{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ipywidgets import interact\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Piecewise Linear Interpolation\n",
    "\n",
    "References: https://cds.cern.ch/record/1456844/files/CERN-OPEN-2012-016.pdf\n",
    "\n",
    "We wish to understand interpolation using the piecewise linear function. This is `interpcode=0` in the above reference. This function is defined as (nb: vector denotes bold)\n",
    "\n",
    "$$\n",
    "\\eta_s (\\vec{\\alpha}) = \\sigma_{sb}^0(\\vec{\\alpha}) + \\underbrace{\\sum_{p \\in \\text{Syst}} I_\\text{lin.} (\\alpha_p; \\sigma_{sb}^0, \\sigma_{psb}^+, \\sigma_{psb}^-)}_\\text{deltas to calculate}\n",
    "$$\n",
    "\n",
    "with\n",
    "\n",
    "$$\n",
    "I_\\text{lin.}(\\alpha; I^0, I^+, I^-) = \\begin{cases} \\alpha(I^+ - I^0) \\qquad \\alpha \\geq 0\\\\ \\alpha(I^0 - I^-) \\qquad \\alpha < 0 \\end{cases}\n",
    "$$\n",
    "\n",
    "In this notebook, we'll demonstrate the technical implementation of these interplations starting from simple dimensionality and increasing the dimensions as we go along. In all situations, we'll consider a single systematic that we wish to interpolate, such as Jet Energy Scale (JES).\n",
    "\n",
    "Let's define the interpolate function. This function will produce the deltas we would like to calculate and sum with the nominal measurement to determine the interpolated measurements value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolate_deltas(down, nom, up, alpha):\n",
    "    delta_up = up - nom\n",
    "    delta_down = nom - down\n",
    "    if alpha > 0:\n",
    "        return delta_up * alpha\n",
    "    else:\n",
    "        return delta_down * alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why are we calculating deltas? This is some additional foresight that you, the reader, may not have yet. Multiple interpolation schemes exist but they all rely on calculating the change with respect to the nominal measurement (the delta)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case 1: The Single-binned Histogram\n",
    "\n",
    "Let's first start with considering evaluating the total number of events after applying JES corrections. This is the single-bin case. Code that runs through event selection will vary the JES parameter and provide three histograms, each with a single bin. These three histograms represent the nominal-, up-, and down- variations of the JES nuisance parameter.\n",
    "\n",
    "When processing, we find that there are 10 events nominally, and when we vary the JES parameter downwards, we only measure 8 events. When varying upwards, we measure 15 events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "down_1 = np.array([8])\n",
    "nom_1 = np.array([10])\n",
    "up_1 = np.array([15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to generate a function $f(\\alpha_\\text{JES})$ that linearly interpolates the number of events for us so we can scan the phase-space for calculating PDFs. The `interpolate_deltas()` function defined above does this for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.linspace(-1.0, 1.0)\n",
    "deltas = [interpolate_deltas(down_1, nom_1, up_1, alpha) for alpha in alphas]\n",
    "deltas[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now that we've generated the deltas from the nominal measurement, we can plot this to see how the linear interpolation works in the single-bin case, where we plot the measured values in black, and the interpolation in dashed, blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(alphas, [nom_1 + delta for delta in deltas], linestyle=\"--\")\n",
    "plt.scatter((-1, 0, 1), (down_1, nom_1, up_1), color=\"k\")\n",
    "plt.xlabel(r\"$\\alpha_\\mathrm{JES}$\")\n",
    "plt.ylabel(r\"Events\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can imagine building a 1-dimensional tensor (column-vector) of measurements as a function of $\\alpha_\\text{JES}$ with each row in the column vector corresponding to a given $\\alpha_\\text{JES}$ value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case 2: The Multi-binned Histogram\n",
    "\n",
    "Now, let's increase the computational difficulty a little by increasing the dimensionality. Assume instead of a single-bin measurement, we have more measurements! We are good physicists after all. Imagine continuing on the previous example, where we add more bins, perhaps because we got more data. Imagine that this was binned by collection year, where we observed 10 events in the first year, 10.5 the next year, and so on..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "down_hist = np.linspace(8, 10, 11)\n",
    "nom_hist = np.linspace(10, 13, 11)\n",
    "up_hist = np.linspace(15, 20, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we still need to interpolate. Just like before, we have varied JES upwards and downwards to determine the corresponding histograms of variations. In order to interpolate, we need to interpolate by bin for each bin in the three histograms we have here (or three measurements if you prefer).\n",
