05_gna_vs_lma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gauss-Newton vs. Levenberg-Marquardt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <div style=\"width: 75%; float: left;\">\n",
    "         The Gauss-Newton Algorithm (GNA) and Levenberg-Marquardt Algorithm (LMA) are often utilized as generic methods to solve non-linear least-squares problems, which is interactively demonstrated and compared with each other in this notebook. For GNA and LMA implementation, a numerical approximation of the Jacobian is employed in this example.<br>\n",
    "         <img src=\"./img/lm-fit_anim.gif\" style=\"width:450px;text-align:left;vertical-align: middle;\"/><br>\n",
    "         last update: 30/04/2022\n",
    "     </div>\n",
    "     <div style=\"margin-left: 80%;\">\n",
    "        <b>Author</b><br><br>\n",
    "        <a href='http://www.christopherhahne.de', style=\"text-decoration: None; text-align: left;\">\n",
    "            <img align=\"left\" src=\"http://www.christopherhahne.de/images/about.jpg\" width=\"78\"/>\n",
    "            <br><br><br><br><br><br>\n",
    "            <b>Christopher<br>Hahne, PhD</b>\n",
    "        </a>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "For the parametrization, we choose a function model $M(\\mathbf{p};x)$, which may be of the form\n",
    "\n",
    "$$\n",
    "M(\\mathbf{p};x)=M(a, \\mu, \\sigma, \\lambda; x)=\n",
    "\\frac{a}{\\sigma\\sqrt{2\\pi}} e^{\\frac{-\\left(x-\\mu\\right)^2}{2\\sigma^2}} \\left(1 + \\text{erf}\\left(\\lambda\\frac{x-\\mu}{\\sigma\\sqrt{2}}\\right)\\right)\n",
    "$$\n",
    "\n",
    "with $\\text{erf}(\\cdot)$ as the [error function](https://en.wikipedia.org/wiki/Error_function) and $\\mathbf{p}=(a, \\mu, \\sigma, \\lambda)$ as the fit coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.special import erf\n",
    "\n",
    "def asym_gaussian(p, x=0):\n",
    "    \"\"\"\n",
    "    Asymmetric Gaussian function model\n",
    "    \"\"\"\n",
    "    amp = p[0] / (p[2] * np.sqrt(2 * np.pi))\n",
    "    spread = np.exp((-(x - p[1]) ** 2.0) / (2 * p[2] ** 2.0))\n",
    "    skew = 1 + erf((p[3] * (x - p[1])) / (p[2] * np.sqrt(2)))\n",
    "    return amp * spread * skew"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generation of synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fda9a8f9210>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 1000)\n",
    "norm, mean, sigm, skew = 10, -1, 2, 5\n",
    "data = asym_gaussian([norm, mean, sigm, skew], x)\n",
    "data_raw = data + .3 * np.random.randn(len(x))\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.title('Synthetic data')\n",
    "plt.plot(x, data_raw, label='raw signal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Residual Function\n",
    "Estimation of $\\mathbf{p}$ is achieved through iterative evaluation of a residual function $f(\\mathbf{p}, y, x)$ which is defined as\n",
    "\n",
    "$$\n",
    "\\mathbf{f}=f(\\mathbf{p}, y_i, x_i)=(y_i-M(\\mathbf{p};x_i))^2\n",
    "$$\n",
    "\n",
    "with $y_i$ as the observed data in our model $M(\\mathbf{p};x_i)$ and $i$ as the index of a total number of $N$ samples while $\\mathbf{f} \\in \\mathbb{R}^{N}$ is a vector-valued functional carrying the residuals. This is implemented by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_fun = lambda p, y, x: (y - asym_gaussian(p, x))**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization\n",
    "\n",
    "The optimization goal we aim to solve is formulated as \n",
    "\n",
    "$$\n",
    "\\text{arg min}_{\\mathbf{h}} \\, \\frac{1}{2} \\left(\\mathbf{J}\\mathbf{h}+\\mathbf{f}\\right)^2\n",
    "$$\n",
    "\n",
    "with $\\mathbf{J}$ denoting the Jacobian w.r.t. $\\mathbf{p}$ and the update change rate $\\mathbf{h}$, which together with the residuals $\\mathbf{f}$ is minimized towards zero. To arrive at a minimum, the solution to this objective is obtained by the derivative of the cost function $\\mathbf{F}(\\mathbf{h})$, which writes\n",
    "\n",
    "$$\n",
    "\\nabla_{\\mathbf{h}} \\mathbf{F}\\left(\\mathbf{h}\\right) = \\frac{\\partial \\frac{1}{2} \\left(\\mathbf{J}\\mathbf{h}+\\mathbf{f}\\right)^2}{\\partial \\mathbf{h}}\n",
