{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4d793f20-1a6a-4324-a474-a6807e1c63ed",
   "metadata": {},
   "source": [
    "# HFSS Driven Modal Simulation of a MultiPlanar Design with pyaedt\n",
    "\n",
    "A `MultiPlanar` design reads its layers from a layer-stack file: for each layer, its z position, thickness, material and whether it is filled. The pyaedt renderers use that stack to build the 3D model in Ansys.\n",
    "\n",
    "**Note:** this may work only with the full (paid) version of Ansys, not the student version. This issue is fixable and requires more testing. Please run the notebook and test it out on student license, if you're interested to make a contribution!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "drivenmodal-badges",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qiskit-community/qiskit-metal/blob/main/docs/tut/4-Analysis/pyaedt-multiplanar/DrivenModal-pyaedt-multiplanar.ipynb)\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/qiskit-community/qiskit-metal/main?labpath=docs%2Ftut%2F4-Analysis%2Fpyaedt-multiplanar%2FDrivenModal-pyaedt-multiplanar.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0f2079",
   "metadata": {},
   "source": [
    "> 💡 **Desktop GUI or inline.** `metal.gui(design)` opens the desktop `MetalGUI` when Qt and a display are available and falls back to an inline viewer (Colab, Binder, a server) with the same API, so `gui.rebuild()` and `gui.screenshot()` work in both. See [1.1 Quick start](https://qiskit-community.github.io/qiskit-metal/tut/1-Overview/1.1-Quick-start.html) and [`docs/headless-usage.rst`](https://qiskit-community.github.io/qiskit-metal/headless-usage.html).\n",
    ">\n",
    "> Everything up to and including the layout runs anywhere. The rest drives Ansys through pyaedt (HFSS driven modal: setup, frequency sweep, S-parameters); those cells need Ansys Electronics Desktop and are shown here without outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "simulation-pathways",
   "metadata": {},
   "source": [
    "> 🧭 **Other simulation pathways.** This notebook uses Ansys, which needs a license. Without one, the same kind of result can come from the open-source ElmerFEM path for capacitance (tutorial 4.19) or the pip-installable gmsh + scikit-fem solver for eigenmodes, couplings and capacitance (tutorials 4.43–4.45); AWS Palace is available through SQDMetal, with a native integration in progress. [Simulation pathways](https://qiskit-community.github.io/qiskit-metal/simulation-pathways.html) summarizes what each solver computes, how to install it, and which tutorials use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c05f84b-ca11-42ad-aa96-8206b3f1b1fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import Dict\n",
    "from qiskit_metal.designs.design_multiplanar import MultiPlanar\n",
    "from qiskit_metal.renderers.renderer_ansys_pyaedt.hfss_renderer_drivenmodal_aedt import (\n",
    "    QHFSSDrivenmodalPyaedt,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e6f6cfba-fa5b-4b6b-8767-dfb54f604e88",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Layer stack\n",
    "\n",
    "The layer stack is a CSV file with one row per layer and datatype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c83bc98-bfc6-4079-9b7c-0d61d590f235",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# The example stack ships with the tutorials, in docs/tut/resources/.\n",
    "ls_file_path = \"../../resources/layer_stack_data_example_1.csv\"\n",
    "\n",
    "multiplanar_design = MultiPlanar(\n",
    "    metadata={}, overwrite_enabled=True, layer_stack_filename=ls_file_path\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fb957cf0-4fc9-4a5a-b95d-901ff6f6255a",
   "metadata": {},
   "source": [
    "\n",
