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    "# Tutorial 8: Fitting a Hanger Resonator\n",
    "\n",
    "Tutorials 1–3 fitted series resonators where the resonance shows up as a **peak** in $|S_{21}|$. However, many readout chips\n",
    "instead use a **hanger** (notch) resonators that hangs off a continuous through\n",
    "feedline. Here, the resonance appears as a **dip** in\n",
    "$|S_{21}|$:\n",
    "\n",
    "In this tutorial you will learn how to use **simpleLOMs** to\n",
    "\n",
    "1. **build** a CPW hanger and look at its notch,\n",
    "2. Turn an existing or new  `OptimizedFit` into a hanger\n",
    "3. Fit a hanger LOM explicitly with  with `fit_lom_hanger` and `HangerOptimizedFit` to match the notch in $|S_{21}|$.\n",
    "\n"
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      "geometry: d = 7.0 mm,  Cc_tap = 30 fF\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import skrf as rf\n",
    "\n",
    "import simpleLOMs as sl\n",
    "\n",
    "\n",
    "sl.apply_style()\n",
    "\n",
    "cpw = sl.CPWParams(ep_r=11.45)\n",
    "Z0 = 50.0\n",
    "d = 7.0e-3          # resonator length (m)\n",
    "Cc_tap = 30e-15     # coupling (F)\n",
    "freq = rf.Frequency(3e9, 12e9, 8001, unit=\"Hz\")\n",
    "\n",
    "print(\"geometry: d = {:.1f} mm,  Cc_tap = {:.0f} fF\".format(d * 1e3, Cc_tap * 1e15))\n"
   ]
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    "## 1. Build the CPW hanger\n",
    "\n",
    "`hanger_resonator_network_2port` takes a list of hanging segments and one tap\n",
    "capacitance per segment. An open termination, which we use here, is the $\\lambda/2$ hanger, while\n",
    " a shorted termination is the $\\lambda/4$ hanger.\n"
   ]
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      "CPW open  (lambda/2):  notch at 8.383 GHz,  |S21| = 0.026\n",
      "CPW short (lambda/4):  notch at 4.191 GHz,  |S21| = 0.022\n"
     ]
    }
   ],
   "source": [
    "cpw_open = sl.hanger_resonator_network_2port(\n",
    "    freq,\n",
    "    [{\"kind\": \"cpw\", \"d\": d, \"termination\": \"open\"}],\n",
    "    [Cc_tap],\n",
    "    cpw_params=cpw,\n",
    "    Z0=Z0,\n",
    ")\n",
    "cpw_short = sl.hanger_resonator_network_2port(\n",
    "    freq,\n",
    "    [{\"kind\": \"cpw\", \"d\": d, \"termination\": \"short\"}],\n",
    "    [Cc_tap],\n",
    "    cpw_params=cpw,\n",
    "    Z0=Z0,\n",
    ")\n",
    "\n",
    "f_open, dp_open = sl.notch_frequency(cpw_open)\n",
    "f_short, dp_short = sl.notch_frequency(cpw_short)\n",
    "print(\"CPW open  (lambda/2):  notch at {:.3f} GHz,  |S21| = {:.3f}\".format(f_open, dp_open))\n",
    "print(\"CPW short (lambda/4):  notch at {:.3f} GHz,  |S21| = {:.3f}\".format(f_short, dp_short))\n"
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       "Schematic(meta={'title': '', 'description': '', 'source': 'simpleLOMs.networks.hanger.hanger_resonator_network_2port', 'units': {'capacitance': 'F', 'inductance': 'H', 'length': 'm', 'impedance': 'ohm', 'frequency': 'Hz'}}, groups=[{'id': 'hang1', 'label': 'hanger 1'}], components=[Component(id='P1', type='port', label='Port 1', value='50.00 \\\\Omega', ports=['out'], group=None, orient='series', props={'Z0': 50}), Component(id='H1', type='branch', label='', value='', ports=['a', 'gnd'], group='hang1', orient='shunt', props={'stack': [{'type': 'cap', 'label': 'C_{c1}', 'value': '30.00 fF'}, {'type': 'tline', 'label': 'CPW 1', 'value': '\\\\ell = 7.00 mm (open)'}]}), Component(id='P2', type='port', label='Port 2', value='50.00 \\\\Omega', ports=['in'], group=None, orient='series', props={'Z0': 50})], nets=[Net(id='n1', ports=['P1.out', 'H1.a']), Net(id='n2', ports=['H1.gnd', 'GND']), Net(id='n3', ports=['P1.out', 'P2.in'])], annotations={})"
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    "sl.hanger_resonator_schematic_2port(\n",
    "    [{\"kind\": \"cpw\", \"d\": d, \"termination\": \"open\"}],\n",
    "    [Cc_tap],\n",
    ")\n"
