{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89bbcebd",
   "metadata": {},
   "source": [
    "# Example full chip design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4860a96",
   "metadata": {},
   "source": [
    "> 💡 **Using this tutorial without the Qt GUI**\n",
    "> \n",
    "> This tutorial uses the desktop `MetalGUI`. To follow along on Colab, Binder, JupyterHub, or any environment where Qt isn't available, **replace any `gui.rebuild()` / `gui.screenshot()` call with `qm.view(design)`** — it renders the design to a matplotlib `Figure` you can display inline or save with `fig.savefig(...)`.\n",
    "> \n",
    "> See [1.1 Quick start](../1-Overview/1.1-Quick-start.ipynb) for a complete runnable walkthrough and [`docs/headless-usage.rst`](../../docs/headless-usage.rst) for the full reference."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "306c85f5",
   "metadata": {},
   "source": [
    "*Make sure to have the right kernel selected!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70ecabbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <h1 style=\"\n",
       "        background: #12c2e9;  /* fallback for old browsers */\n",
       "        background: -webkit-linear-gradient(to right, #d4418e 0%, #0652c5 74%);  /* Chrome 10-25, Safari 5.1-6 */\n",
       "        background: linear-gradient(315deg, #d4418e 0%, #0652c5 74%); /* W3C, IE 10+/ Edge, Firefox 16+, Chrome 26+, Opera 12+, Safari 7+ */\n",
       "        margin-top: 50px;\n",
       "        border-style: outset;\n",
       "        padding-top:100px;\n",
       "        padding-bottom:50px;\n",
       "        padding-left:25px;\n",
       "        color: white;\n",
       "    \"> Welcome to Qiskit Metal! <h1>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, open_docs\n",
    "\n",
    "%metal_heading Welcome to Qiskit Metal!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3d4614",
   "metadata": {},
   "source": [
    "Welcome to Qiskit Metal! \n",
    "\n",
    "For this example tutorial, we will attempt to create a multi qubit chip with a variety of components. We will want to generate the layout, simulate/analyze and tune the chip to hit the parameters we are wanting, finally rendering to a GDS file.\n",
    "\n",
    "One could generate subsections of the layout and tune individual components first, but in this case we will create all of the layout. We will be using both transmon pockets and crossmons, meandered and simple transmission lines, capacitive couplers, and launchers for wirebond connections. So we will import these, and also create a design instance and launch the GUI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418cec1d",
   "metadata": {},
   "source": [
    "## Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01f94259",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket_6 import TransmonPocket6\n",
    "from qiskit_metal.qlibrary.qubits.transmon_cross_fl import TransmonCrossFL\n",
    "\n",
    "from qiskit_metal.qlibrary.couplers.tunable_coupler_01 import TunableCoupler01\n",
    "\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "from qiskit_metal.qlibrary.tlines.pathfinder import RoutePathfinder\n",
    "from qiskit_metal.qlibrary.tlines.anchored_path import RouteAnchors\n",
    "\n",
    "from qiskit_metal.qlibrary.lumped.cap_n_interdigital import CapNInterdigital\n",
    "from qiskit_metal.qlibrary.couplers.cap_n_interdigital_tee import CapNInterdigitalTee\n",
    "from qiskit_metal.qlibrary.couplers.coupled_line_tee import CoupledLineTee\n",
    "\n",
    "from qiskit_metal.qlibrary.terminations.launchpad_wb import LaunchpadWirebond\n",
    "from qiskit_metal.qlibrary.terminations.launchpad_wb_coupled import (\n",
    "    LaunchpadWirebondCoupled,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11f335b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "design = metal.designs.DesignPlanar()\n",
    "\n",
    "gui = metal.MetalGUI(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59941f9b",
   "metadata": {},
   "source": [
    "Since we are likely to be making many changes while tuning and modifying our design, we will enable overwriting. We can also check all of the chip properties to see if we want to change the size or any other parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e3cdd42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'material': 'silicon',\n",
       " 'layer_start': '0',\n",
       " 'layer_end': '2048',\n",
       " 'size': {'center_x': '0.0mm',\n",
       "  'center_y': '0.0mm',\n",
       "  'center_z': '0.0mm',\n",
       "  'size_x': '9mm',\n",
       "  'size_y': '6mm',\n",
       "  'size_z': '-750um',\n",
       "  'sample_holder_top': '890um',\n",
       "  'sample_holder_bottom': '1650um'}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "design.overwrite_enabled = True\n",
    "design.chips.main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce352d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "design.chips.main.size.size_x = \"11mm\"\n",
    "design.chips.main.size.size_y = \"9mm\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03dfb41a",
   "metadata": {},
   "source": [
    "#### The Qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47293c0",
   "metadata": {},
   "source": [
    "We will add a collection of qubits. First we will place a transmon pocket with six connection pads. We can see any options the qubit qcomponent has to figure out what we might want to modify when creating the component. This will include the components default options (which the component designer included) as well as renderer options (which are added based on what renderers are present in Metal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a083915a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pos_x': '0um',\n",
       " 'pos_y': '0um',\n",
       " 'connection_pads': {},\n",
       " '_default_connection_pads': {'pad_gap': '15um',\n",
       "  'pad_width': '125um',\n",
       "  'pad_height': '30um',\n",
       "  'pad_cpw_shift': '0um',\n",
       "  'pad_cpw_extent': '25um',\n",
       "  'cpw_width': '10um',\n",
       "  'cpw_gap': '6um',\n",
       "  'cpw_extend': '100um',\n",
       "  'pocket_extent': '5um',\n",
       "  'pocket_rise': '0um',\n",
       "  'loc_W': '+1',\n",
       "  'loc_H': '+1'},\n",
       " 'chip': 'main',\n",
       " 'pad_gap': '30um',\n",
       " 'inductor_width': '20um',\n",
       " 'pad_width': '455um',\n",
       " 'pad_height': '90um',\n",
       " 'pocket_width': '650um',\n",
       " 'pocket_height': '650um',\n",
       " 'orientation': '0',\n",
       " 'hfss_wire_bonds': False,\n",
       " 'q3d_wire_bonds': False,\n",
       " 'hfss_inductance': '10nH',\n",
       " 'hfss_capacitance': 0,\n",
       " 'hfss_resistance': 0,\n",
       " 'hfss_mesh_kw_jj': 7e-06,\n",
       " 'q3d_inductance': '10nH',\n",
       " 'q3d_capacitance': 0,\n",
       " 'q3d_resistance': 0,\n",
       " 'q3d_mesh_kw_jj': 7e-06,\n",
       " 'gds_cell_name': 'my_other_junction'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TransmonPocket6.get_template_options(design)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "235602f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = dict(\n",
    "    pad_width=\"425 um\",\n",
    "    pocket_height=\"650um\",\n",
    "    connection_pads=dict(\n",
    "        readout=dict(loc_W=0, loc_H=-1, pad_width=\"80um\", pad_gap=\"50um\"),\n",
    "        bus_01=dict(loc_W=-1, loc_H=-1, pad_width=\"60um\", pad_gap=\"10um\"),\n",
    "        bus_02=dict(loc_W=-1, loc_H=+1, pad_width=\"60um\", pad_gap=\"10um\"),\n",
    "        bus_03=dict(loc_W=0, loc_H=+1, pad_width=\"90um\", pad_gap=\"30um\"),\n",
    "        bus_04=dict(loc_W=+1, loc_H=+1, pad_width=\"60um\", pad_gap=\"10um\"),\n",
    "        bus_05=dict(loc_W=+1, loc_H=-1, pad_width=\"60um\", pad_gap=\"10um\"),\n",
    "    ),\n",
    ")\n",
    "\n",
    "q_main = TransmonPocket6(\n",
    "    design,\n",
    "    \"Q_Main\",\n",
    "    options=dict(\n",
    "        pos_x=\"0mm\",\n",
    "        pos_y=\"-1mm\",\n",
    "        gds_cell_name=\"FakeJunction_01\",\n",
    "        hfss_inductance=\"14nH\",\n",
    "        **options,\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8842df69",
   "metadata": {},
   "source": [
    "We then will add a mixture of additional qubits. This is not (though do not let me stop any experimental investigation) a design one would normally create for any experiment of computational purpose, but allows for having a mixture of different components on one chip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8d85b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pos_x': '0um',\n",
       " 'pos_y': '0um',\n",
       " 'connection_pads': {},\n",
       " '_default_connection_pads': {'connector_type': '0',\n",
       "  'claw_length': '30um',\n",
       "  'ground_spacing': '5um',\n",
       "  'claw_width': '10um',\n",
       "  'claw_gap': '6um',\n",
       "  'connector_location': '0'},\n",
       " 'cross_width': '20um',\n",
       " 'cross_length': '200um',\n",
       " 'cross_gap': '20um',\n",
       " 'orientation': '0',\n",
       " 'layer': '1',\n",
       " 'make_fl': True,\n",
       " 'fl_options': {'t_top': '15um',\n",
       "  't_offset': '0um',\n",
       "  't_inductive_gap': '3um',\n",
       "  't_width': '5um',\n",
       "  't_gap': '3um'},\n",
       " 'hfss_wire_bonds': False,\n",
       " 'q3d_wire_bonds': False,\n",
       " 'hfss_inductance': '10nH',\n",
       " 'hfss_capacitance': 0,\n",
       " 'hfss_resistance': 0,\n",
       " 'hfss_mesh_kw_jj': 7e-06,\n",
       " 'q3d_inductance': '10nH',\n",
       " 'q3d_capacitance': 0,\n",
       " 'q3d_resistance': 0,\n",
       " 'q3d_mesh_kw_jj': 7e-06,\n",
       " 'gds_cell_name': 'my_other_junction'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TransmonCrossFL.get_template_options(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2855c4",
   "metadata": {},
   "source": [
