source_injector.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "a0226ddc",
   "metadata": {},
   "source": [
    "# Source injector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3cf1f63-9d4c-4922-9555-c6a33bdc6fc1",
   "metadata": {},
   "source": [
    "The \"source injector\" is a cosipy module that will generate mocked binned data based on the detector response and a source hypothesis (provided by the users). This should result in the same output as simulating the source using MEGAlib, but be much quicker. MEGAlib is only needed when the event-by-event data is required, or to create the detector response itself. \n",
    "\n",
    "The goal of this notebook is to get an idea how the source injector will work in practice. We need to take it from here to something that is user friendly and compatible with the rest of the modules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12989d27-f8eb-4764-947f-e5197b13b3b5",
   "metadata": {},
   "source": [
    "First, let's load all dependecies:"
   ]
  },
  {
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   "id": "1863fe19-1d2b-4d9d-aacc-6e1eba99b882",
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   "source": [
    "# We'll use histpy's Histogram and mhealpy's HealpixMap as the basis\n",
    "# develop cosipy. These object (or a derivative) will be passed around by \n",
    "# the different modules.\n",
    "from histpy import Histogram\n",
    "from mhealpy import HealpixMap\n",
    "from scoords import SpacecraftFrame, Attitude\n",
    "\n",
    "# Needed for coordinate conversion.\n",
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "# cosipy uses astropy units\n",
    "import astropy.units as u\n",
    "\n",
    "#Other standard libraries\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e713e7ed-3956-403e-919d-5f0e1330d1ce",
   "metadata": {},
   "source": [
    "## Signal from a source"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe2b03c-e736-46e2-bcbb-bfb8d3fc3424",
   "metadata": {},
   "source": [
    "Here we obtain the expected number of counts for a given source hypothesis.\n",
    "\n",
    "We need the detector response, which can convert from physical values --e.g. source position, spectrum-- to detected counts. For this tutorial we'll use the detector response for an idealized detector, called the \"Compton sphere. The goals of this tutorial is not to get realistics simulations, but to understand the mechanics of of the code.. The response can be found in the sftp server at `/uploads/cosipy/test_data/FlatContinuumIsotropic.LowRes.binnedimaging.imagingresponse.area.nside8.cosipy.h5.zip` (remember to unzip it). "
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5ff96a4",
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   "outputs": [
    {
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      "/Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/numba-0.57.0rc1-py3.10-macosx-10.9-x86_64.egg/numba/np/ufunc/dufunc.py:84: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  dispatcher = jit(_target='npyufunc',\n"
     ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">03:46:31 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The naima package is not available. Models that depend on it will not be         </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py#48\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">48</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                         </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
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       "\u001b[38;5;46m03:46:31\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=342730;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=444799;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py#69\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">69</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">will not be available.                                                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=878118;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=453808;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/functions.py#69\u001b\\\u001b[2m69\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mwill not be available.                                                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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      "/Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/numba-0.57.0rc1-py3.10-macosx-10.9-x86_64.egg/numba/core/decorators.py:262: NumbaDeprecationWarning: \u001b[1mnumba.generated_jit is deprecated. Please see the documentation at: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-generated-jit for more information and advice on a suitable replacement.\u001b[0m\n",
      "  warnings.warn(msg, NumbaDeprecationWarning)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The ebltable package is not available. Models that depend on it will not be     </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/absorption.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">absorption.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/absorption.py#36\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">36</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                        </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The ebltable package is not available. Models that depend on it will not be    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=375059;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/absorption.py\u001b\\\u001b[2mabsorption.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=150851;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/functions/functions_1D/absorption.py#36\u001b\\\u001b[2m36\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                \u001b[0m\n"
