{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phased Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fermipy provides several options to support analysis with selections on pulsar phase.\n",
    "\n",
    "This example script will use the pre-made event files included in the repository. These event files have the PULSE_PHASE column added, which lists the pulsar phase for each recorded event. We can then use this information to perform analyses on the pulsar both during and between the pulses.\n",
    "\n",
    "Also included is a tutorial on how to add the PULSE_PHASE column to an event file. Note that performing the analysis from the raw data can take multiple hours to process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To perform a phased analysis, the configuration file must have some additional parameters.\n",
    "\n",
    "The gtlike.expscale parameter defines the correction that should be applied to the nominal exposure to account for the phase selection defined by selection.phasemin and selection.phasemax. Normally this should be set to the size of the phase selection interval, i.e. $\\rm{expscale}=\\rm{phasemax}-\\rm{phasemin}$.\n",
    "\n",
    "To perform a joint analysis of multiple phase selections we need to use the components section to define separate ON- and OFF-phase components. We use the gtlike.src_expscale parameter to zero out the 3FGL source component in the OFF phase. For the ON-phase of ``3FGL J0633.9+1746'' we set $\\rm{src\\_expscale}=1$, and for the OFF-phase we set $\\rm{src\\_expscale}=0$.\n",
    "\n",
    "The components selection of the config file is then constructed as:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "components:\n",
    "  - selection : {phasemin : 0.05, phasemax: 0.20}\n",
    "    gtlike    : {expscale : 0.15, src_expscale : {'3FGL J0633.9+1746':1.0}}\n",
    "  - selection : {phasemin : 0.20 , phasemax: 0.55}\n",
    "    gtlike    : {expscale : 0.35, src_expscale : {'3FGL J0633.9+1746':0.0}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where the process of determining the values used for the phases will be shown later. In general the src_expscale parameter can be used to define an exposure correction for indvidual sources."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "This section will go over adding the pulsar ephemeris to the event files. This will require downloading the raw data from the [Fermi SSC website](https://fermi.gsfc.nasa.gov/cgi-bin/ssc/LAT/LATDataQuery.cgi).\n",
    "\n",
    "This section can be skipped by using the pre-processed data included in the repository. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse from scratch, download the data from the [FSSC website](https://fermi.gsfc.nasa.gov/cgi-bin/ssc/LAT/LATDataQuery.cgi) using the following selections:\n",
    "\n",
    "* Object name or coordinates: 98.4792,17.7729\n",
    "* Coordinate system: J2000\n",
    "* Search radius (deg): 12\n",
    "* Observation dates: 239804670,743358091\n",
    "* Time system: MET\n",
    "* Energy Range (MeV): 1000, 316228\n",
    "* LAT data type: photon\n",
    "* Spacecraft data: yes\n",
    "\n",
    "The following cell can be used to set up the directory structure and download the data from the FSSC servers. Note that you will likely need to change the file names for the `wget' lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-07-17T22:33:26.692633Z"
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   "source": [
    "# # Set up a subdirectory for the data\n",
    "# !mkdir -p Geminga_data/\n",
    "\n",
    "# # Set up a subdirectory for the raw data\n",
    "# !mkdir -p Geminga_rawdata/PH\n",
    "# !mkdir -p Geminga_rawdata/SC\n",
    "\n",
    "# # Download the data and place into the subdirectory\n",
    "# !wget -P ./Geminga_rawdata/PH/ https://fermi.gsfc.nasa.gov/FTP/fermi/data/lat/queries/L240723161301912CDF4657_PH00.fits\n",
    "# !wget -P ./Geminga_rawdata/PH/ https://fermi.gsfc.nasa.gov/FTP/fermi/data/lat/queries/L240723161301912CDF4657_PH01.fits\n",
    "# !wget -P ./Geminga_rawdata/PH/ https://fermi.gsfc.nasa.gov/FTP/fermi/data/lat/queries/L240723161301912CDF4657_PH02.fits\n",
    "# !wget -P ./Geminga_rawdata/PH/ https://fermi.gsfc.nasa.gov/FTP/fermi/data/lat/queries/L240723161301912CDF4657_PH03.fits\n",
    "# !wget -P ./Geminga_rawdata/PH/ https://fermi.gsfc.nasa.gov/FTP/fermi/data/lat/queries/L240723161301912CDF4657_PH04.fits\n",
    "\n",
    "# !wget -P ./Geminga_rawdata/SC/ https://fermi.gsfc.nasa.gov/FTP/fermi/data/lat/queries/L240723161301912CDF4657_SC00.fits\n",
    "\n",
    "# # As the preprocessing steps append data to the SC file, create a copy\n",
    "# !cp ./Geminga_rawdata/SC/L240723161301912CDF4657_SC00.fits ./Geminga_rawdata/SC/SC_copy.fits\n",
    "\n",
    "# # Make a file list of the photon files\n",
    "# !ls ./Geminga_rawdata/PH/*PH*.fits > ./Geminga_rawdata/PH.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the ephemeris data from the FSSC website. A list of all ephermiris data [can be found here](https://fermi.gsfc.nasa.gov/ssc/data/access/lat/ephems/), though more up-to-date ephemerides will likely be required for use in current analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:26.694870Z",
     "iopub.status.busy": "2026-07-17T22:33:26.694688Z",
     "iopub.status.idle": "2026-07-17T22:33:26.697108Z",
     "shell.execute_reply": "2026-07-17T22:33:26.696436Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Download the ephemeris data for the Geminga pulsar\n",
    "# !wget -P ./Geminga_data/ https://fermi.gsfc.nasa.gov/ssc/data/access/lat/ephems/0633+1746/0633+1746_ApJ_720_272_2010_D4.fits\n",
    "# !wget -P ./Geminga_data/ https://fermi.gsfc.nasa.gov/ssc/data/access/lat/ephems/0633+1746/0633+1746_ApJ_720_272_2010.par"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is a brief tutorial of using ephemeris data to add the PULSE_PHASE column to the event file.\n",
    "We will use the bash fermitools to both prepare the evfile and to add the column.\n",
    "\n",
    "For this tutorial we will use a simplified form of ephemeris file (named a D4 file). These files cover shorter time frames. The benefit is that we can use the built-in functions within the bash fermitools to perform the calculations.\n",
    "\n",
    "For more complicated ephemerides that cover larger time frames, such as those stored in .par files, you will need to install TEMPO2 to perform the required calculations to add the PULSE_PHASE column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bash commands we use to prepare the data are: [gtselect](https://fermi.gsfc.nasa.gov/ssc/data/p6v11/analysis/scitools/help/gtselect.txt), [gtmktime](https://fermi.gsfc.nasa.gov/ssc/data/p6v11/analysis/scitools/help/gtmktime.txt), [gtbin](https://fermi.gsfc.nasa.gov/ssc/data/p6v11/analysis/scitools/help/gtbin.txt), [gtpphase](https://fermi.gsfc.nasa.gov/ssc/data/p6v11/analysis/scitools/help/gtpphase.txt) (click for documentation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:26.698779Z",
     "iopub.status.busy": "2026-07-17T22:33:26.698593Z",
     "iopub.status.idle": "2026-07-17T22:33:26.701369Z",
     "shell.execute_reply": "2026-07-17T22:33:26.700519Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%bash\n",
    "# gtselect evclass=128 evtype=3\n",
    "#     @./Geminga_rawdata/PH.txt\n",
    "#     ./Geminga_data/Geminga.fits\n",
    "#     INDEF\n",
    "#     INDEF\n",
    "#     INDEF\n",
    "#     INDEF\n",
    "#     INDEF\n",
    "#     100\n",
    "#     316227.76\n",
    "#     105"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:26.702752Z",
     "iopub.status.busy": "2026-07-17T22:33:26.702579Z",
     "iopub.status.idle": "2026-07-17T22:33:26.704961Z",
     "shell.execute_reply": "2026-07-17T22:33:26.704253Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%bash\n",
    "# gtmktime\n",
    "#     ./Geminga_rawdata/SC/SC_copy.fits\n",
    "#     (DATA_QUAL>0)&&(LAT_CONFIG==1)\n",
    "#     no\n",
    "#     ./Geminga_data/Geminga.fits\n",
    "#     ./Geminga_data/Geminga_gtis.fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:26.706281Z",
     "iopub.status.busy": "2026-07-17T22:33:26.706145Z",
     "iopub.status.idle": "2026-07-17T22:33:26.708793Z",
     "shell.execute_reply": "2026-07-17T22:33:26.708091Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%bash\n",
    "# gtbin\n",
    "#     CMAP\n",
    "#     ./Geminga_data/Geminga_gtis.fits\n",
    "#     ./Geminga_data/Geminga_cmap.fits\n",
    "#     ./Geminga_rawdata/SC/SC_copy.fits\n",
    "#     300\n",
    "#     300\n",
    "#     0.1\n",
    "#     CEL\n",
    "#     98.4756\n",
    "#     17.7703\n",
    "#     0\n",
    "#     CAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:26.710105Z",
     "iopub.status.busy": "2026-07-17T22:33:26.709965Z",
     "iopub.status.idle": "2026-07-17T22:33:26.712424Z",
     "shell.execute_reply": "2026-07-17T22:33:26.711562Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%bash\n",
    "# gtpphase\n",
    "#     ./Geminga_data/Geminga_gtis.fits\n",
    "#     ./Geminga_rawdata/SC/SC_copy.fits\n",
    "#     ./Geminga_data/0633+1746_ApJ_720_272_2010_D4.fits\n",
    "#     J0633+1746\n",
    "#     DB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combined ON- and OFF-phase analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:26.713792Z",
     "iopub.status.busy": "2026-07-17T22:33:26.713623Z",
     "iopub.status.idle": "2026-07-17T22:33:28.539358Z",
     "shell.execute_reply": "2026-07-17T22:33:28.538493Z"
    }
   },
   "outputs": [],
   "source": [
    "from fermipy import utils\n",
    "from fermipy.gtanalysis import GTAnalysis\n",
    "import argparse\n",
    "from fermipy.castro import CastroData\n",
    "from fermipy.plotting import ROIPlotter, SEDPlotter\n",
    "import astropy.io.fits as pyfits\n",
    "from math import *\n",
    "import matplotlib.pyplot as plt\n",
    "utils.init_matplotlib_backend()\n",
    "import numpy as np\n",
    "from scipy.integrate import quad\n",
    "from IPython.display import Image\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the data is not available, download it from the repository.\n",
    "\n",
    "If you want to perform the analysis on data that hasn't been pre-processed, only unpack the config file from the tarball."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:28.541070Z",
     "iopub.status.busy": "2026-07-17T22:33:28.540811Z",
     "iopub.status.idle": "2026-07-17T22:33:29.411398Z",
     "shell.execute_reply": "2026-07-17T22:33:29.410314Z"
    }
   },
   "outputs": [],
   "source": [
    "if not os.path.isfile('./Geminga_data/config_phase.yaml'):\n",
    "\n",
    "    if not os.path.isfile('./../data/Geminga_data.tar.gz'):\n",
    "\n",
    "        !curl -OL --output-dir ./../data/ https://raw.githubusercontent.com/fermiPy/fermipy-extras/master/data/Geminga_data.tar.gz\n",
    "\n",
