{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sweeps - Capacitance matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prerequisite\n",
    "You need to have a working local installation of Ansys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Perform the necessary imports and create a QDesign in Metal first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings\n",
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "from qiskit_metal.renderers.renderer_ansys.ansys_renderer import QAnsysRenderer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "design = designs.DesignPlanar()\n",
    "gui = MetalGUI(design)\n",
    "\n",
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.variables['cpw_width'] = '15 um'\n",
    "design.variables['cpw_gap'] = '9 um'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this example, the design consists of 4 qubits and 4 CPWs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow running the same cell here multiple times to overwrite changes\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "## Custom options for all the transmons\n",
    "options = dict(\n",
    "    # Some options we want to modify from the defaults\n",
    "    # (see below for defaults)\n",
    "    pad_width = '425 um', \n",
    "    pocket_height = '650um',\n",
    "    # Adding 4 connectors (see below for defaults)\n",
    "    connection_pads=dict(\n",
    "        readout = dict(loc_W=+1,loc_H=-1, pad_width='200um'),\n",
    "        bus1 = dict(loc_W=-1,loc_H=+1, pad_height='30um'),\n",
    "        bus2 = dict(loc_W=-1,loc_H=-1, pad_height='50um')\n",
    "    )\n",
    ")\n",
    "\n",
    "## Create 4 transmons\n",
    "\n",
    "q1 = TransmonPocket(design, 'Q1', options = dict(\n",
    "    pos_x='+2.42251mm', pos_y='+0.0mm', **options))\n",
    "q2 = TransmonPocket(design, 'Q2', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='-0.95mm', orientation = '270', **options))\n",
    "q3 = TransmonPocket(design, 'Q3', options = dict(\n",
    "    pos_x='-2.42251mm', pos_y='+0.0mm', orientation = '180', **options))\n",
    "q4 = TransmonPocket(design, 'Q4', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='+0.95mm', orientation = '90', **options))\n",
    "\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "RouteMeander.get_template_options(design)\n",
    "\n",
    "options = Dict(\n",
    "        lead=Dict(\n",
    "            start_straight='0.2mm',\n",
    "            end_straight='0.2mm'),\n",
    "        trace_gap='9um',\n",
    "        trace_width='15um')\n",
    "\n",
    "def connect(component_name: str, component1: str, pin1: str, component2: str, pin2: str,\n",
    "            length: str, asymmetry='0 um', flip=False, fillet='90um'):\n",
    "    \"\"\"Connect two pins with a CPW.\"\"\"\n",
    "    myoptions = Dict(\n",
    "        fillet=fillet,\n",
    "        hfss_wire_bonds = True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(\n",
    "                component=component1,\n",
    "                pin=pin1),\n",
    "            end_pin=Dict(\n",
    "                component=component2,\n",
    "                pin=pin2)),\n",
    "        total_length=length)\n",
    "    myoptions.update(options)\n",
    "    myoptions.meander.asymmetry = asymmetry\n",
    "    myoptions.meander.lead_direction_inverted = 'true' if flip else 'false'\n",
    "    return RouteMeander(design, component_name, myoptions)\n",
    "\n",
    "asym = 140\n",
    "cpw1 = connect('cpw1', 'Q1', 'bus2', 'Q2', 'bus1', '5.6 mm', f'+{asym}um')\n",
    "cpw2 = connect('cpw2', 'Q3', 'bus1', 'Q2', 'bus2', '5.7 mm', f'-{asym}um', flip=True)\n",
    "cpw3 = connect('cpw3', 'Q3', 'bus2', 'Q4', 'bus1', '5.6 mm', f'+{asym}um')\n",
    "cpw4 = connect('cpw4', 'Q1', 'bus1', 'Q4', 'bus2', '5.7 mm', f'-{asym}um', flip=True)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Metal passes information to the simulator \"q3d\" to extract the capacitance matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare data to pass as arguments for method run_sweep()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "render_design_argument_qcomps = ['Q1']\n",
    "\n",
    "render_design_argument_endcaps = [('Q1', 'readout'), ('Q1', 'bus1'),('Q1', 'bus2')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'reuse_setup': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 15,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.5,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': True,\n",
       " 'solution_order': 'High',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To identify the agruments that you can change.\n",
    "# They will change based on the simulation software used.\n",
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the simulation software, if you don't want to use the default values, \n",
    "# you can update them as seen below.\n",
    "\n",
    "# If a setup named \"sweeper_q3d_setup\" exists in the project, it will be deleted, \n",
    "# and a new setup will be added.\n",
    "c1.sim.setup.name = \"sweeper_q3d_setup\"\n",
    "\n",
    "c1.sim.setup.freq_ghz = 5.6\n",
    "c1.sim.setup.max_passes = 9\n",
    "c1.sim.setup.min_passes = 2\n",
    "c1.sim.setup.percent_error = 0.45"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We will look at modifying the pad_gap of qubit 1, to see how it impacts the anharmonicity of the qubit.\n",
    "\n",
    "The steps will be;\n",
    "- Connect to Ansys Q3D.\n",
    "- Rebuild QComponents in Metal.\n",
    "- Render QComponents within Q3D and setup the simulation.\n",
    "- Delete/Clear the Q3D between each simulation.\n",
    "- Using the capacitance matrices, LOM for each value in option_sweep is found.\n",
    "\n",
    "#### Returns a dict and return code.  If the return code is zero, there were no errors detected.  \n",
    "#### The dict has:  key = each value used to sweep, value = capacitance matrix\n",
    "\n",
