template_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "checked-traffic",
   "metadata": {},
   "source": [
    "## Main template analysis\n",
    "\n",
    "\n",
    "This notebook will generate model-independent MiniBooNE templates.\n",
    "\n",
    "We then unfold them using our unfolder and plot a few examples against MicroBooNE (inclusive) data. \n",
    "\n",
    "Finally, we select templates with a given $p_{\\rm val}^{\\rm MB} > (80, 10, 1)\\%$, and plot them according to their MicroBooNE chi2 and the total number of MiniBooNE events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "virtual-removal",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy \n",
    "\n",
    "from MicroTools import unfolder\n",
    "from MicroTools.TemplateTools import mcmc_signal\n",
    "from MicroTools.TemplateTools import miniboone_fit as mbfit\n",
    "\n",
    "from MicroTools.InclusiveTools import inclusive\n",
    "\n",
    "\n",
    "from MicroTools.TemplateTools import template_analysis as templ\n",
    "\n",
    "from MicroTools.plot_tools import *\n",
    "\n",
    "PATH_MCMC_CHAINS = 'MicroTools/mcmc_results'\n",
    "PATH_PLOTS = 'plots/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mysterious-bermuda",
   "metadata": {},
   "source": [
    "### generate model-independent templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "impressed-macro",
   "metadata": {},
   "outputs": [],
   "source": [
    "example = templ.Templates()\n",
    "\n",
    "example.load_templates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "registered-variation",
   "metadata": {},
   "source": [
    "#### Get microBooNE templates through unfolding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "disciplinary-moore",
   "metadata": {},
   "outputs": [],
   "source": [
    "example.unfold(analysis='1eX', remove_high_energy=False, unfold=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "economic-professor",
   "metadata": {},
   "source": [
    "#### Calculate chi2s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "documented-blend",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-c318e25dc015>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_chi2s\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[0m",
      "\u001b[0;32m~/Repos/MicroBooNE-analysis-2021/MicroTools/TemplateTools/template_analysis.py\u001b[0m in \u001b[0;36mcompute_chi2s\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmb_chi2s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmb_template\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmub_template\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmcmc_chain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmicro_templates\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[0;32m--> 113\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmb_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmb_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmb_template\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnominal_nevents\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    114\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmb_chi2s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmb_chi2s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmbfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCalculateChi2SignalExpectations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmb_template\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    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "example.compute_chi2s()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wicked-remains",
   "metadata": {},
   "source": [
    "#### MiniBooNE p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ruled-peter",
   "metadata": {},
   "outputs": [],
   "source": [
    "example.compute_MB_pval(ndf=4.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automatic-norway",
   "metadata": {},
   "source": [
    "#### Null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "equivalent-participant",
   "metadata": {},
   "outputs": [],
   "source": [