    "\n",
    "Let's go ahead and plot these histograms as a function of the bin index with black as the nominal measurements, red and blue as the down and up variations respectively. The black points are the measurements we have, and for each bin, we would like to interpolate to get an interpolated histogram that represents the measurement as a function of $\\alpha_\\text{JES}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_measurements(down_hist, nom_hist, up_hist):\n",
    "    bincenters = np.arange(len(nom_hist))\n",
    "    for i, h in enumerate(zip(up_hist, nom_hist, down_hist)):\n",
    "        plt.scatter([i] * len(h), h, color=\"k\", alpha=0.5)\n",
    "\n",
    "    for c, h in zip([\"r\", \"k\", \"b\"], [down_hist, nom_hist, up_hist]):\n",
    "        plt.plot(bincenters, h, color=c, linestyle=\"-\", alpha=0.5)\n",
    "\n",
    "    plt.xlabel(\"Bin index in histogram\")\n",
    "    plt.ylabel(\"Events\")\n",
    "\n",
    "\n",
    "plot_measurements(down_hist, nom_hist, up_hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this look like if we evaluate at a single $\\alpha_\\text{JES} = 0.5$? We'll write a function that interpolates and then plots the interpolated values as a function of bin index, in green, dashed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_interpolated_histogram(alpha, down_hist, nom_hist, up_hist):\n",
    "    bincenters = np.arange(len(nom_hist))\n",
    "    interpolated_vals = [\n",
    "        nominal + interpolate_deltas(down, nominal, up, alpha)\n",
    "        for down, nominal, up in zip(down_hist, nom_hist, up_hist)\n",
    "    ]\n",
    "\n",
    "    plot_measurements(down_hist, nom_hist, up_hist)\n",
    "    plt.plot(bincenters, interpolated_vals, color=\"g\", linestyle=\"--\")\n",
    "\n",
    "\n",
    "plot_interpolated_histogram(0.5, down_hist, nom_hist, up_hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can go one step further in visualization and see what it looks like for different $\\alpha_\\text{JES}$ using iPyWidget's interactivity. Change the slider to get an idea of how the interpolation works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = interact(\n",
    "    lambda alpha: plot_interpolated_histogram(alpha, down_hist, nom_hist, up_hist),\n",
    "    alpha=(-1, 1, 0.1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The magic in `plot_interpolated_histogram()` happens to be that for a given $\\alpha_\\text{JES}$, we iterate over all measurements bin-by-bin to calculate the interpolated value\n",
    "\n",
    "```python\n",
    "    [nominal + interpolate_deltas(down, nominal, up, alpha) for down, nominal, up in zip(...hists...)]\n",
    "```\n",
    "\n",
    "So you can imagine that we're building up a 2-dimensional tensor with each row corresponding to a different $\\alpha_\\text{JES}$ and each column corresponding to the bin index of the histograms (or measurements). Let's go ahead and build a 3-dimensional representation of our understanding so far!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolate_alpha_range(alphas, down_hist, nom_hist, up_hist):\n",
    "    at_alphas = []\n",
    "    for alpha in alphas:\n",
    "        interpolated_hist_at_alpha = [\n",
    "            nominal + interpolate_deltas(down, nominal, up, alpha)\n",
    "            for down, nominal, up in zip(down_hist, nom_hist, up_hist)\n",
    "        ]\n",
    "        at_alphas.append(interpolated_hist_at_alpha)\n",
    "    return np.array(at_alphas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then with this, we are interpolating over all histograms bin-by-bin and producing a 2-dimensional tensor with each row corresponding to a specific value of $\\alpha_\\text{JES}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.linspace(-1, 1, 11)\n",
    "\n",
    "interpolated_vals_at_alphas = interpolate_alpha_range(\n",
    "    alphas, down_hist, nom_hist, up_hist\n",
    ")\n",
    "\n",
    "print(interpolated_vals_at_alphas[alphas == -1])\n",
    "print(interpolated_vals_at_alphas[alphas == 0])\n",
    "print(interpolated_vals_at_alphas[alphas == 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a way to generate the 2-dimensional tensor. Let's go ahead and add in all dimensions. Additionally, we'll add in some extra code to show the projection of the 2-d plots that we made earlier to help understand the 3-d plot a bit better. Like before, let's plot specifically colored lines for $\\alpha_\\text{JES}=0.5$ as well as provide an interactive session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_wire(alpha):\n",
    "    alphas = np.linspace(-1, 1, 51)\n",
    "    at_alphas = interpolate_alpha_range(alphas, down_hist, nom_hist, up_hist)\n",
    "    bincenters = np.arange(len(nom_hist))\n",
    "    x, y = np.meshgrid(bincenters, alphas)\n",
    "    z = np.asarray(at_alphas)\n",