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      "/Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/numba-0.57.0rc1-py3.10-macosx-10.9-x86_64.egg/numba/np/ufunc/dufunc.py:84: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  dispatcher = jit(_target='npyufunc',\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">03:46:32 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> We have set the min_value of K to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1e-99</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> because there was a postive transform   </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">704</span></a>\n",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> We have set the min_value of K to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1e-99</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> because there was a postive transform   </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">704</span></a>\n",
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=369448;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=202717;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> We have set the min_value of K to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1e-99</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> because there was a postive transform   </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">704</span></a>\n",
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=81293;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=586458;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> We have set the min_value of K to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1e-99</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> because there was a postive transform   </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">704</span></a>\n",
       "</pre>\n"
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=527526;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=881648;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> We have set the min_value of F to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1e-99</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> because there was a postive transform   </span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">704</span></a>\n",
       "</pre>\n"
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of F to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=153981;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=768809;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
      ],
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=470813;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=648880;file:///Users/ckierans/Software/COSItools/astromodels-2.4.0/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m but are inform you about optional packages that can be installed                 \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=606590;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=213595;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#37\u001b\\\u001b[2m37\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=271693;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=712682;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n"
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    {
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     "text": [
      "\n",
      "WARNING NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The cthreeML package is not installed. You will not be able to use plugins which  </span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#94\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">94</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">require the C/C++ interface (currently HAWC)                                       </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
       "</pre>\n"
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=835924;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=327807;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m                                      \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m              \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin HAWCLike.py. Do you have the relative instrument         </span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#144\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">144</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=459659;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=622022;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin FermiLATLike.py. Do you have the relative instrument     </span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#144\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">144</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
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       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=326198;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=278429;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
      ]
     },
     "metadata": {},
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">03:46:33 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> No fermitools installed                                              </span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lat_transient_builder.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[38;5;46m03:46:33\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=805049;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=971490;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">03:46:33 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable OMP_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;46m03:46:33\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=49739;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=155903;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable MKL_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=147812;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=494163;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal     </span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=475536;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=939003;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_path = \"/Users/ckierans/Software/COSItools/COSItools/intro_cosipy/test_data/FlatContinuumIsotropic.LowRes.binnedimaging.imagingresponse.area.nside8.cosipy.h5\"\n",
    "\n",
    "from cosipy.response import FullDetectorResponse\n",
    "\n",
    "response = FullDetectorResponse.open(response_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f2b36b-b919-4f8b-91ee-fb761590ad71",
   "metadata": {},
   "source": [
    "These is the source hypothesis. Let's inject a GRB-like event, lasting only 1s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c583e3b-6779-4712-94ef-d42b54ecf69f",
