add syst variations plots, reintroduce mcmc fr script

1 files changed, 204 insertions, 213 deletions

diff --git a/plot_llh/thesis.ipynb b/plot_llh/thesis.ipynb
index c30371e..ff31a79 100644
--- a/plot_llh/thesis.ipynb
+++ b/plot_llh/thesis.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline"
@@ -14,21 +12,19 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/data/icecube/software/LVTools_package/anaconda/envs/lvtools/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",
+      "/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 2yr numu sample\"\"\"\n",
+    "\"\"\"Systematic variations of HESE 7yr sample\"\"\"\n",
     "from copy import deepcopy\n",
     "import numpy as np\n",
     "import matplotlib as mpl\n",
@@ -41,145 +37,183 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/cvmfs/icecube.opensciencegrid.org/py2-v3/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/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/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/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 lvsearchpy as lv\n",
-    "\n",
-    "# paths\n",
-    "effective_area_path='/data/icecube/data/Astro_numu_north_2yr/effective_area_release/effective_area.h5'\n",
-    "events_path='/data/icecube/software/LVTools_package/LVTools/data/simple_corrected_data_release.dat'\n",
-    "chris_flux_path='/data/icecube/data/Astro_numu_north_2yr/effective_area_release/conventional_flux.h5'\n",
-    "kaon_flux_path='/data/icecube/software/LVTools_package/LVTools/data/kaon_flux.h5'\n",
-    "pion_flux_path='/data/icecube/software/LVTools_package/LVTools/data/pion_flux.h5'\n",
-    "prompt_flux_path='/data/icecube/software/LVTools_package/LVTools/data/prompt_flux.h5'"
+    "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": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "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": [
-    "# constructing object\n",
-    "lvsearch = lv.LVSearch(effective_area_path,events_path,chris_flux_path,kaon_flux_path,pion_flux_path,prompt_flux_path)\n",
-    "lvsearch.SetEnergyExponent(6.)\n",
-    "lvsearch.SetVerbose(False)"
+    "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": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "binning = np.logspace(np.log10(400), np.log10(18e3), 18)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1, 0, 1, 1, 1, 0, 1e-100, 1e-100, 1e-100]\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      0.111      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": [
-    "# Null\n",
-    "n = [1e-100] * 3\n",
-    "\n",
-    "# norm_conv, dCR_index, pik, norm_prompt, norm_astro, dastro_index\n",
-    "# null_bf = lvsearch.llh(n)[:-1] + n\n",
-    "null_bf = [1, 0, 1, 1, 1, 0] + n\n",
+    "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",
-    "print null_bf"
+    "livetime = FITTER.GetLivetime()/(3600.*24)\n",
+    "print livetime"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def parse_exp(x):\n",
-    "    h = np.sum(x, axis=(0,1))\n",
+    "    h = np.sum(x, axis=(0,1,2,3))\n",
     "    return np.concatenate([[h[0]], h])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "null_exp = parse_exp(lvsearch.GetExpectationDistribution(null_bf))\n",
-    "\n",
-    "s = deepcopy(null_bf)\n",
-    "s[0] = 1.4\n",
-    "norm_conv_p = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "s[0] = 0.6\n",
-    "norm_conv_n = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "\n",
-    "s = deepcopy(null_bf)\n",
-    "s[1] = 0.05\n",
-    "dCR_index_p = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "s[1] = -0.05\n",
-    "dCR_index_n = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "\n",
-    "s = deepcopy(null_bf)\n",
-    "s[2] = 1.1\n",
-    "pik_p = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "s[2] = 0.9\n",
-    "pik_n = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "\n",
-    "s = deepcopy(null_bf)\n",
-    "s[3] = 0\n",
-    "prompt_p = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "s[3] = 5\n",
-    "prompt_n = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "\n",
-    "s = deepcopy(null_bf)\n",
-    "s[4] = 0\n",
-    "astro_p = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "s[4] = 5\n",
-    "astro_n = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "\n",
