{
 "cells": [
  {
   "cell_type": "raw",
   "id": "3fa93af2",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "/// script\n",
    "requires-python = \">=3.12,<3.15\"\n",
    "dependencies = [\n",
    "  \"qpdk[models] @ git+https://github.com/gdsfactory/quantum-rf-pdk.git\",\n",
    "]\n",
    "///"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb2aec59",
   "metadata": {},
   "source": [
    "# Lumped Josephson parametric amplifiers with VACASK\n",
    "\n",
    "::::{admonition} Required extras\n",
    ":class: tip\n",
    "\n",
    "Needs `models` and the external\n",
    "[VACASK](https://codeberg.org/arpadbuermen/VACASK) simulator:\n",
    "`uv sync --extra models` (or `pip install \"qpdk[models]\"`). Install VACASK,\n",
    "put `vacask` on `PATH` or set `VACASK`; it compiles Verilog-A with the bundled\n",
    "`openvaf-r`, and Linux also needs `binutils`. See the\n",
    "{ref}`extras reference <notebook-extras>`.\n",
    "::::\n",
    "\n",
    "A parametric amplifier uses a strong pump and a nonlinear inductance to\n",
    "amplify a weak signal\n",
    "{cite:p}`clerkIntroductionQuantumNoise2010,royIntroductionParametricAmplification2016`.\n",
    "The pump creates gain. It also creates idlers: a signal at $f_\\text{s}$\n",
    "produces waves at $f_\\text{s} + k f_\\text{p}$.\n",
    "\n",
    "A classical simulation needs two steps. First find the pumped steady state\n",
    "with harmonic balance, HB\n",
    "{cite:p}`kundertSimulationNonlinearCircuits1986`. Then find the small-signal\n",
    "waves around that state. In VACASK the second step is `hbac`, a\n",
    "conversion-matrix analysis {cite:p}`maasNonlinearMicrowaveRF2003`. The\n",
    "{ref}`vacask-analysis-guide` below compares these analyses.\n",
    "\n",
    "This notebook runs the two lumped examples in released VACASK: a\n",
    "current-pumped Kerr junction with four-wave mixing, and a flux-pumped SQUID\n",
    "with three-wave mixing. It ends with signal compression. The\n",
    "{doc}`TWPA notebook <josephsoncircuits_twpa>` covers the traveling-wave\n",
    "circuit with JosephsonCircuits.jl. The\n",
    "{doc}`CPW companion <vacask_cpw_parametric_amplifier>` qualifies\n",
    "the distributed resonator model.\n",
    "\n",
    "The schematic shows the Kerr circuit on the left and the distributed\n",
    "flux-pumped circuit from the CPW companion on the right. The lumped flux\n",
    "example replaces the line with a shunt capacitor.\n",
    "\n",
    "![A current-pumped Kerr JPA (junction and shunt capacitor coupled to a 50 Ω port) and a flux-pumped JPA (quarter-wave CPW terminated in a SQUID whose flux is modulated)](figures/vacask-jpa-circuits.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a308724",
   "metadata": {},
   "source": [
    "(vacask-analysis-guide)=\n",
    "## Choose a VACASK analysis\n",
    "\n",
    "An analysis chooses the circuit problem to solve. VACASK then uses numerical\n",
    "methods such as Newton iteration or time stepping to solve it. The manual\n",
    "groups the analyses by the state they use {cite:p}`vacaskCircuitAnalyses`.\n",
    "\n",
    "| Question | Analysis | State and result |\n",
    "| --- | --- | --- |\n",
    "| What is the DC state? | `op` | Solve the nonlinear DC equations. |\n",
    "| What is the unpumped response? | `ac`, `acsp` | Linearize at a DC state. `ac` returns voltages and currents; `acsp` returns port S-parameters. |\n",
    "| What happens in time? | `tran` | Integrate the nonlinear equations, including startup and transients. |\n",
    "| What periodic state repeats in time? | `pss` | Use shooting to match the state after one period to its initial state. |\n",
    "| What weak signal responds to that state? | `pac` | Linearize around a shooting-based periodic state. |\n",
    "| What is the periodic pump spectrum? | `hb` | Solve for Fourier coefficients using harmonic balance. |\n",
    "| What weak signal and idlers respond to the pump? | `hbac` | Linearize around an HB state and couple its frequency sidebands. |\n",
    "\n",
    "The manual also covers transfer functions, feedback-loop analysis and noise.\n",
    "`noise` uses a DC state and `hbnoise` uses an HB state. Those are classical\n",
    "device-noise calculations. They do not by themselves give quantum added\n",
    "noise. The current manual includes features that an installed release may\n",
    "lack.\n",
    "\n",
    "For a parametric amplifier, use `hb` for the pump, then `hbac` for the weak\n",
    "probe. `hbac` labels the response at $f_\\text{s}+k f_\\text{p}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd40b8c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T06:55:41.639908Z",
     "iopub.status.busy": "2026-10-21T06:55:41.639860Z",
     "iopub.status.idle": "2026-10-21T06:55:41.642023Z",
     "shell.execute_reply": "2026-10-21T06:55:41.641752Z"
    },
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "if \"google.colab\" in sys.modules:\n",
    "    import subprocess\n",
    "\n",
    "    print(\"Running in Google Colab. Installing dependencies...\")\n",
    "    subprocess.check_call([\n",
    "        sys.executable,\n",
    "        \"-m\",\n",
    "        \"pip\",\n",
    "        \"install\",\n",
    "        \"-q\",\n",
    "        \"qpdk[models] @ git+https://github.com/gdsfactory/quantum-rf-pdk.git\",\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e07c09f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T06:55:41.642912Z",
     "iopub.status.busy": "2026-10-21T06:55:41.642863Z",
     "iopub.status.idle": "2026-10-21T06:55:45.223095Z",
     "shell.execute_reply": "2026-10-21T06:55:45.222736Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "from pathlib import Path\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "from jax.typing import ArrayLike\n",
    "from matplotlib import font_manager, pyplot as plt\n",
    "\n",
    "import qpdk.colormaps  # ruff: ignore[unused-import] - registers the style colormap\n",
    "from qpdk import PDK\n",
    "from qpdk.config import PATH\n",
    "from qpdk.simulation.vacask import (\n",
    "    Netlist,\n",
    "    Vacask,\n",
    "    VacaskError,\n",
    "    format_value,\n",
    ")\n",
    "\n",
    "# Agg suppresses notebook figure outputs in headless CI.\n",
    "if \"ipykernel\" in sys.modules:\n",
    "    from IPython import get_ipython\n",
    "    from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
    "\n",
    "    if (ipython := get_ipython()) is not None:\n",
    "        ipython.run_line_magic(\"matplotlib\", \"inline\")\n",
    "        set_matplotlib_formats(\"png\", \"svg\")\n",
    "\n",
    "jax.config.update(\"jax_enable_x64\", True)\n",
    "PDK.activate()\n",
    "\n",
    "# The checkout stylesheet is absent from installed wheels.\n",
    "for _style in (PATH.docs / \"qpdk.mplstyle\", \"qpdk\"):\n",
    "    try:\n",
    "        plt.style.use(_style)\n",
    "    except OSError:\n",
    "        continue\n",
    "    break\n",
    "\n",
    "# The stylesheet names fonts that only the docs build ships; register them so\n",
    "# they resolve without docs/ipython_config.py.\n",
    "_font_dir = os.environ.get(\"QPDK_DOCS_FONTS_DIR\")\n",
    "for _fonts in (Path(_font_dir) if _font_dir else None, PATH.repo / \"build/docs-fonts\"):\n",
    "    if _fonts is not None and _fonts.is_dir():\n",
    "        for _font in sorted(_fonts.iterdir()):\n",
    "            if _font.suffix in {\".otf\", \".ttf\"}:\n",
    "                font_manager.fontManager.addfont(str(_font))\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1008ee",
   "metadata": {},
   "source": [
    "## Finding VACASK\n",
    "\n",
    "{meth}`Vacask.find() <qpdk.simulation.vacask.Vacask.find>` checks the\n",
    "`VACASK`/`QPDK_VACASK` environment variables and `PATH`. The notebook stops\n",
    "if the binary is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fac2487b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T06:55:45.224484Z",