    "mub_chi2_null_hypothesis = cern.Chi2_Inclusive(np.zeros(25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "deluxe-ethiopia",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(\"PyModules/HarvardPerimeter/data/mubMCMC_chi2s.dat\", np.array([mub_x, mub_chi2s, mb_x, mb_chi2s]))\n",
    "# np.save(\"PyModules/HarvardPerimeter/data/mubMCMC_miniboone_templates.dat\", mb_templates)\n",
    "# np.save(\"PyModules/HarvardPerimeter/data/mubMCMC_microboone_templates.dat\", mub_templates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "republican-jamaica",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13b1436d0>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = (plt.figure(constrained_layout=True, figsize=(9,3)).subplots(1, 3))\n",
    "args = {'bins':50, 'density': True, 'facecolor': 'None', 'lw': 0.5}\n",
    "_ = axs[0].hist(example.signal_strength, edgecolor='purple', range=(0,3), label=f'MiniBooNE', **args)\n",
    "_ = axs[0].hist(example.mub_signal_strength, edgecolor='dodgerblue', range=(0,3), label=f'MicroBooNE', **args)\n",
    "axs[0].set_xlabel(r\"signal strength $N / N_{\\rm LEE}$\")\n",
    "axs[0].legend()\n",
    "\n",
    "_ = axs[1].hist(example.avg_energy, edgecolor='purple', range=(100,600), label=f'MiniBooNE', **args)\n",
    "_ = axs[1].hist(example.mub_avg_energy, edgecolor='dodgerblue', range=(100,600), label=f'MicroBooNE', **args)\n",
    "axs[1].set_xlabel(r\"average $E_{\\nu}^{\\rm reco}$ / MeV\")\n",
    "axs[1].legend()\n",
    "\n",
    "_ = axs[2].hist(example.mb_chi2s, edgecolor='purple', range=(0,60), label=f'MiniBooNE',**args)\n",
    "_ = axs[2].hist(example.mub_chi2s, edgecolor='dodgerblue', range=(0,60), label=f'MicroBooNE',**args)\n",
    "axs[2].set_xlabel(r\"$\\chi^2$\")\n",
    "axs[2].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "minimal-cartoon",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# mb_template_worst=mb_templates[pval_1 & (mb_x>0.6)][np.argmin(mub_chi2s[pval_1 & (mb_x>0.6)])]\n",
    "mb_template_worst = [94.40563491,41.31592711,44.36387293,29.99242961, -6.79081795,19.26734292,-8.77917357, -6.88138706, 7.63200631, -9.47116872, -22.11263405]\n",
    "mub_template_worst=np.append(0.0, example.converter.miniToMicro(mb_template_worst))\n",
    "\n",
    "# mb_template_best=mb_templates[pval_1 & (mb_x<1.3)][np.argmax(mub_chi2s[pval_1 & (mb_x<1.3)])]\n",
    "mb_template_best=[170.56228436, 84.38734816, 91.74304699, 49.08114012, 19.81931372, 41.51597723, 14.62351973, 12.99344466, 24.8044534, 9.92501774, 7.46580514]\n",
    "mub_template_best=np.append(0.0, example.converter.miniToMicro(mb_template_best))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "seventh-prize",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = 0.5081723123333333\n",
      "x = 1.463670420138889\n"
     ]
    }
   ],
   "source": [
    "print(f\"x = {np.sum(mb_template_worst)/360}\")\n",
    "print(f\"x = {np.sum(mb_template_best)/360}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "grand-writing",
   "metadata": {},
   "outputs": [],
   "source": [
    "from MicroTools.plot_tools import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "assured-gauge",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Our chi2\n",
    "# micro_nominal_template = get_micro_templates(np.array([mbfit.mb_nominal_excess]))\n",
    "# x_nominal, chi2_nominal = get_microboone_chi2s_in_x(micro_nominal_template, Npoints_for_x=100)\n",
    "# ax.plot(x_nominal, chi2_nominal[0] - mub_chi2_null_hypothesis, color='black', lw=1.5, label=r'Our $\\Delta \\chi^2_{\\rm nominal}$', zorder=5)\n",
    "\n",
    "# # Official\n",
    "# df=pd.read_csv(\"kk/InclusiveChannel/muB_1eX_eLEE_Chi2_Official.csv\", sep=',', header=None)\n",
    "# x_official, chi2official = df.values.T[0], df.values.T[1] \n",
    "# ax.plot(x_official, chi2official, c='black', dashes=(3,1), label=r'\\noindent MicroBooNE $\\Delta \\chi^2_{\\rm nominal}$\\\\\\ \\,', zorder=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "neither-collar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 319.68x221.055 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = std_fig(ax_form=[0.12,0.16,0.81,0.8])\n",
