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    "# Tutorial 2: Ranking LOMs and Predicting Loaded Shifts\n",
    "\n",
    "Tutorial 1 fit each lumped oscillator model (LOM) to a single, strongly and\n",
    "asymmetrically coupled device. Here, we'll see how to construct LOMs for a variety of parameters and test their accuracy under loading.\n",
    "\n",
    "\n",
    "In this tutorial we will see how to:\n",
    "\n",
    "1. **Rank the accuracy of LOMs.** We rank all three LOMs (**Foster**,\n",
    "   **Analytical**, and **Optimized**) against the CPW circuit model over a grid of coupling capacitances. \n",
    "2. **Predict a frequency shift from capacitive loads.** We attach capacitive loads to a\n",
    "   fitted device and check that its `(L, C)` reproduce the shifted CPW\n",
    "   frequency — not just the bare resonance.\n",
    "\n",
    "\n",
    "This helps you see whether the LOM that you have chosen is capable of standing in for a resonator inside a larger network with arbitrary loads.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba808a3b",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "For the setup we will first construct the same $7000$ um CPW as in Tutorial 1:"
   ]
  },
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import skrf as rf\n",
    "from scipy.signal import find_peaks\n",
    "\n",
    "import simpleLOMs as sl\n",
    "\n",
    "sl.apply_style()   # one shared simpleLOMs figure style\n",
    "\n",
    "\n",
    "cpw    = sl.CPWParams(ep_r=11.45)                    # substrate\n",
    "coarse = rf.Frequency(4e9, 12e9, 8_001, unit=\"Hz\")   # fit grid\n",
    "Ctog   = 1e-14\n",
    "Z0     = 50.0\n",
    "d      = 7.0e-3                                      # 7 mm lambda/2, f0 ~ 7.5-8 GHz\n",
    "MODELS = (\"optimized\", \"foster\", \"analytical\")       # best first\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab54e0e2",
   "metadata": {},
   "source": [
    "We will test the performance of all three available lumped element models in this tutorial."
   ]
  },
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   },
   "outputs": [],
   "source": [
    "MODELS = (\"optimized\", \"foster\", \"analytical\")       # best first"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef5bd6d",
   "metadata": {},
   "source": [
    "In order to use the Analytical expression, we need $f_{bare}$ and the line impedance, which we'll compute as part of the setup.\n"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPWParams(w=1.17e-05, s=5.1e-06, t=0.0, h=0.0005, rho=1e-19, ep_r=11.45, has_metal_backside=False, tand=0.0)\n",
      "bare-line f_r = 8.5830 GHz,  line impedance = 46.43 ohm\n"
     ]
    }
   ],
   "source": [
    "f_bare = sl.bare_cpw_resonance_hz(d, cpw_params=cpw)   # unloaded lambda/2 resonance\n",
    "z_line = sl.cpw_line_impedance(cpw, f_hz=f_bare)       # line impedance \n",
    "print(cpw)\n",
    "print(f\"bare-line f_r = {f_bare/1e9:.4f} GHz,  line impedance = {z_line:.2f} ohm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3455fd9e",
   "metadata": {},
   "source": [
    "## 1. Ranking the LOMs across the coupling plane\n",
    "\n",
    "Tutorial 1 ranked the models at a *single* strongly-coupled device\n",
    "($C_{c1}=30$, $C_{c2}=70$ fF). Here we rank them across the whole plane: a grid\n",
    "of coupling capacitances $(C_{c1}, C_{c2})$ from 15 fF (weak) to 150 fF (very\n",
    "strong), allowing them to differ — real devices couple asymmetrically to input\n",
    "and output.\n",
    "\n",
    "At each grid point, we measure the CPW ground-truth $(f_0, \\kappa)$ with\n",
    "`extract_f0_kappa`, fit each LOM with `fit_lom` — handing the Optimized fit a\n",
    "window sized to the *measured* linewidth, since at 150 fF the resonance is the\n",
    "better part of a GHz wide and `fit_lom`'s default internal window cannot\n",
    "bracket it — then rebuild each model through **its own `get_network()`**,\n",
    "circle-fit $f_0$, and record the signed percent error $\\delta f_0 / f_0$. The Analytical\n",
    "model is anchored to the *bare-line* frequency and *line* impedance printed\n",
    "above — the same anchoring Tutorial 1 used — so its errors here are genuinely\n",
    "the closed form's, not an artifact of feeding it an already-loaded frequency.\n",
    "\n",
    "> Note: this is a deliberately small grid (9 Optimized fits) so the notebook\n",
    "> executes in about three minutes. Feel free to increase `N` to map the plane in\n",
    "> more detail.\n"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1= 15.0 Cc2= 15.0 fF | Optimized: -0.000%  Foster: +0.037%  Analytical: +0.069%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1= 47.4 Cc2= 15.0 fF | Optimized: -0.001%  Foster: +0.110%  Analytical: +0.161%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1=150.0 Cc2= 15.0 fF | Optimized: +0.060%  Foster: +0.335%  Analytical: +0.432%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1= 15.0 Cc2= 47.4 fF | Optimized: -0.001%  Foster: +0.110%  Analytical: +0.161%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1= 47.4 Cc2= 47.4 fF | Optimized: -0.004%  Foster: +0.232%  Analytical: +0.299%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1=150.0 Cc2= 47.4 fF | Optimized: +0.045%  Foster: +0.579%  Analytical: +0.690%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1= 15.0 Cc2=150.0 fF | Optimized: +0.060%  Foster: +0.335%  Analytical: +0.432%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1= 47.4 Cc2=150.0 fF | Optimized: +0.045%  Foster: +0.579%  Analytical: +0.690%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cc1=150.0 Cc2=150.0 fF | Optimized: -0.002%  Foster: +1.462%  Analytical: +1.614%\n"
     ]
    },
    {
     "data": {
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       "<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>Optimized</th>\n",
       "      <th>Foster</th>\n",
       "      <th>Analytical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min |df0| (%)</th>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.036749</td>\n",
       "      <td>0.068749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median |df0| (%)</th>\n",
       "      <td>0.003654</td>\n",
       "      <td>0.335301</td>\n",
       "      <td>0.431841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max |df0| (%)</th>\n",
       "      <td>0.060479</td>\n",
       "      <td>1.461949</td>\n",
       "      <td>1.613534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Optimized    Foster  Analytical\n",
       "min |df0| (%)      0.000042  0.036749    0.068749\n",
       "median |df0| (%)   0.003654  0.335301    0.431841\n",
       "max |df0| (%)      0.060479  1.461949    1.613534"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 3                                          # grid points per axis (N x N Optimized fits)\n",
    "cc = np.geomspace(15e-15, 150e-15, N)\n",
    "f0_err = {m: np.full((N, N), np.nan) for m in MODELS}   # [row=Cc2, col=Cc1]\n",
    "\n",
    "for i, Cc2 in enumerate(cc):\n",
    "    for j, Cc1 in enumerate(cc):\n",
    "        f0c, kc = sl.extract_f0_kappa(cpw, d, Cc1=Cc1, Cc2=Cc2, Ctog=Ctog, Z0=Z0, n_fine=8001)\n",
    "        # evaluation window sized to the (broad, at strong coupling) linewidth\n",
    "        half = max(0.8e9, 5 * kc)\n",
    "        fr = rf.Frequency(max(1e9, f0c - half), f0c + half, 8_001, unit=\"Hz\")\n",
    "        for m in MODELS:\n",
    "            fit_freq = fr if m == \"optimized\" else coarse\n",
    "            mod = sl.fit_lom(d, model=m, Cc1=Cc1, Cc2=Cc2, Ctog=Ctog, Z0=Z0,\n",
    "                             cpw_params=cpw, freq=fit_freq, return_model=True)\n",
    "            net = mod.get_network(fr, Cc1=Cc1, Cc2=Cc2, Ctog1=Ctog, Ctog2=Ctog, Z0=Z0)\n",
    "            # Probe the stronger port — same convention as extract_f0_kappa\n",
    "            # under asymmetric coupling.\n",
    "            port = 0 if Cc1 >= Cc2 else 1\n",
    "            f0m, km = sl.circle_fit_f0_kappa(net, port, port)\n",
    "            f0_err[m][i, j] = 100.0 * (f0m - f0c) / f0c\n",
    "        print(f\"Cc1={Cc1*1e15:5.1f} Cc2={Cc2*1e15:5.1f} fF | \"\n",
    "              + \"  \".join(f\"{sl.MODEL_LABELS[m]}: {f0_err[m][i, j]:+.3f}%\" for m in MODELS),\n",
    "              flush=True)\n",
    "\n",
    "# summary: |delta f0| statistics per model\n",
    "summary = pd.DataFrame(\n",
    "    {sl.MODEL_LABELS[m]: [np.abs(f0_err[m]).min(), np.median(np.abs(f0_err[m])), np.abs(f0_err[m]).max()]\n",
    "     for m in MODELS},\n",
    "    index=[\"min |df0| (%)\", \"median |df0| (%)\", \"max |df0| (%)\"],\n",
    ")\n",
    "summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ea008e",
   "metadata": {},
   "source": [
    "We can now plot the error maps (on a log color scale, since the models differ by\n",
    "orders of magnitude). The star marks Tutorial 1's device.\n"
   ]
  },
  {
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      "text/plain": [
       "<Figure size 2117.1x846.84 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = sl.plot_heatmaps(\n",
    "    cc * 1e15, cc * 1e15,\n",
    "    {sl.MODEL_LABELS[m]: np.abs(f0_err[m]) for m in MODELS},\n",
    "    xlabel=sl.axis_label(\"Coupling\", r\"C_{c1}\", \"fF\"),\n",
    "    ylabel=sl.axis_label(\"Coupling\", r\"C_{c2}\", \"fF\"),\n",
    "    cbar_label=r\"$|\\delta f_0/f_0|$  (%)\",\n",
    "    norm=\"log\",\n",
    "    show=False,\n",
    ")\n",
    "for k, ax in enumerate(np.atleast_1d(axes)):\n",
    "    ax.plot(30, 70, marker=\"*\", ms=13, mfc=\"w\", mec=\"0.15\", mew=0.9, ls=\"none\")\n",
    "    if k == 0:\n",
    "        ax.annotate(\"Tut. 1\", (30, 70), xytext=(4, 5), textcoords=\"offset points\",\n",
    "                    color=\"w\", fontsize=9)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9cfd74",
   "metadata": {},
   "source": [
    "The **Optimized** method tracks the CPW to a few *thousandths* of a percent\n",
    "over most of the plane — never worse than $0.06\\%$, at the strongly asymmetric\n",
    "corners (15 fF against 150 fF) — while the other two, which do not inherently\n",
    "incorporate the loading, do not.\n",
    "\n",
    "The Foster and Analytical errors instead grow with the *total* coupling, reaching\n",
    "$\\approx 1.5\\%$ at the strongest symmetric point (150 fF on both ports).\n"
   ]
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    "## 2. Predicting the loaded frequency shift\n",
    "\n",
    "The point of a fitted `(L, C)` is to *stand in* for the resonator when\n",
    "something else is attached to it. So to test this we attach a load and ask whether each model\n",
    "reproduces how far the CPW resonance moves.\n",
    "\n",
