examples/microboone_study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39de5788",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0e1e379",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import DarkNews as dn\n",
    "from DarkNews import const\n",
    "from DarkNews.GenLauncher import GenLauncher\n",
    "from DarkNews import Cfourvec as Cfv\n",
    "from DarkNews.MC import get_merged_MC_output\n",
    "from DarkNews.fourvec import df_dot3\n",
    "\n",
    "from particle import literals as lp\n",
    "from pathlib import Path\n",
    "\n",
    "import ToyAnalysis as ta\n",
    "from ToyAnalysis import analysis_decay as ad\n",
    "from ToyAnalysis import analysis as an\n",
    "from ToyAnalysis import fit_functions as fit\n",
    "from ToyAnalysis.plot_tools import plot_all_rates as MBplotter\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "def get_pval(df_prob, ndof=8.7):\n",
    "    bin_e = np.genfromtxt(f'ToyAnalysis/include/MB_data_release/numode/miniboone_binboundaries_nue_lowe.txt')\n",
    "    NP_MC, _ = np.histogram(df_prob['reco_Enu']*1e3, weights=df_prob['reco_w'], bins=bin_e)\n",
    "    bw = bin_e[1:]-bin_e[:-1]\n",
    "    # plt.step((bin_e[1:]+bin_e[:-1])/2,NP_MC/bw, where ='mid')\n",
    "    chi2=fit.chi2_MiniBooNE_2020(NP_MC, NP_MC.sum())\n",
    "    return scipy.stats.chi2.sf(chi2, ndof)\n",
    "\n",
    "def print_summary_of_event(df, col='w_event_rate', summary='Events'):\n",
    "    coherent = (df['scattering_regime'] == 'coherent')\n",
    "    pel = (df['scattering_regime'] == 'p-el')\n",
    "    nel = (df['scattering_regime'] == 'n-el')\n",
    "    print(f\"{summary}: {df[col].sum():.2g} total ({df[col][coherent].sum():.2g} coh : {df[col][pel].sum():.2g} pel : {df[col][nel].sum():.2g} nel)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abb8ea93",
   "metadata": {},
   "outputs": [],
   "source": [
    "common_kwargs = {'loglevel': 'ERROR', 'HNLtype': 'dirac', 'neval': 1e4}\n",
    "\n",
    "# TMM\n",
    "kwargs = {'mu_tr_mu4': 1.4e-6, 'm4': 0.470, 'decay_product': 'photon'}\n",
    "gen_1 = dn.GenLauncher(experiment='miniboone_fhc', **kwargs, **common_kwargs)\n",
    "gen_1_dirt = dn.GenLauncher(experiment='miniboone_fhc_dirt', **kwargs, **common_kwargs)\n",
    "genmuB_1 = dn.GenLauncher(experiment='microboone', nopelastic=True, **kwargs, **common_kwargs)\n",
    "genmuB_1_dirt = dn.GenLauncher(experiment='microboone_dirt', nopelastic=False,  **kwargs, **common_kwargs)\n",
    "df_1 = gen_1.run()\n",
    "df_1_dirt = gen_1_dirt.run()\n",
    "df_1_muB = genmuB_1.run()\n",
    "df_1_muB_dirt = genmuB_1_dirt.run()\n",
    "\n",
    "# Vectors\n",
    "M4 = 0.107\n",
    "M5 = 0.150\n",
    "kwargs = {'d_mu5': 0.0002, 'd_45': 1, 'dprotonV': 3.4e-4*const.eQED, 'deV': 3.4e-4*const.eQED, 'mzprime': 0.03, 'm4': M4, 'm5': M5}\n",
    "gen_2 = dn.GenLauncher(experiment='miniboone_fhc', **kwargs, **common_kwargs)\n",
    "gen_2_dirt = dn.GenLauncher(experiment='miniboone_fhc_dirt', **kwargs, **common_kwargs)\n",
    "gen_2_muB = dn.GenLauncher(experiment='microboone', nopelastic=True, **kwargs, **common_kwargs)\n",
    "gen_2_muB_dirt = dn.GenLauncher(experiment='microboone_dirt', nopelastic=False,  **kwargs, **common_kwargs)\n",
    "df_2 = gen_2.run()\n",
    "df_2_dirt = gen_2_dirt.run()\n",
    "df_2_muB = gen_2_muB.run()\n",
    "df_2_muB_dirt = gen_2_muB_dirt.run()\n",
    "\n",
    "kwargs = {'d_mu5': 0.0002, 'd_45': 1, 'dprotonV': 3.4e-4*const.eQED, 'deV': 3.4e-4*const.eQED, 'mzprime': 0.03, 'm4': M4, 'm5': M5}\n",
    "kwargs = {'UD4': 1/np.sqrt(2), 'UD5': 1/np.sqrt(2), 'Umu5': 1e-3, 'Umu4': 1e-3, 'gD': 2, 'epsilon': 0.75e-2, 'mzprime': 1.25, 'm4': M4, 'm5': M5}\n",
    "gen_3 = dn.GenLauncher(experiment='miniboone_fhc',  **kwargs, **common_kwargs)\n",
    "gen_3_dirt = dn.GenLauncher(experiment='miniboone_fhc_dirt',  **kwargs, **common_kwargs)\n",
    "gen_3_muB = dn.GenLauncher(experiment='microboone', nopelastic=True, **kwargs, **common_kwargs)\n",
    "gen_3_muB_dirt = dn.GenLauncher(experiment='microboone_dirt', nopelastic=False,  **kwargs, **common_kwargs)\n",
    "df_3 = gen_3.run()\n",
    "df_3_dirt = gen_3_dirt.run()\n",
    "df_3_muB = gen_3_muB.run()\n",
    "df_3_muB_dirt = gen_3_muB_dirt.run()\n",
    "\n",
    "# Scalars\n",
    "M4 = 0.140\n",
    "M5 = 0.2\n",
    "theta_e = 2.5e-3\n",
    "theta_numu = 4e-3\n",
    "theta_nucleon = 4e-3\n",
    "kwargs = {'mzprime': None, 'mhprime': 1.0, 'm4': M4, 'm5': M5, 's_mu5': theta_numu, 's_45': 1.0,  'deS': theta_e, 'dprotonS': theta_nucleon, 'dneutronS': theta_nucleon}\n",
    "gen_4 = dn.GenLauncher(experiment='miniboone_fhc', **kwargs, **common_kwargs)\n",
    "gen_4_dirt = dn.GenLauncher(experiment='miniboone_fhc_dirt', **kwargs, **common_kwargs)\n",
    "gen_4_muB = dn.GenLauncher(experiment='microboone', nopelastic=True, **kwargs, **common_kwargs)\n",
    "gen_4_muB_dirt = dn.GenLauncher(experiment='microboone_dirt', nopelastic=False,  **kwargs, **common_kwargs)\n",
    "df_4 = gen_4.run()\n",
    "df_4_dirt = gen_4_dirt.run()\n",
    "df_4_muB = gen_4_muB.run()\n",
    "df_4_muB_dirt = gen_4_muB_dirt.run()\n",
    "\n",
    "\n",
    "theta_e = 0.8e-3\n",
    "theta_numu = 1e-3\n",
    "theta_nucleon = 1.4e-4\n",
    "kwargs = {'mzprime': None, 'mhprime': 0.03, 'm4': M4, 'm5': M5, 's_mu5': theta_numu, 's_45': 1.0,  'deS': theta_e, 'dprotonS': theta_nucleon, 'dneutronS': theta_nucleon}\n",
    "gen_5 = dn.GenLauncher(experiment='miniboone_fhc', **kwargs, **common_kwargs)\n",
    "gen_5_dirt = dn.GenLauncher(experiment='miniboone_fhc_dirt', **kwargs, **common_kwargs)\n",
    "gen_5_muB = dn.GenLauncher(experiment='microboone', nopelastic=True, **kwargs, **common_kwargs)\n",
    "gen_5_muB_dirt = dn.GenLauncher(experiment='microboone_dirt', nopelastic=False, **kwargs, **common_kwargs)\n",
    "df_5 = gen_5.run()\n",
    "df_5_dirt = gen_5_dirt.run()\n",
    "df_5_muB = gen_5_muB.run()\n",
    "df_5_muB_dirt = gen_5_muB_dirt.run()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b701a42",
   "metadata": {},
   "source": [
    "### Performing analysis\n",
    "\n",
    "Apply decay probability, smear, select overlapping and asymmetric events for\n",
    "\n",
    "* MiniBooNE \n",
    "* MicroBooNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb7f8a7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:296: RuntimeWarning: invalid value encountered in sqrt\n",
      "  Pe  = np.sqrt(Evis**2 - const.m_e**2)\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:296: RuntimeWarning: invalid value encountered in sqrt\n",
      "  Pe  = np.sqrt(Evis**2 - const.m_e**2)\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:195: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_asym\t= w_asym.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:196: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_ovl\t\t= w_ovl.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:197: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_sep\t\t= w_sep.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:198: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_inv\t\t= w_inv.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:304: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff = weights[final_selected].sum()/weights.sum()\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:195: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_asym\t= w_asym.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:196: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_ovl\t\t= w_ovl.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:197: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_sep\t\t= w_sep.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:198: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_inv\t\t= w_inv.