    "We will add two crossmons with flux lines to the west side of the chip, which we will couple to each other using a tunable coupler. To make sure the various readout and control lines will have space to connect to launchers at the chip edge, we have to be mindful of where we place them, and making sure we have enough space for routing while avoiding cross talk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ee0d446",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = TransmonCrossFL(\n",
    "    design,\n",
    "    \"Q1\",\n",
    "    options=dict(\n",
    "        pos_x=\"-2.75mm\",\n",
    "        pos_y=\"-1.8mm\",\n",
    "        connection_pads=dict(\n",
    "            bus_01=dict(connector_location=\"180\", claw_length=\"95um\"),\n",
    "            readout=dict(connector_location=\"0\"),\n",
    "        ),\n",
    "        fl_options=dict(),\n",
    "    ),\n",
    ")\n",
    "\n",
    "Q2 = TransmonCrossFL(\n",
    "    design,\n",
    "    \"Q2\",\n",
    "    options=dict(\n",
    "        pos_x=\"-2.75mm\",\n",
    "        pos_y=\"-1.2mm\",\n",
    "        orientation=\"180\",\n",
    "        connection_pads=dict(\n",
    "            bus_02=dict(connector_location=\"0\", claw_length=\"95um\"),\n",
    "            readout=dict(connector_location=\"180\"),\n",
    "        ),\n",
    "        fl_options=dict(),\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "tune_c_Q12 = TunableCoupler01(\n",
    "    design,\n",
    "    \"Tune_C_Q12\",\n",
    "    options=dict(pos_x=\"-2.81mm\", pos_y=\"-1.5mm\", orientation=90, c_width=\"500um\"),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ea3f08",
   "metadata": {},
   "source": [
    "We then will add three transmon pockets to the north side of the chip, with the intention of having them in a linear series of coupling to each other, as well as the 'main' qubit to the south."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f20e798b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3 = TransmonPocket6(\n",
    "    design,\n",
    "    \"Q3\",\n",
    "    options=dict(\n",
    "        pos_x=\"-3mm\",\n",
    "        pos_y=\"0.5mm\",\n",
    "        gds_cell_name=\"FakeJunction_01\",\n",
    "        hfss_inductance=\"14nH\",\n",
    "        connection_pads=dict(\n",
    "            bus_03=dict(loc_W=0, loc_H=-1, pad_width=\"80um\", pad_gap=\"15um\"),\n",
    "            bus_q3_q4=dict(loc_W=1, loc_H=-1, pad_width=\"80um\", pad_gap=\"15um\"),\n",
    "            readout=dict(loc_W=0, loc_H=1, pad_width=\"80um\", pad_gap=\"50um\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "Q4 = TransmonPocket6(\n",
    "    design,\n",
    "    \"Q4\",\n",
    "    options=dict(\n",
    "        pos_x=\"0mm\",\n",
    "        pos_y=\"1mm\",\n",
    "        gds_cell_name=\"FakeJunction_01\",\n",
    "        hfss_inductance=\"14nH\",\n",
    "        connection_pads=dict(\n",
    "            bus_04=dict(loc_W=0, loc_H=-1, pad_width=\"80um\", pad_gap=\"15um\"),\n",
    "            bus_q3_q4=dict(loc_W=-1, loc_H=-1, pad_width=\"80um\", pad_gap=\"15um\"),\n",
    "            bus_q4_q5=dict(loc_W=1, loc_H=-1, pad_width=\"80um\", pad_gap=\"15um\"),\n",
    "            readout=dict(loc_W=0, loc_H=1, pad_width=\"80um\", pad_gap=\"50um\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "Q5 = TransmonPocket6(\n",
    "    design,\n",
    "    \"Q5\",\n",
    "    options=dict(\n",
    "        pos_x=\"3mm\",\n",
    "        pos_y=\"0.5mm\",\n",
    "        gds_cell_name=\"FakeJunction_01\",\n",
    "        hfss_inductance=\"14nH\",\n",
    "        connection_pads=dict(\n",
    "            bus_05=dict(loc_W=0, loc_H=-1, pad_width=\"80um\", pad_gap=\"15um\"),\n",
    "            bus_q4_q5=dict(loc_W=-1, loc_H=-1, pad_width=\"80um\", pad_gap=\"15um\"),\n",
    "            readout=dict(loc_W=0, loc_H=1, pad_width=\"80um\", pad_gap=\"50um\"),\n",
    "        ),\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb9a627",
   "metadata": {},
   "source": [
    "#### The Busses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b172ee0b",
   "metadata": {},
   "source": [
    "We now couple the qubits to each other, primarily using RouteMeander. Although one needs to run simulations to properly tune the line lengths for target frequencies, an initial estimate could be determined from the below method;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1c12fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.em.cpw_calculations import guided_wavelength\n",
    "\n",
    "\n",
    "def find_resonator_length(frequency, line_width, line_gap, N):\n",
    "    # frequency in GHz\n",
    "    # line_width/line_gap in um\n",
    "    # N -> 2 for lambda/2, 4 for lambda/4\n",
    "\n",
    "    [lambdaG, etfSqrt, q] = guided_wavelength(\n",
    "        frequency * 10**9,\n",
    "        line_width * 10**-6,\n",
    "        line_gap * 10**-6,\n",
    "        750 * 10**-6,\n",
    "        200 * 10**-9,\n",
    "    )\n",
    "    return str(lambdaG / N * 10**3) + \" mm\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55c1b29",
   "metadata": {},
   "source": [
    "As we are not worried about a creating a functional chip in this tutorial, we will give the resonators somewhat arbitraty lengths. First coupling the two crossmons to Q_Main."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae45ae71",
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_01 = RouteMeander(\n",
    "    design,\n",
    "    \"Bus_01\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q_Main\", pin=\"bus_01\"),\n",
    "            end_pin=Dict(component=\"Q1\", pin=\"bus_01\"),\n",
    "        ),\n",
    "        lead=Dict(start_straight=\"125um\", end_straight=\"225um\"),\n",
    "        meander=Dict(asymmetry=\"1305um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"6mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "bus_02 = RouteMeander(\n",
    "    design,\n",
    "    \"Bus_02\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q_Main\", pin=\"bus_02\"),\n",
    "            end_pin=Dict(component=\"Q2\", pin=\"bus_02\"),\n",
    "        ),\n",
    "        lead=Dict(start_straight=\"325um\", end_straight=\"125um\"),\n",
    "        meander=Dict(asymmetry=\"450um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"6.4mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d10cc8c",
   "metadata": {},
   "source": [
    "Then the three transmon pockets on the north side to Q_Main."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea425425",
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_03 = RouteMeander(\n",
    "    design,\n",
    "    \"Bus_03\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q_Main\", pin=\"bus_03\"),\n",
    "            end_pin=Dict(component=\"Q3\", pin=\"bus_03\"),\n",
    "        ),\n",
    "        lead=Dict(start_straight=\"225um\", end_straight=\"25um\"),\n",
    "        meander=Dict(asymmetry=\"50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"6.8mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "# To help set the right spacing, jogs can be used to set some initially controlled routing paths\n",
    "from collections import OrderedDict\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", \"250um\"]\n",
    "jogs_start[1] = [\"R\", \"200um\"]\n",
    "\n",
    "jogs_end = OrderedDict()\n",
    "jogs_end[0] = [\"L\", \"600um\"]\n",
    "\n",
    "bus_04 = RouteMeander(\n",
    "    design,\n",
    "    \"Bus_04\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q_Main\", pin=\"bus_04\"),\n",
    "            end_pin=Dict(component=\"Q4\", pin=\"bus_04\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"225um\",\n",
    "            # end_straight = '25um',\n",
    "            start_jogged_extension=jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"150um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"7.2mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "bus_05 = RouteMeander(\n",
    "    design,\n",
    "    \"Bus_05\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q_Main\", pin=\"bus_05\"),\n",
    "            end_pin=Dict(component=\"Q5\", pin=\"bus_05\"),\n",
    "        ),\n",
    "        lead=Dict(start_straight=\"225um\", end_straight=\"25um\"),\n",
    "        meander=Dict(asymmetry=\"50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"7.6mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295979ad",
   "metadata": {},
   "source": [
    "Finally the three transmon pockets on the north side to each other. This concludes the interconnectivity between the qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e02f6ff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_q3_q4 = RouteMeander(\n",
    "    design,\n",
    "    \"Bus_Q3_Q4\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q3\", pin=\"bus_q3_q4\"),\n",
    "            end_pin=Dict(component=\"Q4\", pin=\"bus_q3_q4\"),\n",
    "        ),\n",
    "        lead=Dict(start_straight=\"125um\", end_straight=\"125um\"),\n",
    "        meander=Dict(asymmetry=\"50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"6.4mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "bus_q4_q5 = RouteMeander(\n",
    "    design,\n",
    "    \"Bus_Q4_Q5\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q4\", pin=\"bus_q4_q5\"),\n",
    "            end_pin=Dict(component=\"Q5\", pin=\"bus_q4_q5\"),\n",
    "        ),\n",
    "        lead=Dict(start_straight=\"125um\", end_straight=\"25um\"),\n",
    "        meander=Dict(asymmetry=\"50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"6.8mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8afab96f",
   "metadata": {},
   "source": [
    "#### The Readouts and Control Lines\n",
    "\n",
    "The intention for this design is to have the three north transmon pockets be multiplexed to one readout line. The crossmons to their own readouts, as well as Q_Main. The tunable coupler, and the two crossmons also have flux lines which need to be connected to launchers.\n",
    "First we will place the wirebond launchers at the edges of the chip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f10294f",
   "metadata": {},
   "outputs": [],
   "source": [
    "launch_qmain_read = LaunchpadWirebond(\n",
    "    design,\n",
    "    \"Launch_QMain_Read\",\n",
    "    options=dict(pos_x=\"2mm\", pos_y=\"-4mm\", orientation=\"90\"),\n",
    ")\n",
    "\n",
    "launch_q1_fl = LaunchpadWirebond(\n",
    "    design,\n",
