Design for  transmon-resonator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qubit-Resonator Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Senario: We want to design a resonator coupled to a transmon. What kind of parameters need to be determined before drawing a mask\n",
    "\n",
    "For the resonator, determine:\n",
    "- $\\omega_r$ : resonant (angular) frequency \n",
    "- $\\kappa$ : linewidth of the resonance\n",
    "\n",
    "Then calculate:\n",
    "- $l$ : length of the resonator\n",
    "- $C_c$ : coupling capacitance to the feedline.\n",
    "\n",
    "For the transmon, determine:\n",
    "- $f_{01}$ : qubit frequency\n",
    "- $\\alpha$ : anharmonicity\n",
    "- $g$ : coupling strength between the resonator and transmon. \n",
    "- $\\chi$: $\\chi$ shift\n",
    "\n",
    "Then calculate:\n",
    "- $E_j$ : Josepthon energy\n",
    "- $E_c$ : Charging energy\n",
    "- $C_g$ : coupling capacitance between qubit island to the resonator\n",
    "- $C_s$ : shunt capacitance across Josephson junction\n",
    "\n",
    "Note on choosing parameters. $g$, $\\chi$, $\\kappa$, $\\Delta$, $\\omega_r$.\n",
    "\n",
    "- For charge-sensitive qubit, small $E_j/E_c$. Charge dispersion and qubit frequency determine $E_j$ and $E_c$ uniquely.\n",
    "- For a normal transmon, $E_j/E_c = 50-60$.There is a room for anharmoncity. It affects the tyep of gate operation, gate speed, etc.\n",
    "- Depending on weak-dispersive ($\\kappa \\gg 2\\chi$) vs strong-dispersive ($\\kappa\\ll2\\chi$) vs intermediate regime (for high-fidelity measurement, $\\kappa=2\\chi$), $\\chi$ and $\\kappa$ are determined. $\\chi=\\left(\\frac{g^2}{\\Delta}\\right)\\cdot\\left(\\frac{\\alpha}{\\Delta+\\alpha}\\right)$.\n",
    "- Put relation Qc vs Cc for capacitive and inductive coupling\n",
    "\n",
    "\n",
    "## Lumped Element Approximation (LEA)\n",
    "Ref: Madison Github, Simulation-and-design \n",
    "\n",
    "Consider a quarter-wave-resonator coupled inductively or capacitively to a feedline, qubit, etc. The undressed (i.e. no loading) resonator is pulled to lower frequency due to the extra capacitance or inductance via such couplings. We want to estimate the length of the resonator for a given undressed or dressed frequency, using lumped element approximation (LEA) where the resonator is approximated by two lumped elements of inductor ($L_{\\rm LEA}$) and capacitor ($C_{\\rm LEA}$).\n",
    "\n",
    "Parameter defination:\n",
    "\n",
    "$\\omega_{\\rm ud}$: undressed resonator frequency\n",
    "\n",
    "$\\omega_{\\rm d}$: dressed resonator frequency\n",
    "\n",
    "\\begin{eqnarray}\n",
    "\\omega_{\\rm d} &=& \\frac{1}{\\sqrt{L_{\\rm tot}C_{\\rm tot}}} \\\\\n",
    "C_{\\rm tot} &=& C_{\\rm LEA} + C_{\\rm parasitic} \\\\\n",
    "L_{\\rm tot} &=& L_{\\rm LEA} + L_{\\rm parasitic} \\\\\n",
    "C_{\\rm LEA} &=& \\left( \\frac{\\pi}{4 Z_0}\\right)  \\frac{1}{\\omega_{\\rm ud}}\\\\\n",
    "L_{\\rm LEA} &=&\\left(\\frac{4Z_0}{\\pi}\\right) \\frac{1}{\\omega_{\\rm ud}}\n",
    "\\end{eqnarray}\n",
    "\n",
    "To rewrite,\n",
    "\\begin{eqnarray}\n",
    "\\frac{1}{\\omega_{\\rm d}^2} &=& L_{\\rm tot}C_{\\rm tot} \\\\   \n",
    "x^2 &+&\\left( \\frac{1}{\\omega_{C_p}} +\\frac{1}{\\omega_{L_p}}\\right)x + \\frac{1}{\\omega_{L_pC_p}^2}-\\frac{1}{\\omega_{\\rm d}^2} =0\\\\\n",
    " \\end{eqnarray}\n",
    ", where\n",
    "\\begin{eqnarray}\n",
    "x&=&\\frac{1}{\\omega_{\\rm ud}}\\\\\n",
    "\\omega_{C_p}&=&\\frac{\\pi}{4Z_0 C_{\\rm paracitic}} \\\\\n",
    "\\omega_{L_p}&=&\\frac{4Z_0}{\\pi L_{\\rm paracitic}} \\\\\n",
