140,  Dout=0.230, epsilon_r=1, sigma=self.conductivity_Al)\n",
    "        C3 = _C3.line(self.L_CEA, unit='mm', name='C3')\n",
    "\n",
    "        # DUT Branch\n",
    "        D0 = Coaxial(frequency=self.frequency, Dint=0.140,  Dout=0.230, epsilon_r=1, sigma=self.conductivity_Ag).line(200, unit='mm', name='D0') # T (1/2)\n",
    "        D1 = Coaxial(frequency=self.frequency, Dint=0.140, Dout=0.230, epsilon_r=1, sigma=self.conductivity_Ag).line(100, unit='mm', name='D1')\n",
    "        LB1 = Coaxial(frequency=self.frequency, Dint=0.140, Dout=0.410, epsilon_r=1, sigma=self.conductivity_Cu).line(239, unit='mm', name='LB1')\n",
    "        LB2 = Coaxial(frequency=self.frequency, Dint=0.140, Dout=0.230, epsilon_r=1, sigma=self.conductivity_Cu).line(1224, unit='mm', name='LB2')\n",
    "        LB3 = Coaxial(frequency=self.frequency, Dint=0.140, Dout=0.230, epsilon_r=1, sigma=self.conductivity_Cu).line(200, unit='mm', name='LB3')\n",
    "        _LB5 = Coaxial(frequency=self.frequency, Dint=0.140, Dout=0.230, epsilon_r=1, sigma=self.conductivity_Cu)\n",
    "        LB5 = _LB5.line(self.L_DUT, unit='mm', name='LB5')\n",
    "\n",
    "        # T-Resonator Input port\n",
    "        port1 = rf.Circuit.Port(frequency=self.frequency, z0=30, name='port1')\n",
    "        # Short circuits\n",
    "        resistor_dut = _LB5.resistor(self.Z_short_DUT, name='short_dut')\n",
    "        resistor_cea = _C3.resistor(self.Z_short_CEA, name='short_cea')\n",
    "        gnd_dut = rf.Circuit.Ground(frequency=self.frequency, z0=_LB5.z0[0], name='gnd_dut')\n",
    "        gnd_cea = rf.Circuit.Ground(frequency=self.frequency, z0=_C3.z0[0], name='gnd_cea')\n",
    "\n",
    "        cnx = [\n",
    "            # T-junction\n",
    "            [(port1, 0), (Tee, 0)], \n",
    "            [(Tee, 1), (C0, 0)], [(Tee, 2), (D0, 0)],\n",
    "            # DUT Branch\n",
    "            [(D0, 1), (D1, 0)],\n",
    "            [(D1, 1), (LB1, 0)],\n",
    "            [(LB1, 1), (LB2, 0)],\n",
    "            [(LB2, 1), (LB3, 0)],\n",
    "            [(LB3, 1), (self.DUT, 1)],\n",
    "            [(self.DUT, 0), (LB5, 0)],\n",
    "            [(LB5, 1), (resistor_dut, 0)],\n",
    "            [(resistor_dut, 1), (gnd_dut, 0)],\n",
    "            # CEA branch\n",
    "            [(C0, 1), (C1, 0)],\n",
    "            [(C1, 1), (C2, 0)],\n",
    "            [(C2, 1), (C3, 0)],\n",
    "            [(C3, 1), (resistor_cea, 0)],\n",
    "            [(resistor_cea, 1), (gnd_cea, 0)],\n",
    "        ]\n",
    "        self._circuit = rf.Circuit(cnx)\n",
    "        return self._circuit\n",
    "    \n",
    "    @property\n",
    "    def network(self):\n",
    "        return self.circuit.network\n",
    "\n",
    "def plot_s11(L_CEA=100, L_DUT=200):\n",
    "    fig, ax = plt.subplots()\n",
    "    Tresonator(L_CEA, L_DUT).network.plot_s_db(ax=ax)\n",
    "    ax.set_ylim(-30,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "501747ef947e45ea86d6e1487077bdb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=100.0, description='L_CEA', max=500.0, min=1.0, step=1.0), FloatSlider…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_s11(L_CEA=100, L_DUT=200)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(plot_s11, \n",
    "         L_CEA=widgets.FloatSlider(min=1, max=500, step=1, value=100), \n",
    "         L_DUT=widgets.FloatSlider(min=1, max=500, step=1, value=200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "L_CEAs = np.linspace(0, 300, 50)\n",
    "fs = np.linspace(50, 60, 50)\n",
    "\n",
    "LL_CEA, LL_fs = np.meshgrid(L_CEAs, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def S11_f_match(d_CEA, f):\n",
    "    _res = tres(L_CEA=d_CEA, f=f)\n",
    "    \n",
    "    return np.squeeze(_res.s_db[0])\n",
    "\n",
    "vfunc = np.vectorize(S11_f_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tres' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-df79aeca1b38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mS11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLL_CEA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLL_fs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2111\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2113\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2189\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2190\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2191\u001b[0;31m             \u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0motypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2193\u001b[0m             \u001b[0;31m# Convert args to object arrays first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_get_ufunc_and_otypes\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2150\u001b[0m             \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2151\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2153\u001b[0m             \u001b[0;31m# Performance note: profiling indicates that -- for simple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-b43abfded928>\u001b[0m in \u001b[0;36mS11_f_match\u001b[0;34m(d_CEA, f)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mS11_f_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md_CEA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0m_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtres\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL_CEA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md_CEA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ms_db\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tres' is not defined"
     ]
    }
   ],
   "source": [
    "S11 = vfunc(LL_CEA, LL_fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots()\n",
    "c=ax.pcolor(LL_CEA, fs, S11, cmap='Greens_r')\n",
    "fig.colorbar(c)\n",
    "# ax.axvline(d_DUT_opt*1e3, ls='--')\n",
    "# ax.axhline(d_CEA_opt*1e3, ls='--')\n",
    "\n",
    "ax.set_xlabel('L CEA [mm]')\n",
    "ax.set_ylabel('f [MHz]')\n",
    "ax.set_title('s11 [dB]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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