    "For this part of the tutorial we'll choose just one device, strong, asymmetric regime ($C_{c1}=60$,\n",
    "$C_{c2}=35$ fF) and add a **100 fF shunt capacitor** on each side — the kind\n",
    "of capacitive loading a neighbouring element presents. The loaded topology (drawn\n",
    "below) probes the circuit through weak `cc_port` capacitors (1 fF by default,\n",
    "exposed as a parameter of the loaded builders), so the measured resonance\n",
    "belongs to the resonator + coupling + load, not to the 50 $\\Omega$\n",
    "terminations.\n",
    "\n",
    "**Measure bare and loaded the same way.** The bare reference is the *same*\n",
    "weak-probe topology with a negligible load, so the shift\n",
    "\n",
    "$$\\Delta f = f_0^{\\text{loaded}} - f_0^{\\text{bare}}, \\qquad\n",
    "  \\epsilon = \\frac{\\Delta f_{\\text{LC}} - \\Delta f_{\\text{CPW}}}{\\Delta f_{\\text{CPW}}}$$\n",
    "\n",
    "isolates the *load's* effect. (If you instead compared against the 50 $\\Omega$-\n",
    "terminated bare device of Part 1, the change of probe convention alone would\n",
    "move $f_0$ by more than the load does — always difference two measurements of\n",
    "the same circuit convention.) With a clean convention the physics reads off\n",
    "directly: a shunt-capacitor load adds capacitance, so it *pulls the resonance\n",
    "down*.\n",
    "\n",
    "\n"
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       "Schematic(meta={'title': 'Loaded CPW resonator, weak-probe convention', 'description': '', 'source': 'simpleLOMs.networks.cpw.cpw_resonator_loaded_network_2port', 'units': {'capacitance': 'F', 'inductance': 'H', 'length': 'm', 'impedance': 'ohm', 'frequency': 'Hz'}}, groups=[{'id': 'resonator', 'label': 'resonator'}], components=[Component(id='P1', type='port', label='Port 1', value='50.00 \\\\Omega', ports=['out'], group=None, orient='series', props={'Z0': 50.0}), Component(id='cc_port1', type='cap', label='C_{p1}', value='1.00 fF', ports=['a', 'b'], group=None, orient='series', props={'C': 1e-15}), Component(id='load1', type='lc', label='load 1', value='L = 1.00 kH\\nC = 100.00 fF', ports=['a', 'gnd'], group='load', orient='shunt', props={'L': 1000.0, 'C': 1e-13}), Component(id='Cc1', type='cap', label='C_{c1}', value='60.00 fF', ports=['a', 'b'], group=None, orient='series', props={'C': 6e-14}), Component(id='Ctog1', type='cap', label='C_{tog1}', value='10.00 fF', ports=['a', 'gnd'], group='resonator', orient='shunt', props={'C': 1e-14}), Component(id='TL', type='tline', label='CPW', value='\\\\ell = 7.00 mm', ports=['in', 'out'], group='resonator', orient='series', props={'length': 0.007, 'Z0': 46.426279489739365}), Component(id='Ctog2', type='cap', label='C_{tog2}', value='10.00 fF', ports=['a', 'gnd'], group='resonator', orient='shunt', props={'C': 1e-14}), Component(id='Cc2', type='cap', label='C_{c2}', value='35.00 fF', ports=['a', 'b'], group=None, orient='series', props={'C': 3.5e-14}), Component(id='load2', type='lc', label='load 2', value='L = 1.00 kH\\nC = 100.00 fF', ports=['a', 'gnd'], group='load', orient='shunt', props={'L': 1000.0, 'C': 1e-13}), Component(id='cc_port2', type='cap', label='C_{p2}', value='1.00 fF', ports=['a', 'b'], group=None, orient='series', props={'C': 1e-15}), Component(id='P2', type='port', label='Port 2', value='50.00 \\\\Omega', ports=['in'], group=None, orient='series', props={'Z0': 50.0})], nets=[Net(id='n1', ports=['P1.out', 'cc_port1.a']), Net(id='n2', ports=['cc_port1.b', 'load1.a']), Net(id='n3', ports=['load1.gnd', 'GND']), Net(id='n4', ports=['cc_port1.b', 'Cc1.a']), Net(id='n5', ports=['Cc1.b', 'Ctog1.a']), Net(id='n6', ports=['Ctog1.gnd', 'GND']), Net(id='n7', ports=['Cc1.b', 'TL.in']), Net(id='n8', ports=['TL.out', 'Ctog2.a']), Net(id='n9', ports=['Ctog2.gnd', 'GND']), Net(id='n10', ports=['TL.out', 'Cc2.a']), Net(id='n11', ports=['Cc2.b', 'load2.a']), Net(id='n12', ports=['load2.gnd', 'GND']), Net(id='n13', ports=['Cc2.b', 'cc_port2.a']), Net(id='n14', ports=['cc_port2.b', 'P2.in'])], annotations={'load': '100 fF shunt (inductor opened)', 'probe': 'cc_port = 1 fF'})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cc1, Cc2 = 60e-15, 35e-15          # strong, asymmetric design point\n",
    "Cload = 100e-15                    # shunt-capacitor load on each port side\n",
    "L_OPEN = 1.0e3                     # open the load inductor -> capacitor-only load\n",
    "C_NEG = 1e-21                      # negligible load capacitance -> \"bare\" reference\n",
    "\n",
    "from pathlib import Path\n",
    "figdir = Path(\"figures\")\n",
    "figdir.mkdir(exist_ok=True)\n",
    "sch = sl.cpw_loaded_schematic_2port(\n",
    "    d, Cc1, Cc2, Ctog, Ctog, L_OPEN, Cload, L_OPEN, Cload,\n",
    "    cpw_params=cpw, freq=coarse, Z0=Z0,\n",
    "    annotations={\"load\": \"100 fF shunt (inductor opened)\", \"probe\": \"cc_port = 1 fF\"},\n",
    "    title=\"Loaded CPW resonator, weak-probe convention\",\n",
    ")\n",
    "sch.save_svg(figdir / \"tut02_loaded_topology.svg\")\n",
    "sch.save_html(figdir / \"tut02_loaded_topology.html\")\n",
    "print(\"saved figures/tut02_loaded_topology.svg|.html\")\n",
    "sch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2e67cd",
   "metadata": {},
   "source": [
    "We first fit all three models on the bare, 50-ohm terminated device.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f24d43c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T14:25:36.081161Z",
     "iopub.status.busy": "2026-07-20T14:25:36.081093Z",
     "iopub.status.idle": "2026-07-20T14:25:52.976165Z",
     "shell.execute_reply": "2026-07-20T14:25:52.975812Z"
    }
   },
   "outputs": [],
   "source": [
    "def measure_f0(build):\n",
    "    \"\"\"Locate a resonance by its |S21| peak (wide grid), then circle-fit f0 on a\n",
    "    fine local grid. Robust for the sharp, weakly-probed resonances here.\"\"\"\n",
    "    wide = rf.Frequency(5.0e9, 9.5e9, 9_001, unit=\"Hz\")\n",
    "    f_approx = wide.f[np.argmax(np.abs(build(wide).s[:, 1, 0]))]\n",
    "    fine = rf.Frequency(f_approx - 60e6, f_approx + 60e6, 20_001, unit=\"Hz\")\n",
    "    f0, _ = sl.circle_fit_f0_kappa(build(fine), 0, 0)\n",
    "    return f0\n",
    "\n",
    "models = {m: sl.fit_lom(d, model=m, Cc1=Cc1, Cc2=Cc2, Ctog=Ctog, Z0=Z0,\n",
    "                        cpw_params=cpw, freq=coarse, return_model=True)\n",
    "          for m in MODELS}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9fefd2",
   "metadata": {},
   "source": [
    "Now we can determine the shift in frequency when comparing the bare and loaded CPWs. This acts as the ground truth for the comparsion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e37fd706",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T14:25:52.977497Z",
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     "shell.execute_reply": "2026-07-20T14:25:53.713994Z"
    }
   },
   "outputs": [],
   "source": [
    "b_cpw = lambda fr, C1, C2: sl.cpw_resonator_loaded_network_2port(\n",
    "    fr, d, Cc1, Cc2, Ctog, Ctog, L_OPEN, C1, L_OPEN, C2, cpw_params=cpw, Z0=Z0)\n",
    "f0_cpw_bare = measure_f0(lambda fr: b_cpw(fr, C_NEG, C_NEG))\n",
    "f0_cpw_load = measure_f0(lambda fr: b_cpw(fr, Cload, Cload))\n",
    "df_cpw = f0_cpw_load - f0_cpw_bare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bed32b2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T14:25:53.715719Z",
     "iopub.status.busy": "2026-07-20T14:25:53.715637Z",
     "iopub.status.idle": "2026-07-20T14:25:55.478758Z",
     "shell.execute_reply": "2026-07-20T14:25:55.478480Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 fF loads pull the CPW resonance -387.1 MHz (8.4354 -> 8.0484 GHz)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>f0_bare_GHz</th>\n",
       "      <th>f0_loaded_GHz</th>\n",
       "      <th>shift_MHz</th>\n",
       "      <th>loaded_err_MHz</th>\n",
       "      <th>shift_err_%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CPW (truth)</th>\n",
       "      <td>8.4354</td>\n",
       "      <td>8.0484</td>\n",
       "      <td>-387.0558</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimized</th>\n",
       "      <td>8.4469</td>\n",
       "      <td>8.0487</td>\n",
       "      <td>-398.2113</td>\n",
       "      <td>0.3463</td>\n",
       "      <td>2.8822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foster</th>\n",
       "      <td>8.4354</td>\n",
       "      <td>8.0583</td>\n",
       "      <td>-377.0917</td>\n",
       "      <td>9.9430</td>\n",
       "      <td>-2.5743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Analytical</th>\n",
       "      <td>8.4366</td>\n",
       "      <td>8.0624</td>\n",
       "      <td>-374.2112</td>\n",
       "      <td>14.0569</td>\n",
       "      <td>-3.3185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             f0_bare_GHz  f0_loaded_GHz  shift_MHz  loaded_err_MHz  \\\n",
       "model                                                                \n",
       "CPW (truth)       8.4354         8.0484  -387.0558          0.0000   \n",
       "Optimized         8.4469         8.0487  -398.2113          0.3463   \n",
       "Foster            8.4354         8.0583  -377.0917          9.9430   \n",
       "Analytical        8.4366         8.0624  -374.2112         14.0569   \n",
       "\n",
       "             shift_err_%  \n",
       "model                     \n",
       "CPW (truth)       0.0000  \n",
       "Optimized         2.8822  \n",
       "Foster           -2.5743  \n",
       "Analytical       -3.3185  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lc_builder(m, mod):\n",
    "    if m == \"optimized\":   \n",
    "        return lambda fr, C1, C2: sl.lc_resonator_loaded_network_2port(\n",
    "            mod.L, mod.C, Cc1, Cc2, L_OPEN, C1, L_OPEN, C2, freq=fr, Z0=Z0)\n",
    "    return lambda fr, C1, C2: sl.lc_resonator_loaded_network_with_grounds_2port(\n",
    "        mod.L, mod.C, Cc1, Cc2, Ctog, Ctog, L_OPEN, C1, L_OPEN, C2, freq=fr, Z0=Z0)\n",
    "\n",
    "rows = [{\"model\": \"CPW (truth)\", \"f0_bare_GHz\": f0_cpw_bare / 1e9,\n",
    "         \"f0_loaded_GHz\": f0_cpw_load / 1e9, \"shift_MHz\": df_cpw / 1e6,\n",
    "         \"loaded_err_MHz\": 0.0, \"shift_err_%\": 0.0}]\n",
    "for m in MODELS:\n",
    "    b = lc_builder(m, models[m])\n",
    "    fb = measure_f0(lambda fr: b(fr, C_NEG, C_NEG))\n",
    "    fl = measure_f0(lambda fr: b(fr, Cload, Cload))\n",
    "    df = fl - fb\n",
    "    rows.append({\"model\": sl.MODEL_LABELS[m], \"f0_bare_GHz\": fb / 1e9,\n",
    "                 \"f0_loaded_GHz\": fl / 1e9, \"shift_MHz\": df / 1e6,\n",
    "                 \"loaded_err_MHz\": (fl - f0_cpw_load) / 1e6,\n",
    "                 \"shift_err_%\": 100.0 * (df - df_cpw) / df_cpw})\n",
    "\n",
