1 files changed, 0 insertions, 539 deletions
diff --git a/plot_llh/syst_variations.ipynb b/plot_llh/syst_variations.ipynb
deleted file mode 100644
index bafca99..0000000
--- a/plot_llh/syst_variations.ipynb
+++ /dev/null
@@ -1,539 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/smandalia/.local/lib/python2.7/site-packages/matplotlib2tikz/__init__.py:31: UserWarning: matplotlib2tikz has been renamed to tikzplotlib (which is Python-3-only). matplotlib2tikz will no longer be supported.\n",
-      "  warnings.warn(\"matplotlib2tikz has been renamed to tikzplotlib (which is Python-3-only). matplotlib2tikz will no longer be supported.\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"Systematic variations of HESE 7yr sample\"\"\"\n",
-    "from copy import deepcopy\n",
-    "import numpy as np\n",
-    "import matplotlib as mpl\n",
-    "from matplotlib import pyplot as plt\n",
-    "plt.style.use('./paper.mplstyle')\n",
-    "\n",
-    "import matplotlib2tikz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/cvmfs/icecube.opensciencegrid.org/py2-v3.1.1/RHEL_7_x86_64/lib/python2.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: to-Python converter for std::vector<golemfit::UnfoldingSegment, std::allocator<golemfit::UnfoldingSegment> > already registered; second conversion method ignored.\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n",
-      "/cvmfs/icecube.opensciencegrid.org/py2-v3.1.1/RHEL_7_x86_64/lib/python2.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: to-Python converter for std::vector<golemfit::AstrophysicalNeutrinoModel, std::allocator<golemfit::AstrophysicalNeutrinoModel> > already registered; second conversion method ignored.\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n",
-      "/cvmfs/icecube.opensciencegrid.org/py2-v3.1.1/RHEL_7_x86_64/lib/python2.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: to-Python converter for nusquids::marray<double, 3u, std::allocator<double> > already registered; second conversion method ignored.\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n",
-      "/cvmfs/icecube.opensciencegrid.org/py2-v3.1.1/RHEL_7_x86_64/lib/python2.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: to-Python converter for nusquids::marray<double, 5u, std::allocator<double> > already registered; second conversion method ignored.\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import GolemFitPy as gf\n",
-    "\n",
-    "# setup fitter\n",
-    "datapaths = gf.DataPaths()\n",
-    "npp = gf.NewPhysicsParams()\n",
-    "\n",
-    "sparams = gf.SteeringParams(gf.sampleTag.MagicTau)\n",
-    "sparams.quiet = False\n",
-    "sparams.fastmode = False\n",
-    "sparams.minFitEnergy = 6E4 # GeV\n",
-    "sparams.maxFitEnergy = 1E7 # GeV\n",
-    "sparams.load_data_from_text_file = False\n",
-    "sparams.use_legacy_selfveto_calculation = False\n",
-    "sparams.do_HESE_reshuffle=False\n",
-    "\n",
-    "FITTER = gf.GolemFit(datapaths, sparams, npp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0 2.5 2.0 0.444444447756 0.0 8.0 7.0 1.0 1.0 0.0 10.0 9.99999968266e-21 0.990000009537 0.0 1.0 1.0 1.0 1.0 56400.0 0.0\n",
-      "convNorm = 1.0\n",
-      "promptNorm = 1.0\n",
-      "muonNorm = 1.0\n",
-      "astroNorm = 8.0\n",
-      "astroDeltaGamma = 2.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "fitparams = gf.FitParameters(gf.sampleTag.MagicTau)\n",
-    "print fitparams.anisotropyScale,\n",
-    "print fitparams.astroDeltaGamma,\n",
-    "print fitparams.astroDeltaGammaSec,\n",
-    "print fitparams.astroFlavorAngle1,\n",
-    "print fitparams.astroFlavorAngle2,\n",
-    "print fitparams.astroNorm,\n",
-    "print fitparams.astroNormSec,\n",
-    "print fitparams.astroParticleBalance,\n",
-    "print fitparams.convNorm,\n",
-    "print fitparams.CRDeltaGamma,\n",
-    "print fitparams.cutoffEnergy,\n",