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:304: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff = weights[final_selected].sum()/weights.sum()\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:195: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_asym\t= w_asym.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:196: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_ovl\t\t= w_ovl.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:197: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_sep\t\t= w_sep.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:198: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_inv\t\t= w_inv.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:321: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff = weights_fs.sum()/weights.sum()\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:117: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  muB_eff = (w*nueCCQE_numi_eff_func(Evis)).sum()/w.sum()\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:195: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_asym\t= w_asym.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:196: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_ovl\t\t= w_ovl.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:197: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_sep\t\t= w_sep.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:198: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff_inv\t\t= w_inv.sum()/w_tot\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:321: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  eff = weights_fs.sum()/weights.sum()\n",
      "/Users/matheushostert/Repos/DarkNews-generator/examples/ToyAnalysis/analysis.py:117: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  muB_eff = (w*nueCCQE_numi_eff_func(Evis)).sum()/w.sum()\n"
     ]
    }
   ],
   "source": [
    "my_dfs = [df_1, df_2, df_3, df_4, df_5]\n",
    "my_dfs_dirt = [df_1_dirt, df_2_dirt, df_3_dirt, df_4_dirt, df_5_dirt]\n",
    "my_dfs_muB = [df_1_muB, df_2_muB, df_3_muB, df_4_muB, df_5_muB]\n",
    "my_dfs_muB_dirt = [df_1_muB_dirt, df_2_muB_dirt, df_3_muB_dirt, df_4_muB_dirt, df_5_muB_dirt]\n",
    "\n",
    "##########\n",
    "# MiniBooNE -- smear, signal selection and filter decays\n",
    "my_dfs_prob = []\n",
    "my_dfs_dirt_prob = []\n",
    "final_dfs = []\n",
    "for df, dfd in zip(my_dfs, my_dfs_dirt):\n",
    "    if df.attrs['model'].mu_tr_mu4 > 0:\n",
    "        dfp   = an.compute_spectrum(ad.decay_selection(df, l_decay_proper_cm=df.attrs['N4_ctau0'], experiment='miniboone'), EVENT_TYPE='photon')\n",
    "        dfp_d = an.compute_spectrum(ad.decay_selection(dfd, l_decay_proper_cm=dfd.attrs['N4_ctau0'], experiment='miniboone'), EVENT_TYPE='photon')\n",
    "    else:\n",
    "        dfp   = an.compute_spectrum(ad.decay_selection(df, l_decay_proper_cm=df.attrs['N5_ctau0'], experiment='miniboone'), EVENT_TYPE='both')\n",
    "        dfp_d = an.compute_spectrum(ad.decay_selection(dfd, l_decay_proper_cm=dfd.attrs['N5_ctau0'], experiment='miniboone'), EVENT_TYPE='both')\n",
    "    my_dfs_prob.append(dfp)\n",
    "    my_dfs_dirt_prob.append(dfp_d)\n",
    "    final_dfs.append(get_merged_MC_output(dfp,dfp_d))\n",
    "\n",
    "##########\n",
    "# MicroBooNE -- smear, signal selection and filter decays\n",
    "my_dfs_muB_prob = []\n",
    "my_dfs_muB_dirt_prob = []\n",
    "final_dfs_muB = []\n",
    "for df, dfd in zip(my_dfs_muB, my_dfs_muB_dirt):\n",
    "    if df.attrs['model'].mu_tr_mu4 > 0:\n",
    "        dfp   = an.compute_spectrum(ad.decay_selection(df, l_decay_proper_cm=df.attrs['N4_ctau0'], experiment='microboone'), EVENT_TYPE='photon', EXP='microboone')\n",
    "        dfp_d = an.compute_spectrum(ad.decay_selection(dfd, l_decay_proper_cm=dfd.attrs['N4_ctau0'], experiment='microboone'), EVENT_TYPE='photon', EXP='microboone')\n",
    "    else:\n",
    "        dfp   = an.compute_spectrum(ad.decay_selection(df, l_decay_proper_cm=df.attrs['N5_ctau0'], experiment='microboone'), EVENT_TYPE='both', EXP='microboone')\n",
    "        dfp_d = an.compute_spectrum(ad.decay_selection(dfd, l_decay_proper_cm=dfd.attrs['N5_ctau0'], experiment='microboone'), EVENT_TYPE='both', EXP='microboone')\n",
    "    my_dfs_muB_prob.append(dfp)\n",
    "    my_dfs_muB_dirt_prob.append(dfp_d)\n",
    "    final_dfs_muB.append(get_merged_MC_output(dfp,dfp_d))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9a5904",
   "metadata": {},
   "source": [
    "### Position of upscattering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9db7a9f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from DarkNews import fourvec as fv\n",
    "# _=plt.hist(np.arccos(fv.df_cos_azimuthal(final_dfs_muB[0]['P_decay_photon']))*180/np.pi)\n",
    "_=plt.hist(final_dfs_muB[0]['P_decay_photon','0'], bins=100, range=(0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "954a67ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 103.69233832414525)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAENCAYAAACbyHl0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYLElEQVR4nO3dv28c6X3H8c83uviqOCv6XAknyiunS2HzqPwDJp3eoaTC3QGmynMRSLYblQqVRqoS6oDrDEQi4ZSBzT0gtU3KTcpwTQLX5axbp/PlDt8U8ww1XO7u7OzOj2dm3y9gcdpnZmce7lEfPc88zzxj7i4A6KK/aLoCAFAVAg5AZxFwADqLgAPQWQQcgM4i4AB0FgGHpZjZqZkdTCjfMzMPf+6b2WlJ5/vCzI7C69TM9ko43slY2amZ9bLbM+c8MrONZc6J+rzTdAXQCf0JZVuSRpLk7kNJt0s83113H0kXQbrn7o+WOaCZbbj76ymbf5CeD+1CCw5lGJjZTvomtHCOazr3S00O2CIeSfp5CXVBZAg4lOGlpPuZ9/clXXRbzayX7Qaa2W7o9p2Y2cOw/Sj8+SCzT9ol3Jp00hCkH0t6MnbsK5+bdTx3H0jaMLNlgxKRoYuKpbn767HrUjvu/sjMruwb9nvg7h+E972waUvSkbvfDftsu/t22OfEzLLdxINw7L6kvbRrOe1zYb9Zx5OkPSUtuQcTfsSD7M+SHgfxowWHshya2U4ImcGM/e4rafFJkjIhM3L3p+HPD7L7hONlW1133X3b3W9LUmaQY9rn8o4nd38haSsTuFnp+bYJt3Yh4FCWfSVBcql7WsCbnO1rkwrTYCr6uSnlh5J2c+qBFiHgUIowUromaStc05rm0vW6zHWvUWafA13uKm5pSqswXE9Lw3Ha5+Y93hNN7qKipbgGhzLtS+rN2iFcr9vPDDoMlBkkCPsMzGwjs8+TEKCpj8M1sfRc2zmfG+YcLz3vyMwGutqKOzGzUeb9E3c/nPVzIg7GenAAuoouKoDOIuAAdBYBB6CzCDgAndXaUVQz+x9J503XA0Dj1t3925M2tDbgJJ27+2bTlQDQLDOburADXVQAnUXAAeisyrqo4ablXSU3Ub/IlO8ouS2nJ2mYWQliYjkALKrKa3BXro+F0Nt29wfh/ZGk7WnlFdYNwAqorIsabrgejRXfk5Rdm38UlteZVg4AC6v7GlxPl0PvjZIVKKaVA8DCYpgm0pu33Mx29Xalh/cqqg+Ajqg74Ea6HFxrktJ1xCaVXxIGK15Is+e+pNaf60zS+oJ1neT8/CPdKvF4ACpUd8C9UrL2faoX1gcbTiov4Xzrkj6U9Mvzj/TllY3P9Q1JP553+/pzzb22VGhtDpV0t++PP9bOzPqT1iQDUJ4qp4lsKRkJ7ZnZ0N0HYUHB7FON9qSLhQavlJeklHBb9OQhqCeF9Z6ku4seF0C+ygIujKJeWRZ62kqoVa2QWme4hekuB3rbFd8Lo8H3lax2+0hJ9/tMyQNO9iU94qHCQDViGGSoVcUtt11J++5+aGYPJ2y/J+k7ocX6vXTeH4BqrNStWjV0S29rcnc09YrWGlCflQm4ZcMtbM9zIimdoPytCdtHc1cYwNK63kU9nzDy+cn685mfmbU9b/25V5I+NbNt5U9UHoUHFj9iNBWoRmufqmVmx6wHB2BWFqxMFxXA6iHgAHQWAQegswg4AJ1FwAHorE4HnJmdmZmX+Dqb45z9Zfcxsw0zK/N+XGAldTrglDwv0cp6ab6ll+YJJsILqEHXA65W4f7TLTPbDzfey8wOwmt/0j5m1g9/PggP3gFQkq7fyVArd39qZtmH5zxUcvP9wMx2zGx3fB8lt29lH7ZTyaoqwCqiBVetO5LSlYdfS/pg0k7hmtuupNzrdwDmR8BV63eS0kU8d3T5yWGSLlb+3VJyH+uotpoBK4CAK98oXE/ru/tTSfdD1/NOeH9pHyXLmm9L+nlTFQa6qtM324dpHaU+dMbdb5V4PABLmpUFnR5kIIyA1UYXFUBnEXAAOouAA9BZBByAziLgAHQWAQegswg4AJ1FwAHoLAIOQGcRcAA6i4AD0FkEHIDOauRmezPbktQLb0fuPgjlO0rWROtJGrr76ybqB6Abam/BhWcV9Nz90N0PlayFlpZvu/sglPNgFgBLaaqL+vMJj867p8sr3o7MbKPGOgHomNq7qO4+MrNHkk7MbODud8Omni4v2f1G0lrN1QPQIU214PoKD2AJy3lP08u+MbNdMzs2s2NJ71VXPaAZczys/KzpOrZJE9fgdiQdu/swtN5eh0GHkS4H2pqS5xVccPcX7r4Zlif+vKYqA3Wa+bBylbsEf+c10YJb0+Wu6JGSIHsl6XamvMcoKoBlNHEN7oWZPTSzUSgauvtQSrqroTUnMYoKYEmNzIPLPD5vvJynugMoDXcyAOgsAg5AZxFwADqLgAPQWQQcgM4i4AB0FgEHoLMamQcHYLr15/Km69AVBBwQnw8l/fL8I305vsF+SvgVQRcViM/EcFt/rm80UZk2I+CAyMwItx83UJ1WI+CAyGXC7ZdN16VtCDggYtlwm9Syw2wEHBApwm15BBwQIcKtHAQcEBnCrTwEHBAfwq0kBBwQH+bBlYSAAyLDPLjyEHBA5JgHtzgCDogYAw7LIeCASBFuyyPggAgRbuUg4IDIEG7lIeCA+BBuJSHggPgwD64kBBwQGebBlYeAAyLHPLjFEXBAxBhwWA4BB0SKcFseAQdEiHArR2OPDTSzXUlDSXL3QSjbkTSS1JM0dPfXTdUPaArhVp5GAs7MDiT9xN1H4c8DM+tJ2nb3B2GfI0nbTdQPaBjhVpLau6hmtiFJIdz67n43bLon6TSz6yjdF1gxzIMrSRPX4DYlKbTYema2F8p7SrqnqTeS1uqsGBAD5sGVp4mA60lJCy5cY9sws/6sfVNmtmtmx2Z2LOm9SmsJRIJ5cItbOODM7JsLfnQYXqmRpL7eDi6k1sb2k7u/cPdNd9+U9PmC5wdagwGH5cwdcGb2azP7ppl9x8z+KOnAzP5lgXMOdDnI+pKOJb2SdDtT3mMUFauMcFtekVHUNXf/XzP7haSP3f1nZvaboicMgwtHYZqIJD1x95GUjJya2VYo35t4AGAFEG7lKBJwZmY/krQjaStv51nc/bBIObBKCLfyFLkGd1fS30l64O5nZvYdSQfVVAtYaYRbSYq04P7B3X+WvnH3PzBPDagE8+BKUqQFd+muAjP7a4U5bQDKwzy48uS24DJd0e+b2e/SYkku6dMK6wZAV+bBfdJwdVolN+Dc/Q+SNs3sn7JdVADVGx9wsJ82XKGWmbuLSrgB9WI0dXlzDzKY2RNJDyT9MS2S5O7+N1VUDFhlhFs5ioyibrs7N78DFSPcylNkFPV3ZvZXldUEQIpwK0mRFtwdSX8ys1MlN8anXdQ7VVQMWGHMgytJkYD7QWW1AHCBeXDlKdJFdSU3wB+5+5+ULGf0k0pqBeAC68EtrkjAHSgJuDPpYn4cz0wAKsSAw3KKBNy3Qqh5psxKrg+AgHBbXpGAexXWf+ub2Y/M7NdKFqkEUDLCrRxzDzK4+9PwKL/7SpZN+pm7/76ymgEratlwM7MzSes5u527+63itWuXIncy/LekE0kv3f1X1VUJ6KZ3rr//1dejz67N2ufa9XVp+ZbburvPvHxkZj5re1cUacF918y+L+l+eNTfa0n/5u7/XlntgA75evTZtZvP/EPNnuf255ztKKDQU7Xc/ffhpvsfSvpCEkuMA0paZ2bms16hdTYrvH4sMQ+uTEWeqvU9M/un0FX9V0m/EQ9mBiRdtM7edXcbf9185u/efOYf3nh8lhdeE+e5MQ9ucUVacL+Q9Ft3/667/727/ypM+AWg3JZXbnjlfZ7R1OKKBNxPJP3QzH4rJSv9mtk/VlMtoP2WDS/CbXmF5sGJOxmAuRBuceBOBqBkhFs8igTcS+5kAGYrKbwIt5IUmQf3z2Y2EHcyABMtG16ZeW7MgytJkfXgFAKNUAMmWza86pwHd55zN0MnbuUqNNEXwEzLhldt8+Dc/dakOXvpS/n3srYCAQeUhHlw8SHggIowmto8Ag6oAOEWh0KDDGUzsx1JI3cfZN9L6kkauvvr5moHLIZwi0djLTgz60l6MPZ+290H7n6o5K4JoFWYBxeXJruom5KOMu/vSTrNvB+Z2Ua9VQIWxzy4+DQScCG4jseKe0q6p6k3GluOycx2zezYzI4lvVdlHYEFtGkeXJ7zvPXtwtLoUWusBefuozl264195oW7b7r7pqTPq6gXsITWzIPLkzdPri1z5WoPODPbVXI/646kO5K2zayvt4MLqTVJw7rrByyKeXDxqT3gQivsMAwkDCUduftQyY37tzO79hhFRZsxmtq8xqaJhOtwG5J6ZjZ096GZHZnZVtiFUVS0FuEWh8YCLrTOtsfKeIgNWo9wiwd3MgAlYh5cXAg4oCTMg4sPAQeUp0vz4DqBgAPK05l5cF1BwAElYR5cfAg4oCKMpjaPgAMqQLjFgYADSka4xYOAA0rEPLi4EHBASZgHFx8CDigP8+AiQ8AB5WEeXGQIOKAkKzgPLm/V37OmK0jAARXp+mhq3qq/imDFXwIOqEDXw60tCDigZIRbPAg4oETMg4sLAQeUhHlw8SHggPIwDy4yBBwwh3euv//VrCkR166vS8yDiw4BB8zh69Fn1yZNhbj5zN+9+cw/vPH4bBXnwUWPgAMWVHV4EW7LI+CABVQdXoRbOQg4oCDCrT0IOKCAmsKLcCsJAQfMqerwYh5c+Qg4YH5Vhxfz4EpGwAHzqzq8mAdXMgIOmFPV4cWAQ/kIOGBBzIOLHwEHLICpIu3wThMnNbMdSWuSPpB04O6DTPlIUk/S0N1fN1E/YBbCrT1qDzgz21ASXofh/ReSrptZT9K2uz8I5UeStuuuHzAL8+DapYku6pqkB5n3b0Lo3ZN0mikfhXIgCsyDa5/aA87dB2krLVgLXdGeku5p6o2SMLxgZrtmdmxmx5Leq7quwBjmwbVMo4MMZrYn6e6MXXrZN+7+wt033X1T0udV1g2YgHlwLdNYwIUBhZfpAIPeDi6k1iQNa64WVlDeYpbpgpbMgyss77mplT87talR1A1Jr919aGb9UPxK0l5mtx6jqKhDupjlePlYOP05Z/sns7aff6Qv15/P3p73+eI/WbPc/VbePmbmVdah9hZcCLdPJR2Z2amkE3cfuvsolG2Z2ZYuhx1Qq6angrQ93GJRewsutMquT9l2WHN10HHvXH//q69Hn12btU94nsKFpsOLcCtPI11UoC7j3c9J4bH+XD5rexbz4NqFW7WwMmIPL+bBlY+Aw0qoqWXFPLjIEHDovBpbVsyDiwwBh1VQS8uKeXDxIeCwChppWTGa2jwCDp3XRMuKcIsDAYeV03Q4EW71YR4cojXPJN08LZ3ES7iVhIBDtOadpHv+kWzW9lmfz2o6vJgHVz66qGiF2MMpbzvz4JpBwCF6TYcX8+Dai4BD1NrUsmIeXHwIOMSutS0rRlObR8Ahdq1sWRFucSDgELU2tqwIt3gQcGiVpsOl6QEPFEPAoTWaDp+IRmsxJyb6ohViD6e87WUNeFy7vj7Pg1rOc7avDFpwKGyex+zN83rn+vtfzXO+psMrptHaG4/P5O6W87o15WdcOQQcCktvoXJ3u/nM3735zD+8+czfzf4lu/nMNWu7u9s895m2pWU1x3bmwTWAgMPClv3LmXa3Zj1seY7ja8ntzIPrMAJuxczTvZyn61hGt+/G4zNNatmlLb8bj8806/OKuGVFuMWBgFsxX48+uzar2zhv11Et6fblbY8xvAi38hBwq2mecMrTyW5f09sDwq0kBNxqmvmXb55rY13s9jUdXsyDKx/z4FZQ3l/OG4/PPjkPi0hO2i7pz7M+f/6Rvlx/Pnt73udj2x5EMVqL+dGC65B5BhDGl/CWmg+PprcHrR2tbbnzOeZMni16cAKuQ7Lz06bNUwsjkxeaDpcutayYB1ecu9/Km7gs6eq/ynMi4Fokr4XW0QestLZlxWhq86IKODPbMbOt8N+Npuszr7LmluXJu4Mg2zqLPbxqDKdODohgPtEEnJn1JG27+8DdDyXtNVyluY13DSeFj+zatWXv3UxbaE2HU972mMIpb3uM4UW4lSeagJN0T9Jp5v1oZivu2l9+UMcN30Uv3E/75Uxvkl7m3s0bj8+61LJayXBiHly9Ygq4nqRR5v0bSWtT9/76/66sqjAeDmloLNOykl3LvbE87RpG8svfipZVjOHS9P8/5sGVz9zzlpaqh5k9lDRy9xfh/b6ko9BdTffZlbQb3v6tpP+qvaLLeU/S501XoqC21blt9ZWo87LW3f3bkzbENNF3pKQVl1qTNMzuEMIvDcBjd9+sq3JloM7Va1t9JepcpZi6qK8k3c6877n766YqA6D9omnBufvIzI7MbCsUtWYUFUCcogk4Scpeb5vDi8oqUh3qXL221VeizpWJZpABAMoW0zU4ACgVAQegs6K6BjdJmA+3H95uufvTUL6jt1NLhumIa9Hyiuu+o2Ru3yDmOofzDCVtShfTcaKtb+Zca5I+kHQQ+3ccztVTMo/zYr5n7HUe1+S5F+LuUb8kHSm5hWs/U9Ybe3+0SHnF9e6Fum/FXOdwnpPM+y9irm849oakjTbVORx/S9JDSbtj33+0dZ7wu9LIuRd9Rd+CUzJd5FiXb9uadt/qZpFyr/Zfn00lAZeKss7uPlLSCpKZ9ZV819HWN1iTdFfSg/D+zSJ1q7nOcvdB+I6zYv6ex02sa03nXkgbrsH1lfxC98wsnRvX0+T7VouWVyL8Ih6PFRet27TySoT5hztKgkML1Gtaeek8WXHmQaZoLfwlK1q3aeV1mlaHouV1aPLcC4k+4Nz9hbunff2tCf8CpnollZcitIzyTKtD0fKleXIN67Xe3utb5PxFy0sT/tG7O2OXaXUoWl6nXknldWjy3Lka7aKGm+d7k7a5+9NwQbPvYWBByb8Y0vT7VtcKlldR510lXaa+pDuSvmVmw6bqnFffsfcDMzsws0FT9S1S5/D78TLTRYq+zhOMCtattDovYNTguRfT9EXAnIua4xeTT2Zd7CxaXkP99xT/IMOupL3M+9PwvUdZ37HfjX74cz+8oq5z5vtmkKGmV/R3MoR/paXkF/jQ3YeZ8lG6n1+dJjBXeYX13lAScEMlATKMsc5h6kK6KsRGOM/4VJxo6hvOsyHpU71t0a+5+/WY6xzOtaVkYKSn5HdiobrV/buc1eS5FxF9wAHAoqIfZACARRFwADqLgAPQWQQcgM4i4AB0FgGHVjKzvpmd5u9Z77HGjrsTpuGgIQQcGjF+y92MW/Am8uT2vdv5e9Z7rDF9n++WPVSEgENTDnLet1pouY0arsbKI+BWgJn1zOzEzPbN7DQsIppu2w1PM7t4olnosp2E19GUY17ZJxwrLXsYytJjn6TdtXCD/EYofzj+PuzzMD12qH8vs/9B+jNN+PlOMqvOyMz2MnXYD/eMTvx+8o419pmdzHG/yH6nwT0lj8LMfs8X303mPHuZ/z5Mv6uc/6WYV9P3ivGq/qXk1qAvMu9PlSy+uKFkNdy0/CTsuydpJ/3slGNe2iccK7twZm9s/11dvo/xZGx79rMbenvvZV/Jis49SS7pYeac2XuTPT2n3i6A2U9/PmXuC57y/cw8Vs53ezrh53049vOcjH1m/Dye+T6PlLkHm9firzYseIlyvMn8eV/JX7rbkl5mygdKgu9I0r6Z3Qn7jiYcb3yf+9ljebj2FK6tbUnaLlDX+5LWzCzttqbX50Y+fWWOob+93pX9WXvhv3/M/DnPtGNNciDpUWb/9GfOrrJx5bsJrdnseYZKvv/0z1Gvs9YWdFFX02jGtjVPbqBOA2lid2mefcJN8QdKFv8c78LleeLud8Prg1A2K2xGE+o4VBKUR5Lu+PzP3b1yrEnShSAmHHdrznNdOo8zIFE6Am51ZFsED5S0Fg70dtlvKWlpDcys78nI4iNJx5OmOozvI+k/lLRULrYrWaUkXa/tyijp+HEz719m65UZYR3N84OOfe6lu2+7+6xFMQsLdZ222GZv7P1LXf1uUAO6qCskdPn6kgb+9glNG5mL2k88WdZp18zSv7jDKS2LrbF9/jO9MB/KBpKeSDoxs20l4ZRtgQ0kfWpmx54sP37pfRhISAc4jrTYk9TfSNozszQsD0Mgl2FPyT8aB2YmJdcMn4ZW66VnFLj76ynfDSrGckkrILQ2TryauV7RCiOgp+7+InwHHytp0c3bVV3knA9nXCdEzeiiost6Cq3G0Arl4v2KoQW3Ala4BddT0mrrhaKhX34aV9nnS1dEjvYxequGgAPQWXRRAXQWAQegswg4AJ1FwAHoLAIOQGcRcAA66/8BkTgwQ7ZIPRgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = dn.plot_tools.std_fig()\n",
    "df = final_dfs[2]\n",
    "dirt_mask = (df['pos_scatt','3']<-610)\n",
    "position = df['pos_scatt','3']\n",
    "tmax = position.max()*1.3\n",
    "tmin = -5e3 #position.min()\n",
    "nbins = 31\n",
    "\n",
    "_ = ax.hist(position[dirt_mask], weights=df['w_event_rate'][dirt_mask], bins=np.linspace(tmin,tmax, nbins), histtype='bar', \n",
    "                hatch='///', facecolor='None', edgecolor='dodgerblue', label='dirt')\n",
    "ht,_,_ = ax.hist(position, weights=df['w_event_rate'], bins=np.linspace(tmin,tmax, nbins), histtype='step', color='black',\n",
    "            label='total')\n",
    "ax.set_title(\"MiniBooNE\")\n",
    "ax.legend(loc='upper left', frameon=False)\n",
    "ax.set_xlabel(\"pos scattering in z/cm\")\n",
    "ax.set_ylabel(\"events\")\n",
    "ax.set_xlim(tmin,tmax)\n",
    "ax.set_ylim(0,ht.max()*1.3)\n",
    "\n",
    "\n",
    "fig, ax = dn.plot_tools.std_fig()\n",
    "df = final_dfs_muB[2]\n",
    "dirt_mask = (df['pos_scatt','3']<-1100/2-215)\n",
    "position = df['pos_scatt','3']\n",
    "tmax = position.max()*1.3\n",
    "tmin = -5e3 #position.min()\n",
    "nbins = 31\n",
    "\n",
    "_ = ax.hist(position[dirt_mask], weights=df['w_event_rate'][dirt_mask], bins=np.linspace(tmin,tmax, nbins), histtype='bar', \n",
    "                hatch='///', facecolor='None', edgecolor='dodgerblue', label='dirt')\n",
    "ht,_,_ = ax.hist(position, weights=df['w_event_rate'], bins=np.linspace(tmin,tmax, nbins), histtype='step', color='black',\n",
    "            label='total')\n",
    "\n",
    "ax.set_title(\"MicroBooNE\")\n",
    "ax.legend(loc='upper left', frameon=False)\n",
    "ax.set_xlabel(\"pos scattering in z/cm\")\n",
    "ax.set_ylabel(\"events\")\n",
    "ax.set_xlim(tmin,tmax)\n",
    "ax.set_ylim(ht.min(),ht.max()*1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "33b33a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1000.0, 1000.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = dn.plot_tools.std_fig()\n",
    "df = final_dfs[2]\n",
    "dirt_mask = (df['pos_scatt','3']<-610) #& (-1e4<df['pos_scatt','3'])\n",
    "position_x = df['pos_scatt','2']\n",
    "position_y = df['pos_scatt','1']\n",
    "\n",
    "_ = ax.scatter(position_x[dirt_mask], position_y[dirt_mask], c=np.log10(df['w_event_rate'][dirt_mask]),\n",