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    "## 2. Option 1 — rewire an inline `OptimizedFit`\n",
    "\n",
    "If you already fit a `LC` tank to your resonator against a series CPW with two-sided coupling\n",
    "using the `fit_lom` / `OptimizedFit` methods of Tutorial 1, dropping those values into a single-tap hanger is the quick\n",
    "path.\n",
    "\n",
    "As an example here we can use a new `OptimizedFit` instance to calibrate $(L, C)$ for the hanger by rewiring it into a hanger.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b0dc8b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-19T04:34:31.043385Z",
     "iopub.status.busy": "2026-07-19T04:34:31.043086Z",
     "iopub.status.idle": "2026-07-19T04:34:46.788608Z",
     "shell.execute_reply": "2026-07-19T04:34:46.787746Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Cc_tap' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Cc1 \u001b[38;5;241m=\u001b[39m Cc2 \u001b[38;5;241m=\u001b[39m \u001b[43mCc_tap\u001b[49m\n\u001b[1;32m      2\u001b[0m freq_fit \u001b[38;5;241m=\u001b[39m rf\u001b[38;5;241m.\u001b[39mFrequency(\u001b[38;5;241m7e9\u001b[39m, \u001b[38;5;241m9e9\u001b[39m, \u001b[38;5;241m3001\u001b[39m, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHz\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mreuse_inline\u001b[39m(Ctog):\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Cc_tap' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "Cc1 = Cc2 = Cc_tap\n",
    "freq_fit = rf.Frequency(7e9, 9e9, 3001, unit=\"Hz\")\n",
    " \n",
    "def reuse_inline(Ctog):\n",
    "    \"Fit an inline LOM at the given ground loading, then rewire into a hanger.\"\n",
    "    L, C = sl.fit_lom(\n",
    "        d, model=\"optimized\",\n",
    "        Cc1=Cc1, Cc2=Cc2, Ctog1=Ctog, Ctog2=Ctog,\n",
    "        cpw_params=cpw, freq=freq_fit, Z0=Z0,\n",
    "    )\n",
    "    net = sl.hanger_resonator_network_2port(\n",
    "        freq, [{\"kind\": \"lc\", \"L\": L, \"C\": C}], [Cc_tap], Z0=Z0,\n",
    "    )\n",
    "    f_r, dp = sl.notch_frequency(net)\n",
    "    return L, C, net, f_r, dp\n",
    "\n",
    "\n",
    "# Matched reuse: drop the inline grounds to mirror the ground-free hanger tap.\n",
    "L_inline, C_inline, lc_reused, f_reuse, dp_reuse = reuse_inline(0.0)\n",
    "err_reuse = 100.0 * (f_reuse - f_open) / f_open\n",
    "\n",
    "\n",
    "print(\"matched reuse (Ctog =  0 fF):  L = {:.4f} nH,  C = {:.4f} pF\".format(\n",
    "    L_inline * 1e9, C_inline * 1e12))\n",
    "print(\"                               notch at {:.3f} GHz  ({:+.3f} % vs CPW open)\".format(\n",
    "    f_reuse, err_reuse))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17623a6a",
   "metadata": {},
   "source": [
    "## 3. Option 2 — fit a hanger LOM with `fit_lom_hanger`\n",
    "\n",
    "Instead of rewiring an inline fit, you can instead fit the hanger directly. `fit_lom_hanger`\n",
    "builds a CPW hanger reference and runs `HangerOptimizedFit` against the S21\n",
    "notch, instead of the Re(S11)/Im(S11) pair that is used in `OptimizedFit`.\n",
    "\n",
    "Let's try to use `fit_lom_hanger` for our device geometry.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49f23aef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-19T04:34:46.791276Z",
     "iopub.status.busy": "2026-07-19T04:34:46.791148Z",
     "iopub.status.idle": "2026-07-19T04:34:48.799435Z",
     "shell.execute_reply": "2026-07-19T04:34:48.799142Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HangerOptimizedFit(L=5.5415e-10 H, C=6.2046e-13 F)\n",
      "L = 0.5542 nH,  C = 0.6205 pF\n",
      "hanger-fitted LC:     notch at 8.383 GHz  (-0.000 % vs CPW open)\n"
     ]
    }
   ],
   "source": [
    "hanger_model = sl.fit_lom_hanger(\n",
    "    d,\n",
    "    Cc_tap=Cc_tap,\n",
    "    termination=\"open\",\n",
    "    cpw_params=cpw,\n",
    "    freq=freq,\n",
    "    Z0=Z0,\n",
    "    return_model=True,\n",
    ")\n",
    "print(hanger_model)\n",