    "## Multi-Planar design has default of chip named main. If there is more than one, add information to multiplanar design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1549d1f-3e3d-4620-95bd-29a14a856b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "multiplanar_design._chips.main.size.size_x = \"7mm\"\n",
    "multiplanar_design._chips.main.size.size_y = \"7mm\"\n",
    "multiplanar_design._chips[\"qubit_chip\"] = Dict()\n",
    "multiplanar_design._chips.qubit_chip.size = Dict(\n",
    "    center_x=\"0.0mm\",\n",
    "    center_y=\"0.0mm\",\n",
    "    size_x=\"7mm\",\n",
    "    size_y=\"7mm\",\n",
    ")\n",
    "\n",
    "\n",
    "# Override the default values size for chip=='main'.\n",
    "multiplanar_design.chips.main.size[\"size_x\"] = \"2mm\"\n",
    "multiplanar_design.chips.main.size[\"size_y\"] = \"2mm\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c2a579d-c9ef-450a-a465-95faaca17f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{'main': {'size': {'center_x': '0.0mm',\n   'center_y': '0.0mm',\n   'size_x': '2mm',\n   'size_y': '2mm'}},\n 'qubit_chip': {'size': {'center_x': '0.0mm',\n   'center_y': '0.0mm',\n   'size_x': '7mm',\n   'size_y': '7mm'}}}"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiplanar_design.chips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0db2f86-d8d7-4a3f-b6a9-ce307044427c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remember, the layers may not always be unique, since there can be multiple datatypes.\n",
    "ls_unique = multiplanar_design.ls.is_layer_data_unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bd1d556e-bc9c-44a2-a2b5-bb8073e10da7",
   "metadata": {},
   "source": [
    "## Open the GUI on the MultiPlanar design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e99d6f99-210a-419d-b360-9641c89ea3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "gui = metal.gui(multiplanar_design)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "195a5996-d589-4cfc-be91-09a0b1ed3985",
   "metadata": {},
   "source": [
    "## Add all components to design.\n",
    "## Note the layer number and chip names are used when adding components to design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1aa31cfd-ba75-4265-9e3b-f7bbb8ce4c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "from qiskit_metal.qlibrary.terminations.open_to_ground import OpenToGround\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "from qiskit_metal.qlibrary.qubits.transmon_concentric import TransmonConcentric\n",
    "\n",
    "from qiskit_metal.qlibrary.couplers.coupled_line_tee import CoupledLineTee\n",
    "from qiskit_metal.qlibrary.tlines.straight_path import RouteStraight\n",
    "\n",
    "# As precaution, remove any components already previously to design.\n",
    "multiplanar_design.delete_all_components()\n",
    "\n",
    "qubit_cpw_otg_layer = 2\n",
    "qubit_chip_name = \"qubit_chip\"\n",
    "\n",
    "\n",
    "##############################################################################\n",
    "\n",
    "\n",
    "options = dict(\n",
    "    # Some options we want to modify from the defaults\n",
    "    pad_width=\"425 um\",\n",
    "    pocket_height=\"650um\",\n",
    "    layer=qubit_cpw_otg_layer,\n",
    "    chip=qubit_chip_name,\n",
    "    # Adding 4 connectors (see below for defaults)\n",
    "    connection_pads=dict(\n",
    "        a=dict(loc_W=+1, loc_H=+1),\n",
    "        b=dict(loc_W=-1, loc_H=+1, pad_height=\"30um\"),\n",
    "        c=dict(loc_W=+1, loc_H=-1, pad_width=\"200um\"),\n",
    "        d=dict(loc_W=-1, loc_H=-1, pad_height=\"50um\"),\n",
    "    ),\n",
    ")\n",
    "\n",
    "## Create 2 transmons\n",
    "\n",
    "q1 = TransmonPocket(\n",
    "    multiplanar_design,\n",
    "    \"Q1\",\n",
    "    options=dict(pos_x=\"+1.4mm\", pos_y=\"0mm\", orientation=\"90\", **options),\n",
    ")\n",
    "q2 = TransmonPocket(\n",