    "                label='dirt', s=10)\n",
    "# c = ax.scatter(position_x, position_y, c=df['reco_w'],\n",
    "#                 label='dirt', s=10)\n",
    "\n",
    "ax.set_title(\"MiniBooNE\")\n",
    "# ax.legend(loc='upper left', frameon=False)\n",
    "ax.set_xlabel(\"pos scattering in x/cm\")\n",
    "ax.set_ylabel(\"pos scattering in z/cm\")\n",
    "# ax.set_ylabel(\"events\")\n",
    "ax.set_xlim(-1000,1000)\n",
    "# fig.colorbar(c)\n",
    "# ax.set_ylim(position_y.min()/1.3,position_y.max()*1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f8614dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 417.45297415839076)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df = final_dfs[2]\n",
    "# dirt_mask = (df['pos_scatt','3']<-610)\n",
    "# delay = (df['pos_decay','0'] - np.min(df['pos_decay','0']))*1e9\n",
    "# tmax = 100 #delay.max()*1.01\n",
    "# tmin = delay.min()\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = dn.plot_tools.std_fig()\n",
    "df = final_dfs[2]\n",
    "dirt_mask = (df['pos_scatt','3']<-610)\n",
    "position = (df['pos_scatt','0'] - np.min(df['pos_scatt','0']))*1e9\n",
    "tmax = 5000\n",
    "tmin = position.min()\n",
    "nbins = 31\n",
    "\n",
    "_ = ax.hist(position[dirt_mask], weights=df['w_event_rate'][dirt_mask], bins=np.linspace(tmin,tmax, nbins), histtype='bar', \n",
    "                hatch='///', facecolor='None', edgecolor='dodgerblue', label='dirt')\n",
    "ht,_,_ = ax.hist(position, weights=df['w_event_rate'], bins=np.linspace(tmin,tmax, nbins), histtype='step', color='black',\n",
    "            label='total')\n",
    "ax.set_title(\"MiniBooNE\")\n",
    "ax.vlines(10,0,1e5, colors='black', ls='--')\n",
    "ax.legend(loc='upper left', frameon=False)\n",
    "ax.set_xlabel(\"time delay in ns\")\n",
    "ax.set_ylabel(\"events\")\n",
    "ax.set_xlim(tmin,tmax)\n",
    "ax.set_ylim(0,ht.max()*1.3)\n",
    "\n",
    "\n",
    "fig, ax = dn.plot_tools.std_fig()\n",
    "df = final_dfs_muB[2]\n",
    "dirt_mask = (df['pos_scatt','3']<-1100/2-215)\n",
    "position = (df['pos_scatt','0'] - np.min(df['pos_scatt','0'])) ## already in ns....\n",
    "tmax = position.max()*1.3\n",
    "tmin = 0 #position.min()\n",
    "nbins = 31\n",
    "\n",
    "_ = ax.hist(position[dirt_mask], weights=df['w_event_rate'][dirt_mask], bins=np.linspace(tmin,tmax, nbins), histtype='bar', \n",
    "                hatch='///', facecolor='None', edgecolor='dodgerblue', label='dirt')\n",
    "ht,_,_ = ax.hist(position, weights=df['w_event_rate'], bins=np.linspace(tmin,tmax, nbins), histtype='step', color='black',\n",
    "            label='total')\n",
    "\n",
    "ax.set_title(\"MicroBooNE\")\n",
    "ax.legend(loc='upper left', frameon=False)\n",
    "ax.set_xlabel(\"time delay in ns\")\n",
    "ax.set_ylabel(\"events\")\n",
    "ax.set_xlim(tmin,tmax)\n",
    "ax.set_ylim(ht.min(),ht.max()*1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5b8ffae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.142260345626248e-07\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = final_dfs[2]\n",
    "dirt_mask = (df['pos_scatt','3']<-610)\n",
    "delay = (df['pos_scatt','0'] - np.min(df['pos_scatt','0']))*1e9\n",
    "tmax = 5000 #delay.max()*1.01\n",
    "tmin = delay.min()\n",
    "\n",
    "_ = plt.hist(delay[dirt_mask], weights=df['reco_w'][dirt_mask], bins=np.linspace(tmin,tmax, 41), histtype='bar', \n",
    "                hatch='///', facecolor='None', edgecolor='dodgerblue', label='dirt')\n",
    "ht,_,_ = plt.hist(delay, weights=df['reco_w'], bins=np.linspace(tmin,tmax, 41), histtype='step', color='black',\n",
    "            label='total')\n",
    "plt.vlines(10,0,1e3, colors='black', ls='--')\n",
    "plt.legend(loc='upper right', frameon=False)\n",
    "plt.xlabel(\"t (ns)\")\n",
    "plt.ylabel(\"events\")\n",
    "plt.xlim(tmin,tmax)\n",
    "plt.ylim(0,ht.max()*1.5)\n",
    "\n",
    "print(df['pos_scatt','0'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "36d695ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Benchmark 1: transition mag moment\n",
      "ctau0 = 2.43688 cm\n",
      "Raw events at MiniBooNE: 5.7e+03 total (2.3e+03 coh : 3.4e+03 pel : 0 nel)\n",
      "Selected events at MiniBooNE: 7e+02 total (2.6e+02 coh : 4.4e+02 pel : 0 nel)\n",
      "MiniBooNE p-value: 0.3885635860914325\n",
      "Raw events at MicroBooNE: 1.4e+05 total (7.2e+04 coh : 6.9e+04 pel : 0 nel)\n",
      "\n",
      "Benchmark 2: mzprime = 0.03, mhprime = None\n",
      "ctau0 = 5.07357e-12 cm\n",
      "Raw events at MiniBooNE: 8.4e+03 total (6.7e+03 coh : 1.7e+03 pel : 0 nel)\n",
      "Selected events at MiniBooNE: 6.6e+02 total (5.3e+02 coh : 1.3e+02 pel : 0 nel)\n",
      "MiniBooNE p-value: 0.41384983555366595\n",
      "Raw events at MicroBooNE: 3.5e+05 total (3.1e+05 coh : 3.4e+04 pel : 0 nel)\n",
      "\n",
      "Benchmark 3: mzprime = 1.25, mhprime = None\n",
      "ctau0 = 183.162 cm\n",
      "Raw events at MiniBooNE: 6.8e+03 total (2.6e+03 coh : 4.2e+03 pel : 0 nel)\n",
      "Selected events at MiniBooNE: 3.7e+02 total (1.5e+02 coh : 2.2e+02 pel : 0 nel)\n",
      "MiniBooNE p-value: 0.22862892420725456\n",
      "Raw events at MicroBooNE: 2.7e+05 total (1.2e+05 coh : 1.5e+05 pel : 0 nel)\n",
      "\n",
      "Benchmark 4: mzprime = None, mhprime = 1.0\n",
      "ctau0 = 49.7234 cm\n",
      "Raw events at MiniBooNE: 3.8e+04 total (1.5e+04 coh : 2.2e+04 pel : 0 nel)\n",
      "Selected events at MiniBooNE: 7.4e+02 total (3.1e+02 coh : 4.4e+02 pel : 0 nel)\n",
      "MiniBooNE p-value: 0.1636155468910185\n",
      "Raw events at MicroBooNE: 9.3e+05 total (4.5e+05 coh : 4.7e+05 pel : 0 nel)\n",
      "\n",
      "Benchmark 5: mzprime = None, mhprime = 0.03\n",
      "ctau0 = 1.53629e-11 cm\n",
      "Raw events at MiniBooNE: 1.6e+04 total (1.5e+04 coh : 1.2e+03 pel : 0 nel)\n",
      "Selected events at MiniBooNE: 7.3e+02 total (6.9e+02 coh : 49 pel : 0 nel)\n",
      "MiniBooNE p-value: 0.5892522094787966\n",
      "Raw events at MicroBooNE: 6.3e+05 total (6.1e+05 coh : 2.2e+04 pel : 0 nel)\n"
     ]
    }
   ],
   "source": [
    "## Printing summary for MiniBooNE benchmarks \n",
    "\n",
    "for i, (df, df_mub) in enumerate(zip(final_dfs,final_dfs_muB)):\n",
    "    if i>0:\n",
    "        print(f'\\nBenchmark {i+1}: mzprime = {df.attrs[\"model\"].mzprime}, mhprime = {df.attrs[\"model\"].mhprime}')\n",
    "        print(f\"ctau0 = {df.attrs['N5_ctau0']:2g} cm\")\n",
    "\n",
    "    else:\n",
    "        print(f'\\nBenchmark {i+1}: transition mag moment')\n",
    "        print(f\"ctau0 = {df.attrs['N4_ctau0']:2g} cm\")\n",
    "\n",
    "    print_summary_of_event(df, col='w_event_rate',summary='Raw events at MiniBooNE')\n",
    "    print_summary_of_event(df, col='reco_w', summary='Selected events at MiniBooNE')\n",
    "    print(f'MiniBooNE p-value: {get_pval(df)}')\n",
    "    print_summary_of_event(df_mub, col='w_pre_decay',summary='Raw events at MicroBooNE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69d8c8dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MB events: 7e+02\n",
      "MB events: 6.5e+02\n",
      "MB events: 4e+02\n",
      "MB events: 7.6e+02\n",
