{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mach-Zehnder Interferometer (MZI)\n",
    "\n",
    "**We use SiEPIC EBeam library in this tutorial.**\n",
    "\n",
    "   This notebook walks through the process of setting up and simulating a mach-zehnder interferometer device using the OPICS package. \n",
    "\n",
    "   A mach-zehnder interferometer is a basic waveguide interference device. It consists of two couplers (or Y branches) connected by two waveguides of different length (see below). The difference between the two waveguide lengths causes differential delay, which contributes to the frequency dependent interference pattern.\n",
    "\n",
    "<img style=\"width:70%;height:50%;\" src=\"../_static/_images/MZI1.svg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "   ____  ____  _______________\n",
      "  / __ \\/ __ \\/  _/ ____/ ___/\n",
      " / / / / /_/ // // /    \\__ \\\n",
      "/ /_/ / ____// // /___ ___/ /\n",
      "\\____/_/   /___/\\____//____/\n",
      "\n",
      "OPICS version 0.2.1\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import opics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import component library\n",
    "Import `ebeam` library from `libs` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ebeam = opics.libraries.ebeam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define network \n",
    "Create an instance of `Network` class, which is used to add, connect, and simulate circuit components. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining custom frequency data points for a component\n",
    "f = np.linspace(opics.C*1e6/1.5, opics.C*1e6/1.6, 2000)\n",
    "circuit_name = \"mzi\"\n",
    "circuit = opics.Network(network_id=circuit_name, f=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mInit signature:\u001b[0m\n",
      "\u001b[0mebeam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWaveguide\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1.99861639e+14\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.99855390e+14\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.99849141e+14\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m...\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m       \u001b[1;36m1.87382784e+14\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.87376535e+14\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.87370286e+14\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mlength\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5e-06\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mheight\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2.2e-07\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mwidth\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5e-07\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mloss\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m700\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mOID\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDocstring:\u001b[0m     \n",
      "Waveguides are components that guide waves. Although these are individual components that can\n",
      "be adjusted for use, it is recommended to draw paths in KLayout and convert them to waveguides\n",
      "using the built-in SiEPIC features.\n",
      "\n",
      "Model schematic:\n",
      "~~~~~~~~~~~~~~~~\n",
      "\n",
      "0 ┌─────────┐ 1\n",
      "  └─────────┘\n",
      "\u001b[1;31mType:\u001b[0m           type\n",
      "\u001b[1;31mSubclasses:\u001b[0m     \n"
     ]
    }
   ],
   "source": [
    "ebeam.Waveguide?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add circuit components\n",
    "\n",
    "Add grating couplers, 3dB power splitters (e.g. Y-splitter or Y-branch), and waveguides to circuit. You can define custom frequency data points for a component as well (see the example for output_GC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#define component instances\n",
    "input_gc  = circuit.add_component(ebeam.GC)\n",
    "y1 =   circuit.add_component(ebeam.Y)\n",
    "wg1 =  circuit.add_component(ebeam.Waveguide, params=dict(length=50e-6))\n",
    "wg2 =  circuit.add_component(ebeam.Waveguide, params=dict(length=150e-6))\n",
    "y2 =  circuit.add_component(ebeam.Y)\n",
    "output_gc = circuit.add_component(ebeam.GC)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define circuit connectivity\n",
    "\n",
    "In this section, we define the component connections. The connections are defined using `Network.connect`, e.g.\n",
    "\n",
    "`Network.connect(component1, component1_port, component2, component2_port)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define circuit connectivity\n",
    "circuit.connect(input_gc, 1, y1, 0)\n",
    "circuit.connect(y1, 1, wg1, 0)\n",
    "circuit.connect(y1, 2, wg2, 0)\n",
    "circuit.connect(y2, 0, output_gc, 1)\n",
    "circuit.connect(wg1, 1, y2, 1)\n",
    "circuit.connect(wg2, 1, y2, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simuate the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypingError",
     "evalue": "Failed in nopython mode pipeline (step: nopython frontend)\n\u001b[1m\u001b[1m\u001b[1mNo implementation of function Function(<function reshape at 0x00000202BBD769D0>) found for signature:\n \n >>> reshape(array(complex128, 2d, A), UniTuple(int64 x 3))\n \nThere are 2 candidate implementations:\n\u001b[1m     - Of which 2 did not match due to:\n     Overload in function 'np_reshape': File: numba\\np\\arrayobj.py: Line 1702.\n       With argument(s): '(array(complex128, 2d, A), UniTuple(int64 x 3))':\u001b[0m\n\u001b[1m      Rejected as the implementation raised a specific error:\n        TypingError: Failed in nopython mode pipeline (step: nopython frontend)\n      \u001b[1m\u001b[1m\u001b[1m\u001b[1m- Resolution failure for literal arguments:\n      \u001b[1mreshape() supports contiguous array only\u001b[0m\n      \u001b[0m\u001b[1m- Resolution failure for non-literal arguments:\n      \u001b[1mNone\u001b[0m\n      \u001b[0m\u001b[0m\n      \u001b[0m\u001b[1mDuring: resolving callee type: BoundFunction(array.reshape for array(complex128, 2d, A))\u001b[0m\n      \u001b[0m\u001b[1mDuring: typing of call at C:\\Users\\jeida\\anaconda3\\lib\\site-packages\\numba\\np\\arrayobj.py (1705)\n      \u001b[0m\n      \u001b[1m\n      File \"..\\..\\..\\..\\..\\..\\..