    "    bottom = np.zeros_like(x)\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax1 = fig.add_subplot(111, projection=\"3d\")\n",
    "    ax1.plot_wireframe(x, y, z, alpha=0.3)\n",
    "\n",
    "    x, y = np.meshgrid(bincenters, [alpha])\n",
    "    z = interpolate_alpha_range([alpha], down_hist, nom_hist, up_hist)\n",
    "\n",
    "    ax1.plot_wireframe(x, y, z, edgecolor=\"g\", linestyle=\"--\")\n",
    "    ax1.set_xlim(0, 10)\n",
    "    ax1.set_ylim(-1.0, 1.5)\n",
    "    ax1.set_zlim(0, 25)\n",
    "    ax1.view_init(azim=-125)\n",
    "    ax1.set_xlabel(\"Bin Index\")\n",
    "    ax1.set_ylabel(r\"$\\alpha_\\mathrm{JES}$\")\n",
    "    ax1.set_zlabel(\"Events\")\n",
    "\n",
    "    # add in 2D plot goodness\n",
    "\n",
    "    for c, h, zs in zip(\n",
    "        [\"r\", \"k\", \"b\"], [down_hist, nom_hist, up_hist], [-1.0, 0.0, 1.0]\n",
    "    ):\n",
    "        ax1.plot(bincenters, h, color=c, linestyle=\"-\", alpha=0.5, zdir=\"y\", zs=zs)\n",
    "        ax1.plot(bincenters, h, color=c, linestyle=\"-\", alpha=0.25, zdir=\"y\", zs=1.5)\n",
    "\n",
    "    ax1.plot(bincenters, z.T, color=\"g\", linestyle=\"--\", zdir=\"y\", zs=alpha)\n",
    "    ax1.plot(bincenters, z.T, color=\"g\", linestyle=\"--\", alpha=0.5, zdir=\"y\", zs=1.5)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_wire(0.5)\n",
    "\n",
    "interact(plot_wire, alpha=(-1, 1, 0.1));"
   ]
  }
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "3e7ebc468b474ed7b83c9ede45e4779f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_c8f142b845b0449299186613727574f0",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "430b397473c44b1da7b767ee95dc203a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8f1b4c16543c4199a7f72171b6576c67",
       "outputs": [
        {
         "ename": "NameError",
         "evalue": "name 'histogram_down' is not defined",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[0;32m~/.local/share/virtualenvs/pyhf-EFAVEj2h/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-14-5242cbe84103>\u001b[0m in \u001b[0;36mplot_wire\u001b[0;34m(alpha)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_wire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0malphas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m51\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mat_alphas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpolate_alpha_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malphas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mbincenters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistogram_nom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbincenters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malphas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-14-5242cbe84103>\u001b[0m in \u001b[0;36minterpolate_alpha_range\u001b[0;34m(alphas)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mat_alphas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;32min\u001b[0m \u001b[0malphas\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0minerpolated_hist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnom\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minterpolate_deltas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mup\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistogram_down\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhistogram_nom\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhistogram_up\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0mat_alphas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minerpolated_hist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mat_alphas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mNameError\u001b[0m: name 'histogram_down' is not defined"
         ]
        }
       ]
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8f4ee3642c89485d8fa6881e4e385c82",
       "outputs": [
        {
         "ename": "NameError",
         "evalue": "name 'histogram_nom' is not defined",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[0;32m~/.local/share/virtualenvs/pyhf-EFAVEj2h/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-15-6e56ffc330b4>\u001b[0m in \u001b[0;36mplot_wire\u001b[0;34m(alpha)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0malphas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m51\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mat_alphas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpolate_alpha_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malphas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mbincenters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistogram_nom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbincenters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malphas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mat_alphas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mNameError\u001b[0m: name 'histogram_nom' is not defined"
         ]
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