    "#### This could take minutes or hours based on the complexity of the design.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 05:12PM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 05:12PM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 05:12PM [load_ansys_project]: \tOpened Ansys Desktop v2021.2.0\n",
      "INFO 05:12PM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    J:/Ansoft/\n",
      "\tProject:   Project5\n",
      "INFO 05:12PM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
      "INFO 05:12PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 05:12PM [connect]: \tConnected to project \"Project5\" and design \"GetCapacitance_q3d\" 😀 \n",
      "\n",
      "INFO 05:12PM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
      "INFO 05:12PM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 05:12PM [analyze]: Analyzing setup sweeper_q3d_setup\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpasduwpn9.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmprvnct2sk.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpj_89pabg.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmplp2v7wsi.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpr1583hvq.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpcv77_j8q.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmptehz4r2y.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpv2q0vlmb.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpfyfnx19b.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
      "INFO 05:13PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpj_fn6u7x.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.266513 [GHz]\n",
      "EC 284.490474 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -328.027635 [MHz]\n",
      "dispersion 16.258044 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 68.087440 [fF]\n",
      "T1 53.831750 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 7.457921 [fF]\n",
      "gbus1_in_MHz 108.061520 [MHz]\n",
      "χ_bus1 -2.196070 [MHz]\n",
      "1/T1bus1 2005.686739 [Hz]\n",
      "T1bus1 79.351845 [us]\n",
      "\n",
      "tCqbus2 -6.588291 [fF]\n",
      "gbus2_in_MHz -82.021155 [MHz]\n",
      "χ_bus2 -5.704388 [MHz]\n",
      "1/T1bus2 655.760880 [Hz]\n",
      "T1bus2 242.702711 [us]\n",
      "\n",
      "tCqbus3 5.453079 [fF]\n",
      "gbus3_in_MHz 70.106609 [MHz]\n",
      "χ_bus3 -2.763400 [MHz]\n",
      "1/T1bus3 295.077910 [Hz]\n",
      "T1bus3 539.365834 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 7.422809 [MHz]\n",
      "gbus1_3 10.345326 [MHz]\n",
      "gbus2_3 5.654427 [MHz]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 05:13PM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
      "INFO 05:13PM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 05:13PM [analyze]: Analyzing setup sweeper_q3d_setup\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpczb1qvs3.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpefiqwjuk.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpb7_eoa61.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpcjsjfzlz.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmptyounyv7.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpuqz5ofoi.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpghifad5m.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpu9yh6bfc.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpipgeyx_p.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
      "INFO 05:14PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpczimzks4.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.462897 [GHz]\n",
      "EC 307.600806 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -357.287459 [MHz]\n",
      "dispersion 33.474349 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 62.971968 [fF]\n",
      "T1 31.181639 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 7.545643 [fF]\n",
      "gbus1_in_MHz 116.213396 [MHz]\n",
      "χ_bus1 -3.378233 [MHz]\n",
      "1/T1bus1 3075.191676 [Hz]\n",
      "T1bus1 51.754479 [us]\n",
      "\n",
      "tCqbus2 -6.637477 [fF]\n",
      "gbus2_in_MHz -87.835128 [MHz]\n",
      "χ_bus2 -11.519528 [MHz]\n",
      "1/T1bus2 1457.770105 [Hz]\n",
      "T1bus2 109.176984 [us]\n",
      "\n",
      "tCqbus3 5.526346 [fF]\n",
      "gbus3_in_MHz 75.520647 [MHz]\n",
      "χ_bus3 -5.083082 [MHz]\n",
      "1/T1bus3 571.162003 [Hz]\n",
      "T1bus3 278.651140 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 7.279458 [MHz]\n",
      "gbus1_3 10.197376 [MHz]\n",
      "gbus2_3 5.507283 [MHz]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 05:14PM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
      "INFO 05:15PM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 05:15PM [analyze]: Analyzing setup sweeper_q3d_setup\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp4w4vtfuv.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpd317tmlb.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpi6iwsdi9.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp8l_x9l2z.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp1q5inamq.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp9t4l6dmy.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp2sgn1_3h.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmp482h5dug.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmphez85936.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
      "INFO 05:16PM [get_matrix]: Exporting matrix data to (C:\\Users\\JAGATH~1\\AppData\\Local\\Temp\\tmpayf6wj5t.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.603196 [GHz]\n",
      "EC 324.747859 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -379.244809 [MHz]\n",
      "dispersion 54.335718 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 59.646977 [fF]\n",
      "T1 19.153152 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 7.638504 [fF]\n",
      "gbus1_in_MHz 122.751924 [MHz]\n",
      "χ_bus1 -4.681162 [MHz]\n",
      "1/T1bus1 4274.193323 [Hz]\n",
      "T1bus1 37.236253 [us]\n",
      "\n",
      "tCqbus2 -6.694974 [fF]\n",
      "gbus2_in_MHz -92.444073 [MHz]\n",
      "χ_bus2 -21.099340 [MHz]\n",
      "1/T1bus2 3037.626181 [Hz]\n",
      "T1bus2 52.394513 [us]\n",