    "    \"Launch_Q1_FL\",\n",
    "    options=dict(\n",
    "        pos_x=\"0mm\",\n",
    "        pos_y=\"-4mm\",\n",
    "        orientation=\"90\",\n",
    "        trace_width=\"5um\",\n",
    "        trace_gap=\"3um\",\n",
    "    ),\n",
    ")\n",
    "launch_q1_read = LaunchpadWirebondCoupled(\n",
    "    design, \"Launch_Q1_Read\", options=dict(pos_x=\"-2mm\", pos_y=\"-4mm\", orientation=\"90\")\n",
    ")\n",
    "\n",
    "launch_tcoup_fl = LaunchpadWirebond(\n",
    "    design,\n",
    "    \"Launch_TuneC_FL\",\n",
    "    options=dict(\n",
    "        pos_x=\"-4mm\",\n",
    "        pos_y=\"-4mm\",\n",
    "        orientation=\"90\",\n",
    "        trace_width=\"5um\",\n",
    "        trace_gap=\"3um\",\n",
    "    ),\n",
    ")\n",
    "launch_tcoup_read = LaunchpadWirebondCoupled(\n",
    "    design,\n",
    "    \"Launch_TuneC_Read\",\n",
    "    options=dict(pos_x=\"-5mm\", pos_y=\"-3mm\", orientation=\"0\"),\n",
    ")\n",
    "\n",
    "launch_q2_read = LaunchpadWirebondCoupled(\n",
    "    design, \"Launch_Q2_Read\", options=dict(pos_x=\"-5mm\", pos_y=\"-1mm\", orientation=\"0\")\n",
    ")\n",
    "launch_q2_fl = LaunchpadWirebond(\n",
    "    design,\n",
    "    \"Launch_Q2_FL\",\n",
    "    options=dict(\n",
    "        pos_x=\"-5mm\",\n",
    "        pos_y=\"1mm\",\n",
    "        orientation=\"0\",\n",
    "        trace_width=\"5um\",\n",
    "        trace_gap=\"3um\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "launch_nw = LaunchpadWirebond(\n",
    "    design, \"Launch_NW\", options=dict(pos_x=\"-5mm\", pos_y=\"3mm\", orientation=0)\n",
    ")\n",
    "launch_ne = LaunchpadWirebond(\n",
    "    design, \"Launch_NE\", options=dict(pos_x=\"5mm\", pos_y=\"3mm\", orientation=180)\n",
    ")\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a8e228f",
   "metadata": {},
   "source": [
    "We then will add in the readout resonators for Q_Main, Q1, Q2 and the tuneable coupler.\n",
    "We will add a finger capacitor for the Q_Main readout, instead of just using the LaunchpadWirebondCoupled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adbb0e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main Readout\n",
    "\n",
    "\n",
    "read_q_main_cap = CapNInterdigital(\n",
    "    design,\n",
    "    \"Read_Q_Main_Cap\",\n",
    "    options=dict(pos_x=\"2mm\", pos_y=\"-3.5mm\", orientation=\"0\"),\n",
    ")\n",
    "\n",
    "jogs_end = OrderedDict()\n",
    "jogs_end[0] = [\"L\", \"600um\"]\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", \"250um\"]\n",
    "\n",
    "read_q_main = RouteMeander(\n",
    "    design,\n",
    "    \"Read_Q_Main\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q_Main\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Read_Q_Main_Cap\", pin=\"north_end\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"725um\",\n",
    "            end_straight=\"625um\",\n",
    "            start_jogged_extension=jogs_start,\n",
    "            end_jogged_extension=jogs_end,\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"5.6mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "read_q_main_cap_launch = RoutePathfinder(\n",
    "    design,\n",
    "    \"Read_Q_Main_Cap_Launch\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=dict(\n",
    "            start_pin=Dict(component=\"Read_Q_Main_Cap\", pin=\"south_end\"),\n",
    "            end_pin=Dict(component=\"Launch_QMain_Read\", pin=\"tie\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"0um\",\n",
    "            end_straight=\"0um\",\n",
    "            # start_jogged_extension = jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5928f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crossmon's Readouts\n",
    "\n",
    "jogs_end = OrderedDict()\n",
    "jogs_end[0] = [\"L\", \"600um\"]\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", \"250um\"]\n",
    "\n",
    "read_q1 = RouteMeander(\n",
    "    design,\n",
    "    \"Read_Q1\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q1\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Launch_Q1_Read\", pin=\"tie\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"250um\",\n",
    "            end_straight=\"25um\",\n",
    "            # start_jogged_extension = jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"6.8mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "jogs_end = OrderedDict()\n",
    "jogs_end[0] = [\"L\", \"600um\"]\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", \"250um\"]\n",
    "\n",
    "read_tunec = RouteMeander(\n",
    "    design,\n",
    "    \"Read_TuneC\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Tune_C_Q12\", pin=\"Control\"),\n",
    "            end_pin=Dict(component=\"Launch_TuneC_Read\", pin=\"tie\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"1525um\",\n",
    "            end_straight=\"125um\",\n",
    "            # start_jogged_extension = jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"5.8mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "jogs_end = OrderedDict()\n",
    "jogs_end[0] = [\"L\", \"600um\"]\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", \"250um\"]\n",
    "\n",
    "\n",
    "read_q2 = RouteMeander(\n",
    "    design,\n",
    "    \"Read_Q2\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q2\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Launch_Q2_Read\", pin=\"tie\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"350um\",\n",
    "            end_straight=\"0um\",\n",
    "            # start_jogged_extension = jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"-450um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"5.4mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc67a802",
   "metadata": {},
   "source": [
    "Finishing off this section of the chip by connecting the flux lines to appropraite wirebond launch pads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d78b73e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crossmon flux lines\n",
    "\n",
    "flux_line_Q1 = RoutePathfinder(\n",
    "    design,\n",
    "    \"Flux_Line_Q1\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q1\", pin=\"flux_line\"),\n",
    "            end_pin=Dict(component=\"Launch_Q1_FL\", pin=\"tie\"),\n",
    "        ),\n",
    "        fillet=\"99um\",\n",
    "        trace_width=\"5um\",\n",
    "        trace_gap=\"3um\",\n",
    "        # anchors = anchors\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", \"750um\"]\n",
    "\n",
    "flux_line_tunec = RoutePathfinder(\n",
    "    design,\n",
    "    \"Flux_Line_TuneC\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Tune_C_Q12\", pin=\"Flux\"),\n",
    "            end_pin=Dict(component=\"Launch_TuneC_FL\", pin=\"tie\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"875um\",\n",
    "            end_straight=\"350um\",\n",
    "            start_jogged_extension=jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        fillet=\"99um\",\n",
    "        trace_width=\"5um\",\n",
    "        trace_gap=\"3um\",\n",
    "        # anchors = anchors\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", \"525um\"]\n",
    "jogs_start[1] = [\"R\", \"625um\"]\n",
    "\n",
    "flux_line_Q2 = RoutePathfinder(\n",
    "    design,\n",
    "    \"Flux_Line_Q2\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q2\", pin=\"flux_line\"),\n",
    "            end_pin=Dict(component=\"Launch_Q2_FL\", pin=\"tie\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"175um\",\n",
    "            end_straight=\"150um\",\n",
    "            start_jogged_extension=jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        fillet=\"99um\",\n",
    "        trace_width=\"5um\",\n",
    "        trace_gap=\"3um\",\n",
    "        # anchors = anchors\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979aa998",
   "metadata": {},
   "source": [
    "Shifting our focus now to the three transmon pockets in the north. As we want these to be multiplexed to a single readout line, we will add in a few three port components, such as the CoupledLineTee and CapNInterdigitalTee.\n",
    "Q3 will have an inductive coupling to the readout line (as we want a lambda/4 resonator), Q4 will have a simple gap capacitor, and Q5 will have an interdigitated capacitor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5d1bed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "q3_read_T = CoupledLineTee(\n",
    "    design,\n",
    "    \"Q3_Read_T\",\n",
    "    options=dict(\n",
    "        pos_x=\"-3mm\",\n",
    "        pos_y=\"3mm\",\n",
    "        orientation=\"0\",\n",
    "        coupling_length=\"200um\",\n",
    "        open_termination=False,\n",
    "    ),\n",
    ")\n",
    "# We use finger count to set the width of the gap capacitance, -> N*cap_width + (N-1)*cap_gap\n",
    "q4_read_T = CapNInterdigitalTee(\n",
    "    design,\n",
    "    \"Q4_Read_T\",\n",
    "    options=dict(\n",
    "        pos_x=\"0mm\", pos_y=\"3mm\", orientation=\"0\", finger_length=\"0um\", finger_count=\"8\"\n",
    "    ),\n",
    ")\n",
    "q5_read_T = CapNInterdigitalTee(\n",
    "    design,\n",
    "    \"Q5_Read_T\",\n",
    "    options=dict(\n",
    "        pos_x=\"3mm\",\n",
    "        pos_y=\"3mm\",\n",
    "        orientation=\"0\",\n",
    "        finger_length=\"50um\",\n",
    "        finger_count=\"11\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61751402",
   "metadata": {},
   "source": [
    "We add in the readout resonators to each respective qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "020a31e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "read_q3 = RouteMeander(\n",
    "    design,\n",
    "    \"Read_Q3\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q3\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Q3_Read_T\", pin=\"second_end\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"150um\",\n",
    "            end_straight=\"150um\",\n",
    "            # start_jogged_extension = jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"0um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"5mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "read_q4 = RouteMeander(\n",
    "    design,\n",
