{
 "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",
    "[cube_analysis_part1.ipynb](../_static/notebooks/cube_analysis_part1.ipynb) |\n",
    "[cube_analysis_part1.py](../_static/notebooks/cube_analysis_part1.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cube analysis with Gammapy (part 1)\n",
    "\n",
    "## Introduction \n",
    "\n",
    "In order to run a cube likelihood analysis (combined spectrum and morphology fitting), we first have to prepare the dataset and compute the following inputs by stacking data from observation runs:\n",
    "* counts, background and exposure cube\n",
    "* mean PSF\n",
    "* mean RMF\n",
    "\n",
    "These are the inputs to the likelihood fitting step, where you assume a parameterised gamma-ray emission model and fit it to the stacked data.\n",
    "\n",
    "This notebook is part 1 of 2 of a tutorial how to use [gammapy.cube](http://docs.gammapy.org/dev/cube/index.html) to do cube analysis of IACT data. Part 1 is about these pre-computations, part 2 will be about the likelihood fitting.\n",
    "\n",
    "We will be using the following classes:\n",
    "* [gammapy.data.DataStore](http://docs.gammapy.org/dev/api/gammapy.data.DataStore.html) to load the data to stack in the cube\n",
    "* [gammapy.image.SkyImage](http://docs.gammapy.org/dev/api/gammapy.image.SkyImage.html) to work with sky images\n",
    "* [gammapy.cube.SkyCube](http://docs.gammapy.org/dev/api/gammapy.cube.SkyCube.html) and [gammapy.cube.StackedObsCubeMaker](http://docs.gammapy.org/dev/api/gammapy.cube.StackedObsCubeMaker.html) to stack the data in the Cube\n",
    "* [gammapy.data.ObservationList](http://docs.gammapy.org/dev/api/gammapy.data.ObservationList.html) to make the computation of the mean PSF and mean RMF from a set of runs.\n",
    "\n",
    "The data used in this tutorial is 4 Crab runs from H.E.S.S.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Setup\n",
    "\n",
    "As always, we start with some setup for the notebook, and with imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.getLogger().setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord, Angle\n",
    "from astropy.visualization import simple_norm\n",
    "\n",
    "from gammapy.data import DataStore\n",
    "from gammapy.image import SkyImage\n",
    "from gammapy.cube import SkyCube, StackedObsCubeMaker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n",
    "\n",
    "Gammapy only supports binned likelihood fits, so we start by defining the spatial and energy grid of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define WCS specification\n",
    "WCS_SPEC = {'nxpix': 50,\n",
    "            'nypix': 50,\n",
    "            'binsz': 0.05,\n",
    "            'xref': 83.63,\n",
    "            'yref': 22.01,\n",
    "            'proj': 'TAN',\n",
    "            'coordsys': 'CEL'}\n",
    "\n",
    "# define reconstructed energy binning\n",
    "ENERGY_SPEC = {'mode': 'edges',\n",
    "               'enumbins': 5,\n",
    "               'emin': 0.5,\n",
    "               'emax': 40,\n",
    "               'eunit': 'TeV'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we create an empty `SkyCube` object, that we will use as reference for all other sky cube data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instanciate reference cube\n",
    "REF_CUBE = SkyCube.empty(**WCS_SPEC, **ENERGY_SPEC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create a `DataStore` object, which allows us to access and load DL3 level data. In this case we will use four Crab runs of simulated HESS data, which is bundled in gammapy-extra:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up the data store\n",
    "data_store = DataStore.from_dir(\"$GAMMAPY_EXTRA/test_datasets/cube/data\")\n",
    "\n",
    "# temporary fix for load psftable for one of the run that is not implemented yet...\n",
    "data_store.hdu_table.remove_row(14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the later background estimation we define an exclusion mask. The background image will normalized on the counts map outside the exclusion region (the so called FOV background method). For convenience we use and all-sky exclusin mask, which was created from the TeVCat catalog and reproject it to our analysis region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in TeVCat exclusion mask\n",
    "exclusion_mask = SkyImage.read('$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits')\n",
    "\n",
    "# reproject exclusion mask to reference cube\n",