-    "s = deepcopy(null_bf)\n",
-    "s[5] = 0.5\n",
-    "dastro_index_p = parse_exp(lvsearch.GetExpectationDistribution(s))\n",
-    "s[5] = -0.5\n",
-    "dastro_index_n = parse_exp(lvsearch.GetExpectationDistribution(s))"
+    "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": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
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N4h3sPbvs+T2F6s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+VvHp4Hlr7edbDYyLbNVibeed2IR9WwgE1m28jb85914b1Fr7x3TnZhadu3t9exEttOiz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\nAIAzSgEqAAAAwJ6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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd49f93edd0>"
+       "<matplotlib.figure.Figure at 0x7f11881b6a10>"
       ]
      },
      "metadata": {},
@@ -193,13 +227,13 @@
     "ax = fig.add_subplot(111)\n",
     "\n",
     "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(1E1, 1E4)\n",
+    "ax.set_ylim(1E-1, 40)\n",
     "\n",
     "ax.set_xscale('log')\n",
     "ax.set_yscale('log')\n",
     "\n",
-    "ax.set_xlabel(r'Muon Energy Proxy (arb. units)', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events', fontsize=fontsize)\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",
@@ -210,86 +244,33 @@
     ")\n",
     "\n",
     "ax.step(\n",
-    "    binning, norm_conv_p, drawstyle='steps-pre', label='$+40\\% N_{\\rm{atm}}$',\n",
+    "    binning, norm_conv_p, drawstyle='steps-pre', label=r'$+40\\%\\:N_{\\rm{atm}}$',\n",
     "    linewidth=2, linestyle='--', color='blue'\n",
     ")\n",
     "ax.step(\n",
-    "    binning, norm_conv_n, drawstyle='steps-pre', label='$-40\\% N_{\\rm{atm}}$',\n",
+    "    binning, norm_conv_n, 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",
-    "matplotlib2tikz.save(\"syst_convnorm.tex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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IUJ8ud+0n3F70WvEUu+P2wo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\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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd497101b10>"
-      ]
-     },
-     "metadata": {},
-     "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(1E1, 1E4)\n",
-    "\n",
-    "ax.set_xscale('log')\n",
-    "ax.set_yscale('log')\n",
-    "\n",
-    "ax.set_xlabel(r'Muon Energy Proxy (arb. units)', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events', 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",
+    "ax.text(\n",
+    "    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
     ")\n",
     "\n",
-    "ax.step(\n",
-    "    binning, dCR_index_p, drawstyle='steps-pre', label='$\\Delta\\gamma_{CR}=0.05$',\n",
-    "    linewidth=2, linestyle='--', color='blue'\n",
-    ")\n",
-    "ax.step(\n",
-    "    binning, dCR_index_n, drawstyle='steps-pre', label='$\\Delta\\gamma_{CR}=-0.05$',\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",
-    "matplotlib2tikz.save(\"syst_dCR_index.tex\")"
+    "matplotlib2tikz.save(\"plots/tex/syst_convnorm.tex\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "data": {
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vv7XWfvgqG1lSrFNaa/99metj7f7hjNeHHbEfzf6fzMP+biq+rTr3r6pHWfB84wSndULf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\nNoCWjW28Y+6/mPOaHB+qilB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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd4983b2250>"
+       "<matplotlib.figure.Figure at 0x7f1179a73a10>"
       ]
      },
      "metadata": {},
@@ -303,13 +284,13 @@
     "ax = fig.add_subplot(111)\n",
     "\n",
     "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(1E1, 1E4)\n",
+    "ax.set_ylim(1E-1, 40)\n",
     "\n",
     "ax.set_xscale('log')\n",
     "ax.set_yscale('log')\n",
     "\n",
-    "ax.set_xlabel(r'Muon Energy Proxy (arb. units)', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events', fontsize=fontsize)\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",
@@ -320,31 +301,33 @@
     ")\n",
     "\n",
     "ax.step(\n",
-    "    binning, pik_p, drawstyle='steps-pre', label='$+10\\% \\pi/K ratio$',\n",
+    "    binning, prompt_p, drawstyle='steps-pre', label=r'$N_{\\rm{prompt}}=2.4$',\n",