-    "print fitparams.darkNorm,\n",
-    "print fitparams.domEfficiency,\n",
-    "print fitparams.holeiceForward,\n",
-    "print fitparams.muonNorm,\n",
-    "print fitparams.NeutrinoAntineutrinoRatio,\n",
-    "print fitparams.piKRatio,\n",
-    "print fitparams.promptNorm,\n",
-    "print fitparams.timeSplit,\n",
-    "print fitparams.zenithCorrection\n",
-    "\n",
-    "nuisance = ('convNorm', 'promptNorm', 'muonNorm', 'astroNorm', 'astroDeltaGamma')\n",
-    "for n in nuisance:\n",
-    "    print n, '=', fitparams.__getattribute__(n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.111      0.111      0.111      0.111      0.111      0.111\n",
-      " 0.111      0.111      0.111      0.111      0.111      0.111\n",
-      " 0.111      0.111      0.111      0.111      0.111      0.111\n",
-      " 0.111      0.111      0.00184875]\n",
-      "[0.11109244 0.11109244 0.11109244 0.11109244 0.11109244 0.11109244\n",
-      " 0.11109244 0.11109244 0.11109244 0.11109244 0.11109244 0.11109244\n",
-      " 0.11109244 0.11109244 0.11109244 0.11109244 0.11109244 0.11109244\n",
-      " 0.11109244 0.11109244]\n",
-      "2635.5112\n"
-     ]
-    }
-   ],
-   "source": [
-    "binning = FITTER.GetEnergyBinsMC()\n",
-    "print np.diff(np.log10(binning))\n",
-    "print np.diff(np.log10(np.logspace(np.log10(6E4), np.log10(1E7), 21)))\n",
-    "\n",
-    "livetime = FITTER.GetLivetime()/(3600.*24)\n",
-    "print livetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_exp(x):\n",
-    "    h = np.sum(x, axis=(0,1,2,3))\n",
-    "    return np.concatenate([[h[0]], h])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "null_exp = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "\n",
-    "d = fitparams.convNorm\n",
-    "fitparams.convNorm = 1.4\n",
-    "norm_conv_p = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.convNorm = 0.6\n",
-    "norm_conv_n = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.convNorm = d\n",
-    "\n",
-    "d = fitparams.promptNorm\n",
-    "fitparams.promptNorm = 2.4\n",
-    "prompt_p = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.promptNorm = 0\n",
-    "prompt_n = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.promptNorm = d\n",
-    "\n",
-    "d = fitparams.muonNorm\n",
-    "fitparams.muonNorm = 1.5\n",
-    "muon_p = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.muonNorm = 0.5\n",
-    "muon_n = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.muonNorm = d\n",
-    "\n",
-    "d = fitparams.astroNorm\n",
-    "fitparams.astroNorm = 12\n",
-    "astro_p = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.astroNorm = 4\n",
-    "astro_n = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.astroNorm = d\n",
-    "\n",
-    "d = fitparams.astroDeltaGamma\n",
-    "fitparams.astroDeltaGamma = 3\n",
-    "dastro_index_p = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.astroDeltaGamma = 2\n",
-    "dastro_index_n = parse_exp(FITTER.GetExpectation(fitparams))\n",
-    "fitparams.astroDeltaGamma = d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8e1459e850>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fontsize = 16\n",
-    "\n",
-    "fig = plt.figure(figsize=(7, 5))\n",
-    "ax = fig.add_subplot(111)\n",
-    "\n",
-    "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(0.9, 1.1)\n",
-    "\n",
-    "ax.set_xscale('log')\n",
-    "#ax.set_yscale('log')\n",
-    "\n",
-    "ax.set_xlabel(r'Deposited Energy [GeV]', fontsize=fontsize)\n",
-    "#ax.set_ylabel(r'Events per 2635 days', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Ratio to baseline', fontsize=fontsize)\n",
-    "\n",
-    "ax.tick_params(axis='x', labelsize=fontsize, which='both', direction='in')\n",
-    "ax.tick_params(axis='y', labelsize=fontsize, which='both', direction='in')\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, null_exp / null_exp, drawstyle='steps-pre',\n",