    "exclusion_mask = exclusion_mask.reproject(reference=REF_CUBE.sky_image_ref, order='nearest-neighbor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's what the exclusion mask looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rIqqS0IqIqiS0IqIqCa2IqEpCKyKqktCKiKoktCKiKgmtiKhKQisiqpLQioiqJLQioioJ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\n5f8DkALnXGMhM/4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d7d3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exclusion_mask.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can feed all the input to the `StackedObsCubeMaker` class. In addition we have to select a offset cut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select the offset band on which you want to select the events in the FOV of each observation\n",
    "offset_band = Angle([0, 2.49], 'deg')\n",
    "\n",
    "# instanciate StackedObsCubeMaker\n",
    "cube_maker = StackedObsCubeMaker(\n",
    "    empty_cube_images=REF_CUBE,\n",
    "    empty_exposure_cube=REF_CUBE,\n",
    "    offset_band=offset_band,\n",
    "    data_store=data_store,\n",
    "    obs_table=data_store.obs_table,\n",
    "    exclusion_mask=exclusion_mask,\n",
    "    save_bkg_scale=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run the cube maker by calling `.make_cubes`. We pass an correlation radius of 4 pixels to compute the Li&Ma significance image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run cube maker\n",
    "cube_maker.make_cubes(make_background_image=True, radius=4.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the computed result cubes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bin edges of the count cubes: \n",
      "[  0.5          1.20112443   2.88539981   6.93144843  16.65106415  40.        ] TeV\n"
     ]
    }
   ],
   "source": [
    "# get counts cube\n",
    "counts = cube_maker.counts_cube\n",
    "\n",
    "energies = counts.energies(mode=\"edges\")\n",
    "print(\"Bin edges of the count cubes: \\n{0}\".format(energies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vRcTKiFgJ3B0RT5mpAklPSesiaT+aIOb/ozk1f56kndP/kM9LZX36HrAq3dXa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db152b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy range: 0.50 TeV to 1.20 TeV\n"
     ]
    }
   ],
   "source": [
    "# show counts image with idx = 0\n",
    "idx = 0\n",
    "counts.sky_image_idx(idx=idx).show()\n",
    "print('Energy range: {emin:.2f} to {emax:.2f}'.format(emin=energies[idx], emax=energies[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc53a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy range: 6.93 TeV to 16.65 TeV\n"
     ]
    }
   ],
   "source": [
    "idx = 3\n",
    "counts.sky_image_idx(idx=idx).show()\n",
    "print('Energy range: {emin:.2f} to {emax:.2f}'.format(emin=energies[idx], emax=energies[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EsXLl3OVsVqgD77uvaVwaLvKzh6GjNpX6JB9xI7l0qulTpGkVtuuKYk2rQJsq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\nQUsI0SsUtIQQvUJBSwjRK/4/x6HidljF50gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df47080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy range: 0.50 TeV to 1.20 TeV\n"
     ]
    }
   ],
   "source": [
    "background = cube_maker.bkg_cube\n",
    "\n",
    "idx = 0\n",
    "background.sky_image_idx(idx=idx).show()\n",
    "print('Energy range: {emin:.2f} to {emax:.2f}'.format(emin=energies[idx], emax=energies[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uiIiNzd/XbzmH5n+yHYBjpgrTFPu/AL9LM2OA5n/FW0babkhlRMTT+jmj7DEP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc50c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy range: 0.50 TeV to 1.20 TeV\n"
     ]
    }
   ],
   "source": [
    "excess = cube_maker.excess_cube\n",
    "\n",
    "idx = 0\n",
    "excess.sky_image_idx(idx=idx).show()\n",
    "print('Energy range: {emin:.2f} to {emax:.2f}'.format(emin=energies[idx], emax=energies[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5nAfsM813FwN7j1e6oij6yIL08hArHbULZbQYdZ/sVmARpQOpdFF3y2x/nlnx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\nqEmrKIpeUZNWURS9oiatoih6xf8HDYJveSbkWh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df84b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy range: 0.50 TeV to 1.20 TeV\n"
     ]
    }
   ],
   "source": [
    "significance = cube_maker.significance_cube\n",
    "\n",
    "idx = 0\n",
    "significance.sky_image_idx(idx=idx).show()\n",
    "print('Energy range: {emin:.2f} to {emax:.2f}'.format(emin=energies[idx], emax=energies[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CiE6xLDWteH0dl2uu2/Pr/eTCvJXJmuhOadnGmuTSGk2rpspDzZReUJcAGm21yaU1lUv7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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc5bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy range: 0.50 TeV to 1.20 TeV\n"
     ]
    }
   ],
   "source": [
    "exposure = cube_maker.exposure_cube\n",
    "\n",
    "idx = 0\n",
    "exposure.sky_image_idx(idx=idx).show()\n",