      "MB events: 7.2e+02\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WcVAGDnR1r1ge8bUJSYQ0pjQCWpfWKI5wj0E4K5s5ESmq6ox506dlqXrPOHD9w6hoj5gXfDy2A3AycJR5bGty+SsxXJ+QREhjCqFW2RTLqijKt3+47Yd9uge2R2jPMc/xD0XkKa+oAUD3ihXtlyedEFJB5rdU+aX7Xt7V1Z2/+ZbPvX27urtCC9uGzbfe4ncOv3N3dXfpRx+831OP3YSQxsm8sPml+35858DdPzhy7O+bOe9fvHb87/za/c79B//06/c2cy1CSH1wr2gbIKxeT0hdUNjaADNPuImZeAkJB4WNEJI5Mj/HlgZWrlrV5bfQEARrlhLSHBS2CJk9NrPmpvz6xdljM2vs4i9/9qO/WQAQerHi4bs2c8sYIU1AYbP49mN/cosuLlQMzz89fb77W2PPbbbbhu68eu1f/od9v7Tb+gcGL/pUsyKEJAyFzUIXF5atfeiPPvS2L/zsR73e9itvjW329iOEpINQiwci8jXz+3YR+b6I3B6nUa3i6vwvVnjbLr15oHfx3Ds9l9480Gu3n/3w/Yq+hJB0ENZjGwPwXTipR56Ek4jun8dlVKu48v6J1aef/erddtviuXd6sHh52fzU9woXX3vuepHky8sucMM8ISklrLCtE5FvAjivqkdFJI2pUZpmxZbtlzb8/v9412679OaB3vmp7xXyIy8WV9/3yAW3fdP//M3bk7aPEBKOsHFsbqEHNxfW8XjMaS1d+S9XZPdYfd8jF7rX3bFgixoAbNi8hZlACEkpYT2246p61DpONBsmIYTUQ1iPzZsnLY68aYQQEglVPTYRuQOOiA2aylNu+p9SzHYRQkjDVBU2VX0HwC4R+Y+q+lxCNhFCSFOEGoqq6nMi8k37J27D4sLe9tRqWwgh8RBq8UBEjgM4ivLqz6/FYlHMcNsTIdkn7KroUVWNrpgjIYTESFhh6xOR/wWr5qeqfjcekwghpDnCCpu3XiGrOcVI77r1m+38bZ8vLAiA7htvyl1Z2dNT9t6fP3vmjKp+KXEjCUkxYePYtsPZL7pLVd8CsK/eC4lITkR8499EZEJE+szPnnrPnTW2bN12eXVv71X35+rileUAcHXxynK7fXVv71UAzN1GiIewHtuQqu4WkWfM8fkGrlUAMCoiw+Y4D+AZVd1vHpsCMBN11ejzn1xe7s2lFoR091wLe97//cG6a2+EPO/Ar/Z0f+vf/7uwp8a/+LePnrEXOI69cqh34ul9hbGnnykOPLirbGvXw3dt3hL6xIR0CGGFTUTkt+FUd//tRi+mqmutEw5bxZLHAczCEbtoUYVfjrVmye38w1/W7uWw+NMn6vKqvKu2Aw/uuvDKyy8ueEXNsODTRkhHEzaObReArXB2HuRV9dF6L6Sqc+7fxmuzq8UX4IhateHqqIjMisgsOPwihFQhbBzb16LaeSAiOQAFVS25bao6aT3+kogUVLVoP8/0mTR9ZqOwhRCSTcIuHgyJyI9E5M/dbLpNsA+WtyYiw54Fg/kmz08I6XBCb6lS1QcA7AfwOyLy/5q45iDKN9EXUT4szXu9NUIIqYewQ9GbAOwGMAJHlJrahWALl6rOGa+tAGeubaSZc6eRH5/O4/+GXEEF/CtgEULCE3ZV9DCAvzBeW1Oo6naftsN+fbNC7wNPLQIIvTLLCliENEfYObYpAA+IyB8CgIgcjM8kQghpjrDCNmhCPNz+jQToEkJIIoQVNjdAt7eZAF1CCEmCxAJ0CSEkKcIuHoCpwQkh7ULYoWjH8tnJI2uWrd6w+NnJI0wlTkibENpj61RW3bPz4qp7djKVOCFtRCiPzd1GJSK3i8j3ReT2OI0ihJBmCDsUdXOkTQF4C8BEPOYQQkjzhBW2dabk3nlVPQrALy8YIYSkgrBzbBMAdgEYNcfH4zGHAP7ZeT89fb47IBPwqoTMIqRtCCts51T1UXeODc6QlMSENzvvZyePrLnywVs39Gz7ZsVCxsU3/vgryVpHSPrhHFsbsOqenRc3Pvb6z7k6S0g4OMdGCMkcYYVtAk6eNHeO7c14zCFRICIDIvKqiAy02hZCWkFYYSvA2SfqDkl3xGMOiQJVPQZgk/lNSMdRb9oiF6YtIoSklnrTFjVVV5QQQpKAaYsIIZkjbDGXg6q6O+iYtI5Vq9fcmL/5ls+97cu7urq97efPnnlXVe9MzjpCWkPYAF2pcUxaxP0P/+7Zb+97uqJQzOM7B+7+wZFjf2+3PXzX5vXJWUZI6wgrbPMi8gcAZuGsiDZU1FhEJrAU3DuoqvtN+zCcsn45AEVVnWvk/IQQAoSfY3sUjpf2KIDeJubYCnB2L4xZopYDMKSqM6YM33iD5yaEEAB1FExW1ZcAvGSOv6aqP2ngeuNwvL681bYLwCnruCQiffTaCCGNEjbc42WzpQoi8h0sBerWSwGOqOVExPXMcnCGoS7zKBc+Qgipi3rCPR4QkeNwMn18t5GLqeqkqrpzaIMiUgjomvM2iMioiMyKyCwAToITQgIJmxr8m3C8rScB/E4jqcFFZFhE9lhN7gJECeVClgdQ9D7fiGK/qvYD+Lje6xNCOoewQ9EHVHWXqh413tveBq5VBDBjHedVtQjgEJzgX5cc59cIIc1QdfHAXSRQ1Sc9D03XeyFVnTNeWwGO9zdi2ksiMi0ig6YrV0UJIU1Ra1V0H4DdACAif27NrY0B+Ot6L2bCOUK3E0JII9QaikqIv0kLOfvh+yu8bcdeOdR75v33eo69cqjX81BPQmYR0lJqeWwa8DfTFjXJZyePrPnk9Rc23vSNJ840k/L71Mm/Xf34zoG77bYz77/Xc2Xhs2UTT+8rvPLyiwvWQ/yHRDqCWsI2ZEI8BEBBRPrN31+P3bKMs+qenRdLrz65pdk6Blvv+bVLe/7k5XfttmOvHOqdeHpfYezpZ4oDD+66nsb94bs2b2nmWoS0C7WE7Y5ErCANs2HzlivetoEHd1145eUXF2xRMyx4+xKSRaoKm6qyaEvK8atBCgTWIWUNUtIRhM3uQVKKtwapy8LPftS79qE/KktnxBqkpFMIG6BLMgorWpEsQmHrcFjRimQRChshJHNQ2AghmYPCRgjJHFwV7SDqqWgFVFa1MgsMTwB4gXNyJM1Q2DqIeipaAZVVrVT1mIg8T1EjaYdDUUJI5qCwEUIyB4WNEJI5KGyEkMxBYSOEZA4KWwb57OSRNctWb1j87OSRNa22hZBWwHCPDLLqnp0Xm01gSUg7Q2HrIF7720+6vbnbrpz5h5WfF99Z9XsP/v4dKzbe5Q3SpcdH2pJEhU1EhuEURN4OYEpVZ0z7BIAJ021QVfcnaVcSfHbyyBrbi7r05oHexXPv9Fx680Dv6vseuVCtb1R8cfGX3Yunf3qD3bZ47p0eXFuUz0+9kbtW+kdvht2KQjFZh7srsoGoau1eUVxIpA9w6oua4/Oqutb8PQ2n1uiMqo6FONesqQhf+7or11z90v6LP2nY8Gi4+8yffmPx2qWz3W7D4rl3erB4eRm6b7jWve6OMkFZtnrD4sbHXv951Eb4CealNw/0zk99r5AfebHoFdh/fFzuUtXVdls973270gmvMSsEfVZJemx5OEWSXeGaF5E+I3TjAGZNn0zirUZVTVDimvT38wJX3/fIhYuvPbfgtcEPEXkIwDYReUhVf+h5bIAeDkkLia2KqqrXG8u73hscby0PICcimawE7xWV1fc9cqF73R2+gpKiif+VIjLr/gA4AGfe7YDdbh57IimjmPWX1KIliwdGvEbcY1WdtB57SUQKqlr0PGcUwKg5LNucTeJhRc8quXHNTfe6x58vLMjlixdww5renpU9Pffafc+fPXNzUnZxMz6pReLCZhYQDlpzbcMACtaCwbzf84z4TZrnzCZha6fzwO5//ZE3G0jYTCCEtJKkV0X7AMypalFECqa5aH5c8l5vjRBC6iExYTOidhTOogHgCJi7KjpshK4Aa4hKCCGNkJiwmaHn2oDHDidlByEk+3DnAQnEr8p8QIV5gFXmSYqgsJFA/KrM+1WYB9qzyrxf7F1QrB7j9NoLChvpZJ4Qkec9bduwFKv3lNV+GsCxpAyrB24Dq4TCRjqZCiEwHtsBAI94PbZELasDxvVVwnxspGPxEwIjZm97t4xRNNoLChuJhai2Pfk9354HC9OfdB4cinY4drbdKPeoRjg8qmceDEjxXBhJDgpbh1NPtt24RLAGoefBzGMDCdlFUgyFjYSmmgiuWr3mxvzNt5Rl4F3e1dXtbQOA82fPvKuqd4a5ZtA8mIg85RW1oP6k86CwkUi4/+HfPcsN8/HD2LtwtKWwrejp+bqfJ+CHLFueTIpgQpIhE7F3cdOWwrZxy5cW/DwBPwK2/xDSriQWe9fOgb9tKWwkfWRlX6m5mU+ndRhXz5xjs/a3c+AvhY1EQrP7StPiHZhrt+z6JBraUtg+/OhSV9ghpnT3XIvbnkZoUehEKvBbQV27YSM+OX+u+6a16x7I33zL9fZ6VlD9SItgpoVOeT/aUtiW3bD2qp8n0E50crV2vxVUwH8VtdkV1LQMp9IiKGl5P+KGW6pIJHhLBtoFob19z374fscVYjZCsimMoLSiClfWtq4lVjA5SlZs+uqnm578u1CroinhbgDtZG/dnPnTb3wlbEHoa1cuL7vh3gfPe8/x6exfrb+x/199bLd9MfffNqzsWVU2nbB45YosXP50ec8NN37RvWJF2RfYb+ialgLIdRX6rtPmZs8tIq8C2OTp6oaRXATwtuex06r6W2Hti4s0FEwmGabegtB+w3C/xYZfu/1T+A1bg+jE4N+IVnIztXWNwkYiwa8gdFCF+XrmFv3CSGqZUkffTBDFSm7Wtq5lQtg+O3lkzSevv7DR6zWQ9scvjKQaV0+8/Otx7FltRzp5+1UqhM0UTS4ByAEousWUw7Lqnp0XS68+uYWiRrr/yWNnri4ulC2K6exfrb/6q793rqLzG3+cWPX6FtGx269aLmwikgMwpKpj5ngawFA957BX4MLO55BsUk+gsJ93F0S93p3XAwrylPz6RkhqU5/HHf7ScmEDsAvAKeu4JCJ91by2xY9P9Zx+9qt3Xz82K3DzU98rXHztubIVuGWrNyxS2NKF3z+boH9O9f5j8vav9k9PfuU3S1dv2rQY6sR1eHc337ploat7RXdXd7fYlzO//7qru9sbivA5rLnBqESw2e1XcW4vC4qnCxg+/ycAjwP4gar+F8+p1sCHlod7iMgeACVVnTTHEwCmVHUm6DnecI9GVuASJvPhHn6cfvard/uF5XhDQ4Dg8JBlqzcsbnzs9Z+HvWY9YSf1nPu9J3O/rte+kNo9nYwyPb9y/4Wu/JevuG1XzvzDys/fnlm7ctvg+RUb7yrzEi++8V9vgWDp3HrNPln5uQWaW7chlBif/+Tycvjd34uXl6P7hi/spp4Vy5avWrXqSmXngHPX4cFWmevz8xz/BoA3zrFa2MlmVa1YXEqrsE17q8OLyCiAUXO4XlVv9zyeilglskTQZ1LnF70ubyFgCBgYspDkhHnY96OGzZMAwoa/LKjqs2HsEJEnAfSEPC8APIbwI74bAXj/KSwzbQrAu+3xkue4C8ANAC4DuOp57IyvwKpqS3/giNUe63gKQF+N58x6jgcAvApgoNWvhz/Bn1PU/Vt93jjtiNPmOM5d7R4MaHsIwCcAHgrRt9q5fV9LGrZUHQKw1TrOaZ2roqp6TFV/SzO0XE1IHNjzZlGeV6tsGQtoC13msJH7u+WLB6paEpFpERk0TeMtNYiQDKMpScsUd967lgsbAKhnPo2QdiAtGTvakbgFNg1DUZJB4hrypIlqw6+s4/1c05YJJBUeG8keaRnykNjw7mpIVRFrChshCVDPnFLa6y4YyobfacsE0vI4tkZgzFo2ietzjfP7wu/iEq14L4KuyTk2kmk6Ya6PVMKhKMk0nOtLhrQNnylsJBWk7cYg9ZG2fyAcipJUkPbdI/UUO+Gwt/Vw8YCQENRZ7CQVhU46ARZzIaQ5Upu0kVRCj42QJuB3sbUw3IMQ0jFQ2AghmYPCRgjJHBQ2QkjmoLARQjIHhY2QBuE+1PTCODZCGiRt24jIEvTYCCGZo10DdM8C+EUMp14P4OMYzhsntDkZaHMy1Gvzl1V1g7exLYUtLtoxipw2JwNtToaobOZQlBCSOShshJDMQWErZ7LVBjQAbU4G2pwMkdjMOTZCSOagx0YIyRwUNkJI5ujInQciMgygBCAHoKiqcwF98gC2A5hS1ZkkbQywp4QqNnv7tovNIjIKoAgA7WCziAyax4EWv88ikgMwauzwnZ+q57uTBHXY3PD913HCZt7UIVUdM8fTAIY8ffrgfAEOm+PzANYmbKptTw41bPb0HQMwnpR9VeyoabOITAH4jqqWzN+tFola340cgJz13RhHC20GUDXmq57vToLUsrnp+68Th6K7AJyyjkvmjbTJwxEHl3mfPkkSxmaXfgDT8ZtUk5o2u8dG1AqqOpKkgT6EfZ/3iUghIZuqYjyZUpUu9Xx3EiGEzU3ff50obDmUv6nzcN7I66jqjPsfzpBvsfueQw2bgetCMZuMSTXJobbN/cCSF2S8n1aSQ+3vRgnAXgAnRGRKVfcmZVyD5BDiu5Mmorj/OlHY/MgFPWButlZ7En7k/BrNjZdWcn7HqloyX9y+tHhCFjmftgKcuR93aNdu5FptQFgavf86UdhKKP9g8zAT117MBObBVk9oI4TNZgK+YGzeAWCoxSJRQu33uehpK8ERjVZRQu33eRjArKoWzdB5ziwmpJUSQn7f00Yz918nCtshAFut41zAylcfgDlVnRORQotFoqbNqjqpqofNhGsRwLSqtvILHOZ9nkH5TVdAa4fSYWzOo3xoN410C0Wo73vaaPb+68idB9byN4ClEAMROQHgfjg32FE48xGAM8Zv2aooUNtmdwhqvhDjcG628VaKWxibrWV9AJh3V8JaRUib91h9ii0O9xiEM9Geg/N5B73HJfc5rR6B1LIZEdx/HSlshJBs04lDUUJIxqGwEUIyB4WNEJI5KGyEkMxBYSOEZA4KG0kUERkWkVPm97CI7GlkK5WI9Nn7B0VkQkSmRGQ66Hzmce9+1T0pD7AlDdBx2T1IyykBOGzHqzUoLP1uyhuTFWTCiofKBTxnAsA+lG/R2a2q+xu4Pkkx9NhI0gwBmBaRnAkcbSpg1ESk5+xz2PtljZc2JSKjpo/t5Q2itSmHSExQ2EjSuEkaRxGQusaIXmCaGiOIh8xhAcF7fffA2Ws4ArNpHcBhs68WcKLfJ+q0n7QBHIqSxFHVwyKSc/OwwfGiCnAqFO2Ds/+yICJ5ONtqrg87DXnLKyua57pD0HEAu8wWnK0AtorIDiyJ6ASAKXOtXIv305KYoLCRxLCHfpYw9Rmhm4CzZ/QZ0+4K3jpYXpURwutipKpFEYGIDJqh5piIuBlaT8Cz/9T0nzcLDPTWMgqHoiQRjCDthZNQctCsiE5hSaRKAAqefHKTAA6iPJvqoHdOTlWHAIyYubQJmAwhxssbcldMradMABht9YZ7Eh/cBE9SgZlTKxjvbRDOyuUpOB5e3lrxHA0qAEKIC4WNtA1G8IqcFyO1oLARQjIH59gIIZmDwkYIyRwUNkJI5qCwEUIyB4WNEJI5KGyEkMxBYSOEZI7/D80vtcb/ELcFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qF1E1algYxMrwmZumqmOqulVVt67pWBPaR0ZEZDtNqk9VnwYgZvty1BvMw4IZM7u/YibUeh2GM0HWVQrrTyMismH7oEDMUI4umyEdJqAdg/OAAHD61NaYfScBPG4yYE54hnuMxK8+EdFCVkFNVQdF5FtwWmpl02oLKz8DYE3APm8epiN+ZYiIGmU9pENVD6RZESKiJEQN6Qh8IKCqO5OvDhFRc6JaajUAD8Hp1D+M+QcFRES5FBrUPFOYngLwV3BmAIyq6tn0q3anC3J52RfP7IkcfHvu5sXlNuUAXE+gWkSUI7HmforIQ3DSmaxpxe3nv/i9T17786NTv4oq9/Ud2x+xKffHv3/fOlV9MGg/k00S5VPTcz9F5B4AO+HMDJgBh18QUU5FPSj4LJxJ6bMAxlX1iUxqRUTUoKiW2iScltkmANtEROE8LFBV3ZZ25YiI4op6UMDVpoiorUStUfBYRvUgIkpE1O2nmOEcCuCwqv4ugzoRETUs6vbzNQCvAYCI/LGIrIGz5P3PsqgcEVFcceZ+/i3gjFXztN4msxyIe+63V5d9afhA5KDaa+cvL7cpB6AjgWoRUY7EXszYpPN+CQBE5HEAZxOuU6Alq9bcXPP5P4vMaHv99Z922ZS7cvyFh5OpGRHlhdXTTfeBgYg8KCLfdNcAbSbNNxFRGmyHbLirQo3