    "\\omega_{L_pC_p}&=&\\frac{1}{\\sqrt{L_{\\rm paracitic}C_{\\rm paracitic}}} \\\\\n",
    "\\end{eqnarray}\n",
    "\n",
    "Solving for $\\omega_{\\rm ud}$,\n",
    "\n",
    "\\begin{eqnarray}\n",
    "x=\\frac{1}{\\omega_{\\rm ud}} &=& \\frac{1}{2}\\left( -\\left(\\frac{1}{\\omega_{L_p}}+\\frac{1}{\\omega_{C_p}}\\right)+ \\sqrt{\\left(\\frac{1}{\\omega_{L_p}}+\\frac{1}{\\omega_{C_p}}\\right)^2 + 4\\left(\\frac{1}{\\omega_{\\rm d}^2}-\\frac{1}{\\omega_{L_pC_p}^2}\\right)}\\right )\n",
    "\\end{eqnarray}\n",
    "\n",
    "The length of quarter-wave-resonator $l$ is\n",
    "\\begin{equation}\n",
    "l=\\frac{\\lambda}{4} = \\frac{\\pi v_{ph}}{2\\omega_{ud}}.\n",
    "\\end{equation}\n",
    "\n",
    "## Inductive coupling vs Qc\n",
    "\\begin{eqnarray}\n",
    "Q_c &=& \\frac{2 Z_0 L_{LEA}}{\\omega_{\\rm ud} M^2} \\\\\n",
    "&=&\\frac{8Z_0^2}{\\pi\\omega_{\\rm ud}^2 M^2} \\\\\n",
    "&=&\\frac{2}{\\pi}\\left(\\frac{2Z_0}{\\omega_{\\rm ud} M}\\right)^2\n",
    "\\end{eqnarray}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Design example for quart-wave resonator + transmon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "from math import pi\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.constants import h, hbar, e, epsilon_0, c, mu_0\n",
    "import pandas as pd\n",
    "\n",
    "from transmonCPW import CPW, QuarterWaveResonator, Transmon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# readout cavity - quarterwave resonator\n",
    "res = QuarterWaveResonator(w=20\n",
    "                           , s=10\n",
    "                           , h=500\n",
    "                           , t=0.1\n",
    "                           , epsilon_r=11.6                                                     \n",
    "                           , w_r=6.6e9 * (2*pi)\n",
    "                           , kappa=0.66e6 * (2*pi)                           \n",
    "                          )\n",
    "\n",
    "# Transmon\n",
    "q = Transmon.in_GHz(Ej=15, Ec=0.286)\n",
    "q.C_g = 3e-15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004977268865722193"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.Z0\n",
    "res.resonator_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#624.50000 find Ej and Ec, given f01 and anharmonicity\n",
    "\n",
    "# sol = q.find_Ej_Ec(4e9, -0.45e9)\n",
    "# (q.Ej, q.Ec) = sol\n",
    "# print(q.anharmonicity/1e6)\n",
    "# print(q.f01/1e9)\n",
    "# print(q.charge_dispersion/1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ej (GHz)</th>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ec (MHz)</th>\n",
       "      <td>286.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ej/Ec</th>\n",
       "      <td>52.447552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charge disp. (kHz)</th>\n",
       "      <td>7.527600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f01 (GHz)</th>\n",
       "      <td>5.556169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anharm. (MHz)</th>\n",
       "      <td>-327.144891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$g/2\\pi$ (MHz)</th>\n",
       "      <td>55.030849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\chi/2\\pi$ (MHz)</th>\n",
       "      <td>-0.351930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lj (nH)</th>\n",
       "      <td>10.897434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ic (nA)</th>\n",
       "      <td>30.200318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_sigma (fF)</th>\n",
       "      <td>67.728075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_sh (fF)</th>\n",