    "    !tar xzf ./../data/Geminga_data.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a phase diagram for the pulsar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:33:29.413441Z",
     "iopub.status.busy": "2026-07-17T22:33:29.413256Z",
     "iopub.status.idle": "2026-07-17T22:33:29.621428Z",
     "shell.execute_reply": "2026-07-17T22:33:29.620475Z"
    }
   },
   "outputs": [],
   "source": [
    "table = pyfits.open('./Geminga_data/Geminga_gtis.fits')\n",
    "\n",
    "tabledata = table[1].data \n",
    "phasevec = tabledata.field('PULSE_PHASE')\n",
    "\n",
    "phase_bins = np.arange(0.,1.01,0.01)\n",
    "phase_val = np.arange(0.+0.005,1.0,0.01)\n",
    "histo_phase = np.histogram(phasevec,phase_bins)\n",
    "\n",
    "plt.clf()\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "plt.errorbar(phase_val, histo_phase[0], yerr=np.sqrt(histo_phase[0]), fmt=\"o\", color=\"black\",label=\"Geminga\")\n",
    "plt.ylabel(r'$N counts$')\n",
    "plt.xlabel(r'$x$')\n",
    "plt.axis([0.,1.0,0.0,1.5e4])\n",
    "plt.grid(True)\n",
    "plt.yscale('linear')\n",
    "plt.xscale('linear')\n",
    "plt.xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],\n",
    "           [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])\n",
    "plt.legend(loc=3,prop={'size':15},numpoints=1, scatterpoints=1, ncol=1)\n",
    "fig.tight_layout(pad=0.5)\n",
    "plt.savefig(\"./Geminga_data/phase_Geminga.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the phase plot we can see P1 in the range $0.05<x<0.20$, off-peak in the range $0.20>x>0.55$, P2 in the range $0.55<x<0.70$, and bridge emission for $0.70<x<1.00$.\n",
    "\n",
    "In this tutorial we will compare the ON- and OFF-phase emission, i.e. we will analyse P1 and the off-peak emission. Hence, we choose phasemax=0.20, phasemin=0.05 for the ON-phase analysis, and phasemax=0.55, phasemin=0.20 for the OFF-phase analysis.\n",
    "These values are included in the configuration file that we will use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin the analysis. If using the pre-processed data this step should take on the order of minutes. If the pre-processed data is not available, this step will take on the order of hours.\n",
    "If re-running this block after generating the srcmap .fits files, it should take approximately a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-07-17T22:36:08.237963Z",
     "shell.execute_reply": "2026-07-17T22:36:08.237061Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:29 INFO    GTAnalysis.__init__(): \n",
      "--------------------------------------------------------------------------------\n",
      "fermipy version 1.4.2+32.g764b \n",
      "ScienceTools version 2.5.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Prefactor': 0, 'Index1': 1, 'Scale': 2, 'Cutoff': 3, 'Index2': 4}\n",
      "{'Prefactor': 0, 'Index1': 1, 'Scale': 2, 'Cutoff': 3, 'Index2': 4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTAnalysis.setup(): Running setup.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis.setup(): Running setup for component 00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis._select_data(): Skipping data selection.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis._create_ltcube(): Skipping LT Cube.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtexpcube2 infile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ltcube_00.fits cmap=none outfile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/bexpmap_00.fits irfs=\"P8R2_SOURCE_V6\" evtype=3 edisp_bins=-1 nxpix=360 nypix=180 binsz=1.0 coordsys=\"GAL\" xref=0.0 yref=0.0 axisrot=0.0 proj=\"CAR\" ebinalg=\"LOG\" emin=1000.0 emax=316227.76 enumbins=20 ebinfile=\"NONE\" hpx_ordering_scheme=\"RING\" hpx_order=6 bincalc=\"EDGE\" ignorephi=no thmax=180.0 thmin=0.0 table=\"EXPOSURE\" chatter=3 clobber=yes debug=no mode=\"ql\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:30 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 3.58 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtexpcube2 infile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ltcube_00.fits cmap=none outfile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/bexpmap_roi_00.fits irfs=\"P8R2_SOURCE_V6\" evtype=3 edisp_bins=0 nxpix=150 nypix=150 binsz=0.08 coordsys=\"GAL\" xref=195.13259155245234 yref=4.269832906893274 axisrot=0.0 proj=\"CAR\" ebinalg=\"LOG\" emin=1000.0 emax=316227.76 enumbins=20 ebinfile=\"NONE\" hpx_ordering_scheme=\"RING\" hpx_order=6 bincalc=\"EDGE\" ignorephi=no thmax=180.0 thmin=0.0 table=\"EXPOSURE\" chatter=3 clobber=yes debug=no mode=\"ql\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:34 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 1.37 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2001-01-01T00:01:04.184' from MJDREF.\n",
      "Set MJD-OBS to 54682.655283 from DATE-OBS.\n",
      "Set MJD-END to 55195.999977 from DATE-END'. [astropy.wcs.wcs]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): Running gtsrcmaps.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtsrcmaps scfile=./Geminga_rawdata/SC/SC_copy.fits sctable=\"SC_DATA\" expcube=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ltcube_00.fits cmap=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ccube_00.fits srcmdl=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/srcmdl_00.xml bexpmap=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/bexpmap_00.fits wmap=none outfile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/srcmap_00.fits irfs=\"P8R2_SOURCE_V6\" evtype=3 convol=yes resample=yes rfactor=2 minbinsz=0.05 ptsrc=yes psfcorr=yes emapbnds=no edisp_bins=-1 copyall=no chatter=3 clobber=yes debug=no gui=no mode=\"ql\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): This is gtsrcmaps version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:35 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0626.8+1743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0631.2+2019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0647.8+1751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.1+1606\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.8+1516\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0650.5+2055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0610.6+1728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0611.5+1957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0619.4+2242\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0700.0+1709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0609.2+2051c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0659.5+1414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0605.9+2039c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named isodiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:39 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named galdiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:40 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0605.9+2039c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:41 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0609.2+2051c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:41 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0610.6+1728 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:42 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0611.5+1957 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:42 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0619.4+2242 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:43 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0626.8+1743 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:43 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0631.2+2019 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:44 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0633.9+1746 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:44 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0647.8+1751 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:45 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.1+1606 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:46 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.8+1516 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:46 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0650.5+2055 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:47 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0659.5+1414 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:33:47 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0700.0+1709 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:18 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for galdiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:42 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for isodiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:42 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtsrcmaps. Execution time: 66.93 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:42 INFO    GTBinnedAnalysis.setup(): Finished setup for component 00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:42 INFO    GTBinnedAnalysis.setup(): Running setup for component 01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:42 INFO    GTBinnedAnalysis._select_data(): Skipping data selection.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:42 INFO    GTBinnedAnalysis._create_ltcube(): Skipping LT Cube.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:43 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:43 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtexpcube2 infile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ltcube_01.fits cmap=none outfile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/bexpmap_01.fits irfs=\"P8R2_SOURCE_V6\" evtype=3 edisp_bins=-1 nxpix=360 nypix=180 binsz=1.0 coordsys=\"GAL\" xref=0.0 yref=0.0 axisrot=0.0 proj=\"CAR\" ebinalg=\"LOG\" emin=1000.0 emax=316227.76 enumbins=20 ebinfile=\"NONE\" hpx_ordering_scheme=\"RING\" hpx_order=6 bincalc=\"EDGE\" ignorephi=no thmax=180.0 thmin=0.0 table=\"EXPOSURE\" chatter=3 clobber=yes debug=no mode=\"ql\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:43 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:43 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:43 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:43 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 3.58 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtexpcube2 infile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ltcube_01.fits cmap=none outfile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/bexpmap_roi_01.fits irfs=\"P8R2_SOURCE_V6\" evtype=3 edisp_bins=0 nxpix=150 nypix=150 binsz=0.08 coordsys=\"GAL\" xref=195.13259155245234 yref=4.269832906893274 axisrot=0.0 proj=\"CAR\" ebinalg=\"LOG\" emin=1000.0 emax=316227.76 enumbins=20 ebinfile=\"NONE\" hpx_ordering_scheme=\"RING\" hpx_order=6 bincalc=\"EDGE\" ignorephi=no thmax=180.0 thmin=0.0 table=\"EXPOSURE\" chatter=3 clobber=yes debug=no mode=\"ql\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:46 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 1.36 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2001-01-01T00:01:04.184' from MJDREF.\n",