    "$$\n",
    "\n",
    "and yields\n",
    "\n",
    "$$\n",
    "\\nabla_{\\mathbf{h}} \\mathbf{F}\\left(\\mathbf{h}\\right) = \\mathbf{J}^\\intercal \\left(\\mathbf{J}\\mathbf{h}+\\mathbf{f}\\right)\n",
    "$$\n",
    "\n",
    "where $\\intercal$ is the matrix transpose. We set $\\nabla_{\\mathbf{h}} \\mathbf{F}\\left(\\mathbf{h}\\right) = \\mathbf{0}$, so that after multiplication and rearranging we have\n",
    "\n",
    "$$\n",
    "- \\mathbf{J}^\\intercal\\mathbf{f} = \\mathbf{J}^\\intercal \\mathbf{J}\\mathbf{h}\n",
    "$$\n",
    "\n",
    "and finally\n",
    "\n",
    "$$\n",
    "\\mathbf{h} = -\\left(\\mathbf{J}^\\intercal \\mathbf{J}\\right)^{-1} \\mathbf{J}^\\intercal\\mathbf{f}\n",
    "$$\n",
    "\n",
    "which gives the parameter update step at iteration $k$ by\n",
    "\n",
    "$$\n",
    "\\mathbf{p}_{k+1}=\\mathbf{p}_k+\\mathbf{h}\n",
    "$$\n",
    "\n",
    "where $\\mathbf{h}=\\mathbf{H}^{-1}\\mathbf{g}$ contains the second-order derivative from Newton's method with the Hessian $\\mathbf{H} \\in \\mathbb{R}^{m\\times m}$ and gradient $\\mathbf{g}\\in\\mathbb{R}^{m}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Jacobian\n",
    "As opposed to the analytically derived Jacobian $\\mathbf{J}$, a multi-variate numerical approximate $\\mathbf{\\tilde{J}}(\\mathbf{p})\\approx \\mathbf{J}$ via $\\mathbf{p}$ and the function $\\mathbf{f}(\\cdot)$ is provided hereafter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jacobian_approx(p, f, dp=1e-8, args=()):\n",
    "    \"\"\"\n",
    "    Numerical approximation for the multivariate Jacobian\n",
    "    :param p: initial value(s)\n",
    "    :param f: function handle\n",
    "    :param dp: delta p for approximation\n",
    "    :param args: additional arguments passed to function handle\n",
    "    :return: jacobian\n",
    "    \"\"\"\n",
    "    n = len(p)\n",
    "    jac = np.zeros(n) if len(args) == 0 else np.zeros([n, len(args[0])])\n",
    "    for j in range(n):  # through columns to allow for vector addition\n",
    "        dpj = abs(p[j]) * dp if p[j] != 0 else dp\n",
    "        p_plus = [(pi if k != j else pi + dpj) for k, pi in enumerate(p)]\n",
    "        # compute jacobian while passing optional arguments\n",
    "        jac[j] = (f(*((p_plus,) + args)) - f(*((p,) + args))) / dpj\n",
    "\n",
    "    return jac if len(args) == 0 else jac.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gauss-Newton Algorithm (GNA)\n",
    "From the numerical estimate of a Jacobian, we can further compute a multivariate gradient $\\mathbf{g}$ from $\\mathbf{J}$ and residuals $\\mathbf{f}$ using\n",
    "\n",
    "$$\\mathbf{g}=\\mathbf{J}^\\intercal \\mathbf{f}$$\n",
    "\n",
    "Similary, the Hessian can be approximated via\n",
    "\n",
    "$$\\mathbf{H}\\approx\\mathbf{J}^\\intercal \\mathbf{J}$$\n",
    "\n",
    "which then allows to replace the first- and second-order derivatives of the conventional Newton method so that\n",
    "\n",
    "$$\\mathbf{h}_{gn}=-(\\mathbf{J}^\\intercal\\mathbf{J})^{-1}\\mathbf{J}^\\intercal \\mathbf{f}$$\n",
    "\n",
    "where $\\mathbf{h}_{gn}$ denotes the *Gauss-Newton step*. This is used in an iterative scheme and can be accomplished via\n",
    "\n",
    "$$\\mathbf{p}_{k+1}=\\mathbf{p}_k+\\alpha\\mathbf{h}_{gn}$$\n",
    "\n",
    "with variable $\\alpha$ which represents the learning rate in the context of machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lsq_gna(p, function, args=(), l=.1, tol=1e-12, max_iter=500):\n",
    "    \"\"\"\n",
    "    Gauss-Newton implementation\n",
    "    :param p: initial value(s)\n",
    "    :param function: user-provided function which takes p (and additional arguments) as input\n",
    "    :param args: optional arguments passed to function\n",
    "    :param l: learning rate\n",
    "    :param tol: tolerance for stop condition\n",
    "    :param max_iter: maximum number of iterations\n",
    "    :return: list of results, eps\n",
    "    \"\"\"\n",
