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   "source": [
    "# FITS_tutorial8\n",
    "Open a FITS file with columns containing variable-length arrays.\n",
    "\n",
    "ABOUT THE DATA: To illustrate such a case we use a Redistribution\n",
    "Matrix File (RMF) conform with the HEASARC OGIP specifications\n",
    "OGIP definition\n",
    "https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html\n",
    "RMF defintion\n",
    "https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#tth_sEc3.1\n",
    "\n",
    "Variable-length arrays are used to return the non-null row elements of a\n",
    "sparse matrix containing the probability of detecting a photon of a given\n",
    "energy at a given detector channel.\n",
    "\n",
    "The test data file, rmf.fits, is taken from the test data of the sherpa X-ray\n",
    "analysis tools\n",
    "https://cxc.harvard.edu/sherpa/\n",
    "the original file is available under the repository\n",
    "https://github.com/sherpa/sherpa-test-data\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Cosimo Nigro  \n",
    "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Tuesday, May 19, 2026 at 08:14 PM.</small></i>"
   ]
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  {
   "cell_type": "markdown",
   "id": "ddea0077",
   "metadata": {},
   "source": [
    "let us read the first header data unit, opening the file with a FITS viewer\n",
    "we see that the following cells contain variable-length arrays with values\n",
    "row 215 column \"F_CHAN\": (1, 68)\n",
    "row 215 column \"N_CHAN\": (66, 130)\n",
    "row 215 column \"MATRIX\" : (5.8425176E-6, 7.290097E-6, 8.188037E-6, 9.157882E-6, 1.018355E-5, ..)"
   ]
  },
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   "id": "455f4255",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading in row 215, column F_CHAN \n",
      "(1.000000, 68.000000) \n",
      "reading in row 215, column N_CHAN \n",
      "(66.000000, 130.000000) \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TFITSHDU::LoadHDU>: The selected HDU contains a Table Extension\n"
     ]
    }
   ],
   "source": [
    "TString dir = gROOT->GetTutorialDir();\n",
    "TFITSHDU* hdu = new TFITSHDU(dir + \"/io/fitsio/rmf.fits\", 1);\n",
    "int rownum = 214; // FITS tables are indexed starting from 1\n",
    "TString colname1 = \"F_CHAN\";\n",
    "TString colname2 = \"N_CHAN\";\n",
    "TString colname3 = \"MATRIX\";\n",
    "\n",
    "printf(\"reading in row %d, column %s \\n\", rownum+1, colname1.Data());\n",
    "TArrayD *arr1 = hdu->GetTabVarLengthVectorCell(rownum, colname1);\n",
    "printf(\"(%f, %f) \\n\", arr1->At(0), arr1->At(1));\n",
    "\n",
    "printf(\"reading in row %d, column %s \\n\", rownum+1, colname2.Data());\n",
    "TArrayD *arr2 = hdu->GetTabVarLengthVectorCell(rownum, colname2);\n",
    "printf(\"(%f, %f) \\n\", arr2->At(0), arr2->At(1));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1a9f9c",
   "metadata": {},
   "source": [
    "printing only the first 5 values in the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac85e132",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:14:15.376363Z",
     "iopub.status.busy": "2026-05-19T20:14:15.376197Z",
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     "shell.execute_reply": "2026-05-19T20:14:15.580627Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading in row 215, column MATRIX \n",
      "(5.842518e-06, 7.290097e-06, 8.188037e-06, 9.157882e-06, 1.018355e-05, ...) \n"
     ]
    }
   ],
   "source": [
    "printf(\"reading in row %d, column %s \\n\", rownum+1, colname3.Data());\n",
    "TArrayD *arr3 = hdu->GetTabVarLengthVectorCell(rownum, colname3);\n",
    "printf(\"(%e, %e, %e, %e, %e, ...) \\n\", arr3->At(0), arr3->At(1), arr3->At(2), arr3->At(3), arr3->At(4));"
   ]
  },
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   "cell_type": "markdown",
   "id": "04a8a3cc",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4127705",
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     "shell.execute_reply": "2026-05-19T20:14:15.786583Z"
    }
   },
   "outputs": [],
   "source": [
    "gROOT->GetListOfCanvases()->Draw()"
   ]
  }
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