    "    \"Read_Q4\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q4\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Q4_Read_T\", pin=\"second_end\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"125um\",\n",
    "            end_straight=\"125um\",\n",
    "            # start_jogged_extension = jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"0um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"5.8mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "read_q5 = RouteMeander(\n",
    "    design,\n",
    "    \"Read_Q5\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q5\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Q5_Read_T\", pin=\"second_end\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"125um\",\n",
    "            end_straight=\"125um\",\n",
    "            # start_jogged_extension = jogs_start,\n",
    "            # end_jogged_extension = jogs_end\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"0um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"5.4mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb625fab",
   "metadata": {},
   "source": [
    "We complete the layout by connecting the multiplexed readout line to the launchpads on either side of the chip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c48dd3d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_tl_01 = RoutePathfinder(\n",
    "    design,\n",
    "    \"ML_TL_01\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=dict(\n",
    "            start_pin=Dict(component=\"Launch_NW\", pin=\"tie\"),\n",
    "            end_pin=Dict(component=\"Q3_Read_T\", pin=\"prime_start\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "mp_tl_02 = RoutePathfinder(\n",
    "    design,\n",
    "    \"ML_TL_02\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=dict(\n",
    "            start_pin=Dict(component=\"Q3_Read_T\", pin=\"prime_end\"),\n",
    "            end_pin=Dict(component=\"Q4_Read_T\", pin=\"prime_start\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "mp_tl_03 = RoutePathfinder(\n",
    "    design,\n",
    "    \"ML_TL_03\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=dict(\n",
    "            start_pin=Dict(component=\"Q4_Read_T\", pin=\"prime_end\"),\n",
    "            end_pin=Dict(component=\"Q5_Read_T\", pin=\"prime_start\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "mp_tl_04 = RoutePathfinder(\n",
    "    design,\n",
    "    \"ML_TL_04\",\n",
    "    options=dict(\n",
    "        hfss_wire_bonds=True,\n",
    "        pin_inputs=dict(\n",
    "            start_pin=Dict(component=\"Q5_Read_T\", pin=\"prime_end\"),\n",
    "            end_pin=Dict(component=\"Launch_NE\", pin=\"tie\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     }
    }
   ],
   "source": [
    "# Rebuild the design in the GUI and save a screenshot as a .png file.\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n",
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675eb409",
   "metadata": {},
   "source": [
    "With this, we have completed the construction of our layout. \n",
    "\n",
    "Now, anyone familiar with chip design might find some of the location choices to be sub-optimal, with large sections of your chip left unused, or perhaps some CPW transmission lines running a bit closer to each other than would be ideal for avoiding cross talk concerns. These could be address by shifting the origin of your chip, or modifying component options to better compact your layout and alleviate crosstalk concerns.\n",
    "\n",
    "For this tutorial, we aren't too concerned how much space we may use up on our fictional chip, so we will instead continue on to analysis and tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb81a2a",
   "metadata": {},
   "source": [
    "## Analyze\n",
    "## Capacitance Extraction and LOM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95566cae",
   "metadata": {},
   "source": [
    "First we want to quickly look at the qubit parameters. Initial simulation and analysis is to use a lumped element approximation, by extracting the capacitance matrix of the qubit. We first analyze the qubit `Q_Main`, by first extracting the matrix and then using LOM analysis on it.\n",
    "\n",
    "For starters, let's select the appropriate QAnalysis class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1482d0b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "\n",
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc98c4e0",
   "metadata": {},
   "source": [
    "We can check if we wish to change any of the default options for the analysis. You should modify the number of passes and convergence based on the accuracy you require for your simulation.\n",
    "\n",
    "Depending on the complexity of the simulation, it could take a minute, or multiple hours. It is generally best to start with a small number of `max_passes` if you are unsure, so can you get a sense on the timing. As each adaptive pass adds additional tetrahedrons, the simulation time per pass will increase significantly (as well as the amount of system memory necessary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c86588c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Setup',\n",
       " 'reuse_selected_design': 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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9338fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Tune_Q_Main',\n",
       " 'reuse_selected_design': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 16,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.05,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': True,\n",
       " 'solution_order': 'High',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.setup.name = \"Tune_Q_Main\"\n",
    "c1.sim.setup.max_passes = 16\n",
    "c1.sim.setup.min_converged_passes = 2\n",
    "c1.sim.setup.percent_error = 0.05\n",
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02584fde",
   "metadata": {},
   "source": [
    "Next we will want to run the simulation for Q_Main. To obtain the complete capacitance matrix from this simulation, we will want to terminate the unconnected pins of Q_Main with opens, so that they are regarded as isolated charge islands in the simulation. You will need to list all of the pin names in the call. To recall them, one can look at the GUI, or check the pin dictionary attached to Q_Main: `q_main.pins.keys()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "67cf8411",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 09:51AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 09:51AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 09:51AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 09:51AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project23\n",
      "INFO 09:51AM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 09:51AM [connect]: \t Connected to project \"Project23\". No design detected\n",
      "INFO 09:51AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Q_Main_q3d [Solution type: Q3D]\n",
      "WARNING 09:51AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 09:51AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 09:51AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 09:51AM [get_setup]: \tOpened setup `Tune_Q_Main`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 09:51AM [analyze]: Analyzing setup Tune_Q_Main\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpjks6h_rl.txt, C, , Tune_Q_Main:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpmpomn8hh.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpo1bs65q6.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp27ap2rm7.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpid24udcc.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpanpc3fp0.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp7xe__y8o.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpzbj0ejdi.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp_kgnraw6.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 8, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpa99kh4q1.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 9, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp_e2tr4z4.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 10, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpfth1uezt.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 11, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpzhz29lld.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 12, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp2ly1bynz.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 13, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmppd5sj06r.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 14, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpx3ryjtku.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 15, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpqgpyoudh.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 16, False\n",
      "INFO 09:55AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp0ypb80s2.txt, C, , Tune_Q_Main:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 17, False\n"
     ]
    }
   ],
   "source": [
    "c1.sim.run(\n",
    "    name=\"Q_Main\",\n",
    "    components=[\"Q_Main\"],\n",
    "    open_terminations=[\n",
    "        (\"Q_Main\", \"readout\"),\n",
    "        (\"Q_Main\", \"bus_01\"),\n",
    "        (\"Q_Main\", \"bus_02\"),\n",
    "        (\"Q_Main\", \"bus_03\"),\n",
    "        (\"Q_Main\", \"bus_04\"),\n",
    "        (\"Q_Main\", \"bus_05\"),\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e435710",
   "metadata": {},
   "source": [
    "With the simulation completed, we can look at the capacitance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "938c6265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>bus_01_connector_pad_Q_Main</th>\n",
       "      <th>bus_02_connector_pad_Q_Main</th>\n",
       "      <th>bus_03_connector_pad_Q_Main</th>\n",
       "      <th>bus_04_connector_pad_Q_Main</th>\n",
       "      <th>bus_05_connector_pad_Q_Main</th>\n",
       "      <th>ground_main_plane</th>\n",
       "      <th>pad_bot_Q_Main</th>\n",
       "      <th>pad_top_Q_Main</th>\n",