    "\n",
    "# plot the vertical region\n",
    "plot_MB_vertical_region(ax)\n",
    "\n",
    "\n",
    "# Banana plot\n",
    "color1=(0.6,0.61,0.97)\n",
    "# color1='green'\n",
    "color2=(0.60,0.95,0.48)\n",
    "# color2='yellow'\n",
    "color3=(0.95,0.6,0.6)\n",
    "# color3='blue'\n",
    "\n",
    "ndf = 4.7\n",
    "pvals = [0.8, 0.1, 0.01]\n",
    "masks = [example.mb_pval > pvals[i] for i in range(len(pvals))]\n",
    "\n",
    "colors=[color1, color2, color3]\n",
    "legends = [r'$ p_{\\rm val}^{\\rm \\footnotesize MB} =  80\\%$',\n",
    "            r'$ p_{\\rm val}^{\\rm \\footnotesize MB} = 10\\%$',\n",
    "          r'$ p_{\\rm val}^{\\rm \\footnotesize MB} = 1\\%$']\n",
    "\n",
    "def plot_banana(ax, x_vals, chi2_vals, mask, color='black'):\n",
    "    ax.scatter(x_vals[mask], chi2_vals[mask] - mub_chi2_null_hypothesis,\n",
    "               s=2, color=color, alpha=1, zorder=3, rasterized=True)\n",
    "handles =[]\n",
    "for i in [2,1,0]:\n",
    "    plot_banana(ax, example.mb_x, example.mub_chi2s, mask=masks[i], color=colors[i])\n",
    "    handles.append(mpatches.Patch(facecolor=colors[i], edgecolor='black', lw=0.5, label=legends[i]))\n",
    "\n",
    "# Horizontal lines\n",
    "dchi2_thresholds=[1,4,9,16]    \n",
    "for i in range(4):\n",
    "#     ax.annotate(fr\"$\\Delta \\chi^2_{{\\mu B}} = $ {dchi2_thresholds[i]:.3g}\", fontsize=8, xy=(0.05,0.5+dchi2_thresholds[i]), xycoords='data', zorder=12)\n",
    "    ax.hlines(dchi2_thresholds[i], 0,3, colors='black', zorder=10, linewidths=[0.75], linestyles=['--'])\n",
    "ax.legend(handles=handles[::-1], loc='upper left', fontsize=9)\n",
    "\n",
    "\n",
    "ax.plot([np.sum(mb_template_worst)/example.nominal_nevents],\n",
    "            [cern.Chi2_Inclusive(mub_template_worst)-mub_chi2_null_hypothesis], markersize=8, color='red', marker='*',zorder=100)\n",
    "ax.annotate(r'1',\n",
    "            color='red',\n",
    "            xy=(np.sum(mb_template_worst)/example.nominal_nevents,\n",
    "            cern.Chi2_Inclusive(mub_template_worst)-20), \n",
    "            xycoords='data',fontsize=25)\n",
    "\n",
    "ax.plot([np.sum(mb_template_best)/example.nominal_nevents],\n",
    "            [cern.Chi2_Inclusive(mub_template_best)-mub_chi2_null_hypothesis], markersize=8, color='red', marker='*',zorder=100)\n",
    "\n",
    "\n",
    "ax.set_xlim(0,2)\n",
    "ax.set_ylim(0,30)\n",
    "\n",
    "ax.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(0.1))\n",
    "\n",
    "ax.yaxis.set_major_locator(MultipleLocator(5))\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "\n",
    "ax.set_xlabel(r\"$N_{\\rm events}(\\vec{\\theta})/N_{\\rm events}^{\\rm nominal}$\")\n",
    "ax.set_ylabel(r\"$\\Delta \\chi^2_{\\mu B}$\")\n",
    "ax.annotate(r\"Arg\\\"uelles\\,et.\\,al.\\,2021\", fontsize=8, xy=(0.7,1.02), xycoords='axes fraction')\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_2.png\", dpi=400)\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_2.pdf\", dpi=400)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "upset-finance",
   "metadata": {},
   "source": [
    "### Plot the signal strengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "parliamentary-mechanism",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 319.68x221.055 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = std_fig(ax_form=[0.12,0.16,0.81,0.8])\n",
    "   \n",
    "def plot_banana_x(ax, x_vals_mb, mub_templates, mask, color='black'):\n",
    "    x_mub = np.sum(mub_templates, axis=1)/38.8\n",
    "    ax.scatter(x_vals_mb[mask], x_mub[mask], s=2.5, color=color, alpha=1, zorder=3, rasterized=True)\n",
    "    \n",
    "    \n",
    "handles =[]\n",
    "for i in [2,1,0]:\n",
    "    plot_banana_x(ax, example.mb_x, example.micro_templates, mask=masks[i], color=colors[i])\n",
    "    handles.append(mpatches.Patch(facecolor=colors[i], edgecolor='black', lw=0.5, label=legends[i]))\n",
    "ax.legend(handles=handles, loc='upper left', fontsize=9)\n",
    "# plot_banana(ax, mub_x*360/381.2, mub_chi2s, mask=np.full(len(mub_x),True), color='black')\n",
    "\n",