      "Set MJD-OBS to 54682.655283 from DATE-OBS.\n",
      "Set MJD-END to 55195.999977 from DATE-END'. [astropy.wcs.wcs]\n",
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): Running gtsrcmaps.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtsrcmaps scfile=./Geminga_rawdata/SC/SC_copy.fits sctable=\"SC_DATA\" expcube=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ltcube_01.fits cmap=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ccube_01.fits srcmdl=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/srcmdl_01.xml bexpmap=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/bexpmap_01.fits wmap=none outfile=/home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/srcmap_01.fits irfs=\"P8R2_SOURCE_V6\" evtype=3 convol=yes resample=yes rfactor=2 minbinsz=0.05 ptsrc=yes psfcorr=yes emapbnds=no edisp_bins=-1 copyall=no chatter=3 clobber=yes debug=no gui=no mode=\"ql\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): This is gtsrcmaps version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:48 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0626.8+1743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0631.2+2019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0647.8+1751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.1+1606\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.8+1516\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0650.5+2055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0610.6+1728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0611.5+1957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0619.4+2242\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0700.0+1709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0609.2+2051c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0659.5+1414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0605.9+2039c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named isodiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named galdiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:52 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0605.9+2039c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:53 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0609.2+2051c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:54 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0610.6+1728 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:54 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0611.5+1957 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:55 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0619.4+2242 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:55 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0626.8+1743 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:56 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0631.2+2019 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:56 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0633.9+1746 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:57 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0647.8+1751 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:57 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.1+1606 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:58 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.8+1516 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:59 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0650.5+2055 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:34:59 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0659.5+1414 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:35:00 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0700.0+1709 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:35:30 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for galdiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:35:54 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for isodiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:35:55 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtsrcmaps. Execution time: 66.92 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:35:55 INFO    GTBinnedAnalysis.setup(): Finished setup for component 01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:35:55 INFO    GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:35:59 INFO    GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 01.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:03 WARNING GTBinnedAnalysis._scale_srcmap(): The expscale parameter was zero, setting it to 1e-8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:04 INFO    GTAnalysis.setup(): Initializing source properties\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.setup(): Finished setup.\n"
     ]
    }
   ],
   "source": [
    "gta = GTAnalysis('./Geminga_data/config_phase.yaml')\n",
    "gta.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:08.239548Z",
     "iopub.status.busy": "2026-07-17T22:36:08.239383Z",
     "iopub.status.idle": "2026-07-17T22:36:08.243199Z",
     "shell.execute_reply": "2026-07-17T22:36:08.242471Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.000   2.976   0.00151   3.12       nan     42003.8     \n",
      "3FGL J0626.8+1743      1.684   2.300  1.69e-06   2.70       nan        21.4     \n",
      "3FGL J0631.2+2019      2.634   0.529  1.95e-06   2.46       nan        20.1     \n",
      "3FGL J0647.8+1751      3.317   2.178  9.76e-07   2.70       nan        12.4     \n",
      "3FGL J0648.1+1606      3.793   0.431  2.26e-06   1.77       nan         7.6     \n",
      "3FGL J0648.8+1516      4.358   1.092   1.2e-05   1.83       nan        45.1     \n",
      "3FGL J0650.5+2055      5.040   0.802  6.84e-06   1.56       nan        13.7     \n",
      "3FGL J0610.6+1728      5.546   0.522  2.26e-06   4.85       nan        42.9     \n",
      "3FGL J0611.5+1957      5.714   0.967  3.93e-06   2.38       nan        36.5     \n",
      "3FGL J0619.4+2242      5.980   0.381  7.81e-06   2.01       nan        27.4     \n",
      "3FGL J0700.0+1709      6.273   0.544  1.29e-06   2.69       nan        14.9     \n",
      "3FGL J0609.2+2051c     6.582   1.310  3.62e-06   2.27       nan        27.4     \n",
      "3FGL J0659.5+1414      7.107   2.874  7.42e-07  15.34       nan        11.0     \n",
      "3FGL J0605.9+2039c     7.210   1.036  4.63e-06   2.36       nan        36.1     \n",
      "isodiff                  ---   1.000    0.0302   2.12       nan       682.3     \n",
      "galdiff                  ---   1.000     0.131   0.00       nan      9763.6     \n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.print_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we perform a first fit to the ROI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:08.244823Z",
     "iopub.status.busy": "2026-07-17T22:36:08.244662Z",
     "iopub.status.idle": "2026-07-17T22:36:09.981978Z",
     "shell.execute_reply": "2026-07-17T22:36:09.981180Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0631.2+2019     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0647.8+1751     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0648.1+1606     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0610.6+1728     : ['norm', 'alpha', 'beta']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0611.5+1957     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0700.0+1709     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0609.2+2051c    : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0659.5+1414     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0605.9+2039c    : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:08 INFO    GTAnalysis.optimize(): Starting\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joint fit  ['3FGL J0633.9+1746', 'galdiff']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting shape 3FGL J0633.9+1746 TS: 179148.448\n",
      "Drm_Cache::update Measured counts < 0 3FGL J0633.9+1746 19 -1.08122e-12 2.76718e-12\n",
      "0.024396 0.0141712 0.00789076 0.00421511 0.00216053 0.00106886 0.000514622 0.000240783 0.000106373 4.40127e-05 1.69327e-05 6.0463e-06 1.95686e-06 5.5132e-07 1.31927e-07 2.62205e-08 4.17742e-09 5.10758e-10 4.5519e-11 2.76718e-12 \n",
      "Fitting shape galdiff TS:  49872.792\n",
      "Fitting shape 3FGL J0619.4+2242 TS:    520.300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting shape 3FGL J0648.8+1516 TS:    126.285\n",
      "Fitting shape 3FGL J0650.5+2055 TS:     31.704\n",
      "Fitting shape isodiff TS:     30.288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:09 INFO    GTAnalysis.optimize(): Finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:09 INFO    GTAnalysis.optimize(): LogLike: -26231.637120 Delta-LogLike: 7660.387064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:09 INFO    GTAnalysis.optimize(): Execution time: 1.72 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loglike0': -33892.024184608636,\n",
       " 'loglike1': -26231.63712026418,\n",
       " 'dloglike': 7660.387064344457,\n",
       " 'config': {'npred_threshold': 1.0,\n",
       "  'npred_frac': 0.95,\n",
       "  'shape_ts_threshold': 25.0,\n",
       "  'max_free_sources': 5,\n",
       "  'skip': [],\n",
       "  'optimizer': {'optimizer': 'MINUIT',\n",
       "   'tol': 0.001,\n",
       "   'max_iter': 100,\n",
       "   'init_lambda': 0.0001,\n",
       "   'retries': 3,\n",
       "   'min_fit_quality': 2,\n",
       "   'verbosity': 0}}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gta.free_sources()\n",
    "gta.optimize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete sources with TS<12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:09.983458Z",
     "iopub.status.busy": "2026-07-17T22:36:09.983314Z",
     "iopub.status.idle": "2026-07-17T22:36:10.077144Z",
     "shell.execute_reply": "2026-07-17T22:36:10.076440Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:09 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.000   1.109    0.0008   2.80 201341.12     20155.5    *\n",
      "3FGL J0626.8+1743      1.684   6.861  5.03e-06   2.70     13.40        63.8    *\n",
      "3FGL J0631.2+2019      2.634   0.022  8.15e-08   2.46      0.02         0.8    *\n",
      "3FGL J0647.8+1751      3.317   0.690  3.09e-07   2.70      0.40         3.9    *\n",
      "3FGL J0648.1+1606      3.793   0.057  2.97e-07   1.77      0.11         1.0    *\n",
      "3FGL J0648.8+1516      4.358   1.096  2.07e-05   1.60    134.69        45.6    *\n",
      "3FGL J0650.5+2055      5.040   0.559  8.65e-06   1.26     34.12         8.7    *\n",
      "3FGL J0610.6+1728      5.546   0.000  4.32e-11   4.85     -0.00         0.0    *\n",
      "3FGL J0611.5+1957      5.714   0.000  4.07e-11   2.38     -0.00         0.0    *\n",