      "\n",
      "tCqbus3 5.592313 [fF]\n",
      "gbus3_in_MHz 79.741791 [MHz]\n",
      "χ_bus3 -8.315547 [MHz]\n",
      "1/T1bus3 997.775951 [Hz]\n",
      "T1bus3 159.509701 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 7.162962 [MHz]\n",
      "gbus1_3 10.056266 [MHz]\n",
      "gbus2_3 5.376093 [MHz]\n"
     ]
    }
   ],
   "source": [
    "sweep_data, return_code = c1.run_sweep(q1.name,\n",
    "                                       'pad_gap',\n",
    "                                       ['20um', '30um', '40um'],\n",
    "                                       render_design_argument_qcomps,\n",
    "                                       render_design_argument_endcaps,\n",
    "                                       design_name=\"GetCapacitance\",\n",
    "                                       box_plus_buffer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sweep Value</th>\n",
       "      <th>Ec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20um</td>\n",
       "      <td>284.490474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30um</td>\n",
       "      <td>307.600806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40um</td>\n",
       "      <td>324.747859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sweep Value          Ec\n",
       "0        20um  284.490474\n",
       "1        30um  307.600806\n",
       "2        40um  324.747859"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "ec_val = []\n",
    "for opt_val in sweep_data.keys():\n",
    "    ec_val.append([opt_val,sweep_data[opt_val]['variables']['lumped_oscillator']['EC']])\n",
    "\n",
    "df=DataFrame(ec_val,columns = ['Sweep Value', 'Ec'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can grab specific values from the results as seen below;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['20um', '30um', '40um'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['option_name', 'variables', 'sim_variables'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each value of option, there is a set of data.\n",
    "sweep_data['20um'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['lumped_oscillator', 'lumped_oscillator_all'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_data['20um']['variables'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['sim_setup_name', 'cap_matrix', 'units', 'cap_all_passes', 'is_converged'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_data['20um']['sim_variables'].keys()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A boolean flag indicating whether a sweep has converged can be obtained from the `is_converged` key within the `sim_variables`. For instance, the following code retrieves the convergence result for the sweep parameter, 20um. The convergence is extracted from the Ansys Q3D renderer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_data['20um']['sim_variables']['is_converged']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['20um', '30um', '40um']) \n",
      "\n",
      "\n",
      "key=20um\n",
      "option_name['20um']['option_name']=pad_gap\n",
      "variables={'lumped_oscillator': {'fQ': 5.266513054956589, 'EC': 284.4904739015804, 'EJ': 13.616300010297985, 'alpha': -328.02763545951046, 'dispersion': 16.25804376029968, 'gbus': array([108.06151986, -82.02115476,  70.10660922]), 'chi_in_MHz': array([-2.19607046, -5.70438759, -2.76340023])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha dispersion  \\\n",
      "1  5.694647  336.214178  13.6163 -394.047036  73.540872   \n",
      "2  5.597383  324.026735  13.6163 -378.317097  53.282182   \n",
      "3  5.486697   310.47202  13.6163 -360.949348  36.405901   \n",
      "4  5.417928  302.217736  13.6163 -350.437946  28.505987   \n",
      "5  5.355959  294.888507  13.6163 -341.145422  22.741803   \n",
      "6  5.311963  289.747388  13.6163 -334.649978  19.308807   \n",
      "7  5.289385  287.129029  13.6163 -331.349078   17.73438   \n",
      "8  5.266513  284.490474  13.6163 -328.027635  16.258044   \n",
      "\n",
      "                                                gbus  \\\n",
      "1  [102.81449386064145, -70.90109936803726, 74.96...   \n",
      "2  [107.77093095701179, -80.12086229266069, 65.69...   \n",
      "3  [105.54815295436816, -80.44038588561413, 67.29...   \n",
      "4  [104.90594389814552, -79.89581633369512, 68.00...   \n",
      "5  [106.33897608793431, -81.08798197198121, 67.81...   \n",
      "6  [107.12981125367459, -81.3102683906494, 68.975...   \n",
      "7  [107.7012390104641, -81.6418443459811, 69.3910...   \n",
      "8  [108.06151985814759, -82.02115475685893, 70.10...   \n",
      "\n",
      "                                          chi_in_MHz    χr MHz      gr MHz  \n",
      "1  [-3.8088097481735166, -18.580483394229866, -9....  3.808810  102.814494  \n",
      "2  [-3.575143906278383, -15.485277632259882, -5.5...  3.575144  107.770931  \n",
      "3  [-2.8885762215855513, -10.445672739152704, -4....  2.888576  105.548153  \n",
      "4  [-2.5743348275853832, -8.277915423268908, -3.6...  2.574335  104.905944  \n",
      "5  [-2.4159333566958128, -7.106373744825535, -3.1...  2.415933  106.338976  \n",
      "6  [-2.3018225059552377, -6.324281107516913, -2.9...  2.301823  107.129811  \n",
      "7  [-2.2529865943188865, -6.001303111687568, -2.8...  2.252987  107.701239  \n",
      "8  [-2.1960704619822993, -5.704387588554193, -2.7...  2.196070  108.061520  }\n",
      "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
      "bus1_connector_pad_Q1                  50.68289               -0.45605   \n",
      "bus2_connector_pad_Q1                  -0.45605               54.98763   \n",
      "ground_main_plane                     -34.03296              -36.27948   \n",
      "pad_bot_Q1                             -1.61149              -14.25514   \n",
      "pad_top_Q1                            -13.44472               -1.90696   \n",
      "readout_connector_pad_Q1               -0.21628               -1.03969   \n",
      "\n",
      "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