     "    linewidth=2, linestyle='--', color='blue'\n",
     ")\n",
     "ax.step(\n",
-    "    binning, pik_n, drawstyle='steps-pre', label='$-10\\% \\pi/K ratio$',\n",
+    "    binning, prompt_n, 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",
-    "matplotlib2tikz.save(\"syst_pik.tex\")"
+    "ax.text(\n",
+    "    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
+    ")\n",
+    "\n",
+    "matplotlib2tikz.save(\"plots/tex/syst_prompt.tex\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd497101790>"
+       "<matplotlib.figure.Figure at 0x7f1179863390>"
       ]
      },
      "metadata": {},
@@ -358,13 +341,13 @@
     "ax = fig.add_subplot(111)\n",
     "\n",
     "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(1E1, 1E4)\n",
+    "ax.set_ylim(1E-1, 40)\n",
     "\n",
     "ax.set_xscale('log')\n",
     "ax.set_yscale('log')\n",
     "\n",
-    "ax.set_xlabel(r'Muon Energy Proxy (arb. units)', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events', fontsize=fontsize)\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",
@@ -375,31 +358,33 @@
     ")\n",
     "\n",
     "ax.step(\n",
-    "    binning, prompt_p, drawstyle='steps-pre', label='$N_{prompt}=0$',\n",
+    "    binning, muon_p, drawstyle='steps-pre', label=r'$N_{\\rm{muon}}=1.5$',\n",
     "    linewidth=2, linestyle='--', color='blue'\n",
     ")\n",
     "ax.step(\n",
-    "    binning, prompt_n, drawstyle='steps-pre', label='$N_{prompt}=5$',\n",
+    "    binning, muon_n, 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",
-    "matplotlib2tikz.save(\"syst_prompt.tex\")"
+    "ax.text(\n",
+    "    8E4, 0.2, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n",
+    ")\n",
+    "\n",
+    "matplotlib2tikz.save(\"plots/tex/syst_muon.tex\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd497e81f90>"
+       "<matplotlib.figure.Figure at 0x7f1179a3fa90>"
       ]
      },
      "metadata": {},
@@ -413,13 +398,13 @@
     "ax = fig.add_subplot(111)\n",
     "\n",
     "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(1E1, 1E4)\n",
+    "ax.set_ylim(1E-1, 40)\n",
     "\n",
     "ax.set_xscale('log')\n",
     "ax.set_yscale('log')\n",
     "\n",
-    "ax.set_xlabel(r'Muon Energy Proxy (arb. units)', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events', fontsize=fontsize)\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",
@@ -430,31 +415,33 @@
     ")\n",
     "\n",
     "ax.step(\n",
-    "    binning, astro_p, drawstyle='steps-pre', label='$N_{astro}=0$',\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='$N_{astro}=5$',\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",
-    "matplotlib2tikz.save(\"syst_astro.tex\")"
+    "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": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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s85ww7FJJcmlXizTUWv+S5KPM/4wxlnnTojiebi0KAABglBSgAgAAALDISsWjpZQfzlz1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\nWWPXjTbR6Is0yY4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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd49a534c10>"
+       "<matplotlib.figure.Figure at 0x7f11796f0e50>"
       ]
      },
      "metadata": {},
@@ -468,13 +455,13 @@
     "ax = fig.add_subplot(111)\n",
     "\n",
     "ax.set_xlim(binning[0], binning[-1])\n",
-    "ax.set_ylim(1E1, 1E4)\n",
+    "ax.set_ylim(1E-1, 40)\n",
     "\n",
     "ax.set_xscale('log')\n",
     "ax.set_yscale('log')\n",
     "\n",
-    "ax.set_xlabel(r'Muon Energy Proxy (arb. units)', fontsize=fontsize)\n",
-    "ax.set_ylabel(r'Events', fontsize=fontsize)\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",
@@ -485,26 +472,30 @@
     ")\n",
     "\n",
     "ax.step(\n",
-    "    binning, dastro_index_p, drawstyle='steps-pre', label='$\\Delta\\gamma_{astro}=0.5$',\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='$\\Delta\\gamma_{astro}=-0.5$',\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",
-    "matplotlib2tikz.save(\"syst_dastro_index.tex\")"
+    "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 [conda env:lvtools]",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "conda-env-lvtools-py"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
@@ -516,7 +507,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,