{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example full chip design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Make sure to have the right kernel selected!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
   "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",
   "metadata": {},
   "source": [
    "## Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 LaunchpadWirebondCoupled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "design = metal.designs.DesignPlanar()\n",
    "\n",
    "gui = metal.MetalGUI(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.chips.main.size.size_x = '11mm'\n",
    "design.chips.main.size.size_y = '9mm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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,
   "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",
    "q_main = TransmonPocket6(design,'Q_Main', options = dict(\n",
    "        pos_x='0mm',\n",
    "        pos_y='-1mm',\n",
    "        gds_cell_name ='FakeJunction_01',\n",
    "        hfss_inductance ='14nH',\n",
    "        **options))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = TransmonCrossFL(design, 'Q1', options = dict(pos_x = '-2.75mm', 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",
    "                                                 fl_options = dict()))\n",
    "\n",
    "Q2 = TransmonCrossFL(design, 'Q2', options = dict(pos_x = '-2.75mm', pos_y='-1.2mm', orientation = '180',\n",
    "                                                 connection_pads = dict(\n",
    "                                                     bus_02 = dict(connector_location = '0',claw_length ='95um'),\n",
    "                                                     readout = dict(connector_location = '180')),\n",
    "                                                 fl_options = dict()))\n",
    "\n",
    "\n",
    "tune_c_Q12 = TunableCoupler01(design,'Tune_C_Q12', options = dict(pos_x = '-2.81mm', pos_y = '-1.5mm',\n",
    "                                                                  orientation=90, c_width='500um'))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3 = TransmonPocket6(design,'Q3', 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",
    "Q4 = TransmonPocket6(design,'Q4', 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",
    "Q5 = TransmonPocket6(design,'Q5', 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'))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Busses"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.em.cpw_calculations import guided_wavelength\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(frequency*10**9, line_width*10**-6,\n",
    "                                              line_gap*10**-6, 750*10**-6, 200*10**-9)\n",
    "    return str(lambdaG/N*10**3)+\" mm\""
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_01 = RouteMeander(design,'Bus_01', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q_Main',\n",
    "                                                    pin='bus_01'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q1',\n",
    "                                                    pin='bus_01')\n",
    "                                            ),\n",
    "                                            lead=Dict(\n",
    "                                                start_straight='125um',\n",
    "                                                end_straight = '225um'\n",
    "                                            ),\n",
    "                                            meander=Dict(\n",
    "                                                asymmetry = '1305um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '6mm'))\n",
    "\n",
    "bus_02 = RouteMeander(design,'Bus_02', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q_Main',\n",
    "                                                    pin='bus_02'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q2',\n",
    "                                                    pin='bus_02')\n",
    "                                            ),\n",
    "                                            lead=Dict(\n",
    "                                                start_straight='325um',\n",
    "                                                end_straight = '125um'\n",
    "                                            ),\n",
    "                                            meander=Dict(\n",
    "                                                asymmetry = '450um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '6.4mm'))\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then the three transmon pockets on the north side to Q_Main."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_03 = RouteMeander(design,'Bus_03', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q_Main',\n",
    "                                                    pin='bus_03'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q3',\n",
    "                                                    pin='bus_03')\n",
    "                                            ),\n",
    "                                            lead=Dict(\n",
    "                                                start_straight='225um',\n",
    "                                                end_straight = '25um'\n",
    "                                            ),\n",
    "                                            meander=Dict(\n",
    "                                                asymmetry = '50um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '6.8mm'))\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",
    "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(design,'Bus_04', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q_Main',\n",
    "                                                    pin='bus_04'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q4',\n",
    "                                                    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(\n",
    "                                                asymmetry = '150um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '7.2mm'))\n",
    "\n",
    "bus_05 = RouteMeander(design,'Bus_05', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q_Main',\n",
    "                                                    pin='bus_05'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q5',\n",
    "                                                    pin='bus_05')\n",