    "    multiplanar_design,\n",
    "    \"Q2\",\n",
    "    options=dict(pos_x=\"-0.6mm\", pos_y=\"0mm\", orientation=\"90\", **options),\n",
    ")\n",
    "\n",
    "##############################################################################\n",
    "\n",
    "TQ1 = CoupledLineTee(\n",
    "    multiplanar_design,\n",
    "    \"TQ1\",\n",
    "    options=dict(\n",
    "        pos_x=\"1mm\",\n",
    "        pos_y=\"3mm\",\n",
    "        layer=qubit_cpw_otg_layer,\n",
    "        chip=qubit_chip_name,\n",
    "        coupling_length=\"200um\",\n",
    "    ),\n",
    ")\n",
    "TQ2 = CoupledLineTee(\n",
    "    multiplanar_design,\n",
    "    \"TQ2\",\n",
    "    options=dict(\n",
    "        pos_x=\"-1mm\",\n",
    "        pos_y=\"3mm\",\n",
    "        layer=qubit_cpw_otg_layer,\n",
    "        chip=qubit_chip_name,\n",
    "        coupling_length=\"200um\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "##############################################################################\n",
    "\n",
    "ops = dict(fillet=\"90um\")\n",
    "multiplanar_design.overwrite_enabled = True\n",
    "\n",
    "options1 = Dict(\n",
    "    total_length=\"8mm\",\n",
    "    hfss_wire_bonds=True,\n",
    "    layer=qubit_cpw_otg_layer,\n",
    "    chip=qubit_chip_name,\n",
    "    pin_inputs=Dict(\n",
    "        start_pin=Dict(component=\"TQ1\", pin=\"second_end\"),\n",
    "        end_pin=Dict(component=\"Q1\", pin=\"a\"),\n",
    "    ),\n",
    "    lead=Dict(start_straight=\"0.1mm\"),\n",
    "    **ops,\n",
    ")\n",
    "\n",
    "options2 = Dict(\n",
    "    total_length=\"9mm\",\n",
    "    hfss_wire_bonds=True,\n",
    "    layer=qubit_cpw_otg_layer,\n",
    "    chip=qubit_chip_name,\n",
    "    pin_inputs=Dict(\n",
    "        start_pin=Dict(component=\"TQ2\", pin=\"second_end\"),\n",
    "        end_pin=Dict(component=\"Q2\", pin=\"a\"),\n",
    "    ),\n",
    "    lead=Dict(start_straight=\"0.1mm\"),\n",
    "    **ops,\n",
    ")\n",
    "\n",
    "meanderQ1 = RouteMeander(multiplanar_design, \"meanderQ1\", options=options1)\n",
    "meanderQ2 = RouteMeander(multiplanar_design, \"meanderQ2\", options=options2)\n",
    "\n",
    "##############################################################################\n",
    "\n",
    "otg1 = OpenToGround(\n",
    "    multiplanar_design,\n",
    "    \"otg1\",\n",
    "    options=dict(\n",
    "        pos_x=\"3mm\", layer=qubit_cpw_otg_layer, chip=qubit_chip_name, pos_y=\"3mm\"\n",
    "    ),\n",
    ")\n",
    "otg2 = OpenToGround(\n",
    "    multiplanar_design,\n",
    "    \"otg2\",\n",
    "    options=dict(\n",
    "        pos_x=\"-3mm\",\n",
    "        pos_y=\"3mm\",\n",
    "        layer=qubit_cpw_otg_layer,\n",
    "        chip=qubit_chip_name,\n",
    "        orientation=\"180\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "##############################################################################\n",
    "\n",
    "ops_oR = Dict(\n",
    "    hfss_wire_bonds=True,\n",
    "    layer=qubit_cpw_otg_layer,\n",
    "    chip=qubit_chip_name,\n",
    "    pin_inputs=Dict(\n",
    "        start_pin=Dict(component=\"TQ1\", pin=\"prime_end\"),\n",
    "        end_pin=Dict(component=\"otg1\", pin=\"open\"),\n",
    "    ),\n",
    ")\n",
    "ops_mid = Dict(\n",
    "    hfss_wire_bonds=True,\n",
    "    layer=qubit_cpw_otg_layer,\n",
    "    chip=qubit_chip_name,\n",
    "    pin_inputs=Dict(\n",
    "        start_pin=Dict(component=\"TQ1\", pin=\"prime_start\"),\n",
    "        end_pin=Dict(component=\"TQ2\", pin=\"prime_end\"),\n",
    "    ),\n",
    ")\n",
    "ops_oL = Dict(\n",
    "    hfss_wire_bonds=True,\n",
    "    layer=qubit_cpw_otg_layer,\n",
    "    chip=qubit_chip_name,\n",
    "    pin_inputs=Dict(\n",
    "        start_pin=Dict(component=\"TQ2\", pin=\"prime_start\"),\n",