      "bus1_connector_pad_Q1             -34.03296    -1.61149   -13.44472   \n",
      "bus2_connector_pad_Q1             -36.27948   -14.25514    -1.90696   \n",
      "ground_main_plane                 235.25834   -30.65801   -37.24496   \n",
      "pad_bot_Q1                        -30.65801   104.10961   -36.17944   \n",
      "pad_top_Q1                        -37.24496   -36.17944    93.77409   \n",
      "readout_connector_pad_Q1          -37.12178   -19.15111    -2.31960   \n",
      "\n",
      "                          readout_connector_pad_Q1  \n",
      "bus1_connector_pad_Q1                     -0.21628  \n",
      "bus2_connector_pad_Q1                     -1.03969  \n",
      "ground_main_plane                        -37.12178  \n",
      "pad_bot_Q1                               -19.15111  \n",
      "pad_top_Q1                                -2.31960  \n",
      "readout_connector_pad_Q1                  60.98654  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4197190e-14, -3.9163000e-16, -3.1057080e-14, -1.4393800e-15,\n",
      "        -1.0445580e-14, -1.7242000e-16],\n",
      "       [-3.9163000e-16,  4.6460040e-14, -3.2925350e-14, -1.1641860e-14,\n",
      "        -4.2839000e-16, -3.3658000e-16],\n",
      "       [-3.1057080e-14, -3.2925350e-14,  2.3578951e-13, -2.7958200e-14,\n",
      "        -3.5407050e-14, -3.4017410e-14],\n",
      "       [-1.4393800e-15, -1.1641860e-14, -2.7958200e-14,  8.8124720e-14,\n",
      "        -2.9463370e-14, -1.5955740e-14],\n",
      "       [-1.0445580e-14, -4.2839000e-16, -3.5407050e-14, -2.9463370e-14,\n",
      "         7.9743910e-14, -2.0364200e-15],\n",
      "       [-1.7242000e-16, -3.3658000e-16, -3.4017410e-14, -1.5955740e-14,\n",
      "        -2.0364200e-15,  5.3205550e-14]]), 2: array([[ 4.5639150e-14, -4.5303000e-16, -3.0625850e-14, -1.4292000e-15,\n",
      "        -1.2108900e-14, -2.1362000e-16],\n",
      "       [-4.5303000e-16,  4.9745810e-14, -3.3732750e-14, -1.1903450e-14,\n",
      "        -1.7305600e-15, -1.0127300e-15],\n",
      "       [-3.0625850e-14, -3.3732750e-14,  2.3301395e-13, -2.9685080e-14,\n",
      "        -3.6138470e-14, -3.4679760e-14],\n",
      "       [-1.4292000e-15, -1.1903450e-14, -2.9685080e-14,  9.1678400e-14,\n",
      "        -3.0003290e-14, -1.6686200e-14],\n",
      "       [-1.2108900e-14, -1.7305600e-15, -3.6138470e-14, -3.0003290e-14,\n",
      "         8.4254420e-14, -1.9635700e-15],\n",
      "       [-2.1362000e-16, -1.0127300e-15, -3.4679760e-14, -1.6686200e-14,\n",
      "        -1.9635700e-15,  5.5569040e-14]]), 3: array([[ 4.8119690e-14, -4.5472000e-16, -3.2461830e-14, -1.5757300e-15,\n",
      "        -1.2521480e-14, -2.1691000e-16],\n",
      "       [-4.5472000e-16,  5.1630230e-14, -3.4489740e-14, -1.2800260e-14,\n",
      "        -1.8607800e-15, -1.0098100e-15],\n",
      "       [-3.2461830e-14, -3.4489740e-14,  2.3061805e-13, -3.0084020e-14,\n",
      "        -3.6128450e-14, -3.5501860e-14],\n",
      "       [-1.5757300e-15, -1.2800260e-14, -3.0084020e-14,  9.5969310e-14,\n",
      "        -3.1906680e-14, -1.7425530e-14],\n",
      "       [-1.2521480e-14, -1.8607800e-15, -3.6128450e-14, -3.1906680e-14,\n",
      "         8.7205650e-14, -2.2600600e-15],\n",
      "       [-2.1691000e-16, -1.0098100e-15, -3.5501860e-14, -1.7425530e-14,\n",
      "        -2.2600600e-15,  5.7518970e-14]]), 4: array([[ 4.8610720e-14, -4.5836000e-16, -3.2749720e-14, -1.5978600e-15,\n",
      "        -1.2682000e-14, -2.1824000e-16],\n",
      "       [-4.5836000e-16,  5.2578160e-14, -3.4972530e-14, -1.3197170e-14,\n",
      "        -1.8861900e-15, -1.0344000e-15],\n",
      "       [-3.2749720e-14, -3.4972530e-14,  2.3166021e-13, -3.0284290e-14,\n",
      "        -3.6528300e-14, -3.5808440e-14],\n",
      "       [-1.5978600e-15, -1.3197170e-14, -3.0284290e-14,  9.8250440e-14,\n",
      "        -3.3240560e-14, -1.7712820e-14],\n",
      "       [-1.2682000e-14, -1.8861900e-15, -3.6528300e-14, -3.3240560e-14,\n",
      "         8.9216690e-14, -2.2857800e-15],\n",
      "       [-2.1824000e-16, -1.0344000e-15, -3.5808440e-14, -1.7712820e-14,\n",
      "        -2.2857800e-15,  5.8182900e-14]]), 5: array([[ 4.9363840e-14, -4.5301000e-16, -3.3226850e-14, -1.5928200e-15,\n",
      "        -1.2970540e-14, -2.1557000e-16],\n",
      "       [-4.5301000e-16,  5.3141140e-14, -3.5279460e-14, -1.3497480e-14,\n",
      "        -1.8826900e-15, -1.0073100e-15],\n",
      "       [-3.3226850e-14, -3.5279460e-14,  2.3243426e-13, -3.0607630e-14,\n",
      "        -3.6532310e-14, -3.6209200e-14],\n",
      "       [-1.5928200e-15, -1.3497480e-14, -3.0607630e-14,  1.0081831e-13,\n",
      "        -3.4478550e-14, -1.8382560e-14],\n",
      "       [-1.2970540e-14, -1.8826900e-15, -3.6532310e-14, -3.4478550e-14,\n",
      "         9.0775280e-14, -2.2863000e-15],\n",
      "       [-2.1557000e-16, -1.0073100e-15, -3.6209200e-14, -1.8382560e-14,\n",
      "        -2.2863000e-15,  5.9218980e-14]]), 6: array([[ 4.9985210e-14, -4.5704000e-16, -3.3616870e-14, -1.6030800e-15,\n",
      "        -1.3178630e-14, -2.1615000e-16],\n",
      "       [-4.5704000e-16,  5.3973980e-14, -3.5687360e-14, -1.3866960e-14,\n",
      "        -1.9026000e-15, -1.0226000e-15],\n",
      "       [-3.3616870e-14, -3.5687360e-14,  2.3349389e-13, -3.0591980e-14,\n",
      "        -3.6927510e-14, -3.6485030e-14],\n",
      "       [-1.6030800e-15, -1.3866960e-14, -3.0591980e-14,  1.0237336e-13,\n",
      "        -3.5303540e-14, -1.8736620e-14],\n",
      "       [-1.3178630e-14, -1.9026000e-15, -3.6927510e-14, -3.5303540e-14,\n",
      "         9.2276360e-14, -2.3091300e-15],\n",
      "       [-2.1615000e-16, -1.0226000e-15, -3.6485030e-14, -1.8736620e-14,\n",
      "        -2.3091300e-15,  5.9894930e-14]]), 7: array([[ 5.0248640e-14, -4.5663000e-16, -3.3742160e-14, -1.6131600e-15,\n",
      "        -1.3298120e-14, -2.1674000e-16],\n",
      "       [-4.5663000e-16,  5.4536010e-14, -3.6050740e-14, -1.4045840e-14,\n",
      "        -1.9002700e-15, -1.0348200e-15],\n",