    "\n",
    "ax.set_xlim(0,1.8)\n",
    "ax.set_ylim(0,2)\n",
    "\n",
    "# ax.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "# ax.xaxis.set_minor_locator(MultipleLocator(0.1))\n",
    "\n",
    "# ax.yaxis.set_major_locator(MultipleLocator(5))\n",
    "# ax.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "\n",
    "\n",
    "# ax.hlines([16,9,4,1], 0,3, colors=colors, zorder=10)\n",
    "plt.plot(example.mb_x, example.mb_x)\n",
    "ax.set_xlabel(r\"signal strength miniboone\")\n",
    "ax.set_ylabel(r\"signal strength microboone\")\n",
    "ax.annotate(r\"Arg\\\"uelles\\,et.\\,al.\\,2021\", fontsize=8, xy=(0.7,1.02), xycoords='axes fraction')\n",
    "fig.savefig(f\"{PATH_PLOTS}/x_vs_x.png\", dpi=400)\n",
    "fig.savefig(f\"{PATH_PLOTS}/x_vs_x.pdf\", dpi=400)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reported-pressing",
   "metadata": {},
   "source": [
    "# Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "adjusted-decade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings for Fig. 1\n",
    "TOTAL_RATE = False\n",
    "INCLUDE_MB_LAST_BIN = False\n",
    "STACKED = False\n",
    "PLOT_FAMILY=False\n",
    "colormap=cm.Blues\n",
    "TEMP1_COLOR = 'deeppink'\n",
    "TEMP2_COLOR = 'dodgerblue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "acting-hotel",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_tools' 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<ipython-input-139-4da65c39d8fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# plot data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m plot_tools.data_plot(ax1, \n\u001b[0m\u001b[1;32m     15\u001b[0m           \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmbfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbin_centers\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1e3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m           \u001b[0mY\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmbfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmb_nue_analysis_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plot_tools' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 276.48x293.76 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1, ax2 = double_axes_fig(height=0.33, gap=0.17, fig_size=(3.2*1.2,3.4*1.2))\n",
    "\n",
    "######################################\n",
    "# MiniBooNE \n",
    "if TOTAL_RATE:\n",
    "    units= 1\n",
    "    ax1.set_ylabel(r\"Events\")#/MeV\")\n",
    "else:\n",
    "    units = 1/mbfit.bin_width\n",
    "    ax1.set_ylabel(r\"Events/MeV\")\n",
    "\n",
    "\n",
    "# plot data\n",
    "plot_tools.data_plot(ax1, \n",
    "          X=mbfit.bin_centers/1e3,\n",
    "          Y=(mbfit.mb_nue_analysis_data)*units, \n",
    "          xerr=np.diff(mbfit.bin_edges)/2/1e3, \n",
    "          yerr=np.sqrt(mbfit.mb_nue_analysis_data)*units,\n",
    "          zorder=1)\n",
    "\n",
    "ax1.hist(mbfit.bin_centers/1e3,\n",
    "     weights=mbfit.mb_nue_analysis_predicted_background*units,\n",
    "     bins=mbfit.bin_edges/1e3, \n",
    "     lw = 1, \n",
    "         alpha=0.5,\n",
    "    facecolor=\"#beaed4\",#(0.8,0.33,0.33),\n",
    "     label = \"backgrounds\")    \n",
    "\n",
    "plot_tools.step_plot(ax1,\n",
    "              x=mbfit.bin_edges[:-1]/1e3,\n",
    "              y=mbfit.mb_nue_analysis_predicted_background*units,\n",
    "              color='black',  \n",
    "              lw=1,\n",
    "              label=\"\")\n",
    "\n",
    "if PLOT_FAMILY:\n",
    "    ###\n",
    "    # Family of templates\n",
    "    templates_MB = test_templates_norm\n",
    "    n= len(templates_MB)\n",
    "    rand_indices = np.random.choice(len(templates_MB), n)\n",
    "\n",
    "    templates_muB = test_micro_templates_norm\n",
    "    micro_rand_indices = np.random.choice(len(templates_muB), n)\n",
    "\n",
    "    # plot a few examples\n",
    "    for i in rand_indices:\n",
    "        c = colormap( test_pval_free[i]/np.max(test_pval_free) )\n",
    "        plot_tools.step_plot(ax1,\n",
    "                      x=mbfit.bin_edges[:-1]/1e3,\n",