       "      <th>readout_connector_pad_Q_Main</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bus_01_connector_pad_Q_Main</th>\n",
       "      <td>41.02176</td>\n",
       "      <td>-0.24001</td>\n",
       "      <td>-0.11719</td>\n",
       "      <td>-0.04481</td>\n",
       "      <td>-0.07874</td>\n",
       "      <td>-29.00822</td>\n",
       "      <td>-9.26982</td>\n",
       "      <td>-1.06835</td>\n",
       "      <td>-0.69738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus_02_connector_pad_Q_Main</th>\n",
       "      <td>-0.24001</td>\n",
       "      <td>41.00041</td>\n",
       "      <td>-0.75929</td>\n",
       "      <td>-0.07826</td>\n",
       "      <td>-0.04484</td>\n",
       "      <td>-28.98620</td>\n",
       "      <td>-1.07415</td>\n",
       "      <td>-9.21369</td>\n",
       "      <td>-0.10640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus_03_connector_pad_Q_Main</th>\n",
       "      <td>-0.11719</td>\n",
       "      <td>-0.75929</td>\n",
       "      <td>45.49485</td>\n",
       "      <td>-0.75936</td>\n",
       "      <td>-0.11710</td>\n",
       "      <td>-31.95144</td>\n",
       "      <td>-1.30891</td>\n",
       "      <td>-9.60366</td>\n",
       "      <td>-0.15780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus_04_connector_pad_Q_Main</th>\n",
       "      <td>-0.04481</td>\n",
       "      <td>-0.07826</td>\n",
       "      <td>-0.75936</td>\n",
       "      <td>41.02315</td>\n",
       "      <td>-0.24004</td>\n",
       "      <td>-29.00650</td>\n",
       "      <td>-1.07408</td>\n",
       "      <td>-9.21787</td>\n",
       "      <td>-0.10629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus_05_connector_pad_Q_Main</th>\n",
       "      <td>-0.07874</td>\n",
       "      <td>-0.04484</td>\n",
       "      <td>-0.11710</td>\n",
       "      <td>-0.24004</td>\n",
       "      <td>41.00041</td>\n",
       "      <td>-28.98777</td>\n",
       "      <td>-9.26969</td>\n",
       "      <td>-1.06800</td>\n",
       "      <td>-0.69718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-29.00822</td>\n",
       "      <td>-28.98620</td>\n",
       "      <td>-31.95144</td>\n",
       "      <td>-29.00650</td>\n",
       "      <td>-28.98777</td>\n",
       "      <td>308.85307</td>\n",
       "      <td>-33.31374</td>\n",
       "      <td>-32.77926</td>\n",
       "      <td>-31.47933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q_Main</th>\n",
       "      <td>-9.26982</td>\n",
       "      <td>-1.07415</td>\n",
       "      <td>-1.30891</td>\n",
       "      <td>-1.07408</td>\n",
       "      <td>-9.26969</td>\n",
       "      <td>-33.31374</td>\n",
       "      <td>95.77645</td>\n",
       "      <td>-31.23494</td>\n",
       "      <td>-7.05105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q_Main</th>\n",
       "      <td>-1.06835</td>\n",
       "      <td>-9.21369</td>\n",
       "      <td>-9.60366</td>\n",
       "      <td>-9.21787</td>\n",
       "      <td>-1.06800</td>\n",
       "      <td>-32.77926</td>\n",
       "      <td>-31.23494</td>\n",
       "      <td>97.45815</td>\n",
       "      <td>-1.13125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readout_connector_pad_Q_Main</th>\n",
       "      <td>-0.69738</td>\n",
       "      <td>-0.10640</td>\n",
       "      <td>-0.15780</td>\n",
       "      <td>-0.10629</td>\n",
       "      <td>-0.69718</td>\n",
       "      <td>-31.47933</td>\n",
       "      <td>-7.05105</td>\n",
       "      <td>-1.13125</td>\n",
       "      <td>42.10847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bus_01_connector_pad_Q_Main  \\\n",
       "bus_01_connector_pad_Q_Main                      41.02176   \n",
       "bus_02_connector_pad_Q_Main                      -0.24001   \n",
       "bus_03_connector_pad_Q_Main                      -0.11719   \n",
       "bus_04_connector_pad_Q_Main                      -0.04481   \n",
       "bus_05_connector_pad_Q_Main                      -0.07874   \n",
       "ground_main_plane                               -29.00822   \n",
       "pad_bot_Q_Main                                   -9.26982   \n",
       "pad_top_Q_Main                                   -1.06835   \n",
       "readout_connector_pad_Q_Main                     -0.69738   \n",
       "\n",
       "                              bus_02_connector_pad_Q_Main  \\\n",
       "bus_01_connector_pad_Q_Main                      -0.24001   \n",
       "bus_02_connector_pad_Q_Main                      41.00041   \n",
       "bus_03_connector_pad_Q_Main                      -0.75929   \n",
       "bus_04_connector_pad_Q_Main                      -0.07826   \n",
       "bus_05_connector_pad_Q_Main                      -0.04484   \n",
       "ground_main_plane                               -28.98620   \n",
       "pad_bot_Q_Main                                   -1.07415   \n",
       "pad_top_Q_Main                                   -9.21369   \n",
       "readout_connector_pad_Q_Main                     -0.10640   \n",
       "\n",
       "                              bus_03_connector_pad_Q_Main  \\\n",
       "bus_01_connector_pad_Q_Main                      -0.11719   \n",
       "bus_02_connector_pad_Q_Main                      -0.75929   \n",
       "bus_03_connector_pad_Q_Main                      45.49485   \n",
       "bus_04_connector_pad_Q_Main                      -0.75936   \n",
       "bus_05_connector_pad_Q_Main                      -0.11710   \n",
       "ground_main_plane                               -31.95144   \n",
       "pad_bot_Q_Main                                   -1.30891   \n",
       "pad_top_Q_Main                                   -9.60366   \n",
       "readout_connector_pad_Q_Main                     -0.15780   \n",
       "\n",
       "                              bus_04_connector_pad_Q_Main  \\\n",
       "bus_01_connector_pad_Q_Main                      -0.04481   \n",
       "bus_02_connector_pad_Q_Main                      -0.07826   \n",
       "bus_03_connector_pad_Q_Main                      -0.75936   \n",
       "bus_04_connector_pad_Q_Main                      41.02315   \n",
       "bus_05_connector_pad_Q_Main                      -0.24004   \n",
       "ground_main_plane                               -29.00650   \n",
       "pad_bot_Q_Main                                   -1.07408   \n",
       "pad_top_Q_Main                                   -9.21787   \n",
       "readout_connector_pad_Q_Main                     -0.10629   \n",
       "\n",
       "                              bus_05_connector_pad_Q_Main  ground_main_plane  \\\n",
       "bus_01_connector_pad_Q_Main                      -0.07874          -29.00822   \n",
       "bus_02_connector_pad_Q_Main                      -0.04484          -28.98620   \n",
       "bus_03_connector_pad_Q_Main                      -0.11710          -31.95144   \n",
       "bus_04_connector_pad_Q_Main                      -0.24004          -29.00650   \n",
       "bus_05_connector_pad_Q_Main                      41.00041          -28.98777   \n",
       "ground_main_plane                               -28.98777          308.85307   \n",
       "pad_bot_Q_Main                                   -9.26969          -33.31374   \n",
       "pad_top_Q_Main                                   -1.06800          -32.77926   \n",
       "readout_connector_pad_Q_Main                     -0.69718          -31.47933   \n",
       "\n",
       "                              pad_bot_Q_Main  pad_top_Q_Main  \\\n",
       "bus_01_connector_pad_Q_Main         -9.26982        -1.06835   \n",
       "bus_02_connector_pad_Q_Main         -1.07415        -9.21369   \n",
       "bus_03_connector_pad_Q_Main         -1.30891        -9.60366   \n",
       "bus_04_connector_pad_Q_Main         -1.07408        -9.21787   \n",
       "bus_05_connector_pad_Q_Main         -9.26969        -1.06800   \n",
       "ground_main_plane                  -33.31374       -32.77926   \n",
       "pad_bot_Q_Main                      95.77645       -31.23494   \n",
       "pad_top_Q_Main                     -31.23494        97.45815   \n",
       "readout_connector_pad_Q_Main        -7.05105        -1.13125   \n",
       "\n",
       "                              readout_connector_pad_Q_Main  \n",
       "bus_01_connector_pad_Q_Main                       -0.69738  \n",
       "bus_02_connector_pad_Q_Main                       -0.10640  \n",
       "bus_03_connector_pad_Q_Main                       -0.15780  \n",
       "bus_04_connector_pad_Q_Main                       -0.10629  \n",
       "bus_05_connector_pad_Q_Main                       -0.69718  \n",
       "ground_main_plane                                -31.47933  \n",
       "pad_bot_Q_Main                                    -7.05105  \n",
       "pad_top_Q_Main                                    -1.13125  \n",
       "readout_connector_pad_Q_Main                      42.10847  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b419673",
   "metadata": {},
   "source": [
    "But more importantly, we can use that matrix to run LOM analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a0937ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 7] [8 0 1 2 3 4]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 4.963607 [GHz]\n",
      "EC 298.739021 [MHz]\n",
      "EJ 11.671114 [GHz]\n",
      "alpha -351.473280 [MHz]\n",
      "dispersion 85.768110 [KHz]\n",
      "Lq 13.994355 [nH]\n",
      "Cq 64.839966 [fF]\n",
      "T1 139.259252 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 3.015572 [fF]\n",
      "gbus1_in_MHz 43.820486 [MHz]\n",
      "χ_bus1 -0.287307 [MHz]\n",
      "1/T1bus1 223.182422 [Hz]\n",
      "T1bus1 713.115943 [us]\n",
      "\n",
      "tCqbus2 4.171076 [fF]\n",
      "gbus2_in_MHz 48.504025 [MHz]\n",
      "χ_bus2 -2.645931 [MHz]\n",
      "1/T1bus2 287.292183 [Hz]\n",
      "T1bus2 553.982853 [us]\n",
      "\n",
      "tCqbus3 -3.999772 [fF]\n",
      "gbus3_in_MHz -47.339949 [MHz]\n",
      "χ_bus3 -1.980823 [MHz]\n",
      "1/T1bus3 204.154172 [Hz]\n",
      "T1bus3 779.582126 [us]\n",
      "\n",
      "tCqbus4 -4.073126 [fF]\n",