     "iopub.status.busy": "2026-10-21T06:55:45.224222Z",
     "iopub.status.idle": "2026-10-21T06:55:45.226480Z",
     "shell.execute_reply": "2026-10-21T06:55:45.226206Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using VACASK at <runtime>\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    vacask = Vacask.find()\n",
    "except VacaskError as error:\n",
    "    print(error)\n",
    "    raise SystemExit(\n",
    "        \"VACASK is not installed; skipping the rest of this notebook.\"\n",
    "    ) from None\n",
    "print(f\"Using VACASK at {vacask.binary}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1551985f",
   "metadata": {},
   "source": [
    "Every netlist uses `tolscale=1e-6 reltol=1e-6`. The default tolerances shift\n",
    "the gain by more than the differences resolved here. Port conventions:\n",
    "\n",
    "- A port is a Norton source $I_\\text{s}$ in parallel with $R=50\\,\\Omega$.\n",
    "  So $V_\\text{inc}=I_\\text{s}R/2$ and $\\Gamma=2V/(I_\\text{s}R)-1$\n",
    "  {cite:p}`m.pozarMicrowaveEngineering2012`.\n",
    "- VACASK reports a `sine` source of amplitude $A$ as $-\\text{j}A$, so the\n",
    "  incident HB phasor is $-\\text{j}I_\\text{s}R/2$.\n",
    "- `hbac` writes the response at $f_\\text{s}+k f_\\text{p}$ as `node;k`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2247b00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T06:55:45.227428Z",
     "iopub.status.busy": "2026-10-21T06:55:45.227386Z",
     "iopub.status.idle": "2026-10-21T06:55:45.230053Z",
     "shell.execute_reply": "2026-10-21T06:55:45.229673Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "R_PORT = 50.0\n",
    "I_SIG = 1e-9\n",
    "OPTIONS = \"options tolscale=1e-6 reltol=1e-6\"\n",
    "\n",
    "# Kerr junction; both circuits share the 2 pF shunt.\n",
    "IC, C_SHUNT = 1e-6, 2e-12\n",
    "\n",
    "\n",
    "def db(x: ArrayLike) -> jax.Array:\n",
    "    r\"\"\"Power gain in dB of a wave amplitude ratio, :math:`20\\log_{10}|A|`. See :cite:p:`m.pozarMicrowaveEngineering2012`.\"\"\"\n",
    "    return 20 * jnp.log10(jnp.abs(x))\n",
    "\n",
    "\n",
    "def reflection(v: ArrayLike, source_current: float) -> jax.Array:\n",
    "    r\"\"\"Reflection coefficient of a Norton port from its node voltage.\n",
    "\n",
    "    Uses :math:`\\Gamma=2V/(I_\\text{s}R)-1`\n",
    "    :cite:p:`m.pozarMicrowaveEngineering2012`.\n",
    "\n",
    "    Returns:\n",
    "        Complex reflected wave amplitude divided by the incident amplitude.\n",
    "    \"\"\"\n",
    "    return 2 * jnp.asarray(v) / (source_current * R_PORT) - 1\n",
    "\n",
    "\n",
    "def conversion(v: ArrayLike, source_current: float) -> jax.Array:\n",
    "    r\"\"\"Conversion gain into a sideband other than the one driven.\n",
    "\n",
    "    The sideband amplitude is :math:`2V/(I_\\text{s}R)`, as in\n",
    "    frequency-conversion analysis :cite:p:`maasNonlinearMicrowaveRF2003`.\n",
    "\n",
    "    Returns:\n",
    "        Complex sideband wave amplitude divided by the incident amplitude.\n",
    "    \"\"\"\n",
    "    return 2 * jnp.asarray(v) / (source_current * R_PORT)\n",
    "\n",
    "\n",
    "def resonance(f: jax.Array, gamma: jax.Array) -> tuple[jax.Array, jax.Array]:\n",
    "    r\"\"\"Peak-delay frequency and :math:`\\kappa/2\\pi` of a lossless one-port.\n",
    "\n",
    "    Group delay is :math:`\\tau=-\\text{d}\\arg\\Gamma/\\text{d}\\omega`, which peaks\n",
    "    at :math:`4/\\kappa` on resonance :cite:p:`m.pozarMicrowaveEngineering2012`.\n",
    "\n",
    "    Returns:\n",
    "        Resonance frequency and linewidth, both in Hz.\n",
    "    \"\"\"\n",
    "    grad = jnp.gradient(jnp.unwrap(jnp.angle(gamma)), 2 * jnp.pi * f)\n",
    "    tau = -jnp.asarray(grad[0] if isinstance(grad, list) else grad)\n",
    "    k = tau.argmax()\n",
    "    return f[k], 4 / tau[k] / (2 * jnp.pi)\n",
    "\n",
    "\n",
    "def show_plot() -> None:\n",
    "    \"\"\"Show the current figure with Outfit titles, as the docs config does.\"\"\"\n",
    "    fig = plt.gcf()\n",
    "    for text in [ax.title for ax in fig.axes] + list(fig.texts):\n",
    "        if text.get_text():\n",
    "            text.set_fontfamily(\"Outfit\")\n",
    "            text.set_fontweight(\"bold\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d677ae7",
   "metadata": {},
   "source": [
    "## Current-pumped Kerr JPA\n",
    "\n",
    "A 1 µA junction with a 2 pF shunt is coupled to the 50 Ω port through\n",
    "0.185 pF. The junction is a nonlinear inductor. Expanding\n",
    "$I=I_\\text{c}\\sin\\varphi$ for a junction current\n",
    "$I_\\text{p}\\cos\\omega_\\text{p}t$ gives the first-harmonic inductance\n",
    "\n",
    "```{math}\n",
    ":label: eq:vacask-kerr-inductance\n",
    "L_\\text{J}(I_\\text{p}) \\simeq L_\\text{J}\\left(1+\\frac{I_\\text{p}^2}{8I_\\text{c}^2}\\right),\n",
    "\\qquad L_\\text{J}=\\frac{\\Phi_0}{2\\pi I_\\text{c}} = 329\\,\\text{pH}.\n",
    "```\n",
    "\n",
    "A stronger pump therefore lowers the resonance. This Kerr shift is why the\n",
    "pump at 5.84 GHz sits below the unpumped resonance measured below. The pump\n",
    "also modulates $L_\\text{J}$ at $2f_\\text{p}$. A signal at $f_\\text{p}+\\delta$ is\n",
    "amplified, with an idler at $f_\\text{p}-\\delta$. This is four-wave mixing,\n",
    "$2f_\\text{p}=f_\\text{s}+f_\\text{i}$\n",
    "{cite:p}`royIntroductionParametricAmplification2016,eichlerControllingDynamicRange2014`.\n",
    "\n",
    "The pump source injects the signal on its first-harmonic spur (`spur={[1]}`).\n",
    "So `in;1` is the reflected signal and `in;-1` is the idler.\n",
    "\n",
    "`hbac` treats the signal as infinitesimal around the pump. It returns the\n",
    "signal and idler as a linear response to the pumped state. By default it also\n",
    "solves the HB for the pump, so no separate HB run is needed here. See the\n",
    "{ref}`vacask-analysis-guide`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3202425",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-21T06:55:47.207546Z"
    },
    "tags": [
     "hide-input",
     "jpa-kerr"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpumped f0 = 5.9598 GHz, κ/2π = 158.1 MHz, pump detuning (f_p - f0)/(κ/2π) = -0.76\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 pump amplitudes × 80 offsets in 0.26 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I_p =  38.0 nA: G =   4.20 dB, idler   2.13 dB at 0.5 MHz; G =   4.17 dB at 5 MHz; signed photon sum = 0.999998\n",
      "I_p =  40.0 nA: G =  11.81 dB, idler  11.51 dB at 0.5 MHz; G =  11.50 dB at 5 MHz; signed photon sum = 0.999995\n",
      "I_p =  41.0 nA: G =  23.08 dB, idler  23.05 dB at 0.5 MHz; G =  20.78 dB at 5 MHz; signed photon sum = 0.999987\n",
      "I_p =  42.0 nA: G =  16.95 dB, idler  16.86 dB at 0.5 MHz; G =  16.61 dB at 5 MHz; signed photon sum = 0.999995\n",
      "I_p = 100.0 nA: G =   1.16 dB, idler  -5.13 dB at 0.5 MHz; G =   1.16 dB at 5 MHz; signed photon sum = 1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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   "source": [
    "F_PUMP_KERR = 5.84e9\n",
    "KERR_LOADS = [\"josephson_junction.va\", \"capacitor.osdi\", \"resistor.osdi\"]\n",
    "\n",
    "\n",
    "def kerr_circuit(pump_source: str) -> str:\n",
    "    \"\"\"Kerr JPA circuit with the given pump source parameters.\"\"\"\n",
    "    return f\"\"\"\n",
    "        model isource isource\n",
    "        model resistor resistor\n",
    "        model capacitor capacitor\n",
    "        model jj jj\n",
    "\n",
    "        ipump (0 in) isource dc=0 {pump_source}\n",
    "        rpump (in 0) resistor r={R_PORT}\n",
    "        cc (in 1) capacitor c=0.185e-12\n",
    "        j1 (1 0) jj ic={IC}\n",
    "        c1 (1 0) capacitor c={C_SHUNT}\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "ac = vacask.run(\n",
    "    Netlist(\n",
