{
 "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": {
     "iopub.execute_input": "2026-02-25T23:43:01.909681Z",
     "iopub.status.busy": "2026-02-25T23:43:01.909487Z",
     "iopub.status.idle": "2026-02-25T23:43:01.912865Z",
     "shell.execute_reply": "2026-02-25T23:43:01.912410Z"
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   "outputs": [],
   "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": "2026-02-25T23:43:01.914383Z",
     "iopub.status.busy": "2026-02-25T23:43:01.914227Z",
     "iopub.status.idle": "2026-02-25T23:43:01.916405Z",
     "shell.execute_reply": "2026-02-25T23:43:01.915956Z"
    }
   },
   "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": "2026-02-25T23:43:01.917980Z",
     "iopub.status.busy": "2026-02-25T23:43:01.917811Z",
     "iopub.status.idle": "2026-02-25T23:43:01.920268Z",
     "shell.execute_reply": "2026-02-25T23:43:01.919716Z"
    }
   },
   "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": "2026-02-25T23:43:01.921783Z",
     "iopub.status.busy": "2026-02-25T23:43:01.921615Z",
     "iopub.status.idle": "2026-02-25T23:43:01.924089Z",
     "shell.execute_reply": "2026-02-25T23:43:01.923503Z"
    }
   },
   "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": "2026-02-25T23:43:01.925678Z",
     "iopub.status.busy": "2026-02-25T23:43:01.925485Z",
     "iopub.status.idle": "2026-02-25T23:43:01.927632Z",
     "shell.execute_reply": "2026-02-25T23:43:01.927165Z"
    }
   },
   "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": "2026-02-25T23:43:01.929099Z",
     "iopub.status.busy": "2026-02-25T23:43:01.928943Z",
     "iopub.status.idle": "2026-02-25T23:43:01.931113Z",
     "shell.execute_reply": "2026-02-25T23:43:01.930668Z"
    }
   },
   "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": "2026-02-25T23:43:01.932765Z",
     "iopub.status.busy": "2026-02-25T23:43:01.932524Z",
     "iopub.status.idle": "2026-02-25T23:43:03.233393Z",
     "shell.execute_reply": "2026-02-25T23:43:03.232746Z"
    }
   },
   "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": "2026-02-25T23:43:03.235655Z",
     "iopub.status.busy": "2026-02-25T23:43:03.235367Z",
     "iopub.status.idle": "2026-02-25T23:43:04.085987Z",
     "shell.execute_reply": "2026-02-25T23:43:04.085174Z"
    }
   },
   "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": "2026-02-25T23:43:04.088343Z",
     "iopub.status.busy": "2026-02-25T23:43:04.088118Z",
     "iopub.status.idle": "2026-02-25T23:43:04.418302Z",
     "shell.execute_reply": "2026-02-25T23:43:04.417710Z"
    }
   },
   "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": "2026-02-25T23:45:39.919092Z",
     "shell.execute_reply": "2026-02-25T23:45:39.918488Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:04 INFO    GTAnalysis.__init__(): \n",
      "--------------------------------------------------------------------------------\n",
      "fermipy version 1.4.1 \n",
      "ScienceTools version 2.2.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Prefactor': 0, 'Index1': 1, 'Scale': 2, 'Cutoff': 3, 'Index2': 4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTAnalysis.setup(): Running setup.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis.setup(): Running setup for component 00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis._select_data(): Skipping data selection.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis._create_ltcube(): Skipping LT Cube.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Prefactor': 0, 'Index1': 1, 'Scale': 2, 'Cutoff': 3, 'Index2': 4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 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": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:05 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:09 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:09 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 4.12 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:09 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:09 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=195.13259294771035 yref=4.269835915614621 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": [