\\anaconda3\\lib\\site-packages\\numba\\np\\arrayobj.py\", line 1705:\u001b[0m\n      \u001b[1m    def np_reshape_impl(a, shape):\n      \u001b[1m        return a.reshape(shape)\n      \u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n\u001b[0m\n  raised from C:\\Users\\jeida\\anaconda3\\lib\\site-packages\\numba\\core\\typeinfer.py:1071\n\u001b[0m\n\u001b[0m\u001b[1mDuring: resolving callee type: Function(<function reshape at 0x00000202BBD769D0>)\u001b[0m\n\u001b[0m\u001b[1mDuring: typing of call at c:\\users\\jeida\\documents\\github\\dev-jaspreetj\\opics\\opics\\sparam_ops.py (166)\n\u001b[0m\n\u001b[1m\nFile \"..\\..\\..\\opics\\sparam_ops.py\", line 166:\u001b[0m\n\u001b[1mdef v_broadcast_sim(A: np.ndarray, k: int, l: int) -> np.ndarray:\n    <source elided>\n\n\u001b[1m    e = np.reshape(A[:, k, :nA], A.shape)\n\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypingError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-b0141dfa6b62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#simulate network\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mcircuit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimulate_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"simulation finished in %ss\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0msim_start\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jeida\\documents\\github\\dev-jaspreetj\\opics\\opics\\network.py\u001b[0m in \u001b[0;36msimulate_network\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    188\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m                 \u001b[0mcombination_f\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt_components\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m                 combination_s = connect_s(\n\u001b[0m\u001b[0;32m    191\u001b[0m                     \u001b[0mt_components\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mntp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mntp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt_components\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mntp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mntp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m                 )\n",
      "\u001b[1;32mc:\\users\\jeida\\documents\\github\\dev-jaspreetj\\opics\\opics\\sparam_ops.py\u001b[0m in \u001b[0;36mconnect_s\u001b[1;34m(A, k, B, l, create_composite_matrix)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m         \u001b[1;31m# call innerconnect_s() on composit matrix C\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[0mmat_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv_broadcast_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnA\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmat_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numba\\core\\dispatcher.py\u001b[0m in \u001b[0;36m_compile_for_args\u001b[1;34m(self, *args, **kws)\u001b[0m\n\u001b[0;32m    418\u001b[0m                 \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch_message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 420\u001b[1;33m             \u001b[0merror_rewrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'typing'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    421\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUnsupportedError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m             \u001b[1;31m# Something unsupported is present in the user code, add help info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numba\\core\\dispatcher.py\u001b[0m in \u001b[0;36merror_rewrite\u001b[1;34m(e, issue_type)\u001b[0m\n\u001b[0;32m    359\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 361\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    363\u001b[0m         \u001b[0margtypes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypingError\u001b[0m: Failed in nopython mode pipeline (step: nopython frontend)\n\u001b[1m\u001b[1m\u001b[1mNo implementation of function Function(<function reshape at 0x00000202BBD769D0>) found for signature:\n \n >>> reshape(array(complex128, 2d, A), UniTuple(int64 x 3))\n \nThere are 2 candidate implementations:\n\u001b[1m     - Of which 2 did not match due to:\n     Overload in function 'np_reshape': File: numba\\np\\arrayobj.py: Line 1702.\n       With argument(s): '(array(complex128, 2d, A), UniTuple(int64 x 3))':\u001b[0m\n\u001b[1m      Rejected as the implementation raised a specific error:\n        TypingError: Failed in nopython mode pipeline (step: nopython frontend)\n      \u001b[1m\u001b[1m\u001b[1m\u001b[1m- Resolution failure for literal arguments:\n      \u001b[1mreshape() supports contiguous array only\u001b[0m\n      \u001b[0m\u001b[1m- Resolution failure for non-literal arguments:\n      \u001b[1mNone\u001b[0m\n      \u001b[0m\u001b[0m\n      \u001b[0m\u001b[1mDuring: resolving callee type: BoundFunction(array.reshape for array(complex128, 2d, A))\u001b[0m\n      \u001b[0m\u001b[1mDuring: typing of call at C:\\Users\\jeida\\anaconda3\\lib\\site-packages\\numba\\np\\arrayobj.py (1705)\n      \u001b[0m\n      \u001b[1m\n      File \"..\\..\\..\\..\\..\\..\\..\\anaconda3\\lib\\site-packages\\numba\\np\\arrayobj.py\", line 1705:\u001b[0m\n      \u001b[1m    def np_reshape_impl(a, shape):\n      \u001b[1m        return a.reshape(shape)\n      \u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n\u001b[0m\n  raised from C:\\Users\\jeida\\anaconda3\\lib\\site-packages\\numba\\core\\typeinfer.py:1071\n\u001b[0m\n\u001b[0m\u001b[1mDuring: resolving callee type: Function(<function reshape at 0x00000202BBD769D0>)\u001b[0m\n\u001b[0m\u001b[1mDuring: typing of call at c:\\users\\jeida\\documents\\github\\dev-jaspreetj\\opics\\opics\\sparam_ops.py (166)\n\u001b[0m\n\u001b[1m\nFile \"..\\..\\..\\opics\\sparam_ops.py\", line 166:\u001b[0m\n\u001b[1mdef v_broadcast_sim(A: np.ndarray, k: int, l: int) -> np.ndarray:\n    <source elided>\n\n\u001b[1m    e = np.reshape(A[:, k, :nA], A.shape)\n\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim_start = time.time()\n",
    "\n",
    "#simulate network\n",
    "circuit.simulate_network()\n",
    "\n",
    "print(\"simulation finished in %ss\"%(str(round(time.time()-sim_start,2))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the simulation result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circuit.sim_result.plot_sparameters(show_freq=False, scale=\"log\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}