{
 "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",
    "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.8?urlpath=lab/tree/cta_sensitivity.ipynb)\n",
    "- You can contribute with your own notebooks in this\n",
    "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\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.irf.CTAIrf](..\/api/gammapy.irf.CTAIrf.rst)\n",
    "* [gammapy.spectrum.SensitivityEstimator](..\/api/gammapy.spectrum.SensitivityEstimator.rst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "As usual, we'll start with some setup ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.irf import CTAPerf\n",
    "from gammapy.spectrum import SensitivityEstimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load IRFs\n",
    "\n",
    "First load 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": [
    "sensitivity_estimator = SensitivityEstimator(irf=irf, livetime=\"5h\")\n",
    "sensitivity_estimator.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "The results are given as an Astropy table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=21</i>\n",
       "<table id=\"table4565597656\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>energy</th><th>e2dnde</th><th>excess</th><th>background</th><th>criterion</th></tr></thead>\n",
       "<thead><tr><th>TeV</th><th>erg / (cm2 s)</th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float32</th><th>float64</th><th>float64</th><th>float32</th><th>str12</th></tr></thead>\n",
       "<tr><td>0.0158489</td><td>1.26569e-10</td><td>339.143</td><td>3703.48</td><td>significance</td></tr>\n",
       "<tr><td>0.0251189</td><td>2.41235e-11</td><td>311.106</td><td>3106.66</td><td>significance</td></tr>\n",
       "<tr><td>0.0398107</td><td>1.5914e-11</td><td>459.213</td><td>6852.06</td><td>significance</td></tr>\n",
       "<tr><td>0.0630957</td><td>4.26714e-12</td><td>163.204</td><td>825.794</td><td>significance</td></tr>\n",
       "<tr><td>0.1</td><td>3.04454e-12</td><td>169.361</td><td>891.645</td><td>significance</td></tr>\n",
       "<tr><td>0.158489</td><td>1.55368e-12</td><td>90.0926</td><td>236.905</td><td>significance</td></tr>\n",
       "<tr><td>0.251189</td><td>1.0771e-12</td><td>51.5349</td><td>69.8381</td><td>significance</td></tr>\n",
       "<tr><td>0.398107</td><td>7.83236e-13</td><td>35.6905</td><td>29.8996</td><td>significance</td></tr>\n",
       "<tr><td>0.630957</td><td>5.93807e-13</td><td>26.4005</td><td>14.2506</td><td>significance</td></tr>\n",
       "<tr><td>1</td><td>4.28759e-13</td><td>18.1072</td><td>5.11857</td><td>significance</td></tr>\n",
       "<tr><td>1.58489</td><td>3.62852e-13</td><td>15.6871</td><td>3.31032</td><td>significance</td></tr>\n",
       "<tr><td>2.51189</td><td>3.21257e-13</td><td>11.7016</td><td>1.17059</td><td>significance</td></tr>\n",
       "<tr><td>3.98107</td><td>3.39152e-13</td><td>12.0962</td><td>1.33607</td><td>significance</td></tr>\n",
       "<tr><td>6.30957</td><td>3.9511e-13</td><td>10</td><td>0.424068</td><td>gamma</td></tr>\n",
       "<tr><td>10</td><td>5.65043e-13</td><td>10.898</td><td>0.865076</td><td>significance</td></tr>\n",
       "<tr><td>15.8489</td><td>8.36566e-13</td><td>10</td><td>0.216136</td><td>gamma</td></tr>\n",
       "<tr><td>25.1189</td><td>1.26771e-12</td><td>10</td><td>0.00979249</td><td>gamma</td></tr>\n",
       "<tr><td>39.8107</td><td>2.00893e-12</td><td>10</td><td>0.0053102</td><td>gamma</td></tr>\n",
       "<tr><td>63.0957</td><td>3.24246e-12</td><td>10</td><td>0.00170479</td><td>gamma</td></tr>\n",
       "<tr><td>100</td><td>5.10213e-12</td><td>10</td><td>0.00101395</td><td>gamma</td></tr>\n",
       "<tr><td>158.489</td><td>9.04831e-12</td><td>10</td><td>0.00566093</td><td>gamma</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=21>\n",
       "  energy      e2dnde     excess background  criterion  \n",
       "   TeV    erg / (cm2 s)                                \n",
       " float32     float64    float64  float32      str12    \n",
       "--------- ------------- ------- ---------- ------------\n",
       "0.0158489   1.26569e-10 339.143    3703.48 significance\n",
       "0.0251189   2.41235e-11 311.106    3106.66 significance\n",
       "0.0398107    1.5914e-11 459.213    6852.06 significance\n",
       "0.0630957   4.26714e-12 163.204    825.794 significance\n",
       "      0.1   3.04454e-12 169.361    891.645 significance\n",
       " 0.158489   1.55368e-12 90.0926    236.905 significance\n",
       " 0.251189    1.0771e-12 51.5349    69.8381 significance\n",
       " 0.398107   7.83236e-13 35.6905    29.8996 significance\n",
       " 0.630957   5.93807e-13 26.4005    14.2506 significance\n",
       "        1   4.28759e-13 18.1072    5.11857 significance\n",
       "  1.58489   3.62852e-13 15.6871    3.31032 significance\n",
       "  2.51189   3.21257e-13 11.7016    1.17059 significance\n",
       "  3.98107   3.39152e-13 12.0962    1.33607 significance\n",
       "  6.30957    3.9511e-13      10   0.424068        gamma\n",
       "       10   5.65043e-13  10.898   0.865076 significance\n",
       "  15.8489   8.36566e-13      10   0.216136        gamma\n",
       "  25.1189   1.26771e-12      10 0.00979249        gamma\n",
       "  39.8107   2.00893e-12      10  0.0053102        gamma\n",
       "  63.0957   3.24246e-12      10 0.00170479        gamma\n",
       "      100   5.10213e-12      10 0.00101395        gamma\n",
       "  158.489   9.04831e-12      10 0.00566093        gamma"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the results table\n",
    "sensitivity_estimator.results_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save it to file (could use e.g. format of CSV or ECSV or FITS)\n",
    "# sensitivity_estimator.results_table.write('sensitivity.ecsv', format='ascii.ecsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the sensitivity curve\n",
    "t = sensitivity_estimator.results_table\n",
    "\n",
    "is_s = t[\"criterion\"] == \"significance\"\n",
    "plt.plot(\n",
    "    t[\"energy\"][is_s],\n",
    "    t[\"e2dnde\"][is_s],\n",
    "    \"s-\",\n",
    "    color=\"red\",\n",
    "    label=\"significance\",\n",
    ")\n",
    "\n",
    "is_g = t[\"criterion\"] == \"gamma\"\n",
    "plt.plot(\n",
    "    t[\"energy\"][is_g], t[\"e2dnde\"][is_g], \"*-\", color=\"blue\", label=\"gamma\"\n",
    ")\n",
    "\n",
    "plt.loglog()\n",
    "plt.xlabel(\"Energy ({})\".format(t[\"energy\"].unit))\n",
    "plt.ylabel(\"Sensitivity ({})\".format(t[\"e2dnde\"].unit))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "* Also compute the sensitivity for a 20 hour observation\n",
    "* Compare how the sensitivity differs between 5 and 20 hours by plotting the ratio as a function of energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "nbsphinx": {
   "orphan": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}