       "      <td>64.728075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_g (fF)</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fr (GHz)</th>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Res. Length (mm)</th>\n",
       "      <td>4.816306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qc</th>\n",
       "      <td>12400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\kappa/2\\pi$ (MHz)</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z0 $(\\Omega)$</th>\n",
       "      <td>50.635160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\epsilon_{\\rm re}$</th>\n",
       "      <td>6.299556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mc (pH)</th>\n",
       "      <td>18.626868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_l (pH)</th>\n",
       "      <td>165.456040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lm_l (nH)</th>\n",
       "      <td>423.629201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lk_l (nH)</th>\n",
       "      <td>11.211442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Values\n",
       "Ej (GHz)                15.000000\n",
       "Ec (MHz)               286.000000\n",
       "Ej/Ec                   52.447552\n",
       "charge disp. (kHz)       7.527600\n",
       "f01 (GHz)                5.556169\n",
       "anharm. (MHz)         -327.144891\n",
       "$g/2\\pi$ (MHz)          55.030849\n",
       "$\\chi/2\\pi$ (MHz)       -0.351930\n",
       "Lj (nH)                 10.897434\n",
       "Ic (nA)                 30.200318\n",
       "C_sigma (fF)            67.728075\n",
       "C_sh (fF)               64.728075\n",
       "C_g (fF)                 3.000000\n",
       "fr (GHz)                 6.200000\n",
       "Res. Length (mm)         4.816306\n",
       "Qc                   12400.000000\n",
       "$\\kappa/2\\pi$ (MHz)      0.500000\n",
       "Z0 $(\\Omega)$           50.635160\n",
       "$\\epsilon_{\\rm re}$      6.299556\n",
       "Mc (pH)                 18.626868\n",
       "C_l (pH)               165.456040\n",
       "Lm_l (nH)              423.629201\n",
       "Lk_l (nH)               11.211442"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show all parameters\n",
    "\n",
    "d = np.array([[q.Ej/1e9/h, q.Ec/1e6/h, q.Ej_to_Ec_ratio, q.charge_dispersion/1e3\n",
    "               , q.f01/1e9, q.anharmonicity/1e6, q.g01(res)/2/pi/1e6\n",
    "               , q.Chi01(res)/2/pi/1e6, q.Lj/1e-9, q.Ic/1e-9, q.total_cap/1e-15, (q.total_cap-q.C_g)/1e-15\n",
    "               , q.C_g/1e-15, res.w_r/2/pi/1e9, res.resonator_length*1e3\n",
    "               , res.Qc, res.kappa/2/pi/1e6, res.Z0, res.epsilon_eff, res.Mc /1e-12\n",
    "               ,res.C_l/1e-12,res.Lm_l/1e-9, res.Lk_l/1e-9\n",
    "              ]])\n",
    "columns = ['Ej (GHz)', 'Ec (MHz)', 'Ej/Ec', 'charge disp. (kHz)', 'f01 (GHz)', 'anharm. (MHz)'\n",
    "           , '$g/2\\pi$ (MHz)', '$\\chi/2\\pi$ (MHz)', 'Lj (nH)','Ic (nA)','C_sigma (fF)'\n",
    "           ,'C_sh (fF)','C_g (fF)','fr (GHz)','Res. Length (mm)','Qc'\n",
    "           , '$\\kappa/2\\pi$ (MHz)', 'Z0 $(\\Omega)$', '$\\epsilon_{\\rm re}$','Mc (pH)'\n",
    "           ,'C_l (pH)', 'Lm_l (nH)','Lk_l (nH)' ]\n",
    "\n",
    "df = pd.DataFrame(d.transpose(), index=columns, columns=['Values'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "169.65506338120574"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/(res.kappa*(q.g01(res)/(q.f01*2*np.pi-res.w_r))**2)/1e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320896483.29664505"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.g01_Madison(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter investigation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C_l vs thickness of substrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "[<matplotlib.lines.Line2D at 0x7f09f1176610>]"