    "        \"Unpumped Kerr JPA\",\n",
    "        loads=KERR_LOADS,\n",
    "        circuit=kerr_circuit(f\"mag={I_SIG}\"),\n",
    "        control=f'{OPTIONS}\\nanalysis ac1 ac from=5e9 to=7e9 mode=\"lin\" points=200001',\n",
    "    )\n",
    ")[\"ac1\"]\n",
    "f0_kerr, kappa_kerr = resonance(\n",
    "    jnp.asarray(ac[\"frequency\"].real), reflection(ac[\"in\"], I_SIG)\n",
    ")\n",
    "print(\n",
    "    f\"unpumped f0 = {f0_kerr / 1e9:.4f} GHz, κ/2π = {kappa_kerr / 1e6:.1f} MHz, \"\n",
    "    f\"pump detuning (f_p - f0)/(κ/2π) = {(F_PUMP_KERR - f0_kerr) / kappa_kerr:.2f}\"\n",
    ")\n",
    "\n",
    "KERR_PUMP = (\n",
    "    f'type=\"sine\" sinedc=0 ampl=40e-9 freq={F_PUMP_KERR} spur={{[1]}} smag=[{I_SIG}]'\n",
    ")\n",
    "pumps = [38e-9, 40e-9, 41e-9, 42e-9, 100e-9]\n",
    "circuit = Netlist(\n",
    "    \"Kerr JPA\",\n",
    "    loads=KERR_LOADS,\n",
    "    circuit=kerr_circuit(KERR_PUMP),\n",
    "    control=f\"\"\"\n",
    "        {OPTIONS}\n",
    "        sweep pump instance=\"ipump\" parameter=\"ampl\" values={format_value(pumps)}\n",
    "        analysis hbac1 hbac freq=[{F_PUMP_KERR}] nharm=7 outspur={format_value([[k] for k in range(-7, 8, 2)])} \\\\\n",
    "            from=0.5e6 to=40e6 mode=\"lin\" points=79\n",
    "    \"\"\",\n",
    ")\n",
    "start = time.perf_counter()\n",
    "plot = vacask.run(circuit)[\"hbac1\"]\n",
    "print(f\"5 pump amplitudes × 80 offsets in {time.perf_counter() - start:.2f} s\")\n",
    "\n",
    "fig, (ax, ax_idler) = plt.subplots(1, 2, figsize=(10, 4), sharey=True)\n",
    "for group in plot.split(\"pump\"):\n",
    "    offset = jnp.asarray(group[\"frequency\"].real)\n",
    "    gain = db(reflection(group[\"in;1\"], I_SIG))\n",
    "    idler = db(conversion(group[\"in;-1\"], I_SIG))\n",
    "    f_signal = F_PUMP_KERR + offset[0]\n",
    "    photon_sum = sum(\n",
    "        jnp.sign(offset[0] + k * F_PUMP_KERR)\n",
    "        * jnp.abs((reflection if k == 1 else conversion)(group[f\"in;{k}\"][0], I_SIG))\n",
    "        ** 2\n",
    "        * f_signal\n",
    "        / jnp.abs(offset[0] + k * F_PUMP_KERR)\n",
    "        for k in range(-7, 8, 2)\n",
    "    )\n",
    "    i5 = jnp.argmin(jnp.abs(offset - 5e6))\n",
    "    label = f\"{group['pump'][0].real * 1e9:.0f} nA\"\n",
    "    print(\n",
    "        f\"I_p = {group['pump'][0].real * 1e9:5.1f} nA: \"\n",
    "        f\"G = {gain[0]:6.2f} dB, idler {idler[0]:6.2f} dB at 0.5 MHz; \"\n",
    "        f\"G = {gain[i5]:6.2f} dB at 5 MHz; \"\n",
    "        f\"signed photon sum = {photon_sum:.6f}\"\n",
    "    )\n",
    "    ax.plot(offset / 1e6, gain, label=label)\n",
    "    ax_idler.plot(offset / 1e6, idler, label=label)\n",
    "ax.set_xlabel(r\"Signal offset $f_\\text{s} - f_\\text{p}$ (MHz)\")\n",
    "ax.set_ylabel(\"Gain (dB)\")\n",
    "ax.set_title(\"Signal\")\n",
    "ax_idler.set_xlabel(r\"Signal offset $f_\\text{s} - f_\\text{p}$ (MHz)\")\n",
    "ax_idler.set_title(\"Idler\")\n",
    "ax_idler.legend(title=\"Pump\")\n",
    "fig.tight_layout()\n",
    "show_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f40f14c",
   "metadata": {},
   "source": [
    "Photon flux should obey the Manley-Rowe balance\n",
    "{cite:p}`manleyGeneralPropertiesNonlinear1956`:\n",
    "\n",
    "$$\\sum_k \\operatorname{sign}(f_k)\\,|S_{k\\text{s}}|^2\n",
    "\\frac{f_\\text{s}}{|f_k|} \\simeq 1, \\qquad\n",
    "f_k = \\delta f + k f_\\text{p}.$$\n",
    "\n",
    "Only the signal sideband subtracts the incident wave; the others carry no\n",
    "incident wave. Negative-frequency sidebands count with a minus sign because the\n",
    "pump amplifies into them. The sum stays within $2\\times10^{-5}$ of 1 at every\n",
    "pump, so the odd sidebands up to $k=\\pm7$ capture the mixing. The residual\n",
    "also includes the small junction dissipation. The balance does not test pump\n",
    "stability.\n",
    "\n",
    "Gain is not monotonic in pump power. The Kerr shift of\n",
    "Eq. {eq}`eq:vacask-kerr-inductance` pulls the resonance down toward the pump,\n",
    "and the gain peaks between 41 and 42 nA, where the pumped resonance crosses\n",
    "the pump. A stronger pump overshoots, and at 100 nA the gain falls to about\n",
    "1 dB. The pump detuning is below $\\sqrt3\\,\\kappa/2$, the critical detuning\n",
    "of a Kerr oscillator, so the pumped state stays single-valued and the gain\n",
    "stays finite {cite:p}`eichlerControllingDynamicRange2014`. A pump detuned\n",
    "further would reach a bistable region instead.\n",
    "\n",
    "## Flux-pumped JPA\n",
    "\n",
    "A SQUID terminates a resonator. The pump modulates its flux at\n",
    "$f_\\text{p}\\approx2f_0$\n",
    "{cite:p}`yamamotoFluxdrivenJosephsonParametric2008`. A signal at\n",
    "$f_\\text{s}$ is amplified with an idler at $f_\\text{p}-f_\\text{s}$. This is\n",
    "three-wave mixing.\n",
    "\n",
    "We treat the symmetric SQUID as one junction with\n",
    "$I_\\text{c,tot}\\cos(\\pi\\Phi/\\Phi_0)$. This neglects loop inductance, valid\n",
    "for $\\beta_L=2L_\\text{loop}I_\\text{c}/\\Phi_0\\ll1$\n",
    "{cite:p}`tescheDcSQUIDNoise1977`. The flux is the third terminal voltage in\n",
    "units of $\\Phi_0$.\n",
    "\n",
    "### Lumped flux-pumped JPA\n",
    "\n",
    "The Kerr circuit with the junction replaced by a SQUID\n",
    "($I_\\text{c,tot}=2$ µA). Its inductance\n",
    "$L_\\text{S}(\\Phi)=\\Phi_0/(2\\pi I_\\text{c,tot}|\\cos\\pi\\Phi/\\Phi_0|)$ tunes the\n",
    "resonance. A flux pump $\\Phi_\\text{p}\\cos\\omega_\\text{p}t$ around the bias\n",
    "modulates the resonance frequency by\n",
    "$\\delta\\omega=2\\pi\\,|\\text{d}f_0/\\text{d}\\Phi|\\,\\Phi_\\text{p}$. The pump does\n",
    "not drive the resonator directly, so it causes almost no Kerr shift.\n",
    "\n",
    "Single-mode theory {cite:p}`royIntroductionParametricAmplification2016` gives\n",
    "the threshold $\\delta\\omega=\\kappa$ and the phase-preserving gain at\n",
    "$f_\\text{s}\\to f_\\text{p}/2$:\n",
    "\n",
    "```{math}\n",
    ":label: eq:vacask-lumped-3wm-gain\n",
    "G_0 = \\left(\\frac{1 + r^2}{1 - r^2}\\right)^2, \\qquad r = \\frac{\\delta\\omega}{\\kappa}.\n",
    "```\n",
    "\n",
    "Above about 10 dB the gain is close to a Lorentzian in signal detuning with\n",
    "full width $B$, and $\\sqrt{G_0}\\,B\\to\\kappa/2\\pi$.\n",
    "\n",
    "The bias is $0.3\\,\\Phi_0$ and the pump is at 12.9358 GHz, within 0.01κ of\n",
    "$2f_0$. An unpumped AC sweep gives $f_0$, $\\kappa$ and\n",
    "$\\text{d}f_0/\\text{d}\\Phi$. The signal is injected on the zeroth sideband\n",
    "(`spur={[0]}`). The `hbac` sweep variable is then the absolute signal\n",
    "frequency, and the idler is `in;-1`. The grid skips the degenerate point\n",
    "$f_\\text{s}=f_\\text{p}/2$, where the gain depends on the signal phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0c35d39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T06:55:47.208955Z",
     "iopub.status.busy": "2026-10-21T06:55:47.208883Z",
     "iopub.status.idle": "2026-10-21T06:55:49.998681Z",
     "shell.execute_reply": "2026-10-21T06:55:49.998332Z"
    },
    "tags": [
     "hide-input",
     "jpa-flux"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f0 = 6.4661 GHz, κ/2π = 183.0 MHz, df0/dΦ = -14.10 GHz/Φ0, single-mode threshold 0.0130 Φ0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pump 0.002 Φ0 (r = 0.15): peak  0.41 dB (single-mode  0.41 dB), 3 dB bandwidth 200.0 MHz, √G·B / (κ/2π) = 1.15\n",
      "pump 0.005 Φ0 (r = 0.39): peak  2.58 dB (single-mode  2.60 dB), 3 dB bandwidth 200.0 MHz, √G·B / (κ/2π) = 1.47\n",
      "pump 0.008 Φ0 (r = 0.62): peak  6.88 dB (single-mode  6.95 dB), 3 dB bandwidth  86.0 MHz, √G·B / (κ/2π) = 1.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pump 0.010 Φ0 (r = 0.77): peak 11.73 dB (single-mode 11.87 dB), 3 dB bandwidth  45.0 MHz, √G·B / (κ/2π) = 0.95\n",
