{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<script type=\"text/javascript\" src=\"../_static/linksdl.js\"></script>\n",
    "<div class='alert alert-info'>\n",
    "**This is a fixed-text formatted version of a Jupyter notebook.**\n",
    "\n",
    " You can contribute with your own notebooks in this\n",
    " [GitHub repository](https://github.com/gammapy/gammapy-extra/tree/master/notebooks).\n",
    "\n",
    "**Source files:**\n",
    "[cta_sensitivity.ipynb](../_static/notebooks/cta_sensitivity.ipynb) |\n",
    "[cta_sensitivity.py](../_static/notebooks/cta_sensitivity.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computation of the CTA sensitivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook explains how to derive the CTA sensitivity for a point-like IRF at a fixed zenith angle and fixed offset. The significativity is computed for the 1D analysis (On-OFF regions) and the LiMa formula.\n",
    "\n",
    "We will be using the following Gammapy classes:\n",
    "\n",
    "* [gammapy.scripts.CTAIrf](http://docs.gammapy.org/dev/api/gammapy.scripts.CTAIrf.html)\n",
    "* [gammapy.scripts.SensitivityEstimator](http://docs.gammapy.org/dev/api/gammapy.scripts.SensitivityEstimator.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "As usual, we'll start with some setup ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.scripts import CTAPerf, SensitivityEstimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load IRFs\n",
    "\n",
    "First import the CTA IRFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '$GAMMAPY_EXTRA/datasets/cta/perf_prod2/point_like_non_smoothed/South_5h.fits.gz'\n",
    "irf = CTAPerf.read(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute sensitivity\n",
    "\n",
    "Choose a few parameters, then run the sentitivity computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens = SensitivityEstimator(\n",
    "    irf=irf,\n",
    "    livetime='5h',\n",
    ")\n",
    "sens.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print and plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gammapy.scripts.cta_sensitivity:** Sensitivity **\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ENERGY         FLUX      \n",
      "   TeV      erg / (cm2 s)  \n",
      "--------- -----------------\n",
      "0.0158489 1.22353358828e-10\n",
      "0.0251189 2.40363677936e-11\n",
      "0.0398107 1.59122601854e-11\n",
      "0.0630957 4.26737356505e-12\n",
      "      0.1 3.04466326156e-12\n",
      " 0.158489 1.55301371261e-12\n",
      " 0.251189 1.07742785338e-12\n",
      " 0.398107 7.82250643556e-13\n",
      " 0.630957 5.93610676157e-13\n",
      "      1.0 4.27244154065e-13\n",
      "  1.58489 3.63853163606e-13\n",
      "  2.51189 3.20427969785e-13\n",
      "  3.98107 3.37974403708e-13\n",
      "  6.30957  3.9510983913e-13\n",
      "     10.0 5.18481585434e-13\n",
      "  15.8489 8.36566298046e-13\n",
      "  25.1189 1.26771004802e-12\n",
      "  39.8107  2.0089332373e-12\n",
      "  63.0957 3.24247655159e-12\n",
      "    100.0 5.10229012813e-12\n",
      "  158.489 9.04770579058e-12\n"
     ]
    }
   ],
   "source": [
    "sens.print_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10db22ac8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9e+Cii9wfhq+8wtZO0b0uPM9Eoqq1VLWOqtbO59a2KIMNVcLM2srSoIHr4rLu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\nGGPCYonEGGNMWCyRGGOMCcv/A+xV1I8NIneEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daf2dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sens.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=21&gt;\n",
       "<table id=\"table4527514568\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ENERGY</th><th>FLUX</th></tr></thead>\n",
       "<thead><tr><th>TeV</th><th>erg / (cm2 s)</th></tr></thead>\n",
       "<thead><tr><th>float32</th><th>float64</th></tr></thead>\n",
       "<tr><td>0.0158489</td><td>1.22353358828e-10</td></tr>\n",
       "<tr><td>0.0251189</td><td>2.40363677936e-11</td></tr>\n",
       "<tr><td>0.0398107</td><td>1.59122601854e-11</td></tr>\n",
       "<tr><td>0.0630957</td><td>4.26737356505e-12</td></tr>\n",
       "<tr><td>0.1</td><td>3.04466326156e-12</td></tr>\n",
       "<tr><td>0.158489</td><td>1.55301371261e-12</td></tr>\n",
       "<tr><td>0.251189</td><td>1.07742785338e-12</td></tr>\n",
       "<tr><td>0.398107</td><td>7.82250643556e-13</td></tr>\n",
       "<tr><td>0.630957</td><td>5.93610676157e-13</td></tr>\n",
       "<tr><td>1.0</td><td>4.27244154065e-13</td></tr>\n",
       "<tr><td>1.58489</td><td>3.63853163606e-13</td></tr>\n",
       "<tr><td>2.51189</td><td>3.20427969785e-13</td></tr>\n",
       "<tr><td>3.98107</td><td>3.37974403708e-13</td></tr>\n",
       "<tr><td>6.30957</td><td>3.9510983913e-13</td></tr>\n",
       "<tr><td>10.0</td><td>5.18481585434e-13</td></tr>\n",
       "<tr><td>15.8489</td><td>8.36566298046e-13</td></tr>\n",
       "<tr><td>25.1189</td><td>1.26771004802e-12</td></tr>\n",
       "<tr><td>39.8107</td><td>2.0089332373e-12</td></tr>\n",
       "<tr><td>63.0957</td><td>3.24247655159e-12</td></tr>\n",
       "<tr><td>100.0</td><td>5.10229012813e-12</td></tr>\n",
       "<tr><td>158.489</td><td>9.04770579058e-12</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=21>\n",
       "  ENERGY         FLUX      \n",
       "   TeV      erg / (cm2 s)  \n",
       " float32       float64     \n",
       "--------- -----------------\n",
       "0.0158489 1.22353358828e-10\n",
       "0.0251189 2.40363677936e-11\n",
       "0.0398107 1.59122601854e-11\n",
       "0.0630957 4.26737356505e-12\n",
       "      0.1 3.04466326156e-12\n",
       " 0.158489 1.55301371261e-12\n",
       " 0.251189 1.07742785338e-12\n",
       " 0.398107 7.82250643556e-13\n",
       " 0.630957 5.93610676157e-13\n",
       "      1.0 4.27244154065e-13\n",
       "  1.58489 3.63853163606e-13\n",
       "  2.51189 3.20427969785e-13\n",
       "  3.98107 3.37974403708e-13\n",
       "  6.30957  3.9510983913e-13\n",
       "     10.0 5.18481585434e-13\n",
       "  15.8489 8.36566298046e-13\n",
       "  25.1189 1.26771004802e-12\n",
       "  39.8107  2.0089332373e-12\n",
       "  63.0957 3.24247655159e-12\n",
       "    100.0 5.10229012813e-12\n",
       "  158.489 9.04770579058e-12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will give you the results as an Astropy table,\n",
    "# which you can save to FITS or CSV or use for further analysis\n",
    "sens.diff_sensi_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "* tbd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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