      "2026-02-25 23:43:09 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:09 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:09 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:09 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:11 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:11 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 1.47 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",
      "2026-02-25 23:43:11 INFO    GTBinnedAnalysis.run_gtapp(): Running gtsrcmaps.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:11 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": [
      "2026-02-25 23:43:11 INFO    GTBinnedAnalysis.run_gtapp(): This is gtsrcmaps version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:11 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:11 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:11 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0626.8+1743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0631.2+2019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0647.8+1751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.1+1606\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.8+1516\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0650.5+2055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0610.6+1728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0611.5+1957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0619.4+2242\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0700.0+1709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0609.2+2051c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0659.5+1414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0605.9+2039c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named isodiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named galdiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:15 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0605.9+2039c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:16 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0609.2+2051c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:17 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0610.6+1728 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:17 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0611.5+1957 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:18 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0619.4+2242 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:18 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0626.8+1743 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:19 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0631.2+2019 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:20 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0633.9+1746 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:20 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0647.8+1751 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:21 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.1+1606 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:21 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.8+1516 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:22 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0650.5+2055 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:22 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0659.5+1414 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:23 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0700.0+1709 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:43:52 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for galdiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:15 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for isodiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtsrcmaps. Execution time: 64.96 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.setup(): Finished setup for component 00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.setup(): Running setup for component 01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis._select_data(): Skipping data selection.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis._create_ltcube(): Skipping LT Cube.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 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": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:16 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:20 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:20 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 3.97 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:20 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:20 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=195.13259294771035 yref=4.269835915614621 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": [
      "2026-02-25 23:44:20 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:20 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:20 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:20 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:22 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:22 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 1.53 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",
      "2026-02-25 23:44:22 INFO    GTBinnedAnalysis.run_gtapp(): Running gtsrcmaps.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:22 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": [
      "2026-02-25 23:44:22 INFO    GTBinnedAnalysis.run_gtapp(): This is gtsrcmaps version HEAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:22 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:22 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R2_SOURCE_V6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:22 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0626.8+1743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0631.2+2019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0647.8+1751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.1+1606\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0648.8+1516\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0650.5+2055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0610.6+1728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0611.5+1957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0619.4+2242\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0700.0+1709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0609.2+2051c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0659.5+1414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 3FGL J0605.9+2039c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named isodiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named galdiff\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:26 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0605.9+2039c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:27 