      "3FGL J0619.4+2242      5.980   3.228  5.79e-05   2.71    602.85       451.2    *\n",
      "3FGL J0700.0+1709      6.273   0.101   2.4e-07   2.69      0.45         2.8    *\n",
      "3FGL J0609.2+2051c     6.582   0.000  2.77e-11   2.27     -0.00         0.0    *\n",
      "3FGL J0659.5+1414      7.107   1.446  3.74e-07  15.34      2.02         5.5    *\n",
      "3FGL J0605.9+2039c     7.210   0.000  3.16e-10   2.36     -0.00         0.0    *\n",
      "isodiff                  ---   0.767    0.0232   2.12     40.51       523.4    *\n",
      "galdiff                  ---   1.360     0.159   0.07  56821.10     12683.5    *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:09 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0631.2+2019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:09 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0647.8+1751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0648.1+1606\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0610.6+1728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0611.5+1957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0700.0+1709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0609.2+2051c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0659.5+1414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0605.9+2039c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.000   1.109    0.0008   2.80 201341.12     20155.5    *\n",
      "3FGL J0626.8+1743      1.684   6.861  5.03e-06   2.70     13.40        63.8    *\n",
      "3FGL J0648.8+1516      4.358   1.096  2.07e-05   1.60    134.69        45.6    *\n",
      "3FGL J0650.5+2055      5.040   0.559  8.65e-06   1.26     34.12         8.7    *\n",
      "3FGL J0619.4+2242      5.980   3.228  5.79e-05   2.71    602.85       451.2    *\n",
      "isodiff                  ---   0.767    0.0232   2.12     40.51       523.4    *\n",
      "galdiff                  ---   1.360     0.159   0.07  56821.10     12683.5    *\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.print_model()\n",
    "gta.delete_sources(minmax_ts=[None,12])\n",
    "gta.print_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:10.078934Z",
     "iopub.status.busy": "2026-07-17T22:36:10.078787Z",
     "iopub.status.idle": "2026-07-17T22:36:11.051303Z",
     "shell.execute_reply": "2026-07-17T22:36:11.050579Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:10 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:11 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:11 INFO    GTAnalysis.fit(): LogLike:   -26228.310 DeltaLogLike:        4.704 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:11 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.000   1.110    0.0008   2.80 180476.66     20144.4    *\n",
      "3FGL J0626.8+1743      1.684  46.829   4.7e-06   5.00     22.63       110.7    *\n",
      "3FGL J0648.8+1516      4.358   1.090  2.06e-05   1.60    131.36        45.4    *\n",
      "3FGL J0650.5+2055      5.040   0.544   8.6e-06   1.25     32.94         8.5    *\n",
      "3FGL J0619.4+2242      5.980   3.203  5.78e-05   2.73    604.13       454.2    *\n",
      "isodiff                  ---   0.900    0.0272   2.12     43.27       613.9    *\n",
      "galdiff                  ---   1.329     0.158   0.06  44669.89     12482.8    *\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.fit()\n",
    "gta.print_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we run the gta.tsmap tool and gta.residmap to make a TS map and residual map to find the residuals in the ROI with respect to our initial model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:11.053022Z",
     "iopub.status.busy": "2026-07-17T22:36:11.052871Z",
     "iopub.status.idle": "2026-07-17T22:36:24.430911Z",
     "shell.execute_reply": "2026-07-17T22:36:24.430113Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:11 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:12 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:24 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:24 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/TSmap_initial_pointsource_powerlaw_2.00_tsmap.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:24 INFO    GTAnalysis.tsmap(): Execution time: 13.37 s\n"
     ]
    }
   ],
   "source": [
    "tsmap_initial = gta.tsmap(prefix='TSmap_initial',\n",
    "                          make_plots=True,\n",
    "                          write_fits=True,\n",
    "                          write_npy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:24.432411Z",
     "iopub.status.busy": "2026-07-17T22:36:24.432266Z",
     "iopub.status.idle": "2026-07-17T22:36:24.643567Z",
     "shell.execute_reply": "2026-07-17T22:36:24.642672Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.clf()\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "ROIPlotter(tsmap_initial['sqrt_ts'],roi=gta.roi).plot(levels=[0,3,6,10],vmin=0,vmax=10,subplot=121,cmap='magma')\n",
    "plt.gca().set_title('Sqrt(TS)')\n",
    "ROIPlotter(tsmap_initial['npred'],roi=gta.roi).plot(vmin=0,vmax=100,subplot=122,cmap='magma')\n",
    "plt.gca().set_title('NPred')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:24.645530Z",
     "iopub.status.busy": "2026-07-17T22:36:24.645361Z",
     "iopub.status.idle": "2026-07-17T22:36:28.735238Z",
     "shell.execute_reply": "2026-07-17T22:36:28.734383Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:24 INFO    GTAnalysis.residmap(): Generating residual maps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:24 INFO    GTAnalysis.add_source(): Adding source residmap_testsource\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:27 INFO    GTAnalysis.delete_source(): Deleting source residmap_testsource\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:28 INFO    GTAnalysis.residmap(): Finished residual maps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:28 WARNING GTAnalysis.residmap(): Saving maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/residualmap_initial_pointsource_powerlaw_2.00_residmap.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:28 INFO    GTAnalysis.residmap(): Execution time: 4.09 s\n"
     ]
    }
   ],
   "source": [
    "resid = gta.residmap('residualmap_initial',\n",
    "                     write_fits=True,\n",
    "                     write_npy=True,\n",
    "                     make_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:28.736798Z",
     "iopub.status.busy": "2026-07-17T22:36:28.736597Z",
     "iopub.status.idle": "2026-07-17T22:36:28.837814Z",
     "shell.execute_reply": "2026-07-17T22:36:28.836974Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.clf()\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "ROIPlotter(resid['data'],roi=gta.roi).plot(vmin=0,vmax=400,subplot=121,cmap='magma')\n",
    "plt.gca().set_title('Data')\n",
    "ROIPlotter(resid['model'],roi=gta.roi).plot(vmin=0,vmax=400,subplot=122,cmap='magma')\n",
    "plt.gca().set_title('Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:28.839310Z",
     "iopub.status.busy": "2026-07-17T22:36:28.839147Z",
     "iopub.status.idle": "2026-07-17T22:36:28.962640Z",
     "shell.execute_reply": "2026-07-17T22:36:28.961836Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.clf()\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "ROIPlotter(resid['sigma'],roi=gta.roi).plot(vmin=-5,vmax=5,levels=[-5,-3,3,5],subplot=121,cmap='RdBu_r')\n",
    "plt.gca().set_title('Significance')\n",
    "ROIPlotter(resid['excess'],roi=gta.roi).plot(vmin=-100,vmax=100,subplot=122,cmap='RdBu_r')\n",
    "plt.gca().set_title('Excess')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:28.964457Z",
     "iopub.status.busy": "2026-07-17T22:36:28.964286Z",
     "iopub.status.idle": "2026-07-17T22:36:32.057649Z",
     "shell.execute_reply": "2026-07-17T22:36:32.056816Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:28 INFO    GTBinnedAnalysis.write_xml(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/initial_00.xml...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:28 INFO    GTBinnedAnalysis.write_xml(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/initial_01.xml...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:28 INFO    GTAnalysis.write_fits(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/initial.fits...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:29 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 00.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:29 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 01.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:30 INFO    GTAnalysis.write_roi(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/initial.npy...\n"
     ]
    }
   ],
   "source": [
    "gta.write_roi('initial',\n",
    "              make_plots=True,\n",
    "              save_model_map=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:32.059387Z",
     "iopub.status.busy": "2026-07-17T22:36:32.059224Z",
     "iopub.status.idle": "2026-07-17T22:36:32.064607Z",
     "shell.execute_reply": "2026-07-17T22:36:32.063906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/initial_counts_spectrum.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:32.066073Z",
     "iopub.status.busy": "2026-07-17T22:36:32.065920Z",
     "iopub.status.idle": "2026-07-17T22:36:32.070258Z",
     "shell.execute_reply": "2026-07-17T22:36:32.069389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/initial_counts_map_xproj_3.000_5.500.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:32.071626Z",
     "iopub.status.busy": "2026-07-17T22:36:32.071464Z",
     "iopub.status.idle": "2026-07-17T22:36:32.075600Z",
     "shell.execute_reply": "2026-07-17T22:36:32.074837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/initial_counts_map_yproj_3.000_5.500.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear from the TS and residual map there is an excess at the location of Geminga."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve our model, we first relocalise the Geminga PSR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:32.077043Z",
     "iopub.status.busy": "2026-07-17T22:36:32.076884Z",
     "iopub.status.idle": "2026-07-17T22:36:41.008313Z",
     "shell.execute_reply": "2026-07-17T22:36:41.007492Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.000   1.110    0.0008   2.80 180476.66     20144.4    *\n",
      "3FGL J0626.8+1743      1.684  46.829   4.7e-06   5.00     22.63       110.7    *\n",
      "3FGL J0648.8+1516      4.358   1.090  2.06e-05   1.60    131.36        45.4    *\n",
      "3FGL J0650.5+2055      5.040   0.544   8.6e-06   1.25     32.94         8.5    *\n",
      "3FGL J0619.4+2242      5.980   3.203  5.78e-05   2.73    604.13       454.2    *\n",
      "isodiff                  ---   0.900    0.0272   2.12     43.27       613.9    *\n",