    "print(\"L = {:.4f} nH,  C = {:.4f} pF\".format(\n",
    "    hanger_model.L * 1e9, hanger_model.C * 1e12))\n",
    "\n",
    "lc_fitted = hanger_model.get_network(freq, Cc_tap=Cc_tap, Z0=Z0)\n",
    "f_fit, dp_fit = sl.notch_frequency(lc_fitted)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a470fe",
   "metadata": {},
   "source": [
    "## 4. Compare the notches\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d6be43",
   "metadata": {},
   "source": [
    "We can now look at the error in the notch feature between these two methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac64324b",
   "metadata": {},
   "outputs": [],
   "source": [
    "err_fit = 100.0 * (f_fit - f_open) / f_open\n",
    "print(\"hanger-fitted LC:     notch at {:.3f} GHz  ({:+.3f} % vs CPW open)\".format(\n",
    "    f_fit, err_fit))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7a880a",
   "metadata": {},
   "source": [
    "Then, we can plot $|S_{21}|$ for the two methods (using `OptimizedFit` and `HangerOptimizedFit` respectively) to see whether the resonance feature is captured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c42acc41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-19T04:34:48.800938Z",
     "iopub.status.busy": "2026-07-19T04:34:48.800801Z",
     "iopub.status.idle": "2026-07-19T04:34:49.136206Z",
     "shell.execute_reply": "2026-07-19T04:34:49.135874Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1012.5x625.759 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "network                           notch GHz   err %   |S21|\n",
      "--------------------------------  ---------  ------  ------\n",
      "CPW open (truth)                     8.3829       —  0.0257\n",
      "LC reuse, Ctog=20 fF (naive)         8.1319  -2.995  0.0505\n",
      "LC reuse, Ctog=0 (Option 1)          8.3845  +0.019  0.0348\n",
      "LC HangerOptimizedFit (Option 2)     8.3829  -0.000  0.0260\n"
     ]
    }
   ],
   "source": [
    "f_ghz = freq.f / 1e9\n",
    "sl.plot_curves(\n",
    "    f_ghz,\n",
    "    (np.abs(cpw_open.s[:, 1, 0]), \"CPW hanger, open (truth)\",\n",
    "     dict(color=sl.COLOR_PRIMARY, lw=2.2)),\n",
    "    (np.abs(lc_fitted.s[:, 1, 0]), \"LC, HangerOptimizedFit (Option 2)\",\n",
    "     dict(color=sl.COLOR_HIGHLIGHT, lw=1.8, ls=\"-.\")),\n",
    "    (np.abs(lc_reused.s[:, 1, 0]), \"LC, reused inline (Option 1)\",\n",
    "     dict(color=sl.COLOR_SECONDARY, lw=1.5, ls=\"--\")),\n",
    "    xlabel=sl.LABEL_FREQ,\n",
    "    ylabel=r\"$|S_{21}|$\",\n",
    "    xlim=(8.25, 8.5),\n",
    ")\n",
    "\n",
    "print(sl.utils.format_comparison_table(\n",
    "    [\n",
    "        (\"CPW open (truth)\", \"{:.4f}\".format(f_open), \"—\", \"{:.4f}\".format(dp_open)),\n",
    "        (\"LC reuse, Ctog=20 fF (naive)\", \"{:.4f}\".format(f_bad), \"{:+.3f}\".format(err_bad), \"{:.4f}\".format(dp_bad)),\n",
    "        (\"LC reuse, Ctog=0 (Option 1)\", \"{:.4f}\".format(f_reuse), \"{:+.3f}\".format(err_reuse), \"{:.4f}\".format(dp_reuse)),\n",
    "        (\"LC HangerOptimizedFit (Option 2)\", \"{:.4f}\".format(f_fit), \"{:+.3f}\".format(err_fit), \"{:.4f}\".format(dp_fit)),\n",
    "    ],\n",
    "    [\"network\", \"notch GHz\", \"err %\", \"|S21|\"],\n",
    "))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a1252d",
   "metadata": {},
   "source": [
    "Although both methods reproduce the notch frequency quite well, only the dedicated fit\n",
    "also reproduces the notch depth.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681f419c",
   "metadata": {},
   "source": [
    "## 5. Draw the fitted LOM hanger\n",
    "\n",
    "As usual we can draw the fitted hanger using the schematic builder, which uses the same segment dicts as the network API. \n",
    "\n",
    "Let's plot the lumped model we fitted using Option 2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84c2cb6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-19T04:34:49.137586Z",
