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 "cells": [
  {
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   "metadata": {},
   "source": [
    "# Data preparation for 1DC How-tos"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<div align=\"right\"><a href=\"../../../_downloads/howto_prepare_1dc.ipynb\" download=\"howto_prepare_1dc.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial assumes that you have downloaded the data for the first CTA Data Challenge. If this is not the case, please read first [how to get the 1DC data](../1dc/getting_data.rst).\n",
    "\n",
    "Start by importing the relevant Python modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gammalib\n",
    "import ctools\n",
    "import cscripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now set the `CTADATA` and `CALDB` environment variables. Please adjust the path below so that it points to the relevant location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CTADATA=/project-data/cta/data/1dc\n",
      "env: CALDB=/project-data/cta/data/1dc/caldb\n"
     ]
    }
   ],
   "source": [
    "%env CTADATA=/project-data/cta/data/1dc\n",
    "%env CALDB=/project-data/cta/data/1dc/caldb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now prepare a dataset that comprises the Galactic Centre observations that have been performed during the Galactic Plane Scan. Start with selecting the observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "obsselect = cscripts.csobsselect()\n",
    "obsselect['inobs']     = '$CTADATA/obs/obs_gps_baseline.xml'\n",
    "obsselect['pntselect'] = 'CIRCLE'\n",
    "obsselect['coordsys']  = 'GAL'\n",
    "obsselect['glon']      = 0.0\n",
    "obsselect['glat']      = 0.0\n",
    "obsselect['rad']       = 3.0\n",
    "obsselect['tmin']      = 'NONE'\n",
    "obsselect['tmax']      = 'NONE'\n",
    "obsselect['outobs']    = 'obs.xml'\n",
    "obsselect.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now select the events with energies comprised between 1 and 100 TeV from the observations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "select = ctools.ctselect()\n",
    "select['inobs']   = 'obs.xml'\n",
    "select['ra']      = 'NONE'\n",
    "select['dec']     = 'NONE'\n",
    "select['rad']     = 'NONE'\n",
    "select['tmin']    = 'NONE'\n",
    "select['tmax']    = 'NONE'\n",
    "select['emin']    = 1.0\n",
    "select['emax']    = 100.0\n",
    "select['outobs']  = 'obs_selected.xml'\n",
    "select.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to stack the selected events into a counts cube."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "binning = ctools.ctbin()\n",
    "binning['inobs']    = 'obs_selected.xml'\n",
    "binning['xref']     = 0.0\n",
    "binning['yref']     = 0.0\n",
    "binning['coordsys'] = 'GAL'\n",
    "binning['proj']     = 'CAR'\n",
    "binning['binsz']    = 0.02\n",
    "binning['nxpix']    = 300\n",
    "binning['nypix']    = 300\n",
    "binning['ebinalg']  = 'LOG'\n",
    "binning['emin']     = 1.0\n",
    "binning['emax']     = 100.0\n",
    "binning['enumbins'] = 20\n",
    "binning['outobs']   = 'cntcube.fits'\n",
    "binning.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now compute the corresponding stacked exposure cube, point spread function cube and background cube."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "expcube = ctools.ctexpcube()\n",
    "expcube['inobs']   = 'obs_selected.xml'\n",
    "expcube['incube']  = 'cntcube.fits'\n",
    "expcube['outcube'] = 'expcube.fits'\n",
    "expcube.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "psfcube = ctools.ctpsfcube()\n",
    "psfcube['inobs']    = 'obs_selected.xml'\n",
    "psfcube['incube']   = 'NONE'\n",
    "psfcube['ebinalg']  = 'LOG'\n",
    "psfcube['emin']     = 1.0\n",
    "psfcube['emax']     = 100.0\n",
    "psfcube['enumbins'] = 20\n",
    "psfcube['nxpix']    = 10\n",
    "psfcube['nypix']    = 10\n",
    "psfcube['binsz']    = 1.0\n",
    "psfcube['coordsys'] = 'GAL'\n",
    "psfcube['proj']     = 'CAR'\n",
    "psfcube['xref']     = 0.0\n",
    "psfcube['yref']     = 0.0\n",
    "psfcube['outcube']  = 'psfcube.fits'\n",
    "psfcube.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bkgcube = ctools.ctbkgcube()\n",
    "bkgcube['inobs']    = 'obs_selected.xml'\n",
    "bkgcube['inmodel']  = '$CTOOLS/share/models/bkg_irf.xml'\n",
    "bkgcube['incube']   = 'cntcube.fits'\n",
    "bkgcube['outcube']  = 'bkgcube.fits'\n",
    "bkgcube['outmodel'] = 'bkgcube.xml' \n",
    "bkgcube.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you are done. All data structures are prepared for the following tutorials."
   ]
  }
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