      "galdiff                  ---   1.329     0.158   0.06  44669.89     12482.8    *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Fixing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:32 INFO    GTAnalysis.localize(): Running localization for 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:38 INFO    GTAnalysis._localize(): Localization succeeded.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:38 INFO    GTAnalysis._localize(): Updating source 3FGL J0633.9+1746 to localized position.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:38 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:38 INFO    GTAnalysis.add_source(): Adding source 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:40 WARNING GTBinnedAnalysis._scale_srcmap(): The expscale parameter was zero, setting it to 1e-8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:40 INFO    GTAnalysis._localize(): Localization completed with new position:\n",
      "(  ra, dec) = (   98.4767 +/-   0.0015,   17.7718 +/-   0.0015)\n",
      "(glon,glat) = (  195.1329 +/-   0.0015,    4.2674 +/-   0.0015)\n",
      "offset =   0.0024 r68 =   0.0022 r95 =   0.0036 r99 =   0.0045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:40 INFO    GTAnalysis._localize(): LogLike:   -26228.501 DeltaLogLike:       -0.190\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:40 INFO    GTAnalysis.localize(): Finished localization.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:41 WARNING GTAnalysis.localize(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/3fgl_j0633.9+1746_loc.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:41 INFO    GTAnalysis.localize(): Execution time: 8.91 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:41 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.028  0.000766   2.78 201386.60     20144.7    *\n",
      "3FGL J0626.8+1743      1.684  46.829   4.7e-06   5.00     22.63       110.7    *\n",
      "3FGL J0648.8+1516      4.358   1.090  2.06e-05   1.60    131.36        45.4     \n",
      "3FGL J0650.5+2055      5.040   0.544   8.6e-06   1.25     32.94         8.5     \n",
      "3FGL J0619.4+2242      5.980   3.203  5.78e-05   2.73    604.13       454.2     \n",
      "isodiff                  ---   0.900    0.0272   2.12     43.27       613.9    *\n",
      "galdiff                  ---   1.329     0.158   0.06  44669.89     12482.8    *\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.print_model()\n",
    "gta.free_sources(free=False)\n",
    "gta.free_sources(skydir=gta.roi[gta.roi.sources[0].name].skydir,distance=[3.0],free=True)\n",
    "localsmc = gta.localize(gta.roi.sources[0].name, update=True, make_plots=True)\n",
    "gta.print_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find new sources. This step will look in the OFF-phase evfile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:36:41.009810Z",
     "iopub.status.busy": "2026-07-17T22:36:41.009629Z",
     "iopub.status.idle": "2026-07-17T22:37:24.468459Z",
     "shell.execute_reply": "2026-07-17T22:37:24.467748Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:41 INFO    GTAnalysis.find_sources(): Starting.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:41 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:41 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:53 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:53 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/sourcefind_00_pointsource_powerlaw_2.00_tsmap.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:53 INFO    GTAnalysis.tsmap(): Execution time: 12.67 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:53 INFO    GTAnalysis._build_src_dicts_from_peaks(): Found source\n",
      "name: PS J0633.9+1746\n",
      "ts: 10107.000681\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:53 INFO    GTAnalysis._build_src_dicts_from_peaks(): Found source\n",
      "name: PS J0617.2+2224\n",
      "ts: 905.764302\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:53 INFO    GTAnalysis.add_source(): Adding source PS J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:55 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:55 INFO    GTAnalysis.add_source(): Adding source PS J0617.2+2224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0617.2+2224       : ['Prefactor']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis._find_sources_iterate(): Performing spectral fit for PS J0633.9+1746.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:791: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  spline = UnivariateSpline(x, y, k=2,\n",
      "2026-07-17 22:36:56 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis.fit(): LogLike:   -19915.445 DeltaLogLike:      783.998 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis._find_sources_iterate(): {'Index': {'error': np.float64(0.03871051688389276),\n",
      "           'value': -2.938229486763812},\n",
      " 'Prefactor': {'error': np.float64(6.324057058591687e-12),\n",
      "               'value': 2.183858722732961e-10},\n",
      " 'Scale': {'error': np.float64(nan), 'value': 1000.0}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis._find_sources_iterate(): Performing spectral fit for PS J0617.2+2224.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:56 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:57 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:57 INFO    GTAnalysis.fit(): LogLike:   -19894.881 DeltaLogLike:       20.564 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:57 INFO    GTAnalysis._find_sources_iterate(): {'Index': {'error': np.float64(0.06465165721981292),\n",
      "           'value': -2.378913238770611},\n",
      " 'Prefactor': {'error': np.float64(2.924628228313707e-12),\n",
      "               'value': 3.6493044806302575e-11},\n",
      " 'Scale': {'error': np.float64(nan), 'value': 1000.0}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:57 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:57 INFO    GTAnalysis.find_sources(): Found 2 sources in iteration 0.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:57 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:36:58 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:10 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:10 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/sourcefind_01_pointsource_powerlaw_2.00_tsmap.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:10 INFO    GTAnalysis.tsmap(): Execution time: 12.61 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:10 INFO    GTAnalysis._build_src_dicts_from_peaks(): Found source\n",
      "name: PS J0616.4+2221\n",
      "ts: 29.926298\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:10 INFO    GTAnalysis.add_source(): Adding source PS J0616.4+2221\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Each lower bound must be strictly less than each upper bound.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0616.4+2221       : ['Prefactor']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis._find_sources_iterate(): Performing spectral fit for PS J0616.4+2221.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0616.4+2221       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:791: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  spline = UnivariateSpline(x, y, k=2,\n",
      "2026-07-17 22:37:11 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis.fit(): LogLike:   -19879.938 DeltaLogLike:        0.031 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis._find_sources_iterate(): {'Index': {'error': np.float64(0.18610706557569456),\n",
      "           'value': -1.976838603385781},\n",
      " 'Prefactor': {'error': np.float64(1.4432149297199825e-12),\n",
      "               'value': 3.3783622533915712e-12},\n",
      " 'Scale': {'error': np.float64(nan), 'value': 1000.0}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0616.4+2221       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis.find_sources(): Found 1 sources in iteration 1.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:11 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:12 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/sourcefind_02_pointsource_powerlaw_2.00_tsmap.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.tsmap(): Execution time: 12.51 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.find_sources(): Found 0 sources in iteration 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.find_sources(): Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.find_sources(): Execution time: 43.45 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.003  0.000737   2.78  78090.18     19242.0    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12045.06      3625.9     \n",
      "3FGL J0626.8+1743      1.684   0.000  4.07e-11   4.94     -0.00         0.0    *\n",
      "3FGL J0648.8+1516      4.358   1.090  2.06e-05   1.60    131.36        45.4     \n",
      "3FGL J0650.5+2055      5.040   0.544   8.6e-06   1.25     32.94         8.5     \n",
      "3FGL J0619.4+2242      5.980   3.203  5.78e-05   2.73    604.13       454.2     \n",
      "PS J0617.2+2224        6.065   3.649  8.54e-05   2.38   1087.43       544.2     \n",
      "PS J0616.4+2221        6.142   0.338  2.08e-05   1.98     29.93        66.2     \n",
      "isodiff                  ---   0.497     0.015   2.12     19.74       339.4    *\n",
      "galdiff                  ---   0.994      0.13  -0.00  36755.78      9721.1    *\n",
      "\n"
     ]
    }
   ],
   "source": [
    "findsource = gta.find_sources(sqrt_ts_threshold=5,\n",
    "                              min_separation=0.2,\n",
    "                              tsmap_fitter='tsmap')\n",
    "\n",
    "gta.print_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:24.470010Z",
     "iopub.status.busy": "2026-07-17T22:37:24.469869Z",
     "iopub.status.idle": "2026-07-17T22:37:26.308577Z",
     "shell.execute_reply": "2026-07-17T22:37:26.307710Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0616.4+2221       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:24 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:791: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  spline = UnivariateSpline(x, y, k=2,\n",
      "2026-07-17 22:37:26 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   2 Status: 102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:26 INFO    GTAnalysis.fit(): LogLike:   -19821.425 DeltaLogLike:       58.513 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:26 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.003  0.000737   2.78  59675.81     19239.6    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.39      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.09e-12   4.94     -0.00         0.0    *\n",
      "3FGL J0648.8+1516      4.358   1.310  2.03e-05   1.68    155.83        53.9    *\n",
      "3FGL J0650.5+2055      5.040   0.731   8.2e-06   1.41     38.27        11.6    *\n",
      "3FGL J0619.4+2242      5.980   1.279  2.25e-05   2.67    132.27       169.7    *\n",