D6WMbTac6RETNsQ1qa83sgsumdRa69BwRUavYBrVROOt0uut9nkinOpkL7FOMWumdq7sT5ZPtg4ITdf1n42lUpgXuEpHpgH2bAXQCOCgi3/bZfx7AVFoVI6LG2Aa1EQBfrdsuQubbD4PSl5gW2kEAT6rqj3z2b0+3akTUiKgsHQ/BCWC9Zok7N/NtLeV6ZSVwHVFV/ZGIfNsvoJn9U6nViogaFjWj4E0AgyLyLS68QkTtwHaJvAPm6af3NU6VIqLcsc18ewLO4sSXPC8zqBFR7tg+KDimqntSrQkRUQJsg1q3iPw9gKr7gqp+NaQ8EVFL2Aa13XXboUtQiUgJzkDdmqqOBZQZxfx0q15V3W9ZFyKiQLZBrQfAVjjTpPaaldvDxqnZLCtXgTOId9JdX5SIqFm2Qa1PVXeKyD6zfTmssKpOmnFtYUYATAMoW9YhcZ0dq1Z/Y/ALgSmK1neV1gXtf2H8h99X1efSqx0RNcI2qImI/BGAkvk/CRU4fXQlERlR1fpbXPfEQzBzTpfcszF2/rcwq5d13br+dlfg/ts3liJkfy8ABjWinLEdpzYoIt+CM6OgrKpPN3tib1+biLwkIhVVrQaUGwOAFRu3XIs67genjnYuWb3+xgenjnZ2PLrjSljZezvv/ejZzz0b2FJ7+Y2Xu4L2v/jKi4kGWCJKhu04tceSnFEgIv0AKp6HA7NJHbvj0R1XooIZERWXbeqhPhH5qYi8mNCyeVU4CyW7yn6tNCKiuKynSQE4YCa4D4vIuKr+XlB5EekF0Aenv6yqqpPm9ZMAHlfVGRHpNw8TKgAGmv4kRESwv/28B84QjgE4GTpCZxeYIDbp83qP5+sjcSqahmvrl+gXz+wJXHXq3M2Ly0P2X0+pWkTUBNvO7iMAfqCqT6RZmaxtfqzn6pf3fifwQcE//XyqK2j/0YNj69KrGRE1yrZPbRzAEyLyXQAwg2+JiHLHNqj1mmEcbvnQwbdERK1iG9TcwbddCQ6+JSJKnFVQU9VBAJuQ4OBbIqI0WI+KL2I67394Z+3t48MHAp9+Xjt/efmXgvd3pFQtImrCop7qU9rx3d8E7fvg1NHOj955bdXKzZ/1naFw5fgLD6dbOyJqxKIOamE43YqoPVn1qblTo0TkQRH5pog8mGaliIgaZfv0003iOA7gNcxnrCUiyhXboLbWLJF3WVWPAXgvxToRETXMNqiNAhiESdYI4EQ61SEiao5tULvkjk0TkW/CuQ0lIsod9qkRUaGwT42ICiVOn9oA5vvUXk2nOkREzbENahU48z7d29Bt6VSnWERku4j8RES2t7ouRItF3NRDLqYesqCqUwA2mv+JKANxUw8lue4nEVHimHqIiArFduGVQ6q6M2ibiCgvrG8/I7aJiHLBNvXQrIh8BcA0nCefia2oTkSUJNs+tafhtM6eBtDFPjUiyivrxYxV9SUAL5ntx1T1F2lWjIioEbZ9an9lpklBRJ7C/CBcXyJSEpFdIjIUUqZfRHrN/932VSYiChZnSMcTInICTsaOr0a8ZWvYThEpAehT1UlVPQJgxKYeRERRbG8/PwtnqtQeAMMiMqOqZ4PKq+qkiFRCDjkI4IxnuyYi3ao6Y1OfPOjsWLX6G4NfOBdVbn1XaZ1NuRfGf/h9VX0umdoRLV62Tz+fMK01ADgmIi8CiGqthSkBqHm2ZwGUmzhe5lYv67p1/e2uyHK3byyFTTkAvQAY1IiaFBrU3AcCqrqnbtdECnUpBdRhCCY7yJJ7NuZm9at7O+/96NnPPRvZAnv5jZe7bMq9+MqLuflsRO0sqk9tr/uFaZ25Qh8UWKhhYRArA6j6FVTVMVXdqqpbl64q32zyvERUcFFBTSy+bsRhOHNJXaV26k8jovyKCmoa8HVo6iER6QXQB2DAfO2+flJESqpaAzBhhnT0gk8/AzEnG1E8Uf04fWYYhwCoiMhW8/Wnwt6kqpMAJn1e7/F8fSR+dfPj2vol+sUze+6LKnfu5sXlNuUAXPd7UVWnROR55mQjshMV1B7KpBZtaPNjPVe/vPc7kQ8A/unnU1025Y4eHFuXTM2IFrfQ209VfS/oX1YVzKuL595eEVVm6u8Od114+62VU3932GZMx8oEqkW06ImqRpfKiRUbt1zbuOeXv2p1PQDg/f0PbOlYtepWWJkLb7+18qPrHyxZsbLj9oaPP+B7e+l6643Toqqr/PaJyLSqhs7SIFpMwq4Jjo1q0Psb/u2VD+/ZeCOsjCz7f9dxenKNPLi9Vtvw+x+GHvCN052JVpBokWJQa9C6J//7r23KnX9uy8oNT/+PN6PKXTn+wsPN14qIbLN0EBG1BQY1IioUBjUiKhQGNSIqFAY1IioUBjUiKhQGNSIqFAa1/PAdMyginwew2fzvixk8iOZx8G1+3CUi0z6vbwbQCeCgiHw74L3nAUylVTGidsKglh8f+s1lMy20gwCeVNUf+b2RLTWiebz9zA/fVOUmkJ0OCmimzFRalSJqNwxqRFQovP3Mic4Vnau77+++4revY3nHqqB9APDaudf+l6p+Lr3aEbUPBrWceHjdwx8ce/rY6377Pv1fPv1I0D4AKP/nckd6NSNqL7z9JKJCYVAjokLh7WdOhK1OZbEiVWiqcKLFhEEtRR+cOtq5ZPX6Gx+cOtrZ8eiOwI5+IHx1qqgVqbgSFdE8BrUUdTy640pUMCOiZDGo5cQ/vLP29vHhA763mNfOX17+pYB9Bp9+EhmpBTUR6QdQA1ACUFXVGZ8yowBGzWavqu5Pqz55V9rx3d8E7bv++k+71nz+zwJvP7loC9G8VIKaiJQA9KnqsNmeANDnU7QCYBzApFuWiKgZabXUBgGc8WzXRKTbp7U2AmAaQDmlehDRIpPWOLUSnFtP1yz8A1fFvF4SkZGU6kJEi0iWg29L9S+o6piquv1tvSJSqS8jIkMiMi0i07fen110Dza8w0JaXReidpBWkKhhYRArA6h6C5gHCRXPw4FZvwOp6hiAMQBYsXHLtaQrmnccFkIUT1ottcMANnm2Sz79aVUAk57tsqpWQW1PRLaLyE+YvJJaIZWWmqrWRGRCRHrNS3P9ZSJyEsDjqjojIv3mlrMCYCCNuiwGYWmLkhInvZGqTonI80xeSa2QWh+Vqh4JeL0nqgzFE5a2KClMb0TtYtF1vBdR2GT4BHHSPLUFBrUCCJsMnxROmqd2waBWAGHzRhPE209qCwxqBRA2bzQpnF9K7YKZb4moUBjUFpkPTh3tvPAXn3mYMxSoqBjUFpmOR3dcuX314vIGZilYd1WYVeU3m/+tcbAuJYF9amTrLhGZtiy7GUAngIMi8u0Y5zgPYCpuxYi8GNTI1oequtWmoGmhHQTwpKr+yPYEbKlREnj7SbZu2hY0gex0nIBm3jcVt1JE9dhSIytx55d2LO9YFXc+apz5pURBGNTIysZHNr9//2M9V23Ln/nxP991f9+2eEHt4GufjF8zooUY1MjKb9Y/8bvzZ69bd1dc+2gJjp+9O+5p1sR9A1E9BjWyEnfWQtQKWH44a6Ex5gHLMwC+x35JPiggansmkG1kQHMwqBFRoTCoEVGhsE+NEuddASvOdKzOjlWrvzH4hVTzwvl5YfyH31fV57I+L6WDQa0A4gSPq68e7Lpx6c2VV1892LX6D558L41zNLoC1oqu0s0f/+r/tmKi/Z8AiBXU2DmfXwxqBfC7l7+3ofaTPR+3KXvj0psrceP9JbPjX6tc+dkB6xTdS1avv5H2Un03tjz57m9v2A8bcd2c/fWKj94+uXrFx3uuLit/4qP4Z/5F7Ky+zS4uIyLbkwqG3gQCcWdxhBwzsfpljUGtAO75zDMX4rTUZse/VikP/GU1bkut8RraaSbZ5fnntjyy/k/+9mwj723RUJJnROT5hI7VaAKBMIklF8i6VcugVgBxWlCr/+DJ96787MD1OAEt7jnISmIXeKMJBCKOuT2J4wDZL5nIp59ELZDkBd5oAoGIY04ldayssaVGi17H6s67yx+798O471u6bNnyRt4HAJcvXjirqpzrmgIGNVr0lv+b/3jhZgMPKHT6v627+S//w6WGTnr8hY819D6KxKBGi95dD336Wtw+w6uvHuy6/b9HNyy//19fi9s/CQA3f/6DT8VNzRSmkVRPYdo5DVRqQU1E+gHUAJQAVFV1ppEyRGmLMyTG1ejQGNfN5Stvn1qa3OV3A4Ikj4flq56QuzqtE4OGunVTACyRZR23sHSZJnJM4EzQjlSCmoiUAPSp6rDZngDQF7cMURbiDIlxNTo0xhV3tkWU889teeTeZ159PanjXfiLzzx8++rF5Ukc68alN1fiFoAlS7C8/ImG+iDvOOZvTgUOMUqrpTaIhZG0JiLddS0xmzJEqWskuDQ6NKaZcwZpdFpamEYCfZBm/wD4+eevS2BQE9WkWoOeg4rsAlBT1TGzPQpgXFUn45Qxrw8BGDKbWwD8MvEKJ2cdgHdbXYkQea/fxwG83cD7ugBsAHABQCMXTSeAqAs479+7xVa/T6jqer8dWT4oKDVSxgQ9N/BN265o1AqsX3PyXL881w1g/bzSGnxbw8IAVQZQbaAMEVEsaQW1wwA2ebZLPn1lNmWIiGJJ5fZTVWsiMiEivealEXefiJwE8HhYmRBjSdc1Yaxfc/JcvzzXDWD95qTyoICIqFU4oZ2ICoVBjYgKJZdzP2NMsSoD6IHP+LZW1q2+bFZ1854TEfUz4/+qAJC3+pl+1pLZzOz7Z2a5DMEzftKnTMum9sWoX+bXhW39PGVTuzZyF9Qsp1h1w/mFOmK2LyOD1b3jTO0yZYdh9wAk0/qJyDiAp8zDmnEAWQYNm+lzJc/PdiSr+gEIHUeVg6l9UfVryXXhYTUOLe1rI4+3n77Tp+rKlOF8U1yzPmXSYFM311YAE+lXaYHI+rnbJqBVVHUgT/Uz9opIJaM6zTGthlpIkTg//8RZ1K9V1wUAq/q5Ur028hjUSlj4jZmF88Oao6qT7l9Lo5zRbUAJEXUD5gLHdAb1qVdCdP22AvMtItMSykoJ0T/bGoDdAE6KyLiq7s6qchZKsPj5t0oLrwtrWVwbeQxqfkpBO8xFmWVro17J70VzceZByW9bVWvmF767Fa2i+vrUqcDpE3Jv8fKs1OoK+MnBdREo7Wsjd31qiDF9ynQ2Hsrwr1ENEXUzHfCzJlBsA7BWRKqqmsUUsMj6me21de+p+JRLQw3R379+ANPm+zUgIiMi0ptlh3eIGtpgal8LrgsrWV0beWypWU2fMs3YGVWdEZFKRq2NyLqp6piqHjGdtVUAExkFNKv6wel0L3m2K8juVtmmfmUsvMWbQH4CR+6n9rXourCS1bWRu5aazRQrOBfiMThRH3D6DlJ/ymM7/ctsdwPohtNvlUlLLeb0NDed076sbpUt6zcmIrtExK1TVq1cdyhJH+Z/ZpN1dWtkal9m9UOLrgvb+mV1bXCaFBEVSh5vP4mIGsagRkSFwqBGRIXCoEZEhcKgRkSFwqBGmRORfhE5Y/7vN0M4QodHiEi3dx6jiIyKyLgZYuH7XrO/fu7rLs+QDCqg3I1To0WhBsAdhAlgboxTmK2e5RTHAYx6xkGVAt4zCmAvFk4X2qmq+xusN7UBttSoFfoATIhIyUzpsc7pZkbIl7zlvYOHTetsXESGTBlv664X2aUxohZhUKNWcJNADsEiVY0JfIfNZuA8VXEWyD5k0in1mJePeGZPDMNpvVGBMahRS5hbzzFVnTRzFEsiMgrMBSevsqc1VoUT2OC+xyRDBJx5mX2mj80tP4r5HGOlDOfhUouwT40y5b0F9ASqbvOau73JU35By0xVqyICT+aOYRFxM66eBDDr7asz5WdNoGMrbRHg3E/KjAlQo3CC1DicW9CdAPaZIhU4WToOeyY/D/nluzetujKcRI3wpNh2A1fZzeprbl9fynJyN7UOgxrlgqff63Bdx79vUCMKwqBGuWVuVTNLPUTFwKBGRIXCp59EVCgMakRUKAxqRFQoDGpEVCgMakRUKAxqRFQoDGpEVCj/H0fnOuI9UQ3EAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 319.68x272.068 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ta.plot_tools.batch_plot_signalMB(final_dfs[0], PATH='plots/BP0', title=r'$m_5 = 150$ MeV,  $\\mu_{\\rm tr} = 1.25 \\times 10^{-6}$ GeV$^{-1}$', prefix='BP0')\n",
    "ta.plot_tools.batch_plot_signalMB(final_dfs[1], PATH='plots/BP1', title=r'$m_5 = 150$ MeV,  $\\Delta_{54} = 0.4$,  $m_{Z^\\prime} = 30$ MeV', prefix='BP1')\n",
    "ta.plot_tools.batch_plot_signalMB(final_dfs[2], PATH='plots/BP2', title=r'$m_5 = 150$ MeV,  $\\Delta_{54} = 0.4$,  $m_{Z^\\prime} = 1.25$ GeV', prefix='BP2')\n",
    "ta.plot_tools.batch_plot_signalMB(final_dfs[3], PATH='plots/BP3', title=r'$m_5 = 200$ MeV,  $\\Delta_{54} = 0.4$,  $m_{h^\\prime} = 1$ GeV', prefix='BP3')\n",