   "metadata": {},
   "outputs": [],
   "source": [
    "duration = 1*u.s\n",
    "\n",
    "# The attitude defines the rotation of the spacecraft with respect\n",
    "# to the inertial ICRS frame. During flight this information will come from the\n",
    "# spacecraft telemetry data, but for now let's assume the spacecraft is aligned with the ICRS.\n",
    "coord = SkyCoord(ra = 20*u.deg, dec = 40*u.deg, \n",
    "                 frame = 'icrs', \n",
    "                 attitude = Attitude.identity())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27769bb-e830-43ab-a372-52500d71985f",
   "metadata": {},
   "source": [
    "The detector response contains the response as a function of the incoming direction in the spacecraft coordinates. As the spacecraft (SC) rotates, the fixed sky location (RA, Dec) of a given source will move in SC coordinate system, and we need to integrate all locations weighted by the time spent in that direction. These is encoded in the \"dwell time map\", which will be produced by the \"spacecraft orientation\" cosipy module, based on the telemetry information. For our case though, we can assume the GRB occurred at a fixed location because the duration is very short. \n",
    "\n",
    "Side note: at some point I was incorrectly calling the \"dwell time map\" a \"exposure map\", which might be confusing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28548161",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dwell time map has the same pixelation (base) as the detector response.\n",
    "# We start with an empty map\n",
    "dwell_time_map = HealpixMap(base = response, \n",
    "                            unit = u.s, \n",
    "                            coordsys = SpacecraftFrame())\n",
    "\n",
    "# Here we add duration of the GRB, at the location where it happend (in spacecraft coordinates)\n",
    "# We use interpolation to get a better result\n",
    "pixels, weights = dwell_time_map.get_interp_weights(coord)\n",
    "\n",
    "for p,w in zip(pixels, weights):\n",
    "    dwell_time_map[p] += w*duration\n",
    "    \n",
    "# Without interpolating, it would look like these. Give it a try.\n",
    "# pixel = dwell_time_map.ang2pix(coord)\n",
    "# dwell_time_map[pixel] += duration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b32b999-486b-4c05-b608-2df0028a58f9",
   "metadata": {},
   "source": [
    "This is how the dwell time map looks. The red dot shows the exact location of the GRB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "718ff8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fea636de0e0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax = dwell_time_map.plot(coord = SpacecraftFrame(attitude = Attitude.identity()));\n",
    "\n",
    "ax.scatter(coord.ra, coord.dec, transform=ax.get_transform('world'), color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d532c332-d566-4787-8830-d42271c22442",
   "metadata": {},
   "source": [
    "The sum of all pixel in the dwell time map is simply the duration of the data that was integrated. In this case is just the duration of the GRB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6bbffb4e-853a-4925-8987-7cfd57d33029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$1 \\; \\mathrm{s}$"
      ],
      "text/plain": [
       "<Quantity 1. s>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dwell_time_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6baed9-14e7-48fc-84f5-af349de2b715",
   "metadata": {},
   "source": [
    "The detector response is then convolved with the dwell time map to get the point source response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2d58300-deae-4c26-9469-e1ac07fa8313",
   "metadata": {},
   "outputs": [],
   "source": [
    "psr = response.get_point_source_response(dwell_time_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f817be35-5398-48b9-b088-d308195a302f",
   "metadata": {},
   "source": [
    "The point source response is still quite generic. We obtained the response for a give location and duration, but we still convolved this with a given spectral assumption:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d93229f-7c8e-4c9a-a582-54cc73bf9eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from threeML import *\n",
    "from astropy.units import Quantity\n",
    "\n",
    "# This is not a very realistic spectrum, but help to illustrate the method.\n",
    "# You can play with other parameters. \n",
    "spectrum = Powerlaw()\n",
    "index = -1.7\n",
    "piv = 1 * u.keV\n",
    "K = 1e-1 / u.cm / u.cm / u.s / u.keV\n",
    "spectrum.index.value = index\n",
    "spectrum.K.value = K.value\n",
    "spectrum.piv.value = piv.value \n",
    "spectrum.K.unit = K.unit\n",
    "spectrum.piv.unit = piv.unit\n",
    "    \n",
    "# We project into the only event parameters that we can measure in COSI\n",
    "signal = psr.get_expectation(spectrum).project(['Em', 'Phi', 'PsiChi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6e60b5-65e4-4171-a8a7-e8e4f4d72008",
   "metadata": {},
   "source": [
    "The result `signal` is histogram that contains the expected counts in measured energy (`Em`) and the \"Compton Data Space\": Compton scatter angle (`Phi`) and direction (in SC coordinates) of the scattered photon in he (`PsiChi`). For reference, see the following figure from [this](https://arxiv.org/abs/2102.13158) paper. The only different is that here we are using spacecraft coordinate instead of galactic coordinates. ![](cds.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea7ffcb-d555-4da0-913a-87c7087b60c0",
   "metadata": {},
   "source": [
    "The `signal` object is a 3D histogram. Note that the last axis, `PsiChi`, is actually a 2D axis encoding the coordinates in a sphere as pixels in a HEALPix grid. So, in a sense, it's really a 4D histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "237a7410-e46a-45f0-ac49-36222ed3e719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "histpy.histogram.Histogram"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(signal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "545a83b9-eb40-4e00-86fe-a1a3a2848272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d821639-d2c7-42a8-9f3b-d6ef53268356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Em', 'Phi', 'PsiChi'], dtype='<U6')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal.axes.labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503cd496-8485-423e-a7f0-1aaac6745487",