-    "    linewidth=2, linestyle='-', color='black'\n",
-    ")\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, norm_conv_p / null_exp, drawstyle='steps-pre', label=r'$+40\\%\\:N_{\\rm{atm}}$',\n",
-    "    linewidth=2, linestyle='--', color='blue'\n",
-    ")\n",
-    "ax.step(\n",
-    "    binning, norm_conv_n / null_exp, drawstyle='steps-pre', label=r'$-40\\%\\:N_{\\rm{atm}}$',\n",
-    "    linewidth=2, linestyle=':', color='red'\n",
-    ")\n",
-    "\n",
-    "ax.legend(loc='upper right', prop={'size': fontsize})#, bbox_to_anchor=(0.96, 0.96), markerfirst=False)\n",
-    "\n",
-    "#ax.text(\n",
-    "#    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
-    "#)\n",
-    "ax.text(\n",
-    "    0.5, 0.1, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r', transform=ax.transAxes\n",
-    ")\n",
-    "\n",
-    "matplotlib2tikz.save(\"plots/tex/syst_convnorm.tex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8e14e42290>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fontsize = 16\n",
-    "\n",
-    "fig = plt.figure(figsize=(7, 5))\n",
-    "ax = fig.add_subplot(111)\n",
-    "\n",
-    "ax.set_xlim(binning[0], binning[-1])\n",
-    "#ax.set_ylim(1E-1, 40)\n",
-    "ax.set_ylim(0.9, 1.1)\n",
-    "\n",
-    "ax.set_xscale('log')\n",
-    "#ax.set_yscale('log')\n",
-    "\n",
-    "ax.set_xlabel(r'Deposited Energy [GeV]', fontsize=fontsize)\n",
-    "#ax.set_ylabel(r'Events per 2635 days', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Ratio to baseline', fontsize=fontsize)\n",
-    "\n",
-    "ax.tick_params(axis='x', labelsize=fontsize, which='both', direction='in')\n",
-    "ax.tick_params(axis='y', labelsize=fontsize, which='both', direction='in')\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, null_exp / null_exp, drawstyle='steps-pre',\n",
-    "    linewidth=2, linestyle='-', color='black'\n",
-    ")\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, prompt_p / null_exp, drawstyle='steps-pre', label=r'$N_{\\rm{prompt}}=2.4$',\n",
-    "    linewidth=2, linestyle='--', color='blue'\n",
-    ")\n",
-    "ax.step(\n",
-    "    binning, prompt_n / null_exp, drawstyle='steps-pre', label=r'$N_{\\rm{prompt}}=0$',\n",
-    "    linewidth=2, linestyle=':', color='red'\n",
-    ")\n",
-    "\n",
-    "ax.legend(loc='upper right', prop={'size': fontsize})#, bbox_to_anchor=(0.96, 0.96), markerfirst=False)\n",
-    "\n",
-    "#ax.text(\n",
-    "#    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
-    "#)\n",
-    "ax.text(\n",
-    "    0.5, 0.1, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r', transform=ax.transAxes\n",
-    ")\n",
-    "\n",
-    "matplotlib2tikz.save(\"plots/tex/syst_prompt.tex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8e14be2150>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fontsize = 16\n",
-    "\n",
-    "fig = plt.figure(figsize=(7, 5))\n",
-    "ax = fig.add_subplot(111)\n",
-    "\n",
-    "ax.set_xlim(binning[0], binning[-1])\n",
-    "#ax.set_ylim(1E-1, 40)\n",
-    "ax.set_ylim(0.9, 1.1)\n",
-    "\n",
-    "ax.set_xscale('log')\n",
-    "#ax.set_yscale('log')\n",
-    "\n",
-    "ax.set_xlabel(r'Deposited Energy [GeV]', fontsize=fontsize)\n",
-    "#ax.set_ylabel(r'Events per 2635 days', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Ratio to baseline', fontsize=fontsize)\n",
-    "\n",
-    "ax.tick_params(axis='x', labelsize=fontsize, which='both', direction='in')\n",
-    "ax.tick_params(axis='y', labelsize=fontsize, which='both', direction='in')\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, null_exp / null_exp, drawstyle='steps-pre',\n",
-    "    linewidth=2, linestyle='-', color='black'\n",
-    ")\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, muon_p / null_exp, drawstyle='steps-pre', label=r'$N_{\\rm{muon}}=1.5$',\n",