      "PS J0617.2+2224        6.065   4.158  7.34e-05   2.54    820.72       549.4    *\n",
      "PS J0616.4+2221        6.142   0.481  2.49e-05   2.04     44.31        88.3    *\n",
      "isodiff                  ---   0.458    0.0138   2.12     15.97       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36770.01      9808.9    *\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.free_sources()\n",
    "gta.fit()\n",
    "gta.print_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see how much the model is improved let's produce a TS map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:26.310081Z",
     "iopub.status.busy": "2026-07-17T22:37:26.309935Z",
     "iopub.status.idle": "2026-07-17T22:37:39.450801Z",
     "shell.execute_reply": "2026-07-17T22:37:39.450086Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:26 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:27 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:39 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:39 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/TSmap_relnewsources_pointsource_powerlaw_2.00_tsmap.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:39 INFO    GTAnalysis.tsmap(): Execution time: 13.14 s\n"
     ]
    }
   ],
   "source": [
    "tsmap_relnewsources = gta.tsmap(prefix='TSmap_relnewsources',\n",
    "                                make_plots=True,\n",
    "                                write_fits=True,\n",
    "                                write_npy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:39.452310Z",
     "iopub.status.busy": "2026-07-17T22:37:39.452159Z",
     "iopub.status.idle": "2026-07-17T22:37:39.656523Z",
     "shell.execute_reply": "2026-07-17T22:37:39.655672Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.clf()\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "ROIPlotter(tsmap_relnewsources['sqrt_ts'],roi=gta.roi).plot(levels=[0,3,6,10],vmin=0,vmax=5,subplot=121,cmap='magma')\n",
    "plt.gca().set_title('Sqrt(TS)')\n",
    "ROIPlotter(tsmap_relnewsources['npred'],roi=gta.roi).plot(vmin=0,vmax=100,subplot=122,cmap='magma')\n",
    "plt.gca().set_title('NPred')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TSmap now shows that the OFF-phase pulsar has been successfully modelled out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:39.658153Z",
     "iopub.status.busy": "2026-07-17T22:37:39.657992Z",
     "iopub.status.idle": "2026-07-17T22:37:42.757434Z",
     "shell.execute_reply": "2026-07-17T22:37:42.756627Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:39 INFO    GTBinnedAnalysis.write_xml(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/rel_newsources_00.xml...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:39 INFO    GTBinnedAnalysis.write_xml(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/rel_newsources_01.xml...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:39 INFO    GTAnalysis.write_fits(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/rel_newsources.fits...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:40 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 00.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:40 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 01.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:40 INFO    GTAnalysis.write_roi(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/rel_newsources.npy...\n"
     ]
    }
   ],
   "source": [
    "gta.write_roi('rel_newsources',make_plots=True,save_model_map=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:42.759424Z",
     "iopub.status.busy": "2026-07-17T22:37:42.759245Z",
     "iopub.status.idle": "2026-07-17T22:37:42.764197Z",
     "shell.execute_reply": "2026-07-17T22:37:42.763442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/rel_newsources_counts_spectrum.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:42.765683Z",
     "iopub.status.busy": "2026-07-17T22:37:42.765520Z",
     "iopub.status.idle": "2026-07-17T22:37:42.769517Z",
     "shell.execute_reply": "2026-07-17T22:37:42.768843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/rel_newsources_counts_map_xproj_3.000_5.500.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:42.770948Z",
     "iopub.status.busy": "2026-07-17T22:37:42.770786Z",
     "iopub.status.idle": "2026-07-17T22:37:42.774873Z",
     "shell.execute_reply": "2026-07-17T22:37:42.774146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/rel_newsources_counts_map_yproj_3.000_5.500.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plots above all the residuals have disappeared."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we derive the SED of the on and off-phase components of Geminga."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:42.776248Z",
     "iopub.status.busy": "2026-07-17T22:37:42.776109Z",
     "iopub.status.idle": "2026-07-17T22:37:42.794853Z",
     "shell.execute_reply": "2026-07-17T22:37:42.794077Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.003  0.000737   2.78  59675.81     19239.6    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.39      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.09e-12   4.94     -0.00         0.0    *\n",
      "3FGL J0648.8+1516      4.358   1.310  2.03e-05   1.68    155.83        53.9    *\n",
      "3FGL J0650.5+2055      5.040   0.731   8.2e-06   1.41     38.27        11.6    *\n",
      "3FGL J0619.4+2242      5.980   1.279  2.25e-05   2.67    132.27       169.7    *\n",
      "PS J0617.2+2224        6.065   4.158  7.34e-05   2.54    820.72       549.4    *\n",
      "PS J0616.4+2221        6.142   0.481  2.49e-05   2.04     44.31        88.3    *\n",
      "isodiff                  ---   0.458    0.0138   2.12     15.97       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36770.01      9808.9    *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0616.4+2221       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.003  0.000737   2.78  59675.81     19239.6    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.39      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.09e-12   4.94     -0.00         0.0    *\n",
      "3FGL J0648.8+1516      4.358   1.310  2.03e-05   1.68    155.83        53.9     \n",
      "3FGL J0650.5+2055      5.040   0.731   8.2e-06   1.41     38.27        11.6     \n",
      "3FGL J0619.4+2242      5.980   1.279  2.25e-05   2.67    132.27       169.7     \n",
      "PS J0617.2+2224        6.065   4.158  7.34e-05   2.54    820.72       549.4     \n",
      "PS J0616.4+2221        6.142   0.481  2.49e-05   2.04     44.31        88.3     \n",
      "isodiff                  ---   0.458    0.0138   2.12     15.97       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36770.01      9808.9    *\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.print_model()\n",
    "gta.free_sources(free=False)\n",
    "gta.free_sources(skydir=gta.roi[gta.roi.sources[0].name].skydir,distance=[3.0],free=True)\n",
    "gta.print_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the SED for both the ON- and OFF-phase components of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:42.796340Z",
     "iopub.status.busy": "2026-07-17T22:37:42.796177Z",
     "iopub.status.idle": "2026-07-17T22:37:44.380885Z",
     "shell.execute_reply": "2026-07-17T22:37:44.380169Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.sed(): Computing SED for 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis._make_sed(): Fitting SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:42 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:791: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  spline = UnivariateSpline(x, y, k=2,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:43 INFO    GTAnalysis.sed(): Finished SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:44 INFO    GTAnalysis.sed(): Execution time: 1.58 s\n"
     ]
    }
   ],
   "source": [
    "Geminga_onphase = gta.sed(gta.roi.sources[0].name, bin_index=2.2, outfile='sedGeminga_onphase.fits', loge_bins=None,write_npy=True,write_fits=True,make_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:44.382542Z",
     "iopub.status.busy": "2026-07-17T22:37:44.382385Z",
     "iopub.status.idle": "2026-07-17T22:37:45.903556Z",
     "shell.execute_reply": "2026-07-17T22:37:45.902761Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:44 INFO    GTAnalysis.sed(): Computing SED for PS J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:44 INFO    GTAnalysis._make_sed(): Fitting SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:44 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:44 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:44 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:44 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:791: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  spline = UnivariateSpline(x, y, k=2,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.sed(): Finished SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.sed(): Execution time: 1.52 s\n"
     ]
    }
   ],
   "source": [
    "Geminga_offphase = gta.sed(gta.roi.sources[1].name, bin_index=2.2, outfile='sedGeminga_offphase.fits', loge_bins=None,write_npy=True,write_fits=True,make_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the SED plots for both components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:45.905126Z",
     "iopub.status.busy": "2026-07-17T22:37:45.904962Z",
     "iopub.status.idle": "2026-07-17T22:37:45.909305Z",
     "shell.execute_reply": "2026-07-17T22:37:45.908535Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/3fgl_j0633.9+1746_sed.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:45.910681Z",
     "iopub.status.busy": "2026-07-17T22:37:45.910526Z",
     "iopub.status.idle": "2026-07-17T22:37:45.914705Z",
     "shell.execute_reply": "2026-07-17T22:37:45.914099Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./Geminga_data/ps_j0633.9+1746_sed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SED of the on phase and off-phase components of Geminga are very similar and they are both compatible with a power-law with an exponential cutoff. The off-phase flux is about a factor of two lower than the on-phase flux, which is approximately what would be expected by looking at the phase plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now search for a possible extension of the off-phase component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-17T22:37:45.916155Z",
     "iopub.status.busy": "2026-07-17T22:37:45.916002Z",
     "iopub.status.idle": "2026-07-17T22:38:07.175871Z",
     "shell.execute_reply": "2026-07-17T22:38:07.175172Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Fixing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.003  0.000737   2.78  59675.81     19239.6     \n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.39      3625.7     \n",
      "3FGL J0626.8+1743      1.684   0.000  8.09e-12   4.94     -0.00         0.0     \n",