    "        end_pin=Dict(component=\"otg2\", pin=\"open\"),\n",
    "    ),\n",
    ")\n",
    "\n",
    "cpw_openRight = RouteStraight(multiplanar_design, \"cpw_openRight\", options=ops_oR)\n",
    "cpw_middle = RouteStraight(multiplanar_design, \"cpw_middle\", options=ops_mid)\n",
    "cpw_openLeft = RouteStraight(multiplanar_design, \"cpw_openLeft\", options=ops_oL)\n",
    "\n",
    "##############################################################################\n",
    "\n",
    "# Place the concentric transmon within design.\n",
    "concentric_options = dict(\n",
    "    # chip='main',\n",
    "    pos_x=\"400um\",\n",
    "    pos_y=\"900um\",\n",
    "    # layer='1',  # default is 1, this is just for example.\n",
    "    pocket_w=\"1500um\",  # transmon pocket width\n",
    "    pocket_h=\"900um\",  # transmon pocket height\n",
    ")\n",
    "\n",
    "# Create a new Concentric Transmon object with name 'Q1'\n",
    "qc1 = TransmonConcentric(multiplanar_design, \"cc_qubit\", options=concentric_options)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n",
    "gui.screenshot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7c86e086-56be-470f-b379-6d6d6f16df25",
   "metadata": {},
   "source": [
    "## Start the HFSS Driven Modal renderer for Ansys by passing arguments to QHFSSDrivenmodalPyaedt.\n",
    "## The default will use default names for Ansys ProjectName and Ansys DesignName."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8d0b141-cc65-4cd3-a188-8f603fa4486c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ansys will not start here since default for initiate is False.\n",
    "\n",
    "# Example with defaults.\n",
    "# hfss_dm_default = QHFSSDrivenmodalPyaedt(multiplanar_design)\n",
    "\n",
    "hfss_dm = QHFSSDrivenmodalPyaedt(\n",
    "    multiplanar_design, \"hfss_dm_project_1\", \"hfss_dm_design_1\"\n",
    ")\n",
    "\n",
    "# # Ansys will start here.\n",
    "# hfss_dm_2 = QHFSSDrivenmodalPyaedt(multiplanar_design,\n",
    "#                                    'hfss_dm_project_2',\n",
    "#                                    'hfss_dm_design_2',\n",
    "#                                    initiate=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c5c1bc10-7b06-4af6-bdd9-d5d1c0c57c33",
   "metadata": {},
   "source": [
    "## Vacuum Box size given to renderer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0591c6f-d090-4b91-a6ac-0b90d629da8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample Holder options will determine the z values of vacuum box.\n",
    "hfss_dm.options[\"sample_holder_top\"] = \"1.0mm\"\n",
    "hfss_dm.options[\"sample_holder_bottom\"] = \"-1.0mm\"\n",
    "\n",
    "# Note, the xy size of vacuum box is determined by box_plus_buffer.\n",
    "# If False, the chip size will be used.\n",
    "# If True, a buffer will be added to the box which contains the rendered components."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a948a838-3997-4f83-a2ac-9174b950f777",
   "metadata": {},
   "source": [
    "## Examine arguments to pass for Ansys-setup for driven modal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "498d4180-1e4e-4dfe-a9d5-8bf9df3854f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{'name': 'QHFSSDrivenmodalPyaedt_setup',\n 'SolveType': 'Single',\n 'Frequency': '5.0',\n 'MaxDeltaE': '0.01',\n 'MaximumPasses': '10',\n 'MinimumPasses': '1',\n 'MinimumConvergedPasses': '1',\n 'PercentRefinement': '30',\n 'BasisOrder': '1',\n 'MultipleAdaptiveFreqsSetup': {'1GHz': [0.02],\n  '2GHz': [0.02],\n  '5GHz': [0.02]},\n 'BroadbandLowFreq': '2',\n 'BroadbandHighFreq': '8'}"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Create a solution setup in Ansys HFSS Driven-Modal solution type. If user does not provide\n",