    "                                            ),\n",
    "                                            lead=Dict(\n",
    "                                                start_straight='225um',\n",
    "                                                end_straight = '25um'\n",
    "                                            ),\n",
    "                                            meander=Dict(\n",
    "                                                asymmetry = '50um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '7.6mm'))\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_q3_q4 = RouteMeander(design,'Bus_Q3_Q4', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q3',\n",
    "                                                    pin='bus_q3_q4'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q4',\n",
    "                                                    pin='bus_q3_q4')\n",
    "                                            ),\n",
    "                                            lead=Dict(\n",
    "                                                start_straight='125um',\n",
    "                                                end_straight = '125um'\n",
    "                                            ),\n",
    "                                            meander=Dict(\n",
    "                                                asymmetry = '50um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '6.4mm'))\n",
    "\n",
    "bus_q4_q5 = RouteMeander(design,'Bus_Q4_Q5', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q4',\n",
    "                                                    pin='bus_q4_q5'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q5',\n",
    "                                                    pin='bus_q4_q5')\n",
    "                                            ),\n",
    "                                            lead=Dict(\n",
    "                                                start_straight='125um',\n",
    "                                                end_straight = '25um'\n",
    "                                            ),\n",
    "                                            meander=Dict(\n",
    "                                                asymmetry = '50um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '6.8mm'))\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "launch_qmain_read = LaunchpadWirebond(design, 'Launch_QMain_Read', options = dict(pos_x = '2mm', pos_y ='-4mm', orientation = '90'))\n",
    "\n",
    "launch_q1_fl = LaunchpadWirebond(design, 'Launch_Q1_FL', options = dict(pos_x = '0mm', pos_y ='-4mm', orientation = '90',\n",
    "                                                                        trace_width = '5um',\n",
    "                                                                        trace_gap = '3um',))\n",
    "launch_q1_read = LaunchpadWirebondCoupled(design, 'Launch_Q1_Read', options = dict(pos_x = '-2mm', pos_y ='-4mm', orientation = '90'))\n",
    "\n",
    "launch_tcoup_fl = LaunchpadWirebond(design, 'Launch_TuneC_FL', options = dict(pos_x = '-4mm', pos_y ='-4mm', orientation = '90',\n",
    "                                                                             trace_width = '5um',\n",
    "                                                                             trace_gap = '3um',))\n",
    "launch_tcoup_read = LaunchpadWirebondCoupled(design, 'Launch_TuneC_Read', options = dict(pos_x = '-5mm', pos_y ='-3mm', orientation = '0'))\n",
    "\n",
    "launch_q2_read = LaunchpadWirebondCoupled(design, 'Launch_Q2_Read', options = dict(pos_x = '-5mm', pos_y ='-1mm', orientation = '0'))\n",
    "launch_q2_fl = LaunchpadWirebond(design, 'Launch_Q2_FL', options = dict(pos_x = '-5mm', pos_y ='1mm', orientation = '0',\n",
    "                                                                       trace_width = '5um',\n",
    "                                                                        trace_gap = '3um',))\n",
    "\n",
    "launch_nw = LaunchpadWirebond(design, 'Launch_NW',options = dict(pos_x = '-5mm', pos_y='3mm', orientation=0))\n",
    "launch_ne = LaunchpadWirebond(design, 'Launch_NE',options = dict(pos_x = '5mm', pos_y='3mm', orientation=180))\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Main Readout\n",
    "\n",
    "\n",
    "read_q_main_cap = CapNInterdigital(design,'Read_Q_Main_Cap', options = dict(pos_x = '2mm', pos_y ='-3.5mm', orientation = '0'))\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(design,'Read_Q_Main', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q_Main',\n",
    "                                                    pin='readout'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Read_Q_Main_Cap',\n",
    "                                                    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(\n",
    "                                                asymmetry = '50um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '5.6mm'))\n",
    "\n",
    "read_q_main_cap_launch = RoutePathfinder(design, 'Read_Q_Main_Cap_Launch', options = dict(hfss_wire_bonds = True,\n",
    "                                                    pin_inputs = dict(\n",
    "                                                        start_pin=Dict(\n",
    "                                                    component='Read_Q_Main_Cap',\n",
    "                                                    pin='south_end'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Launch_QMain_Read',\n",
    "                                                    pin='tie')),\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",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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(design,'Read_Q1', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q1',\n",
    "                                                    pin='readout'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Launch_Q1_Read',\n",
    "                                                    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(\n",
    "                                                asymmetry = '50um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '6.8mm'))\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(design,'Read_TuneC', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Tune_C_Q12',\n",
    "                                                    pin='Control'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Launch_TuneC_Read',\n",