    "ta.plot_tools.batch_plot_signalMB(final_dfs[4], PATH='plots/BP4', title=r'$m_5 = 200$ MeV,  $\\Delta_{54} = 0.4$,  $m_{h^\\prime} = 30$ MeV', prefix='BP4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "393516e0",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "batch_plot_signalMB() got an unexpected keyword argument 'BP'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb Cell 14\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X16sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Im missing the miniboone plots for TMM\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X16sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m MBplotter(final_dfs[\u001b[39m1\u001b[39;49m], \u001b[39m'\u001b[39;49m\u001b[39mBP light vector\u001b[39;49m\u001b[39m'\u001b[39;49m, title\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m$m_5 = 150$ MeV,  $\u001b[39;49m\u001b[39m\\\u001b[39;49m\u001b[39mDelta_\u001b[39;49m\u001b[39m{54}\u001b[39;49;00m\u001b[39m = 0.4$,  $m_\u001b[39;49m\u001b[39m{\u001b[39;49m\u001b[39mZ^\u001b[39;49m\u001b[39m\\\u001b[39;49m\u001b[39mprime} = 30$ MeV\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X16sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m MBplotter(final_dfs[\u001b[39m2\u001b[39m], \u001b[39m'\u001b[39m\u001b[39mBP heavy vector\u001b[39m\u001b[39m'\u001b[39m, title\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m$m_5 = 150$ MeV,  $\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mDelta_\u001b[39m\u001b[39m{54}\u001b[39;00m\u001b[39m = 0.4$,  $m_\u001b[39m\u001b[39m{\u001b[39m\u001b[39mZ^\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mprime} = 1.25$ GeV\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X16sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m MBplotter(final_dfs[\u001b[39m3\u001b[39m], \u001b[39m'\u001b[39m\u001b[39mBP light scalar\u001b[39m\u001b[39m'\u001b[39m, title\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m$m_5 = 200$ MeV,  $\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mDelta_\u001b[39m\u001b[39m{54}\u001b[39;00m\u001b[39m = 1$,  $m_\u001b[39m\u001b[39m{\u001b[39m\u001b[39mh^\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mprime} = 30$ MeV\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/Repos/DarkNews-generator/examples/ToyAnalysis/plot_tools.py:43\u001b[0m, in \u001b[0;36mplot_all_rates\u001b[0;34m(df, case_name, Nevents, truth_plots, title, path, loc)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[39m# get observables at MiniBooNE\u001b[39;00m\n\u001b[1;32m     38\u001b[0m bag_reco_MB \u001b[39m=\u001b[39m analysis_decay\u001b[39m.\u001b[39mdecay_selection(\n\u001b[1;32m     39\u001b[0m                                             analysis\u001b[39m.\u001b[39mcompute_spectrum(df, EVENT_TYPE\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mboth\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     40\u001b[0m                                             experiment\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mminiboone\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m     41\u001b[0m                                             l_decay_proper_cm\u001b[39m=\u001b[39mdf\u001b[39m.\u001b[39mattrs[\u001b[39m'\u001b[39m\u001b[39mN5_ctau0\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m---> 43\u001b[0m batch_plot_signalMB(bag_reco_MB, PATH_MB, BP\u001b[39m=\u001b[39;49mcase_name, title\u001b[39m=\u001b[39;49mtitle, NEVENTS\u001b[39m=\u001b[39;49mNevents, loc\u001b[39m=\u001b[39;49mloc)\n\u001b[1;32m     45\u001b[0m \u001b[39m# plot true variables for MiniBooNE\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[39mif\u001b[39;00m truth_plots:\n",
      "\u001b[0;31mTypeError\u001b[0m: batch_plot_signalMB() got an unexpected keyword argument 'BP'"
     ]
    }
   ],
   "source": [
    "# Im missing the miniboone plots for TMM\n",
    "\n",
    "MBplotter(final_dfs[1], 'BP light vector', title='$m_5 = 150$ MeV,  $\\Delta_{54} = 0.4$,  $m_{Z^\\prime} = 30$ MeV')\n",
    "MBplotter(final_dfs[2], 'BP heavy vector', title='$m_5 = 150$ MeV,  $\\Delta_{54} = 0.4$,  $m_{Z^\\prime} = 1.25$ GeV')\n",
    "MBplotter(final_dfs[3], 'BP light scalar', title='$m_5 = 200$ MeV,  $\\Delta_{54} = 1$,  $m_{h^\\prime} = 30$ MeV')\n",
    "MBplotter(final_dfs[4], 'BP heavy scalar', title='$m_5 = 200$ MeV,  $\\Delta_{54} = 1$,  $m_{h^\\prime} = 1.0$ MeV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bf36342",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6d886bce",
   "metadata": {},
   "source": [
    "# Test the geometries a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39753cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# geometry of muBoone\n",
    "#cryostat vessel\n",
    "r_muB = 191.61\n",
    "l_muB  = 1086.49\n",
    "#detector\n",
    "z_muB = 1040.\n",
    "x_muB = 256.\n",
    "y_muB = 232.\n",
    "dif_z = l_muB - z_muB\n",
    "#outer spheres\n",
    "r_s_muB = 305.250694958\n",
    "theta_lim_muB = 38.8816337686 * np.pi / 180.0\n",
    "#how much volume for each - rates\n",
    "sphere_cut_muB = 0.030441980173709752\n",
    "cylinder_cut_muB = 1. - 2*sphere_cut_muB\n",
    "\n",
    "precision = 1e-8\n",
    "def get_distances(p0, phat, experiment):\n",
    "    \"\"\"get_distances_in_muB gets the distance to the entry and exit \n",
    "    of the MicroBooNE fiducial volume\n",
    "\n",
    "    NOTE: this function uses a coordinate system with (0,0,0) being the center \n",
    "    of the MicroBooNE detector in the xy plane AND the most upstream point of the \n",
    "    detector in the z position (the tip of the first spherical cap).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    p0 : np.ndarray\n",
    "        point of HNL production\n",
    "    phat : np.ndarray\n",
    "        direction of travel of HNL\n",
    "    experiment :\n",
    "        what experiment to find the distances for 'microboone', 'sbnd'\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    2 np.ndarrays\n",
    "        one array for the distance of entry and one for the exit\n",
    "    \"\"\"\n",
    "\n",
    "    # number of events\n",
    "    n = len(p0.T)\n",
    "\n",
    "    # positions of the 6 walls of the cryostat in order (2 for X, 2 for Y, 2 for Z)\n",
    "    if experiment == 'microboone':\n",
    "        planes = np.array([-x_muB/2,x_muB/2,-y_muB/2,y_muB/2,dif_z/2,z_muB + dif_z/2])\n",
    "    elif experiment == 'sbnd':\n",
    "        planes = np.array([-x_sbnd/2,x_sbnd/2,-y_sbnd/2,y_sbnd/2,dif_z_sbnd/2,z_sbnd + dif_z_sbnd/2])\n",
    "\n",
    "    # suitable forms for parameters\n",
    "    p0_6 = np.array([p0[0], p0[0], p0[1], p0[1], p0[2], p0[2]]).T\n",
    "    phat_6 = np.array([phat[0], phat[0], phat[1], phat[1], phat[2], phat[2]]).T\n",
    "\n",
    "    # find solutions and intersections of P0 + phat*t = planes, for parameter t\n",
    "    solutions = (planes - p0_6) / phat_6\n",
    "    # intersections = [[p0[:,i] + solutions[i,j] * phat[:,i] for j in range(6)] for i in range(n)]\n",
    "    intersections = [p0 + solutions[:,j] * phat for j in range(6)]\n",
    "\n",
    "\n",
    "    # create a mask with invalid intersections\n",
    "    mask_inter = np.array([[\n",
    "        (planes[0] - precision <= intersections[i][j][0] <= planes[1] + precision)\\\n",
    "      & (planes[2] - precision <= intersections[i][j][1] <= planes[3] + precision)\\\n",
    "      & (planes[4] - precision <= intersections[i][j][2] <= planes[5] + precision)\\\n",
    "      & (solutions[i,j] > -precision) for j in range(6)] for i in range(n)])\n",
    "\n",
    "    # compute the distances from the previous calculations\n",
    "    distances = np.zeros((n,2))\n",
    "    for i in range(n):\n",
    "        dist_temp = solutions[i][mask_inter[i]]\n",
    "        if len(dist_temp) == 2:\n",
    "            distances[i] = dist_temp\n",
    "        elif len(dist_temp) == 1:\n",
    "            distances[i] = [0, dist_temp[0]]\n",
    "        else:\n",
    "            distances[i] = [0,0]\n",
    "\n",
    "    # return the distances\n",
    "    return distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ac79e71",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'p0' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb Cell 17\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X31sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m d2, d1 \u001b[39m=\u001b[39m get_distances(p0, phat, \u001b[39m'\u001b[39m\u001b[39mmicroboone\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'p0' is not defined"
     ]
    }
   ],
   "source": [