   "metadata": {},
   "source": [
    "Let's explore this simulated signal and see how it looks.\n",
    "\n",
    "First, we can get the measured energy distribution by projecting the histogram into the `Em`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6bf0e6b-ff96-4b30-9d11-c31e1e6d625f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot: xlabel='Em [keV]'>, <ErrorbarContainer object of 3 artists>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "signal.project('Em').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05960a53-f5ee-41f0-a3d3-1cb41d141f64",
   "metadata": {},
   "source": [
    "This shape is a combination of the spectrum, the energy resolution, and the effective area of the detector as a function of energy.\n",
    "\n",
    "We can get the total number of events we expect from the GRB by summing over all bins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08eb4fae-d2e7-4336-82ca-cf8b301a1252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.8148381352382"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(signal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea9bc5f-3a6d-45ab-96ae-bd1d767f0df8",
   "metadata": {},
   "source": [
    "Now let's explore the CDS. It's easier to visualize if we take slices in energy and scatter angle. For reference, these are the bin edges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7b7bc781-6100-4123-b805-d4c3742aff04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[100,~200,~500,~1000,~2000,~5000] \\; \\mathrm{keV}$"
      ],
      "text/plain": [
       "<Quantity [ 100.,  200.,  500., 1000., 2000., 5000.] keV>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal.axes['Em'].edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1339c2c6-48b4-4ce0-9c6f-c33263b6bc03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0,~10,~20,~30,~40,~50,~60,~70,~80,~90,~100,~110,~120,~130,~140,~150,~160,~170,~180] \\; \\mathrm{{}^{\\circ}}$"
      ],
      "text/plain": [
       "<Quantity [  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90.,\n",
       "           100., 110., 120., 130., 140., 150., 160., 170., 180.] deg>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal.axes['Phi'].edges"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4b2946-6165-4d08-b045-d80f9292748a",
   "metadata": {},
   "source": [
    "This is the plot of the distribution of events within the energy range 1-2 MeV (bin 3) and scattered angles between 40-50deg (bin 4):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "db0b2090-2ad3-4807-87bc-9269b214d68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fea4993be20>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Since `PsiChi` is encoded as pixel in a HEALPix grid, we need mhealpy to render it back to a sphere\n",
    "m_signal = HealpixMap(signal.slice[{'Em':3, 'Phi':4}].project('PsiChi').todense().contents,\n",
    "                      coordsys = SpacecraftFrame())\n",
    "\n",
    "fig = plt.figure(dpi = 150)\n",
    "\n",
    "# Try also other projections, e.g. projection = 'mollview'\n",
    "ax = fig.add_subplot(projection = 'orthview')\n",
    "\n",
    "m_signal.plot(ax, coord = SpacecraftFrame(attitude = Attitude.identity()))\n",
    "\n",
    "# Location of the source\n",
    "ax.scatter(coord.ra, coord.dec, transform=ax.get_transform('world'), color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290a9873-c011-4bbd-a2e5-3605f0a56931",
   "metadata": {},
   "source": [
    "This is a horizontal slice of the Compton cone shown in the figure above, spread by detector effects and the finite size of the `Em` and `Phi` bins. Try selecting different `Phi` bins to see how these circle grows or shrinks, and relate that to the CDS figure.\n",
    "\n",
    "You can also try selecting different energy bins. The opening of the cone in the CDS is geometrically constrained and does not depend on the energy. This circle becomes more blurry at different energies though, which is related to the energy resolution and the bin width."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b32eb9-b8d4-49c1-9c38-52def6410278",
   "metadata": {},
   "source": [
    "## Getting a fake background"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90c008e-40b3-46b0-a20d-fd3d85c7c58c",
   "metadata": {},
   "source": [
    "The background from Compton telescopes can be complex, and in general we need to either simulate all the different components with MEGAlib and/or use real data to constrain it. For the purpose of having a toy background that we can use to develop our algorithms, let's use the detector response to simulate an (unrealistic) isotropic gamma-ray background. The final source injector should use a background model as input instead.\n",
    "\n",
    "We'll repurpose the point source convolution by generating an effective dwell time map with the same value for all pixels. Since all pixels have the same area, this is simulating an isotropic distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4700ced8-79da-4d5f-87e3-8771f050caa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "iso_map = HealpixMap(base = response, \n",
    "                     unit = u.s, \n",
    "                     coordsys = SpacecraftFrame())\n",
    "\n",
    "# Filling all pixels with a constant. The actual value doesn't\n",
    "# since we will renormalize it\n",
    "iso_map[:] = 1*u.s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0dcee61-d3e5-478c-a2a5-206b80812f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Non-realistic spectrum\n",
    "bkg_spectrum = Powerlaw()\n",
    "bkg_index = -2\n",
    "bkg_piv = 1 * u.keV\n",
    "bkg_K = 1 / u.cm / u.cm / u.s / u.keV\n",
    "bkg_spectrum.index.value = bkg_index\n",
    "bkg_spectrum.K.value = bkg_K.value\n",
    "bkg_spectrum.piv.value = bkg_piv.value\n",
    "bkg_spectrum.K.unit = bkg_K.unit\n",
    "bkg_spectrum.piv.unit = bkg_piv.unit\n",
    "    \n",
    "iso_response = response.get_point_source_response(iso_map)\n",
    "    \n",
    "bkg = iso_response.get_expectation(bkg_spectrum).project(['Em', 'Phi', 'PsiChi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0de926a-f12e-4c79-b7df-3cd94edd0c88",