    "loaded = pd.DataFrame(rows).set_index(\"model\")\n",
    "print(f\"100 fF loads pull the CPW resonance {df_cpw/1e6:+.1f} MHz \"\n",
    "      f\"({f0_cpw_bare/1e9:.4f} -> {f0_cpw_load/1e9:.4f} GHz)\")\n",
    "loaded.round(4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a99ab03",
   "metadata": {},
   "source": [
    "Now we can compare the bare vs loaded CPW resonance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46dfe30c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T14:25:55.479880Z",
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     "shell.execute_reply": "2026-07-20T14:25:56.391426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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grLPOsutZpkyZAq+jkw2aq16N/RToh6Pf85JLLrGN9/Q3uuGGG+z7xlNRob+d/s4qfW/atGnQ/e7EgCoANLWeC85Vov/MM8+Yzp07F5jnXiXuOjGhEwYqyVfQX5iMPgAAQFERoANx+uKLL2wpLsKrUqVKsZuuxWPRokWB65E6tqeKglM1qHPUJV6BspeCZt3mSt+VDVeg6OXNkHsby2lMubrOqwxcQfxhhx0W8XmqJNAJgkR3y1fZuQLxUHo/BdP77ruvDYKVVVe2OVwJv+aq19jwaOPjFQB//PHHpmvXrrYxnjryP/DAAwWGEIRScK7GeJ9//nnQsACnXLly9lKv991339nr++23n63ECPd4nZD497//bYNzBemqblBTwuHDh0ddDwAAgEQgQAfipOCcxlGRKaBJNgW5LtMs1atXN+miKdFOPfXUoA7rajoWSkGzMrA6wRMrQNfv453+zZtN12NCA3RvNUEyplfT+4cLzr0nSC677DLz8MMP2581ZjvSGPt4KPuuTL0CdGWyNaZcwX0syniHC7a93DqKMuexHn/jjTeaxx57zJa6q7mdxtqXLElfVQAAkFwE6AAyRmgFg8YNJ9Mff/xhhgwZEnSbSs1///13O6Wa96SExiurhDsclV+7AD10PnRlqfVaLivtDQLbtGlju53/9ddfBcah6zbvWOpkNIhT2XosZ599diD4VdM4ZZzV6T0SBbya4m7mzJl2rLfGoHsb0M2ePTtwXePQYwXoyrAfccQRUR+zYsUK+1qi8e8acx6LMulqfKfMvE7C6IRMp06dYj4PAACgOAjQAWRUGb2XgrtkUndwZU6jUdn2lVdeabt+Ryrh9ma3FVQruFbgLQrOlS0OzZh7y9xHjRplJkyYYIN5NWkTb8CusekqDU+0aJ3ZHZWX60SJ/i801Zmy3+Gep3HlN910kx0zHq6bezj6O8WiIQShpf35eXlmt+e5P/7fyRE3TV/oSZdI/vzzz8D1JUuWEKADPlZ68xbT9angoSi/XHZJ2tYHAIqKAB1AxtBYbnXqdmXuysammjKrKkVXdlvB87nnnmuaN28e9Tkam64gWtl3Zd2VRR8wYEDY+c8jBegK4hXMu/nRvZl4BcThmrMVl7rkx6KTEmp65zLf3inwnKlTp9osd2GHiMTT80FzmIeTn/f/qxuWr1gR1Iwu1kmXcBjeAvhbCZNvym7ZUuA2AMg0DKgDkFGaNGkSuK5S82RS2bgCau+iDKxKptVwTPODxwrORcGzGpM53qDcXdeY6HBZcBeQR3pessafS2jX+Ei8Je2hQbWy5Sr/dwGuAupbb73V/v2UlVZJvErc3d93xIgRged6S98jiTWWXDZsLH5zR2/vAwAAgGQhgw4Usbwa6fn7KGB1pccq+84UCqLdHO4uuFZJuLqzi5tjPFSXLl0C2Xc97+abb7bN8jRfeDLHn4veM57/VwXZTujjP/jgA5u1lgYNGthx6nvuuWfE10rGTAmVPCcaNGe6m5ceAADAbwjQgTgdddRR6V4FX1P2M56MZ3H16dMnMLWZplxTgKsu6X7nDaIVXCvInjt3biAgDVfe7rLvCt6VcdbvqqBec5Dr0k0jlqz54BcvXhxzKjv9v2u6OceNrXfcSQm56qqrogbnodPoJcoenjL4lStXJvz1AQAAEoUSdwAZRZ3SvUGgd/osP1OJu8uQuyA71vjz0PsUzGs8t3f8effu3e24+GRQt/VY1N3cnWRQs7bQDulqtOdtKBdLaLf6ROjepUvgurq5ezP+AAAAfkKADiCjaMzxFVdcEfj5/ffft0thKUhz5eWpoCBawbQ3EHXBqBrfReuY7h2HPnbs2KAgNlnl7fLaa6/FfMx///vfoGZ4oVOseaeNU8l8NJMnT7Yl8InWvEkT07ZtW3tdnfBffPHFhL8HAABAIhCgA8g4Q4cOtWOznTPPPNN8/PHHcT9fWV9ltL/66iuTSt5mbgq0f/zxR3u9c+fOUed0V/DuphLTOnuD2GQ1iJMxY8bYadEi0ZRxTz75ZODnwYMHF3iMt4neRx99FPG1FLxfeOGFJlmGXn994Lqa1E2fPj3u51IWD2QuDcMBgExCgA4g42jc9bvvvmv22GMP+7M6q/fv39+cddZZNmiMdJCmwPbss8+2ZdgK0lPNG0yPGzfOrF27NmZ5uyh413zfLkBXFthl3pM5/l4l+fqbvvnmmwXu0/ofeeSRgTncNVZdJ0pCHXvssYHr6h3w0EMPBcbPO/PmzbPTsE2ZMqVABj5RzjjjDNu/QFSSr6qEZ599NvC3DKUeAa+//ro5+OCDzeWXX56UdQIAAAhFkzgAGUmZWXVxP/74422wrQZ1r776ql2aNm1qOnXqZMeqKxhUBlRjj1etWpXWzvy9evUKmsfdiRWgu8eoBNxr3333TVpAK8OGDbON3U4//XTz73//2zajU9M6/b29WXydQFDwHa4LvQJvrbvGzeskyXXXXWfnIVcFRLVq1cwff/wRaH6nLu9XXnmlrZBINFUgvPPOO+bwww+34/gVgF988cX2vVShoPfWYzQd3Jw5c+yJHvf/dMIJJyR8fQCkRokSJdK9CgBQKAToADKWAnFlch955BHbLG79+vX29oULF9olEmXQb7/9dpt1TyUFsipnDx1n7R1jHoke8+ijjwbdlszx56JgWVn+u+66ywbSWkLVr1/fBr46WRDJW2+9ZU+kKEMumnbNTb3mtGvXzlZFTJw40SRLrVq1bHO+a665xrzwwgs2AFeg/uWXX0bteRDtdwPgb5S4A8g0BOgAMpqC3n/961+2cdxnn31mvv76a5tpXr16tVm3bp3N6tasWdO0adPGZoAVlHvHr6eagmpvgK7mZaFTk4UTLohP5vhz5z//+Y855phjzHPPPWez4OrKrix6ixYtzMCBA81ll11mM+HR1K1b1wbGas6mYF0ZeI051xCF1q1bm1NOOcUMGjTIzveezADdBdzDhw83N9xwg22Cp2ngNN2dTkSoCkO/i6ozdBLn0EMPtdMrVq1aNanrBAAA4JTI59QiMtDMmTNNhw4dAj/rgD/WfM2Osma///57geydSo+RuHnQ1b2b0sLclc7tIT8vz+xavSbottJ71DElPB3lc1U69n87d+4M9FtwlQw6yYPclIztQSfbdq5ZY3o+EDzt5rjrrzH7HnqoHfIE/2HfAD9vCzOLEWsUF0crAAAAAAD4AAE6AAAAAAA+QE0vACDrlKycvO72APxnd5kyZlHvgwrcBgCZhgAdAJBVNNa8VOXK6V4NACmUV7asWXRIcme2AIBUoMQdAAAAGYuGpACyCQE6AAAAAAA+QIAOAAAAAIAPEKADAAAAAOADBOgAAAAAAPgAXdwBAFklPy/P7F63Lui2UjVr2u7uALJT6S1bzd4jXg66bdq556RtfQCgqAjQAQBZJ3/X7nSvAoAUKpGfZyqt+avAbQCQaUgnAAAAAADgAwToAAAAAAD4AAE6AAAAAAA+QIAOAAAAAIAPEKADAAAAAOADBOgAAAAAAPgAAToAAAAyVn5+frpXAQAShgAdAAAAAAAfIEAHAABAVipRokS6VwEACoUAHQAAABmLEncA2YQAHQAAAAAAHyBABwAAQFaixB1ApiFABwAENG3a1B7Qalm4cKHJFeecc07g93755ZeNX+Xq/w8AALmCAB1AVti4caN5++23zeDBg80+++xjGjRoYMqVK2eqVKliGjdubA477DBz0003mXHjxkV9ndtvvz0QAIVbKlSoYOrXr2/69OljbrvtNrNgwYKwr/P8888HnlOrVq24xkiuWrUq6L3atm0b1+8+bdq0oOfNnj3b5LqSFSsELQCyl/aveaVLm+XdugYtug0AMg17LgAZbcuWLebRRx81Dz74oPn7778L3L9jxw7zzz//mCVLlpjRo0eb++67z7Rq1coG4qeeemqhyx+3bdtmVqxYYZfvvvvO3H333ebqq68299xzjylbtmzgcQcddFDg+rp168zMmTNNhw4dor72999/H/SzAu01a9aYOnXqxP28unXrmjZt2phcVqJkSVOqatV0rwaAFNpdrpyZd+zRBW6nxB1ApiFAB5CxFi9ebI4//njz22+/Bd2ujHmnTp1sYLt7926zcuVKm2VWhlrmzp1rTj/9dBu0Dx06NOLrK1M+YMCAoNs2b95sZs2aZSZOnPi/rE1ennnooYdswP7aa68FDgZbt25tg2X3ngqiCxugu9tOOOGEuJ934IEHRn0sAGQburgDyCYE6AAyksbf9uzZ0wbfosD4tNNOMzfffLNp37592AO4X375xTzxxBPm9ddft4G1su/RtGzZ0jz55JNh75s+fbp9P2XG5Y033jD9+/c3J510UlAW/d133w0E0ZdeemlcgbbK8xXwax3jCdB/+OGHwPXevXtHfSwAAAD8izHoADKOytYVCLvgvHz58uaDDz6wgXe44NwF8N26dTP//e9/bTY9VjY7lo4dO5ovv/zSVKtWLXDbI488EvQYb7DsDaLDWb9+vZkxY4a9fuSRR9rXj5RV91I1gMvSh5bWA0Cuo8QdQKYhQAeQcYYNG2az4c4rr7xis9fxUnA+fvx4c/jhhxdrPZTpVvdvR2XvalYXLlhevny5mTdvXsTXUgCvjLkrU3el6irf37BhQ8TneQP4mjVrBgJ7AAAAZB4C9BynMmGV/KqEVuW8FStWtNnIhg0b2oDnrbfeMrt27Yr79ZQBvOiii8xee+1lu11rDLACjWeeeaZQrwNEsnXrVvP4448Hfh44cKA5+eSTC/06lSpVMr169Sr2+uy///6B6xrvrnHx3hMBCpqdaNnw0HHkBxxwgL2uoP3HH3+M63l6TqqzRYsWLbLd7Pfbbz875l6N8nSp4QdqxKdx/vGaPHmyuffee81xxx1nmjdvbipXrhx4Pf2db7nllqC/b7xGjRpl+vXrF+jsr/2bTs68+uqrRd4vacjEyJEjzdlnn22bDqqSQvvORo0a2X2nThoV5rW3b99u98X6v9d+U/tP7UfVK0HNCAFExhh0ANmEMeg57F//+pftQB3ui23ZsmV2+fDDD83DDz9s3nvvPdt4KxpNKzVkyBBbfuzteK3gQsuIESPMp59+amrXrp2U3we5QduiOps711xzTVrXp0aNGkE/ezPoCpYVcOlz5ILp8847L2qgveeeewZOcHnvO/bYY303/lz7j7vuust+zr1Wr15tlwkTJtju+grgb7zxxqiv1b17dzNp0qSw97nX0xR5DzzwgH3PaM398vPyzO716233/tMHDzaff/112P3bN998Y5577rlAn4B4qapBgfmvv/5a4L6lS5faRf/nOtmgoRft2rWL+npqOqhmhHPmzAm6ff78+XZ58803zcUXXxx0YgpAsFJbt5n2b70TdNvMU0+mxB1AxiFAz2FqQqXgXJlEHRweeuihNouuLJAOGHUwqANmLZpDesqUKTajFc5nn31mDyCV7VO2S5muHj162OmlFLjrIFXlv3qfMWPGmFKlSqX890V2+PbbbwPXddIoEVnw4gid2s07Jt2VuXsD9HDUGV6fL3Gl7eog36xZMzvPeqTnKRBUFUw6AnSdjHvqqacCP2vfcMghh5h69erZ3gDK+ipAVvCuxn0aJ6/p8CJxmXFluNVHoEWLFvZvqX2U9lUK9v/66y+zc+dOc8MNN9jHRgvSd2zeYvqecor5Yfz4wG1aN/1/VKlSxQ43cCcPtV9Sxj4e+r/QzAHuREyZMmVsbwPtO3Vd/x96Tf3eCriV+deJhUhz2qsCQfte/Y6Ofv8uXbrYwELbhSqTVIWkCicA4ZXM222qL1pU4DYAyDQE6DmsVq1a5v777zeXXHKJPWD12nfffW2HapVXvvPOO+aPP/6wmXRlwkLpgPnyyy+3wXnVqlXNTz/9ZDOAzlFHHWUuu+wy8/TTT9sDV5WVesftZgqtsw6mEV6TJk3Miy++mPT38WaMdRIo3bS9OzrxpBJnL2/QrGBbQbVKrL1+/vnnQDm0d5o0XddzVPqtjvOhAdrYsWMD1/XZ22effUwqaJ/gDc712XjsscfsOjgaN6/PvRr3ie5XcKwhCeHodpW2K8j3Vg94hw9o36ETAzqhceutt9pGgTqJEc6wJ54IBOcKdO+8806bxfeeHFSDPQ2PUD8Cd4IkGp140Hu64Pyss84y9913n6168NLJCO1XVQKvv8Mpp5xipk6dGvbE5Pnnnx8IznVCQlP16e/g9fnnn5tBgwbZfbBOAgAIRok7gGxCgJ7DFJxHo4NJBdUav6mydZUWhwvQdRCqMky56aabgoJzR2WpKtNUtlHXMzFAV3CuA3qkl/ckSaSO7amiMmmNNXaUSfUGqaKgWbe5oE4ZWJ348vJmyL2N5TSmXF3ndRJMQbwqWSI9T5UEqahM0Yk4b7m6AtaXXnqpQBmpfueXX37ZbNq0yXz00UeBjLfGZ5csWbD9ifY10eh3035Dwfupp55q/ybKKofbjykofuDJJwI/33rttebmm24yJULeV2PHv/76a7P33nsHZbAjUWWQSu3liiuusCcdwlEVkcrmjzjiCFvxoSn5tP9UoO6l9x49erS9rr+f9qU6QRHq6KOPtlUYOtmj3xtA/AjeAWQamsQhZpa9U6dO9vqff/4Z9jEK4J1Igbcyf66R1++//06giyJRkOttvFW9evW0rYvKjjUdmrfD+tVXXx02sPQ2kgtXru5u0+/jnf7Nm00P9zxvNUGqplf76quvbFZf1MBNQ2EijfHU7Wp85rK+2ocoKC2OE088MTDURmPIw9Gc9Fu2brXXG9avb264/IqIr6eGbHfccUfM91XfA2W3Xal8PCc4NUbfcZUEXi+88ELQ7xUuOPduC6pqAlAQ48wBZBMy6Iiru7BEys65DtOtW7e2B66RKPvz7LPPBsqClb0CCkPZWK9IPRESRUM7VFLtpVJznWRSTwVvZkYnoJRNjrTtf/HFF2HnQ1d1il7LZcG92eU2bdrYpooaex0aoOs29YrwvkeqewAcc8wxUT/zos7pOpHxySef2J81Nl0/x2rCppJwjefWSRm3Dwo9GFdmWhn90Iy8+lw4J/Xta08kRKOMfGiDy1A6GeDuVzm+enXEoiEY6vGhkvxwnfi93dlVLh+LGtPp5AOA+JFBB5BpCNARlco5XRAQrsmRmkC5aZQUTETjvd8bWBSHGkrpy1cZ+XhLP/V4d1DvTjroNr7EEy/Rf9PQgFwBe6Lfw/t6mrvcO9Y6HAWLKnfWWOTQ54fLhGvbVzbWzWag5meuC7pK2kOfr9tUpaLHKVB1waZ3/LkqVNQ3Ihl/i9DXVODsaCq1eN5TFQQuQNdY70jP0XABdT6Pt8JGn/n169cX6KQ/1dNdvUfXrsYono/yGdd2pcoFNw493O+tIQbeEwgaX18YGt6j/aUCdjc8wjsbgYL5WH9LPUbbm3tcUfZb3se76/o7JnP/p6oX9RDw/ozclYztwX4uSpQweeXKhd5h34uhIf7EvgF+3hby8vLsyfjQmWpSgQAdUWm8uPuQhJtrWg2vnNDGV6G8zbMKMzdyrBJ8BVGRGk+Fo0ZMmt9dB8puvLA+hFqiIYCP78RHMimQKl26dGCbVNCT6PeM9f+snbVK0VUxooy3hnW4DuCR1kXBs4JoZd/1+sqGayx2aKCt1wt9DQW3CtD1BaFmZm5+dG9GXXOQ62RTov8W4T4X3qBSXfSjvae7zztFozL/oc/R3+SCCy6wY9YLS0MMQjvne9exUePGJr90aZOn/9co66r9kzdAD11H7Wcc1/29sNauXRtogKdGco62jZo1a8b8/9P2r99VJyXi3W+F0uPdNu6eq9k2ktm/QJ9Xb/WL3l+fY+SmZGwPen5+hQpmS6uWQbeX0W1bttjPHvyHfQP8vC1s3brVdOzYMeIUsMnEpwARKWPnpkVS8K2uxKG8H6ZY5cYucyTKJGVil3Kk/++j93H9EBJViRGJxnV7S7qLSmOwlf10Jc3eAN2VvCtw66psbwjvNHJ6rAvQvQGiN0OfbN7PbrzTfnk/+6HDFNxYbG9wrhJ4lZ137tzZ7nv0Pt4ydTWidM0CwwWoxV3HcLy9BorKmxEoyjq69XQBOgAAyD4E6AhL2R01LdIBpUrHVHoa7iDSW/YRa5ynytG9Z6UyjTpVI7pkZ9BdwOoCdDd2OxMoiHYBuguuVc6lObKle/fuYT9Dmg/bZd8VoGumBI3LnjZtWsobxIWeiNM6xUNjsJ3QKR1F04c5//73v82//vWvqK8XLsgPXUcXUBdlHWMF8A8++KC56qqr4nrdaOvoxLuO8awnkItsiXuE+6h+A5BpCNBzpDvpiBEj4p7aTAe/xx57bKB8XWNr+/TpE/ax3kZJ0RosibfRU7h5jotCZWsKalSyHmk+5Ghj0N3fVtfDTf2EokvW31PboqYeE2VRVfbt7ZKeyM+brifq9zj44IPNf/7zH3tdwbUyqBpr7YJNBfDh3ksntlz2XcG8tl9durFaul9jwZPx9w73uVDXc+9QlXjed/HixYHrGnvvfY5eQ834REMHbr755qivqZMTGtoQax1dgL5k8WLTrVMnU1L/r1Fe1ztcJ9z/u6ZO857ALO7f2/t6CtD1O2nITjTaVryZ/KLst0L3e6Ly+mSWEroTvU6y3w/+loztwY5f3brVVJz7v32Js3PrVruPjPXZQnqwb4Cft4U1a9bYZrTpwKcABTLi/fr1M5MnT7Y/X3fddXbu4ki82bBYZevezE+ium+7oF8d4eOdE1s7AXXh9tJOgWlaiidcliIZf1P1QtB2qbHM8sgjjwSVgRdX6Don6ndQEK2TSTqRpeBaTce8JfrKgkd6L5d9V4D266+/BpW3K/OeqBNeocJ9LlR27qY304mCSOvs3R5clYCrCPA+xzv/uBpJxqrE0QwQ3tcOu4777GPmzZtnr0+cPNkMPPY42ywq0rpq36Vp86K9psb5P//88/a6/u+Ku12odF8nEtx4eQ0p0onRaPSYWL97YU9AuSEYyT4Q8o5x13u5qfeQmxK9PdhtOT/flAyZ8UG36T62N/9i3wC/bgslS5ZMS4M4+95peVcUig7ii7sMGDAgrsBVwY8rwx08eLBtEhdrCqVwGahwvI3hvA3jgMJQMKqu6c77779vl8LSCSNvZ+5kU7WJgmlH49Bdozd9ESmAj8SNO3dN5bwN4lI1vZrjrab57LPP7EwP0ai5mptiLvT54s0Ax1PqPXz48LiqFZx3P/woZgfnt99+u8BUbqE0Lt4FsdpuvEMMiso77/mrr74a8/GucgRA/ChxB5BpCNAzgLJKxV1CuxyHUsn3mWeeaT7++GP78ymnnBKYszwaZdBdsD179uyoj/XeH27KNiBequpQJtbxbrvxULZUGdGvvvrKpJJ3rLgCbZcJV1Y6WlWJgnd3Zlnr7O0omsrx53LEEUcEhpMoqI02FlsHxldeeWUgQFZzt8MOOyzoMXotl8nV/8v8+fOjBtJuurZoTj/9dFPx/6oKlixfZh548smow2Ruu+22mK+pk5FnnHFG4PfSvOUqt4+H9q/ezvKOToI677zzToG57kMrB5gDHSg8AnQAmYYAHdZFF11kx3HL8ccfb1577bW4xza67N6cOXPMypUrIz4udDopoKg0pvDdd981e+yxR6DpoLqiK2iK1NldB2kKbM8++2yz9957B5U0p4o3mFbZt5v6J1YXdgXv++yzTyBAd/0elNFN5Pj7eGi/4OZ8lzfffNNOkRY6xEXl+Oeff74ZOXJk4LZhw4YV2K9oTLpOlrhAVs0ptS/x0u2aj14nYnSiwtv7IhydkLzWM0/5HQ8MM/cPGxY0x6po7Pvhhx9us/yxSuvl7rvvNnvuuWdgLnRVREQ7yaOqIg3B0JR8OrkQSu/tsujaPrUNqyohlN6jb9++9u+Q7pI/INMQoAPINIxBh7nmmmvsNEdy6KGH2sCnMOMRdVCpg3TRVEk33nhjgceodFUZImnXrp0dMw4Uh+Ye15hcnVBSsK3gRWXCWpo2bWo6depkgz8FZTpxpLHb3rmnI3UUTyadmPLO416YadL0GNcbwju/eqzpwZJBQ2GU7VXQLNp/KABVsKnmZyp7Hz16dFDQrkz6wIEDw77enXfeaTPz+j+cOnWqnXdUfyv9H+s11L