     "iopub.status.busy": "2026-07-19T04:34:49.137486Z",
     "iopub.status.idle": "2026-07-19T04:34:49.140396Z",
     "shell.execute_reply": "2026-07-19T04:34:49.140129Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "Schematic(meta={'title': '', 'description': '', 'source': 'simpleLOMs.networks.hanger.hanger_resonator_network_2port', 'units': {'capacitance': 'F', 'inductance': 'H', 'length': 'm', 'impedance': 'ohm', 'frequency': 'Hz'}}, groups=[{'id': 'hang1', 'label': 'hanger 1'}], components=[Component(id='P1', type='port', label='Port 1', value='50.00 \\\\Omega', ports=['out'], group=None, orient='series', props={'Z0': 50}), Component(id='H1', type='branch', label='', value='', ports=['a', 'gnd'], group='hang1', orient='shunt', props={'stack': [{'type': 'cap', 'label': 'C_{c1}', 'value': '30.00 fF'}, {'type': 'lc', 'label': 'LC 1', 'value': 'L = 554.15 pH\\nC = 620.46 fF'}]}), Component(id='P2', type='port', label='Port 2', value='50.00 \\\\Omega', ports=['in'], group=None, orient='series', props={'Z0': 50})], nets=[Net(id='n1', ports=['P1.out', 'H1.a']), Net(id='n2', ports=['H1.gnd', 'GND']), Net(id='n3', ports=['P1.out', 'P2.in'])], annotations={})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.hanger_resonator_schematic_2port(\n",
    "    [{\"kind\": \"lc\", \"L\": hanger_model.L, \"C\": hanger_model.C}],\n",
    "    [Cc_tap],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbb7778",
   "metadata": {},
   "source": [
    "This can now be saved or exported for further use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b1a4b7",
   "metadata": {},
   "source": [
    "## 6. Multiple hangers on one feedline\n",
    "\n",
    "Much line in Tutorial 3, resonator networks with multiple hangers on the same feedline can be constructed.\n",
    "\n",
    "To do so, we will pass several segments (and one `Cc` per segment) to put several notches on the\n",
    "same through line. Fit each resonator\n",
    "in isolation with `fit_lom_hanger` at its own length, then reassemble.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f10940",
   "metadata": {},
   "source": [
    "### 6a. Constructing the network\n",
    "First we construct the network with our CPW from before and a CPW at a length of $8000$ mm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6314c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "d2 = 8.0e-3\n",
    "cpw_two = sl.hanger_resonator_network_2port(\n",
    "    freq,\n",
    "    [\n",
    "        {\"kind\": \"cpw\", \"d\": d,  \"termination\": \"open\"},\n",
    "        {\"kind\": \"cpw\", \"d\": d2, \"termination\": \"open\"},\n",
    "    ],\n",
    "    [Cc_tap, Cc_tap],\n",
    "    cpw_params=cpw,\n",
    "    Z0=Z0,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176baed0",
   "metadata": {},
   "source": [
    "Next we fit the LOM for each individual resonator using `fit_lom_hanger`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d404c0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "L1, C1 = sl.fit_lom_hanger(d,  Cc_tap=Cc_tap, termination=\"open\",\n",
    "                           cpw_params=cpw, freq=freq, Z0=Z0)\n",
    "L2, C2 = sl.fit_lom_hanger(d2, Cc_tap=Cc_tap, termination=\"open\",\n",
    "                           cpw_params=cpw, freq=freq, Z0=Z0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e417cf6e",
   "metadata": {},
   "source": [
    "We now reconstruct the LC-fitted network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48dbe69f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_two = sl.hanger_resonator_network_2port(\n",
    "    freq,\n",
    "    [\n",
    "        {\"kind\": \"lc\", \"L\": L1, \"C\": C1},\n",
    "        {\"kind\": \"lc\", \"L\": L2, \"C\": C2},\n",
    "    ],\n",
    "    [Cc_tap, Cc_tap],\n",
    "    Z0=Z0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc73863",
   "metadata": {},
   "source": [
    "### 6b. Comparing LC and CPW networks\n",
    "\n",