    "    j = jacobian_approx(p, function, args=args)\n",
    "    g = np.dot(j.T, function(*((p,) + args))) if len(args) != 0 else np.dot(j.T, function(p))\n",
    "    H = np.dot(j.T, j)\n",
    "    eps = 1\n",
    "    p_list = []\n",
    "    while len(p_list) < max_iter:\n",
    "        h = -l*np.dot(np.linalg.inv(H), g)\n",
    "        p += h\n",
    "        p_list.append(p.copy())\n",
    "        j = jacobian_approx(p, function, args=args)\n",
    "        g = np.dot(j.T, function(*((p,) + args))) if len(args) != 0 else np.dot(j.T, function(p))\n",
    "        H = np.dot(j.T, j)\n",
    "        eps = max(abs(g))\n",
    "        if eps < tol:\n",
    "            break\n",
    "    return p_list, eps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Levenberg-Marquardt Algorithm (LMA)\n",
    "Next, we employ the LMA as a gradual blend of the Gauss-Newton and Gradient Descent method. The main motivation for LMA is to automatically fade between the two in an optimal way. To achieve this, Levenberg and Marquardt introduced the term $\\mu\\mathbf{D}$ to the *Gauss-Newton step* as seen in\n",
    "\n",
    "$$\\mathbf{h}_{lm}=-(\\mathbf{H}+\\mu\\mathbf{D})^{-1}\\mathbf{g}$$\n",
    "\n",
    "or respectively\n",
    "\n",
    "$$\\mathbf{h}_{lm}=-(\\mathbf{J}^\\intercal\\mathbf{J}+\\mu\\mathbf{D})^{-1}\\mathbf{J}^\\intercal\\mathbf{f}$$\n",
    "\n",
    "where $\\mathbf{D}$ is a diagonal matrix and $\\mu\\ge0$ is the damping parameter, which shifts between the influence of the Gradient Descent and Gauss-Newton method. In practice, $\\mu$ is reduced following an update which decreases the error so that $\\mathbf{h}_{lm} \\approx \\mathbf{h}_{gn}$ and $\\mu$ is increased once the error goes up. More precisely, Levenberg initially proposed to use an identity matrix $\\mathbf{I}$ that scales with $\\mu$ so that $\\mathbf{D}=\\mathbf{I}$. For the initialization of $\\mu$, Levenberg used\n",
    "\n",
    "$$\\mu_0=\\tau\\max\\{\\text{diag}{[\\mathbf{J}^\\intercal\\mathbf{J}]}\\}$$\n",
    "\n",
    "where $\\tau$ is set by the user. Then $\\mu_k$ is regularily updated on each iteration $k$ by\n",
    "\n",
    "$$\\mu_{k+1}=\\mu_k\\max\\{1/3,1-(2\\rho_k-1)^3\\}$$\n",
    "\n",
    "with $\\rho_k$ acting as the *gain ratio* given by\n",
    "\n",
    "$$\\rho_k = \\frac{\\mathbf{f}(\\mathbf{p}_k)-\\mathbf{f}(\\mathbf{p}_k+\\mathbf{h}_{lm})}{L(\\mathbf{0})-L(\\mathbf{h}_{lm})}$$\n",
    "\n",
    "where the denominator is predicted by\n",
    "\n",
    "$$L(\\mathbf{0})-L(\\mathbf{h}_{lm})=-\\mathbf{h}_{lm}^\\intercal\\mathbf{J}^\\intercal\\mathbf{f}-\\frac{1}{2}\\mathbf{h}_{lm}^\\intercal\\mathbf{J}^\\intercal\\mathbf{J}\\mathbf{h}_{lm}$$\n",
    "\n",
    "After substituting $\\mathbf{g}$ for $\\mathbf{J}^\\intercal \\mathbf{f}$ and placing $-\\frac{1}{2}\\mathbf{h}_{lm}^\\intercal$ outside the brackets, this may be written as\n",
    "\n",
    "$$L(\\mathbf{0})-L(\\mathbf{h}_{lm})=-\\frac{1}{2}\\mathbf{h}_{lm}^\\intercal (\\mathbf{2}\\mathbf{g}+(\\mathbf{J}^\\intercal\\mathbf{J}+\\mu\\mathbf{D}-\\mu\\mathbf{D})\\mathbf{h}_{lm})$$\n",
    "\n",
    "Since $\\mathbf{J}^\\intercal\\mathbf{J}+\\mu\\mathbf{D} = -\\mathbf{g}$ by definition of LMA, this becomes\n",
    "\n",
    "$$L(\\mathbf{0})-L(\\mathbf{h}_{lm}) = \\frac{1}{2}\\mathbf{h}_{lm}^\\intercal (\\mu\\mathbf{h}_{lm}-\\mathbf{g})$$\n",
    "\n",
    "after rearranging.\n",
    "\n",
    "Later, Marquardt suggested to replace the identity matrix with the diagonal of the local Hessian resulting in\n",
    "$\\mathbf{D}_k=\\text{diag}[\\mathbf{J}^\\intercal_k\\mathbf{J}_k]$. There exist alternatives to this approach including $\\mathbf{D}_{k+1}=\\max\\limits_{1\\leq i\\leq m}\\{\\text{diag}{[\\mathbf{J}^\\intercal\\mathbf{J}]}_{(ii)}, {\\mathbf{D}_{k}}_{(ii)}\\}$. Updates on $\\mu_k$ have also been modified by Marquardt as follows\n",
    "\n",
    "$$\n",
    "\\mu_k = \\beta\\mu_k \\quad \\text{if} \\, \\rho_k < \\rho_1 \\quad \\text{and} \\quad\n",
    "\\mu_k = \\mu_k/\\gamma \\quad \\text{if} \\, \\rho_k > \\rho_2\n",
    "$$\n",
    "\n",