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0609.2+2051c 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:28 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0610.6+1728 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:28 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0611.5+1957 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:29 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0619.4+2242 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:29 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0626.8+1743 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:30 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0631.2+2019 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:31 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0633.9+1746 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:31 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0647.8+1751 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:32 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.1+1606 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:32 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0648.8+1516 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:33 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0650.5+2055 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:34 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0659.5+1414 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:44:34 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 3FGL J0700.0+1709 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:03 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for galdiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:26 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for isodiff 21....................!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:27 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtsrcmaps. Execution time: 64.93 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:27 INFO    GTBinnedAnalysis.setup(): Finished setup for component 01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:27 INFO    GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:31 INFO    GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 01.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:35 WARNING GTBinnedAnalysis._scale_srcmap(): The expscale parameter was zero, setting it to 1e-8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:36 INFO    GTAnalysis.setup(): Initializing source properties\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 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": "2026-02-25T23:45:39.920939Z",
     "iopub.status.busy": "2026-02-25T23:45:39.920752Z",
     "iopub.status.idle": "2026-02-25T23:45:39.924931Z",
     "shell.execute_reply": "2026-02-25T23:45:39.924445Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 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": "2026-02-25T23:45:39.926467Z",
     "iopub.status.busy": "2026-02-25T23:45:39.926309Z",
     "iopub.status.idle": "2026-02-25T23:45:41.815752Z",
     "shell.execute_reply": "2026-02-25T23:45:41.815158Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0631.2+2019     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0647.8+1751     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0648.1+1606     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0610.6+1728     : ['norm', 'alpha', 'beta']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0611.5+1957     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0700.0+1709     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0609.2+2051c    : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0659.5+1414     