    "                      y=(templates_MB[i]+0*mbfit.mb_nue_analysis_predicted_background)*units,\n",
    "                      color=c,  lw=0.1,\n",
    "                      label=\"\")\n",
    "\n",
    "    \n",
    "MB_LEE = mbfit.mb_nominal_excess\n",
    "plot_tools.step_plot(ax1, \n",
    "              x=mbfit.bin_edges[:-1]/1e3,\n",
    "              y=(MB_LEE+ STACKED*mbfit.mb_nue_analysis_predicted_background)*units,\n",
    "              color='black',\n",
    "              label=r'nominal')\n",
    "\n",
    "\n",
    "plot_tools.step_plot(ax1,\n",
    "              x=mbfit.bin_edges[:-1]/1e3,\n",
    "              y=(np.clip(mb_template_worst, 0, np.inf) + STACKED*mbfit.mb_nue_analysis_predicted_background) *units,\n",
    "              color=TEMP1_COLOR,  \n",
    "              lw=1.5,\n",
    "              label=\"template 1\")\n",
    "\n",
    "\n",
    "plot_tools.step_plot(ax1,\n",
    "              x=mbfit.bin_edges[:-1]/1e3,\n",
    "              y=(np.clip(mb_template_best, 0, np.inf) + STACKED*mbfit.mb_nue_analysis_predicted_background) *units,\n",
    "              color=TEMP2_COLOR,  \n",
    "              lw=1.5,\n",
    "              label=\"template 2\")\n",
    "\n",
    "# # plot sterile\n",
    "# MiniBooNE event rates\n",
    "plot_tools.step_plot(ax1, \n",
    "              x=mbfit.bin_edges[:-1]/1e3,\n",
    "              y=(mbfit.mb_sterile_best_fit+STACKED*mbfit.mb_nue_analysis_predicted_background)*units,\n",
    "              color='black',\n",
    "                dashes=(1,1),\n",
    "              label=r'3+1 best-fit')\n",
    "\n",
    "\n",
    "\n",
    "ax1.legend(fontsize=8, markerfirst=False, ncol=2)\n",
    "ax1.annotate(text=r'\\textbf{MiniBooNE 2018}', xy=(0.0,1.05), xycoords='axes fraction', fontsize=8)\n",
    "ax1.set_xlabel(r\"Reconstructed $E_\\nu^{\\rm QE}$ (GeV)\", fontsize=9, labelpad=2.5)\n",
    "if INCLUDE_MB_LAST_BIN:\n",
    "    ax1.set_xticks([0.2,0.5,1,1.5,2,2.5,3])\n",
    "    ax1.set_xlim(0.2,3)\n",
    "else:\n",
    "    ax1.set_xticks([0.2,0.4,0.6,0.8,1,1.2,1.4])\n",
    "    ax1.set_xlim(0.2,1.5)\n",
    "\n",
    "ax1.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "ax1.xaxis.set_minor_locator(MultipleLocator(0.1))\n",
    "\n",
    "\n",
    "######################################\n",
    "# MicroBooNE\n",
    "n= 100\n",
    "if PLOT_FAMILY:\n",
    "#     plot a few examples\n",
    "    for i in micro_rand_indices:\n",
    "#         c = colormap( test_pval_norm[i]/np.max(test_pval_norm) )\n",
    "        plot_tools.step_plot(ax2,\n",
    "                      x=cern.BinEdge[:-1],\n",
    "                      y=templates_muB[i]+cern.BkgVec*0,\n",
    "                      color=c,  lw=0.1,\n",
    "                      label=\"\")\n",
    "    \n",
    "micro_nominal_template=get_micro_templates(np.array([ mbfit.mb_nue_analysis_data -  mbfit.mb_nue_analysis_predicted_background]))\n",
    "plot_tools.step_plot(ax2,\n",
    "              x=cern.BinEdge[:-1],\n",
    "              y=micro_nominal_template+cern.BkgVec*STACKED,\n",
    "              color='black',  lw=1.5,\n",
    "              label=r\"nominal\")\n",
    "\n",
    "plot_tools.step_plot(ax2,\n",
    "              x=cern.BinEdge[:-1],\n",
    "              y=np.clip(mub_template_worst, 0, np.inf)+cern.BkgVec*STACKED,\n",
    "              color=TEMP1_COLOR,  lw=1.5,\n",
    "              label=r\"template 1\")\n",
    "\n",
    "plot_tools.step_plot(ax2,\n",
    "              x=cern.BinEdge[:-1],\n",
    "              y=np.clip(mub_template_best, 0, np.inf)+cern.BkgVec*STACKED,\n",
    "              color=TEMP2_COLOR,  lw=1.5,\n",
    "              label=r\"template 2\")\n",
    "\n",
    "\n",
    "plot_tools.step_plot(ax2, \n",
    "              x=cern.BinEdge[:-1],\n",
    "              y=(np.append(0,converter.miniToMicro(mbfit.mb_sterile_best_fit))+cern.BkgVec*STACKED),\n",
    "              color='black',\n",
    "              dashes=(1,1),\n",
    "              label=r'3+1 best-fit')\n",
    "\n",
    "\n",
    "# background\n",
    "ax2.hist(cern.BinCenter,\n",