    "    arguments, they will be obtained from QHFSSDrivenmodalPyaedt.default_setup dict.\n",
    "\n",
    "    Args:\n",
    "        name (str, optional): _description_. Defaults to None.\n",
    "        SolveType (str, optional): Solution frequency type. Accepted values are in self.__supported_SolveType__.\n",
    "                                Defaults to self.default_setup.\n",
    "        Frequency (float, optional):  Minimum frequency in GHz. Defaults to self.default_setup.\n",
    "        MaxDeltaE (float, optional):  This is correlated to MaxDeltaS. The definition\n",
    "                                of MaxDeltaS is, absolute value of maximum difference in\n",
    "                                scattering parameter S. Defaults to self.default_setup.\n",
    "        MaximumPasses (int, optional):  Maximum number of passes. Defaults to self.default_setup.\n",
    "        MinimumPasses (int, optional): Minimum number of passes.Defaults to self.default_setup.\n",
    "        MinimumConvergedPasses (int, optional): Minimum number of converged passes. Defaults to self.default_setup.\n",
    "        PercentRefinement (int, optional): Percent refinement. Defaults to self.default_setup.\n",
    "        BasisOrder (int, optional): Basis order. Defaults to self.default_setup.\n",
    "        MultipleAdaptiveFreqsSetup (dict, optional): Frequencies and their associated MaxDeltaS.\n",
    "                                Defaults to self.default_setup.\n",
    "        BroadbandLowFreq (float, optional): Minimum frequency for Broadband SolveType in GHz. \n",
    "                                Defaults to self.default_setup.\n",
    "        BroadbandHighFreq (float, optional): Maximum frequency for Broadband SolveType in GHz. \n",
    "                                Defaults to self.default_setup.\n",
    "\n",
    "    Returns:\n",
    "        new_setup (pyaedt.modules.SolveSetup.SetupHFSS): pyAEDT simulation setup object.\n",
    "\"\"\"\n",
    "\n",
    "hfss_dm.default_setup"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ff9a5658-0669-4f33-a07d-9b069de72821",
   "metadata": {},
   "source": [
    "## Add solution setup to Ansys Driven Modal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d6212d8-fcc1-4ed2-9a3b-aa35037a6f97",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "# Ansys should add project and design for hfss_dm.\n",
    "hfss_dm.add_hfss_dm_setup()\n",
    "\n",
    "# Reduce MaxPass when running notebook as an example.\n",
    "hfss_dm.add_hfss_dm_setup(name=\"fun_dm_1\", MaximumPasses=3)\n",
    "\n",
    "\n",
    "#### We have access to other solution frequency types as well\n",
    "#### Normally it defaults to `SolveType=Single` (single frequency).\n",
    "\n",
    "## 1. Multiple Frequencies\n",
    "# multiple_freqs = OrderedDict([('4GHz', [0.01]),\n",
    "#                               ('6GHz', [0.001]),\n",
    "#                               ('11GHz', [0.0001])])\n",
    "# hfss_dm.add_hfss_dm_setup(\n",
    "#     name=None,\n",
    "#     SolveType='MultiFrequency'\n",
    "#     MultipleAdaptiveFreqsSetup=multiple_freqs)\n",
    "\n",
    "## 2. Broadband\n",
    "# hfss_dm.add_hfss_dm_setup(\n",
    "#     name=None,\n",
    "#     SolveType='Broadband',\n",
    "#     BroadbandLowFreq=\"4\",  # GHz\n",
    "#     BroadbandHighFreq=\"11\"  # GHz\n",
    "# )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "497ce2f5-bb21-4097-8707-48cf91346eca",
   "metadata": {},
   "source": [
    "## Since user can edit the GUI, it is \"better\" to clean the user's project and design denoted when starting the renderer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05031079-0742-4279-9b47-58814860a2f0",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "hfss_dm.clean_user_design()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b91a2fb8-a92f-4daf-ab4a-c6b8504f74f8",