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot C_l vs thickness of substrate\n",
    "\n",
    "h_list = np.linspace(1, 100, 1000)\n",
    "C_l_list = []\n",
    "for h in h_list:\n",
    "    res.h = h\n",
    "    C_l_list.append(res.C_l)\n",
    "    \n",
    "plt.plot(h_list, C_l_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asymmetric transmon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USS = 8.25352013910591, LSS=4.60676711805653\n"
     ]
    }
   ],
   "source": [
    "Ej_ratio = 2\n",
    "d = (Ej_ratio - 1)/(Ej_ratio + 1)\n",
    "Ej_USS = 28\n",
    "Ec = 0.33\n",
    "\n",
    "\n",
    "Ej_LSS = Ej_USS*d\n",
    "\n",
    "q1 = Transmon.in_GHz(Ej_USS, Ec)\n",
    "USS = q1.f01\n",
    "q1 = Transmon.in_GHz(Ej_LSS, Ec)\n",
    "LSS = q1.f01\n",
    "print(f'USS = {USS/1e9}, LSS={LSS/1e9}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc4f7bce460>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.C_g = 3e-15\n",
    "Ej_AT = lambda Ej_USS, d, f: Ej_USS*np.cos(np.pi*f)*np.sqrt(1+d**2*np.tan(np.pi*f)**2) \n",
    "f_list = np.linspace(-1,1, 51)\n",
    "Ej_list = Ej_AT(Ej_USS, d, f_list)\n",
    "f01_list = []\n",
    "for Ej in Ej_list:\n",
    "    q1.Ej = Ej*h*1e9\n",
    "    f01_list.append(q1.f01)\n",
    "plt.plot(f_list, f01_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anharmonicity vs Ej/Ec for different N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# conclusion: Use N>8. I use N=10.\n",
    "\n",
    "Ej = 10\n",
    "Ec = np.linspace(0.2,5, 100) \n",
    "Ec = np.logspace(-1, 1.5, 50)\n",
    "\n",
    "Ns = [4, 6, 8, 10, 12]\n",
    "qs = [Transmon.in_GHz(Ej=Ej,Ec=0, N=n) for n in Ns]\n",
    "anharms = []\n",
    "\n",
    "for q in qs:\n",
    "    anh = np.array([])\n",
    "    for E in Ec:\n",
    "        q.Ec = E *1e9*h\n",
    "        anh = np.append(anh, q.anharmonicity)\n",
    "    anharms.append(anh)\n",
    "\n",
    "# anharms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "data": {
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       "<matplotlib.legend.Legend at 0x7f54351c10d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import plotly\n",
    "# import plotly.graph_objs as go\n",
    "# from plotly.offline import iplot\n",
    "# plotly.offline.init_notebook_mode(connected=True)\n",
    "\n",
    "for anh in anharms:        \n",
    "    plt.plot(Ej/Ec, anh/(Ec*1e9), '-')\n",
    "\n",
    "plt.tick_params\n",
    "plt.xlabel('Ej/Ec')\n",
    "plt.ylabel(r'$\\alpha/Ec$', fontsize=20)\n",
    "plt.grid()\n",
    "plt.legend([f'N={n}' for n in Ns])\n",
    "\n",
    "# # iplot\n",
    "# data = []\n",
    "# for anh in anharms:\n",
    "#     data.append(go.Scatter(x=Ej/Ec, y=anh/(Ec*1e9)))\n",
    "    \n",
    "# iplot({\n",
    "#     \"data\": data,\n",
    "#     \"layout\": go.Layout(title=\"Anharmoniciity vs Ej/Ec\")\n",
    "    \n",
    "# })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7415902026988156\n",
      "0.7415901873874097\n"
     ]
    }
   ],
   "source": [
    "from scipy.special import ellipk\n",
    "k=10/(10+12)\n",
    "k_p = np.sqrt(1-k**2)\n",
    "\n",
    "k_sq = k**2\n",
    "k_p_sq=k_p**2\n",
    "\n",
    "print(ellipk(k_sq)/ellipk(k_p_sq))\n",
    "print(np.pi/np.log(2*(1+np.sqrt(k_p))/(1-np.sqrt(k_p))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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