    "where $\\rho_1=0.25$ and $\\rho_2=0.75$ represent thresholds for the *gain ratio* controlling the increase from $\\beta=2$ and decrease via $\\gamma=3$ as user-defined parameters.\n",
    "\n",
    "All of the above-mentioned methodological solutions have the update procedure in common, which simplifies to\n",
    "\n",
    "$$\\mathbf{p}_{k+1}=\\mathbf{p}_k+\\mathbf{h}_{lm}$$\n",
    "\n",
    "A full implementation of the LMA is provided below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def lsq_lma(p, function, args=(), tol=1e-12, tau=1e-3, meth='lev', rho1=.25, rho2=.75, bet=2, gam=3, max_iter=500):\n",
    "    \"\"\"\n",
    "    Levenberg-Marquardt implementation for least-squares fitting of non-linear functions\n",
    "    :param p: initial value(s)\n",
    "    :param function: user-provided function which takes p (and additional arguments) as input\n",
    "    :param args: optional arguments passed to function\n",
    "    :param tol: tolerance for stop condition\n",
    "    :param tau: factor to initialize damping parameter\n",
    "    :param meth: method which is default 'lev' for Levenberg and otherwise Marquardt\n",
    "    :param rho1: first gain factor threshold for damping parameter adjustment for Marquardt\n",
    "    :param rho2: second gain factor threshold for damping parameter adjustment for Marquardt\n",
    "    :param bet: multiplier for damping parameter adjustment for Marquardt\n",
    "    :param gam: divisor for damping parameter adjustment for Marquardt\n",
    "    :param max_iter: maximum number of iterations\n",
    "    :return: list of results, eps\n",
    "    \"\"\"\n",
    "    j = jacobian_approx(p, function, args=args)\n",
    "    g = np.dot(j.T, function(*((p,) + args))) if len(args) != 0 else np.dot(j.T, function(p))\n",
    "    H = np.dot(j.T, j)\n",
    "    u = tau * np.max(np.diag(H.diagonal()))\n",
    "    v = 2\n",
    "    eps = 1\n",
    "    p_list = []\n",
    "    while len(p_list) < max_iter:\n",
    "        D = np.identity(j.shape[1])\n",
    "        D *= 1 if meth == 'lev' else np.max(np.maximum(H.diagonal(), D.diagonal()))\n",
    "        h = -np.dot(np.linalg.inv(H+u*D), g)\n",
    "        f = function(*((p,) + args)) if len(args) != 0 else function(p)\n",
    "        f_plus = function(*((p+h,) + args)) if len(args) != 0 else function(p+h)\n",
    "        rho = (np.dot(f.T, f) - np.dot(f_plus.T, f_plus)) / np.dot(.5*h.T, u*h-g)\n",
    "        if rho > 0:\n",
    "            p += h\n",
    "            p_list.append(p.copy())\n",
    "            j = jacobian_approx(p, function, args=args)\n",
    "            g = np.dot(j.T, function(*((p,) + args))) if len(args) != 0 else np.dot(j.T, function(p))\n",
    "            H = np.dot(j.T, j)\n",
    "        if meth== 'lev':\n",
    "            u, v = (u*np.max([1/3, 1-(2*rho-1)**3]), 2) if rho > 0 else (u*v, v*2)\n",
    "        else:\n",
    "            u = u*bet if rho < rho1 else u/gam if rho > rho2 else u\n",
    "        eps = max(abs(g))\n",
    "        if eps < tol:\n",
    "            break\n",
    "    return p_list, eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNA: [ 9.13028684 -1.02513265  1.91222146  4.72345346] with an error of 6.494116083 after 500 iterations.\n",
      "LMA: [ 9.99998965 -0.99999916  1.99999408  4.99991457] with an error of 0.0 after 25 iterations.\n"
     ]
    }
   ],
   "source": [
    "initials = [.5, .5, .5, .5]\n",
    "coeffs_gn, eps_gn = lsq_gna(initials, cost_fun, args=(data, x), l=.01)\n",
    "coeffs_lm, eps_lm = lsq_lma(initials, cost_fun, args=(data, x), meth='marq')\n",
    "print('GNA: %s with an error of %s after %s iterations.' % (coeffs_gn[-1], round(eps_gn,9), len(coeffs_gn)))\n",
    "print('LMA: %s with an error of %s after %s iterations.' % (coeffs_lm[-1], round(eps_lm,9), len(coeffs_lm)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Illustration of the result\n",
    "\n",
    "The animation below indicates the optimization process with intermediate results from the iterative LMA procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "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",