      "gbus4_in_MHz -49.034442 [MHz]\n",
      "χ_bus4 -1.716249 [MHz]\n",
      "1/T1bus4 169.570131 [Hz]\n",
      "T1bus4 938.578876 [us]\n",
      "\n",
      "tCqbus5 -4.001869 [fF]\n",
      "gbus5_in_MHz -49.021470 [MHz]\n",
      "χ_bus5 -1.415561 [MHz]\n",
      "1/T1bus5 135.025422 [Hz]\n",
      "T1bus5 1178.703542 [us]\n",
      "\n",
      "tCqbus6 4.171183 [fF]\n",
      "gbus6_in_MHz 51.958983 [MHz]\n",
      "χ_bus6 -1.335697 [MHz]\n",
      "1/T1bus6 123.643673 [Hz]\n",
      "T1bus6 1287.206527 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 3.325733 [MHz]\n",
      "gbus1_3 1.912509 [MHz]\n",
      "gbus1_4 2.174045 [MHz]\n",
      "gbus1_5 1.979171 [MHz]\n",
      "gbus1_6 3.559683 [MHz]\n",
      "gbus2_3 2.144945 [MHz]\n",
      "gbus2_4 2.034516 [MHz]\n",
      "gbus2_5 1.819268 [MHz]\n",
      "gbus2_6 1.928359 [MHz]\n",
      "gbus3_4 3.378480 [MHz]\n",
      "gbus3_5 1.912662 [MHz]\n",
      "gbus3_6 1.881624 [MHz]\n",
      "gbus4_5 3.496435 [MHz]\n",
      "gbus4_6 2.177719 [MHz]\n",
      "gbus5_6 2.376495 [MHz]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
       "      <th>alpha</th>\n",
       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
       "      <th>chi_in_MHz</th>\n",
       "      <th>χr MHz</th>\n",
       "      <th>gr MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.334856</td>\n",
       "      <td>349.017036</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-418.169804</td>\n",
       "      <td>304.17916</td>\n",
       "      <td>[46.33925002733205, 44.187331992065, -42.60569...</td>\n",
       "      <td>[-0.5299131495471634, -9.027355440831576, -5.3...</td>\n",
       "      <td>0.529913</td>\n",
       "      <td>46.339250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.293479</td>\n",
       "      <td>343.185353</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-410.316354</td>\n",
       "      <td>266.531163</td>\n",
       "      <td>[44.48347532561871, 43.91119291374231, -40.557...</td>\n",
       "      <td>[-0.46063358481393896, -7.215266047760587, -4....</td>\n",
       "      <td>0.460634</td>\n",
       "      <td>44.483475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.17092</td>\n",
       "      <td>326.250466</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-387.687301</td>\n",
       "      <td>177.943279</td>\n",
       "      <td>[43.075350634431345, 44.952056360711815, -42.4...</td>\n",
       "      <td>[-0.3648586676742546, -4.484695488473718, -2.8...</td>\n",
       "      <td>0.364859</td>\n",
       "      <td>43.075351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.108373</td>\n",
       "      <td>317.801003</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-376.493737</td>\n",
       "      <td>143.687618</td>\n",
       "      <td>[43.13448774561514, 43.30507017705921, -44.091...</td>\n",
       "      <td>[-0.3364016456250303, -3.3214019525977982, -2....</td>\n",
       "      <td>0.336402</td>\n",
       "      <td>43.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.074637</td>\n",
       "      <td>313.297364</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-370.553464</td>\n",
       "      <td>127.750448</td>\n",
       "      <td>[43.277211774574475, 45.44982592921459, -43.97...</td>\n",
       "      <td>[-0.3238225188245248, -3.2664264401609686, -2....</td>\n",
       "      <td>0.323823</td>\n",
       "      <td>43.277212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.041406</td>\n",
       "      <td>308.897746</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-364.767715</td>\n",
       "      <td>113.602489</td>\n",
       "      <td>[43.129687798475445, 46.338514084696286, -45.2...</td>\n",
       "      <td>[-0.3078723909186688, -3.0514480686225833, -2....</td>\n",
       "      <td>0.307872</td>\n",
       "      <td>43.129688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.023692</td>\n",
       "      <td>306.56726</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-361.709895</td>\n",
       "      <td>106.643367</td>\n",
       "      <td>[43.19413642807277, 46.657953431831764, -45.44...</td>\n",
       "      <td>[-0.3017295453629039, -2.9276804873799436, -2....</td>\n",
       "      <td>0.301730</td>\n",
       "      <td>43.194136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.008631</td>\n",
       "      <td>304.593954</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-359.124452</td>\n",
       "      <td>101.0268</td>\n",
       "      <td>[43.35990049055785, 46.99778023673387, -46.254...</td>\n",
       "      <td>[-0.2981493080591482, -2.837011006665539, -2.1...</td>\n",
       "      <td>0.298149</td>\n",
       "      <td>43.359900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.995019</td>\n",
       "      <td>302.81687</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-356.799016</td>\n",
       "      <td>96.177974</td>\n",
       "      <td>[43.46480555843615, 47.44716630141518, -46.470...</td>\n",
       "      <td>[-0.29435169781569703, -2.77574777235584, -2.0...</td>\n",
       "      <td>0.294352</td>\n",
       "      <td>43.464806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.989574</td>\n",
       "      <td>302.107679</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-355.871761</td>\n",
       "      <td>94.296886</td>\n",
       "      <td>[43.597944296844695, 47.654276741440626, -46.5...</td>\n",
       "      <td>[-0.294078462456382, -2.7551199777315323, -2.0...</td>\n",
       "      <td>0.294078</td>\n",
       "      <td>43.597944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.98144</td>\n",
       "      <td>301.050061</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-354.489758</td>\n",
       "      <td>91.547639</td>\n",
       "      <td>[43.550558591120705, 48.01342068567195, -46.89...</td>\n",
       "      <td>[-0.2903720998583706, -2.7305526898818075, -2....</td>\n",
       "      <td>0.290372</td>\n",
       "      <td>43.550559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.975829</td>\n",
       "      <td>300.321846</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-353.538756</td>\n",
       "      <td>89.693095</td>\n",
       "      <td>[43.677893070257284, 48.10303127872349, -47.06...</td>\n",
       "      <td>[-0.28996661844835814, -2.6961457251648193, -2...</td>\n",
       "      <td>0.289967</td>\n",
       "      <td>43.677893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.97308</td>\n",
       "      <td>299.965387</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-353.073412</td>\n",
       "      <td>88.796586</td>\n",
       "      <td>[43.74422948775013, 48.268028637087, -47.10813...</td>\n",
       "      <td>[-0.28981954135701093, -2.693030802856854, -2....</td>\n",
       "      <td>0.289820</td>\n",
       "      <td>43.744229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.969275</td>\n",
       "      <td>299.47245</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-352.430083</td>\n",
       "      <td>87.568934</td>\n",
       "      <td>[43.73302237009102, 48.38885048108347, -47.226...</td>\n",
       "      <td>[-0.2882553488551673, -2.6768170445299937, -2....</td>\n",
       "      <td>0.288255</td>\n",
       "      <td>43.733022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.966898</td>\n",
       "      <td>299.164667</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-352.028505</td>\n",
       "      <td>86.809485</td>\n",
       "      <td>[43.77570019556721, 48.46146286708871, -47.343...</td>\n",
       "      <td>[-0.2879360055703413, -2.6664620128030103, -1....</td>\n",
       "      <td>0.287936</td>\n",
       "      <td>43.775700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.963607</td>\n",
       "      <td>298.739021</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-351.47328</td>\n",
       "      <td>85.76811</td>\n",
       "      <td>[43.82048616757409, 48.50402461483827, -47.339...</td>\n",
       "      <td>[-0.2873072774566821, -2.6459305257029584, -1....</td>\n",
       "      <td>0.287307</td>\n",
       "      <td>43.820486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fQ          EC         EJ       alpha  dispersion  \\\n",
       "1   5.334856  349.017036  11.671114 -418.169804   304.17916   \n",
       "2   5.293479  343.185353  11.671114 -410.316354  266.531163   \n",
       "3    5.17092  326.250466  11.671114 -387.687301  177.943279   \n",
       "4   5.108373  317.801003  11.671114 -376.493737  143.687618   \n",
       "5   5.074637  313.297364  11.671114 -370.553464  127.750448   \n",
       "6   5.041406  308.897746  11.671114 -364.767715  113.602489   \n",
       "7   5.023692   306.56726  11.671114 -361.709895  106.643367   \n",
       "8   5.008631  304.593954  11.671114 -359.124452    101.0268   \n",
       "9   4.995019   302.81687  11.671114 -356.799016   96.177974   \n",
       "10  4.989574  302.107679  11.671114 -355.871761   94.296886   \n",
       "11   4.98144  301.050061  11.671114 -354.489758   91.547639   \n",
       "12  4.975829  300.321846  11.671114 -353.538756   89.693095   \n",
       "13   4.97308  299.965387  11.671114 -353.073412   88.796586   \n",
       "14  4.969275   299.47245  11.671114 -352.430083   87.568934   \n",
       "15  4.966898  299.164667  11.671114 -352.028505   86.809485   \n",
       "16  4.963607  298.739021  11.671114  -351.47328    85.76811   \n",
       "\n",
       "                                                 gbus  \\\n",
       "1   [46.33925002733205, 44.187331992065, -42.60569...   \n",
       "2   [44.48347532561871, 43.91119291374231, -40.557...   \n",
       "3   [43.075350634431345, 44.952056360711815, -42.4...   \n",
       "4   [43.13448774561514, 43.30507017705921, -44.091...   \n",
       "5   [43.277211774574475, 45.44982592921459, -43.97...   \n",
       "6   [43.129687798475445, 46.338514084696286, -45.2...   \n",
       "7   [43.19413642807277, 46.657953431831764, -45.44...   \n",
       "8   [43.35990049055785, 46.99778023673387, -46.254...   \n",
       "9   [43.46480555843615, 47.44716630141518, -46.470...   \n",
       "10  [43.597944296844695, 47.654276741440626, -46.5...   \n",
       "11  [43.550558591120705, 48.01342068567195, -46.89...   \n",