   "metadata": {},
   "source": [
    "## Render the multi-planar design using the similar convention as planar-design to identify ports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c94a2654-d252-489e-a2a4-2ee2ac4dc3ca",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "hfss_dm.render_design(\n",
    "    selection=[\"TQ1\", \"TQ2\", \"cpw_openRight\", \"cpw_openLeft\", \"cpw_middle\", \"Q1\", \"Q2\"],\n",
    "    open_pins=[],\n",
    "    port_list=[(\"cpw_openRight\", \"end\", 50), (\"cpw_openLeft\", \"end\", 50)],\n",
    "    jj_to_port=[(\"Q2\", \"rect_jj\", 51)],\n",
    "    ignored_jjs=[(\"Q1\", \"rect_jj\")],\n",
    "    box_plus_buffer=True,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "790ad6da-b9f2-44eb-923b-9a1bf9445ec0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Examine arguments to pass for Ansys-sweep for driven modal.\n",
    "\n",
    "```\n",
    "Add a frequency sweep to a driven modal setup.\n",
    "\n",
    "Args:\n",
    "    setup_name (str, optional): Name of driven modal simulation setup.\n",
    "                            Defaults to \"QHFSSDrivenmodalPyaedt_setup\".\n",
    "    unit(str, optional): The units of start and stop.\n",
    "    start_ghz (float, optional): Starting frequency of sweep in GHz.\n",
    "                            Defaults to 2.0.\n",
    "    stop_ghz (float, optional): Ending frequency of sweep in GHz.\n",
    "                            Defaults to 8.0.\n",
    "    count (int, optional): Total number of frequencies.\n",
    "                            Defaults to 101.\n",
    "    step_ghz (float, optional): Difference between adjacent\n",
    "                            frequencies. Defaults to None.\n",
    "    name (str, optional): Name of sweep. Defaults to \"QHFSSDrivenmodalPyaedt_sweep\".\n",
    "    type (str, optional): Type of sweep.  Options are \"Fast\", \"Interpolating\",\n",
    "                        and \"Discrete\". Defaults to \"Fast\".\n",
    "    save_fields (bool, optional): Whether or not to save fields.\n",
    "                        Defaults to False.\n",
    "    interpolation_tol (float, optional): Error tolerance threshold \n",
    "                                for the interpolation type sweep. Defaults to 0.5.\n",
    "    interpolation_max_solutions (int, optional): Maximum number of solutions\n",
    "                                evaluted for the interpolation process. \n",
    "                                Defaults to 250.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1789fcf-6ece-47fe-8f17-39fe3fab1ec1",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "# Add sweep for specific setup and give new name for sweep.\n",
    "\n",
    "hfss_dm.add_sweep(\n",
    "    setup_name=\"fun_dm_1\",\n",
    "    name=\"fun_dm_1_sweep\",\n",
    "    start_ghz=4.0,\n",
    "    stop_ghz=8.0,\n",
    "    count=2001,\n",
    "    type=\"Interpolating\",\n",
    "    interpolation_tol=0.1,\n",
    "    interpolation_max_solutions=500,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f9d94ac1-7aa8-4478-b375-e2b12bf1ae10",
   "metadata": {},
   "source": [
    "## Select the name to analyze. Presently, we have two to choose from.\n",
    "## They are default=='QHFSSDrivenmodalPyaedt_setup' and 'fun_dm_1'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59789cef-1925-4914-9175-c301b1fb8ffd",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "# This has some error checking before using pyaedt's analyze_setup.\n",
    "result_bool = hfss_dm.analyze_setup(setup_name=\"fun_dm_1\", sweep_name=\"fun_dm_1_sweep\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b249bfbd-4986-4f7d-9eb2-480a5f50f497",
   "metadata": {},
   "source": [
    "## Get the solution data!\n",
    "\n",