3ejVVXkPzcc88FplkLRycwrrnySvPN2LFm3KRJ9gBd3e8ff/xxOyRAJzw0Rl2vq+1DTfiU3Y+Voa5fv7758MMPzTHHHGN7IOhEgkrflV1XsK4x5aoW0H3aJhcsWBDzb/niiy/aCgltm2oUp3HoHTp0CIzV199DJwPcvlrDOaL97kAu0mclr1Qps6ZdcHWebiNAB5BpCNBz3O23324zPKJMnA4+vdOhxUPj23UgrdLUe++915x00kn2YNfr+uuvD3Re1nUgERSIKxOtbVhTdbn5oRcuXGiXSJRB17bv5iJPFQWGKmf3lqiHjjGPRI959NFHg25L9fhzryeffNLUq1fP3HXXXbbUXRnzjz76qMDjlO2+9dZbbXf2SHRiUMH+5Zdfbk9eKMgdM2aMXRxl3vU6CrQVoEei567fsMHkly5tXnn9dVsd5PpqKMh3lUKO9nvvvfeefd14dOvWzfzyyy+2OkAnIWTZsmVBlQKhdNKiZcuWYe9Tib9eR/tR181ewX1ohYeqFFS5UJR+C0Au2F2+vJl18okFbidAB5BpCNBz2BNPPGHuuOMOe10ZIJWfxsr4qFQztMRSP+u1lMnUmExlvnQgrYySgnJ1PnYHlQoyVKYKJDLo1bzZahyn8uCvv/7aZpqVxV23bp0tXdZ0HerFoEypgnLv+PVUU1DtDdDVj0GZ/ljCBfGpHn8eSp9zfZ6VQf/yyy/t/kMnSTRdmk7aqYRbgWzjxo1jvtbFF19s9x062aKAWmXnagiofZMay5133nn25EYsGu7gDshVIaGs+KeffmqDcFVRaJvQ31t/90GDBtlx5YUtG2/SpIntZK+TQ6o4UjWBmmBqf6cKCU3ppIC8a9eutjJATeuiVSVpBgplyXXiQZUI6tehqiOV0+uEgMaq628JILxosxkQoAPINCXy2XPlLB00eseFx0MH4MpahqNAfMiQIRHnQ1fArgPleIKRWGbOnGnLQB1lm4ozzZrK7tM932Kmc/PLh5tvGbknXdtDtD4YyvjnunTs/1TV4PotiE5gMJY+dyVje1DfnEizQGi4k+tzAX9h3wA/bwszixFrFBdN4pAwKsFU5lKXyp6ptFUfMGX+NDWSuhAnIjgHAACIB3koAJmGlGEO847vTBSdaYo2PhQAACBVCNABZBoy6AAAAMhKBOgAMg0ZdABAVtFI98ohtwXP0g4g24LwUtu2mVYffRJ0+9y+xxGgA8g4BOgAgKwL0Mvaf723cZAOZLOSu3ebOr/PCrpt3rFHE6ADyDiUuAMAACArEaADyDQE6AAAAMhYBOEAsgkBOgAAALJSXl5eulcBAAqFAB0AAAAAAB8gQAcAAEBWlriTQQeQaQjQAQAAAADwAQJ0AAAAZCUayAHINAToyDklSgTPjyy7d+9Oy7oAQCqFK/cNt08EsiUIJ0AHkGkI0JFzSpYsWeCAdPPmzWlbHwBIla1bt4bdJwKZjAAdQDbhWxk5R8F55cqVg25bs2aN2bVrV9rWCQCSTYGK9nVe2heSQUemI0AHkE1Kp3sFgHSoWbOm2bRpU+Dn7du3mz/++MPUqFHDVKlSxZQpU4asUiHpIMhbPhuuUgG5I13bg4ar6HB8lwl+L92aqyfh9P+gzLmC8y1btgTdV61atbStF5DI/U2kvQsBOoBMQ4COnFS1alVTunTpoAP2nTt3mtWrV9sFReM9ECI4Rzq2B9dPIvRTrFMFa9auTck6ZAr9nxCgIxuQQQeQTUgRImcPTJs1a2aDdADIxX1gkyZN2AciKzAPOoBswjczclaFChVMy5YtzYIFC8y2bdvSvToZjxJ3+GF7UBm3zjxXCSl43WTyzR716iX9/TMpOFclEZAN+xq7vylZyqxv0iToPt1GgA4g0xCgI6dprLmCdHVx15j0jRs32vHoADKXDsc32JHoCG0Ip5J2LWTOkW3Z890Vypvfzj2rwP0E6AAyDd/QyHmuq7uWPffcM5D5Y9xa4WgM/7p164Ia8ekECHJTuraHk08+OeJ9v/32W042f9Q+jooWZKtY39UE6AAyDQE6EEIHsaVKlUr3amTkQZL376YMHVm63JWu7cE7O0MorQ+fbSC3AnTXOBIAMkXupRIAADmJqhgg+8TKkJNBB5BpCNABAACQkQjQAWQbAnQAQE4ggw5kHwJ0ANmGAaIAgKxSId+YC0KmWXve/G8qJgDZxQXgpbZvN82++TbovgWH9TF5lSqlac0AoGgI0AEAWaWsMaZfSID+CtOuAVnJNYEruWuXqT/pl6D7Fh18EBl0ABmHEncAQE4ggw5kH0rcAWQbAnQAQE4gQAeyT6xp1AjQAWQaAnQAQE4gQAeyDxl0ANmGAB0AAAAZiQAdQLYhQAcAAEBWlrircobqGQCZhAAdAJATOEgHsk88GXI++wAyCQE6ACAncJAO5GaAHivLDgB+QoAOAACAjBRP8M04dACZhAAdAJATyKAD2See4JsAHUAmIUAHAOQEAnQg+xCgA8g2BOgAgJxAgA5kH0rcAWQbAnQAAABkJJrEAcg2BOgAgJxABh3IPvEE37t27UrJugBAIpROyKsAAOATyqctNPkFbiNAB7I3g55foqTZXKd20H26TQjQAWQSAnQAQFbZVMKY80MCdCFAB7I3g76rYgUz+bJLoj4GADIBJe4AAADISPFkxwnQAWQSAnQAQE4ggw5kH8agA8g2BOgAAADISPEE3wToADIJAToAICeQQQeyDyXuALJN6Uw7uPrrr7/M5s2b7c+VKlUytWvXNiVKlEj3qgEAACDFCNABZBtfB+iTJ082X3/9tZk4caKZNm2aWbp0aYEdcenSpU3Dhg3N3nvvbbp3724OP/xws++++6ZtnQEA6VU+35iTTfCJ23dMPhl0IAu54Lvkjh2m0U/jgu5b0qunyStblhJ3ABnFdwH677//bkaMGGHeeOMNs3LlyqD7wh1c7dy50yxcuNAuH374obnllltMvXr1zOmnn27OOecc0759+xSuPQAg3coZY84OCdA/JEAHspILvkvt3GmajP0+6L7l3bsSoAPIOL4Zg/7tt9+aI4880nTs2NE8/PDDNjjXwZR3iST0cXquXqNTp072NfXaAAAAyC6UuAPINmnPoE+ZMsVcf/31ZsyYMfZnF4hrXHmHDh3MgQceaPbZZx/Ttm1b06BBAzvmvGLFivZxW7duNWvWrDHLli0zs2bNsmXwP/zwg5k5c2bgdb755hu7HHzwweaBBx4wXbp0SevvCwBIDzLoQPahizuAbJPWAP2iiy4yL730ksnLy7MHTgrKDz30UHPyySeb/v37mzp16kR9fpUqVezSvHlzG8g7CtpHjRpl3n33XTN69Gj72joB0KNHD3P++eebZ555JgW/HQDATwjQgewTT3acDDqATJLWEvfnn3/e7jQVZA8dOtTMmzfPNoW74IILYgbn0ei5eo2vvvrK/PnnnzZDr/fQe+k9AQC5hwAdyD5k0AFkm7QG6Aqa77jjDrN48WJz3333mWbNmiX8PZo2bWruv/9++x633367qVy5csLfAwAAAKlHgA4g26S1xF3ZbY0pT4WqVaua2267zVx66aUpeT8AgL+QQQeyTzzBt2b8AYBMkdYMeqqC83S/JwAg/QjQgeyioYvqYxQLATqATOKbadYAAEgmAnQgu8QbeBOgA8gkBOgAAADIODt27Ejo4wDADwjQEdbnn39up71zixrsxWPGjBl2+ry99trLVKhQwXbU1xR4mtqOJi0AACBRyKADyEZpbRJXXKtXrzZjx441ixYtMiVLljQNGjQwffr0KdYUbTBm8+bN5pJLLin08zSF3ZAhQ4LOVG/bts38+OOPdhkxYoT59NNP6QMAIC0ocQdyN0DX518JBwDwu9J+a/YxZswY8/vvv9sgsV69ejb7qmys14YNG8w111xjXnvttQJZWe18Bw0aZB566CECwSL617/+ZU967LHHHvYkSDw+++wzc/HFF9tmLXXr1jW33HKL6dGjh1m3bp0N3D/44AMzceJEM2DAAPt/XKpUqaT/HgDgRYAOZJd4S9f12dcxZunSvjrsBYCwfLOncgHesmXLCtw3cOBA8+qrr5ry5cub9evXm4MOOsjMnDmzwNlQ/axFgfukSZNsIKggE/GbPHmyefzxx025cuXM3XffbS644IK4zkxffvnlNjjXdHY//fRT0EmVo446ylx22WXm6aeftpl0/V+ec845Sf5NAABANitM6bqCeQJ0AJnAF2PQP/74Y9O/f38bnLsg27so+3rmmWfax15xxRV2nLPjfZz3tjlz5tiAH/HT2WUF5Lq8+eabTYsWLeJ63siRI838+fPt9ZtuuqlAxYM88MADpkaNGoHrAJAs+jZYb/KDFt1GBh3I3gA935QwOypWDFp0W7jHAoCfpT1A/+eff8yFF14YVKrerFkzs//++5vmzZvbn12Q/tFHH5k33njDZs3r169vnnvuOVuKrbOiyqx/++235rjjjgs858MPPzRTp05N2++WaR555BH792rVqpW54YYb4n7eqFGjAtcjZcYrVqxoTj75ZHtdQxjmzp2bgDUGgII2ljDmhBL5QYtuI0AHsrfEfVelimb80GuDFt3mEKADyBRpD9Dfeusts2rVKht0N2zY0IwbN878+eefthR63rx5dtxy06ZN7WMvvfRSW0atJnC6ffDgwaZRo0a2ZEml1QcffLAN4r1l2QroEdvChQvNv//9b3t9+PDhtsQ9Xvq/ktatW9u+AZH07t07cF1l8ACQSgToQG6XuANAJkj7YBx19fYG62os5tW1a1fz9ttv29uXL19uA/mhQ4eaPffcM+JrDhs2zI5DVwfx8ePHJ3X9s4W6tm/ZssU22FMn/MJUQCxZssReb9OmTdTHeu+fNWuWSQSdSNBBtzLyhT07rhM99ChIHFXBaHiE92fkrnRtD9FOLmp9yKKlHvsGJGt72L59e9yd2XVMyOffX9g3wM/bQl5enu1/pn1HzgXo06ZNs5ft2rUzPXv2DPuYbt26mQ4dOgTGnh999NFRX7NatWpmv/32M999953NwiM6VRl88cUXpnr16ubhhx8u1HOXLl0auK4KiGhU7eC4oL64atWqZU/cqJFgYV177bXmuuuuS8h64H87002bNgV+1okTGvLkrnRtDxqiE+2E4tq1a5O+DgjGvgHJ2h6UWChTpkxcj9VQSA23g3+wb4Cft4WtW7eajh072sbjOVfi/tdff9mzn3vvvXfUx3Xq1Clw3ZW8R+MeoynZEJmmQbv66qvt9XvvvbfQGWXvh6ly5cpRH1upUqWgA2UASCVK3IHsUpiydUrcAWSKtAfomu9clL2NlRV3KlSoEPN13VlSlT8hMmWQNde5hhCoWV9hecs+ypYtG3fpqc5KAUAqEaAD2aUwJeuUtwPIFGmvI3FzmZcsGf1cQaz7s1m846uiGTFiRIEO65onXreXKlXKPPPMM0X6G2tsRrxnp70nS+I5yRIPlavqxID6F6j7f2HHoKtEHokrT/JuqzVr1kx7eRJyb3tQP4qy+cYcHnL71/9XxcNnPvXYNyBZ24OOO1zgXXLnTlNn2m9B96/Zu5PJ85TA8/n3F/YN8PO2sGbNGjN9+vS0vDefghylYPmiiy4KzC2/zz77FOl1qlSpEnfZuquWiKccPl4u6Ne40/bt2yfkNVF0OtnjaMca79hAZKd0bA/aJ5TPN+aykAKx0SbPnoRkm0wP9g1IxvagKj5XGVNy+3bT6uP/33hY1rZpbXb/30G+gnm2O/9h3wC/bgslS5ZMS4M4IUDPAInoeB7a9V7zyivTpI1fDfqUgQ6l+codNehzj1E5vMtWN2jQIGzDuHC8jeG8DeMAAAAKQ4F5YYYxputAGwAKiwA9A8Savqwo3JeaSsO888ZH8v7779tFVBbvAnRl0BVsK/iePXt21Nfw3t+2bdti/gYAUPgpUwBkT0lsYT7T9CQCkCl8E6BPnDjR/Oc//4l6vxPtceEej+Q64IADzJtvvmnmzJljVq5caerVqxf2cWPHjg1c79WrVwrXEAD+Nw86gOxQ2ICbAB1ApvBNgK455mLNM+eaB9xxxx0pWqvspYZxoU3jQqmJ3CGHHGKv//vf/za333572Mf179/fBujy8ssvmxtvvDHsXKXvvPOOva6S+mhzFQNAMpBBB7JHYUvWKXEHkClK+mksUaIXpMaAAQNM8+bNA3Op//nnnwUec/3115u///47cB0AUo0AHcgehc2Ia0gfVTQAMkHaM+gHHXRQQqYRQ/qo0dwTTzxhjj/+eLNx40Zbvn7rrbea7t2726D8+eefD4xfVzn8mWeeme5VBpCDCNCB7LF169YiBfUVK1ZMyvoAQNYE6CqjRuY75phj7FzqQ4YMMatWrTKXX355gccoYB85cmTQNAoAkCpkz4DsoaFzRXkOAToAv/NNiTsyn7rBT5482V6q5L18+fKmVq1aNms+fPhw89NPP5natWunezUB5Cgy6ED2KGqADgB+l/YMOvzr4IMPLvRY/g4dOpjnnnsuaesEAJHE2l8RoAO5XeJOgA4gE5BBBwDkRAk7Je5A9iCDDiBbEaADALJCrACcDDqQPQjQAWQrAnQAQFaIFYCTQQeyByXuALJVWsegf//99ymbyg0AkN1iBeCF7akBwL8n47Zt21bo5xGgA8gEpdPdhCzZc6Dr9Xft2pXU9wAApB9j0IHcUNRAmwAdQCZIexd3MhoAgERwAfiGEsYcagqWuxOgA9nhn3/+KXDbzkqVzPe3/8t07drVrFixwixbtixsgK7jzmQnhwAgYwN0lZ7Hs5McN26c2blzZyCYr1mzpmncuLGpVKmS2bx5s1myZIlZu3atvU+vV7ZsWbPffvslff0BAJkzBp0TwkD2BuhO5cqV7fFhpH2Exq5XrFgxiWsHABkcoI8ZMybmDvjcc881O3bssDvTq666ypx99tmmZcuWBR47b94888orr5hHH33UniGtXbu2efnllyPupAEA2YUSdyA3bNq0KWqAriXacwnQAfhZ2kvcoznnnHPMyJEjTdOmTc2XX34ZNjB3WrRoYe68804bwB9++OHmgw8+sAdjugQAZD+mWQNyQ6wMepUqVaIG6HXr1k3SmgFAFk+z9uGHHwaC63feeSdqcB4aqL/77ruB11CADwDIfkyzBuR2gK5hjrEC9GjBPQD4gW8D9BEjRtjL7t2724YfhaHH63miMncAQPZjmjUgt0vcVbpesmTJqAH6xo0bk7hmAJDFJe7Tpk2zZ0Lbt29fpOfreRMmTLCvAwDInQC9TL4xPUPuG0cGHcgKOtEWLgteYtcuU2/OXLPx40/sY+r8Psv81aqlyS8dfKhLBh2A3/k2QF+5cqW93L59e5Ge7563atWqhK4XAMDfJe5q//TvkAKxgSaPAB3IAtu2bTO7du0qcHvp7dtNgxdeMstfeMn+3FYn5q6/xuwMCdCjNZgDAD/wbYl79erV7RlQTbFWFO551apVS/CaAQD8iCZxQPbbsGFDsZ6vAJ3hLgD8zLcBeseOHe3lggUL7PRphfHf//7XzJ8/35bId+rUKUlrCADwEwJ0IPsVN0DXfoAsOgA/822APmjQoMD1iy++2Lz22mtxPe+NN96wjw/3OgCA7BUrACdABzLf+vXrffEaAJBzAfpZZ51lO7GrDEnjyTW/ec+ePc3TTz9tpk6datauXWu2bt1qL/Xz8OHDTa9evcyZZ55pxycpe65u7nodAED2i5VBZww6kPmKm0EXAnQAfubbJnEKsDWP+SGHHGJmz55tb5s4caJd4tGqVSv7fL0OACD7UeIO5G6AXq5c+bhfgwAdgJ/5NoMudevWtc3ezj33XPuzsunxLOecc459Xr169dL9KwAAUoQAHchu+gxHCtCrVata4LYSJcMf5hKgA/AzXwforgv7iy++aLPoQ4cONd26dTNly5YNeox+1u26f86cOeall16yXeABALkjVmdmStyBzKY5zCOdaKtStWCAXjXMbfL3338nfN0AIOtL3EO1bNnS3HfffYGfdQZVO+rKlSszlRoAgAw6kOXUdyiSalULHgtWq1rV/BXmOeptpD5GFSpUSPg6AkDWZ9AjUVDeoEEDgnMAgEWTOCB3A/QaNQpWTkY7Rly3bl3C1gsAEiljA3QAAAqTId+1a1fK1gVAqgP0mgVuq169RsTH//XXXwlbLwBIJAJ0AEBWiJUh37lzZ8rWBUDiRcp6q1S9QsWC5eo1axUM2h0CdAB+ldYAffny5Sl/zxUrVqT8PQEAyUeADmSvHTt2mE2bNoW9r2bN8IF4lSpVTJkyZcLeR4AOwK/SGqBrrvJbb701Jd009R4333yzfU8AQPaJVcJOiTuQneXttWrVCnt7iRIlIt6nZsOctAPgR2kN0Lds2WLuvfde06RJE3PdddeZP//8M+Hvode85ppr7Hvcf//99j0BANmZYSvO/QD8a82aNRHvixSES+3atYsU9ANATk6zdsUVV5inn37aTpf2yCOPmEcffdQcdNBB5pRTTjH9+/c3devWLdLrrlq1yowaNcq8/fbbZuzYsYH5cUuXLm2GDBmS4N8CAOAHLhu20Rgz0AQ3jNNtZNCBzKVju2hBeKlq1UyL6b8G3V6qRo2oAbqC/nr16iV0PQEgowN0BeSDBw82Q4cONV988YUNohVQa7n00ktNmzZtzIEHHmg6depkrzds2NCeJa1YsaJ9rOaw1BiipUuXmjlz5php06aZH3/80cyePTvwHnqcHH300WbYsGGmffv2afyNAQDJ4gLw/BLGbIhyP4DMs3r16rC3ly1b1lSvXt2Ws5cOk0mPFqCvXLnSdOzYMaHrCQAZHaBLhw4dzGeffWZ++uknc99999nrLqhWoO0NtuPlnq+d9XHHHWduuukm07Nnz4SvOwDAPyhxB7KTKi03b94c9r499tjDHu9FouBdjeLCjTdXVl7HjNGeDwA5O81ar169zMcff2wz4TfeeKNp3Lix3WkWZdFzFZTrtT766COCcwDIAbEaPpFBB7Ire+4C9GhKliwZccik+hJF6gwPADmbQQ/VokULc88999hl+vTp5uuvvzYTJkwwv/32m1m0aJHZtm1b0OPLly9vmjZtasvgu3fvbg4//HDKlQAgB8UK0OnYDGTf+PN4+hVpnLmGQ0Yqc69atWqx1g8AsjpA91KgHRpsa1oMV+ZUqVIlU61atTStHQDATwjQgey0fPnyImfQJVojOAXoTMELwE98HaCHo4CcoBwAECkAL51vTGg70JkE6EBGUuVkpOnQatSoYcqVK2ev5+/YYbb8Mjno/opd9zUlypYNjFN3PYq8VqxYkaQ1B4AcCdABAAjHBeCVjDEPh7RY0bRrjEEHsit7Xr9+/cD13Zs2mSUnnhx0v6ZdU2d3TbOrbu7h5lJXZabGoVepUiXBaw4AGd4kDgCA4qDEHcitAL1BgwZxv060x0Yanw4A6UCADgDICrGmUSNABzLPsmXLwt6ukvU999wz7tdp1KhRxPuWLFlSpHUDgGQgQAcAZIVYJeyUuAOZZePGjbYEPRyVrLvx5/HQOHTNhx4pS5+Xl1fk9QSARCJABwBkhVgZ8lgZdgD+snjx4oSUt0upUqWCxqyH7huizbUOAKlEgA4AyAqxAnAy6EBmWbRoUcT7GjduXOjXa9iwYcT7FixYUOjXA4BkIEAHAGSFWAE4Y9CBzDrhFqlBnErb45n/vDDj0BcuXBh2GjYASDUCdABAVqBJHJA91LgtUsDcpEkTU7Jk4Q9hq1atamrVqhX2Pk21Fmm+dQBIJQJ0AEBWiBWAb9++PWXrAqB45s+fH/E+BehF1axZs4j3UeYOwA8I0AEAOVHivm3btpStC4DiVcNEahCnzHm0seSxNG3aNOpJAcrcAaQbAToAICts3bo16v1k0IHMoPHgu3fvDnufgvNI06XFo0aNGqZatWph79OUbnRzB5BuBOgAgKwQK0OurFykg34A/jFv3ryI97Vo0aJYr12iRAnTvHnziPfPnTu3WK8PAMVFgA4AyArxlLCTRQf8bcuWLWbZsmVh7ytdunSxxp87LVu2jHjfn3/+yZSMANKKAB0AkBMl7sI4dMDflMGONA5c48eLU97uVK9ePeI0bdHGvwNAKhCgAwCyQjzBdzxBPID0UGA+a9aspJW3e7Vq1SrifdHWAQCSrbRJo1KlSpl001gkSpkAIHsC9E3GmPNMXtB9m0IeA8B/VNqu+cjDqVixYtTu7aWqVTPNvvumwG2RaBz6zz//bPLy8sKux99//20bygFATgXoTGUBAEgUF3znlTBmUYzHAPCf33//PeJ9rVu3tlOsRVKidGlTrnXruN+rfPnytmQ+0nzrM2fONAcccEDcrwcAWRGgH3TQQTaDDQBAcU/4xhN8E6AD/qTM+aJFkU6tGdOmTZuEv2eHDh0iBugaC9+9e3dTtmzZhL8vAPg2QB8zZkw63x4AkCXi7c5OgA740/Tp0yNWVjZq1MhUqVIl4e9Zt25dU6tWLbN27doC92n4ozL6++yzT8LfFwCioUkcACDjxRt4E6AD/jzBNnv27Ij3t23bNinvqyrO9u3bRz1pQJ8iAKlGgA4AyHgE6EDmUqY6UiBcrVo107hx46S9tzrDlytXLuKsD9FOHABAMhCgAwAynnf6tJL5xjQJWXRbYUrhAaSGAvMZM2ZEvL9jx45Rm8M5+bt2me1z5gQtui2W0qVL27HokUybNs3s3r075usAQFaMQQcAIBG8mXGNVH0p5PzzQJNnNpBBB3xHc457T7CFdlqPNl+51+4NG8yCQw4Luq3F9F9N6Vq1Yj5XZe6//fab2blzZ4H7Nm/ebObMmWPatWsX13oAQHGRQUfQl9BTTz1lDj30UNOgQQNb8qUGKl26dDGXX365+eqrr2K+hs6CX3TRRWavvfYyFSpUMHXq1DEHHnigeeaZZxjHBSBptmzZktDHAUg+BcQa5x0tcFaGO9l0IiBaAD558mSzY8eOpK8HAAgZdFjfffedOffccwtMcbJ69Wq7TJ061fzwww/miCOOiPgazz//vBkyZEjQl5iyVT/++KNdRowYYT799FNTu3btpP4uAHLzBGM8/vnnn6SvC4D46JgjUuBbpkyZqA3cEk2l9EoyhCtnV4ZfGfauXbumbH0A5K6cy6BfeeWV5vzzz0/3avjKN998Y4455hj7RVm9enVz4403mi+++MJMmTLFBtYKvPv162cz4pF89tln5uKLL7ZftMq6P/7442bChAnm888/NwMHDrSPmThxohkwYABjuQAkXLyBd7yBPIDkUj+IhQsXRry/U6dONrOdKhUrVox6QkABOhU4AFIh5zLob7zxhlm3bp158cUX070qvrBmzRpz6qmn2ky35vpUYK4A26tXr15m8ODBEc9yq0RNJfB5eXmmatWq5qeffrIl7s5RRx1lLrvsMvP000/bgP/VV18155xzTtJ/NwC5I94AfdOmTUlfFwCx/fHHHxGHvmmInTLaqabjIHVtD3e8o3VVouHggw9O+XoByC05l0FHsJtuusmsXbvWnjkeNWpUgeDcq2zZsmFvHzlypJk/f37g9bzBufPAAw+YGjVqBK4DQCLFmxkngw6k34YNG8zSpUsj3r/33ntHPOZIJmXsFaRHMnfuXLNixYqUrhOA3EOAnsP+/vtvW1EgZ5xxhmnSpEmRXkeBvRMpM64TACeffHJgvlN9yQFAosSbGSeDDqRftLnFK1WqlNKx56E05ZrWIRJVAqpiEACSJSNL3Js3b16soBT/88knnwSmNunbt2/gdo2xWr58ualcubLNqJcoUSLq6+jLSlq3bm3q1asX8XG9e/c2zz77rL2uMvh4p04BgFhoEgdkTml7tGOx7t272wZx6aKu8VoHNc8NR+uu8ejRMu0AkHMBupqKKGjMz88v0vNjBZy5Yvz48YHrGus1adIkc8stt5jRo0cHzg5rmjRlvv/1r3+FLX/Xwe6SJUvs9TZt2kR9P+/9mvc0ETROTduBMvLh5i+NRr/bHnvskZD1wP/G53kbADKtXm5L9fagE4vaH0jZfGPyQgrEypo8U67E/xpTFXZfgeJh3wDv51TjuCMdk+k7uWnTpkX6jO7etcvk/d8+wLut5RfhtbQOSjisWrUq7P1qolu/fv3A0D0UDfsG+HlbyMvLs8Ne1Kcr1TIyQFfpkXbyjz76qJ2vuzDOO+88Mij/R6Xmjs4UqxFc6AdCTeQ0N/r7779vG8hpXJiXdwxZw4YNo75fo0aNAtddUF9ctWrVstl+1ym+MK699lpz3XXXJWQ98L+dqbd8WCdOUjF/Lfwp1duDMm6uKqdivjFbTPCJ2OYm32wpYUzNmjVt3w2kDvsGOJqyVQG59//fm3BRJV5RP5+7N240W1q1DLpt7YYNplQRkzmaF13Z8kjJIFUP7rfffqZkSUaLFhX7Bvh5W9i6dWsggZlqGfkpUFnRzz//bIPzE044oVDP1VRg+B91s/f+XfQledddd5mzzjrLZsvnzZtnG7q9/PLLZuXKlaZ///5m2rRptlO74/0wqSQ+Gu+YLk6SAEikeM9wu2E9AFJLzdWUkY5UxaiT/Jrq1S90TNOsWbNAE9xQGzdutPe1aNEi5esGILtl5Gm/rl272svJkyene1WyZsymDm419ZxK3JXpVvdUnT0eMWKEufDCCwNDC4YPHx7xoDhWx1VXfiocJANIR4CejlI1INfpO99btRfu+EDZc7/RrDTRGsb9+eef9DYCkHClMzVAV+nDL7/8UujnFnXcejolYsy8Au3QDusaV+F06tTJnHnmmWGfe88995hXXnnFjt18++23zQ033BD2NSLNk+7o+U6FChVMIqgUTicG3nrrLXumu7Bj0FUij8SVJ3m3VZUSp7s8CbmzPSxYsCAwO0TVfGMqhpx/nm/yzMb/Wx1l6UqVKpW0dUEw9g25Tcddn3/+uR2aKKE9hLR99OrVK2qT2XjsLlHCbJj7R9BttapVM6Vq1izW6+6///52/SMdP2o8uhrteo+HEB/2DfDztrBmzRozffr0tLx3Rn4KDjvsMPPII48ElVrH69dffw1qQpDLqlSpErh+xBFHRHycglidFFHndZW4KxB32XLva8QqW/dm7GOVw8fLBf0ae5rOaVnwP96gRzvWdHbiRW5tD/oidfuDHfkqDwsO0HeYPLO9xP/PovuplDYXsG/IXQpgIzVbE1XtJaJMvETp0qakJxHgtrXSxdzWNJxS41B1/BPp2OaHH34wRx99NE2Ii4B9A/y6LZQsWTJtVXcZGaBrfPSVV15ZpOfGamTmR4noeL7nnnuG/VJ0ndy9DdzCcfero6HGrrsz3d4mfd6GceF4G8PFej8AKIz169fH/dgNGzYQoAMpoOOCaMMRdbK/Z8+exu+UpFi8eHHEcnb3e7ohmACQcwF6rok1fVlRKeP87rvv2uuxqgq893tLTpRBV7Ct4Hv27NlRX8N7f9u2bYux5gDw/+nEoYJuR7U615j/TRXpvc0bzDdp0iSFawjkHjWR1bSt0YYWKjOdqCFvpXQ88t47BW5LyGuXKmUOPfRQM3LkyIjHS6oU0LRrGrcOADnXJA6JcdBBBwWuR+pS6m2EIhpjpXEhXgcccIC9nDNnju32HsnYsWMD1zXeDAASFQgoSHd2lTBmWsii2xyaOgHJH0/61VdfBfWeCdW4cWPbCyZRSpQtayrt3zNo0W2JomOfAw88MOpjxowZY1avXp2w9wSQmwjQczxAd1+OH3/8ccSzwmq+pLH7LrAOnfNT0685mpItHDWHeeed/53ZVnd4N18xAKSyvF0I0IHkUcZcJ+SjzWeuPjR+7Noei45doq23jqO+/PJLppIFkNsBurrrqUz7008/DZrXOxIFkP/5z39Ssm5+p5Kt6667zl5ftGiRufPOO8OeBb/00ksD2alw88gPGDDANG/e3F6/9957A9l2r+uvvz5wUKzrAJAoBOiAf0yaNCnscYB3mNw+++yTsTMpKFERbQYYTSmnY1KmkwWQcwG6GnJoJ6md/KmnnmqnuFAjtMsvvzzqmUvN9X3HHXekdF397IorrjBdunSx1/V3Oe2008wXX3xhx1LpxIey7PpZjjnmGHPCCScUeA11WXziiSdsZn3jxo32/+XJJ580EydOtGeSTzzxRPP0008HyuEjTecGAEVBgA74g+Y6dxV3kei4IlEzuaSDTjAceeSRUcfOqyeGpmaLNf0sAGRNgK6zkppqTR3IVUql6da0o9y5c6cNBBVw/vFH8FyYCE9jyj/55BOz77772p81n7imCtHPJ598shk3blwgONd9kaYQ0f3PPPOM7ciq6VR0oqRHjx7mqKOOMu+//759TPfu3W2DlUw9aw7AnwjQgfRTJZ6mY41Gx2cae57pdIJBQXq045m//vrLJilUiQgAWR+gKwifO3eu7cL7448/2oMtna3UGGd1FJ83b549Qztjxox0r2pGUOWBTnYowO7du7cdl66suKZSU2XCBx98YMu1vHOeh3PBBRfYaUZ0qZJ3Bf8qA1PWfPjw4faLu3bt2in7vQDkhtCAu0S+MdVCFt0W6fEAil/V+PXXX0ft2K7A3CUDkiE/L8/sWrs2aNFtybLHHnvYY6ZoVqxYQZAOIDemWVNGVpnc119/PTB/ps5iqpRamfWTTjrJTu3Rp08fu2Ps3Llzulc5I0q2LrroIrsUR4cOHcxzzz2XsPUCgMJm0KsaYz4IOf880OQZNxEbATqQOMuXL7fHWt6ZFELpxL+mKYtUhZcIu//+28zruE/QbS2m/2pKRxkvXlwtWrSwwyo1pC+SZcuW2XJ3VRQq+QEAWZlBnzVrlmnQoEEgOPeqXr263RGecsoptrxIXwgTJkxIy3oCAJIvWrfocAjQgcTQ1KrqUxNpFhhR9Z3KwbM1OFUvpL333jtmJv2zzz5jTDqA7A3QN2/ebMuyo2WD33jjDXP22WfbzMoRRxxhS+EBANmnsPMOxzPjB4DolBlW0BmtfLtcuXK2r03FihVNNlOPnTZt2kR9jPrzaEpbTTsLAFkXoGtcs0qqolEZ1YgRI8z5559vNm3aZJuYfffddylbRwBAaqxZs6bQAX20sbIAolu4cKHNnEcLzpUxV1m3KhuznY451W/HTTkbrdpn1KhRhW5sCSC3ZGSArnHOCtD1BRHL888/b+fu1hih4447ju7uAJDjGXSVmXKADBSNGvGqIVy0snb1BVJwXrduXZMrNNWseh9pXHo0Oh798MMP7fAAAMiaAF0d2uW1116Lu+v7kCFD7PRshc20AAD8rSj79cIG9UCuU9XJtGnTzLfffhu1AkXBucacRxuKmM1B+sEHH2xatWoV9XHbt2+3s+NoRiIAyIoAvX///vbL4cknn7RBdzwef/xxc91111HWCABZROM5lZEqLI0HBRAfdWj/4YcfYjbdVXB++OGHm4YNG5pcpSBd06/FGpOuCoQxY8aYn3/+OWoHfAC5JyOnWevYsaMtsVKwrS+DeA0bNswMHDjQnrkEAGS+olZFEaAD8Q8JUUm7msJFowa9ypxrlp1cpzHpBx54oG2ON2XKlKiPnTFjhp1ZQrMOlS9fPmXrCMC/MjJAl1iNOCLZb7/9Er4uAID0KGqpOiXuQGwKHBWcx+rZ4Lq177HHHilbt0wI0rt27WqD9J9++ilqBadOfrz//vs2SK9Xr15K1xOA//iixH3btm3pXgUAQAYqaqBNBh2Ibv78+XF1HK9QoYJtwktwHl67du1s2X+sik9NIaxp2KZOncpwTCDHpT1AV7m5unyqocYZZ5xhlixZku5VAgBkiBUrVhTpeQToQHgaDz1+/HjzzTffmJ07d0Z9rKZQ69evn53+FpE1bdrU9O3b11SqVCnq4xSYT5o0yc4vz3zpQO5Ke4n76NGj7TzlWvSlEG9jEU2x9tVXX9mDs6pVq5q99trLHHHEEYzfAYAcsnTp0iI9jymOgILUcPG7776L68RX/fr1bWZY5e2IrU6dOmbAgAH2xEes/Y9K3t999107t7qObwHklrQH6D/++GNgrI7mK9dlrLOL11xzje3gHtr1Umcmr7zySvOvf/3LlC1bNqnrDQBIv1iNqxId2APZXNL+/fff26ZwsbRu3doGj4Vp1Atjx6Mfe+yxtnP7rFmzYlaYKom1YMEC+7cmAQXkjrSXuHu7W5500kkxH3/77bebxx57zE5P4R2jo+s683vPPffYedKVkQcAZLeiBtoqcWdGD8DYMnZN96XMbqzgXEkUNdvVcRbBedHo76YO7/H+DXXiRNl0XTI2HcgNaQ/QtcORxo0bmyZNmkR9rMana6o0fUG4TLt2VqGBusbvHH/88UlecwBApgbo+q4oavYdyBYqZVf38Llz58Z8rJrBKfvbqVOnmNWOiE3zpGvq3xo1asR87NatW+0JlC+//JIEFJADSvqhA6929Pvss0/Mxz777LNBGY8+ffqYb7/91pb/aAqLwYMH29fSgdcPP/xgnnjiiSSvPQAgXXTQum7dugK3q7XSh/t0MvWfHW5mnjjQ3GHy7G2haEqKXKVMuYYYqmv4xo0bYz5eHdo1flrjzv2qZOXK9jPvXXSbnyk4199VwXo8Fi9ebLPpv/76a4FhngCyR9rHoGtaCdc8I5a33347EIC3bdvWfPHFF6Z06f/9Csq+9+zZ0/Tu3ducddZZ9jF33323ueSSSwKPAQBkj0gZ8J0ljNncdV9T9fjjTPmSJcz3778X9nEE6MjVqhONNdewwHi0b9/elrX7vaS9ZLly9jOfaXSMqnJ3NUnWSZNYUw/v2rXLTJw40fzxxx+mV69evj5pAiBDM+hqmCGxxtWo/OrPP/+01xWkX3XVVWED70GDBpnzzjvPXl+zZo358MMPk7LeAAD/lre7GUE0fCoSAnTkEk3bpbHmmsIrnuBcTcmOPPJIGwT6PTjPBs2bNzcnnniinZItHn///bf55JNP7IxGGzZsSPr6AcihAN2NvYk1J63O9rpAvmTJknbcTiQ33nhj4Lo6YAIAso+m24wVoEebupMAHblApdAzZsww77zzTlxjzd3nRsFirN5ASHzSSlPXHXLIIXHPRqT9oMrex40bR+NLIEukvfa7Xbt2dkyNt5t7ODrrK268eq1atSI+VnNGajzP7NmzbcM4AED2cU1Gw2nWrJm9rF69uqlSpUrYxkqLFi1K6voB6abkh8qm165dG9fjlSnv1q2b6dixI43g0kR/95YtW9rSdQXd0fZz3pMw06dPtydgOnfubI+tGd4JZK60Z9BVOuU6ibosebgdj0p43JeFxpnHoi8XYa5bAMhObthTKH1XuDJRXY9U5q7nM20RspFK2L/77js7zC/e4Lxu3brmhBNOoEu7T1SqVMkcdthh5qijjrInGeOhDPr48ePNW2+9ZX7//Xc7JTGAzJP2AP20004LfBEMHTrUNr8IpWZwf/31V+BASjuseEvnGZcDANkpUmapQYMGdkoop0WLFmEfp6x6rOFVQKZ1Z1cDMTXVVROxeCjTuv/++9vpaVVxAn/RCcaTTjrJVo9qiGe8/QZUOaFhDXPmzKHjO5BhSvuhKUa/fv3MqFGjbDm6xjy99NJLpmbNmoGzwLfffnvg8ZUrV7Zjc2Jx43DYKQFA9tE+PtIY8g4NG5nZ9RsFfr5Cw6R0wjZMUlBBTL169ZK5qkDS6Vhn1qxZZvLkyTG7gHupjFodxKtWrWoy3a61a828jsFT9raY/qspHWVIZKbQSZTu3bvb0ndlyOPtn6GTkGPHjrXTsu299972+TT8A/wv7QG6PPnkk3aMubLdmpOzUaNG9myummVMmDDBdmMXZdp1FrFcuXIxX9M9J96yIABA5tD48Ujl6U2aNjFm3IS4Xkdl7gceeGCC1w5IXWCubVh9fApTMagKE01Nq549lLNnDlWHHn300TZAV6CuTu7x0LahYaQ6gaMhDOrTVKZMmaSvL4AMDtB1Bve9994zffv2NVu3brXLt99+a+/TAZj78tBZv+uuuy6u19SXlZ7H/JAAkH3mzZsX8b54pymSeMuAAT/RsZGGeCjgWr9+fdzP03FRhw4dzL777ht3l3D4jxJZGsqjZsi//PJL3FUTmzdvto3ndIysXk2a4z6epBeAHAzQpU+fPnZKNM1hrjItlxnRl4m7fu+999qzfrGoi+XKlSsDnTABANlFB6aRhJsayo7dzM8rVKAP+I2OhxYsWGAD83izp46Gcqgxb7RZcJA5tE9Tt3b12Pjtt99sF/edO3fGPURIgb1K31u1amVP2tB/APAP3wTo0qNHDzNt2jRb5v7RRx/Zs8M626cSLAXuRx55ZFyv88orrwSud+3aNYlrDABIB53IjaRVy1YmtNi3QcMG5u8w4zbjnRca8EMpu46R1q1bV6jnaqifjq809SDl7NlHlRA61lU2XNvHzJkz4+7ersbM6vauRVl5BeoNGzZkOwHSzFcBumuEMWDAALsUhTpXvvjii4Gf1fwEAJBddEAZjhqM1q5Tu0CAroakM8IE6KtXr7bLHnvskaQ1BYpOAZSqRZQhVdPcwlDpcpcuXWyWlcZg2U99Bfbbbz9buj516lS73RSmUbLGtWtRJl3BvipQGQYBpIfvAvTieuihh+zBltSuXds2QQEAZA9VVi1cuDDsfW3btg2b/WnTurX5aOzYsM+ZMWOGHWYF+IXGFOsklLbNwnRld6XPyoR27tyZ8cU5On/6AQccYKdlU0ZdgXph5kNXT4OffvrJNmlWBav2qXXq1CGrDqRQ1gXomrJNmRA1wNA4RHYoAJA748+VLQynbdvwt4vGbhKgww8UHCko19ALZc8LQ8c7rVu3toE5M9hA0xKr54CqKLSPU+l7vGPURduf5lDXor4FCtQ13p2sOpB8WRega/oILQCA3CpvFx1Ehr29XfjbRQERkM7Gbyot1na4dOnSQj9fgbkafSkwz4b5zJH40nfNoa6MuoJ0bWeaLakw1q5da3788Uc7tZuGC2l723PPPUmCAUmSdQE6ACC7qWyzsBl0dbDW2MpwU1IRoCMdduzYYbOTCpo2btxY6OcrOFJGUxnSatWqJWUdkT2U+dZJHI1RV8NBZdUL23BQWXVVd2hRhl7j1LXQAR5ILAJ0AEBG0RCmSAegyuyYMM20FMzowPSHH34ocN/ixYvtlFU1atRIyvoC3mz5mjVr7DANTfFX2DJ2N8Zc2/nee+9NYI4iNWPWUAhtQytWrLCB+qJFiwr9OmpaqGZ0WjS0VIG6xqyXL18+KesN5BICdABAxti0aVPEqdHUGEtNsXZF6HYdKUAXzQd8yCGHJHRdAe+80wrINT1gYbOWTpkyZWyFiLbjihUrJnwdkVt00rJ+/fp2UQWH632gyo7CcrNh/Pzzz3aaNgXq6gNFk0IgAwN0P0z7oR1UUc5gAwBST4G0spDhqHwzmmj9SSZOnEiAjoTSdrpq1SoblM+fP79QnbRDxxArKFd/BQIeJIN6F+y///52rLq2VW2z2naL2k9Biyo9FKxrzHrTpk1pLgdkSoAe6SALAIBwVE4ZicbiRtOtW7eI902aNKlY6wU4ykYqW/7HH3+YDRs2FPl11Dlb81FrnLnKkoFk03am0nctqvRQoK7tuChZdc3BruFDWpSQc8F648aNOdEExJDWPf5BBx1EB0gAQNwmT54c8b5YGXQ1ilPZZbjxlgr8dRBKlgdFLWFX5lHBzMqVK4v8OjomUrZRwzW0vXKMhHSpWbOmnaatR48etqmcyt81Zr0oVD2i/a4WV1qvfbGCdgA+C9DHjBmTzrcHAGQQHeSpFD1StlGZGSlZsaKpe/edQffrNpdFDxegb9u2zY7BjJWFB7wZQpXyKijXNlXUEnZRRrFNmzZ2jDlzmBdNtM89it9UTot6gGh7L051iKpnly1bZhcF69p3q8mcFl0HQJM4AECGUAAdaToqZXlctrFkhQqmxrnnhH2cAvT33nsv7H0K/gnQESsoX758uc0oLly40GbOi6Nu3bo2MFdTLcrYiyfa5x6JoZNH2keqWkmzESirrs9CcT4H2qe7YSGqkGrUqJE92aosO58J5Cq2fABARvjpp58i3qcGR/FQE6RI1OH94osvLtK6IfuDcpWwL1iwoNhBubLlmpJKgbnKiIFMo5OhLuvds2dPW0miQF2VJMVpvKyp237//Xe7aNz6nnvuaQN2LZpSkCEfyBUE6ACAjKApfIoboKvhlsoo165dW+C+8ePHm61bt9qu2chtCso1llxBh4JyDYEoLgUbCsqbNWtGZhBZQ4G0+iZoUXCupnA6maXL4gTrGjKydOlSu4wbN85m712wruy6ph0EshXfEAAA39u5c2fE8ee1a9e2XYfjoal/1KB05MiRBe5TEKb36N27d7HXF5m5jSkYUOm6goviZsqlcuXKNluupXr16glZT8CvdOJJndq16PPkDdaL06NBNP7dZdeVSdfwEAXqDRo0sJl8P0zdDCQKAToAwPcUOG/evDli9rwwpY+RAnQZO3YsAXoOUcWEynIVlKtpVXGDCFFmTwGKgnJlzSnLRS7S50C9FbQk+uSXGs2pwkXLlClT7IkBfdZcwK4qKT53yGQE6AAA3/vmm28i3qepgAojWgCuAB3ZSwf26j7tgvJVq1Yl5HUVDKj0VkG5po+ihB0IDtY1tEOLGz6iz58+h4moVFEpvcbBa3F9HhSsa1HgXqNGDQJ2ZBS+QQAAGR2g9+nTJ+jnXWvXmQW9Dwm6rdnY70zpWv9ryKXSyLZt25pZs2YVeK3Zs2fbDI+bsg2ZT9k7NXlzB/AqlU0UHfwrW67AoyJTeqVVrM89/EHDjFzwrFk11ONBHeFXr15t/vrrr4S8h4J+va4WF7DXq1fPLvrMaliU1gPwKwJ0AICvqVGXxjGG0759e3ugFyzf7F63rsBtXgcffHDYAF0+++wzurlnQZbcBeQrVqxISOm6o4N8N86WoNxPYn/u4T9Vq1a1i0rhK1WqZLPr+tyqJD4RzRldwK5svRZRhYtO1LqAXWPYqXqBn7A1AgAyNnt+2GGHFek1jzjiCDN8+PCw933++ecE6BmYJVcgruqHRGfJRQfzLihXEAEg8cqXLx9oqqhSeGXU3Yk2ZdgTRSXx6jmhRZRNV1Zdgbo+61r0OacsHulCgA4A8LVPPvkk4QH6vvvua+rUqWNLK0P98ssvNouj7Ar8SQfvOmB3B9kaS67MeaLowFyVGRpPrumj1I0dQOooaHZzrWt/rWy6suoarqLPfCJPwrn9iZYZM2bY21Qd4wJ2Xer7giw7UoUtDQDgW8qcqEtvOMp47LPPPkV6XU3Jc9RRR5lXX3017P1ffPGFOeecc4r02kg8Bd9///13ICBXtlxZ80Q3slKjNwXkutS4VQD+ya63aNHCLrJx48bA/kBBe6LK4Z0tW7bYRnZa3Ek7dYd3AbsWleaTZUcyEKADAHzr448/jnjfMcccU6xGP3p+pABd07ARoKc3IFeGTIG4OwjXlGiJVqFCBRuQK1Ou6ZmYSxnIrLHraviZihN4eg+V3GuZOXNm4KSeMus6WaxLLVWqVCFoR7ERoAMAfOujjz6KeF/fvn2L9do9e/Y01atXN+vXrw9b5q7mdGpchNQ1dtOBtVsizXtfXDqIVoZcnfp1nYNpILPpM1yzZk27dOzY0Zasr1271u5HNFxJl4mYzi3SDBFaHFXeuIDdXWqIDPsZFAYBOgDAl/744w