      "3FGL J0648.8+1516      4.358   1.310  2.03e-05   1.68    155.83        53.9     \n",
      "3FGL J0650.5+2055      5.040   0.731   8.2e-06   1.41     38.27        11.6     \n",
      "3FGL J0619.4+2242      5.980   1.279  2.25e-05   2.67    132.27       169.7     \n",
      "PS J0617.2+2224        6.065   4.158  7.34e-05   2.54    820.72       549.4     \n",
      "PS J0616.4+2221        6.142   0.481  2.49e-05   2.04     44.31        88.3     \n",
      "isodiff                  ---   0.458    0.0138   2.12     15.97       312.8     \n",
      "galdiff                  ---   1.002     0.132  -0.00  36770.01      9808.9     \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.003  0.000737   2.78  59675.81     19239.6    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.39      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.09e-12   4.94     -0.00         0.0     \n",
      "3FGL J0648.8+1516      4.358   1.310  2.03e-05   1.68    155.83        53.9     \n",
      "3FGL J0650.5+2055      5.040   0.731   8.2e-06   1.41     38.27        11.6     \n",
      "3FGL J0619.4+2242      5.980   1.279  2.25e-05   2.67    132.27       169.7     \n",
      "PS J0617.2+2224        6.065   4.158  7.34e-05   2.54    820.72       549.4     \n",
      "PS J0616.4+2221        6.142   0.481  2.49e-05   2.04     44.31        88.3     \n",
      "isodiff                  ---   0.458    0.0138   2.12     15.97       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36770.01      9808.9    *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:45 INFO    GTAnalysis.extension(): Running extension fit for PS J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:37:58 INFO    GTAnalysis._extension(): Fitting extended-source model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:04 INFO    GTAnalysis._extension(): Generating TS map.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:05 INFO    GTAnalysis._extension(): Testing point-source model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/extension.py:314: RuntimeWarning: invalid value encountered in sqrt\n",
      "  np.sqrt(o['ts_ext']) > sqrt_ts_threshold):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:06 INFO    GTAnalysis._extension(): Best-fit extension: 0.0032 + 0.0355 -    nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:06 INFO    GTAnalysis._extension(): TS_ext:        -0.004\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:06 INFO    GTAnalysis._extension(): Extension UL: 0.0487\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:06 INFO    GTAnalysis._extension(): LogLike:   -19821.380 DeltaLogLike:        0.045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:06 INFO    GTAnalysis.extension(): Finished extension fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:07 WARNING GTAnalysis.extension(): Saving maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/Geminga_data/ps_j0633.9+1746_ext.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:07 INFO    GTAnalysis.extension(): Execution time: 21.24 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-07-17 22:38:07 INFO    GTAnalysis.print_model(): \n",
      "sourcename            offset    norm  eflux     index        ts       npred free\n",
      "--------------------------------------------------------------------------------\n",
      "3FGL J0633.9+1746      0.002   1.003  0.000737   2.78  59675.81     19239.6    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.39      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.09e-12   4.94     -0.00         0.0     \n",
      "3FGL J0648.8+1516      4.358   1.310  2.03e-05   1.68    155.83        53.9     \n",
      "3FGL J0650.5+2055      5.040   0.731   8.2e-06   1.41     38.27        11.6     \n",
      "3FGL J0619.4+2242      5.980   1.279  2.25e-05   2.67    132.27       169.7     \n",
      "PS J0617.2+2224        6.065   4.158  7.34e-05   2.54    820.72       549.4     \n",
      "PS J0616.4+2221        6.142   0.481  2.49e-05   2.04     44.31        88.3     \n",
      "isodiff                  ---   0.458    0.0138   2.12     15.97       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36770.01      9808.9    *\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'spatial_model': 'RadialGaussian', 'width': [], 'fit_position': False, 'width_min': 0.01, 'width_max': 1.0, 'width_nstep': 21, 'free_background': False, 'fix_shape': False, 'free_radius': None, 'fit_ebin': False, 'update': True, 'save_model_map': False, 'sqrt_ts_threshold': 3.0, 'psf_scale_fn': None, 'make_tsmap': True, 'tsmap_fitter': 'tsmap', 'make_plots': True, 'write_fits': True, 'write_npy': True, 'reoptimize': False, 'ra_format': 'hour', 'optimizer': {'optimizer': 'MINUIT', 'tol': 0.001, 'max_iter': 100, 'init_lambda': 0.0001, 'retries': 3, 'min_fit_quality': 2, 'verbosity': 0}, 'prefix': '', 'outfile': None, 'loge_bins': []}\n",
      "{'spatial_model': 'RadialGaussian', 'width': [], 'fit_position': False, 'width_min': 0.01, 'width_max': 1.0, 'width_nstep': 21, 'free_background': False, 'fix_shape': False, 'free_radius': None, 'fit_ebin': False, 'update': True, 'save_model_map': False, 'sqrt_ts_threshold': 3.0, 'psf_scale_fn': None, 'make_tsmap': True, 'tsmap_fitter': 'tsmap', 'make_plots': True, 'write_fits': True, 'write_npy': True, 'reoptimize': False, 'ra_format': 'hour', 'optimizer': {'optimizer': 'MINUIT', 'tol': 0.001, 'max_iter': 100, 'init_lambda': 0.0001, 'retries': 3, 'min_fit_quality': 2, 'verbosity': 0}, 'prefix': '', 'outfile': None, 'loge_bins': []}\n",
      "{'name': 'PS J0633.9+1746', 'file': None, 'config': {'spatial_model': 'RadialGaussian', 'width': [], 'fit_position': False, 'width_min': 0.01, 'width_max': 1.0, 'width_nstep': 21, 'free_background': False, 'fix_shape': False, 'free_radius': None, 'fit_ebin': False, 'update': True, 'save_model_map': False, 'sqrt_ts_threshold': 3.0, 'psf_scale_fn': None, 'make_tsmap': True, 'tsmap_fitter': 'tsmap', 'make_plots': True, 'write_fits': True, 'write_npy': True, 'reoptimize': False, 'ra_format': 'hour', 'optimizer': {'optimizer': 'MINUIT', 'tol': 0.001, 'max_iter': 100, 'init_lambda': 0.0001, 'retries': 3, 'min_fit_quality': 2, 'verbosity': 0}, 'prefix': '', 'outfile': None, 'loge_bins': []}, 'width': array([0.        , 0.01      , 0.01258925, 0.01584893, 0.01995262,\n",
      "       0.02511886, 0.03162278, 0.03981072, 0.05011872, 0.06309573,\n",
      "       0.07943282, 0.1       , 0.12589254, 0.15848932, 0.19952623,\n",
      "       0.25118864, 0.31622777, 0.39810717, 0.50118723, 0.63095734,\n",
      "       0.79432823, 1.        ]), 'dloglike': array([-2.04409624e-03, -2.13602535e-02, -3.56409426e-02, -6.07183976e-02,\n",
      "       -1.06479488e-01, -1.93768453e-01, -3.68000347e-01, -7.28994053e-01,\n",
      "       -1.49466688e+00, -3.13190050e+00, -6.58408459e+00, -1.36508779e+01,\n",
      "       -2.74627867e+01, -5.36809642e+01, -9.97990605e+01, -1.72428867e+02,\n",
      "       -2.87720641e+02, -4.53618279e+02, -6.80725791e+02, -9.78339405e+02,\n",
      "       -1.35057867e+03, -1.79256991e+03]), 'loglike': array([-19821.37983072, -19821.39914688, -19821.41342757, -19821.43850502,\n",
      "       -19821.48426611, -19821.57155508, -19821.74578697, -19822.10678068,\n",
      "       -19822.87245351, -19824.50968712, -19827.96187122, -19835.02866456,\n",
      "       -19848.84057328, -19875.05875087, -19921.17684715, -19993.80665404,\n",
      "       -20109.09842782, -20274.99606611, -20502.10357787, -20799.71719147,\n",
      "       -21171.9564579 , -21613.94769206]), 'loglike_ptsrc': -19821.377786627123, 'loglike_ext': -19821.379834477528, 'loglike_init': -19821.424932526108, 'loglike_base': -19821.424932524766, 'ext': 0.0031622776601683794, 'ext_err_hi': np.float64(0.03553599041452472), 'ext_err_lo': np.float64(nan), 'ext_err': np.float64(0.03553599041452472), 'ext_ul95': 0.04867755850688909, 'ts_ext': -0.0040957008095574565, 'ebin_e_min': array([], dtype=float64), 'ebin_e_ctr': array([], dtype=float64), 'ebin_e_max': array([], dtype=float64), 'ebin_ext': array([], dtype=float64), 'ebin_ext_err': array([], dtype=float64), 'ebin_ext_err_hi': array([], dtype=float64), 'ebin_ext_err_lo': array([], dtype=float64), 'ebin_ext_ul95': array([], dtype=float64), 'ebin_ts_ext': array([], dtype=float64), 'ebin_dloglike': array([], shape=(0, 22), dtype=float64), 'ebin_loglike': array([], shape=(0, 22), dtype=float64), 'ebin_loglike_ptsrc': array([], dtype=float64), 'ebin_loglike_ext': array([], dtype=float64), 'ra': np.float64(98.482416321313), 'dec': np.float64(17.768254206806677), 'glon': np.float64(195.13858188951784), 'glat': np.float64(4.270646145648665), 'ra_err': nan, 'dec_err': nan, 'glon_err': nan, 'glat_err': nan, 'pos_offset': 0.0, 'pos_err': nan, 'pos_r68': nan, 'pos_r95': nan, 'pos_r99': nan, 'pos_err_semimajor': nan, 'pos_err_semiminor': nan, 'pos_angle': nan, 'tsmap': <gammapy.maps.wcs.ndmap.WcsNDMap object at 0x7f6ff1133b90>, 'ptsrc_tot_map': None, 'ptsrc_src_map': None, 'ptsrc_bkg_map': None, 'ext_tot_map': None, 'ext_src_map': None, 'ext_bkg_map': None, 'source_fit': {'param_names': array([b'Prefactor', b'Index', b'Scale', b'', b'', b'', b'', b'', b'',\n",
      "       b''], dtype='|S32'), 'param_values': array([ 2.17915008e-10, -2.93927372e+00,  1.00000000e+03,             nan,\n",
      "                   nan,             nan,             nan,             nan,\n",
      "                   nan,             nan]), 'param_errors': array([6.44565178e-12, 4.00507654e-02,            nan,            nan,\n",
      "                  nan,            nan,            nan,            nan,\n",
      "                  nan,            nan]), 'ts': np.float64(12471.876123243965), 'loglike': np.float64(-19821.379834477528), 'loglike_scan': array([-26057.3178961 , -19824.10661788, -19823.45970131, -19822.90220846,\n",
      "       -19822.43312974, -19822.05147334, -19821.75626482, -19821.54654674,\n",
      "       -19821.42137819, -19821.37983448, -19821.40760315, -19821.49063856,\n",
      "       -19821.62845231, -19821.82056275, -19822.0664949 , -19822.36578026,\n",
      "       -19822.71795673, -19823.12256849, -19823.57916587, -19824.08730526]), 'dloglike_scan': array([-6.23593806e+03, -2.72678340e+00, -2.07986683e+00, -1.52237398e+00,\n",
      "       -1.05329526e+00, -6.71638859e-01, -3.76430347e-01, -1.66712263e-01,\n",
      "       -4.15437162e-02,  0.00000000e+00, -2.77686775e-02, -1.10804085e-01,\n",
      "       -2.48617829e-01, -4.40728274e-01, -6.86660420e-01, -9.85945778e-01,\n",
      "       -1.33812225e+00, -1.74273401e+00, -2.19933139e+00, -2.70747079e+00]), 'eflux_scan': array([0.        , 0.00021978, 0.00022119, 0.00022259, 0.000224  ,\n",
      "       0.0002254 , 0.00022681, 0.00022821, 0.00022962, 0.00023102,\n",
      "       0.00023217, 0.00023333, 0.00023448, 0.00023563, 0.00023679,\n",
      "       0.00023794, 0.00023909, 0.00024025, 0.0002414 , 0.00024255]), 'flux_scan': array([0.00000000e+00, 1.07016117e-07, 1.07699957e-07, 1.08383797e-07,\n",