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       "\n",
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       "        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",
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       "\n",
       "    this._init_header(this);\n",
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       "    this._init_toolbar(this);\n",
       "\n",
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       "\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",
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       "\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",
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       "                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",
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       "\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",
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       "\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",
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       "\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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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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\" width=\"576\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from IPython.display import HTML\n",
    "plt.style.use('seaborn-pastel')\n",
    "\n",
    "fig, ax = plt.figure(figsize=(8, 5)), plt.axes()\n",
    "ax.set_xlabel('x', fontsize=16)\n",
    "ax.set_ylabel('y', fontsize=16)\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.yaxis.set_tick_params(labelsize=12)\n",
    "ax.plot(x, data_raw, label='data')\n",
    "line, = ax.plot(x, asym_gaussian(coeffs_lm[-1], x), '--', color='red', lw=2, label='LMA fit')\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "# animation\n",
    "if True:\n",
    "    animate = lambda i: line.set_data(x, asym_gaussian(coeffs_lm[i], x))\n",
    "    anim = FuncAnimation(fig, animate, frames=len(coeffs_lm), interval=100)\n",
    "    HTML(anim.to_jshtml())\n",
    "\n",
    "# styles\n",
    "if False:\n",
    "    import matplotlib as mpl\n",
    "    mpl.rcParams['savefig.facecolor'] = '#c7b897'#'#148ec8'\n",
    "    fig.set_facecolor('#c7b897')#('#148ec8')\n",
    "    #ax.set_facecolor('#c7b897')#('#148ec8')\n",
    "    ax.spines['bottom'].set_color('white')\n",
    "    ax.spines['top'].set_color('white')\n",
    "    ax.spines['left'].set_color('white')\n",
    "    ax.spines['right'].set_color('white')\n",
    "    ax.xaxis.label.set_color('white')\n",
    "    ax.yaxis.label.set_color('white')\n",
    "    ax.tick_params(colors='white')\n",
    "    anim.save('./data/lm-fit_anim.gif', writer='imagemagick', fps=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equivalent Scipy implementation\n",
    "For ease of use, an equivalent of the above implementation is provided hereafter using [Scipy's leastsq](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.05364597 -1.01000325  2.00578554  4.98348821]\n"
     ]
    },
    {
     "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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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"
    },
    {
     "data": {
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\" width=\"576\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fda9b4ebc10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import leastsq\n",
    "\n",
    "residuals = lambda p, y, x: y - asym_gaussian(p, x)\n",
    "\n",
    "# executes least-squares regression analysis to optimize initial parameters\n",
    "coeffs_scipy, _ = leastsq(residuals, [1., 1., 1., 1.], args=(data_raw, x))\n",
    "\n",
    "print(coeffs_scipy)\n",
    "\n",
    "y_scipy = asym_gaussian(coeffs_scipy, x)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(x, data_raw, label='data')\n",
    "plt.plot(x, data, label='ground truth')\n",
    "plt.plot(x, y_scipy, linestyle='--', label='scipy fit')\n",
    "plt.legend()"
   ]
  }
 ],
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