      "pump 0.011 Φ0 (r = 0.85): peak 15.49 dB (single-mode 15.71 dB), 3 dB bandwidth  29.0 MHz, √G·B / (κ/2π) = 0.94\n",
      "pump 0.012 Φ0 (r = 0.92): peak 21.66 dB (single-mode 22.13 dB), 3 dB bandwidth  14.0 MHz, √G·B / (κ/2π) = 0.93\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "F_PUMP_LUMPED = 12.9358e9\n",
    "SQUID_LOADS = [\"squid.va\", \"capacitor.osdi\", \"resistor.osdi\"]\n",
    "SMALL_SIGNAL = f\"mag=1e-9 spur={{[0]}} smag=[{I_SIG}]\"\n",
    "\n",
    "\n",
    "def lumped_circuit(source: str = SMALL_SIGNAL, pump: float = 0.01) -> str:\n",
    "    \"\"\"Lumped flux-pumped JPA circuit.\"\"\"\n",
    "    return f\"\"\"\n",
    "        model isource isource\n",
    "        model vsource vsource\n",
    "        model resistor resistor\n",
    "        model capacitor capacitor\n",
    "        model squid squid\n",
    "\n",
    "        iin (0 in) isource dc=0 {source}\n",
    "        rport (in 0) resistor r={R_PORT}\n",
    "        cc (in 1) capacitor c=0.185e-12\n",
    "        s1 (1 0 fl) squid ic_tot=2e-6\n",
    "        c1 (1 0) capacitor c={C_SHUNT}\n",
    "        vfl (fl 0) vsource type=\"sine\" sinedc=0.3 ampl={pump} freq={F_PUMP_LUMPED}\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "FLUX_BIAS = 0.3\n",
    "ac = vacask.run(\n",
    "    Netlist(\n",
    "        \"Unpumped flux-pumped JPA\",\n",
    "        loads=SQUID_LOADS,\n",
    "        circuit=lumped_circuit(f\"mag={I_SIG}\", pump=0.0),\n",
    "        control=f\"\"\"\n",
    "            {OPTIONS}\n",
    "            sweep flux instance=\"vfl\" parameter=\"sinedc\" \\\\\n",
    "                values={format_value([FLUX_BIAS - 1e-3, FLUX_BIAS, FLUX_BIAS + 1e-3])}\n",
    "            analysis ac1 ac from=5.5e9 to=7.5e9 mode=\"lin\" points=200001\n",
    "        \"\"\",\n",
    "    )\n",
    ")[\"ac1\"]\n",
    "(f_lo, _), (F0_LUMPED, KAPPA_LUMPED), (f_hi, _) = (\n",
    "    resonance(jnp.asarray(g[\"frequency\"].real), reflection(g[\"in\"], I_SIG))\n",
    "    for g in ac.split(\"flux\")\n",
    ")\n",
    "df0_dphi = (f_hi - f_lo) / 2e-3\n",
    "threshold = KAPPA_LUMPED / abs(df0_dphi)\n",
    "print(\n",
    "    f\"f0 = {F0_LUMPED / 1e9:.4f} GHz, κ/2π = {KAPPA_LUMPED / 1e6:.1f} MHz, \"\n",
    "    f\"df0/dΦ = {df0_dphi / 1e9:.2f} GHz/Φ0, single-mode threshold {threshold:.4f} Φ0\"\n",
    ")\n",
    "\n",
    "pumps_flux = [0.002, 0.005, 0.008, 0.010, 0.011, 0.012]\n",
    "circuit = Netlist(\n",
    "    \"Flux-pumped JPA\",\n",
    "    loads=SQUID_LOADS,\n",
    "    circuit=lumped_circuit(),\n",
    "    control=f\"\"\"\n",
    "        {OPTIONS}\n",
    "        sweep pump instance=\"vfl\" parameter=\"ampl\" values={format_value(pumps_flux)}\n",
    "        analysis hbac1 hbac freq=[{F_PUMP_LUMPED}] nharm=5 outspur={{[0],[-1]}} \\\\\n",
    "            from=6.3679e9 to=6.5679e9 mode=\"lin\" points=400\n",
    "    \"\"\",\n",
    ")\n",
    "plot = vacask.run(circuit)[\"hbac1\"]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for group in plot.split(\"pump\"):\n",
    "    f = jnp.asarray(group[\"frequency\"].real)\n",
    "    gain = db(reflection(group[\"in;0\"], I_SIG))\n",
    "    k = gain.argmax()\n",
    "    above = f[gain >= gain[k] - 3]\n",
    "    bandwidth = above.max() - above.min()\n",
    "    gbw = 10 ** (gain[k] / 20) * bandwidth\n",
    "    r = group[\"pump\"][0].real / threshold\n",
    "    single_mode = 20 * jnp.log10((1 + r**2) / (1 - r**2))\n",
    "    print(\n",
    "        f\"pump {group['pump'][0].real:.3f} Φ0 (r = {r:.2f}): peak {gain[k]:5.2f} dB \"\n",
    "        f\"(single-mode {single_mode:5.2f} dB), 3 dB bandwidth {bandwidth / 1e6:5.1f} MHz, \"\n",
    "        f\"√G·B / (κ/2π) = {gbw / KAPPA_LUMPED:.2f}\"\n",
    "    )\n",
    "    (line,) = ax.plot(f / 1e9, gain, label=f\"{group['pump'][0].real:.3f}\")\n",
    "    ax.axhline(float(single_mode), color=line.get_color(), ls=\":\", lw=1)\n",
    "ax.set_xlabel(\"Signal frequency (GHz)\")\n",
    "ax.set_ylabel(\"Signal gain (dB)\")\n",
    "ax.legend(title=r\"Pump ($\\Phi_0$)\")\n",
    "ax.set_title(\"Solid: VACASK hbac; dotted: single-mode peak gain\")\n",
    "show_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a86681e",
   "metadata": {},
   "source": [
    "The peak gain follows Eq. {eq}`eq:vacask-lumped-3wm-gain` to within about\n",
    "0.5 dB, even at 22 dB. Above 10 dB, $\\sqrt{G}\\,B$ is within 7 % of\n",
    "$\\kappa/2\\pi$, the gain-bandwidth limit of a single-mode amplifier\n",
    "{cite:p}`royIntroductionParametricAmplification2016,eichlerControllingDynamicRange2014`.\n",
    "Below about 3 dB the 3 dB bandwidth is wider than the 200 MHz sweep, so the\n",
    "product is not meaningful there. A lumped resonator has no higher modes for\n",
    "the pump to couple into. Compare the\n",
    "{doc}`CPW resonator <vacask_cpw_parametric_amplifier>`, whose gain falls well\n",
    "short of the same formula.\n",
    "\n",
    "## Compression\n",
    "\n",
    "A two-tone `hb` run gives the large-signal gain. HB solves the full nonlinear\n",
    "periodic steady state for a finite drive. This is not `hbac`, which assumes an\n",
    "infinitesimal signal. Here the signal sits 5 MHz above $f_\\text{p}/2$, the\n",
    "pump is 0.011 $\\Phi_0$, and the current is swept from 1 pA to 30 nA. The input\n",
    "power is $P=(I_\\text{s}R/2)^2/(2R)$. Saturation in SQUID JPAs is mostly set\n",
    "by the Kerr shift rather than by pump depletion\n",
    "{cite:p}`eichlerControllingDynamicRange2014`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5900ff5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T06:55:49.999738Z",
     "iopub.status.busy": "2026-10-21T06:55:49.999684Z",
     "iopub.status.idle": "2026-10-21T06:55:52.408151Z",
     "shell.execute_reply": "2026-10-21T06:55:52.407317Z"
    },
    "tags": [
     "hide-input",
     "jpa-compression"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "small-signal gain 15.00 dB, input P1dB ≈ -129.5 dBm\n"
     ]
    },
    {
     "data": {
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8cYqIiFD7V61aRUuXLqV27drR1VdfbS0/bdo0ZfaDDz5o3bdu3Tr69ddf6dJLL1XnYWxJRPPuuHHjrK2NmzZtoh9//JEuvvhiuv76661l33vvPZX5+s4776S6deuqfdu2baPvvvuOmjRpQjfddJO17CeffKK6qG+55RZq1KiR2rdr1y766quvVHf17bffbi372Wef0bFjx+iGG26gZs2aqX379++n2bNnU82aNemee+6xlv3iiy/o4MGDdN1111HLli3VviNHjtDMmTMpLi6OHnjgAes4hK+//pp2795Nw4YNo7Zt26p9KSkp9NFHH1FUVBQ9+uij1teFhu3bt9OVV15JnTt3tgbk7777LoWEhNBTTz1lLYu62bx5Mw0YMEAF9SArK4veeOMNdRGjy12DOl+/fj317t2b+vTpY/X35ZdfVo8nTJhg/ZAuWbKEVq9eTd26daPLL7/c6r0ekzl+/HhrHqMVK1bQ8uXLqWPHjupHheaDDz5QxzzyyCPWevj7779p8eLF1KZNGxo+fLi17Ouvv67OBZnbkeQT/PPPP/Tzzz+r63XkyJHWsu+8847SePfdd1OtWrXUvq1bt9IPP/xATZs2pdGjR1vLfvjhh6rubrvtNmrQoIHah2EK8+bNo4YNG9Ktt95qLYsfOydOnKAxY8ZQYmKi2rdnzx6aM2eOagUfO3asteznn39Ohw8fplGjRlHz5s3VvkOHDqmxqwkJCeoa1sydO5f27dtHI0aMoNatW6t9ycnJNH36dIqJiaGHH37YWhbntXPnThoyZAh16NBB7cO1i7oMDw+nJ554wlp2wYIFSvcVV1xBXbt2VfvwmcDnCDe1Z555xlr2p59+oo0bN1Lfvn2tLfX4rOHzCSZOnGgtC3/Q8t+jRw/q37+/2ofPsPb+6aeftt7kly1bRitXrqQuXbrQoEGDrD7rss7cIx566CGKjY1ld4/QmPkeMXDgwArdI/C6ZrxHTB14Fb25NpOSM3Kprn8GDQzZYy0zsEXNct0j7r33Xpb3CE1V3SNMGdiVBW4+COZQceUFlT9p0qQS+3BRgvT0dNWlm52drcbt6ag9LS1NPcZzmqNHj6oLFGV137yxLG4A+D/IzMxUH1yUM/bj2yuLDy9+PeD9c3JyLnhd3OSMZXFxIKLHedi+rrEsnrdXFppRBhetLov3RTmcB95Dgw8QykMLbl7Gsvj1AJ3GsiiDXx76daEdNzLsN5bFYxxvLHv27FmrB8alXnT9GBdsRllsuKnj/Ixl8XrGXzV4f5TF8cZzQJ3o1zCWxTnjvKDTqAPna2y1RR2gDOoRr6XLQoP2o7Syui7x/rqsPh6eoQyuEejRs75xbravq/3CzVDXqX4/29dFWZTB+oioX90NYSyrj8dfXO8IBlFW/4rFMbZloQVl8KUGL3RZPG+vLMrgxobn9C9T4+vq88J567L4q8viNXRZox/Yhx+FKKt/yBnLGlsMUAb3ANSHvuZ0WezTX6TO3iN0PXC7R6DuUL9mvkcg2EGg6uw9QmO2e0TrOpG08K5EWrkzmTIzM2j/mj10ujiU1hQn0p3Z+RTul+PwHmG8n3C6RwDUZ1XdI4ytjI7ws+hX8yLw6wi/FPWvN/yaxUXx7bffqv/jFxx+leFXWHlb7BB1A/wCdKZZFEbgg2/WbpaKltUfBjN3s1S0qR2tDbhpmKWbxZ1lcb3r1nNOXbH6eufYFXvy5EmlnWNXLL5Eod1buuM8dY/A9Y4GC+y/ccZaWn8ok0Z2qEevjmzj812xp0+fVoGT2bpi0RIMkpKSyPQtdmheR0CHpmtjBA2DUBFG04zgFyo2jb7obMsbLwYjupwx6nZUtjyva2+hZZybvdeoyrK6K8J44Tl6XXtlcYF6sqy9enemLED3kbPXSXnK2qt3b7tOjMvtOPO63uC9K9eJ1u2Mn5VV1tPe43q3fc5V7735OjHWu/bd2XuEJ72vjOsEulEW35HPDmlFI97/i+ZtOEK3dm9ELevEVPh1vd37wMBAdb0b69NbvHemrGlmxZbGokWLVBCHaBVBFjZ0yaKbFeMTKpuKVKovwFU3EO38EM95wtV3rVt1I58+SCMbFRJZiunFRf9auwR9FQ6ee31gh0G6GJhr3DDoFoNW//jjj0p/f+N4Ck5w1Q1EOz/Ec55w9V3rRmCHiRmRJzZRaKAf/bX3FC35N4V8mRwGnntlVyyah3XzKsa36TFuGowNwKwRPRNIEARBEATnQLcpZtuCW2Ib04d/HqD//fwv9b44gYICvL7dRzDT5InKQE/ZL8/AQyPoBrbXh+/rcNUNRDs/38Vzfp5z9t2e7qzcAurz6jI6lZNPk65uSbdcdi59jq9RaFLPnYlhJCR3gJ6wwQ2uuoFo54d4zhOuvtvTHRUaRI8MPJff8M3Fuyjj7LmZor5GLgPPJbBzgJ4GzQ2uuoFo54d4zhOuvpem+/pO9alpjUhKO1NA7y39v+TFvkQBA88lsHNUQU4u5eErcNUNRDs/xHOecPVd60aQg9UusOFxYIA/PTP4EvXcZ6sO0KFT/5cg11fwZ+C57yt0EUzU4AhX3UC080M85wlX37VuDLFHwl5serh9n2YJ1LNpdcovKqaXf9lBvkYMA88lsHOA7VItXOCqG4h2fojnPOHqu9aNSQRYaxabnlCAhL3PDr6E/P2IFm1NpvUHTpMvkcbAcwnsBEEQBIEh6JZE2jBsxi7K5rWiaVSn+urx5EX/UnExi+QZPoMEdg4wLkvGCa66gWjnh3jOE66+l0c3ZshGBAfQ5sPptHDLMfIVQhh4LoGdA8yY78YdcNUNRDs/xHOecPVd68a668iLhg2PjdSICqVxfRLV41d+2Um5BUXkCwQy8FwCOwdwWH7EHlx1A9HOD/GcJ1x917qRrHfevHlqw2Nb7uzZhOrEhNLR9LP06ar95AvkMPBcAjtBEARBYAgmSjRs2FBteGxLaFAAPTHoYvX4/aV76WR2XhWcpeAsEtg5IDIykuVVxVU3EO38EM95wtV3rTsoKIhuvfVWteGxPYa1qUut68VQdl4hvfH7LjI7kQw8l8DOAfn5+cQRrrqBaOeHeM4Trr47o9vf348mDG6hHn+55hB9s/4w/bDpKK3ee4qKTDhbNp+B574/itANF0FERARxg6tuINr5+S6e8/Ocs+/O6u7cOI7a1o+lTYfT6Yl5W6z7a8eE0sShLWhQq9pkFvIZeC4tdg6wN+6AA1x1A9HOD/GcJ1x917qxjNiHH36otrLWUP1lW7IK6mw5npFL42ZvUM+bBT8Gnktg54DY2FjiCFfdQLTzQzznCVfftW4sI3bixAm16SXFbEF366SF2+0+p4/A82bplo1l4LkEdg7gsPyIPbjqBqKdH+I5T7j6blxSbMyYMWorLb/b2v2nKTkjt9TXQjiH51HODKQx8FzG2AmCIAgCQ7CMWGLiuSTEpZGSVXpQV5FyQuUjLXYOCA4OJo5w1Q1EOz/Ec55w9d0Z3ViBwp3lqppgBp5LYOcADheBPbjqBqKdH+I5T7j6rnVjGbFdu3apzXZJMeOMWMx+LW3KAfbjeZQzA8EMPJfAzgHZ2dnEEa66gWjnh3jOE66+a91YRmzOnDlqs7ekGAjw91MpTUBpwR2eRzkzkM3AcwnsBEEQBIEhSP1Rp04dtZWVBgR56j4Y055qxVzY3TqyYz1T5bHjgEyecACH5UfswVU3EO38EM95wtV345JiY8eOLdcxCN4GtqilZr9iosTmw+n06aoDtPFQukqVYpb8cJEMPJcWOweUlbTRl+GqG4h2fojnPOHqe0V1o7u1W2I8DWtblx4Z2IzCgwNod0o2rTtgnhQiHDyXwM4BeXl5xBGuuoFo54d4zhOuvrtDd1RoEA1rW0c9/mLNQTILeQw8l8BOEARBEBiC1qtPP/1UbRVpyRrduaH6+/PW43Qq2/cDJrMggZ0DqlWrRhzhqhuIdn6I5zzh6rvWjbFxhw8fVltpS4qVxaX1YqhNvRjKLyqmef8cITNQjYHnEtg5ID39woWPOcBVNxDt/BDPecLVd60by4iNGjVKbaUtKeaIG7uca7X7cu0hKjbBerHpDDyXwM4BFfkV4wtw1Q1EOz/Ec55w9V3rxpJizZs3VxseV4QhbWpTVGggHTx1hlbtPUnejoWB5xLYOYBDlmp7cNUNRDs/xHOecPXdnbrDgwPp2vb11OMv/j5E3k4wA88lsHMAh4vAHlx1A9HOD/GcJ1x9Ny4pduDAAbWVtqRYeRjdpYH6+/u/J+hEZi55M8EMPJfAzgEclh+xB1fdQLTzQzznCVffjUuKzZo1S22lLSlWHprVjKLOjeKoqNhCX607TN5MNgPPJbATBEEQBIZgtYiEhAS1ubpyxI1dz7XazVl7iAqLKt76J7iOLCnmgIiICOIIV91AtPNDPOcJV9+1biwpdu+997rlNQe1qkVxEcGUnJFLy3am0oAWNckbiWDgubTYOcCV5mkzw1U3EO38EM95wtX3ytAdEhhAIzvU8/qVKAoZeC6BnQM4LD9iD666gWjnh3jOE66+V5buGzqf645dtiuVDp8+Q95IHgPPJbATBEEQBIZgGbHPP/9cbRVZUsyWRtUjqGfT6oRUcXPXeX/qE19FAjsHxMbGEke46gainR/iOU+4+q51I1nvvn371OauxL03nk998tW6I5Rf6H2TKGIZeC6BnQMyMzOJI1x1A9HOD/GcJ1x917qxjNiIESPUVtElxWzpf0lNqhEVQiez8+j37SfI28hk4LkEdg5wJWmjmeGqG4h2fojnPOHqu9aNZcRat26ttoouKWZLUIA/Xd+pvtdOoihm4LkEdg7AdHCOcNUNRDs/xHOecPW9snWP6tyA/P2I/tp7ivameldC4CAGnktg54DQ0FDiCFfdQLTzQzznCVfftW60Xh09elRt7mzJqhsbRv2a11CP56zxrkkUoQw8l8DOAVlZWcQRrrqBaOeHeM4Trr5r3cjpNn36dLW5O7/bjV0aqr/zNhyh3IIi8hayGHgugZ0gCIIgMATLiMXExKjN1SXFbOnVLEG13KWfKaCftia79bWFspHAzgHh4eHEEa66gWjnh3jOE66+a90Yb/bwww+rzd1jzwL8/Wj0+dQnX3hRd2w4A88lsHMAhxk09uCqG4h2fojnPOHqu6d0j+xYjwL9/eifg2n0b7J3pBkpZuC5BHYOyM3NJY5w1Q1EOz/Ec55w9d1TumtEhdLlLWuqx196SatdLgPPJbATBEEQBIZgwsTcuXPV5u7JE7aTKL7beJRy8irnPYSSSGDnAA7Lj9iDq24g2vkhnvOEq+9aN7old+7cqbbK6qLs1iSeGlePoOy8Qlqw+RhVNbEMPJfAzgEcpkbbg6tuINr5IZ7zhKvvWndAQAANGTJEbXhcGfhjEkXnc5MoZv990G1r0laULAaeu2dxOB+mqMh78u94Eq66gWjnh3jOE66+a90I5jp06FDp73dth3r06m87KelYppohGxUaqMbfdW4cp2bPepIiBp5LYOeogty0MLLZ4KobiHZ+iOc84eq7p3XHRQRT2/qxtHb/aZrw/Tbr/toxoTRxaAsa1Kq2x84lkIHn0hXrAA45b+zBVTcQ7fwQz3nC1XetG92iKSkpaqvMLtJftiWroM6W4xm5NG72BvW8pwhn4LkEdg7IzPSO3DuehqtuINr5IZ7zhKvvWndBQQF98MEHasPjyqCo2EKTFm63+5wOJfE8ynmCTAaeS2AnCIIgCExBC1ZltmKhpS45o/TccQjn8Ly9Fj3BBwK7/Px8uvXWWyk0NJSmTZtm3X/69Gm1PyEhgRo0aEDPP/98peXcsSUsLIw4wlU3EO38EM95wtV3rTs4OJieeOIJteFxZZCSlevWcq4SxsBzrxlFmJ6eTsOHD6f9+/dTfHx8icDtP//5j2om/v777yktLY0efPBBNbNl8uTJlX5eVT01u6rgqhuIdn6I5zzh6rsndWP2qzvLuYqFgede02K3cuVK8vPzo/Xr11Pt2v83QwaB3pIlS2jGjBnUvXt3lW/n5Zdfpk8++cQj58Vh+RF7cNUNRDs/xHOecPXdk7qR0gSzX0tLaoL9eB7lPEEuA8+9JrDr0qUL/f7776q71Qha75YuXUqJiYnWfUFBQarbVhAEQRCEioGesfnz56utsoY3IU8dUpqA0oI7PO/pfHa+jNcEdgjo7OWXiY6Opj59+qjWPM306dOpZ8+eZb5eXl6emv2iNyyXUpEm2JiYGOIIV91AtPNDPOcJV9+1bnwvbt26VW2VtaQYQJ66D8a0p1oxJbtbI0IC1H5P5rGLYeC514yxKy/okl28eLHqsi2LqVOn0qRJk0rsq169uhqjp83Nzs5WY/UQUGJWkJ4GrQdXnj17lnJycqhOnTrWssjUHRkZSRkZGaoMJnog6ERZHYieOXNG/fpB2aioKDV+UJf19/dXzwM8h2ZhjB/Efhyry4aEhKjzwvsDvCdaKbHh/bDendaCsmjFxDnalgXVqlVTr4vAFgNksemyERER6lwRCOuy0IZlV/AeOGe9BAvKog50Uzae10GzbR3iMfbrsuWtb3t1aFvfxjo0lrWtQ5TF8boObesb52SsQ13f2GrWrFlqfdvWIc4Pr2msw/LWt7EO4aGxvm3rEGXxXGl1iPezV9+2dVhWfWNfrVq1Sq3vsq7Zsurb3jXr6Pp25prVZW3rsKxr1lgW54EflxW9Zsuqb2+/R+D4evXqOX2PsHfNmu0egXrFOTp7j3BU395+j9Dfa/iLRhJohQZ9/q7Ud2nXbJe6ofT7g91o45FM+mnLUZrzz3GqFhZE3RtGqvf11D0iLy9PxQLO3iNcvWZdvUfo48qDn8ULRxJ27NiRrrvuOnrqqadK7F+9ejX17duX3n77bbrrrrvKfA2Yp03VXb2ouO3b7efTKQ1cZLgouMFVNxDt/HwXz/l5ztn3qtZ9Jr+Q2k/+nXILimnh/T3o0noxbLRXlJYtW6q/SUlJ5umKdcTBgwfVrNm7777bYVCno31EvXrTv0icpbIWRvZ2uOoGop0f4jlPuPpe1brDgwOpf/Oa6vGirZ5bdcIbtHsCUwR2aAYdOnQoderUiV5//XWPvjeaSznCVTcQ7fwQz3nC1XetGx126KLU3ZSe5KpLz42r+2lrskffO5KB514f2KE/+4YbblD9zW+++SalpqbS8ePH1eaJJMW6D5wbXHUD0c4P8ZwnXH3XujEO7q233lJbZS0pVhp9mydQaJA/HTp9hrYd9dwyXxkMPPf6wG758uW0aNEi2rt3LzVt2lTluNPbjh07qvr0BEEQBMG0YIIAtqroju3XvEaVdMf6Ol45K/avv/6y9oP37t3bOlPE3ji6yobD8iP24KobiHZ+iOc84eq7cUmxZ555psrOY/CldeinrcdVd+z4QRdXaBy8s4Qx8NwrAzvjmnWY9ICpwIIgCIIg+A7G7tikY5nUqq7v55jzBF7fFVvVlNZa6Otw1Q1EOz/Ec55w9d1bdBu7Y3/cksxKe2UigZ0gCIIgMAQTEBcsWKA2T0xG9KbZsb6MBHYOQA48jnDVDUQ7P8RznnD1XetG1omNGzeqrTKXFCsLtNgZu2Mrm2gGnktg5wC9TAo3uOoGop0f4jlPuPqudWOSIlZzwlZViXs9PTv2DAPPJbBzQFU1T1c1XHUD0c4P8ZwnXH3XuhHM9erVS21VuSKD7o5dtKXyu2MLGXgugZ0DOCw/Yg+uuoFo54d4zhOuvnubbk92xwZ4mfbKQAI7B3BYfsQeXHUD0c4P8ZwnXH03LimWk5OjtqqcuODJ7thIBp5LYOcADsuP2IOrbiDa+SGe84Sr78YlxaZNm6Y2Ty8pVlXdsRw8l8BOEARBEIQqxdOzY30Zr1x5wpvguuoFV91AtPNDPOcJV9+1bqzyNHHiRPIGdHcslhhDd2xlrUIRysBzabFzVEH+PKuIq24g2vkhnvOEq+/eqtsTyYr9vVS7O/F9hS7CIeeNPbjqBqKdH+I5T7j67q26dXfswVOV1x17xku1uxMJ7ARBEASBIcjp9ssvv6jNG/K7oTu278WeS1bsq0hg54CoqCjiCFfdQLTzQzznCVfftW4sI7ZmzRq1VdWSYrYMbl253bFRDDyXwM4Bubm5xBGuuoFo54d4zhOuvmvdSNbbo0cPtXlL4t7K7o7NZeC5BHYOqOrcPlUFV91AtPNDPOcJV9+1bgRz/fv3V5u3BHaV3R1bwMBzCewcVRCDGTT24KobiHZ+iOc84eq7t+uuzNmx/l6u3R34vkIXiY6OJo5w1Q1EOz/Ec55w9V3rRtCUn5+vtqpcUsyT3bHRDDyXwM4B6enpxBGuuoFo54d4zhOuvmvd6JacOnWq2rypizIipPK6Y9MZeO7SyhOYHv3PP//Q2rVrKTk5mY4fP66aORMSEqht27ZqQGbdunXdd7aCIAiCIPg86I79edtx1R375BUXk5+fX1Wfku8GdmiuXbhwIc2cOZOWLFlCWVlZqmmzdu3aVLNmTSoqKqLly5fTyy+/rMojuLvnnnto1KhRXjM40xk4LD9iD666gWjnh3jOE66+a91BQUH09NNPWx97E+iODQn8v+5Ydy0xFsrAc6e7Ym+77TYaMWKEmjKM5tutW7eqps0dO3aogG7lypW0e/dutW/+/PnUoEEDuvPOO2nIkCFkRswYjLoDrrqBaOeHeM4Trr5r3WgFw3qx2LytRQzdsQjuAFrt3EUAA8+dDuwQ1B04cIB+/vlnuu+++6hVq1Z2Lwi04g0fPpxmz55NR44coTvuuIPMSE5ODnGEq24g2vkhnvOEq+9m0a1nx2Kcnbsmd+SYRLtHu2KHDRvm9JvExcXRdddd5/RxgiAIgiBUDhg6tWzZMvW4T58+XteaVVndsb5OhWfFHjx4kD7//HNatGiRdSmS77//nq688kpq06YNjRw5ktatW0dmh8PyI/bgqhuIdn6I5zzh6rvWjcAOw6ew4bG3URndsVEMPK9QYPfVV19R06ZN6dZbb1Vj526++WaaM2eO6qbNy8tTkT/G2fXq1YuSkpLIzHBYfsQeXHUD0c4P8ZwnXH3XupHFokuXLmrz1sS97k5WnMvAc6edRK6b+++/n8aMGaP6qvfu3UsbNmyg8ePH01NPPUV//PEHvfXWWyoNyiWXXELvvPMOmRlvyu3jSbjqBqKdH+I5T7j6rnUHBgbSoEGD1IbH3ojujj3gpmTFBQw8dzqwQyB38uRJevbZZ9W04SZNmtAzzzxDhw8fVsGdBn31uFi2b99OZsbbZgp5Cq66gWjnh3jOE66+m0m3MVmxO7pj/Uyk3WOBXXZ2tvobHx9v3VevXj31NzIy8oKZsWfOnCEzExsbSxzhqhuIdn6I5zzh6rvZdA9u7b7u2FiTaa8I3tmp7kWkpaURR7jqBqKdH+I5T7j6rnVjjdhJkyapDY+9FWN37Nx1h+iHTUdp9d5TVFTsfJCXxsDzCneqv/nmm9YMzvv27VN/X3nllRIDMJGwWBAEQRAEwZXu2EtqR9Omw+n09Pxt1v21Y0Jp4tAWNKjVuRY9oYKBHcbORURE0LRp00rsx77//e9/F5Rv3749mZmQkBDiCFfdQLTzQzznCVfftW4sI/b4449bH3srv2xLVkGdLcczcmnc7A30wZj25Q7uQhh47nRg165dO+s4Ow5460yhyoarbiDa+SGe84Sr71o3JhKgUcabQXfrpIX2J2GiIxZTIfD8wBa1KMDf8cQIDp7LGDsHcFh+xB5cdQPRzg/xnCdcfTeT7rX7T1NyRum55xDc4XmU8zXtFcXp0HXr1q00dOjQcpdv3bo1LViwwNm3EQRBEAShEsFqE6tWrVKPu3fv7nVLioGUrFy3luOA04FdQkIC3XPPPSWyOL/77rvUsGFD6t+/v1quA5Mp5s2bp1agQCJjM2ObwoULXHUD0c4P8ZwnXH3XuhHYLV26VD3u2rWrVwZ2NaJC3VoukoHnTgd2tWrVUitMaLA2LJYWs51M8eKLL6pfAOHh4WRmMAXcmweVVhZcdQPRzs938Zyf55x917qRxQLj5oG3LinWuXGcmv2KiRL2kptgVF2tmFBVrjzkM/DcJSexHuwvv/xC99133wXP1alTh6677jqVFsXMeHNun8qEq24g2vkhnvOEq+9aNyYSXH311Wrz1kkFmBCBlCbAdmqE/j+eL8/ECS6euxTY6QsBS4zZIzU11WsvlvLCYfkRe3DVDUQ7P8RznnD13Wy6kcoEKU3QMmekZnSoU6lOzKjd44Fd48aNVZ66O++8kzZv3mxd6uPs2bOqpW727Nk0cuRIMjMclh+xB1fdQLTzQzznCVffzagbwdvK8f1oztguFBdxriv1hatbOp2cONaE2p3F5U71b775RgV0bdu2VRMnmjRpQtWqVaPHHntMbXfccQeZmfT0C5MicoCrbiDa+SGe84Sr71o3uiWxsAA2M3RRoru1W2J1GtK6jvr/st2pTr9GOgPPXe4nRSC3ceNGtXwY/p45c4Zq165NAwYMoEaNGpHZcXXBYbPCVTcQ7fwQz3nC1Xej7oKCAjIbfZvXoP+3+iAt3ZGitDjTvWph4LlbBsBhinS/fv3U5msEBwcTR7jqBqKdH+I5T7j6rnVjduhDDz1kfWwWujWJp7CgAJWY+N/kLGpRJ7rcxwYz8Nzprtj33nuP/vjjD6ei3i1bttDrr79OZoTDRWAPrrqBaOeHeM4Trr5r3WjpwpgzbGaaVBAaFEDdL4pXj5fuTHHq2GAGnjsd2B0+fFglIr7oootUpP/dd9/RiRMnSgR6hYWFtGnTJnrttdeoS5cu1KZNG0pKSiIzwmldXCNcdQPRzg/xnCdcffcF3eiOBUv+PcFOu9u7Yl966SW66aabaObMmfTbb7/RO++8o4I6NONiVYri4mI6deqU6rfHr4BrrrlGrUzRqVMnZ99KEARBEIRKAitPrFu3Tj3Gd7Q3rjxRGv3OB3YbD6fT6Zx8iovw/Za4Sh1j17JlS+tKEwjiNmzYQMeOHVMtd2jORYCHVrpLL73U9HnsIiIiiCNcdQPRzg/xnCdcfde6Edj9+uuv6jFSl5kpsKsdE0aX1I6mf5MzadnOFLqmfb1yHRfBwHOXo674+HgaOHAg+SroVubQJ28LV91AtPPzXTzn5zln37VuLCOGBhhvXlKsLPo1T1CB3R87yh/YFTLw3HxOepi8vDziCFfdQLTzQzznCVfftW70qGG4FDYz9q71a15T/f1zVyoVFBWX65g8Bp5LYCcIgiAIguloWz9Wja3LzC2kfw6mVfXpeA0S2DkAq2hwhKtuINr5IZ7zhKvvvqIbK1H0bpagHiNZMSftZSGBnQM4LD9iD666gWjnh3jOE66+G5cUe/XVV9VmhiXFypodi3F25SGdgedeF9ghdQryzNhb5gTPYRaPp8+HI1x1A9HOD/GcJ1x9N+rGMqDYzEqvZgmq5W53SjYdPu1YBwfPXQ7sMMPk448/pltuuYWuvPJKGjRoUIntwQcfLPdr5eTk0NVXX01RUVEqubFxsOM999yjpimHhYXRyJEj6fTp0+QJzLTMijvhqhuIdn6I5zzh6rvWjb/jxo1Tm1nrIiYsiDo2rFbuVrsgk+r0aGA3atQoFXQdPHiQatSoQbVq1SqxIR1KeUhJSaGePXvS3r17qUGDBiWee+KJJ2jp0qW0fPlytaLF8ePHVZJkTxAaGkoc4aobiHZ+iOc84eq71o28s/jexmamJcVc6Y4NZeC5S/Obk5OTaf78+arFbuzYsS6dyNq1a6lRo0Y0a9Ys6tu3r3V/Wloaffjhh7Rw4ULr6hVY9aJp06a0efNmlQi5MsnKymIx2NIWrrqBaOfnu3jOz3POvvuabgR2U3/eQav3naIz+YUUHhzIRrvbW+z0mmtXXXWVyyfSo0cP+vbbb1U3rJEVK1ao7lesT6vBOrVY/WLJkiUuv68gCIIgcARj1v/55x+1eXr8uju5qEYk1Y8Lo/zCYlq15xRxx6XADq1mHTt2VN2kroJ1Ze01Be/fv5/q169/QfJEtO7hudLAuLzMzEzrhjVsKzJoksPyI/bgqhuIdn6I5zzh6rtxSbEff/xRbWYO7BA79Lu4fN2xEQw8d6krFt2kN998M02YMEF1paIVzXatOawbO2zYsAq/B2brxMTEXLAf+86ePVvqcVOnTqVJkyaV2Fe9enV1zvp4tDjiYkbQGB4ergJAgBZCgNdHgIjxB7os9EVGRlJGRoa1vx4XlT6X6Ohodc6YVIKyaIHU06tRFsu26BlIeC43N1fNAMZ+HKvLhoSEqPPChBKA98R0dGx4PwTCWgvKYkCobkE1lgVodsbrIrDFUirYdFlc5DhXnY0bZaENevA6OGc0XeuyqAOcM8A56KDZtg7xGPt12fLWt706tK1vYx0ay9rWIcrieF2HtvWNczLWoa5v1AXGhpZW37Z1iPPDaxrrsLz1baxDeGisb9s6RFk8V1od4v3s1bdtHZZV39CBz0lp9V3WNVtWfdu7Zh1d385cs7qsbR2Wdc0ay+K18DoVvWbLqm9vv0dAO8ZDO3uPsHfNmu0eoXH2HuGovr39HmH8XmvSpIm1UUWfvyv1XVX3iF5N42nW6oO0ZPtxyhvcTNWBvfpGfcFXZ+8Rrl6zrt4j9HHlwc/iwtzfjRs3qgkPZYGFhf/880+nXhetgNdddx099dRT9Pbbb9NHH31ESUlJJcoMHjxYdcm+9dZbdl8DhhqXDunSpYuquO3btzt1LrjIfL0/3h5cdQPRzs938Zyf55x990XduQVF1O6F3+lsQREterAHtaxzYYOQmbWj4QzYxkJub7Fr166d9VdGZYFfE4cOHVJRunHh3j179tAVV1xR6nGI9rFpzLjAsSAIgiAIjgkNCqDuF1Wnxf+eoD/+TSk1sOOA10c7aBFEM+1PP/1k3bdt2zbatWsXDRw4sNLfH82yHOGqG4h2fojnPOHqu6/qtqY92ZnCTrtLLXbouX366aepQ4cOKi3J999/X2Z5V8fYoV/6oYceogceeMDah/3oo4/Sf/7zH7rkkkuoskF/ub0xfr4OV91AtPPzXTzn5zln37VuNJq89957at99991n+uS9OrDbdDidTmXnUXzk//XacfLc6cAOg/veeecdlX4EY9wefvhhh2PsnA3sELwZu11ffPFF9X9ceLgQhw8frta28wQYKMkRrrqBaOeHeM4Trr5r3Wio0QP4fWGprVoxodSidjRtT86kZTtT6doO9Vh67nRgh6Br9+7dKuLFbJLKGGNnO9kCLXWTJ09Wm6cx+y+YisJVNxDt/BDPecLVd60b36133nmn9bEv0K95DRXYoTvWXmDHwfMKjbGrU6cOi1wwXJYfsQdX3UC080M85wlX37VuTCqsW7eu2nxlgmG/S851x/65K5UKiopZeu6WEB1NuVjeS+exMYKWvW7dupFZ4bD8iD246gainZ/v4jk/zzn77su629SLpbiIYDqdk0/rD6RRt8R4NtrdFtjNmzePbrnlFrtBHcAki/Xr17v6NoIgCIIguBGMN0OWCdCqVSufaLUL8PejPhcn0PwNR2npzpQLAjsOuOQiJjLcf//9NHr0aDp48KBKPzJlyhRKSUmhadOmqTx3CxYsIDODMYUc4aobiHZ+iOc84eq71o2VDb777ju14bHPpT3ZkcLSc5cCOyQJPnHiBD333HPUoEEDuuyyy1T0jxQnjz32mMqU/Nprr5GZ4TCDxh5cdQPRzg/xnCdcfde6sRoTFgEwLivmC/RsmqBa7vakZNOhU2fYee5SYIf1y4CeLt2sWTPauXOn9fnExETTd8PqNeG4wVU3EO38EM95wtV3rRszRG+66Sa1+dJs0ZiwIOrU6Nw4uj92nGDnuUuBHWbSIJfd448/rpL+de7cmbZs2aKCOSwB9uuvv6oZtIIgCIIgCJ7ujl1ipzvW13EpsEPT7WeffUZr166l1NRUFeQheTACvLi4ONUt+8QTT5CZ8fUM1aXBVTcQ7fwQz3nC1XcOunVgt2bfacrJK2Sl3eUpMN27d1cTJ9DtCr744gv6/PPP1eSJpKQktfKEmamMBMxmgKtuINr5IZ7zhKvvWjcmQL7//vtqw2NfIjEhkhrEhVN+UTGt2nOSlecupTspKipSOWFAenq6df/gwYOtj/G8HotnRqCRI1x1A9HOD/GcJ1x917qxjBh62/RjX8LPz0+12n321wE1O/bylrXYeO5SYLdx40bq1KmTw3JIBvjkk0/SU089RWbDV5ZZcRauuoFo54d4zhOuvmvd+Is8tMZ9vkTf84Ed8tkhcEWw54s6bXFJYfPmzembb76hRx99lHr37k1XXHEFxcbG0qFDh2jGjBmqpW78+PG0bt06mjBhAjVt2pSuvfZaMhMcct7Yg6tuINr5IZ7zhKvvWjcSEjdq1Ih8lS6N4yg8OIBOZOZR0rFMalU3hoXnLo2xCwsLoxdeeEHNisW4ujFjxtCQIUPo3nvvpdWrV6u+bKQ/QZ47BHRz584ls4HZvhzhqhuIdn6I5zzh6jsX3aFBAdT9ouolkhVz0O5SYIegbevWrTRixIgLngsODqbLL7+cvv32W/V/JCs+duyYK28nCIIgCIKbQLLeHTt2qM1XE/f2K2MVCl/FpcAOKU0Agjt7IKedXmwXK1TEx5tvzTa0SnKEq24g2vkhnvOEq+9aN5YR++qrr9TmS0uKGel78bnAbvORdDqZncfCc5cCu1q1atE111xDN998M3366aeUnJxMOTk5Kn/duHHjaNGiRXTHHXeodCizZ8+moUOHuu/MBUEQBEGoMJhMUL9+fbX50pJiRmrFhFLLOtGESb/Ldp6bAezruDw9ZNasWfTAAw/QXXfdVWIacXR0NH344Yc0bNgw2r9/v+qWvf3228lsnD17lkJDQ4kbXHUD0c7Pd/Gcn+ecfde6sYyYGb+XnaV/8xpq8sTSHSnUv0mEz3vucmAXGRlJM2fOpNdee402bNigBibWqFFDpUEJCQlRZRo3bqxmzwqCIAiCIHg67cnbf+xR68b+2CCCLqpTTJ0bx1GAv2+2UrotoQvG2w0YMIB8DQ7Lj9iDq24g2vkhnvOEq+/cdCen5xJiuLMFxfTfRXuIaA/VjgmliUNb0KBWtcnXcHlJMV+Hw/Ij9uCqG4h2fojnPOHqu3FJsU8++URtvrakmOaXbcl035cbqNhmYY3jGbk0bvYG9byvIYGdAzgsP2IPrrqBaOeHeM4Trr4blxRDGjJsvrakGCgqttCkhdvJnjK9D8+jnC/h+2truEhAQABxhKtuINr5IZ7zhKvvWjeW17rhhhusj32NtftPU3JGbqnPI5zD8yjXLdF86dhKw/ecdDOYHMIRrrqBaOeHeM4Trr5r3VhSrFmzZuSrpGTlurWczwZ2J0+epO+//77c5RMSElTKE7OSkZFhTbLMCa66gWjn57t4zs9zzr5z0V0jKtSt5Xw2sDt8+DA9/PDD5S7fvn17Uwd2giAIguCLYBkx5JnVacnQgudLdG4cp2a/YqKEvVF0fucTGKMc68CuXbt2rGYS+Xoiw9LgqhuIdn6I5zzh6rvWjWXEsCoUePrpp9Ua775EgL+fSmmC2a8I4uwFd3je1/LZ+VZ4Xgn46jIrjuCqG4h2fojnPOHqu9aNvzVr1lSbr9bFoFa16YMx7VXLnJHIkEC13xfz2AW6q79+8+bNdObMGbuJELt160ZmhfuSMxwR7fx8F8/5ec7Zd+OSYvfccw/5OoNa1aaBLWqp2a/frttP8zadoMbVw30yqHNLYDdv3jy65ZZb7AZ1oEOHDrR+/XpX30YQBEEQBKFCBPj7qZQm8cGFKrDbdiyTTufkU1yEb3U/u9wVi0zV999/P40ePZoOHjxIAwcOpClTplBKSgpNmzZNjcdbsGABmZno6GjiCFfdQLTzQzznCVffueoGiXWqU/NaUYR8zCv3nCRfxKXAbs+ePXTixAl67rnnqEGDBnTZZZfRtm3bVIqTxx57jFq2bEmvvfYamZnSWiJ9Ha66gWjnh3jOE66+a91onPnss8/U5qtLitnT3qtZgnq8Ylcq+SIuBXZRUVHWMXYAiQ537txpfT4xMdH03bCYNcQRrrqBaOeHeM4Trr5r3VhGDL1t2HxxSbHStPdsWl09XrH7pE/qdimwq1u3Ll100UX0+OOPU2ZmJnXu3Jm2bNmigrn8/Hz69ddfqU6dOmRmuC85wxHRzg/xnCdcfTcuKXbdddepzReXFCtNe6dGcRQS6E/HM3Npd4rvpW9zyUlMj0YT7tChQyk1NVUFecOHD1cBXnh4uHr+vffeIzOjWyW5wVU3EO38EM95wtV3rRsJiTFkihPQjtikS5N4+nNXqtqa1fSt68DlPHbdu3dXzbjodgVffPEFff7552ryRFJSklp5wsykp6cTR7jqBqKdH+I5T7j6zlW3UXuv892xf+72vQkUge7+1RMSEkI33nijO15WEARBEIRKXFLsyJEj6nG9evV8bkmxslATKBb9S2v2naLcgiIKDfKdbvlAdwxE/PTTT2nVqlUqzYntQERMqHj77bfJrHBMXslZNxDt/BDPecLVd+OSYjNnzvTZJcXK0t60RiTVjA6hE5l5tP5AGvU434LnC7gc2I0aNYq+++476tWrFzVs2PCCZUni4+PJzHD6BWOEq24g2vkhnvOEq+9aN76v4+LirI+5ae/ZNIHm/XOE/tydKoGdJjk5mebPn08ff/wxjR07lnw15w26l7nBVTcQ7fx8F8/5ec7Zd60bS4o98MADxNXzXs3OB3a7UumZqy4hX8GlnyvZ2eemCV911VXuOh9BEARBEIRKp8dF1QkNlTuOZ1FKZq7P1LhLgV3Tpk2pY8eOtHTpUvJVuE+H54ho54d4zhOuvnPVbasd68ReWjfGmqzYV3BpjF1aWhrdfPPNNGHCBFq7dq3Kh2Ob8BHLiw0bNozMSm5uLkVGRhI3uOoGop2f7+I5P885+651Y/LE119/rfb95z//YZGkONfGc6xCseVIBq3YnUrXdqhHvoBLLh44cEDNpAGYGWsP5LEzc2DHZf08W7jqBqKdH+I5T7j6rnUj3cnu3butjzl63qtpAr23dK9qsSsutpC/vx/vwK5du3bWcXa+CvdZUxwR7fwQz3nC1XetGz1suuGFy/Jq/jaet2tQjSKCA+hUTj5tT86kVue7ZtkGdkVFRZSVlVVmGVwsZu7Pj46OJo5w1Q1EOz/Ec55w9V3rxvdz27ZtibPnwYH+1C0xnhb/m6LSnvhCYOfSz5WNGzdStWrVytxQiciT89JLL5EZ4br0ClfdQLTzQzznCVffueouTbtahQITKHb5xgQKl1rsmjdvTt988w09+uij1Lt3b7riiisoNjaWDh06RDNmzFAtdePHj6d169apCRaYRXvttde67+wFQRAEQagQGFeHFaNAjRo12HZNI1ExWH/wNJ3JL6TwYHNPInHp7MPCwuiFF16gxx9/nB588MESz91555102WWX0c6dO+m5556jpKQkmjt3rukCO47JKznrBqKdH+I5T7j6rnVjVuxHH33EakmxEDueN4oPp/pxYXT49Fn6e98p6te8JpkZl8JzBG1bt26lESNGXPAcLpDLL7+cvv32W/V/pEI5duwYmQ0O07/twVU3EO38EM95wtV3rRvLaqFnDRuXJcUC7XiulxcDf/pAd6xLgZ1eYw7BnT22bNmixtmBEydOmHLd2JycHOIIV91AtPNDPOcJV9+1biwphqFU2PCYs+e9mlZXfzGBgnVgV6tWLbrmmmtUkmLkscPasai0bdu20bhx42jRokV0xx130MGDB2n27Nk0dOhQ9525IAiCIAiCG+iWWJ0C/P1oX2oOHUk7Y+o6dbkdetasWWoR4bvuukulP9FgNuyHH36ocuTs379fdcvefvvtZDY4ZiXnrBuIdn6I5zzh6jtX3WVpjwkLorb1Y+mfg2m0cvdJur5zAzIr/u6opJkzZ6qZNb///rsaU7dixQr1/7vvvluVady4sZo9a8YEiPn5+cQRrrqBaOeHeM4Trr5r3Zg8ge9mbHjM3fNeepydybtj3Ta3GePtBgwYoLpme/ToUSmzjfLy8tRYPYvFQp6C+wefI6KdH+I5T7j6rnUj3cn27dvVxmVJsfwyPO/Z7Nw4O7TYFRV7Ls6o8q5YBFWYFt2hQwfq27cvff/992WWT0hIcHmtWLwn8uG98847agHfevXqqSnaV111FVU2XGYK2cJVNxDt/BDPecLVd60bvWhXXnml9TF3z1vXjaHo0EDKzC2kzUfSqX2Dc5M/fT6wO3v2rAqw+vfvTxdddBE9/PDDZZZv3769y4EdxuphcsbixYupTZs29P7776t8eLt27aL69etTZYKEyxzhqhuIdn6I5zzh6rvWjWCuc+fOxInYMjwPDPCnHk2r009bj6tVKMwa2DndFRseHk67d++mOXPmULt27Sg7O7vM7c8//3T5JBcsWKBm3nbv3l2N6XvyySepevXq9Ntvv1Flk5aWRhzhqhuIdn6I5zzh6jtX3eXRrvPZrTDxOLsKzYqtU6cOeRIEc2gp1GAsAMbbcZ7ZIwiCIAiuDnM6ffq0dZw8165pIz3P57PbeDidMnMLKDo0iM/kCbTEYakwI0hrcv3111O3bt1UHrujR4+64xzp3nvvpS+++IK+/vpr2rNnDz300EMUERFBgwcPLvUYBH6ZmZnWDcFgRSZdcF9yhiOinR/iOU+4+q51FxQU0Lvvvqs2POZAiAPP61ULpyYJEWryxF97ThGbFjus+Tp69Gjq16+fdd+pU6dUQIf1YwcOHEg//fQTLVy4UK0+oVeoqChYc/bqq6+mUaNGqf8jQ/aMGTPKbLGbOnUqTZo0qcQ+dN/qZtiYmBjVVYzce1hiBF3MCAABNAC0EmIKeGhoqLUsxiTgfTMyMlQZPIdfObpFEfn7zpw5o45DWSzVkp6ebi2LRZbxPMBzmAyCDxT241hdFhcfzktnycZ7YjYPNrwfxgloLSiLOsE52pYFWP0Dr4vAFku9YdNlESDjXBEI67LQhmMRDOOcs7KyrGVRBzhngHPQQbNtHeIx9uuy5a1ve3VoW9/GOjSWta1DlMXxug5t6xvnZKxDXd94LdRnafVtW4c4P7ymsQ7LW9/GOsR7Guvbtg5RFs+VVod4P3v1bVuHZdU3XgPnUFp9l3XNllXf9q5ZR9e3M9esLmtbh2Vds8ay+lqr6DVbVn17+z0C5wFtzt4j7F2zZrtH4FjUgbP3CEf17e33CP29hrJ4b5w/Xkefvyv17e33CByLsmVds10bRqtExUu2H6MBzauXWt+evEfo48qDn8XJZiyIwqzUIUOG0Mcff2zd/+yzz9Kbb76pVp1A3jo072J92HvuuYcmTpxIroDExv/88w998skn1KhRIzXm7sEHH6R58+aVOjMWhmpTQZcuXVTFYVq3M+Ai08uicYKrbiDa+fkunvPznLPvXHWXV/sfO07Q7Z+tp3rVwmjFk329oosa8RRISkpyf4vdvn371NJhWFvOyJdffkkjRoxQQR1AKx1a2FatWkWugAj2888/VzNi9eydO++8UwWQr7/+eqmBHaJ9Y5MrfjkIgiAIgiCURZfG8RQU4EdH0s7SwVNnqFH1CDITTkc7unvP2A2KCPLAgQMqObERNEu6mgASTbRogrR9HU8t3sx1ggZX3UC080M85wlX37nqLq/2iJBA6tgwzrSrUDgd2DVp0kRVzK+//mrdhy5Y9B8PGjToggkWiYmJLp0g+prHjBlDY8eOpenTp9PPP/9MEyZMoM8++0ytUVvZcM9MzhHRzg/xnCdcfTcuKYZFBrDJkmL2V6H4c9dJMhtOd8VikB8Cqvvvv1+NV8M4NkxkwAxZtNCBY8eO0auvvqq6YfHXVRDQIXjEX/SPI1jExAzbQLKyPgAYXMkNrrqBaOfnu3jOz3POvmvdGDO/efNmtc8TKzmZyfNeTRPolV920uq9Jym/sJiCA/19e1bslClT1Pi1WbNmqW5SLPf13//+1/r8/Pnz1eoQmEKNmbKughksSEqMTRAEQRAE18EsS6zxrh8L/0eL2tEUHxFMp3LyaeOhNOrSJJ7MgtOzYstDSkqKioi96ZeQMzNKBEEQBEHgzUNzN9IPm47RfX0T6YkrmpsmhqmUtsUaNWp4VVDnCjr/Dje46gainR/iOU+4+s5Vt7Pa0R0LVuw21zg783QaVxGV0KBpCrjqBqKdH+I5T7j6rnXjr16diUtdWJzQqZcX23o0g07nmGeijQR25RjfxxGuuoFo54d4zhOuvmvdSCf2xhtvqI3LkmLBTnheIzqUmteKIsSCK/eYp9VOAjsHcP/gc0S080M85wlX3426kbyfUwL/YCc979XsfHfsLvPks+PjZgXRa89xg6tuINr5IZ7zhKvvWjeCHGS0wMYlyM120nM9zg6Jis3SXS2BnSAIgiAIgh06NqpGIYH+dCIzj3anmOOHgAR2DvCV2b3OwlU3EO38EM95wtV3rroroj00KMCaw+5Pk3THSmDnAC7LrNjCVTcQ7fwQz3nC1XetG38XLVqkNi51UVgBnb3Oz45dsPkY/bDpKK3ee4qKir23W1YCOwdgyTSOcNUNRDs/xHOecPVd68aSYuvXr1cbHnMgrwKe+/v5qb9bjmTQQ3M30Q2f/E09Xv6DftmWTN6IBHaCIAiCwBAsI9a7d2+1yZJi9kHwNvnH7RfsP56RS+Nmb/DK4K5SlhTzRmRJMUEQBEEQygu6W9Eyl5yRa/d5tOPVigmlleP7UYD/uVY9n11SzJfIyMggjnDVDUQ7P8RznnD1natuZ7Wv3X+61KAOoFUMz6OcNxFY1Sfg7XAZd2ALV91AtPNDPOcJV9+1bnTY6TFnISEh5Hd+LJkvU+yE5ylZuW4t5ymkxc4BQUFBxBGuuoFo54d4zhOuvmvdWEbs5ZdfVhuXJcWCnPC8RlSoW8t5CgnsHBAa6l2GeQquuoFo54d4zhOuvnPV7az2zo3jqHZMqBpLZw/sx/Mo501IYOeArKws4ghX3UC080M85wlX37VutF5NmDBBbVxaL7Oc8BwTIiYObaEe2wZ3+v94vrInTjiLBHaCIAiCwBCMqUOaE2wcxtdVhEGtatMHY9qr2a9GqkeGqP143tuQyRMO4Lr0ClfdQLTzQzznCVffuequqHYEbwNb1FKzX19YmET/Hs+isb0ae2VQB6TFzgFFRUXEEa66gWjnh3jOE66+a934+9tvv6mNS11UVCe6W7slxtO1Heqp/6/ac4q8FQnsHJCb613TmD0FV91AtPNDPOcJV9+1bgQ5q1evVhuXwC7XRc97nF83ds3+U5RX6J11Jl2xgiAIgsAQjK3r1q2b9bHgmItrRlFCVAilZuXRPwfT6LLEc4GeNyEtdg6IjY0ljnDVDUQ7P8RznnD1XetGMHf55ZerjUtgF+ui55hk0vOic8Hcit0nyRuRwM4BmZmZxBGuuoFo54d4zhOuvnPV7S7tujt2pQR25oT7kjMcEe38EM95wtV345JiGFuHDY85UOwGz3ucb7HbdiyDTufkk7chLXYOCAzkOQyRq24g2vkhnvOEq+9aN5YRmzJlitq4LCkW6AbPa0SHUvNaUYRYeNUe7+uOlcDOAeHh4cQRrrqBaOeHeM4Trr5z1e1O7brVzhu7YyWwcwDXsQhcdQPRzg/xnCdcfde6sYzY+PHj1cZlSbFMN3nes1mC+rtyz0mv68aWwE4QBEEQGIIZnqGhoWqTJcWco3OjOAoO8Kej6Wdp38kc8iYksHMA1yZrrrqBaOeHeM4Trr5z1e1O7WHBAdSxUTWv7I6VwM4B3GdNcUS080M85wlX37VuzIZdtmyZ2risPFHsRs912hNvy2cngZ0DuC85wxHRzg/xnCdcfTcuKbZ8+XK1cQnsct3oea+m58bZ/b3vFBUUec+PBJ5zvQVBEASBOf7+/tSxY0frY8E5WtSOpriIYJXLbtPhdOrUKM4rqlCcdEBMTAxxhKtuINr5IZ7zhKvvWjdyug0ePFhtXHL6xbjRc39/P7osMd7rumMlsHNAdnY2cYSrbiDa+SGe84Sr71x1V4Z23R27YncqeQsS2DmAy7gDW7jqBqKdH+I5T7j6zlV3ZWjXEyg2H06njLPesXqHBHYO4NI8bQtX3UC080M85wlX37Xu/Px8mjx5strwmAOBbva8TmwYNUmIoGIL0eq9p8gbkMDOAVzz/XDVDUQ7P8RznnD13agb6T84pX0JrwTPva07VgI7B3BfcoYjop0f4jlPuPpuXFLskUceUZssKeaGdWP3eMcECgnsBEEQBIEhWEYsOjpabbKkWMXpmhhPgf5+dPDUGTp06gxVNRLYOSAsLIw4wlU3EO38EM95wtV3rrorS3tkSCC1b3BuebEVe6q+O1YCO0EQBEFgOkN01apVauM8U9ads2O9Yd1YCewccPbsWeIIV91AtPNDPOcJV9+1bgRzixcvVhuXwO5sJXmuA7tVe05SEabIViE853oLgiAIAnOwjFibNm2sj4WK07puDEWHBlJmbiFtOZJO7c53zVYFEtg5AINKOcJVNxDt/BDPecLVd60bOd2GDx9OnIiuJM8DA/zpssTq9EvScdUdW5WBnYToDjhzpupnuFQFXHUD0c4P8ZwnXH3nqruytevu2BVVnPZEAjsHFBYWEke46gainR/iOU+4+s5Vd2Vr73k+sNt4KI2y86qujiWwc0BAQABxhKtuINr5IZ7zhKvvWjeWEXvppZfUxmVJsYBK9LxhfAQ1iAungiILrdlXdcuLSWDngMjISOIIV91AtPNDPOcJV9+NuvPy8tTGhchK9tzaHVuFaU8ksHNARkYGcYSrbiDa+SGe84Sr71o3lhG7//771cZlSbGMSva8pxcsLyazYgVBEASBIVhGLD4+vqpPw6e4LLE6+fsR7UnJpuSMs1Q7xvOrfEiLnQNCQ0OJI1x1A9HOD/GcJ1x956rbE9pjwoOodb3YKu2OlcDOUQUxTdrIVTcQ7fwQz3nC1XetG6tNrF27Vm1cVp7w94DnPat4eTFTXdW5ubm0Y8cOyszM9Nh7cs33w1U3EO38EM95wtV3rRvB3M8//6w2LoHdGQ943rNpgnWcXXEVLC9mmsDuyy+/pFq1aqnlT2rUqEETJ06s6lMSBEEQBFO3XrVo0UJtXFsvK4N2DWIpIjiATufk0/ZkzzVEaUzh5IYNG+iuu+6i2bNnq2nZv/32G73yyis0b968Sn9v7kvOcES080M85wlX341Lio0cOVJteMyBaA94HhTgT12bxFfZ7FhTBHbTpk2jm266iYYMGaL+36tXL5owYQJt37690t+be1M9R0Q7P8RznnD1natuT2rX4+xW7E4lT2OKEH3x4sX0xRdfUHFxMR05coSqV69Ozz77rEfem+vSK1x1A9HOD/GcJ1x956rbk9p7nB9nt+5AGuUWFFFokOdWOfE3QzLB1NRUFdA1adKEGjVqpAK7KVOmlHkcumwxyUJvCAotFucHMXIdd8BVNxDt/BDPecLVd627oKCAXn/9dbXhMQc85XliQgTVjgml/MJiWrv/NHkSr2+xy8rKUn+ffPJJ1Wo3YMAA+u677+j666+npk2b0qhRo+weN3XqVJo0aVKJfQgI09LS1OOYmBjKzs5WM4EwtiA8PNw62zYs7FxCwbNnz6pgEEGhLot15rAkic5ejZw4SPKIsrr/Hk29+FWAslFRUZSenm4ti4tKNwXjOcz0xQcK+3GsLhsSEqLOKycnR/0f74m1/LDh/WJjY61aUBZZw3GOtmVBtWrV1OtCS3BwsNp02YiICHWuekkZlIU2aEUZnLP2AGWxH+cMcA46aLatQzzGfl22vPVtrw5t69tYh8aytnWIsjhe16FtfeOcjHWo6xv1hDKl1bdtHeL8UN5Yh+Wtb2MdwkNjfdvWIcriudLqEO9nr75t67Cs+ka94D1Lq++yrtmy6tveNevo+nbmmtVlbeuwrGvWWBbH4vUqes2WVd9muEcAZ+8R9q5Zs90jUC+oA2fvEY7q29vvEfp7Dc/p19L3fFfr29vvEYGBgeo5Z+8RFblmuzWuRvM3JdPvW49Qj4viXbpH6OPKg5+lIs1YHgStdZgF++qrr9Ljjz9u3Y8lUPbu3aumadvDdv27Ll26qIpzdlweLjJcFNzgqhuIdn6+i+f8POfsu9aNICUlJUXtw/cshxbMNA96vmDzMXpwzkZqXiuKfnm4l0uv1bJlS/U3KSnJ/C12WO4EkS1a54ygS3bNmjWlHodoH5uGwwUrCIIgCOUF34tIIyZUDt0Tz82M3XE8i1KycqlGlGdW/PA3w4U3cOBAWrBggXUffmUsWrSIWrduXenvz3XpFa66gWjnh3jOE66+c9Xtae3xkSHUss659CqrPJj2xOtb7MDzzz9PXbt2VS1wSHXy9ddfq9x2H330UaW/d3n7tH0NrrqBaOeHeM4Trr5r3Ri3tXXrVvX40ksvZVEfAR7WiFUoko5lqnVjR7Sr55H39PoWO4CWuZUrV6oBjG+99ZYaWIhu2GbNmlX6e+uBm9zgqhuIdn6I5zzh6rvWjcDuhx9+UBuXJcVyPOy5cd1YT01pMEWLHWjfvj19/vnnVX0agiAIguATYKiTHr8u49Arhw4Nq1FwgB+lZOXRR8v3UZv6sdS5cRwF+PtV0juaKLCrKjDNmCNcdQPRzg/xnCdcfde6kaJj9OjRxIkoD3u+bGcKkR+COAu99MsOtQ/57SYObUGDWtXm2xVbleh8NdzgqhuIdn6I5zzh6jtX3Z7W/su2ZBo3e4NKUmzkeEau2o/nKwMJ7BzAJRu3LVx1A9HOD/GcJ1x956rbk9qLii00aeF2sjeqTu/D8yjnbiSwc1RBTPPfcdUNRDs/xHOecPXduKTYO++8ozYuwZ6/hzzHMmLJGaW3DiKcw/OVsdyYjLFzAJYM4QhX3UC080M85wlX37VuzNI8ffpcYOHli1CZzvOUrFy3lnMGnj9XnECvW8cNrrqBaOeHeM4Trr5r3Zg8cdttt6kNjzngKc/Lu8pEZaxGwcNJQRAEQRAu6JZs0KCB1EolgJQmmP2KiRL22kIxT7ZWTKgq526kxc4BxvVmOcFVNxDt/BDPecLVd666PakdeeqQ0gSUlrEOz1dGPjsJ7BzApXnaFq66gWjnh3jOE66+a91Ydz0pKUlteMyBQA96jjx1H4xpr1rmjIQG+av9kseuiuC+5AxHRDs/xHOecPVd6y4sLKR58+apDY85kONhzxG8rRzfj+aM7UqPDDy3ykeQvx8NuKRmpb2ntNgJgiAIAkP8/PyoYcOGasNjoXJAd2u3xHi6v29Tig0Poqy8Itp4OL2S3k0mTzgkMjKSOMJVNxDt/BDPecLVd607KCiIbr31VuJEZBV6jgCvZ9MEWrj5GC3fmUqdGrl/4gSQFjsH5OfnE0e46gainR/iOU+4+s5Vtzdo79MsQf1dtiul0t5DAjsvvwiqCq66gWjnh3jOE66+c9XtDdp7nQ/sth3NrJTkxEACOwdwHXfAVTcQ7fwQz3nC1XetG8uIffjhh2rjsqSYXxV7nhAVQq3qRqvHf+46WSnvIYGdA2JjY4kjXHUD0c4P8ZwnXH3XurGM2IkTJ9TGZUmxWC/wvE+zGurv8l2plfL6Etg5gPuSMxwR7fwQz3nC1XfjkmJjxoxRG5ecfmle4Hmfi891x67YnUpFxe4PqHk4KQiCIAjCBUuKJSYmSq14mLb1Yyk6NJDSzxTQpsPp1KFhNbe+vrTYOSA4OJg4wlU3EO38EM95wtV3rrq9RXtggL9Ke1JZ3bES2JngIqgKuOoGop0f4jlPuPqudWMZsV27dqmNy5JiwV7iee/z3bHLd7o/7YkEdg7Izs4mjnDVDUQ7P8RznnD1XevGMmJz5sxRG5clxbK9xPPe59OebDmaQaey89z62hLYCYIgCAJDkPqjTp06aqvqNCDcqBkdSpfUjiZMRl6x271pT2TyhAO4LznDEdHOD/GcJ1x9Ny4pNnbsWOJEpBd5jtmx/yZn0rKdKTS8XV23va602DmAS9JGW7jqBqKdH+I5T7j6zlW3t2nX3bF/7j5JxW5MeyKBnQPy8tzb920WuOoGop0f4jlPuPrOVbe3aUeak6iQQDqdk09bj2a47XUlsBMEQRAEpq1Xn376qdq8qSWLC0EB/tT9ourq8bKd7kt7IoGdA6pVc2/iQLPAVTcQ7fwQz3nC1XetG8uIHT58WG1clhSr5mWe67Qny3a5L+2JBHYOSE9PJ45w1Q1EOz/Ec55w9V3rxjJio0aNUhuXJcXSvcxzvbzY5sPplJaT75bXlMDOAVx+xdjCVTcQ7fwQz3nC1XetG0uKNW/eXG14zAGLl3leOyaMLq4ZRZg7sWKPe9Ke8HDSB7JUexquuoFo54d4zhOuvnPV7a3ard2xblqFQgI7E14EnoCrbiDa+SGe84Sr78YlxQ4cOKA2WVKs6uij057sSnVL2hMJ7Eyy/Iin4aobiHZ+iOc84eq7cUmxWbNmqU2WFKs6OjaKo/DgADqZnU/bkzNdfj0J7ARBEASBIVhGLCEhQW2ypFjVERzoT5clVndbd6wEdg6IiIggjnDVDUQ7P8RznnD1XevGkmL33nuv2vCYAxFe6rmeHeuOfHYS2DmAS/O0LVx1A9HOD/GcJ1x956rbm7XrwG7DoTTKOONasmgJ7Ey0/Ign4aobiHZ+iOc84eo7V93erL1etXC6qEakSnuy0sW0JxLYCYIgCAJDsIzY559/rjZZUqzq6X1+dqyr4+wksHNAbGwscYSrbiDa+SGe84Sr71o3kvXu27dPbd6WuJej533Od8cu35Xqkh8S2DkgM9P1qcdmhKtuINr5IZ7zhKvvWjeWERsxYoTauCwplunFnndqFEdhQQGUkpVH/yZnVfh1JLBzAJekjbZw1Q1EOz/Ec55w9V3rxjJirVu3VhuXJcWKvdjz0KAA6pYYrx4v21Xx7lgeTroAlyngtnDVDUQ7P8RznnD1natuM2i3dse6kPZEAjsHhIaGEke46gainR/iOU+4+q51o/Xq6NGjavPmlixOnvdpVkP9/edgGmXlViztiQR2DsjKqng/t5nhqhuIdn6I5zzh6rvWjZxu06dPV5u35nfj5nmD+HBqXD2CCosttKqCaU8ksBMEQRAEhmAZsZiYGLXJkmLel/YEs2MrAo9pMC4QHh5OHOGqG4h2fojnPOHqu9aN8WYPP/xwVZ+ORwk3gecYZ/fZXwfU8mJIe+Js0C0tdg7gMu7AFq66gWjnh3jOE66+c9VtFu1dm8RTSKA/JWfk0q4T2U4fL4GdA3Jzc4kjXHUD0c4P8ZwnXH3nqtss2pH2BMFdRVehkMBOEARBEBiCCRNz585VG5fJE2bBuAqFs8gYOxMvP1KZcNUNRDs/xHOecPVd60a35M6dO62PORBrEs/1BIp1B05Tdp5zQbe02Jl8anRlwVU3EO38EM95wtV3rTsgIICGDBmiNjzmQJZJPEfKkwZx4VRQZKG/nEx7IoGdA4qKiogjXHUD0c4P8ZwnXH3XuhHMdejQQW1cArsik3iOmbAV7Y6VwM4BXBZGtoWrbiDa+SGe84Sr71x1m0277o5F2hNnkMDOB3LeVAZcdQPRzg/xnCdcfde6kSMtJSVFbXjMgXATed4tMZ6CA/zpaPpZyi8q9u3Abv78+TRv3jyPvFdmZiZxhKtuINr5IZ7zhKvvWndBQQF98MEHasNjDmSayPPw4EDq0iROPc7JK/LdwG7dunV03XXX0YQJE6r6VARBEATB9C1YZmrF4kbv892xWbkFlJVbvtmx5ulsPs8TTzxBCQnnhHqCsLAw4ghX3UC080M85wlX37Xu4OBg9Z3KiTCTeR7gf245sbyCYjqeket7LXYLFiygDRs20OOPP+6x9+Qy7sAWrrqBaOeHeM4Trr5z1W027b9sS6YXFm53+jh/M01Rfuqpp+jJJ5/0aIudGZYfqQy46gainR/iOU+4+s5Vt5m0FxVbaNLC7VSRMNQ0gd2MGTMoIyODHn300XKVz8vLU4Mk9Yas2maK1AVBEAShMsEyYpiMiE2WFPMu1u4/Tcnl7Ho15Ri7nJwcev7552ny5MnlHuQ5depUmjRpUol91atXp7S0NPU4JiaGsrOzVUsg8trgdfVsGd0Hf/bsWRUQYtNlkcQxMjJSBZkgNDRUJRJEWRAdHU1nzpxRHxKUjYqKovT0dGtZf39/9TzAc/j1gNlI2I9jddmQkBB1XtAO8J75+flqw/thWRStBWWDgoLUOdqWBdWqVVOvi8AWYyqw6bIRERHqXBEI67LQpjXjnHWmbpRFHehfPDgHHTTb1iEeY78uW976tleHtvVtrENjWds6RFkcr+vQtr5xTsY61PWN80aZ0urbtg5xfihvrMPy1rexDuGhsb5t6xBl8VxpdYj3s1fftnVYVn1DK96ztPou65otq77tXbOOrm9nrlld1rYOy7pmjWXxf7xeRa/Zsurb2+8R+gdvRe4RztS3N94joBN14Ow9wlF9e/s9Qn+voc62bt2qnr/qqqusr+tKfXv7PSIwMFA95+w9wtVr1tl7xKHUc3VWEfwsJmjGeuGFF+jrr7+mzZs3qwr57LPP6KWXXqIdO3aUegwM1aaCLl26qIrbvt25/mqYhErmBlfdQLTz81085+c5Z9+1bgQZyDQBOnXqxGL1iUyTeL567ym64ZO/rf8/Nv1e9Tf/5EHzt9ghop02bRoNHz6cZs6cqfb99ddfav/06dNp2LBhdsfcIdrHpsEvB19efsTdcNUNRDs/xHOecPXduKRY165diRNFJvG8c+M4qh0TqmbCOtv65vWBHZpAW7RoQbt27VIbSE1NVc2qCOy6detWqZMpOPyCsQdX3UC080M85wlX37nqNpN2pDmZOLQFjZu9gc4lPPGxrlhbENChFa+srlhbWrZsqf4mJSU59V7oT69oa5+Z4aobiHZ+vovn/Dzn7LvWja9/Pc4LY8AwXMnXKTaZ50h5gtmx/7x2e7m7Ys2jzkDz5s1p5MiRHnkvfdFzg6tuINr5IZ7zhKvvWjcmLbz11ltq47KkWIbJPB/UqjatHN+P6lULo1oxob7RFWuPHj16qE0QBEEQhIqDmZ+C93fLhgWXvwvZlIGdJzHb8iPugqtuINr5IZ7zhKvvxiXFnnnmGeJEGAPPTdkVKwiCIAiCIFyIBHYO0AkDucFVNxDt/BDPecLVd666uWiXrlhBEARBYAhWYPjpp5+sK09g9QTB/EiLnQPMkKG6MuCqG4h2fojnPOHqu9aN1B8bN25UGx5zgIPnEtg5QK9/xw2uuoFo54d4zhOuvmvdSNbbt29ftZklca+rnGHgubS7lqOpmiNcdQPRzg/xnCdcfde6Ecz16tWLOFHIwHNpsXMAl18xtnDVDUQ7P8RznnD1natuLtqlxc4BkZGRxBGuuoFo54d4zhOuvmvdWFJMd02Gh4ezWFIskoHn0mLnY8uPuAuuuoFo54d4zhOuvhuXFMO669hkSTHfwc+CkJ0BUVFR6sJNTEx06riioiIWTbe2cNUNRDs/38Vzfp5z9l3rxtf/yZMn1b7q1auzaLErMqnne/fuVcu/ZWVlOSzLpsUuIiLC6TXxcNGfPn1a/eUEV91AtPPzXTzn5zln3426EcglJCSojUNQZzGx54hfEMeUBzYtdhUhMzOTYmJiVLM1h9w33HUD0c7Pd/Gcn+ecfeeqm5N2Ni12giAIgiAIvo4EdoIgCIIgCD6CBHaCIAiCIAg+ggR2ZRASEkITJ05UfznBVTcQ7fx8F8/5ec7Zd666OWmXyROCIAiCIAg+grTYCYIgCIIg+AgS2AmCIAiCIPgIEtgJgiAIgiD4CBLYCYIgCIIg+AgS2J0nOTmZUlJSSq0orDN74MABOnv2bKllcnJy6ODBg1RYWEhmApqgDedvy6lTp8pcHLq4uJhOnDhhOs1G33fs2HHBEjN5eXkO/Ub28sOHD5tyeRqApXWgPTc317oWIf5vu6GObNdaxHWenZ1NZgTXOXTZWwA+LS2Njhw54tJ9wJv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   "source": [
    "F_SIG = 6.4729e9\n",
    "circuit = Netlist(\n",
    "    \"Flux-pumped JPA compression\",\n",
    "    loads=SQUID_LOADS,\n",
    "    circuit=lumped_circuit(f'type=\"sine\" sinedc=0 ampl=1e-12 freq={F_SIG}', pump=0.011),\n",
    "    control=f\"\"\"\n",
    "        {OPTIONS}\n",
    "        sweep sig instance=\"iin\" parameter=\"ampl\" from=1e-12 to=30e-9 mode=\"dec\" points=8\n",
    "        analysis hb1 hb freq=[{F_PUMP_LUMPED}, {F_SIG}] nharm=[3, 5] immax=7\n",
    "    \"\"\",\n",
    ")\n",
    "plot = vacask.run(circuit)[\"hb1\"]\n",
    "\n",
    "groups = plot.split(\"sig\")\n",
    "source_current = jnp.array([g[\"sig\"][0].real for g in groups])\n",
    "v_signal = jnp.array([\n",
    "    g[\"in\"][jnp.argmin(jnp.abs(jnp.asarray(g[\"frequency\"].real) - F_SIG))]\n",
    "    for g in groups\n",
    "])\n",
    "gain_large = db(v_signal / (-1j * source_current * R_PORT / 2) - 1)\n",
    "power_dbm = 10 * jnp.log10((source_current * R_PORT / 2) ** 2 / (2 * R_PORT) / 1e-3)\n",
    "p1db = jnp.interp(gain_large[0] - 1, gain_large[::-1], power_dbm[::-1])\n",
    "print(f\"small-signal gain {gain_large[0]:.2f} dB, input P1dB ≈ {p1db:.1f} dBm\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(power_dbm, gain_large, \"o-\")\n",
    "ax.axhline(float(gain_large[0] - 1), color=\"gray\", ls=\":\")\n",
    "ax.axvline(float(p1db), color=\"gray\", ls=\":\")\n",
    "ax.set_xlabel(\"Input signal power (dBm)\")\n",
    "ax.set_ylabel(\"Signal gain (dB)\")\n",
    "show_plot()"
   ]
  },
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   "source": [
    "## Connect the lumped circuit to layout\n",
    "\n",
    "QPDK has {func}`~qpdk.cells.junction.josephson_junction`, {func}`~qpdk.cells.junction.squid_junction` and {func}`~qpdk.cells.capacitor.interdigital_capacitor` cells. Their geometry is a starting point for these circuits. Junction overlap alone does not set the critical current without process calibration. Likewise, replace the ideal shunt and coupling capacitances with extracted values before predicting a fabricated device. The {doc}`capacitor extraction notebook <elmer_capacitance_interdigital>` shows that step. The {ref}`CPW example <vacask-cpw-layout>` shows how layout and circuit models can share geometry inputs.\n",
    "\n",
    "## Limitations\n",
    "\n",
    "- **Classical.** HB gives classical gain and conversion. It does not give\n",
    "  added noise or squeezing ({cite:t}`clerkIntroductionQuantumNoise2010`;\n",
    "  {cite:t}`royIntroductionParametricAmplification2016`). The junction shunt\n",
    "  conductance is noiseless.\n",
    "- **Threshold and stability.** `hbac` linearizes around the pumped state\n",
    "  whether or not that state is stable. A flux-pumped JPA has a parametric\n",
    "  threshold. There the pump-induced frequency modulation reaches the\n",
    "  resonator linewidth. Above it the gain formula diverges and the reported\n",
    "  gain is not physical. The threshold quoted here is the single-mode\n",
    "  estimate from an unpumped AC sweep, not a stability analysis of the\n",
    "  pumped state.\n",
    "- **Junction model.** RCSJ keeps only the first Josephson harmonic\n",
    "  {cite:p}`McCumber1968,tinkhamIntroductionSuperconductivity2015`, while real\n",
    "  junctions show higher ones\n",
    "  {cite:p}`willschObservationJosephsonHarmonics2024`. The SQUID model\n",
    "  neglects loop inductance and asymmetry.\n",
    "- **Compression.** The two-tone run fixes the pump and samples signal power.\n",
    "  It shows saturation at those points. It does not establish pump depletion\n",
    "  or stability.\n",
    "\n",
    "## References\n",
    "\n",
    "```{bibliography}\n",
    ":filter: docname in docnames\n",
    "```"
   ]
  }
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