      "       [-3.3742160e-14, -3.6050740e-14,  2.3428136e-13, -3.0627100e-14,\n",
      "        -3.7020200e-14, -3.6890900e-14],\n",
      "       [-1.6131600e-15, -1.4045840e-14, -3.0627100e-14,  1.0327317e-13,\n",
      "        -3.5754980e-14, -1.8974920e-14],\n",
      "       [-1.3298120e-14, -1.9002700e-15, -3.7020200e-14, -3.5754980e-14,\n",
      "         9.2955270e-14, -2.3092000e-15],\n",
      "       [-2.1674000e-16, -1.0348200e-15, -3.6890900e-14, -1.8974920e-14,\n",
      "        -2.3092000e-15,  6.0565440e-14]]), 8: array([[ 5.0682890e-14, -4.5605000e-16, -3.4032960e-14, -1.6114900e-15,\n",
      "        -1.3444720e-14, -2.1628000e-16],\n",
      "       [-4.5605000e-16,  5.4987630e-14, -3.6279480e-14, -1.4255140e-14,\n",
      "        -1.9069600e-15, -1.0396900e-15],\n",
      "       [-3.4032960e-14, -3.6279480e-14,  2.3525834e-13, -3.0658010e-14,\n",
      "        -3.7244960e-14, -3.7121780e-14],\n",
      "       [-1.6114900e-15, -1.4255140e-14, -3.0658010e-14,  1.0410961e-13,\n",
      "        -3.6179440e-14, -1.9151110e-14],\n",
      "       [-1.3444720e-14, -1.9069600e-15, -3.7244960e-14, -3.6179440e-14,\n",
      "         9.3774090e-14, -2.3196000e-15],\n",
      "       [-2.1628000e-16, -1.0396900e-15, -3.7121780e-14, -1.9151110e-14,\n",
      "        -2.3196000e-15,  6.0986540e-14]])}, 'is_converged': True}\n",
      "\n",
      "key=30um\n",
      "option_name['30um']['option_name']=pad_gap\n",
      "variables={'lumped_oscillator': {'fQ': 5.46289654950697, 'EC': 307.6008059702114, 'EJ': 13.616300010297985, 'alpha': -357.28745914780166, 'dispersion': 33.47434947013855, 'gbus': array([116.21339556, -87.83512791,  75.52064749]), 'chi_in_MHz': array([ -3.37823347, -11.51952754,  -5.08308206])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha  dispersion  \\\n",
      "1  5.830279  353.645796  13.6163  -416.73565  113.172147   \n",
      "2  5.745499  342.689905  13.6163 -402.449411   86.651292   \n",
      "3  5.652966  330.959668  13.6163 -387.251884    64.14433   \n",
      "4  5.600191  324.374985  13.6163 -378.765066   53.788833   \n",
      "5  5.539417  316.886639  13.6163 -369.151877   43.731781   \n",
      "6  5.501965  312.321897  13.6163 -363.311786    38.40525   \n",
      "7  5.476807  309.277112  13.6163 -359.424666   35.161001   \n",
      "8  5.462897  307.600806  13.6163 -357.287459   33.474349   \n",
      "\n",
      "                                                gbus  \\\n",
      "1  [108.98992719353839, -74.66540858425307, 76.43...   \n",
      "2  [112.91989864116363, -82.54641626955339, 69.19...   \n",
      "3  [112.00253623300011, -84.53126333113148, 72.24...   \n",
      "4  [111.7129552679439, -84.54971279864021, 73.324...   \n",
      "5  [113.80880482565388, -85.29604218811512, 73.28...   \n",
      "6  [115.01096521384784, -86.429109644363, 74.2469...   \n",
      "7  [115.76279818788643, -87.36761701089993, 74.76...   \n",
      "8  [116.21339555843822, -87.83512791363434, 75.52...   \n",
      "\n",
      "                                          chi_in_MHz    χr MHz      gr MHz  \n",
      "1  [-5.3973914316567875, -46.71741582928083, -16....  5.397391  108.989927  \n",
      "2  [-5.002677392991025, -32.844329514281235, -9.9...  5.002677  112.919899  \n",
      "3  [-4.221519632060279, -21.760254163093062, -7.9...  4.221520  112.002536  \n",
      "4  [-3.8586174786700056, -17.438517118939654, -6....  3.858617  111.712955  \n",
      "5  [-3.640928569154806, -14.09716741437999, -5.85...  3.640929  113.808805  \n",
      "6  [-3.510312154331441, -12.695294731236578, -5.4...  3.510312  115.010965  \n",
      "7  [-3.423097890240712, -11.925426143748664, -5.1...  3.423098  115.762798  \n",
      "8  [-3.378233467896783, -11.519527540166766, -5.0...  3.378233  116.213396  }\n",
      "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
      "bus1_connector_pad_Q1                  50.59475               -0.42373   \n",
      "bus2_connector_pad_Q1                  -0.42373               54.94375   \n",
      "ground_main_plane                     -34.00537              -36.29163   \n",
      "pad_bot_Q1                             -1.54779              -14.31897   \n",
      "pad_top_Q1                            -13.49320               -1.83808   \n",
      "readout_connector_pad_Q1               -0.20664               -1.03145   \n",
      "\n",
      "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
      "bus1_connector_pad_Q1             -34.00537    -1.54779   -13.49320   \n",
      "bus2_connector_pad_Q1             -36.29163   -14.31897    -1.83808   \n",
      "ground_main_plane                 237.02640   -31.52366   -38.22709   \n",
      "pad_bot_Q1                        -31.52366    99.53492   -30.61516   \n",
      "pad_top_Q1                        -38.22709   -30.61516    89.15073   \n",
      "readout_connector_pad_Q1          -37.11942   -19.21035    -2.22435   \n",
      "\n",
      "                          readout_connector_pad_Q1  \n",
      "bus1_connector_pad_Q1                     -0.20664  \n",
      "bus2_connector_pad_Q1                     -1.03145  \n",
      "ground_main_plane                        -37.11942  \n",
      "pad_bot_Q1                               -19.21035  \n",
      "pad_top_Q1                                -2.22435  \n",
      "readout_connector_pad_Q1                  60.91761  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4218330e-14, -3.5664000e-16, -3.1113930e-14, -1.3706900e-15,\n",
      "        -1.0520740e-14, -1.6641000e-16],\n",
      "       [-3.5664000e-16,  4.6489100e-14, -3.2944760e-14, -1.1564330e-14,\n",
      "        -5.7548000e-16, -3.1984000e-16],\n",
      "       [-3.1113930e-14, -3.2944760e-14,  2.3767564e-13, -2.8923580e-14,\n",
      "        -3.6263220e-14, -3.4079430e-14],\n",
      "       [-1.3706900e-15, -1.1564330e-14, -2.8923580e-14,  8.5775550e-14,\n",
      "        -2.6142010e-14, -1.6057020e-14],\n",
      "       [-1.0520740e-14, -5.7548000e-16, -3.6263220e-14, -2.6142010e-14,\n",