    "Now we can plot the curved to see how the LC behaves compared to the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d9552c8",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-07-19T04:34:54.574254Z"
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   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1012.5x625.759 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPW notches (GHz): ['7.356', '8.383']\n",
      "LC  notches (GHz): ['7.356', '8.383']\n"
     ]
    }
   ],
   "source": [
    "sl.plot_curves(\n",
    "    freq.f / 1e9,\n",
    "    (np.abs(cpw_two.s[:, 1, 0]), \"CPW\",\n",
    "     dict(color=sl.COLOR_PRIMARY, lw=2.2)),\n",
    "    (np.abs(lc_two.s[:, 1, 0]), \"LC, HangerOptimizedFit\",\n",
    "     dict(color=sl.COLOR_HIGHLIGHT, lw=1.8, ls=\"-.\")),\n",
    "    xlabel=sl.LABEL_FREQ,\n",
    "    ylabel=r\"$|S_{21}|$\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96d7ba1",
   "metadata": {},
   "source": [
    "The notches are very close! To be sure, we can print their frequencies and compare them numerically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd555493",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"CPW notches (GHz):\", [\"{:.3f}\".format(x) for x in sl.notches_below(cpw_two)])\n",
    "print(\"LC  notches (GHz):\", [\"{:.3f}\".format(x) for x in sl.notches_below(lc_two)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f88b6fb3",
   "metadata": {},
   "source": [
    "Finally we can make a schematic of the fitted system and export it. As we can see, the fitted system has the same two-hanger topology as the CPW hanger system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84d01edd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-19T04:34:54.583915Z",
     "iopub.status.busy": "2026-07-19T04:34:54.582786Z",
     "iopub.status.idle": "2026-07-19T04:34:54.596064Z",
     "shell.execute_reply": "2026-07-19T04:34:54.593476Z"
    }
   },
   "outputs": [
    {
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       "Schematic(meta={'title': '', 'description': '', 'source': 'simpleLOMs.networks.hanger.hanger_resonator_network_2port', 'units': {'capacitance': 'F', 'inductance': 'H', 'length': 'm', 'impedance': 'ohm', 'frequency': 'Hz'}}, groups=[{'id': 'hang1', 'label': 'hanger 1'}, {'id': 'hang2', 'label': 'hanger 2'}], components=[Component(id='P1', type='port', label='Port 1', value='50.00 \\\\Omega', ports=['out'], group=None, orient='series', props={'Z0': 50}), Component(id='H1', type='branch', label='', value='', ports=['a', 'gnd'], group='hang1', orient='shunt', props={'stack': [{'type': 'cap', 'label': 'C_{c1}', 'value': '30.00 fF'}, {'type': 'lc', 'label': 'LC 1', 'value': 'L = 554.15 pH\\nC = 620.46 fF'}]}), Component(id='H2', type='branch', label='', value='', ports=['a', 'gnd'], group='hang2', orient='shunt', props={'stack': [{'type': 'cap', 'label': 'C_{c2}', 'value': '30.00 fF'}, {'type': 'lc', 'label': 'LC 2', 'value': 'L = 630.97 pH\\nC = 711.81 fF'}]}), Component(id='P2', type='port', label='Port 2', value='50.00 \\\\Omega', ports=['in'], group=None, orient='series', props={'Z0': 50})], nets=[Net(id='n1', ports=['P1.out', 'H1.a']), Net(id='n2', ports=['H1.gnd', 'GND']), Net(id='n3', ports=['P1.out', 'H2.a']), Net(id='n4', ports=['H2.gnd', 'GND']), Net(id='n5', ports=['P1.out', 'P2.in'])], annotations={})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sl.hanger_resonator_schematic_2port(\n",
    "    [\n",
    "        {\"kind\": \"lc\", \"L\": L1, \"C\": C1},\n",
    "        {\"kind\": \"lc\", \"L\": L2, \"C\": C2},\n",
    "    ],\n",
    "    [Cc_tap, Cc_tap],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4d820a",
   "metadata": {},
   "source": [
    "## What next?\n",
    "\n",
    "In the next tutorial, Tutorial 9, we will see how to cascade a series resonator (Tutorial 1, 3) with hanger resonators (this tutorial) to make networks that include both.\n"
   ]
  }
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