    "d2, d1 = get_distances(p0, phat, 'microboone')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e34cabb2",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb Cell 18\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39m# production point\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m p0 \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([df[\u001b[39m'\u001b[39m\u001b[39mpos_scatt\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m1\u001b[39m\u001b[39m'\u001b[39m], df[\u001b[39m'\u001b[39m\u001b[39mpos_scatt\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m2\u001b[39m\u001b[39m'\u001b[39m], df[\u001b[39m'\u001b[39m\u001b[39mpos_scatt\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m3\u001b[39m\u001b[39m'\u001b[39m]])\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m d2, d1 \u001b[39m=\u001b[39m get_distances(p0, phat, \u001b[39m'\u001b[39;49m\u001b[39mmicroboone\u001b[39;49m\u001b[39m'\u001b[39;49m)\u001b[39m.\u001b[39mT\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m np\u001b[39m.\u001b[39msum(d1 \u001b[39m-\u001b[39m d2)\n",
      "\u001b[1;32m/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb Cell 18\u001b[0m in \u001b[0;36mget_distances\u001b[0;34m(p0, phat, experiment)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m intersections \u001b[39m=\u001b[39m [p0 \u001b[39m+\u001b[39m solutions[:,j] \u001b[39m*\u001b[39m phat \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m6\u001b[39m)]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=59'>60</a>\u001b[0m \u001b[39m# create a mask with invalid intersections\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=60'>61</a>\u001b[0m mask_inter \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=61'>62</a>\u001b[0m     (planes[\u001b[39m0\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m0\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m1\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=62'>63</a>\u001b[0m   \u001b[39m&\u001b[39m (planes[\u001b[39m2\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m1\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m3\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=63'>64</a>\u001b[0m   \u001b[39m&\u001b[39m (planes[\u001b[39m4\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m2\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m5\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=64'>65</a>\u001b[0m   \u001b[39m&\u001b[39m (solutions[i,j] \u001b[39m>\u001b[39m \u001b[39m-\u001b[39mprecision) \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m6\u001b[39m)] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(n)])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=66'>67</a>\u001b[0m \u001b[39m# compute the distances from the previous calculations\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=67'>68</a>\u001b[0m distances \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((n,\u001b[39m2\u001b[39m))\n",
      "\u001b[1;32m/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb Cell 18\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m intersections \u001b[39m=\u001b[39m [p0 \u001b[39m+\u001b[39m solutions[:,j] \u001b[39m*\u001b[39m phat \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m6\u001b[39m)]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=59'>60</a>\u001b[0m \u001b[39m# create a mask with invalid intersections\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=60'>61</a>\u001b[0m mask_inter \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=61'>62</a>\u001b[0m     (planes[\u001b[39m0\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m0\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m1\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=62'>63</a>\u001b[0m   \u001b[39m&\u001b[39m (planes[\u001b[39m2\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m1\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m3\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=63'>64</a>\u001b[0m   \u001b[39m&\u001b[39m (planes[\u001b[39m4\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m2\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m5\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=64'>65</a>\u001b[0m   \u001b[39m&\u001b[39m (solutions[i,j] \u001b[39m>\u001b[39m \u001b[39m-\u001b[39mprecision) \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m6\u001b[39m)] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(n)])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=66'>67</a>\u001b[0m \u001b[39m# compute the distances from the previous calculations\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=67'>68</a>\u001b[0m distances \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((n,\u001b[39m2\u001b[39m))\n",
      "\u001b[1;32m/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb Cell 18\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m intersections \u001b[39m=\u001b[39m [p0 \u001b[39m+\u001b[39m solutions[:,j] \u001b[39m*\u001b[39m phat \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m6\u001b[39m)]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=59'>60</a>\u001b[0m \u001b[39m# create a mask with invalid intersections\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=60'>61</a>\u001b[0m mask_inter \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=61'>62</a>\u001b[0m     (planes[\u001b[39m0\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m0\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m1\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=62'>63</a>\u001b[0m   \u001b[39m&\u001b[39m (planes[\u001b[39m2\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m1\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m3\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=63'>64</a>\u001b[0m   \u001b[39m&\u001b[39m (planes[\u001b[39m4\u001b[39m] \u001b[39m-\u001b[39m precision \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m intersections[i][j][\u001b[39m2\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m planes[\u001b[39m5\u001b[39m] \u001b[39m+\u001b[39m precision)\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=64'>65</a>\u001b[0m   \u001b[39m&\u001b[39m (solutions[i,j] \u001b[39m>\u001b[39m \u001b[39m-\u001b[39mprecision) \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m6\u001b[39m)] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(n)])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=66'>67</a>\u001b[0m \u001b[39m# compute the distances from the previous calculations\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=67'>68</a>\u001b[0m distances \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((n,\u001b[39m2\u001b[39m))\n",
      "\u001b[0;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 3"
     ]
    }
   ],
   "source": [
    "pN = df.P_decay_N_parent.values\n",
    "l_decay_lab_cm = ta.analysis_decay.get_decay_length_in_lab(pN, df.attrs['N5_ctau0'])\n",
    "# direction of travel of the HNL.\n",
    "phat = ta.analysis_decay.get_3direction(pN)\n",
    "\n",
    "# production point\n",
    "p0 = np.array([df['pos_scatt','1'], df['pos_scatt','2'], df['pos_scatt','3']])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "d2, d1 = get_distances(p0, phat, 'microboone').T\n",
    "\n",
    "np.sum(d1 - d2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bb7d4e0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb Cell 21\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/matheushostert/Repos/DarkNews-generator/examples/microboone_study.ipynb#X26sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39mhist(np\u001b[39m.\u001b[39mexp(\u001b[39m-\u001b[39md2\u001b[39m/\u001b[39ml_decay_lab_cm) \u001b[39m-\u001b[39m np\u001b[39m.\u001b[39mexp(\u001b[39m-\u001b[39md1\u001b[39m/\u001b[39ml_decay_lab_cm))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.hist(np.exp(-d2/l_decay_lab_cm) - np.exp(-d1/l_decay_lab_cm))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc9b981",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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  "vscode": {
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