       "12  [43.677893070257284, 48.10303127872349, -47.06...   \n",
       "13  [43.74422948775013, 48.268028637087, -47.10813...   \n",
       "14  [43.73302237009102, 48.38885048108347, -47.226...   \n",
       "15  [43.77570019556721, 48.46146286708871, -47.343...   \n",
       "16  [43.82048616757409, 48.50402461483827, -47.339...   \n",
       "\n",
       "                                           chi_in_MHz    χr MHz     gr MHz  \n",
       "1   [-0.5299131495471634, -9.027355440831576, -5.3...  0.529913  46.339250  \n",
       "2   [-0.46063358481393896, -7.215266047760587, -4....  0.460634  44.483475  \n",
       "3   [-0.3648586676742546, -4.484695488473718, -2.8...  0.364859  43.075351  \n",
       "4   [-0.3364016456250303, -3.3214019525977982, -2....  0.336402  43.134488  \n",
       "5   [-0.3238225188245248, -3.2664264401609686, -2....  0.323823  43.277212  \n",
       "6   [-0.3078723909186688, -3.0514480686225833, -2....  0.307872  43.129688  \n",
       "7   [-0.3017295453629039, -2.9276804873799436, -2....  0.301730  43.194136  \n",
       "8   [-0.2981493080591482, -2.837011006665539, -2.1...  0.298149  43.359900  \n",
       "9   [-0.29435169781569703, -2.77574777235584, -2.0...  0.294352  43.464806  \n",
       "10  [-0.294078462456382, -2.7551199777315323, -2.0...  0.294078  43.597944  \n",
       "11  [-0.2903720998583706, -2.7305526898818075, -2....  0.290372  43.550559  \n",
       "12  [-0.28996661844835814, -2.6961457251648193, -2...  0.289967  43.677893  \n",
       "13  [-0.28981954135701093, -2.693030802856854, -2....  0.289820  43.744229  \n",
       "14  [-0.2882553488551673, -2.6768170445299937, -2....  0.288255  43.733022  \n",
       "15  [-0.2879360055703413, -2.6664620128030103, -1....  0.287936  43.775700  \n",
       "16  [-0.2873072774566821, -2.6459305257029584, -1....  0.287307  43.820486  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.setup.junctions = Dict({\"Lj\": 14, \"Cj\": 2})\n",
    "c1.setup.freq_readout = 7.0\n",
    "c1.setup.freq_bus = [5.6, 5.7, 5.8, 5.9, 6.0]  # list of the bus frequencies\n",
    "\n",
    "c1.run_lom()\n",
    "c1.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdaa6d8",
   "metadata": {},
   "source": [
    "Which gives us the qubits frequency, anharmonicity, and coupling strength to the different connection pads.\n",
    "\n",
    "We can further check if these parameters converged well. If they have not, we may want to modify our simulation in order to get a more accurate result. We then will want to make modifications to our qubit options, such as `pad_gap` of the qubit, or modifying the size of the connection pads, in order to hit the desired qubit anharmonicity or readout chi values respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a161f7c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 09:55AM [hfss_report_full_convergence]: Creating report for variation 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Q_Main_q3d\" info:\n",
      "\t# eigenmodes    0\n",
      "\t# variations    1\n"
     ]
    }
   ],
   "source": [
    "c1.plot_convergence()\n",
    "c1.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f35dc1f",
   "metadata": {},
   "source": [
    "Once the analysis and tuning is complete, we can stop the analysis and the renderer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f092bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc0af12",
   "metadata": {},
   "source": [
    "## Eigenmode and EPR\n",
    "\n",
    "Once each of our qubits have been run through LOM, we can begin to look at the resonant busses and readouts, and larger coupled sections of the chip. One such case could be looking at Q_Main, Q5, and Bus_05. This allows us not only to look at some of the parameters of the individual qubits, but also the bus frequency and if the qubits are coupled (via the bus) to the degree we wish.\n",
    "\n",
    "#### Preparations\n",
    "We will setup the design and simulation in the same manner as we did previously, but with the methods needed for an eigenmode simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb4a2326",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import EPRanalysis\n",
    "\n",
    "eig_qb = EPRanalysis(design, \"hfss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f881cd6f",
   "metadata": {},
   "source": [
    "(optional) you can tune the wirebond size by directly modifying the renderer options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd06f8a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.renderer.options[\"wb_size\"] = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82cb95ed",
   "metadata": {},
   "source": [
    "Review and update the simulation setup by executing following two cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64b8e7f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "em_p = eig_qb.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9b934c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': '3Modes',\n",
       " 'reuse_selected_design': True,\n",
       " 'min_freq_ghz': 4,\n",
       " 'n_modes': 3,\n",
       " 'max_delta_f': 0.1,\n",
       " 'max_passes': 10,\n",
       " 'min_passes': 1,\n",
       " 'min_converged': 2,\n",
       " 'pct_refinement': 30,\n",
       " 'basis_order': 1,\n",
       " 'vars': {'Lj1': '13 nH', 'Cj1': '0 fF', 'Lj2': '15 nH', 'Cj2': '0 fF'}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em_p.name = \"3Modes\"\n",
    "em_p.min_freq_ghz = 4\n",
    "em_p.n_modes = 3\n",
    "em_p.max_passes = 10\n",
    "em_p.max_delta_f = 0.1\n",
    "em_p.min_converged = 2\n",
    "# Design variables can also be added in for direct simulation sweeps.\n",
    "em_p.vars = Dict({\"Lj1\": \"13 nH\", \"Cj1\": \"0 fF\", \"Lj2\": \"15 nH\", \"Cj2\": \"0 fF\"})\n",
    "\n",
    "eig_qb.sim.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13298383",
   "metadata": {},
   "source": [
    "Before we execute the analysis, we want to update the design if needed. For example we may want to modify the junction inductance of the two qubits based on the previous LOM analysis, so they are near the desired frequency. Further, one may want to change the length of the bus after initial simulations to get it to the target frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "98931035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'14nH'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_main.options.hfss_inductance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86973a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'14nH'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q5.options.hfss_inductance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58ab0812",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_main.options.hfss_inductance = \"13nH\"\n",
    "Q5.options.hfss_inductance = \"15nH\"\n",
    "bus_05.options.total_length = \"7.5mm\"\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be69243f",
   "metadata": {},
   "source": [
    "We can now run the simulation on the specified layout. All unconnected pins are left as shorts, as we are only concerned about simulating the resonant mode of the three components listed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "29613501",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 09:55AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 09:55AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 09:55AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 09:55AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project23\n",
      "INFO 09:55AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Q_Main_q3d [Solution type: Q3D]\n",
      "INFO 09:55AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 09:55AM [connect]: \tConnected to project \"Project23\" and design \"Q_Main_q3d\" 😀 \n",
      "\n",
      "INFO 09:55AM [connect_design]: \tOpened active design\n",
      "\tDesign:    QMain_Q5_Bus05_hfss [Solution type: Eigenmode]\n",
      "WARNING 09:55AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 09:55AM [connect_setup]: \tCreating eigenmode default setup.\n",
      "INFO 09:55AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 09:55AM [get_setup]: \tOpened setup `3Modes`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 09:55AM [analyze]: Analyzing setup 3Modes\n",
      "09:59AM 09s INFO [get_f_convergence]: Saved convergences to C:\\workspace\\qiskit-metal\\docs\\circuit-examples\\full-design-flow-examples\\hfss_eig_f_convergence.csv\n"
     ]
    }
   ],
   "source": [
    "eig_qb.sim.run(\n",
    "    name=\"QMain_Q5_Bus05\", components=[\"Q_Main\", \"Q5\", \"Bus_05\"], open_terminations=[]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c8fdfc",
   "metadata": {},
   "source": [
    "Once the simulation is complete, we can check to see if the convergence was good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be6ef967",
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.plot_convergences()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2e7c65",
   "metadata": {},
   "source": [
    "With the eigenmode simulation complete (and nicely converged) we can run some EPR analysis on the result.\n",
    "\n",
    "At first we need to define the junctions in terms of name, inductance_variable, capacitance_variable, rectancle that was defined in the rendering to represent the junction `port` and line that was defined in the rendering to represent the direction of the current thorugh the junction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "40958220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'junctions': {'jj1': {'Lj_variable': 'Lj1',\n",
       "   'Cj_variable': 'Cj1',\n",
       "   'rect': 'JJ_rect_Lj_Q_Main_rect_jj',\n",
       "   'line': 'JJ_Lj_Q_Main_rect_jj_'},\n",
       "  'jj2': {'Lj_variable': 'Lj2',\n",
       "   'Cj_variable': 'Cj2',\n",
       "   'rect': 'JJ_rect_Lj_Q5_rect_jj',\n",
       "   'line': 'JJ_Lj_Q5_rect_jj_'}},\n",
       " 'dissipatives': {'dielectrics_bulk': ['main']},\n",
       " 'cos_trunc': 8,\n",
       " 'fock_trunc': 7,\n",
       " 'sweep_variable': 'Lj1'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eig_qb.del_junction()\n",
    "eig_qb.add_junction(\n",