      "       1.09067637e-07, 1.09751477e-07, 1.10435317e-07, 1.11119157e-07,\n",
      "       1.11802997e-07, 1.12486837e-07, 1.13048311e-07, 1.13609786e-07,\n",
      "       1.14171261e-07, 1.14732735e-07, 1.15294210e-07, 1.15855685e-07,\n",
      "       1.16417159e-07, 1.16978634e-07, 1.17540109e-07, 1.18101584e-07]), 'norm_scan': array([0.        , 2.07316863, 2.08641631, 2.09966399, 2.11291168,\n",
      "       2.12615936, 2.13940704, 2.15265472, 2.1659024 , 2.17915008,\n",
      "       2.19002725, 2.20090441, 2.21178157, 2.22265873, 2.2335359 ,\n",
      "       2.24441306, 2.25529022, 2.26616738, 2.27704455, 2.28792171]), 'npred': 3620.76202881675, 'npred_wt': 3620.76202881675, 'pivot_energy': 1675.9470230649706, 'flux': 1.1248683675379354e-07, 'flux100': 9.790893087582635e-06, 'flux1000': 1.1248683675379354e-07, 'flux10000': 1.2912310560254307e-09, 'flux_err': np.float64(2.375248476371501e-09), 'flux100_err': np.float64(9.248034613913148e-07), 'flux1000_err': np.float64(2.375248476371501e-09), 'flux10000_err': np.float64(1.2185679222348056e-10), 'flux_ul95': 1.1643503603691985e-07, 'flux100_ul95': 1.0134545715615634e-05, 'flux1000_ul95': 1.1643503603691985e-07, 'flux10000_ul95': 1.3365522480588453e-09, 'eflux': 0.0002310204758279805, 'eflux100': 0.0020172331801680387, 'eflux1000': 0.0002310204758279805, 'eflux10000': 2.564380692187872e-05, 'eflux_err': np.float64(6.867317442335187e-06), 'eflux100_err': np.float64(0.00014827749759664224), 'eflux1000_err': np.float64(6.867317442335187e-06), 'eflux10000_err': np.float64(2.8358170692993978e-06), 'eflux_ul95': 0.00023912911238825577, 'eflux100_ul95': 0.0020880364743638764, 'eflux1000_ul95': 0.00023912911238825577, 'eflux10000_ul95': 2.6543884326732725e-05, 'dnde': 4.7766649232244397e-11, 'dnde100': 1.8947890124854134e-07, 'dnde1000': 2.1791500842102193e-10, 'dnde10000': 2.5061867354215313e-13, 'dnde_err': np.float64(1.0096866947886773e-12), 'dnde100_err': np.float64(2.1765862907572933e-08), 'dnde1000_err': np.float64(6.445651780268785e-12), 'dnde10000_err': np.float64(1.8693098573843945e-14), 'dnde_index': np.float64(2.9392737153079516), 'dnde100_index': np.float64(2.9392737153079516), 'dnde1000_index': np.float64(2.9392737153079516), 'dnde10000_index': np.float64(2.9392737153079516), 'name': 'PS J0633.9+1746', 'spectral_pars': {'Prefactor': {'name': 'Prefactor', 'value': 2.179150084210219, 'error': 0.06445651780268785, 'min': 1e-05, 'max': 1000.0, 'free': True, 'scale': 1e-10}, 'Index': {'name': 'Index', 'value': 2.939273715311858, 'error': 0.040050765441167015, 'min': 0.0, 'max': 5.0, 'free': True, 'scale': -1.0}, 'Scale': {'name': 'Scale', 'value': 1.0, 'error': nan, 'min': 0.001, 'max': 1000.0, 'free': False, 'scale': 1000.0}}, 'model_counts': array([1.48946645e+03, 8.98709595e+02, 5.28722001e+02, 3.04525593e+02,\n",
      "       1.72523365e+02, 9.71619037e+01, 5.52349819e+01, 3.18053879e+01,\n",
      "       1.82024300e+01, 1.03765100e+01, 5.93399527e+00, 3.44364036e+00,\n",
      "       2.00897134e+00, 1.15860791e+00, 6.62320606e-01, 3.77837881e-01,\n",
      "       2.15363531e-01, 1.22814144e-01, 7.01758259e-02, 4.00812496e-02]), 'model_counts_wt': array([1.48946645e+03, 8.98709595e+02, 5.28722001e+02, 3.04525593e+02,\n",
      "       1.72523365e+02, 9.71619037e+01, 5.52349819e+01, 3.18053879e+01,\n",
      "       1.82024300e+01, 1.03765100e+01, 5.93399527e+00, 3.44364036e+00,\n",
      "       2.00897134e+00, 1.15860791e+00, 6.62320606e-01, 3.77837881e-01,\n",
      "       2.15363531e-01, 1.22814144e-01, 7.01758259e-02, 4.00812496e-02]), 'covar': array([[0.00415464, 0.00180579],\n",
      "       [0.00180579, 0.00160406]]), 'model_flux': {'energies': array([  1000.        ,   1124.65782212,   1264.85521686,   1422.52931349,\n",
      "         1599.85871961,   1799.29362329,   2023.58964773,   2275.84592607,\n",
      "         2559.5479227 ,   2878.61559235,   3237.45754282,   3641.03194931,\n",
      "         4094.91506238,   4605.37825582,   5179.47467923,   5825.13671247,\n",
      "         6551.2855686 ,   7367.95455966,   8286.42772855,   9319.39576234,\n",
      "        10481.13134155,  11787.68634794,  13257.1136559 ,  14909.71657184,\n",
      "        16768.32936811,  18858.63278773,  21209.5088792 ,  23853.44006431,\n",
      "        26826.9579528 ,  30171.14810529,  33932.21771895,  38162.13407949,\n",
      "        42919.34260129,  48269.57437678,  54286.75439324,  61054.02296585,\n",
      "        68664.88450043,  77224.49945836,  86851.13737514,  97677.81100895,\n",
      "       109854.11419876, 123548.28882567, 138949.54943731, 156270.6976547 ,\n",
      "       175751.06248548, 197659.80717016, 222299.64825262, 250011.03826179,\n",
      "       281176.86979742, 316227.76601684]), 'log_energies': array([3.        , 3.05102041, 3.10204082, 3.15306122, 3.20408163,\n",
      "       3.25510204, 3.30612245, 3.35714286, 3.40816327, 3.45918367,\n",
      "       3.51020408, 3.56122449, 3.6122449 , 3.66326531, 3.71428571,\n",
      "       3.76530612, 3.81632653, 3.86734694, 3.91836735, 3.96938776,\n",
      "       4.02040816, 4.07142857, 4.12244898, 4.17346939, 4.2244898 ,\n",
      "       4.2755102 , 4.32653061, 4.37755102, 4.42857143, 4.47959184,\n",
      "       4.53061224, 4.58163265, 4.63265306, 4.68367347, 4.73469388,\n",
      "       4.78571429, 4.83673469, 4.8877551 , 4.93877551, 4.98979592,\n",
      "       5.04081633, 5.09183673, 5.14285714, 5.19387755, 5.24489796,\n",
      "       5.29591837, 5.34693878, 5.39795918, 5.44897959, 5.5       ]), 'dnde': array([2.17915008e-10, 1.54285175e-10, 1.09234859e-10, 7.73389566e-11,\n",
      "       5.47564601e-11, 3.87679127e-11, 2.74479222e-11, 1.94332989e-11,\n",
      "       1.37588959e-11, 9.74138349e-12, 6.89695982e-12, 4.88309025e-12,\n",
      "       3.45725813e-12, 2.44776016e-12, 1.73302935e-12, 1.22699551e-12,\n",
      "       8.68720415e-13, 6.15059432e-13, 4.35465886e-13, 3.08312543e-13,\n",
      "       2.18287189e-13, 1.54548682e-13, 1.09421423e-13, 7.74710452e-14,\n",
      "       5.48499796e-14, 3.88341252e-14, 2.74948010e-14, 1.94664894e-14,\n",
      "       1.37823950e-14, 9.75802097e-15, 6.90873926e-15, 4.89143017e-15,\n",
      "       3.46316284e-15, 2.45194073e-15, 1.73598922e-15, 1.22909112e-15,\n",
      "       8.70204119e-16, 6.16109904e-16, 4.36209626e-16, 3.08839116e-16,\n",
      "       2.18660006e-16, 1.54812638e-16, 1.09608306e-16, 7.76033594e-17,\n",
      "       5.49436589e-17, 3.89004507e-17, 2.75417599e-17, 1.94997365e-17,\n",
      "       1.38059342e-17, 9.77468688e-18]), 'dnde_lo': array([2.11654534e-10, 1.50301922e-10, 1.06678898e-10, 7.56639917e-11,\n",
      "       5.36186485e-11, 3.79583672e-11, 2.68454424e-11, 1.89698889e-11,\n",
      "       1.33959346e-11, 9.45525011e-12, 6.67153470e-12, 4.70625149e-12,\n",
      "       3.31934051e-12, 2.34086855e-12, 1.65069052e-12, 1.16393368e-12,\n",
      "       8.20677188e-13, 5.78633540e-13, 4.07968013e-13, 2.87635859e-13,\n",
      "       2.02794772e-13, 1.42977980e-13, 1.00804915e-13, 7.10715126e-14,\n",
      "       5.01085279e-14, 3.53289483e-14, 2.49088318e-14, 1.75622473e-14,\n",
      "       1.23825844e-14, 8.73066494e-15, 6.15585569e-15, 4.34045091e-15,\n",
      "       3.06046037e-15, 2.15796462e-15, 1.52162505e-15, 1.07294349e-15,\n",
      "       7.56575032e-16, 5.33498472e-16, 3.76201417e-16, 2.65285667e-16,\n",
      "       1.87073937e-16, 1.31922523e-16, 9.30316642e-17, 6.56067938e-17,\n",
      "       4.62671793e-17, 3.26289738e-17, 2.30112378e-17, 1.62286619e-17,\n",
      "       1.14454162e-17, 8.07210127e-18]), 'dnde_hi': array([2.24360660e-10, 1.58373991e-10, 1.11852059e-10, 7.90510000e-11,\n",
      "       5.59184165e-11, 3.95947236e-11, 2.80639232e-11, 1.99080293e-11,\n",
      "       1.41316916e-11, 1.00361758e-11, 7.13000184e-12, 5.06657378e-12,\n",
      "       3.60090619e-12, 2.55953278e-12, 1.81947535e-12, 1.29347402e-12,\n",
      "       9.19576140e-13, 6.53778392e-13, 4.64817172e-13, 3.30475569e-13,\n",
      "       2.34963144e-13, 1.67055759e-13, 1.18774446e-13, 8.44468145e-14,\n",
      "       6.00400848e-14, 4.26870696e-14, 3.03492388e-14, 2.15772049e-14,\n",
      "       1.53404495e-14, 1.09062682e-14, 7.75370324e-15, 5.51235105e-15,\n",
      "       3.91885384e-15, 2.78596476e-15, 1.98055268e-15, 1.40796322e-15,\n",
      "       1.00089902e-15, 7.11513591e-16, 5.05789796e-16, 3.59542982e-16,\n",
      "       2.55579152e-16, 1.81674458e-16, 1.29138621e-16, 9.17935635e-17,\n",
      "       6.52472379e-17, 4.63773416e-17, 3.29642648e-17, 2.34301342e-17,\n",
      "       1.66532885e-17, 1.18363856e-17]), 'dnde_err': array([6.44565178e-12, 4.08881597e-12, 2.61720002e-12, 1.71204341e-12,\n",
      "       1.16195645e-12, 8.26810835e-13, 6.16001009e-13, 4.74730448e-13,\n",
      "       3.72795669e-13, 2.94792298e-13, 2.33042024e-13, 1.83483529e-13,\n",
      "       1.43648061e-13, 1.11772623e-13, 8.64460046e-14, 6.64785086e-14,\n",
      "       5.08557243e-14, 3.87189592e-14, 2.93512855e-14, 2.21630262e-14,\n",
      "       1.66759544e-14, 1.25070770e-14, 9.35302268e-15, 6.97576929e-15,\n",
      "       5.19010519e-15, 3.85294449e-15, 2.85443778e-15, 2.11071552e-15,\n",
      "       1.55805453e-15, 1.14824721e-15, 8.44963976e-16, 6.20920880e-16,\n",
      "       4.55690994e-16, 3.34024021e-16, 2.44563465e-16, 1.78872102e-16,\n",
      "       1.30694902e-16, 9.54036871e-17, 6.95801701e-17, 5.07038656e-17,\n",
      "       3.69191463e-17, 2.68618197e-17, 1.95303144e-17, 1.41902041e-17,\n",
      "       1.03035790e-17, 7.47689089e-18, 5.42250494e-18, 3.93039764e-18,\n",
      "       2.84735436e-18, 2.06169876e-18]), 'dnde_ferr': array([0.02915386, 0.02615957, 0.02367907, 0.02189717, 0.02099997,\n",
      "       0.02110452, 0.02219623, 0.02413745, 0.0267375 , 0.02981741,\n",
      "       0.0332369 , 0.0368949 , 0.04072095, 0.04466619, 0.04869647,\n",
      "       0.05278762, 0.0569222 , 0.06108747, 0.06527395, 0.06947448,\n",
      "       0.0736836 , 0.07789707, 0.08211158, 0.08632452, 0.09053383,\n",
      "       0.09473783, 0.0989352 , 0.10312485, 0.10730592, 0.11147769,\n",
      "       0.11563959, 0.11979116, 0.12393201, 0.12806185, 0.13218044,\n",
      "       0.13628759, 0.14038315, 0.14446702, 0.14853911, 0.15259938,\n",
      "       0.15664779, 0.16068434, 0.16470903, 0.16872188, 0.17272292,\n",
      "       0.17671219, 0.18068974, 0.18465563, 0.18860992, 0.19255268]), 'pivot_energy': 1675.9470230649706}}}\n"
     ]
    }
   ],
   "source": [
    "gta.free_sources(free=False)\n",
    "gta.print_model()\n",
    "gta.free_sources(skydir=gta.roi[gta.roi.sources[0].name].skydir,distance=[1.0],free=True)\n",
    "gta.print_model()\n",
    "extensionsmc = gta.extension(gta.roi.sources[1].name,update=True,make_plots=True,sqrt_ts_threshold=3.0,spatial_model='RadialGaussian')\n",
    "gta.print_model()"
   ]
  }
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