      "         7.7450100e-14, -1.9410100e-15],\n",
      "       [-1.6641000e-16, -3.1984000e-16, -3.4079430e-14, -1.6057020e-14,\n",
      "        -1.9410100e-15,  5.3242360e-14]]), 2: array([[ 4.6325230e-14, -4.2047000e-16, -3.1584610e-14, -1.4162100e-15,\n",
      "        -1.1900660e-14, -2.0238000e-16],\n",
      "       [-4.2047000e-16,  4.9779490e-14, -3.3812720e-14, -1.1965040e-14,\n",
      "        -1.6715600e-15, -1.0030400e-15],\n",
      "       [-3.1584610e-14, -3.3812720e-14,  2.3584731e-13, -3.0625210e-14,\n",
      "        -3.6991100e-14, -3.4724940e-14],\n",
      "       [-1.4162100e-15, -1.1965040e-14, -3.0625210e-14,  8.9142120e-14,\n",
      "        -2.6367590e-14, -1.6742180e-14],\n",
      "       [-1.1900660e-14, -1.6715600e-15, -3.6991100e-14, -2.6367590e-14,\n",
      "         8.1202660e-14, -1.9176400e-15],\n",
      "       [-2.0238000e-16, -1.0030400e-15, -3.4724940e-14, -1.6742180e-14,\n",
      "        -1.9176400e-15,  5.5596600e-14]]), 3: array([[ 4.781116e-14, -4.224900e-16, -3.238145e-14, -1.508140e-15,\n",
      "        -1.242209e-14, -2.067700e-16],\n",
      "       [-4.224900e-16,  5.170372e-14, -3.449845e-14, -1.299176e-14,\n",
      "        -1.795240e-15, -1.000140e-15],\n",
      "       [-3.238145e-14, -3.449845e-14,  2.328565e-13, -3.103545e-14,\n",
      "        -3.708001e-14, -3.567206e-14],\n",
      "       [-1.508140e-15, -1.299176e-14, -3.103545e-14,  9.285160e-14,\n",
      "        -2.759688e-14, -1.751171e-14],\n",
      "       [-1.242209e-14, -1.795240e-15, -3.708001e-14, -2.759688e-14,\n",
      "         8.360249e-14, -2.166200e-15],\n",
      "       [-2.067700e-16, -1.000140e-15, -3.567206e-14, -1.751171e-14,\n",
      "        -2.166200e-15,  5.763952e-14]]), 4: array([[ 4.8484680e-14, -4.2765000e-16, -3.2827670e-14, -1.5419100e-15,\n",
      "        -1.2581860e-14, -2.0936000e-16],\n",
      "       [-4.2765000e-16,  5.2746780e-14, -3.5034120e-14, -1.3418960e-14,\n",
      "        -1.8169100e-15, -1.0270100e-15],\n",
      "       [-3.2827670e-14, -3.5034120e-14,  2.3360505e-13, -3.1226640e-14,\n",
      "        -3.7280880e-14, -3.5886480e-14],\n",
      "       [-1.5419100e-15, -1.3418960e-14, -3.1226640e-14,  9.4740220e-14,\n",
      "        -2.8469770e-14, -1.7808840e-14],\n",
      "       [-1.2581860e-14, -1.8169100e-15, -3.7280880e-14, -2.8469770e-14,\n",
      "         8.4958130e-14, -2.1791000e-15],\n",
      "       [-2.0936000e-16, -1.0270100e-15, -3.5886480e-14, -1.7808840e-14,\n",
      "        -2.1791000e-15,  5.8221940e-14]]), 5: array([[ 4.8928030e-14, -4.2235000e-16, -3.3016070e-14, -1.5386100e-15,\n",
      "        -1.2845170e-14, -2.0677000e-16],\n",
      "       [-4.2235000e-16,  5.3069610e-14, -3.5143740e-14, -1.3675150e-14,\n",
      "        -1.8136800e-15, -9.9928000e-16],\n",
      "       [-3.3016070e-14, -3.5143740e-14,  2.3401567e-13, -3.1505580e-14,\n",
      "        -3.7644370e-14, -3.6157100e-14],\n",
      "       [-1.5386100e-15, -1.3675150e-14, -3.1505580e-14,  9.6955680e-14,\n",
      "        -2.9440700e-14, -1.8481020e-14],\n",
      "       [-1.2845170e-14, -1.8136800e-15, -3.7644370e-14, -2.9440700e-14,\n",
      "         8.6629220e-14, -2.1968600e-15],\n",
      "       [-2.0677000e-16, -9.9928000e-16, -3.6157100e-14, -1.8481020e-14,\n",
      "        -2.1968600e-15,  5.9144960e-14]]), 6: array([[ 4.9744140e-14, -4.2602000e-16, -3.3507700e-14, -1.5477100e-15,\n",
      "        -1.3151570e-14, -2.0706000e-16],\n",
      "       [-4.2602000e-16,  5.3990280e-14, -3.5717330e-14, -1.3970180e-14,\n",
      "        -1.8331900e-15, -1.0150200e-15],\n",
      "       [-3.3507700e-14, -3.5717330e-14,  2.3538074e-13, -3.1492990e-14,\n",
      "        -3.7927300e-14, -3.6520570e-14],\n",
      "       [-1.5477100e-15, -1.3970180e-14, -3.1492990e-14,  9.8174080e-14,\n",
      "        -3.0017040e-14, -1.8830100e-14],\n",
      "       [-1.3151570e-14, -1.8331900e-15, -3.7927300e-14, -3.0017040e-14,\n",
      "         8.7842500e-14, -2.2091100e-15],\n",
      "       [-2.0706000e-16, -1.0150200e-15, -3.6520570e-14, -1.8830100e-14,\n",
      "        -2.2091100e-15,  5.9890010e-14]]), 7: array([[ 5.0304040e-14, -4.2385000e-16, -3.3825400e-14, -1.5470800e-15,\n",
      "        -1.3383390e-14, -2.0684000e-16],\n",
      "       [-4.2385000e-16,  5.4562730e-14, -3.6109810e-14, -1.4131330e-14,\n",
      "        -1.8331700e-15, -1.0255300e-15],\n",
      "       [-3.3825400e-14, -3.6109810e-14,  2.3631736e-13, -3.1479700e-14,\n",
      "        -3.8084980e-14, -3.6914650e-14],\n",
      "       [-1.5470800e-15, -1.4131330e-14, -3.1479700e-14,  9.8958140e-14,\n",
      "        -3.0432440e-14, -1.9054770e-14],\n",
      "       [-1.3383390e-14, -1.8331700e-15, -3.8084980e-14, -3.0432440e-14,\n",
      "         8.8685850e-14, -2.2178100e-15],\n",
      "       [-2.0684000e-16, -1.0255300e-15, -3.6914650e-14, -1.9054770e-14,\n",
      "        -2.2178100e-15,  6.0541990e-14]]), 8: array([[ 5.059475e-14, -4.237300e-16, -3.400537e-14, -1.547790e-15,\n",
      "        -1.349320e-14, -2.066400e-16],\n",
      "       [-4.237300e-16,  5.494375e-14, -3.629163e-14, -1.431897e-14,\n",
      "        -1.838080e-15, -1.031450e-15],\n",
      "       [-3.400537e-14, -3.629163e-14,  2.370264e-13, -3.152366e-14,\n",
      "        -3.822709e-14, -3.711942e-14],\n",
      "       [-1.547790e-15, -1.431897e-14, -3.152366e-14,  9.953492e-14,\n",
      "        -3.061516e-14, -1.921035e-14],\n",
      "       [-1.349320e-14, -1.838080e-15, -3.822709e-14, -3.061516e-14,\n",
      "         8.915073e-14, -2.224350e-15],\n",
      "       [-2.066400e-16, -1.031450e-15, -3.711942e-14, -1.921035e-14,\n",
      "        -2.224350e-15,  6.091761e-14]])}, 'is_converged': True}\n",
      "\n",
      "key=40um\n",
      "option_name['40um']['option_name']=pad_gap\n",
      "variables={'lumped_oscillator': {'fQ': 5.603196288676896, 'EC': 324.7478590723875, 'EJ': 13.616300010297985, 'alpha': -379.2448093489956, 'dispersion': 54.33571833992004, 'gbus': array([122.75192426, -92.44407275,  79.74179062]), 'chi_in_MHz': array([ -4.68116172, -21.09933964,  -8.31554677])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha  dispersion  \\\n",