    "## Can get scatter data, impedance, and admittance in format of mag/phase or real/imag. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "832d5a23-66df-4577-a733-bd84184be7f9",
   "metadata": {},
   "source": [
    " ```\n",
    "Get the solution data based on expressions.  Return output based on output_type.\n",
    "\n",
    "Args:\n",
    "    sweep_name (str): Name of sweep entry within setup.\n",
    "    expressions (str, optional): This expression is either passed to get_solution_data\n",
    "                                OR either S, Y or Z with port information will be\n",
    "                                gathered by renderer. The renderer will get the\n",
    "                                port names used with render_design() to create expressions.\n",
    "                                So, the user MUST execute render_design() prior to\n",
    "                                getting solution data.\n",
    "\n",
    "                                Defaults to 'S'. S for scattering, Y for admittance, Z for\n",
    "                                impedance.\n",
    "    output_type (int, optional): 1 to return mag/phase,\n",
    "                                2 to return real/imag,\n",
    "                                3 to return mag/phase and real/imag.\n",
    "                                Defaults to 1.\n",
    "\n",
    "Returns:\n",
    "    dict: Key is either mag, phase, real or imag based on output_type.\n",
    "        The value is data from get_solution_data.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38f23d3d-8426-48f2-944c-2e637ed2ff69",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "result_dict_default = hfss_dm.get_ansys_solution_data(sweep_name=\"fun_dm_1_sweep\")\n",
    "\n",
    "# To make more viewable, print some lines.\n",
    "print()\n",
    "print()\n",
    "result_dict_default.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19da00ae-3b0c-4a96-bf35-2a1994c4c05f",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "result_dict_s = hfss_dm.get_ansys_solution_data(\n",
    "    sweep_name=\"fun_dm_1_sweep\", expressions=\"S\", output_type=3\n",
    ")\n",
    "# To make more viewable, print some lines.\n",
    "print()\n",
    "print()\n",
    "result_dict_s.keys()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "48b23ac5-1e17-4324-ae24-91cd631cbc2c",
   "metadata": {},
   "source": [
    "## Remove the hash mark to retrieve the type of data you want to retrieve from Scatter Solution Data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d70f4bc-e490-4501-bafb-dd582a027e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove comment to view all frequencies.\n",
    "# result_dict_s\n",
    "\n",
    "# Remove comment to view pandas dataframe for magnitude for all frequencies for scatter solution data.\n",
    "# result_dict_s['mag']\n",
    "\n",
    "# Remove comment to view pandas dataframe for real data for all frequencies for scatter solution data.\n",
    "# result_dict_s['real']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb728f14-81d2-43a0-916f-6c434f825626",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "# Example of getting impedance data.\n",
    "result_dict_z = hfss_dm.get_ansys_solution_data(\n",
    "    sweep_name=\"fun_dm_1_sweep\", expressions=\"Z\", output_type=3\n",
    ")\n",
    "result_dict_s[\"mag\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fefe90d0-645a-4bf5-be69-b45bd050f8f3",
   "metadata": {
    "tags": [
     "requires-ansys"
    ]
   },
   "outputs": [],
   "source": [
    "# Will disconnect Metal from Ansys, and leave Ansys open.\n",
    "# If the python script closes, the OS will most likely close Ansys.\n",
    "# hfss_dm.close()\n",
    "\n",
    "\n",
    "# Will close Ansys.\n",
    "# hfss_dm.force_exit_ansys()"
   ]
  }
 ],
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