8zffr0sPdpfHj37t2L9frKfhx77LHm9ddfD3v/e++9Z2644YZivQfCcxkvHdi6A+hkZMjdAbMCci0NGza0WXMA2UuVVS6j3alTJ7u/0YlYb8CerBOAOhHgbUDnyvMVrKtE3i3VqlVjqjdERIAOAPClN998M+J9xx13XELKkU866aSoAfr111/PQVQCKKOlMaM6SP71119tU7dkZLTCZcmZ8xjIbcpe16hRwy7t2rWzAfs///wTCNi1P9IJw2RxDe60OPr+UsbfG7Tr57JlyyZtPZA5CNABAL6zY8cO8+6770a8v1+/fgl5n65du9oxyK4RkJeyuz/++KM56KCDEvJeuUTZcB30atEBsIJzNxe5ykIT2XFdVEKqMeRuIUsOIFrArrHiWlq1ahX4znGd3LXf0mUyTyJqf6hZREJnEtG4em/QroUp33IPAToAwHe+/PJLs27durD3tW7d2nTu3Dkh76ODHmXRH3jggbD3jxgxggA9juy4MuMuE6VFAbn3b6zhBIkuW9c0aArGVbZOYyYAxaHMtfYlWrx9MbwBu76TEn1yMZT2nVoWLFgQuE37T2X/lWF3lQC6rhOR7PeyEwE6AMB33njjjYj3nXbaaQk9KFGA/uCDD4Y98Pr666/N4sWLbak0/n+5prI+7sBVS6I7JodSOeiee+5pg3IdQCurxIEpgGTR/kVNRLW4LLv2cy7rrUUd3b0nI5NF7+uy+6EnKl3Q7g3eNeYdmY0AHQDgK3PnzjXff/99xCzHCSeckND3Uxb2kEMOMd9++22B+xS0v/LKK+Zf//qXyUU6MNRBqAvIdanpz5JNAbkaASoo16XmHC5dmkMWAOmjTLZOEmrxnrB006+5wF3j21NBJfhu1guvihUrBoJ1d5JBCxn3zMG3HawvvvjCvPzyy2bixIm2TFEli2qy06VLF3P66afbDFM8TXZmzJhhnnjiCfPNN9/Y8ZsaF9imTRszaNAgM3jwYA6wAMT0wgsvRLzvqKOOspmCRDvvvPPCBujy1ltvmWuvvdYe9GQz7fdVwukCcS1qnJTskk534OsCci1q7Mac5AD8Ttlqb2m8t8rIZdl1mayu8eFs2bLFLt5O8u4Etzdgd4vGvdNI01+IlnKczr4peH7//fcL3Oc6Tmoe4qeeespe6oMcyfPPP2+GDBliG214d1JqsqRFYzk//fRTe+AFAOFo7lp1T4/kjDPOSMr79u7d2zRr1ixo3J+j8dWvvfaaufDCC022jRvX39t7IOkauSWbSjO9AblK1jlABJAtQbub2tF7PKwToNrnukUnQLUvThVvIzwvZdUVpIcL3rWvRuoRoOe4K664IhCcq4Rw6NChNmuubIbmH77//vvNokWLzA8//GBOPfVUm2kP57PPPjMXX3yx3dHUrVvX3HLLLaZHjx52Z6TA/YMPPrDZ+QEDBpgxY8aQGQEQlsrJI3XObd++vdl///1jvkbJChVMrWuuLnBb1OeULGnOPfdcc9ttt4W9/9lnnzVnnXVWRo7t27Vrlz0QdGWY7uAwVcG4qAuxO+Br0qSJrdCi1BKJVJTPPZAq+u4ILY/XPtidKPUuyeweH45riKdFx/xeCtA1Z7sCeF16rxO8J0+J/FTUrsGX1NhHOwoF1Rqn8ttvvwWV6IiaX+y9996BKYgmTZpkpyUKHaOoMvb58+fbD+2UKVPMXnvtFfSYyy67zDz99NP2ujLp55xzTrHWfebMmaZDhw5BpfU6eEf6aDvQF4ujjFiiOzcju7cHjW3eb7/97AFLOI899pg58cQTE76u3vfv1q1bxDHW9957rw3S/UwZEp0YdcG4llSVqTs6AasTvlp0wlb/997yTvYNuY3vCjhsCwVpX639pf4u2pdr/61LfS+mMtse70mHcIG7lsLO5+7HbWFmGmMNMug5bMKECYEPuzJHocG56MN29dVXmyuvvNL+PG7cuAIB+siRI21wLjfddFOB4Fw0hdGbb75pdzS6XtwAHUD2eemllyIG5yqH7tu3b1LfX1N1aV/4+OOPh71fQ31USVTYA49kHcRpjKErmXSZcWVAUk3ZcQXibtGBlbdKSgdeqRx/CQCZSpVF6t+kRdVGjo7XlTTTPt8F7lq0z09XrlVl+1pCS+Zd8O6CdrfoO1aXNKuLjQA9h3nHijdv3jzi47wBt/c5zqhRowLXIwXeaq508skn2zLR33//3XZpdtNWAIAOPJ577rmI9ytwTkVgfMEFF9hhOVu3bi1wn3pyaCy6GsqlukTdHZS5JR1lkKKMhsrT3aIsuQ4kAQDJo2FYbpiQ95jdlcm7TLu7TMVsG/EE76rWDaWG0S5Yd5c60avfRcF7KYbBEqDnstatWweuuwx4OH/++WfY5zhqAOfuU5YrWhMmBejy008/EaADCHjxxRcjZs91QHL22WenZD3UIf7MM8+MeLLgkUcesbNa6KAi0ZQF0UGVC8BdMJ6OrLg7IFQ2XEG4C8aVESHzAQD+oGBW+2ktoSd29d2h79XQJZX9R6L1RdHi6HvFlbSXL1/eBur6vunVq1faS93TgQA9h3Xs2NE2XPr555/tFGuaRsjbvEJ0sPjoo4/a6zpjd8QRRwTdr7kelyxZYq9rHHo03vtnzZqVkN9BDSp0UKuMvMooC8MdcCJxO1zvTl8/I3cVZntQMKreFJEazqgBpb6wC/sZL04W/e2337Zn/0OpVPuZZ54xV111VbHeQxl6HTy5gxSX9Qh34JSqgFgnQjTLhvaNulRvktDO6kX5XLNvgBfbAxy2heRypeWNGzcucOzumsK5IF6X4b7zUsX7Pbdt2za7jjo2UICequ/+UBpWoGOPdPxdCNBznA6KNa+wphZS93bXxV3lJ2qGMGzYMHufDtZef/31AiWmKvl0wo1h9/JON+GC+uLSGUPNtz5w4MBCP1cnJK677rqErAf+98XqLanSiRPmvc9dhdketB8KPTnoqOztuOOOC2oek2wKSnVSQLNThPP999+bY445pkDGIpR+Zw0L0oFG6BLugEPvm4qpxtwYR++4QC2hWQpvdqM42DfAi+0BDttC+ijw1KK+IaH9QvQdpR4nuu7mVE921l3fS97/+xIlStjv/1R+94c7ka5kphpkpxqfghynMnNteMOHD7dTqilo9dIBm4JYNYkLF4B7d6yxxiHqg+boww8AOlmngDeSo48+2pa6FUbeP/+Y1ffcF3TbHjffaEoWYqy0AvCxY8eGbW6mg5g33njDXH755YGDSo0H9wbg7iAnXWf+veWP3mBci35mjB+yTSI+90Au0zG/G+fu5b7jXLDuDdyTGbxXyOFpEgnQYT7++GObHQ8XNOvg8p133rElj9dff32BUktv2UesBk7e8tVwDZgA5BZ96Wt2h0gdaFViffjhhxf+dfPyzK5lywrcVhgKYo8//njz1ltvhb1/8uTJ5qOPPrLZBz8E4qLsQ2i3XJ0YTUVWHki3RHzuARSkY3+XcVeflkjBuzdw13Ud6xcneK9AgA4/S8T4w0hzjytj/vDDD9vr/fv3t0G45j1XdkXjxJ944gn73BtuuMFOy6Zg3Zt50Yc1Wod3L2/H4UR96FT6ohMDOohu1qxZoZ6rkw6xSlRRuFI177aqnTilarkrnu3h008/DZoFItR9990XsfQ9mt0lSpgNc/8Iuq1WtWqmVMiBRSQKtjUeb99997XVRWvWrAn7OI1FHzx4cFoa2Cj41gkMt+jvm4zGdYnAvgGp2B6K+7lH6rFvyH4K1nUSWzO1qOpWi/vZm+TTduA9Wb9r1y67PaTzOF3f/dOnT0/Le/MpyGE6OHbBuYJ3BeJenTt3tvMSq7T9zjvvNB988IF5+umnA2Wd4j0gjFW27i0VTdS0PC7oV6l++/btE/KaKDrvyRt9yeZi503Etz1ofPNtt90WcaqwTp06mRNPPLFI2d8SpUubkiGvq/cv7Xl/NX/RPss1x3HdbXVdBxSOGtSoYVw4mj7mu+++K1KWP16qPHIHKbrUooA80z5b7BuQ7O0hns89/Id9Q3ZTJ3YtDRo0CHsyXAG7gnV99yrppqz71q1b7W0qtU/n9qDjj3Q1ziNAzwCJ6Hi+5557FrjthRdeCJy1uuuuuyI+9+abb7ZTC+lgVgG7N0D3fuC8DePC8TaG8zaMA5B7tM+J1vzl9ttvT2hptqaL3Dh/fiAQ15d/PKV3mj5SJwA1U0Q4mgVDjwntklvUOW5dEO4C8ooVKzKtGQAg6yj4dt95Cta9xwS1atXK6V4pBOgZINb0ZcUN/DXVWLgzW94ydmWnVeI+e/bsoPuUQVewreA79L5Q3vvbtm1b7PUHkJnUFC7S2G433KZHjx5xvZZK4nSG252Bt2V0S5aY0FOSP//0k9npaVQZLwXHalQ3f/78iFMAqUxfXd9j9eFwr6dsgrc8XYtuy+WDEQAAvErmcP8UAvQc5sb5xDPvpGuAFG5s0AEHHGAbPc2ZM8esXLnS1KtXL+xrqCOyt2wUQO7RXN9XX311xPuVRb7jjjvCBuEuAA+9DO1/UWbz5gIBenFonXr37m1Gjx4d9n6V63/11Vd2OjhvIO664RKIAwCAeBGg5zA1VZs5c6YtKVE2PVJWWwfUmhPdPSdctksBurz88svmxhtvLPAYjelUgzlp166dLRkFkFsUaKvhpE7kRaIpHbVPWrBgQVAgnu4u6T179rT7yeXLl0fs6t6nTx/Tt2/fQCCey2f/AQBA0XD0kMM0hZBz1VVXhe3CrkZKV1xxReA+b4bIGTBggGnevLm9fu+999qxnqHUHV5ZJncdQO7RibzPPvss4v1Nmza1QbyqbaZOnWrLyv/666+0B+eiseDnn39+1IY1aqKp9VeATnAOAACKggx6DlPn9kcffdRmhVSe2bVrV9sAzk2z9vvvv9sphsaNG2cfr/l+r7nmmgKvowNWTcemgF/ZLpWv33rrraZ79+42KH/++efN+++/HyiHP/PMM1P+uwJIPp3Q02wNLuut4FrdWFVBM2/ePPPUU09FfK7Gbyv7nM6GaHpvTV+m7LcrT9ein9WLQ/cr8A4twXf0u1900UXm448/zun5WwEAQNERoOcwHRB//vnnpl+/fmbatGl2rr8LL7ww7GNV2q5p1mrXrh32/mOOOcbOCTxkyBA79ZC307ujgH3kyJGMvwQylBsL7p3H1M1tqkvd5uYxVTDrss3KgKspXLRMuBqxKfOcCpq6zAXe3kBcTS9j7Z807/nXX39tu7eHoxOeOpGpbDrd1wEAQGERoOe4Jk2amEmTJtmD5/fee89MmTLFrFmzxh5ka9oDzUWsMeZnnXWWqRSjA/IFF1xgx2k+/vjjtpmSxmrqORrbPmjQIHtgG67JHAB/0Ode2W4XgGtx191lPE0lQ40fPz4wxCUc7SNUuZNM+/fqZao1bmwDcWXDi0oZ9CeffNIceeSRdl8ZzkcffWRatGhhrr322mKsMQAAyEVES7BZLpWdJ6L0vEOHDua5555LyHoBSE4JerjA213qMYmkBpPLli2LeL+y1uptUZRss/ZdyoJrcaXpuqy8a7dZ+sDDQY/da6+9TOlatUwiaLiPMuSnnHJKxL/Xww8/bHtzqEcHAABAvAjQASBLKLsdLQDXfa4EPRUWLlwYmAEiUjb6xBNPtA3Yog3FCQ3A3aUbFx5q19q1Jtn2339/2/Dy/vvvj/gYZdAVzOuxAAAA8SBAB4AMoEytys9d6bkLxL3XNT7cLxYtWmSnHovmiCOOMI0bN7ZDYRRwK5vuvVQgrvHifh3LrZ4bv/32m+3lEc727dvNueeea9599107XAgAACAWAnQASDNltdXt3BtshwbgCs5Tmf0uDo3NVlNJBd/RgvMHH3zQBuKJbhxZQk3gzj6rwG2JpgoA9dxQGXukSgH9/6kHhxpkalw6gORI1eceAJKNAB0AkkhBtTKpkQJvd5nosd+ppAx35cqVbbCtEwmadjFaNr9du3Z2yrVope3FUapyZVPv3rtNKuh3GDFihB1Hrxkswlm3bp059dRTbSNOzfUOILM/9wCQTAToAFDMsnMF2N4l9Lbdu3ebTKYAXGXoCsK1qATdLQrKdZ+yyWoGN3DgQBuQqjQ9nHr16plXXnklacF5OtSvX98G6SeccIKthAhnxYoV9v63336bTDoAAIiIAB0AwmS9NWd3aOAdGoBHCsYyjYJrb/AdeukC8Fhl7coSL126NOJj9DoKzhXQZhtNE/fiiy+as88+O+J87ytXrrRN8d555x3TqlWrlK8jAADwPwJ0ADmZ9Y6V+S7KfN9+pTHe4QJvd6lsdnEasSk7rCnH5s+fH3UdnnnmGTsVY7bq3bu3nSP9kksuiThkQScylEl/4403TMeOHVO+jgAAwN8I0AFkXeAdbVHWO1OarcWrQoUKgRJ0d+kNwCNNR5YIixcvtsG5LqO56667TJ8+fUy201j0jRs32inYItEQAAXpzz33nDn44INTun4AAMDfCNAB+Joy2QqqvVlvb7DtbvPTFGOJpHnAvYG3dyy4FmW/S5dOz6583rx5NjhX6XY0Z5xxhh2bnitOP/10s2PHDnPLLbdEfIy2WZXDDxs2zP4NAQAAhAAdQFponG48GW91QM9WCqzDBd7e62XKlDF+9Msvv9g5vpUNjuakk04yhx12mEml3Rs2mKXnDQ66reFLL5hS1aqlbB3OOecc+393ww03RKzY0Mmna665xixfvtxcddVVvp3vHcgEfvjcA0AiEKADSGiZuTKHWpTd1qXLdHuz4FoiNdLKtuBbi7Lc4bLg6nSeiUHZJ598Yq644oqYJ0/69u1rjj32WJNq+aq6GDe+wG2ppvnPFaRfe+21UafR03zwv//+u3n00UfttgEgcz/3AFBcBOgAolJgofJxb0m599IbfHszhQrAs22sd+iYbwXeLgj3BuO6VGl6Jgbf0ej/89lnnzV33nlnzMcqM5xLZe2RnHzyybYHwJVXXmlPWEXy2Wef2SZ76gTPXOkAAOQuAnQgh4PuWAG3LuMd261g1K/l2PFSp/HQQDtcAB5ryrFspGz5rbfearuPx/Kf//zHnHXWWWbt2rUpWTe/UyVBrVq1zODBg20DuUhmz55tKw4ef/xxc+ihh6Z0HQEAgD8QoANZQuNZXdDtAuvQYNu75BplvRVcRwu8M7XkPNk0RvrCCy80U6dOjfo4/e0eeOABc9ppp2X9EIbC6tWrl/nggw9sw7xoTfXWr19vT25cdNFF5sYbb7SVGAAAIHcQoAMZkOUODbi9193PuRgQKSB0QXe0RcF5Lma9E2HcuHHm4osvNn/99VfUx6mM+6mnnjJHHXVUytYt07Rt29Z8/PHHNgCfNWtW1MdqKMGECRPM008/bZo0aZKydQQAAOlFgA6kcPyuyoRjBdruejZ3L4+n1NwbXLusd2jgncz5vXOdThApSLz33nvN7t27oz62du3a5uWXXzadO3dO2fplqvr165uPPvrIXH311bbZXjS//vqrOfLII+0c8po3nW0dAIDsR4AOFIOy1pEC7HDXs7VpWmE6m8eT8abUPL1Wr15tm5p9//33MR+71157mVdffZUsbyFoG3/mmWdsxcF9990Xdb+wadMm+3+hJnJ67B577JHSdQUAAKlFgA4UgzJhud4IS0G3y3JrvKxbatSoYbPe7j5dZnoTuVzw7bff2jm549muDzroIFuCrf9rFI5OQA0ZMsS0a9fOXm7YsCHq47/88ktb8n733Xebfv36cQILAIAsRYAOFIOCzmwuMdfv5xbvz97Scxd0q5rAG9SpazUBeeZQI0FlaDXNVzwUVA4dOtRuKyi6Pn362OD7sssuM5MnT476WDWQ0+M0jl2d8hs0aJCy9QQAAKlBgA4Ug8ZAZwoFUvEE3C7TTYYud4wfP95ce+21ZuHChTEfq23k0UcftdOBITEaNWpk3n//ffPggw/asvdYQ2G++OILO/xA/2fnn38+J8IAAMgiBOhABmfQdYJA6+Au3eL9WdddppugG16bN2+2TeBGjBgR1+M13vz55583rVu3Tvq65Rp9Pm+66SY7HdsVV1xh1qxZE7Pi4c477zTvvfee/T/s1q1bytYVAAAkDwE64KMAXWO3vQF2aODtvU2N1Jg6DEU1ZswYGxAuXrw4rseffvrp5o477rAne5A8GtevPgC33HKL7XERi6Zr69+/vznxxBPNDTfcYLvEAwCAzEWADiQxQA8tKw/NbIdeZzwvkm358uXm9ttvN59++mlcj69ataoZNmyYOf7445O+bvifmjVrmuHDh9thBDqJsm7dupjPUSZd07ZdcskldlGDRgAAkHkI0IFiUCO0Dh06RMxyq8M5ZeXwAzXxUwO4hx56yJZHx0Nl008++aRp2LBh0tcPBR133HFmv/32MzfffHNcJ1Q0neMjjzxi3njjDdvA76STTuKkHwAAGYYAHSiG2rVr2wXwMzUUU3n67Nmz43q8TjApwBs8eHBGBnglypQxVY47tsBtmUj7l+eee8589dVX5tZbbzXLli2L+ZxVq1bZBnLPPvusue6668zRRx/NcBhkvWz63APIbQToAJCl5syZY+666y47pjleypory66GcJmqVNWqpsFzz5hscsQRR5gDDjjAdtBX4L1r166Yz5k7d6658MILbZXP9ddfbw499FAqepC1svFzDyA3cUodALKMOoCrYdhhhx0Wd3CurLnGpmu6r0wOzrOZGvSp3F3zpvfo0SPu582YMcOcffbZpm/fvraaItY0bgAAIH0I0AEgS2zatMmOQdZUXa+99prJy8uL63nKrCqQv+CCCzKypD3XtGnTxp5IeeaZZ+wc6vGaMmWKOe2002zzOY1pj3f7AAAAqUOADgAZTk3fnnrqKdtQ7MEHH7Tzm8ejQYMG5qWXXjKvvPKKadKkSdLXE4mjUnV11td0eRqbXqVKlbifO23aNFv6fvDBB5u3337b7NixI6nrCgAA4keADgAZSl27n3/+edOzZ09zzz33mPXr18f1vDJlypghQ4bY4O7II49kXHIG09AETav2008/2TJ2zRwRrz///NNcc801tuJC49o3btyY1HUFAACxEaADQIbZunWrnTJNgZXGjf/1119xP/fwww8333zzjZ1fW2OakT1TPuokzdixY82JJ55YqK7ty5cvN//5z3/Mvvvua2655RYzb968pK4rAACIjC7uAJAhlCEfMWKELUtft25doZ6rTt633XabDeqz3e6NG83K64YG3VbvwWG2y3O2a9q0qXnsscdshYS68X/88ceFGirx8ssv26VPnz7mvPPOM71792aKNmSEXP7cA8guBOgA4HMrVqywpexq/Bbv+HKnXr16tqN7YbOqmSx/506z6ZNPg26re+/dJpe0bNnSNpG7/PLLbaCuzu+FoaaBWpo3b24GDRpkTj75ZFOzZs2krS9QXHzuAWSL3DhaA4AM9Pvvv5trr73W7L///naMcGGC8+rVq9sy9h9//NEGV7kSnCNY+/btbcXF6NGjzQknnFDoLv3z5883d955py1/v+yyy8zPP//MNG0AACQRR2wA4CO7d++22U5lvDVe/K233ipUl211877uuuvM+PHjbZlzhQoVkrq+yJyp2R5//HHbTO6cc86xzeUKQ9vgqFGjzEknnWQOPPBAM3z48EL1PgAAAPEhQAcAH1AH7eeee84ccMABduzvuHHjCvV8NXxTObOed/XVVxdq2i3kDs2bfvfdd9sTONpeatSoUejXWLBggbnrrrtMly5dbOf4jz76yM4oAAAAio8x6ACQRjNmzDCvv/66ef/99ws9vlwUiCtI0rzW6uQNxKNOnTrmxhtvNFdeeaUZOXKknRVg9uzZha720IwAWqpWrWqOO+44W0bfvXt3hlQAAFBEBOgAkGIKxJV1VNO3X3/9tUivUbt2bXPBBReYs846ywZHQFFoCMTpp59uTjvtNFv+rkD966+/LvQ4c1WAvPHGG3ZRln7gwIGmb9++pnXr1klbdwAAshEBOgCkMFuuoFwZy3/++adIr9G4cWNz8cUX28ZvjC9HopQoUcIOr9CycOFCu52+/fbbhZ7OT5YsWWKnetOibvIK1DVdW4MGDZKy7gAAZBMCdABIIgU4H374oXn33XfNtGnTivw6++yzjxk8eLA5/vjjTenS7LqR3LnUb731VjN06FDbsFBDMH744YcivdYff/xhnnzySfPVV1/ZAF3l7wcffDCZdQAAIuAoDwASbPv27XYOaQXlmt5q165dRXodTYl17LHHmvPPP99Oc6UsJ5AqZcuWtSeEtCir/uabb9qs+po1a4r0esuWLbPVI/fff79p1qyZOfLII+3SqVMntm0AAP4PAToAJIDG7Go8uYJyZczXr19f5NfSHOZnnHGGbf5Wv379hK4nUNSs+k033WSn8Pvuu+/Me++9Z8eqF2YKQC81pNOiMvh69erZKQUVrO+///6mXLlyCV9/AAAyBQE6ABTDnDlzbMM3LfPnzy/Wa+299942MB8wYADjy+FLZcqUMUcccYRddBLqk08+sTMQTJw4scivuXLlSvPqq6/apXLlyrYEXsH6IYccUqRp4AAAyGQE6ABQSH/++acNyD/++GMboBeHAhIF5IMGDTIdO3ZM2DoCyeYqPbQsWrTIfPDBBzazrnL4olLzRAX9WjRVW+fOnW2grqBdJ7CYvg0AkO0I0AEgDgpAFJArMJ85c2axX89ly/v162cqVaqUkHUE0qVJkybm6quvNldddZX9fOizokWfm6LKy8szkydPtsuDDz5oatasabvBK2DXpaYaBAAg2xCgA0CEMeXKjn/xxRe2k/Vvv/1W7NdUua4C8lNPPZVsObKSmr116NDBLjfeeKOZPn26+eyzz2zQXtTmct4ZEdRkTououZwy65oaTk0Uy5cvn6DfAgCA9CFAB4D/s3v3bputc0F5cUp1vWN2Dz30UHPSSSeZPn362M7YSK4SpUubCj33K3AbUh+sK4hu27at+euvv+znSWPV1ViuuENDRCfNtDz++OM2OO/atasN1nv16mXfl+kIcwufewDZgj0XgJy2bds28+OPP9qAXHM1K5BIBM1brqC8b9++tjQXqVOqWjXT5P13070aCAnWNbWalksvvdSWvuszp2XKlCm2YiURn2MtUqVKFbPffvsFAvY2bdowlVuW43MPIFsQoAPIOZqPWfOUa9EB/ZYtWxI2FZUC8oEDB5qWLVsm5DWBbNSiRQu7XHbZZbb0XVO2KVjX51HBdnFt2rTJvqYWqVWrlunRo4fp3r27vWzXrh0ZdgCAL/HtBCDr7dy505auKyAfPXq0nX85URo2bGiDci0ad0uWDiicOnXqmNNPP90uOln2ww8/2LnWtSxdujQh77F27Vo7Fl6LqDGjSuJdwK6KF6Y2BAD4AQE6gKykrJzLkn///fdm48aNCXvtevXqmeOPP94G5ZoGiqAcSIyKFSvaOdC1qOxdUxoqUB8zZowZN26c2b59e0LeZ/PmzWbs2LF2EfWG0MwKCta7detmunTpwtAUAEBaEKADyArKvE2YMMFm3xSQz5o1K6Gvr0y5gobjjjvOZt6YjxlILp34cqXwF1xwgdm6dasN0hWs68TbggULEvZealw3adIkuzgaL69AXYu6xGscu5o+AgCQTAToADK24/q0adNsQK5FJew6yE4kjVM96qijbGDevn17MuVAGqkEXTMhaPnPf/5je0m4xnA//fSTWbVqVULfTycAtLz//vv2Z3WKV5bdBe1aVE0DAEAiEaADyAgqd9XBsgvIf/75Z7Nhw4aEvoey4hqT6oLyxo0bJ/T1kRq7//nHrLn73qDb6txykylVuXLa1gmJ16BBA3PKKafYxZXDu2Bd+4f169cn9P3UvE5VOlq866Dx65rWzS3Vq1dP6PsiPnzuAWQLAnQAvuQOuFXSOn78eLusXLky4e+j6ZgOOuggO1f5YYcdZrs9I7Plb99u1r/y36Dbal93jTEcqOdEOfw555xjK2x+//13G7BrH/LLL78k/ISeKIuv5dNPPw3c1qRJE9OxY0ebbdelgvZq1aol/L0RjM89gGxBgA7ANwH5H3/8YTNfLiBXo7dkaN26daBUVg2hGFcKZJdSpUrZ4FjLJZdcYvLy8uzsDRMnTrT7Fl0muiTe0RzvWj755JOgKRi9WXbN+EDQDgAIhwAdQFq4A2YdLCvDpbJRTYWUrLGrBxxwQCAoV8M3ALlDw1fUU0KLMuw6IaggWvsdBeu6TGTTuVALFy60y0cffRRUHu/WST0udKnsOw0oASC3EaBnsH/++cdMmTLFHlxoUfdZHQCIvuTd9XjNmDHDPPHEE+abb74xy5cvN5UrV7ZdawcNGmQGDx5sSpeOb3P5/PPPzXPPPWfXRxlQzXGrLOWFF15ojj766CL9rsh8mtZo6tSpdrtQQzctiZz6LFTbtm3NgQceaHr37m32228/2+AJAFxJvLLaWjSGXVavXm33S/pe1fLrr7/acefJ4srjv/7668Btmp9d37vewF37Mk0/BwDIDQToGUzzMGu6mUR4/vnnzZAhQ4K6YOvAxHXIHTFihB1jV7t27agZUQXhL774YtiDkFGjRtlA/9lnnyVDkANWrFgRCMg1/lPjQTUuNFnUTVljybX06tXL7LHHHkl7LwDZR/sMnUR2J5J37txp5syZExS0z58/P+knMt0JzNCTCQrYFbxriI4W3RbviXMAQOZgz57BVKLn1KxZ087NrPG7yqwXxmeffWYuvvhiG2DXrVvX3HLLLaZHjx5m3bp1NnD/4IMPbIZ+wIAB9oSAxvaFo+e54Lxz585m6NChZq+99rKNvoYNG2aDtRdeeMFm1O+5555i/vbwE53YmTlzpq3C0DhyLSoZ3b59e9LeU83d9t9/f5sl16JtjWnQACSKelNorLiWs88+296m70Vl1hWsK4j+7bffEt4tPtIMFlq8zejKli1r93suYNfSqlUrO/tEpO9pAID/EaBnsNNPP91cdNFFtnxcnWtFZ9QLE6ArQ3D55Zfb4Lxq1ap2ehp94Tuabuqyyy4zTz/9tM2kv/rqq3b8Xqi5c+eaBx980F7XiYLvv//ejvsVrV/fvn1tqbEyqQ888IA577zzAuuMzOIOFnWQqkUnXhSciw4Ok0Xbk7YtlatrPLmmNiJ7BCCVdDLc9bJw+8PFixfbQN0t06dPT0rH+HAnRmfNmmUXLw3nadmypd0fK+OuSwXvGvNO9RoA+B9HtxlM5eTFNXLkyEDJ3k033RQUnDsKqN98803z999/2+vhAvRHH33U7Nq1y17XOHYXnDsaP6fbe/bsaR/3yCOPmKeeeqrY64/kUx8BF4jrctq0aWEzRuXKlUvo+2ospk7uaJtRUK7Ox8oYAYBfqGpHPV+0aNiZuAZ02lcqWNelqouS2XPDS8PT9L5aQgN3fcdrcVPSuZ9Dv7MBAOlDgJ7jNC7cCRd4u+D65JNPtmPHNY5Y2XJvplQHIx9++KG9rrP1CqbC0e06i68xfXr8k08+SUmyz6j6QgeS3oB86dKlKXlvlay7gFyLpkciQw4gkxvQ9evXz96mKjU1blXQrIojfZfqUo3pUkWBu97TVTx5KbvugvbmzZsHrmvYG9/TAJBaHP3mOJWtiwJnNdmKROXpCtBFZfDeAF3lzur67h4Xje5XgK6mcTpYadasWYJ+ExSWKiIUjGtx2Rb9X3p7GySTDvxUsr7vvvvakzfqVkxADiAbqbRcga8WF7TLX3/9ZYN1F7DrUj08ktlQMxzXzHXs2LFBt2s2F5dl17rrO1snHnTJPO4AkBwcDed4tnTJkiWBzHc03vtDx7vpgCLc4+J5neIG6CqrVkCprL7G0xeGmtXlSqdvzS/uMifuYDBcZrw4JeT6v1BTJe/P3oNTndTp0qVLYFHGxkv/j4X9P4R/aSiLN8hwQ2BSYfeuXSYvZMiF3j+f7SvntgW/U5DrqoYcNddUc9XZs2fb70ldaklVibyX9snu/UNVr17dButqSqcSf+/1aMF7srYHPveZh30D/Lwt5OXl2aFByZxuMxIC9BzmDdAaNmwY9bGNGjUKXHdBfaJfpyhq1apls/cDBw4s9HOvvfZac91115lsoiBXXYbVtEgVChoHqUXZ8tBhC4lu6KbgvH79+oFAX1MC6QSMG+cYOsZRJw2QvfTlumnTpqBtM1UVErs3bjRbWrUMum3thg2mVIqqQ+CfbSFTqcJIi6tK099MvT+U5dZ3rr73dKmfkzlbRixbtmwJG8Crh4j7HXQi3Htd3wXJ2B743Gce9g3w87awdetWO9xS0wWnGp+CHOb9IKiMLRp92TqhXeIT9TooHB2U6QBNiwJyXeqkhw6YUs2NX1QJ5J577mlq165tZwVI984VALKBxoHXqFHDLpr2LfSkrAvWvUs6K5I0n7sa0IabN179RnSCWFl2LTou0HeGgneddE90w1EAyDQcPecwb8lGrNJm7xemzigl43UQng7ANE7RBeIKwrWsWrUqbdMMKTOurLgbj6iMSOjZTwBA8gN3BbVa9t5776DSTH1vuGBdGfeVK1eaFStWpP27V8cMWjctonXznkxQ0K4haFpc4K5L/azvH6aKA5DtCNCTLBHdT0eMGBGxw3pxaFyFdz7VaLwldKGlyol6naJQmbROCrz11luFHs+uL3sd1PiJqgrmzZtnywXVTM8t6ao2UKZD05u5RaU++ruFowDdu73rQIoMeu5K5/aQV6qU2ZIXXNZau0YNU7J69ZS8P4Kxb0gP7avbtm1b4HYFxhrjrqagynC7SwXyqWgSGpoh13devGX6biiVqrY0ZE4VW/pZy56Vq5iqu/OCtjU+9/7GvgF+3hbWrFlTYLrKVOFTkMMUfDmxAkCVq0UqY0/U6xSF+1JXuZy6gGcK/R3U2E6LAnB36brhp4P+PxSAq3xSS+fOne1Jj8JkK0qVKhW4rh2rt2kcck/atoc6dUzLr79IzXshLuwb/ENBrZYDDjgg6HZl1hWsK2BWAK/FXU/00Clvxlzf4/EG6Hqc++4MR99XGuuufjgK4hu+8IK9tNf/77ZEHHsgcdg3wK/bQsmSJdPSIE4I0JMstON5UeiLNBm8XbRjzXXtbejmbfQW2hiuOK+TjRSIa8ocF4S7QFyZinRS912XEVcwrkt13qV0EAByk6ra1NxTi5ey6hpSpeBdzUe1KOPurqej70kkKu1XGb+WSI2d9P2njHsgiG/YMPCzLlV54A0UACDVCNCTLNa0Y+mkzLeCZAXN4aZQ8fLeH1oy5/0yL87rZDIdoLhA3BuQJ6JTfXEpm+CCcLfoICQRwy8AANlN3xX16tWzi3c6OBe8r169OhC869J73U/Bu6Nu+Fq8U8R6KTjX96aSI/qdXcWBK6fXpcbFF2daUgCIhgA9x6nE7c0337QBpRrI6MsonLFjxwau9+rVK+g+lUHrS0vl2d7HhfP999/bS521VnOxTKbx9hdccEEgEE/F2L1olP1WF3Wd+HBZEAXjOtAAACAZwbubQm2//fYLuk/fiRrD6QJ2Zd31XanstspX/dpUVHMx63gm2pAz/d7KtIcL4t2i2xPRawdA7iFAz3H9+/e3Abq8/PLL5sYbbyzwGJ0Bf+edd+x1BX2h82fri6pfv35m+PDhNkM+fvz4Al/UottdBl2Pz/QMrs6e//bbbzZ7kGoq0fMG4rqu/xcOBgAAfqDveGWatfTo0SNo/LkavLpu7rquwF2zlLiZSnTpx+x7aOWAlmnTpkV8nKbFc8F66Jzw7mct6R5rC8BfCNBz3IABA2zWVWe27733XnPSSSfZ6bO8rr/+evP3338Hrodz1VVXmeeee86eeb788sttptwbLKr5jG4XnTnX47OBguJkBugqtdP/hwvCXVCuL/tMP8EBAMhdmgFG4741PVxogKoAWIG7C9pd4L5o0SJ7qT4uOt7wOx07aYlUTi/6LlfH6tDA3fuzFmXsKasHcgMBegZTd9Uff/wx6DbXRV2Xyoh7HXXUUQVK2PWl+MQTT5jjjz/ebNy40Zav33rrraZ79+72S+X5558377//fqAc/swzz4wYqCp4v++++8wvv/xiX+eGG26wwaU6wN5///1m6tSp9rF6XMuWLU02aN26dYH/g6JS2b9eT39L15W+RYsWQdPYAYgtb8sWs/bpZ4Juq3XpxaZkxYppWycA8VPQqrnPtXTp0iXslExqXKdAXc1pdblywULTZMIEeyyjZefOXeYdk2+2+fxctjsZoSVaIC8ukNexnDeI13U3b7wuK1WqxEl8IIMRoGcwBYbnnntu2Pu0ow+977vvvgs7xvyYY44xzzzzjBkyZIj9wnOZbi8F7CNHjoza2fTuu++22eSXXnrJBuOnnnpqgcecf/755q677jLZIrTcPx4ar++CcG9AztQvQGLkbd1q1j78SNBtNc49mwAdyBKqxHPTp+n4RHatXWvmddzH86gSZtA7b5llmzcHgnhdamy5LjU+PtOsW7fOLrFmCFIFowvYFby7wN397L2uYB6AvxCgw1KzM3Vnffzxx83o0aPtF5h22iqpHjRokBk8eLD9QozVpOzFF180J5xwgi131xQnGl+mL4Fu3bqZiy66yBx99NEmmyjAjkQnQ1wA7g3EvfPGAwCA5GjTtq3pUKtW2Ps0Bl7HOgrc3aJmuW6aNi0bNmwwmUjDCt3QgFgI5gH/IUDPYOecc45dEkVTcSmwLi5l5LXkAgXcCsRd8O1dqlWrlu7VAwAAYWj4mHrwaIlEjeq8AXu4RRWLmawwwbz+ZrVq1Yq4qARfwbz7mVJ7oGgI0IFiUBA+efLkdK8GAABIsIoVK9peOqHNc0Mz8RoeqGA9NAPvFpXTZ0JTu1j0u7pqg3iUK1cuKGjXdV16f9bfuGrVqnYB8D8E6AAAAEARKKvcpEkTu0Si4FyZdvXpURCvSwX1bnG3Z0sg72zfvj1wkiJSAO/t5bNw4UKbdfdm5TVVnQJ5XbrF/axLppdFNiJABwAAAJJEDXbdnOcaThiJgnM1gQsN3F1A7w3k1ck+2+zYscNs2rTJ/o6FOUFSvXr1oKA9NJAPva5eQJTew88I0AEAAAAfBPJqyqYlWiCfl5cXFMgrYFdTXgXwuvT+rClzs5nK7hXQFyaoV9PjWEG8gn4tGsrorjPtLVKFAB0AAADIEJo1x80T3759+6iP3blzpy2vdwF7LgfzjqoP9HsXdqo9l613QXvopXfx3qcl2jTFQCgCdAAAACALlSlTxs42oyWWSMG8C2Z1nxZl73Wpx+eSomTrHTXBCxe8xwrs6YSfmwjQAQAAgBxXmGA+Pz/fbNy4MRC0hy4K4hXce3/WGPNcpb+Vlnims/NS5l1j5hWsu2733uuhP4deJ8DPTAToAAAAAOKmoM9leaPNJe8N6NUALjSA11zzClx1n5roaUy9u19d4HOdGgeuX7/eLkUdDuEN5mMF9KHXFeDrNZBaBOgAAAAAkhrQu+CvWbNmQSX1jqZVUxbfBfQK3jUuXosC9niu6zkIbihY3ABfGXz9v1WuXNle6me3uNvd9Uj3qTEf4sdfCwAAAICvAnplb7U0bNgw7ucp6x4tiA/384YNG5L6u2R6gK+/T3H/RhUrVgwK3kODeGXs3bz2FSpUsLfrhE202QyyGQE6AAAAgIxXrly5uMfRe7u6KwD1Bu4u66zbvZfeRaX5CmARmyobtGgIQ6T/t1atWgV+njt3rmnQoIH54YcfTC4iQAcAZJkSplTNmgVuA5DN+NyjaFR+rWytlsJQcK6x8+GC92iBvW6jFD+2qlWrmlxFgA4AyCqla9U0LWdMS/dqAEghPvdINY3Pdo3yGjduXKjnqqN9rMDedX7X4v1ZJwVyQeXKlU2uIkAHAAAAgBQpW7asqVOnjl2K0tldQXq44D3Wdfe8TFCVDDoAAAAAwM80N3r16tXtUhQK8P/5559CBfcK7F1wr0u9RrJVJoMOAAAAAMj2AN+V5jdq1KjQz9cUeNu2bQsE66GLbncnAMLd7x6zdevWqO9TpUoVk6sI0AEAAAAAcU2B56ZDq1u3bpFfR93zXcDuuuired7WrVttAN+2bVuTqwjQAQAAAAAp7Z5fo0YNu+zcudOsXbs2cF+tWrVMmTJlTK4iQAcAZJW8rVvNhrfeDrqt2qmnmJIVKqRtnQAkF597ANmCAB1AQqxevdo88sgjgZ+vvvpq06BBg7SuE3Jze8jbssWsuuVfQbdV6Xs8B+ppwr4Bqdge+NxnHvYNcNgWghGgA0iINWvWmIceeijw8xlnnJHTO9dcx/YAh20BXmwPcNgW4LAtBCsZ8jMAAAAAAEgDAnQAAAAAAHyAAB0AAAAAAB8gQAcAAAAAwAcI0AEAAAAA8AECdAAAAAAAfIAAHQAAAAAAHyBABwAAAADABwjQAQAAAADwAQJ0AAAAAAB8gAAdAAAAAAAfKJ3uFQCKYvv27UE/z5s3L23rgv9ZsGBBgZ/LlCmTtvVB7m4Pu9avN4t37gy6bfvs2aZ09eopeX8EY9+AVGwPfO4zD/sG+HlbmBcSW4TGHslUIj8/Pz9l7wYkyIcffmj69++f7tWAR9myZc2OHTsi/ozcwvYAh20BXmwPcNgWkEnbwqhRo0y/fv1S8l6UuANIiNAdqd92rEgttgc4bAvwYnuAw7YAh20hGAE6AAAAAAA+QIk7MtL69evN2LFjAz83atTIlCtXLq3rBAAAACDzbd++3SxZsiTwc+/evU31FPW0IEAHAAAAAMAHKHEHAAAAAMAHCNABAAAAAPABAnQAAAAAAHyAAB0AAAAAAB8gQAcAAAAAwAcI0AEAAAAA8AECdAAAAAAAfIAAHQAAAAAAHyBABwAAAADABwjQAQAAAADwAQJ0AAAAAAB8gAAdAAAAAAAfIEAHYA4++GBTokSJQi1jxowp8vvNmDHDDBkyxHTs2NFUrVrVlC1b1tSpU8eux8MPP2w2bdqU0N8PRbNjxw7zwgsvmCOPPNLsueeeply5cqZy5cqmdevW5txzzzU///xzwt7rzTffNEcccYSpV6+eKV++vGnSpIk544wzzLhx4xL2HvDvtrBhwwbz+uuv29fae++9TbVq1UyZMmXsfuGQQw4xDz30kFm/fn3Cfh9kxn7Ba8WKFaZGjRqB7yB9XyC3tof8/Hzz/vvvm5NOOsk0a9bMVKhQwdSsWdO0bdvWfl+MGDHC7N69O2HvB39uCzNy4RgyH0DO6927d752B/EuJUuWzF+6dGmR3uu+++7LL1WqVNTXb9SoUf7UqVMT/nsifgsXLsxv3759zG3h8ssvz8/Lyyvy+2zZsiX/mGOOibqt3X777Qn93eCvbeGzzz7LL1euXMzXr1evXv63336blN8R/tovhHPCCScEvYe+t5A728OiRYvyDzjggJjv9ffffyfs94P/toX7cuQYkgAdQP78+fPzp0+fHnV5++23Azu/ww8/vEjv88YbbwReo2zZsvlXX311/qeffpo/YcIEe5/3y7du3bp80abJjh07gr5oO3XqlP/yyy/njxs3Lv+rr77Kv+222/IrVaoUuP/ee+8t8nudeuqpgdc55JBD8keNGpU/ceLE/BdffDF/r732Ctz37LPPJvR3hH+2hVdffTVwMubII4/Mf+SRR2wgPmXKlPyPPvoo/5RTTgm8fsWKFTP+wCtTpXK/EErbgV5zjz32IEDPwe1h8eLF+c2aNbOvo+Ds7LPPzn/vvffyJ02aZI8f3nrrrfzBgwfn16pVi+OGLN4W3sihY0gCdABxGTp0aGDHpwPqovDuwD/55JOwjxk4cGDgMQ888EAx1xpF8e677wb+D3r27Jm/a9euAo/55Zdf8suUKWMfU7169fydO3cW+n1Gjx4deJ/jjz++wPusWbMmv3HjxoH3WLduXbF+L/hzW9DB9UUXXWQzZJE8/vjjQSdykL37hVCbNm2yGTG95n//+18C9BzbHpRtPeigg+xr1KhRwwZ9kej1E125Af9sC+1z6BiSAB1ATLt3785v0KCB3eFVrlw5f/PmzYV+jQ0bNgR2ml26dIn4uGnTpgUepx0tUk9npd3/gTJXkQwYMCDwuN9++63Q73P00Ufb55YuXTp/yZIlYR/z5ptvBt5j2LBhhX4PZMa2EI+uXbsGMu06eYPc2BZUEus9MUOAnlvbg6uw0aJAELm5LWzIsWNImsQBiGn06NFm2bJl9vqJJ55oKlasWKTmIU7z5s0jPm6vvfYK+xykTir+r9TERduVHHbYYaZhw4ZhHzdw4EDbBEZGjhxZqPdA8fnpc+uaguXl5ZkFCxYk5T3gr21h4sSJ5qmnnrJNoIYPH16s10Jmbg9PPvmkvVSjMR1/IDe3hR0++i5KBQJ0ADH997//DVw/66yzivQatWvXtt1WZf78+REf9+effwau6wsZqef9u8fzf6WOyi1btizUe0yaNCnw5dm7d++Ij9OB+X777Rd4zs6dOwv1PvD/thCv7du3B66XKlUqKe8B/2wLu3btMhdccIE9IXPDDTfwfZCD28PixYvNhAkT7PXjjz8+cLu+BxYuXGiWLFnCd0KObAu1c+0YMt0pfAD+pvF/rrlHkyZNijW+6+abbw6UHn3++edhH3PiiScGyp5nzZpVjDVHUal8uGrVqvb/oVevXmHHk6mBl5q06DGDBg0q9Hs88cQTgW1h5MiRUR97xRVXBB47c+bMQr8X/L0txEuNh/QeGseockdk97agRlJ6nRYtWuRv3bo1cDsl7rmzPbzzzjuB/+9XXnklf8WKFfnnnHOObRbpbRzZv39/mkfmwL7h5hw6hiRABxCVOnG6HeItt9xSrNf6559/bJdmvZamVbr22mvtFEvq2q1GUW66N3VpfeaZZxL2O6DwPvzww8BBUOfOne3BkZrzfP3113basypVqgTGgq1cubLQr3/DDTcEtit14o1GjV7cY7/44oti/Fbw47YQDzUEctvAcccdl5T3gH+2hXnz5uVXqFDBvtaXX34ZdB8Beu5sD3oN9/+t2R3q1KkT+Dl00Ym7ojawRWbsG/7JoWNIAnQAUfXp0yfwBThnzpxiv546d4ZOoeVd1NRDO1ukn84+a+qaEiVKFPh/0hQmjz76aJEaBsqll14aeK1YZ7mffvrpwGM1tQ6ya1uIZe3atbZ6xx14KROD7N4WDjvsMPt6mmIvFAF67mwP3uqp8uXL28szzjjDTv26ffv2/KVLl9pKC5eZVZCubuHI3n3Dzhw5hmQMOoCIli5dasaMGWOvaxxwq1ativ2aavrz2muvRRxD9PXXX5sXX3zRbNiwodjvhaLT+HD1Hvjwww91IrfA/atWrbL/j998802RXn/btm1B48yjKVeuXOD61q1bi/R+8O+2EM3u3bvNoEGDzKJFi+zPt956q+ncuXPC3wf+2Rb0+nq+mkM+8sgjxVxjZPL2sHnz5qDvjPPOO8+8+uqrpkOHDvZ7o0GDBubGG280L7/8sn2MxqNrH4Hs/Z6YmCvHkOk+QwDAv9wYQC3Dhw8v9utpihSVJen1NJ5UY4+VHduxY0f+n3/+mX/PPfcEyhrbtm2bv2zZsoT8Hih8GdmBBx4YyFgOHTrUnhlXxkJjf7/66qv8Aw44wN6vM+UPPfRQod+DDHpmSMW2EM2FF14YVNoebmwjsmdb0FjW2rVr29dQn4pwyKDnzvZw2WWXBf6/NaY4Wmm0dxrGv//+u5i/Hfz4PfFuDh1DEqADiKhdu3aBsT7r1q0r1mvpi9U1