-    "    linewidth=2, linestyle='--', color='blue'\n",
-    ")\n",
-    "ax.step(\n",
-    "    binning, muon_n  / null_exp, drawstyle='steps-pre', label=r'$N_{\\rm{muon}}=0.5$',\n",
-    "    linewidth=2, linestyle=':', color='red'\n",
-    ")\n",
-    "\n",
-    "ax.legend(loc='upper right', prop={'size': fontsize})#, bbox_to_anchor=(0.96, 0.96), markerfirst=False)\n",
-    "\n",
-    "#ax.text(\n",
-    "#    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
-    "#)\n",
-    "ax.text(\n",
-    "    0.5, 0.1, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r', transform=ax.transAxes\n",
-    ")\n",
-    "\n",
-    "matplotlib2tikz.save(\"plots/tex/syst_muon.tex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8e14bc2f10>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fontsize = 16\n",
-    "\n",
-    "fig = plt.figure(figsize=(7, 5))\n",
-    "ax = fig.add_subplot(111)\n",
-    "\n",
-    "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(1E-1, 40)\n",
-    "\n",
-    "ax.set_xscale('log')\n",
-    "ax.set_yscale('log')\n",
-    "\n",
-    "ax.set_xlabel(r'Deposited Energy [GeV]', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events per 2635 days', fontsize=fontsize)\n",
-    "\n",
-    "ax.tick_params(axis='x', labelsize=fontsize, which='both', direction='in')\n",
-    "ax.tick_params(axis='y', labelsize=fontsize, which='both', direction='in')\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, null_exp, drawstyle='steps-pre',\n",
-    "    linewidth=2, linestyle='-', color='black'\n",
-    ")\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, astro_p, drawstyle='steps-pre', label=r'$N_{\\rm{astro}}=12$',\n",
-    "    linewidth=2, linestyle='--', color='blue'\n",
-    ")\n",
-    "ax.step(\n",
-    "    binning, astro_n, drawstyle='steps-pre', label=r'$N_{\\rm{astro}}=4$',\n",
-    "    linewidth=2, linestyle=':', color='red'\n",
-    ")\n",
-    "\n",
-    "ax.legend(loc='upper right', prop={'size': fontsize})#, bbox_to_anchor=(0.96, 0.96), markerfirst=False)\n",
-    "\n",
-    "ax.text(\n",
-    "    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
-    ")\n",
-    "\n",
-    "matplotlib2tikz.save(\"plots/tex/syst_astro.tex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8e14957490>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fontsize = 16\n",
-    "\n",
-    "fig = plt.figure(figsize=(7, 5))\n",
-    "ax = fig.add_subplot(111)\n",
-    "\n",
-    "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(1E-1, 40)\n",
-    "\n",
-    "ax.set_xscale('log')\n",
-    "ax.set_yscale('log')\n",
-    "\n",
-    "ax.set_xlabel(r'Deposited Energy [GeV]', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events per 2635 days', fontsize=fontsize)\n",
-    "\n",
-    "ax.tick_params(axis='x', labelsize=fontsize, which='both', direction='in')\n",
-    "ax.tick_params(axis='y', labelsize=fontsize, which='both', direction='in')\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, null_exp, drawstyle='steps-pre',\n",
-    "    linewidth=2, linestyle='-', color='black'\n",
-    ")\n",
-    "\n",
-    "ax.step(\n",
-    "    binning, dastro_index_p, drawstyle='steps-pre', label=r'$\\gamma_{\\rm{astro}}=3.0$',\n",
-    "    linewidth=2, linestyle='--', color='blue'\n",
-    ")\n",
-    "ax.step(\n",
-    "    binning, dastro_index_n, drawstyle='steps-pre', label=r'$\\gamma_{\\rm{astro}}=2.0$',\n",
-    "    linewidth=2, linestyle=':', color='red'\n",
-    ")\n",
-    "\n",
-    "ax.legend(loc='upper right', prop={'size': fontsize})#, bbox_to_anchor=(0.96, 0.96), markerfirst=False)\n",
-    "\n",
-    "ax.text(\n",
-    "    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
-    ")\n",
-    "\n",
-    "matplotlib2tikz.save(\"plots/tex/syst_astro_index.tex\")"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}