   "metadata": {},
   "source": [
    "Now, let's renormalize the background to a total rate of 1k Hz. This is again not realistic, but was chosen such that the signal will show up clearly enough above the background and we can work on the algorihtms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "02ae055f-9549-4f38-a8e6-d0e57af60cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90238.31928090738"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(bkg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c0cc346-7bd6-4d67-b98b-e94bcf5569aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "bkg = bkg * 1e3 / np.sum(bkg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3bf9becd-f9af-4259-b3af-22d0aa3e100a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000.0000000000001"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(bkg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71024a7d-1742-4a57-9b23-cd47bc1ae603",
   "metadata": {},
   "source": [
    "These are the same plots as we did for the signal, so you can compare:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aa8589a2-cff1-4c68-bae8-c78c1691a0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot: xlabel='Em [keV]'>, <ErrorbarContainer object of 3 artists>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bkg.project('Em').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eee3d467-d795-43b3-857e-b6cc1c09c1f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.image.AxesImage at 0x7fea4a73c280>, <OrthviewSubplot: >)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_bkg = HealpixMap(bkg.slice[{'Em':3, 'Phi':4}].project('PsiChi').todense().contents)\n",
    "\n",
    "fig = plt.figure(dpi = 150)\n",
    "\n",
    "ax = fig.add_subplot(projection = 'orthview')\n",
    "\n",
    "m_bkg.plot(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bcca3df-33c4-4843-aac1-27facf67cafe",
   "metadata": {},
   "source": [
    "Note: I actually don't understand what causes the strip in the middle. Maybe it's a beating pattern caused by converting from FISBEL to HEALPix during the creation of the detector response. I plan to generate a detector response using HEALPix directly, and will revisit this then."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f927b2-1132-4afe-971a-cc649a832ebb",
   "metadata": {},
   "source": [
    "## Putting it togetter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b0ce845-5b17-48e9-8282-97e6d703bcbc",
   "metadata": {},
   "source": [
    "Once we obtain the expected signal, it's easy to add it do the background to simulate how the observed data would look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c9e5f28-0c5c-475c-923b-47b0ba28c38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = signal + bkg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e53ffc-0e27-422d-88c6-f3a6f3969dae",
   "metadata": {},
   "source": [
    "If the user wants to simulate multiple sources, just add those to this sum.\n",
    "\n",
    "This is how it looks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8962acdf-949a-4f02-b932-8a974c068b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.image.AxesImage at 0x7fea4a7371f0>, <OrthviewSubplot: >)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_data = HealpixMap(data.slice[{'Em':3, 'Phi':4}].project('PsiChi').todense().contents)\n",
    "\n",
    "fig = plt.figure(dpi = 150)\n",
    "\n",
    "ax = fig.add_subplot(1,2,1, projection = 'orthview')\n",
    "\n",
    "m_data.plot(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3069c1-22ee-4b5a-bd0e-928d620f4f7d",
   "metadata": {},
   "source": [
    "The final source injector should save the result to disk in the same format as the \"Data classes\" module, including all the appropiate header information. However, for now you can use directly histpy's `write` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "42d5f436-683e-421a-9996-88ab169844fd",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unable to write histogram. Another group with the same name already exists. Choose a different name or use overwrite",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata.h5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m bkg\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbkg.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m signal\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msignal.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/Software/COSItools/histpy/histpy/histogram.py:1733\u001b[0m, in \u001b[0;36mHistogram.write\u001b[0;34m(self, filename, name, overwrite)\u001b[0m\n\u001b[1;32m   1731\u001b[0m         \u001b[38;5;28;01mdel\u001b[39;00m f[name]\n\u001b[1;32m   1732\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1733\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to write histogram. Another group \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1734\u001b[0m                          \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith the same name already exists. Choose \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1735\u001b[0m                          \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma different name or use overwrite\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1737\u001b[0m \u001b[38;5;66;03m# Contents\u001b[39;00m\n\u001b[1;32m   1738\u001b[0m hist_group \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mcreate_group(name)\n",
      "\u001b[0;31mValueError\u001b[0m: Unable to write histogram. Another group with the same name already exists. Choose a different name or use overwrite"
     ]
    }
   ],
   "source": [
    "data.write(\"data.h5\")\n",
    "bkg.write(\"bkg.h5\")\n",
    "signal.write(\"signal.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f22343-ad7e-4cf5-920c-0f0d7cd82ea5",
   "metadata": {},
   "source": [
    "To load them back, use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9bcda82-15c0-433a-ae2a-f59ee707d171",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Histogram.open(\"data.h5\")\n",
    "bkg = Histogram.open(\"bkg.h5\")\n",
    "signal = Histogram.open(\"signal.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49ffb43f-95c3-4637-bf6f-b77314d198b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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