    "    \"jj1\", \"Lj1\", \"Cj1\", rect=\"JJ_rect_Lj_Q_Main_rect_jj\", line=\"JJ_Lj_Q_Main_rect_jj_\"\n",
    ")\n",
    "eig_qb.add_junction(\n",
    "    \"jj2\", \"Lj2\", \"Cj2\", rect=\"JJ_rect_Lj_Q5_rect_jj\", line=\"JJ_Lj_Q5_rect_jj_\"\n",
    ")\n",
    "eig_qb.setup.sweep_variable = \"Lj1\"\n",
    "eig_qb.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23466e00",
   "metadata": {},
   "source": [
    "Note in the previous cell output that the dissipatives have alrady been defined by default.\n",
    "\n",
    "Now we can start looking at the EPR values. First we look at the electric field and subtrate participation. Then extract the kerr matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "636906e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"QMain_Q5_Bus05_hfss\" info:\n",
      "\t# eigenmodes    3\n",
      "\t# variations    1\n",
      "Design \"QMain_Q5_Bus05_hfss\" info:\n",
      "\t# eigenmodes    3\n",
      "\t# variations    1\n",
      "\n",
      "        energy_elec_all       = 5.30807060349227e-24\n",
      "        energy_elec_substrate = 4.86434514599116e-24\n",
      "        EPR of substrate = 91.6%\n",
      "\n",
      "        energy_mag    = 2.32419129800195e-26\n",
      "        energy_mag % of energy_elec_all  = 0.4%\n",
      "        \n",
      "\n",
      "Variation 0  [1/1]\n",
      "\n",
      "  \u001b[1mMode 0 at 4.81 GHz   [1/3]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               99.6%  2.654e-24 1.162e-26\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_0j   sign s_0j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 97.68%\n",
      "\tjj1             3.29205e-06  (+)        7.83062e-08\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 97.33%\n",
      "\tjj2             0.994398  (+)        0.0272922\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=1.41%\n",
      "Calculating Qdielectric_main for mode 0 (0/2)\n",
      "p_dielectric_main_0 = 0.9164055095255939\n",
      "\n",
      "  \u001b[1mMode 1 at 7.39 GHz   [2/3]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "                0.1%  3.353e-24  3.35e-24\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_1j   sign s_1j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 94.69%\n",
      "\tjj1             0.000383918  (+)        2.15125e-05\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 93.93%\n",
      "\tjj2             0.000723443  (+)        4.6774e-05\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=0.00%\n",
      "Calculating Qdielectric_main for mode 1 (1/2)\n",
      "p_dielectric_main_1 = 0.9185431895828703\n",
      "\n",
      "  \u001b[1mMode 2 at 14.79 GHz   [3/3]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "                0.0%  2.501e-24 2.501e-24\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_2j   sign s_2j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 81.67%\n",
      "\tjj1             5.19757e-05  (+)        1.16685e-05\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 79.43%\n",
      "\tjj2             8.11545e-05  (+)        2.1022e-05\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=0.00%\n",
      "Calculating Qdielectric_main for mode 2 (2/2)\n",
      "p_dielectric_main_2 = 0.91725087597056\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 09:59AM [__init__]: <p>Error: <class 'IndexError'></p>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ANALYSIS DONE. Data saved to:\n",
      "\n",
      "C:\\data-pyEPR\\Project23\\QMain_Q5_Bus05_hfss\\2021-07-20 09-59-11.npz\n",
      "\n",
      "\n",
      "\t Differences in variations:\n",
      "\n",
      "\n",
      "\n",
      " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "Variation 0\n",
      "\n",
      "Starting the diagonalization\n",
      "Finished the diagonalization\n",
      "Pm_norm=\n",
      "modes\n",
      "0    1.028611\n",
      "1    1.031457\n",
      "2    1.123854\n",
      "dtype: float64\n",
      "\n",
      "Pm_norm idx =\n",
      "     jj1    jj2\n",
      "0  False   True\n",
      "1  False  False\n",
      "2  False  False\n",
      "*** P (participation matrix, not normlz.)\n",
      "        jj1       jj2\n",
      "0  0.000003  0.967980\n",
      "1  0.000384  0.000723\n",
      "2  0.000052  0.000081\n",
      "\n",
      "*** S (sign-bit matrix)\n",
      "   s_jj1  s_jj2\n",
      "0      1      1\n",
      "1      1      1\n",
      "2      1      1\n",
      "*** P (participation matrix, normalized.)\n",
      "   3.2e-06        1\n",
      "   0.00038  0.00072\n",
      "   5.2e-05  8.1e-05\n",
      "\n",
      "*** Chi matrix O1 PT (MHz)\n",
      "    Diag is anharmonicity, off diag is full cross-Kerr.\n",
      "       264    0.588    0.132\n",
      "     0.588 0.000408 0.000191\n",
      "     0.132 0.000191 2.24e-05\n",
      "\n",
      "*** Chi matrix ND (MHz) \n",
      "       298      0.5    0.116\n",
      "       0.5 0.000317 0.000173\n",
      "     0.116 0.000173 2.28e-05\n",
      "\n",
      "*** Frequencies O1 PT (MHz)\n",
      "0     4550.033083\n",
      "1     7388.261062\n",
      "2    14788.982401\n",
      "dtype: float64\n",
      "\n",
      "*** Frequencies ND (MHz)\n",
      "0     4533.470356\n",
      "1     7388.272216\n",
      "2    14788.988393\n",
      "dtype: float64\n",
      "\n",
      "*** Q_coupling\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0, 1, 2]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Mode frequencies (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Lj1</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eigenmode</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4550.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7388.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14788.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Lj1              13\n",
       "eigenmode          \n",
       "0           4550.03\n",
       "1           7388.26\n",
       "2          14788.98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Kerr Non-linear coefficient table (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lj1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">13</th>\n",
       "      <th>0</th>\n",
       "      <td>297.70</td>\n",
       "      <td>5.00e-01</td>\n",
       "      <td>1.16e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>3.17e-04</td>\n",
       "      <td>1.73e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.12</td>\n",
       "      <td>1.73e-04</td>\n",
       "      <td>2.28e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2\n",
       "Lj1                              \n",
       "13  0  297.70  5.00e-01  1.16e-01\n",
       "    1    0.50  3.17e-04  1.73e-04\n",
       "    2    0.12  1.73e-04  2.28e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eig_qb.run_epr()\n",
    "# (pyEPR allows to switch modes: eprd.set_mode(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b45546e",
   "metadata": {},
   "source": [
    "From the analysis results we can determine the qubits anharmonicities and coupling strength.\n",
    "Once the analysis and tuning is complete, we can close the connection to Ansys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12caade9",
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b735b211",
   "metadata": {},
   "source": [
    "### Rendering to a GDS File"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65fbf4f",
   "metadata": {},
   "source": [
    "Once all of the tuning is complete, we will want to prepare a GDS file so we can create a mask and fabricate our chip. We first create a gds render instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e66f101",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_chip_gds = design.renderers.gds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd624d27",
   "metadata": {},
   "source": [
    "The various options for the gds renderer can also be checked and changed as necessary. A key option is the gds file which holds the cells for your junction ebeam design. Make sure this is pointing at the correct file so they are placed in your final mask at the appropriate locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "db64865a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'short_segments_to_not_fillet': 'True',\n",
       " 'check_short_segments_by_scaling_fillet': '2.0',\n",
       " 'gds_unit': 0.001,\n",
       " 'ground_plane': 'True',\n",
       " 'negative_mask': {'main': []},\n",
       " 'corners': 'circular bend',\n",
       " 'tolerance': '0.00001',\n",
       " 'precision': '0.000000001',\n",
       " 'width_LineString': '10um',\n",
       " 'path_filename': '../resources/Fake_Junctions.GDS',\n",
       " 'junction_pad_overlap': '5um',\n",
       " 'max_points': '199',\n",
       " 'cheese': {'datatype': '100',\n",
       "  'shape': '0',\n",
       "  'cheese_0_x': '25um',\n",
       "  'cheese_0_y': '25um',\n",
       "  'cheese_1_radius': '100um',\n",
       "  'view_in_file': {'main': {1: True}},\n",
       "  'delta_x': '100um',\n",
       "  'delta_y': '100um',\n",
       "  'edge_nocheese': '200um'},\n",
       " 'no_cheese': {'datatype': '99',\n",
       "  'buffer': '25um',\n",
       "  'cap_style': '2',\n",
       "  'join_style': '2',\n",
       "  'view_in_file': {'main': {1: True}}},\n",
       " 'bounding_box_scale_x': '1.2',\n",
       " 'bounding_box_scale_y': '1.2'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_chip_gds.options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15a6c01f",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_chip_gds.options[\"path_filename\"] = \"../resources/Fake_Junctions.GDS\"\n",
    "full_chip_gds.options[\"no_cheese\"][\"buffer\"] = \"50um\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8f939c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_chip_gds.export_to_gds(\"Full_Chip_01.gds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2cea794",
   "metadata": {},
   "source": [
    "(optional) With the design complete, we can close the GUI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "475c9737",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gui.main_window.close()"
   ]
  }
 ],
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