    "                                                    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(\n",
    "                                                asymmetry = '50um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '5.8mm'))\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",
    "\n",
    "read_q2 = RouteMeander(design,'Read_Q2', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q2',\n",
    "                                                    pin='readout'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Launch_Q2_Read',\n",
    "                                                    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(\n",
    "                                                asymmetry = '-450um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '5.4mm'))\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finishing off this section of the chip by connecting the flux lines to appropraite wirebond launch pads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Crossmon flux lines\n",
    "\n",
    "flux_line_Q1 = RoutePathfinder(design,'Flux_Line_Q1', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q1',\n",
    "                                                    pin='flux_line'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Launch_Q1_FL',\n",
    "                                                    pin='tie')),\n",
    "                                            fillet = '99um',\n",
    "                                            trace_width = '5um',\n",
    "                                            trace_gap = '3um',\n",
    "                                            #anchors = anchors\n",
    "                                            ))\n",
    "\n",
    "\n",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", '750um']\n",
    "\n",
    "flux_line_tunec = RoutePathfinder(design,'Flux_Line_TuneC', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Tune_C_Q12',\n",
    "                                                    pin='Flux'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Launch_TuneC_FL',\n",
    "                                                    pin='tie')),\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",
    "jogs_start = OrderedDict()\n",
    "jogs_start[0] = [\"L\", '525um']\n",
    "jogs_start[1] = [\"R\", '625um']\n",
    "\n",
    "flux_line_Q2 = RoutePathfinder(design,'Flux_Line_Q2', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q2',\n",
    "                                                    pin='flux_line'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Launch_Q2_FL',\n",
    "                                                    pin='tie')),\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",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "q3_read_T = CoupledLineTee(design,'Q3_Read_T', options=dict(pos_x = '-3mm', pos_y = '3mm',\n",
    "                                                        orientation = '0',\n",
    "                                                        coupling_length = '200um',\n",
    "                                                        open_termination = False))\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(design,'Q4_Read_T', options=dict(pos_x = '0mm', pos_y = '3mm',\n",
    "                                                           orientation = '0',\n",
    "                                                           finger_length = '0um',\n",
    "                                                           finger_count = '8'))\n",
    "q5_read_T = CapNInterdigitalTee(design,'Q5_Read_T', options=dict(pos_x = '3mm', pos_y = '3mm',\n",
    "                                                           orientation = '0',\n",
    "                                                           finger_length = '50um',\n",
    "                                                           finger_count = '11'))\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add in the readout resonators to each respective qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_q3 = RouteMeander(design,'Read_Q3', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q3',\n",
    "                                                    pin='readout'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q3_Read_T',\n",
    "                                                    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(\n",
    "                                                asymmetry = '0um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '5mm'))\n",
    "\n",
    "read_q4 = RouteMeander(design,'Read_Q4', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q4',\n",
    "                                                    pin='readout'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q4_Read_T',\n",
    "                                                    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(\n",
    "                                                asymmetry = '0um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '5.8mm'))\n",
    "\n",
    "read_q5 = RouteMeander(design,'Read_Q5', options = dict(hfss_wire_bonds = True,\n",
    "                                            pin_inputs=Dict(\n",
    "                                                start_pin=Dict(\n",
    "                                                    component='Q5',\n",
    "                                                    pin='readout'),\n",
    "                                                end_pin=Dict(\n",
    "                                                    component='Q5_Read_T',\n",
    "                                                    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(\n",
    "                                                asymmetry = '0um'),\n",
    "                                            fillet = \"99um\",\n",
    "                                            total_length = '5.4mm'))\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_tl_01 = RoutePathfinder(design, 'ML_TL_01', options = dict(hfss_wire_bonds = True,\n",
    "                                                    pin_inputs = dict(\n",
    "                                                    start_pin=Dict(\n",
    "                                                        component='Launch_NW',\n",
    "                                                        pin='tie'),\n",