m2vfvr3doYfzzTffBMqjMnkOy0x23XXXBQ6K1IMgUpmZ5iV2B0S//vprod6DMeiZIRXbQiQ33nhj4L118Ldly5aEvC78uy2ceeaZ9rkKtnbv3h32MQToubM9ePcB2iaiUY8c91gdRyB1UrEtrMyxY0gCdABhKWhyO9wTTjih2K+nsUfu9V5//fW4xh9qJ17cEwMoHHXpr1mzpv37t2rVKupjf/zxx8D/6VVXXVWo96GLu/+lalsI57777gu8npoK0bU9+7cFZbvc85SBe/PNN8Mu7jHKkLnbxo8fn4DfEn7bNzz11FOB5w4YMCDqY9UUzD1W2wSya1t4NMeOIUunu8QegP/nPj/77LOL/XqzZs0KXO/SpUvUx+677752nJLmv507d67p0aNHsd8f8dE4sXXr1tnrscb66v/JmT17dqHep127dnE/191funTppM2xjfRtC6GefvppO65U2rZta7788ks7HhnZvS1oDKszbNiwuL5TTjvttMB3FN8T2bdvaN++fVA/imi89+u7Atm1LczKsWNImsQBKECNVt566y17vU6dOuboo48u9mt6vzB37doV8/3DPQ/Jl6r/p27dugWaw40dOzbqQfv48eMDzylTpkyh3geZ9ZlVA6ghQ4bY682bN7cHWbVr1y7y6yEx2H8jHdtD165dTYUKFez1SE3BnD///DNwXc3jkF3bQukc2wcRoAMo4PPPPzdr1qyx108//fSE7OCaNWsWuP7DDz9Efez3339vL0uUKGGaNm1a7PdG/GrWrBnIVo4bNy7qF6E3sPb+/8ajSpUq5tBDD7XXFYRpxoBwPvjgA7Nx40Z7fcCAAYV6D2TGtuD9vz733HNtB+CGDRua0aNHm/r16xfptZB524L29f839DLq4vTu3Ttwm+vijezaN1SqVMkcddRR9vrMmTPNH3/8EfZxypSqe7hUrFgxZoYVmbctNMu1Y8h019gD8B+NOXdjfSZPnhzXc0aMGBF4zr///e8C96ujp2vc0aBBAzt/aTjPPvts4HV69uxZ7N8FhXfaaacF/g9uv/32sI/RuC7XRFDLl19+WajtQUaPHh14TN++fQt06FZH58aNG9v7q1evnrFjyTJZqrYFPcfNZbzHHnvkz549Oym/D/y/LcRCk7jc2h4mTJgQeMzhhx9uO3aHuvPOOwOPUed3ZN+2MCvHjiEJ0AEU2Im6aSw6dOgQ9/Pi+aI977zzAo+pU6dO/t13353//fff50+dOjX/o48+yj/99NMD92uqju+++y6BvxnipS/CihUrBv4vjj/+eDu92ZQpU/J//vnn/IcffjgQOGs59NBDC7xGvAfip556auBx6vD64Ycf2gaFL730Uv5ee+0VuE9fusjObWHcuHGB9yhTpoxtADR9+vSoC9MoZfd+IRoC9NzbHrzTcnbv3t02gVPyQLN6nHHGGYH7GjVqZE/sIju3hfNy6BiSAB1AEM137nZww4YNi/t58exct23bln/KKacEHhdp0VQasbp0Irm+/vrrwHzE0ZY+ffqEzWzHe+ClqbOOOeaYiK+vLqxFPZBHZmwLui3Wa4cuek1k734hGgL03NseVF111llnRX2PFi1aUHmT5dvCthw6hszMkfMAkkZNmqRUqVJm0KBBCX3tcuXK2eZzF110kR0zqOZfy5YtM9u3b7djmFq3bm0OO+wwc+GFF9oxqEgf/T+oy+qLL75oexJo/N/69ettP4J69erZhm3qT9C3b187zquo1ADo008/NW+88YbdJqZNm2bfp27duubAAw+0DcN69uyZ0N8N/twW4H9sC0jH9qDjkVdeecV27X/hhRfssYP65FSuXNl2ej/hhBPscUX58uUT+vvBX9tCuRw6hiyhKD3dKwEAAAAAQK6jizsAAAAAAD5AgA4AAAAAgA8QoAMAAAAA4AME6AAAAAAA+AABOgAAAAAAPkCADgAAAACADxCgAwAAAADgAwToAAAAAAD4AAE6AAAAAAA+QIAOAAAAAIAPEKADAAAAAOADBOgAAAAAAPgAAToAAAAAAD5AgA4AAAAAgA8QoAMAAAAA4AME6AAAAAAA+AABOgAAAAAAPkCADgAAAACADxCgAwAAAADgAwToAAAAAAD4AAE6AAAAAAA+QIAOAAAAAIAPEKADAAAAAOADBOgAAAAAAPgAAToAAECG2LZtm3n66afNkUceaRo2bGgqVKhgSpQoYZcWLVoU+XWvuuoq+xoVK1Y0S5cuTeg6Z6M1a9aYqlWr2r/ZBRdckO7VAZBFCNABAL5x8MEHB4KNwixjxoxJ96oDSafAee+99zaXXXaZ+eqrr8yyZctswO7ovqKYMWOGeeqpp+z1K6+80gb+8dq8ebP54IMPzJAhQ0zXrl1N48aNTaVKlUz58uVNvXr17DqdddZZ5oknnjCLFy8u9H7g9ttvL9Tvose75+p1kqVOnTrm2muvtddfeukl88svvyTtvQDkltLpXgEAAABEl5+fb0499VQzd+5c+3O5cuXMoYceaoPpMmXK2NsOP/zwIr320KFDza5du2xgfd1118X1nK1bt9qg+8EHH7TZ5HBWrVpll99++828+uqr5oorrjD777+/ufnmm82xxx5rMp2qDh5++GGzceNGc/3115vvvvsu3asEIAsQoAMAfKlbt26me/fucT22QYMGSV8fIJ2UMf/pp58C2/vPP/9ss9XFpdf8/PPP7XWVateqVSvmcxYtWmT69etnpk2bViCr3KVLF1O7dm1bKv/XX3/ZLP+UKVPsCQDReh933HE2sL366qtNJqtWrZq5+OKLzbBhw2wVzzfffGMOO+ywdK8WgAxHgA4A8KVjjjmm0OWtQLZ67bXXAtdvuOGGhATnct9999lLlYRfeumlMR8/f/5807NnT7N69erA80488US7TgrO9XOoTZs2mdGjR5snn3zSXrrS+Gxw0UUXmQceeMBWONx///0E6ACKjTHoAAAAPufts5Co8vA//vjDfPrpp/b6QQcdZFq2bBmzrP2EE04IBOfKko8cOdK88847Zt999w0bnEuVKlVM//79bYZ5woQJpmPHjiZbNG/e3PTp08de1++n8fwAUBwE6AAAAD62fPnyQGd1lZErKEyEESNG2MyvnHLKKTEfr1LuX3/9NfDz66+/bkvdC0PDVtRQbcCAASZbnHzyyUF/UwAoDgJ0AEBW8nZ5dzRmVl2qO3ToYGrWrGnvU2YvnLVr15qHHnrINt5q1KiR7UpdvXp1065dO9tFu7Bdm/Py8swrr7xiX0/drfV6TZs2tQHOqFGjwnawDtedfuHChYH79fx46HHuOXp+LIn63cP9H8yZM8c212rbtq2pXLmynapKnb5vuukmO2a5MNScS43Kjj/+ePs76vXUPK1+/fq2gdodd9xhZs6cGfQcvZdbpzfffDPu9zr77LMDz7vmmmtMsim77d7P22NBDdlCZzHwBoiFoQDbifQ5cFSS/vjjjwd+Pu2002I+J5KyZcua9u3bm3TQZ6ooM0VEqg4Q/R3c/W+88UbgpAcAFEk+AAA+0bt3bx3Z2uXf//53sV7LvY77qtPrlSpVKuh2Lf369Svw3CeffDK/WrVqBR7rXUqUKJF/3nnn5W/fvj3muqxYsSK/R48eUV9vwIAB+Rs3bgz6G3z33XcFXmvBggWB+5s0aRLX30KPc8/R86NJ5O8e+n8wfPjw/HLlykV83Vq1auVPmjQprt9Jr1WjRo2o6+mWzz//PPC8J554InD7YYcdFtd7bdiwIb9ixYqB582cOTM/2UaNGhXX76bljjvuKPTrT5s2LfD8Nm3axHz8iBEjgt5z4sSJ+X7cD+jx7rl6nVD6TMX7dw1dounQoUNK/jYAsh9N4gAAWU9NnJRNlb322suW2Wr8rLLJbooqR9ndxx57LPCzOlKrKZay3ppzeurUqXacqeJPzX+s8mON4y1ZMnxR2vr16+0Y1VmzZgVua9asmX1NZXuV4Z04caIdyxvpNVIl0b+718svv2wuueQSe71169Z2zuwKFSqY2bNn207iek1l7vv27Wv/VuqQHYmm61Lm3ClVqpTt+q8x1Mr2K8usUmxXLeCdK/yMM86w04ppPLUalukxsSoRlGnfsmWLva6/hyoJkk2/v6oV5MsvvzTz5s2z14888kjTokWLoMceffTRhX79r7/+OnD9wAMPjPl47xRi2n71985EqkZwf9dYtF16S/qj0d/QjT/X3zZT/z4AfCDdZwgAAEh2Br106dI2Kzxy5MgCj9u2bVvg+osvvhh4TtWqVfOff/75/B07dhR4zrfffpvfoEGDwGPvv//+iOuhTLN7XNmyZe17hJowYUIgy63HpCODnozf3ft/oMx5nTp1grLZztixY+17xpMRVubc+7onn3xy/uLFi8M+dvr06flXXHFF/pdffhl0+9lnnx14/m233ZYfS7du3QKPD/f/l2xdunQJvP8vv/ySkNc86aSTAq+pqoJYmjVrFnj8qaeemp9Mycygx2vq1Kn5lSpVCrzWjTfeGPXxzz77bNSqHACIVwn9k+6TBAAAuPHXY8eOLdQ86Bq7HG4edO+YUWV4lQFUp+pINBWUpq5SxltjZL///nvTo0ePiI9XllfTSik7q7mjFy9ebLPyXnPnzjVt2rQJjElVFlljmcPRYzt37hzI1IrWWX8TL2V8lcGUJk2axDWmXBlizV0tCxYsKJAxTsbvHvp/oGoBVQp06tQp7Gs+9dRTZsiQIfa6/mbeigPn77//tr+z1lc0B/Xw4cNNYWku7l69etnr+r31N4lUBTB9+vTAOqsb+YoVK0ylSpVMqmj+cI2t3759uyldurT5559/7N+yuPQ3Vj8Al/GNNT2YKk3cXOaqRrntttuMn/YDjraxSZMm/b/27jzEpv+P4/jx/UP+Ut+yL6kJpciuDBrCH5YI2fd9z3+W0AiRJXvZI7IvIWnIksYa+UNZQvyhCaOUKSXi/np98jm/z9zvvefeO3c7Zp6PunVn3HvuOeeeue7783m/3x9zv6ioKGYfh0Q+fvxoXvP9+/d+jfn58+cD69BLS0v9zxf9femaAoCqIMUdABBK+pJtv2gHmTFjRswA3aV1moOCc1HKtgJU0XrQQQGqqMmZgu29e/ea1OySkhJv+PDhlR5z8OBBPzjXF/54wbm0bt3apJivW7fOy7VsHHu0WbNmxQ3OZdKkSeb4FQQqcFQDODWQc+3bt88PzhWob9u2zauKwsJC06RM5QUaXFCAqtTxWPQeWmPGjMlpcC7Pnz83wbk975kIznVN2gEbadasWeDj9V7Y4FzUMDCRK1eumFuQ1atXm2aNmfgcyBQNOikgt8F5hw4dzBr0QcG5uJ9B6rj/69cvU3oBAKmiizsAoNpTYJWIG0yMGzcuqe3a9Y/lzp07gXW7EydOTLi9oAA+m7Jx7NFGjhwZ+O+anVZ/gFgBpKWBAGvmzJlpBat6fqwg3PXjxw8TnLmDQbnm1kArWMyEr1+/VqrLVxZEEDsoYiUzSKGZbGVFBN0U+IfN9OnTzVrtot4Lly5dSup41a/B0mBGqisSAIDFDDoAIJSKi4u9VatWZWRbnTt3TviY+/fvV5qp1ZJoidi1qcXOuFkKMrWsm6XmYoloFl0zil++fPFyKdPHHku7du0SPsYNFGMFbzZwkj59+njp0Iz90qVLTaB68eJFkwkQHahq+Tv93u5/KqnWmaLGfJkO0LVkmitWeUL04EnQ88P0OaDH2oaQqVq7dq1ZJk3UbFDXhZYZTEb0OczlOQJQvRCgAwCqvfr16wf+u+p63VnCAwcOpPwaqo+OnqXUDKylWudk6HG5DNCzceyxBHVlt9yO+j9//qz0bwrY1XndKigo8NLx77//mtIHzZDrfTp69KhJsXe5M+v5mD3P1gx6tETtiFRqoPp3m+ZuyyESBcrRgbXbPyFsVGPu1tWr7COVARlaOgHIFFLcAQDVnpbzCqJgOl1uja4NfFOZpbRyXeOcjWOPJVENbyLRadZqnJYu1cXHS3NXbfr169fNfaXSa3m26hKgR19j7sBHPO4Ak+riqxNlKagExQbZK1eu9MaOHZvSNqLPYa7/jgFUHwToAIAaL/rLtGaw9WU9lVt0t+joANLtzh4kG6mxv3//zumxZ0N0mnX0AEhVaO1qdTMXrWGtumnr0KFD/nlTA7xEzcyyQTPOdrZaAXKm9kHZDErhtpKpl3bXSnfP099OHduHDBni/32qV0JVUuQ/f/7s31e2gVuTDgCpIEAHANR46krtNhzTl/ZMBEFassydkU1GonpuNw08mZnrRLPk2Tj2bFCatZsJkallrGI1i9OggwL0fKe3Z6P+3GYzuEvtuf0E4nFr/nXuq0OQrv4DQ4cO9Y9fvSrUf6Eq2R5lZWWVuuLTwR1AVRGgAwDwZxk06+7du2lvT1/y27dv7//84MGDhM95/fq135QsHnfpMdV+J6p91cBAom7ZmT72bHGXf7t582ZGtqnO+XaA4uTJk2YmVanttou8at3TbUgXxvpzd8k7ux56ENXru030qrrEXZhMmzbNH2ho0qSJ6dieqBwmnhcvXvj33b97AEgVAToAAJ7nDR482L+/e/fujDR9cgM7d7mueI4cOZJUqrdNdVYw+erVq8DHnz59Oi/Hng0DBgzw7+/fv99fHzwdCjrtGu4ayDhz5kylenQFcenWz4dtBj16UMZdbSCoFGLhwoX+zydOnDBd7v9Wa9asMcdg+0MoOFeQXlXuOcxHt38A1QcBOgAAnufNnj3bpHvLkydPUqpDVQ3vr1+/Yq6p7M6gBwXpb9688bZu3ZryTPLhw4fjPk6pu+vXr8/LsWeD0tFtbb9muKO7rmeiWZxmhm3gqTTlqVOnevnizqB37Ngxo9vu379/SuvYy5IlSyrtx/jx481SZH+bs2fPmuXbRIMvSmtPZinGIKWlpTHPLQCkigAdAIA/NeNugKwgVenP8WrHNcusdPB58+aZBl6xOmFrXfMpU6ZUqmWOtcb448ePzZd6NYhz69bjGTdunH9/y5Yt3rlz5/7zGA0IFBUVmTT4RNvMxrFng5ZG27Bhg//znj17vNGjR8etoX727Jm3aNEi79q1a4Hb7d27t9eqVSs/KLYz85qxT3ZWVQ3dFOzZW9DASTJU6mD7EWjwxK0Zz1SKu+3M/vLlS+/Dhw8Jn6PGcrrWGjRo4GdwDBs2zLwHGtiJl3mhZntqJOgOhOSL9lPXtt1XXetK309HeXm5udakUaNGaQf7AGo21kEHAOAPBdNv37416a825fzYsWMmvVjdvjV7q+7hCggVyCWzRJkC6Pv375s6XwV+eo3Vq1d73bt3N7XP+mKvOlgFDEq1VmB2+/btwG2OGTPG27x5s0mr1RreCjA6depk9lOz2U+fPvXTo7UWtRqe2ZrqXB57NmhQQB3XlYpvU/gVNHbt2tUMiCiIVEdtHb+CZkmmhlyz84sXL670u3w1h4uePc9WTbNmwG2GhbIG5s6dm/A5Wsdc16uaq+n603Wr90C3+vXrm+BUHcx1vWjASdeLrsfo3gp6T9ya9lzZuXOn37Fd+/jp0ydvwYIFST13165dMX+vc2cDfi3P9s8/zH8BSEMEAICQKCoq0rdccysuLk5rW3Y7Vfmv7tSpU5EmTZpU2kbQrVu3bpHv37/H3V5ZWVmkS5cugdsYMmRIpKKiotI5uHXrVtxtvn37NlJQUBB3e7Vq1YosX7488vv370iLFi3837979y5nx57qe5Dsscu2bdsidevWTbh/Og9Xr15N+Nrl5eWR2rVr+89r3Lhx5OfPn5Fk6f1wX/fIkSORdGzatMnf1qJFiyLZ8OrVK3N+9Bo696n49u1bZP369ZF69eolfa3otXr16hW5cOFC1j4H9Hj73FjHNHny5KT3N/oWT79+/fzHPH36NKX9BYBozKADABBl1KhRZoZQXb2vXr3qPXr0yMzKagZZzbKaNm3qtWnTxqwNPXDgQDNzG0Rp0ko5t7PSmlHUDHTDhg3N7KhSbkeMGJFSMzLNZGo7mhE8f/68aRanGXq9lvZLs6FurXq+jj1blLo+YcIEk0qu/Xz+/Lm/nrdmcLWPSvFX+rVNXw+i2d+ePXv63eH1nmg962TpvXBLBtzGe2Hr4G7pvAwaNMi7fPmyydrQKgLJnCvbWG3p0qWmcVxJSYl348YN7+HDhybdW7PlSmtXar7Oq/ZfGQ46J+qKX51oyTkdu/Tt29dr165dvncJwF+ulqL0fO8EAAD4fz20TXG/deuW+RnZp3Rs1Q9rIEIDJRrwaNmyZUoDBjt27DD3VSawYsUK729w7949r0ePHv4xVIfl03JJjfM2btxo7qvXAQ3iAKSLIhkAAFDjnTp1ygTnokGRVIJzsTPvaqCWqe7yuVBYWOgvX3fgwIH/1IojPmXBqFGhKFuD4BxAJhCgAwCAGk3JhCoVsObMmVPlLt7Lli3zl4L7W2gGWOn8yiJQ80EkZ/v27V5FRYVpCrdp06Z87w6AaoIAHQAA1Gjqzm1rvrWcmZYOS4VKERTkN2/ePKlO6GHTtm1bb/78+X7QWVZWlu9dCj31ZbCDGdOmTTM19gCQCdSgAwAQItSgZ5+WCTt+/LhZok7N3bSmu6VGfhMnTszr/gEAai66uAMAgBpFHd81Uxxt5MiRBOcAgLwiQAcAADVWnTp1zFJxU6dONUuGAQCQT6S4AwAAAAAQAjSJAwAAAAAgBAjQAQAAAAAIAQJ0AAAAAABCgAAdAAAAAIAQIEAHAAAAACAECNABAAAAAAgBAnQAAAAAAEKAAB0AAAAAgBAgQAcAAAAAIAQI0AEAAAAACAECdAAAAAAAQoAAHQAAAACAECBABwAAAAAgBAjQAQAAAAAIAQJ0AAAAAABCgAAdAAAAAIAQIEAHAAAAACAECNABAAAAAAgBAnQAAAAAAEKAAB0AAAAAgBAgQAcAAAAAIAQI0AEAAAAACAECdAAAAAAAQoAAHQAAAACAECBABwAAAAAgBAjQAQAAAAAIAQJ0AAAAAABCgAAdAAAAAIAQIEAHAAAAACAECNABAAAAAPDy73/YVlynBuTQKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1012.5x625.759 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delta f = -387.1 MHz\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1012.5x625.759 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 1012.5x625.759 with 1 Axes>,\n",
       " <Axes: title={'center': 'Loaded resonance, all models'}, xlabel='Frequency, $f$ (GHz)', ylabel='$|S_{21}|$ (dB)'>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide = rf.Frequency(7.7e9, 8.8e9, 12_001, unit=\"Hz\")\n",
    "s_db = lambda net: 20 * np.log10(np.abs(net.s[:, 1, 0]))\n",
    "sl.plot_curves(\n",
    "    None,\n",
    "    (wide.f / 1e9, s_db(b_cpw(wide, C_NEG, C_NEG)), \"CPW bare\", dict(color=\"0.6\", lw=1.6)),\n",
    "    (wide.f / 1e9, s_db(b_cpw(wide, Cload, Cload)), \"CPW loaded\", dict(color=\"0.1\", lw=1.6)),\n",
    "    xlabel=sl.LABEL_FREQ,\n",
    "    ylabel=r\"$|S_{21}|$ (dB)\",\n",
    "    vline=[f0_cpw_bare / 1e9, f0_cpw_load / 1e9],\n",
    ")\n",
    "print(rf\"Delta f = {df_cpw/1e6:+.1f} MHz\")\n",
    "\n",
    "# Zoom on the loaded resonance: each LOM vs the CPW ground truth.\n",
    "W = rf.Frequency(f0_cpw_load - 30e6, f0_cpw_load + 30e6, 12_001, unit=\"Hz\")\n",
    "nets = {\"CPW (loaded)\": b_cpw(W, Cload, Cload)}\n",
    "for m in MODELS:\n",
    "    nets[sl.MODEL_LABELS[m]] = lc_builder(m, models[m])(W, Cload, Cload)\n",
    "sl.plot_all_models(nets, m=1, n=0, quantity=\"db\", title=\"Loaded resonance, all models\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa8b5e0",
   "metadata": {},
   "source": [
    "As we expect, the Optimized model is the best at predicting how the element will behave in a distributed network."
   ]
  }
 ],
 "metadata": {
  "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}