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0605.9+2039c    : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:39 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": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.optimize(): Finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.optimize(): LogLike: -26231.637120 Delta-LogLike: 7660.387064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.optimize(): Execution time: 1.87 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": "2026-02-25T23:45:41.817626Z",
     "iopub.status.busy": "2026-02-25T23:45:41.817415Z",
     "iopub.status.idle": "2026-02-25T23:45:41.919176Z",
     "shell.execute_reply": "2026-02-25T23:45:41.918557Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 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": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0631.2+2019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0647.8+1751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0648.1+1606\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0610.6+1728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0611.5+1957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0700.0+1709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0609.2+2051c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0659.5+1414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0605.9+2039c\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 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": "2026-02-25T23:45:41.920900Z",
     "iopub.status.busy": "2026-02-25T23:45:41.920709Z",
     "iopub.status.idle": "2026-02-25T23:45:42.885694Z",
     "shell.execute_reply": "2026-02-25T23:45:42.885142Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:41 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:42 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:42 INFO    GTAnalysis.fit(): LogLike:   -26228.310 DeltaLogLike:        4.704 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:42 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": "2026-02-25T23:45:42.887358Z",
     "iopub.status.busy": "2026-02-25T23:45:42.887189Z",
     "iopub.status.idle": "2026-02-25T23:45:57.419631Z",
     "shell.execute_reply": "2026-02-25T23:45:57.419026Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:42 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:44 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:57 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:57 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": [
      "2026-02-25 23:45:57 INFO    GTAnalysis.tsmap(): Execution time: 14.53 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": "2026-02-25T23:45:57.421694Z",
     "iopub.status.busy": "2026-02-25T23:45:57.421450Z",
     "iopub.status.idle": "2026-02-25T23:45:57.698371Z",
     "shell.execute_reply": "2026-02-25T23:45:57.697772Z"
    }
   },
   "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": "2026-02-25T23:45:57.700308Z",
     "iopub.status.busy": "2026-02-25T23:45:57.700118Z",
     "iopub.status.idle": "2026-02-25T23:46:02.440564Z",
     "shell.execute_reply": "2026-02-25T23:46:02.439898Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:57 INFO    GTAnalysis.residmap(): Generating residual maps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:45:57 INFO    GTAnalysis.add_source(): Adding source residmap_testsource\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:00 INFO    GTAnalysis.delete_source(): Deleting source residmap_testsource\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:02 INFO    GTAnalysis.residmap(): Finished residual maps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:02 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": [