    "                                                    end_pin=Dict(\n",
    "                                                        component='Q3_Read_T',\n",
    "                                                        pin='prime_start'))\n",
    "                                            ))\n",
    "\n",
    "mp_tl_02 = RoutePathfinder(design, 'ML_TL_02', options = dict(hfss_wire_bonds = True,\n",
    "                                                    pin_inputs = dict(\n",
    "                                                    start_pin=Dict(\n",
    "                                                        component='Q3_Read_T',\n",
    "                                                        pin='prime_end'),\n",
    "                                                    end_pin=Dict(\n",
    "                                                        component='Q4_Read_T',\n",
    "                                                        pin='prime_start'))\n",
    "                                            ))\n",
    "\n",
    "mp_tl_03 = RoutePathfinder(design, 'ML_TL_03', options = dict(hfss_wire_bonds = True,\n",
    "                                                    pin_inputs = dict(\n",
    "                                                    start_pin=Dict(\n",
    "                                                        component='Q4_Read_T',\n",
    "                                                        pin='prime_end'),\n",
    "                                                    end_pin=Dict(\n",
    "                                                        component='Q5_Read_T',\n",
    "                                                        pin='prime_start'))\n",
    "                                            ))\n",
    "\n",
    "mp_tl_04 = RoutePathfinder(design, 'ML_TL_04', options = dict(hfss_wire_bonds = True,\n",
    "                                                    pin_inputs = dict(\n",
    "                                                    start_pin=Dict(\n",
    "                                                        component='Q5_Read_T',\n",
    "                                                        pin='prime_end'),\n",
    "                                                    end_pin=Dict(\n",
    "                                                        component='Launch_NE',\n",
    "                                                        pin='tie'))\n",
    "                                            ))\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "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": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.autoscale()\n",
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze 1: Capacitance Extraction and LOM"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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,
   "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",
   "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,
   "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(name=\"Q_Main\", components=['Q_Main'], open_terminations=[('Q_Main', 'readout'), ('Q_Main', 'bus_01'),('Q_Main', 'bus_02'),('Q_Main', 'bus_03'),\n",
    "                                      ('Q_Main', 'bus_04'), ('Q_Main', 'bus_05')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the simulation completed, we can look at the capacitance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
   "metadata": {},
   "source": [
    "But more importantly, we can use that matrix to run LOM analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
   "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,
   "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",
   "metadata": {},
   "source": [
    "Once the analysis and tuning is complete, we can stop the analysis and the renderer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze 2: 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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import EPRanalysis\n",
    "eig_qb = EPRanalysis(design, \"hfss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(optional) you can tune the wirebond size by directly modifying the renderer options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.renderer.options['wb_size'] = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review and update the simulation setup by executing following two cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "em_p = eig_qb.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
   "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,
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'14nH'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q5.options.hfss_inductance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
   "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,
   "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(name=\"QMain_Q5_Bus05\", components=['Q_Main', 'Q5','Bus_05'], open_terminations=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the simulation is complete, we can check to see if the convergence was good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.plot_convergences()"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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('jj1', 'Lj1', 'Cj1', rect='JJ_rect_Lj_Q_Main_rect_jj', line='JJ_Lj_Q_Main_rect_jj_')\n",
    "eig_qb.add_junction('jj2', 'Lj2', 'Cj2', rect='JJ_rect_Lj_Q5_rect_jj', line='JJ_Lj_Q5_rect_jj_')\n",
    "eig_qb.setup.sweep_variable = 'Lj1'\n",
    "eig_qb.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>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",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</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",
   "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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rendering to a GDS File"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_chip_gds = design.renderers.gds"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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": 46,
   "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,
   "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",
   "metadata": {},
   "source": [
    "(optional) With the design complete, we can close the GUI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gui.main_window.close()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
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