    "print('Energy range: {emin:.2f} to {emax:.2f}'.format(emin=energies[idx], emax=energies[idx + 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a last step we'de like to compute a mean PSF across all observations in the list. For this purpose we use the `.make_mean_psf_cube()` method on the `StackedObsCubeMaker` object. We only have to specify a reference cube, which is used to evaluate the PSF model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with np.errstate(divide='ignore', invalid='ignore'):\n",
    "    mean_psf_cube = cube_maker.make_mean_psf_cube(REF_CUBE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick look a the resulting PSF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0f45f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy range: 6.93 TeV to 16.65 TeV\n"
     ]
    }
   ],
   "source": [
    "idx = 3\n",
    "psf_image = mean_psf_cube.sky_image_idx(idx=idx)\n",
    "norm = simple_norm(psf_image.data, stretch='log')\n",
    "psf_image.show(norm=norm)\n",
    "print('Energy range: {emin:.2f} to {emax:.2f}'.format(emin=energies[idx], emax=energies[idx + 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we repeat the computation but assume a different energy binning for the \"true\" energy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define true energy binning\n",
    "ENERGY_TRUE_SPEC = {'mode': 'edges',\n",
    "                    'enumbins': 20,\n",
    "                    'emin': 0.1,\n",
    "                    'emax': 100,\n",
    "                    'eunit': 'TeV'}\n",
    "\n",
    "# init refereence cube\n",
    "REF_CUBE_TRUE = SkyCube.empty(**WCS_SPEC, **ENERGY_TRUE_SPEC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reference cube is now passed to the `empty_exposure_cube` argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cube_maker = StackedObsCubeMaker(\n",
    "    empty_cube_images=REF_CUBE,\n",
    "    empty_exposure_cube=REF_CUBE_TRUE,\n",
    "    offset_band=offset_band,\n",
    "    data_store=data_store,\n",
    "    obs_table=data_store.obs_table,\n",
    "    exclusion_mask=exclusion_mask,\n",
    "    save_bkg_scale=True,\n",
    ")\n",
    "cube_maker.make_cubes(make_background_image=True, radius=10.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PSF can computed using the \"true\" energy reference cube as well: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "with np.errstate(divide='ignore', invalid='ignore'):\n",
    "    mean_psf_cube = cube_maker.make_mean_psf_cube(REF_CUBE_TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy bin edges of the count cube: \n",
      "[  0.5          1.20112443   2.88539981   6.93144843  16.65106415  40.        ] TeV\n",
      "\n",
      "Energy bin edges of the exposure cube: \n",
      "[  1.00000000e-01   1.41253754e-01   1.99526231e-01   2.81838293e-01\n",
      "   3.98107171e-01   5.62341325e-01   7.94328235e-01   1.12201845e+00\n",
      "   1.58489319e+00   2.23872114e+00   3.16227766e+00   4.46683592e+00\n",
      "   6.30957344e+00   8.91250938e+00   1.25892541e+01   1.77827941e+01\n",
      "   2.51188643e+01   3.54813389e+01   5.01187234e+01   7.07945784e+01\n",
      "   1.00000000e+02] TeV\n"
     ]
    }
   ],
   "source": [
    "e_reco = cube_maker.counts_cube.energies(mode=\"edges\")\n",
    "print(\"Energy bin edges of the count cube: \\n{}\\n\".format(e_reco))\n",
    "\n",
    "e_true = cube_maker.exposure_cube.energies(mode=\"edges\")\n",
    "print(\"Energy bin edges of the exposure cube: \\n{}\".format(e_true))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we compute the mean RMF (energy dispersion matrix) across multiple observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get reference energies\n",
    "obs_list = data_store.obs_list(data_store.obs_table['OBS_ID'])\n",
    "position = REF_CUBE.sky_image_ref.center\n",
    "e_true = REF_CUBE_TRUE.energies('edges')\n",
    "e_reco = REF_CUBE.energies('edges')\n",
    "\n",
    "with np.errstate(divide='ignore', invalid='ignore'):\n",
    "    mean_rmf = obs_list.make_mean_edisp(position=position, e_true=e_true, e_reco=e_reco)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/colors.py:1152: UserWarning: Power-law scaling on negative values is ill-defined, clamping to 0.\n",
      "  warnings.warn(\"Power-law scaling on negative values is \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e0ecef0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/colors.py:1113: RuntimeWarning: invalid value encountered in power\n",
      "  np.power(resdat, gamma, resdat)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df7c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_rmf.plot_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "- Take another dataset\n",
    "- Change the Cube binning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now that we created some cubes for counts, background, exposure, psf from a set of runs we will learn in the future tutorials how to use it for a 3D analysis (morphological and spectral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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