      "1  5.942448  368.450236  13.6163 -436.183203   159.21458   \n",
      "2  5.860089  357.545759  13.6163 -421.842741  124.079999   \n",
      "3   5.77753   346.80562  13.6163 -407.805744   95.939037   \n",
      "4  5.737369  341.649788  13.6163 -401.097753   84.424981   \n",
      "5  5.684596  334.942753  13.6163 -392.400966   71.169458   \n",
      "6  5.644678  329.920535  13.6163 -385.910471   62.408863   \n",
      "7  5.619535  326.779545  13.6163 -381.860572   57.396903   \n",
      "8  5.603196  324.747859  13.6163 -379.244809   54.335718   \n",
      "\n",
      "                                                gbus  \\\n",
      "1  [114.38520672445735, -77.91536562297485, 77.96...   \n",
      "2  [118.14507620202656, -86.1504566631403, 72.301...   \n",
      "3  [117.78733127044518, -88.41648711373728, 75.26...   \n",
      "4  [117.84135564008048, -88.71652047403923, 76.68...   \n",
      "5  [120.25622025638054, -89.93776777738493, 76.86...   \n",
      "6  [121.56948305978523, -90.94565691616705, 78.44...   \n",
      "7  [122.23628316058053, -92.09397182340028, 78.79...   \n",
      "8  [122.75192426481861, -92.44407275077926, 79.74...   \n",
      "\n",
      "                                          chi_in_MHz    χr MHz      gr MHz  \n",
      "1  [-7.295942608032298, -186.41543152823883, -29....  7.295943  114.385207  \n",
      "2  [-6.68860344251652, -79.71703699689004, -17.06...  6.688603  118.145076  \n",
      "3  [-5.749406339167892, -45.51994077862497, -13.2...  5.749406  117.787331  \n",
      "4  [-5.373834510954572, -36.26780028758106, -11.8...  5.373835  117.841356  \n",
      "5  [-5.124958369774344, -28.48380922738183, -9.94...  5.124958  120.256220  \n",
      "6  [-4.907144357646617, -24.28714089662891, -9.12...  4.907144  121.569483  \n",
      "7  [-4.764435821161295, -22.382303472279492, -8.5...  4.764436  122.236283  \n",
      "8  [-4.6811617174208555, -21.099339639730957, -8....  4.681162  122.751924  }\n",
      "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
      "bus1_connector_pad_Q1                  50.64383               -0.39535   \n",
      "bus2_connector_pad_Q1                  -0.39535               55.01423   \n",
      "ground_main_plane                     -34.09242              -36.42933   \n",
      "pad_bot_Q1                             -1.48913              -14.37074   \n",
      "pad_top_Q1                            -13.55646               -1.76745   \n",
      "readout_connector_pad_Q1               -0.19817               -1.02182   \n",
      "\n",
      "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
      "bus1_connector_pad_Q1             -34.09242    -1.48913   -13.55646   \n",
      "bus2_connector_pad_Q1             -36.42933   -14.37074    -1.76745   \n",
      "ground_main_plane                 239.00028   -32.35105   -39.13830   \n",
      "pad_bot_Q1                        -32.35105    96.74559   -26.86254   \n",
      "pad_top_Q1                        -39.13830   -26.86254    86.26515   \n",
      "readout_connector_pad_Q1          -37.20723   -19.29195    -2.13165   \n",
      "\n",
      "                          readout_connector_pad_Q1  \n",
      "bus1_connector_pad_Q1                     -0.19817  \n",
      "bus2_connector_pad_Q1                     -1.02182  \n",
      "ground_main_plane                        -37.20723  \n",
      "pad_bot_Q1                               -19.29195  \n",
      "pad_top_Q1                                -2.13165  \n",
      "readout_connector_pad_Q1                  60.96517  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4242490e-14, -3.2589000e-16, -3.1180050e-14, -1.3069000e-15,\n",
      "        -1.0580130e-14, -1.6062000e-16],\n",
      "       [-3.2589000e-16,  4.6524070e-14, -3.2984390e-14, -1.1518980e-14,\n",
      "        -6.7004000e-16, -3.0398000e-16],\n",
      "       [-3.1180050e-14, -3.2984390e-14,  2.3947968e-13, -2.9795340e-14,\n",
      "        -3.7079460e-14, -3.4156100e-14],\n",
      "       [-1.3069000e-15, -1.1518980e-14, -2.9795340e-14,  8.4032180e-14,\n",
      "        -2.3506090e-14, -1.6140410e-14],\n",
      "       [-1.0580130e-14, -6.7004000e-16, -3.7079460e-14, -2.3506090e-14,\n",
      "         7.5733560e-14, -1.8492400e-15],\n",
      "       [-1.6062000e-16, -3.0398000e-16, -3.4156100e-14, -1.6140410e-14,\n",
      "        -1.8492400e-15,  5.3280840e-14]]), 2: array([[ 4.5616390e-14, -3.9686000e-16, -3.0842440e-14, -1.3883800e-15,\n",
      "        -1.1984460e-14, -1.9837000e-16],\n",
      "       [-3.9686000e-16,  4.9820940e-14, -3.3872680e-14, -1.2002070e-14,\n",
      "        -1.6197500e-15, -1.0311400e-15],\n",
      "       [-3.0842440e-14, -3.3872680e-14,  2.3684909e-13, -3.1346120e-14,\n",
      "        -3.7795590e-14, -3.4975700e-14],\n",
      "       [-1.3883800e-15, -1.2002070e-14, -3.1346120e-14,  8.7279510e-14,\n",
      "        -2.3604360e-14, -1.6865180e-14],\n",
      "       [-1.1984460e-14, -1.6197500e-15, -3.7795590e-14, -2.3604360e-14,\n",
      "         7.9292480e-14, -1.8851600e-15],\n",
      "       [-1.9837000e-16, -1.0311400e-15, -3.4975700e-14, -1.6865180e-14,\n",
      "        -1.8851600e-15,  5.5964780e-14]]), 3: array([[ 4.774243e-14, -3.945600e-16, -3.234879e-14, -1.454270e-15,\n",
      "        -1.247981e-14, -1.988300e-16],\n",
      "       [-3.945600e-16,  5.157865e-14, -3.453043e-14, -1.294261e-14,\n",
      "        -1.728270e-15, -9.924400e-16],\n",
      "       [-3.234879e-14, -3.453043e-14,  2.344610e-13, -3.183040e-14,\n",
      "        -3.793489e-14, -3.571656e-14],\n",
      "       [-1.454270e-15, -1.294261e-14, -3.183040e-14,  9.062371e-14,\n",
      "        -2.453722e-14, -1.759460e-14],\n",
      "       [-1.247981e-14, -1.728270e-15, -3.793489e-14, -2.453722e-14,\n",
      "         8.135669e-14, -2.079910e-15],\n",
      "       [-1.988300e-16, -9.924400e-16, -3.571656e-14, -1.759460e-14,\n",
