diff options
Diffstat (limited to 'plot_llh/.ipynb_checkpoints/bayes_factor-checkpoint.ipynb')
| -rw-r--r-- | plot_llh/.ipynb_checkpoints/bayes_factor-checkpoint.ipynb | 228 |
1 files changed, 228 insertions, 0 deletions
diff --git a/plot_llh/.ipynb_checkpoints/bayes_factor-checkpoint.ipynb b/plot_llh/.ipynb_checkpoints/bayes_factor-checkpoint.ipynb new file mode 100644 index 0000000..2dbe742 --- /dev/null +++ b/plot_llh/.ipynb_checkpoints/bayes_factor-checkpoint.ipynb @@ -0,0 +1,228 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "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": [ + "\"\"\"Bayes factor plots\"\"\"\n", + "import sys\n", + "sys.path.extend(['.', '..'])\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import warnings\n", + "with warnings.catch_warnings():\n", + " warnings.simplefilter(\"ignore\")\n", + " from sens import parse_args\n", + " from utils import plot as plot_utils\n", + " from utils.fr import SCALE_BOUNDARIES\n", + " from utils.misc import get_units\n", + "\n", + "import matplotlib2tikz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "DIM = 6\n", + "SC_010 = [(0, 1, 0), 'OET']\n", + "SC_100 = [(1, 0, 0), 'OUT']\n", + "\n", + "DATADIR = '/data/user/smandalia/flavour_ratio/data/sensitivity/DIM{0}/bayesian/real/fr_stat_DIM{0}_sfr'.format(DIM)\n", + "\n", + "SEGMENTS = 10\n", + "\n", + "args_010 = parse_args('--dimension {0} --source-ratio {1} {2} {3} --stat-method bayesian'.format(DIM, *SC_010[0]))\n", + "args_100 = parse_args('--dimension {0} --source-ratio {1} {2} {3} --stat-method bayesian'.format(DIM, *SC_100[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "boundaries = SCALE_BOUNDARIES[DIM]\n", + "eval_scales = np.linspace(boundaries[0], boundaries[1], SEGMENTS-1)\n", + "eval_scales = np.concatenate([[-100.], eval_scales])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scales, stat [(-100.0, -29.555862612249648), (-56.0, -29.766019647372207), (-52.75, -29.623184822825394), (-49.5, -29.928550147551004), (-46.25, -30.610604313154642), (-43.0, -37.94956693781441), (-39.75, -40.40734055766876), (-36.5, -40.60191617254397), (-33.25, -40.302401793524595), (-30.0, -40.61265085007461)]\n", + "[reduced_ev > np.log(10**(BAYES_K))] 264\n", + "limit = -45.1082397392\n", + "scales, stat [(-100.0, -29.555862612249648), (-56.0, -29.766019647372207), (-52.75, -29.623184822825394), (-49.5, -29.928550147551004), (-46.25, -30.610604313154642), (-43.0, -37.94956693781441), (-39.75, -40.40734055766876), (-36.5, -40.60191617254397), (-33.25, -40.302401793524595), (-30.0, -40.61265085007461)]\n", + "[reduced_ev > np.log(10**(BAYES_K))] 264\n" + ] + } + ], + "source": [ + "base_infile = DATADIR + '_{0}_{1}_{2}_'.format(*SC_010[0]) + SC_010[1] + '_scale_'\n", + "\n", + "d_010 = []\n", + "for scale in eval_scales:\n", + " infile = base_infile + '{0:.0E}'.format(np.power(10, scale)) + '.npy'\n", + " d_010.append(np.load(infile)[0])\n", + "d_010 = np.dstack(d_010)[0]\n", + "\n", + "lim_010 = plot_utils.get_limit(d_010[0], d_010[1], args_010, mask_initial=True)\n", + "sc, st = plot_utils.get_limit(d_010[0], d_010[1], args_010, mask_initial=True, return_interp=True)\n", + "d_010 = np.vstack([sc, st])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-100. -56. -52.75 -49.5 -46.25\n", + " -43. -39.75 -36.5 -33.25 -30. ]\n", + " [ -31.98570191 -31.93911116 -31.9159181 -31.26814871 -34.16971184\n", + " -35.1842944 -35.26534428 -35.31729645 -35.24392331 -35.29710952]]\n", + "scales, stat [(-100.0, -31.9857019100975), (-56.0, -31.939111164736115), (-52.75, -31.915918104898985), (-49.5, -31.268148706631592), (-46.25, -34.16971184021791), (-43.0, -35.184294403803335), (-39.75, -35.26534428423956), (-36.5, -35.31729644732499), (-33.25, -35.24392330992847), (-30.0, -35.29710952128992)]\n", + "[reduced_ev > np.log(10**(BAYES_K))] 228\n", + "limit = -46.0829187287\n", + "scales, stat [(-100.0, -31.9857019100975), (-56.0, -31.939111164736115), (-52.75, -31.915918104898985), (-49.5, -31.268148706631592), (-46.25, -34.16971184021791), (-43.0, -35.184294403803335), (-39.75, -35.26534428423956), (-36.5, -35.31729644732499), (-33.25, -35.24392330992847), (-30.0, -35.29710952128992)]\n", + "[reduced_ev > np.log(10**(BAYES_K))] 228\n" + ] + } + ], + "source": [ + "base_infile = DATADIR + '_{0}_{1}_{2}_'.format(*SC_100[0]) + SC_100[1] + '_scale_'\n", + "\n", + "d_100 = []\n", + "for scale in eval_scales:\n", + " infile = base_infile + '{0:.0E}'.format(np.power(10, scale)) + '.npy'\n", + " d_100.append(np.load(infile)[0])\n", + "d_100 = np.dstack(d_100)[0]\n", + "print d_100\n", + "\n", + "lim_100 = plot_utils.get_limit(d_100[0], d_100[1], args_100, mask_initial=True)\n", + "sc, st = plot_utils.get_limit(d_100[0], d_100[1], args_100, mask_initial=True, return_interp=True)\n", + "d_100 = np.vstack([sc, st])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x7f3d24097c50>" + ] + }, + "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(boundaries)\n", + "ax.set_ylim(-1, 12)\n", + "\n", + "ax.set_xlabel(r'${\\rm log}_{10}\\left[\\Lambda^{-1}_{'+ \\\n", + " r'{0}'.format(DIM)+r'}'+ \\\n", + " get_units(DIM)+r'\\right]$', fontsize=fontsize)\n", + "\n", + "ax.set_ylabel(r'$\\text{Bayes\\:Factor}\\:\\left[\\text{ln}\\left(B_{0/1}\\right)\\right]$')\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.plot(d_010[0], d_010[1], color='g', lw=2, ls='-')\n", + "ax.text(\n", + " -40, 9.7, r'$\\left(0:1:0\\right)_\\text{S}\\text{\\:w/\\:}\\mathcal{O}_{e\\tau}\\text{\\:texture}$',\n", + " fontsize=fontsize-3, color='g'\n", + ")\n", + "\n", + "ax.plot(d_100[0], d_100[1], color='b', lw=2, ls='-',)\n", + "ax.text(\n", + " -41, 3.7, r'$\\left(1:0:0\\right)_\\text{S}\\text{\\:w/\\:}\\mathcal{O}_{\\mu\\tau}\\text{\\:texture}$',\n", + " fontsize=fontsize-3, color='b'\n", + ")\n", + "\n", + "ax.axhline(y=np.log(10**(plot_utils.BAYES_K)), color='k', linewidth=1.5, ls='--')\n", + "ax.text(\n", + " -54, 2.6, r'$B_{0/1}='+r'{0}$'.format(int(10**(plot_utils.BAYES_K))),\n", + " fontsize=fontsize-3, color='k'\n", + ")\n", + "ax.vlines(lim_010, ymin=-10, ymax=np.log(10**plot_utils.BAYES_K), color='g', linewidth=1.5, linestyles='--')\n", + "ax.vlines(lim_100, ymin=-10, ymax=np.log(10**plot_utils.BAYES_K), color='b', linewidth=1.5, linestyles='--')\n", + "\n", + "ax.text(\n", + " -55, 11, r'${\\rm\\bf IceCube\\:Preliminary}$', fontsize=fontsize, color='r'\n", + ")\n", + "\n", + "matplotlib2tikz.save(\"plots/tex/bayes_factor.tex\")" + ] + } + ], + "metadata": { + "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": 2 +} |