    "     weights=cern.BkgVec,\n",
    "     bins=cern.BinEdge, \n",
    "     lw = 1, \n",
    "    facecolor=(0.5,0.88,0.55),\n",
    "     label = r\"backgrounds\")    \n",
    "# data plot\n",
    "plot_tools.data_plot(ax2, \n",
    "          X=cern.BinCenter,\n",
    "          Y=cern.DatVec, \n",
    "          xerr=np.diff(cern.BinEdge)/2, \n",
    "          yerr=np.sqrt(cern.DatVec),\n",
    "         zorder=2)\n",
    "plot_tools.step_plot(ax2,\n",
    "              x=cern.BinEdge[:-1],\n",
    "              y=cern.BkgVec,\n",
    "              color='black',  \n",
    "              lw=0.5,\n",
    "              label=\"\")\n",
    "\n",
    "ax2.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "ax2.xaxis.set_minor_locator(MultipleLocator(0.1))\n",
    "\n",
    "ax2.annotate(text=r'\\textbf{MicroBooNE 2021} (Inclusive FC unconstrained)', xy=(0.0,1.05), xycoords='axes fraction', fontsize=8)\n",
    "ax2.legend(fontsize=7, markerfirst=False, ncol=3)\n",
    "ax2.set_xlabel(r\"Reconstructed $E_\\nu$ (GeV)\", fontsize=9, labelpad=2.5)\n",
    "ax2.set_ylabel(r\"Events/100 MeV\")\n",
    "ax2.set_xlim(np.min(cern.BinEdge),np.max(cern.BinEdge))\n",
    "# ax.hlines(0,0,2e3, lw=0.75, ls='--', color='black')\n",
    "ax2.set_ylim(0,45)\n",
    "\n",
    "ax1.annotate(text=r'Arg\\\"uelles\\,et.\\,al.\\,2021', xy=(0.65,1.05), xycoords='axes fraction', fontsize=8.5)\n",
    "\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_1.png\", dpi=400)\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "divided-module",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "absent-crisis",
   "metadata": {},
   "source": [
    "# Paper plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "african-truth",
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_chi2s = chi2s - np.min(chi2s, axis=0)\n",
    "i_chi2_min = np.argmin(np.sum(delta_chi2s, axis=1))\n",
    "i_chi2_max = np.argmax(np.sum(delta_chi2s, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "rocky-income",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the minimal template\n",
    "min_delta_chi2s = np.empty(0)\n",
    "for i in range(len(x)):\n",
    "    min_delta_chi2s = np.append(min_delta_chi2s, np.min(delta_chi2s[:,i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "governmental-divorce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[169.49378    155.84720171 226.41970476  77.69474387  68.70430268\n",
      "  66.02649682  66.08117128  39.7793506   51.0968461   68.55970876\n",
      "  96.98580602]\n"
     ]
    }
   ],
   "source": [
    "best_template = test_templates_free[i_chi2_min]\n",
    "print(best_template)\n",
    "mub_best_template = np.append(0,converter.miniToMicro(best_template))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adolescent-overview",
   "metadata": {},
   "source": [
    "### compute all MicroBooNE chi2s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "academic-quantum",
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings for Fig. 2\n",
    "INCLUDE_BAND = True\n",
    "cmap = matplotlib.colors.LinearSegmentedColormap.from_list('custom greys', [(0.9,0.9,0.9),(0.5,0.88,0.55)], N=1)\n",
    "colormap=cmap#cm.Blues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "alternate-toyota",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 25)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 319.68x221.055 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "official_muB_template = np.load(\"PyModules/InclusiveData/LEE_x1_Expectation.npy\")\n",
    "micro_nominal_template = get_micro_templates(np.array([mbfit.mb_nominal_excess]))\n",
    "\n",
    "\n",
    "fig, ax = plot_tools.std_fig(ax_form=[0.12,0.16,0.81,0.8])\n",
    "\n",
    "# plot a few examples\n",
    "# x, chi2s = get_microboone_chi2s_in_x(filtered_micro_templates_free, Npoints_for_x=100)\n",
    "n=len(chi2s)\n",
    "for i in range(n):\n",
    "    c = colormap( 1 - (np.sum(chi2s, axis=1)[i] - np.min(np.sum(chi2s, axis=1)))/(np.max(np.sum(chi2s, axis=1)) - np.min(np.sum(chi2s, axis=1))) )\n",
    "    ax.plot(x, chi2s[i] - np.min(chi2s[i]), color='lightgrey', alpha=1)\n",