      "        -2.079910e-15,  5.766013e-14]]), 4: array([[ 4.8375450e-14, -3.9882000e-16, -3.2753100e-14, -1.4809000e-15,\n",
      "        -1.2649340e-14, -2.0097000e-16],\n",
      "       [-3.9882000e-16,  5.2595680e-14, -3.5087220e-14, -1.3334920e-14,\n",
      "        -1.7464400e-15, -1.0145900e-15],\n",
      "       [-3.2753100e-14, -3.5087220e-14,  2.3528881e-13, -3.1988810e-14,\n",
      "        -3.8217180e-14, -3.5959440e-14],\n",
      "       [-1.4809000e-15, -1.3334920e-14, -3.1988810e-14,  9.2036790e-14,\n",
      "        -2.5065330e-14, -1.7840350e-14],\n",
      "       [-1.2649340e-14, -1.7464400e-15, -3.8217180e-14, -2.5065330e-14,\n",
      "         8.2460870e-14, -2.0930200e-15],\n",
      "       [-2.0097000e-16, -1.0145900e-15, -3.5959440e-14, -1.7840350e-14,\n",
      "        -2.0930200e-15,  5.8211970e-14]]), 5: array([[ 4.9096690e-14, -3.9415000e-16, -3.3227640e-14, -1.4835300e-15,\n",
      "        -1.2897560e-14, -1.9845000e-16],\n",
      "       [-3.9415000e-16,  5.3034830e-14, -3.5258960e-14, -1.3647410e-14,\n",
      "        -1.7376000e-15, -9.8836000e-16],\n",
      "       [-3.3227640e-14, -3.5258960e-14,  2.3597375e-13, -3.2372580e-14,\n",
      "        -3.8395660e-14, -3.6226080e-14],\n",
      "       [-1.4835300e-15, -1.3647410e-14, -3.2372580e-14,  9.4223890e-14,\n",
      "        -2.5781500e-14, -1.8561290e-14],\n",
      "       [-1.2897560e-14, -1.7376000e-15, -3.8395660e-14, -2.5781500e-14,\n",
      "         8.3649800e-14, -2.0921300e-15],\n",
      "       [-1.9845000e-16, -9.8836000e-16, -3.6226080e-14, -1.8561290e-14,\n",
      "        -2.0921300e-15,  5.9163860e-14]]), 6: array([[ 4.982926e-14, -3.967100e-16, -3.363908e-14, -1.487400e-15,\n",
      "        -1.320745e-14, -1.987400e-16],\n",
      "       [-3.967100e-16,  5.400163e-14, -3.580562e-14, -1.401215e-14,\n",
      "        -1.762220e-15, -1.003590e-15],\n",
      "       [-3.363908e-14, -3.580562e-14,  2.372922e-13, -3.228836e-14,\n",
      "        -3.884504e-14, -3.659899e-14],\n",
      "       [-1.487400e-15, -1.401215e-14, -3.228836e-14,  9.536075e-14,\n",
      "        -2.629024e-14, -1.890722e-14],\n",
      "       [-1.320745e-14, -1.762220e-15, -3.884504e-14, -2.629024e-14,\n",
      "         8.499589e-14, -2.120500e-15],\n",
      "       [-1.987400e-16, -1.003590e-15, -3.659899e-14, -1.890722e-14,\n",
      "        -2.120500e-15,  5.993182e-14]]), 7: array([[ 5.0244360e-14, -3.9629000e-16, -3.3810070e-14, -1.4880600e-15,\n",
      "        -1.3444540e-14, -1.9878000e-16],\n",
      "       [-3.9629000e-16,  5.4549730e-14, -3.6174730e-14, -1.4169490e-14,\n",
      "        -1.7662900e-15, -1.0135700e-15],\n",
      "       [-3.3810070e-14, -3.6174730e-14,  2.3804178e-13, -3.2340100e-14,\n",
      "        -3.8915950e-14, -3.6999550e-14],\n",
      "       [-1.4880600e-15, -1.4169490e-14, -3.2340100e-14,  9.6209560e-14,\n",
      "        -2.6668040e-14, -1.9141080e-14],\n",
      "       [-1.3444540e-14, -1.7662900e-15, -3.8915950e-14, -2.6668040e-14,\n",
      "         8.5726250e-14, -2.1270600e-15],\n",
      "       [-1.9878000e-16, -1.0135700e-15, -3.6999550e-14, -1.9141080e-14,\n",
      "        -2.1270600e-15,  6.0594280e-14]]), 8: array([[ 5.0643830e-14, -3.9535000e-16, -3.4092420e-14, -1.4891300e-15,\n",
      "        -1.3556460e-14, -1.9817000e-16],\n",
      "       [-3.9535000e-16,  5.5014230e-14, -3.6429330e-14, -1.4370740e-14,\n",
      "        -1.7674500e-15, -1.0218200e-15],\n",
      "       [-3.4092420e-14, -3.6429330e-14,  2.3900028e-13, -3.2351050e-14,\n",
      "        -3.9138300e-14, -3.7207230e-14],\n",
      "       [-1.4891300e-15, -1.4370740e-14, -3.2351050e-14,  9.6745590e-14,\n",
      "        -2.6862540e-14, -1.9291950e-14],\n",
      "       [-1.3556460e-14, -1.7674500e-15, -3.9138300e-14, -2.6862540e-14,\n",
      "         8.6265150e-14, -2.1316500e-15],\n",
      "       [-1.9817000e-16, -1.0218200e-15, -3.7207230e-14, -1.9291950e-14,\n",
      "        -2.1316500e-15,  6.0965170e-14]])}, 'is_converged': True}\n"
     ]
    }
   ],
   "source": [
    "if return_code ==0:\n",
    "    print(f'{sweep_data.keys()} \\n')\n",
    "    for key in sweep_data.keys():\n",
    "        print(f'\\nkey={key}')\n",
    "        \n",
    "        option_name = sweep_data[key]['option_name']\n",
    "        print(f'option_name[\\'{key}\\'][\\'option_name\\']={option_name}')\n",
    "        \n",
    "    \n",
    "        variables = sweep_data[key]['variables']\n",
    "        sim_variables = sweep_data[key]['sim_variables']\n",
    "        \n",
    "        print(f'variables={variables}')\n",
    "        print(f'sim_variables={sim_variables}')\n",
    "        \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# READ THIS BEFORE running the cell.\n",
    "This cell is to demonstrate that if you have already executed c1.sim.run(), you don't have to set up \n",
    "the environment again for c1.run_sweep().  In another words, if you don't pass updated arguments to\n",
    "c1.run_sweep(), then c1.run_sweep() looks for the previous desgin arguments. \n",
    "\n",
    "If you pass anything more than these three arguments: qcomp_name, option_name, option_sweep ..... \n",
    "Then NOTHING will be used from previous run.  \n",
    "```\n",
    "c1.sim.solution_order = 'Medium'\n",
    "c1.sim.auto_increase_solution_order = 'False'\n",
    "\n",
    "\n",
    "c1.sim.run(components=render_design_argument_qcomps,\n",
    "           open_terminations=render_design_argument_endcaps)\n",
    "```\n",
    "\n",
    "Because c1.sim.setup.run has the information from last run, this is OK.\n",
    "\n",
    "```\n",
    "sweep_data, return_code = c1.run_sweep(q1.name, \n",
    "                                        'pad_gap', \n",
    "                                        ['20um', '30um', '40um'])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uncomment next line if you would like to close the gui\n",
    "gui.main_window.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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