    "    \n",
    "x_nominal, chi2_nominal = get_microboone_chi2s_in_x(micro_nominal_template, Npoints_for_x=100)\n",
    "ax.plot(x_nominal, chi2_nominal[0] - np.min(chi2_nominal[0]), color='black', lw=1.5, label=r'Our $\\Delta \\chi^2_{\\rm nominal}$')\n",
    "\n",
    "df=pd.read_csv(\"kk/InclusiveChannel/muB_1eX_eLEE_Chi2_Official.csv\", sep=',', header=None)\n",
    "x_official, chi2official = df.values.T[0], df.values.T[1] \n",
    "ax.plot(x_official,chi2official, c='black', dashes=(3,1), label=r'\\noindent MicroBooNE $\\Delta \\chi^2_{\\rm nominal}$\\\\\\ \\,')\n",
    "\n",
    "\n",
    "ax.plot(x, chi2s[i] - np.min(chi2s[i]), color='lightgrey', alpha=1, label=r'$\\{\\Delta\\chi^2_{\\rm shape}\\}$', zorder=-1)\n",
    "ax.plot(x, min_delta_chi2s, c='dodgerblue', lw=2, label=r'min($\\{ \\Delta\\chi^2_{\\rm shapes}\\})$')\n",
    "# ax.plot(x,delta_chi2s[i_chi2_min], c='dodgerblue', label=r'min($\\{ \\Delta\\chi^2_{\\rm shape}\\}$')\n",
    "\n",
    "##########\n",
    "# MINIBOONE 2018\n",
    "\n",
    "matplotlib.rcParams['hatch.linewidth'] = 0.7\n",
    "y = [0,1e10]\n",
    "NEVENTS=381.2\n",
    "ERROR = 85.2\n",
    "xleft = (NEVENTS-ERROR)/NEVENTS\n",
    "xright = (NEVENTS+ERROR)/NEVENTS\n",
    "ax.fill_betweenx(y,[xleft,xleft],[xright,xright], zorder=3,\n",
    "                     ec='orange', fc='None',\n",
    "                     hatch='\\\\\\\\\\\\\\\\\\\\',\n",
    "                     lw=0,\n",
    "                    label=r'MiniBooNE $1 \\sigma$')\n",
    "xleft = (NEVENTS-np.sqrt(NEVENTS))/NEVENTS\n",
    "xright = (NEVENTS+np.sqrt(NEVENTS))/NEVENTS\n",
    "# ax.fill_betweenx(y,[xleft,xleft],[xright,xright], zorder=5,\n",
    "#                      ec='darkorange', fc='None',\n",
    "#                      hatch='xxxxxx',\n",
    "#                      lw=0)\n",
    "ax.vlines(1,0,1e10, zorder=3, lw=1, color='darkorange')\n",
    "\n",
    "\n",
    "ax.set_xlim(0,1.5)\n",
    "ax.set_ylim(0,30)\n",
    "\n",
    "ax.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(0.1))\n",
    "\n",
    "ax.legend(loc='upper left', fontsize=9)\n",
    "\n",
    "ax.set_xlabel(r\"signal strength x\")\n",
    "ax.set_ylabel(r\"$\\Delta \\chi^2$\")\n",
    "# ax.annotate(r\"Arg\\\"uelles et. \\!al. 2021\", xy=(0.63,0.02), xycoords='axes fraction')\n",
    "ax.annotate(r\"Arg\\\"uelles et.\\,al.\\,2021\", xy=(0.04,0.42), xycoords='axes fraction')\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_2.png\", dpi=400)\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_2.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "desperate-following",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Banana plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "illegal-surgeon",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must be the same size",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-84-295fc15b2398>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchi2s\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      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1359\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1360\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4496\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4497\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4498\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\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   4499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4500\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASsAAADSCAYAAAASTubRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAI7ElEQVR4nO3cz3Hb1hqG8fe9owIwkrO+M/Aim7tiqA54O+C4BLqETCrIOB2YLVgdUB2I8v4uwgo8GnTw3YUPLQShCP4BFX7U85vJhOCBxAPTfIQDIXFECADO3b/+6QkAwC6IFYAUiBWAFIgVgBSIFYAUiBWAFHpjZXtqe9EzPrE9G3ZqAPCsN1YRcffSmO1p2ee+bE+GmxoAPDt2GXgraVUerySNjvx+ALDR1ZFfX3W2b7o7lOXheon4s6T/HfmaAPL7d0T8tM8XHBurRtL1th0iYi5pLkm2lxExPvI1ASRne7nv1xy7DHzQ89lVLenFC/EAcIxdfhs4kTReX0wvzy2kHxff67JPtb7QDgBD82v+XxdYBgKQDmsBN4UCSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFI4apvB9tTSY2kOiLm+44DwBC2nlmVECki7sv2pDM+kbQq4yvbo1NNFMDb1rcMvJW0Ko9XkroxWkr6UiJVR8TXgecHAJL6Y1V1tm/aGxHRSPos6YukXzZ9A9sz20vbS0nvDpsmgLeuL1aNpOuXBssy8D4i3ktq1svGtoiYR8Q4IsaSvh0zWQBvV1+sHvR8dlVLWnTGR62l3+/aEjYAOMbWWEXEnaS6nEFVrQvt62jNyzJvIukDvw0EcCqOiNd7MXtZloMA3rBDWsBNoQBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSuOrbwfZUUiOpjoj5hvGRpFqSIuJu6AkCgNRzZlVCpYi4L9uTDbv9ViJ1bbsefooA0L8MvJW0Ko9XkkbtQdszSQ+264iYR8Sq+w0AYAh9sao62zed7ffluSfbn21395ftme2l7aWkd4dOFMDb1herRtJ1zz5/RkQj6VHSrDtYzrjGETGW9O2QSQJAX6we9Hx2VUtabBhfq/Q9bgAwuK2xKhfO63JhvWpdaF+0xqv1hfdNvy0EgCE4Il7vxexlWQ4CeMMOaQE3hQJIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUiBWAFIgVgBSIFYAUihN1a2p7Yntmc9+30abloA8FdbY2V7KkkRcV+2Jy/sN5FUDz47ACj6zqxuJa3K45WkUXcH23VrHwA4ib5YVZ3tmw371BHxYqxsz2wvbS8lvdtzfgAgqT9WjaTrlwZtT9ZLxJdExDwixhExlvRt/ykCgHTVM/6g57OrWtKiM/5UrldVkmrbo4j4OugMAUA9Z1YRcafvEZpIqloX2hdl/Gt57lp/XzICwGAcEa/3YvayLAcBvGGHtICbQgGkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkcNW3g+2ppEZSHRHzzlglqS7/3EbEryeYIwBsP7MqoVJE3JftSWeXD5LGEXFXxmenmCQA9C0DbyWtyuOVpFF7MCLmrbOturUvAAyqbxlYdbZvNu1ku5b0tD4D64zNJK3PuN7tO0EAkPrPrBpJ1zt8n2lEfNw0UM6+xhExlvRtz/kBgKT+WD3o+eyqlrTo7mB7GhF/lMej7jgADGFrrMqF87pcWK9aF9oX5d8TSZ9sP9p+1G5nYQCwN0fE672YvSzLQQBv2CEt4KZQACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKRArACkQKwApECsAKVz17WB7KqmRVEfEfN9xABjC1jOrEiJFxH3ZnuwzDgBD6VsG3kpalccrSaM9xwFgEH3LwKqzfbPnuGzPJM3K5n9sL3edXALvJH37pycxoEs6nks6Funyjufnfb+gL1aNpOsjxlWuY80lyfYyIsZ7zO+scTzn65KORbrM49n3a/qWgQ96PnuqJS32HAeAQWyNVUTcSarLhfOqdSF9sW0cAIbWe+tCRPxRHt63nvvvtvEtLu3WBo7nfF3SsUgcjxwRp5gIAAyKO9gBpNC7DDzUJd35vm2utit9/+VCLek2In599Qnuadc/e9ufLuF4bI/0/f1ZX2c9a5f02ZF+zPdj+/LRhvFGPcdzkjOrS7rzfYe5fpA0Xn8Iyn1lZ2vXP/vyfP2KUzvIjsfzW3l/rm2f9THt8NmZSFqV8VUJ8Vnb9gNinxacahl4SXe+b51rRMxbPw3q1r7nqvfPvnygz/041rYeT/nh8WC7Lu/VuR9X3/uzlPRlfbYYEV9fc3InsHMLThWrqrO9953vZ6TqbG+ca/mAPyW4faPqbG86njrBh3qt6mx3j+d9ee7J9ueybD9nVWf7L8cTEY2kz5K+SPrldaZ0UlVn+8UWnCpWjY688/2MNNptrtOI+HjiuQyh0ZbjsT1JENy2Rv3vz5/lQ/6o5//061w16nl/JN1HxHtJzXoZlVijHVtwqlhd0p3vvXO1PV3fb5bgGkLf8TzZnpQPQX0Bx/PQelzp+4fjnPUdz6i19PtdeX7ov2TnFpwkVpd053vfsZTnP9l+tP2oM//Ls8N787U8d62/n6KfnR3/rlXrC7fn/tuzvuORNLc9K+Mfzv14pB+fkXH7LPCQFnBTKIAUuCkUQArECkAKxApACsQKQArECkAKxApACsQKQArECkAK/wdffPNFKzmUAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 319.68x221.055 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_tools.std_fig(ax_form=[0.12,0.16,0.81,0.8])\n",
    "\n",
    "\n",
    "\n",
    "ax.scatter(x, chi2s)\n",
    "\n",
    "\n",
    "ax.set_xlim(0,1.5)\n",
    "ax.set_ylim(0,30)\n",
    "\n",
    "ax.xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(0.1))\n",
    "\n",
    "ax.legend(loc='upper left', fontsize=9)\n",
    "\n",
    "ax.set_xlabel(r\"signal strength x\")\n",
    "ax.set_ylabel(r\"$\\Delta \\chi^2$\")\n",
    "# ax.annotate(r\"Arg\\\"uelles et. \\!al. 2021\", xy=(0.63,0.02), xycoords='axes fraction')\n",
    "ax.annotate(r\"Arg\\\"uelles et.\\,al.\\,2021\", xy=(0.04,0.42), xycoords='axes fraction')\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_2_bananas.png\", dpi=400)\n",
    "fig.savefig(f\"{PATH_PLOTS}/Fig_2_bananas.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "catholic-wales",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13191a790>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 319.68x221.055 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_tools.std_fig()\n",
    "\n",
    "flat_temp = np.ones(11)*0.2*mbfit.bin_width/1.5\n",
    "mub_flat_temp = np.append(0.0, converter.miniToMicro(flat_temp))\n",
    "\n",
    "ax.plot(cern.BinCenter, mub_flat_temp, label='flat 0.13 events/MeV at MiniBooNE')\n",
    "z=np.ones(11)\n",
    "z[0] = 2\n",
    "ax.plot(cern.BinCenter, np.append(0, converter.miniToMicro(mbfit.mb_nominal_excess*z)), label='nominal')\n",
    "ax.set_xlabel('Energy/MeV')\n",
    "ax.set_ylabel('Events')\n",
    "ax.legend(loc='upper left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greater-plymouth",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "separate-country",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outstanding-attention",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "technological-listening",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}