      "2026-02-25 23:46:02 INFO    GTAnalysis.residmap(): Execution time: 4.74 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": "2026-02-25T23:46:02.442681Z",
     "iopub.status.busy": "2026-02-25T23:46:02.442455Z",
     "iopub.status.idle": "2026-02-25T23:46:02.589498Z",
     "shell.execute_reply": "2026-02-25T23:46:02.588983Z"
    }
   },
   "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": "2026-02-25T23:46:02.591400Z",
     "iopub.status.busy": "2026-02-25T23:46:02.591223Z",
     "iopub.status.idle": "2026-02-25T23:46:02.753389Z",
     "shell.execute_reply": "2026-02-25T23:46:02.752811Z"
    }
   },
   "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": "2026-02-25T23:46:02.755433Z",
     "iopub.status.busy": "2026-02-25T23:46:02.755245Z",
     "iopub.status.idle": "2026-02-25T23:46:06.302189Z",
     "shell.execute_reply": "2026-02-25T23:46:06.301475Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:02 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": [
      "2026-02-25 23:46:02 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": [
      "2026-02-25 23:46:02 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 %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": [
      "2026-02-25 23:46:03 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 00.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:03 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 01.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:03 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": "2026-02-25T23:46:06.304401Z",
     "iopub.status.busy": "2026-02-25T23:46:06.304220Z",
     "iopub.status.idle": "2026-02-25T23:46:06.310216Z",
     "shell.execute_reply": "2026-02-25T23:46:06.309648Z"
    }
   },
   "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": "2026-02-25T23:46:06.311750Z",
     "iopub.status.busy": "2026-02-25T23:46:06.311564Z",
     "iopub.status.idle": "2026-02-25T23:46:06.315634Z",
     "shell.execute_reply": "2026-02-25T23:46:06.315135Z"
    }
   },
   "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": "2026-02-25T23:46:06.317087Z",
     "iopub.status.busy": "2026-02-25T23:46:06.316932Z",
     "iopub.status.idle": "2026-02-25T23:46:06.320895Z",
     "shell.execute_reply": "2026-02-25T23:46:06.320417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ucRXd2rdvX6vtP3DggEzTVFxcXLlznmMZGRle/x6trOe8p2xV6vSl119/3eohuPDCC3XhhRfqxRdfPK66du/eLUl64IEHtHXrVr344otatmyZYmJidMkll+ijjz6qdp0ff/yxXnrpJd12222Kj4+vtOy4ceOUm5urqVOneh2fOHGiFi1apHHjxikuLk4tW7bUCy+8oPfff1/nnHNOpXV6elPK9nRJh4NbRaF36dKlWrlypV588UXNmDFD/fv319/+9je98847+vHHH70m8I8ZM0aGYei5556zjrlcLr300ks655xzlJKSUmn7AMBXIiMjZZYMka8zS+4CvsaYgiD24osv6uSTT/Y65lkGrzI33XSTLr744mOWq0qY8YXK2nzkuaOVrWq5Y50zTbNcz09Vhu4MGjRImzZtUk5Ojj7//HM98MADysjI0LJly6o9TM3tdkuSwsPD9eGHHyo6OlqS1K9fP3Xs2FGzZ8/WoEGDqlzfli1bdOWVV6pnz566//77Ky07ffp0vfzyy1q4cKG6d+/ude7555/X7bffrvHjx+vCCy9UYWGhXnzxRV122WV6++23q9Umj3fffVdDhw617kdGRlqrmi1fvlyNGzfWJZdcYg3Fk6Ru3bopMTFR69at09///ndJUrt27dSvXz+9/PLLeuihh6zXbu/evZo7d2612wUAAI6OABLETj75ZJ1xxhnVflxiYmKFY/+PVJUwUxNNmjSRYRgV9kh4Nj7y9Fp4PrXPyMhQ8+bNy5Ut2+MRHx9fpTorsn79evXr18/r2I4dO9S2bdtjPhfP96Jfv35q3769Ro4cWe4NdlV4nuvZZ59thQ9JatSokfr06aN33nmnynV98803GjBggDp27KgVK1ZUGipnzZqlOXPmaO7cuRo/frzXuQMHDmjcuHEaO3asFixYYB2/8MIL1bdvX91yyy3asWPHUev2DEX7448/lJycbB3v27evNm3aZF1/7dq11rm//vpLBw8eVHh4eIV1Hrkc7N/+9jdde+21eu+99zR8+HA9//zzioqK0pVXXnnUduEwzzKilS3FG6hlRAEAdQsBBNX2z3/+U7NmzTpmuTZt2mjnzp211o6IiAh16NBBW7duLXdu69atioiIsCZhd+nSxTreqVMnq5zL5dL27dt19dVXW8e6dOmiV199VS6Xy6v3wnMdzxrmFenevbv1htijZcuW1X5ungnfP//8c7UfW9GcGA/TNKvco/LNN9+of//+atOmjT7++ONyE8jLmjVrlmbOnKmZM2fq3nvvLXc+NTVV+fn56tGjR7lzZ5xxhtavX69Dhw4pKiqqwvoHDBige++9V++9954GDhxoHW/cuLEV3I4cGpaQkKD4+HitXLmywjrLhjNJGjZsmJo0aaLnnntOffr00fLly3X99dcftU3wlpSUpNTUVGsndM8GhBs2bJBUsiGh0+k85hA+AEDDRwBBtdWlIVhDhw7VI488ol27dql169aSpJycHL399tu69NJLrQBx1llnqUWLFlq8eLGuuuoq6/FvvvmmDh06pGHDhnnV+fTTT+utt97yKvvCCy+oZcuWOuuss47anujo6OPqVTqS55P8Dh06VPuxLVq0UK9evbRx40ZlZ2crJiZGUsnqWOvXr1fPnj2PWce3336r/v3764QTTtAnn3yiJk2aHLXs7NmzNXPmTE2bNk333XdfhWU8IeyLL77QqFGjrOOmaeqLL75QkyZNKh3rfMYZZ2jgwIF6+umnddVVV+ncc8895nO4+OKL9dprr6m4uLjS75mHw+HQNddcoyeeeEIPPPCAioqKmHxeTUlJSUpKSvLaRLNbt26BaxAAoE4igASxbdu2eY2N92jfvr21DGxFWrZseVyf6h/Nm2++KUnWilVff/219anz8OHDrXIXXHCB1q9f79Xmu+66Sy+99JIuuugi/fOf/5Tdbtf8+fPldDo1c+ZMq1xoaKgefPBB/d///Z9uvvlmXX311frll180adIkDRgwQIMHD7bKXnjhhRowYID+/ve/Kzs7Wx06dNCrr76qlStXasmSJQoNDfXZc3/yySf16aefauDAgWrdurVyc3P16aefauHChTr77LN12WWXHddrtWDBAvXr10+DBg3SPffcI8Mw9PDDDys9PV2zZ8+2yv3xxx9q3769Ro0apWeffVZSSW9F//79JUlz587VL7/84rV7etmfj4cfflgzZszQ4MGDddFFF+mLL77waq8n7CQlJWnYsGF66qmnZLfbNWTIEBUUFOiFF17Qxo0bNXv27GMO2VuyZIkGDRqk/v3764YbbtCgQYPUrFkzZWdn6/vvv9eqVaussCVJI0eO1Msvv6whQ4bo9ttv15lnnqmwsDD973//09q1a3XZZZeVG972t7/9TYsWLdK//vUvpaSk6Oyzz660TTi2b7/9ttz9iIgISSW9VMez0hsAoJ4L3BYkvvPNN9+YQ4YMMVu3bm06HA6zSZMmZs+ePSvclGzz5s3mBRdcYEZGRpqxsbHm0KFDj7pZ23/+8x8zOTnZDA8PN9u2bWvOnDmzwg3a/vrrL3PUqFFmfHy8GRERYfbs2dNctWpVtZ+HvzciPNrt6aefrtXrH6mytpTVp0+fcsdM0zR//fVX8/LLLzdjYmLMRo0amRdccMFRNzt75ZVXzK5du5rh4eFmYmKiOWHCBDMnJ6dcuZycHHPChAlmYmKiGR4ebnbt2tV89dVXffOEy9i4caN58cUXmy1btjTDw8PNRo0amaeeeqo5e/ZsMzc3t1z5qr5Wpmman376qdmnTx+zUaNGZqNGjczzzz/f3Lhxo1eZHTt2mJK8dqk91s9H2c0PPd+TqrQpPz/ffOihh8yuXbua0dHRZlxcnNmzZ09zyZIlptvtrtLr5XQ6zYULF5q9e/c2GzdubNpsNjMuLs4899xzzQceeMDMyMjwKl9UVGQuWLDAPPXUU02Hw2FGRUWZKSkp5s0332z+8ssvFV7jtNNOMyWZDz74YJXahIr98ccfpsPhqPTnw+Fw+H13ZwCoKTYirDnDNE2zFnKNX61bt06vvfaaevfurVatWik3N1cvv/yyXnvtNc2ePVvTpk2TJG3fvl1nnnmmunXrpsmTJ8vpdGrGjBk6cOCAvv32W69P/efOnavp06dr8uTJGjhwoDZt2qRp06Zp1KhReuqpp6xyBQUFOuOMM3Tw4EHNnz9fzZo106JFi/TBBx9o1apV6tOnT5WfR3Z2tmJjY5WVleX1SS4A1DdbtmwptxJaRTZv3qzTTz/dDy0CAN/g/ZoPBDoB1aazzjrLbN26tXV/xIgRZkJCgldi3blzpxkWFmZOmjTJOpaenm46HA7zpptu8qpv7ty5pmEY5g8//GAdW7RokSnJ/Oyzz6xjRUVFZqdOncwzzzyzWu0lUQNoKDZv3lxp74fndrTeSgCBVbY3fO3ateXOu91us3379qYks0+fPj67riTzvvvuq/bjPD36ZXvpawvv12quQW9EmJCQYE1CdrlcWr58ua644gqvtNqmTRv169dPy5Yts46tXLlSTqdTo0eP9qpv9OjRMk3TaxnTZcuWKTk5Wb169bKO2Ww2XXfddfrqq6+sTeEAAADqm+joaGuOYFnr16/Xb7/9Vm5FQaAqGlQAcbvdcrlc2r9/vx577DF99NFHuueeeyRJv/32m/Lz8ytcorRr16769ddfrfXrt23bJunw0q0eLVq0UEJCgnXeU/ZodUrSDz/84JsnBwAA4GdXXXWV3nrrLWVnZ3sdf/bZZ9WrVy8WksBxaVAB5NZbb1VYWJiaNWumO+64Q//5z3908803S5K1sVxFm8jFxcXJNE0dOHDAKmu32ytcFjQuLs5rk7qMjIyj1ln2uhUpKChQdna21w0AAKCu8OyT9eqrr1rHsrKy9NZbb1W4VHlmZqZuvfVWtWrVSuHh4TrxxBM1depUFRQUeJXLzs7WjTfeqPj4eEVFRWnw4MFH3fvql19+0TXXXKNmzZrJbrfr5JNP1qJFi3z4LOFvDWoZ3nvvvVdjx47Vvn379P7772v8+PHKzc3VXXfdZZWpbKnPsueqWq66Zcu6//77q7ShHwAAqPtM01R+UXGgm+ElIiz0mMucVyYmJkbDhw/Xc889Z32o++qrryokJERXXXWVHnnkEaus0+lUv3799Ntvv2nWrFnq2rWrPv30U91///369ttv9cEHH0gqeZ0uv/xyffbZZ5oxY4Z69OihjRs36sILLyx3/R9//FFnn322kpKS9PDDDysxMVEfffSRJkyYoPT09KPuP4W6rUEFEM8mWJI0ZMgQSdKUKVM0atQoa/fdinokMjMzZRiGGjduLKlkR2Wn06m8vDw1atSoXNmyK7vEx8cftU6p4h4XjylTpujOO++07mdnZ1ub6QEAgPolv6hYnWZ8FOhmePnxn4PUKLxmb/fGjBmjfv366YcfflDnzp313HPPacSIEeXmf7zwwgv6/vvvtXTpUo0YMUKSNGDAAEVFRemee+7RJ598ogEDBuijjz7S2rVr9eijj2rChAlWufDwcE2dOtWrzjvvvFPR0dHasGGDNYd3wIABKigo0Pz58zVhwoRKN8tF3dSghmAd6cwzz5TL5dLvv/+u9u3bKyIiQlu3bi1XbuvWrerQoYMcDoekw3M/jiy7d+9epaen65RTTrGOdenS5ah1SvIqeyS73a6YmBivGwA0BAkJCdbv1KNxOBxKSEjwU4sAHK8+ffqoffv2eu6557R161Zt2rSpwuFXa9asUWRkpNfGuJJ0ww03SJJWr14tSVq7dq0k6dprr/Uqd80113jddzqdWr16tYYOHapGjRrJ5XJZtyFDhsjpdJbbABf1Q4PqATnS2rVrFRISohNPPFE2m02XXHKJ3n77bT344INWak9LS9PatWt1xx13WI8bPHiwHA6HFi9erLPOOss6vnjxYhmGocsvv9w6NnToUN1666368ssvrbIul0tLlizRWWed5dMdwwGgvkhKSlJqaqrS09OVn5+v3r17S5I2bNjATuhosCLCQvXjPwcFuhleIsJCa1yHYRgaPXq0/vOf/8jpdOqkk07SueeeW65cRkaGEhMTyw35atasmWw2mzViJCMjQzabzRqd4pGYmFiuPpfLpYULF2rhwoUVti09Pb0mTw0B0iACyE033aSYmBideeaZat68udLT0/XGG2/o9ddf1913321tMDhr1iz16NFDF198sddGhAkJCfrHP/5h1RcXF6dp06Zp+vTpiouLszYinDlzpsaOHatOnTpZZceMGaNFixZpxIgR1kaEjz32mFJTU7Vq1Sq/vxYAUFd4hsXm5uZax7p161bhAh9AQ2AYRo2HO9VVN9xwg2bMmKEnnnhCc+fOrbBMfHy8vvzyS5mm6RVC9u3bJ5fLZfV4xsfHy+VyKSMjwyuE7N2716u+Jk2aKDQ0VP/3f/+ncePGVXjNdu3a1fSpIQAaxP+SXr166fnnn9cLL7yggwcPKioqSqeeeqpeeuklXXfddVa5lJQUrVu3Tvfcc4+GDx8um82m888/XwsWLPDaBV2Spk6dqujoaC1atEgLFixQYmKiJk+eXG5sot1u1+rVqzVp0iTddtttysvLU7du3fThhx9Waxd0AACAuqpVq1a6++67tX37do0aNarCMhdccIGWLl2qd955R0OHDrWOv/jii9Z5SerXr58efPBBvfzyy9YcEEl65ZVXvOpr1KiR+vXrp2+++UZdu3ZVeHi4r58WAqRBBJDRo0eX2zTwaLp3717lnokJEyZ4/cc4mubNm+uFF16oUp0AEGwiIyNlmmagmwGghubPn1/p+euvv16LFi3SqFGjtHPnTnXp0kUbNmzQvHnzNGTIEPXv31+SNHDgQJ133nmaNGmScnNzdcYZZ2jjxo166aWXytX56KOPqnfv3jr33HP197//XW3btlVOTo5+/fVXvf/++1qzZk2tPFfUrgYRQAAAABBYDodDa9eu1dSpU/XQQw9p//79atWqle666y6v5XJDQkL03nvv6c4779SDDz6owsJCnXPOOVqxYoVSUlK86uzUqZO2bNmi2bNna9q0adq3b58aN26sjh07Wiueov4xTD6WqjOys7MVGxurrKwsVsQCAACog3i/VnMNehleAAAAAHULAQQAAACA3xBAAAAAAPgNAQQAAACA3xBAAAAAAPgNAQQAAACA3xBAAAAAAPgNAQQAAACA3xBAAAAAAPgNAQQAAACA3xBAAAAAAPgNAQQAAABeFi9eLMMwrJvD4VBiYqL69eun+++/X/v27fMqP3PmTBmG4XWssLBQt9xyi1q0aKHQ0FB169ZNkpSZmamRI0eqWbNmMgxDl19+uZ+eFeoKW6AbAAAAgLrp+eefV0pKioqKirRv3z5t2LBBDzzwgBYsWKDXX39d/fv3lySNHTtWgwcP9nrs448/rieffFILFy5U9+7dFRUVJUmaPXu2li1bpueee07t27dXXFyc358XAosAAgDwuS9+z9Dqn/6SpNJPUKX+JzdXj7a80QDqk1NOOUVnnHGGdf+KK67QHXfcod69e2vYsGH65Zdf1Lx5c51wwgk64YQTvB67bds2RUREaPz48eWOt2/fXtdee61fngPqHoZgAQB87h9Lv9PTn+7Q05/u0FP//V1Prv9dE1/7NtDNAuADSUlJevjhh5WTk6Mnn3xSUvkhWIZh6JlnnlF+fr41jMszrGvVqlX66aefrOPr1q3TunXrrK/L2rlzp/VYj99//10jR45Uy5YtZbfb1bx5c11wwQX69ttvvR77+uuvq1evXoqMjFRUVJQGDRqkb775prZeFlQDPSAAAJ87kFcoSbr6zCRJ0qtfpelg6TGgwTJNqSgv0K3wFtZIOmJuhi8MGTJEoaGh+u9//1vh+c8//1yzZ8/W2rVrtWbNGklSu3bt9Pnnn+vWW29VVlaWXn75ZUlSp06dtGXLlmpdu7i4WA8++KCSkpKUnp6uzz77TAcPHrTKzJs3T9OmTdPo0aM1bdo0FRYW6qGHHtK5556rr776Sp06dTr+J48aI4AAAHzKNE05i4olSXf07yhTJQHE6XIHtmFAbSvKk+a1DHQrvN27RwqP9Hm1kZGRSkhI0J49eyo837NnTzVt2lQhISHq2bOndbx58+aKiYlRYWGh1/GqysjIUGpqqh555BFdd9111vFhw4ZZX+/atUv33Xefxo8fr//85z/W8QEDBqhjx46aNWuWXn/99WpfG75DAAEA+JTLbcptlnxtDwuVSr8udptyFbtlC2X0L9AQmKbp92vGxcWpffv2euihh1RcXKx+/frp1FNPVUjI4d8rH330kVwul66//nq5XC7ruMPhUJ8+fbR27Vq/txveCCAAAJ8qKNPTYbeFlDtHAEGDFdaopMehLglrVCvV5ubmKiMjQ126dKmV+o/GMAytXr1a//znP/Xggw/qH//4h+Li4nTttddq7ty5io6O1l9/lSyA0aNHjwrrKBtWEBgEEACAT3mGX0klAaTsh6TOomJF2vnTgwbKMGpluFNd9MEHH6i4uFh9+/b1SX0Oh0OSVFBQ4HU8PT29XNk2bdro2WeflST9/PPPWrp0qWbOnKnCwkI98cQTSkhIkCS9+eabatOmjU/aB9/irwAAwKc8PSB2W4i1BG+4LUSFLrdX7wiA+iktLU133XWXYmNjdfPNN/ukzrZt20qSvv/+ew0aNMg6/t5771X6uJNOOknTpk3TW2+9ZU1kHzRokGw2m3777TddccUVPmkffIsAAgDwKU8PSNnhV3YCCFAvbdu2TS6XSy6XS/v27dOnn36q559/XqGhoVq2bJmaNm3qk+skJiaqf//+uv/++9WkSRO1adNGq1ev1ttvv+1V7vvvv9f48eM1YsQIdezYUeHh4VqzZo2+//57TZ48WVJJmPnnP/+pqVOn6vfff9fgwYPVpEkT/fXXX/rqq68UGRmpWbNm+aTdOD4EEACATxUUlfaAhIVax+y2UOXI5TU8C0DdN3r0aElSeHi4GjdurJNPPln33HOPxo4d67Pw4fHSSy/ptttu0z333KPi4mJdcsklevXVV702QkxMTFT79u312GOPadeuXTIMQyeeeKIefvhh3XbbbVa5KVOmqFOnTnr00Uf16quvqqCgQImJierRo4duueUWn7Yb1WeYgVjCABXKzs5WbGyssrKyFBMTE+jmAMBx+SbtgIY+9plax0Xo00nnS5J6P7BG/zuQr3fGnaNurRsHtoEAUAO8X6s5lgEAAPiU09MDYivbA1Ly56aAHhAACHoEEACATxW4SkKGI6zsHJCSMMJmhAAAAggAwKcOr4J1uAfEE0boAQEAEEAAAD5V8SpY9IAAAEoQQAAAPuXpAXGE0QMCACiPAAIA8KmyGxF6eHpA2AcEAEAAAQD4VEFFQ7BKe0DYBwQAQAABAPhUhUOw6AEBAJQigAAAfKqyHhACCACAAAIA8CnPSlf2MDYiBACURwABAPiUJ2Q4yvSAeIZj0QMCACCAAAB8yllUSQ+Iix4QAAh2BBAAgE95QkaFGxEW0QMCAMGOAAIA8KmCCuaAWBsR0gMCAEGPAAIA8ClnRatg0QMCAChFAAEA+FSFO6HTAwIAKEUAAQD4VEUbEXp6QAroAQGAoEcAAQD4VIVDsEp7QJz0gABA0COAAAB8quIeEM9GhPSAAECwI4AAAHyqomV42YgQAOBBAAEA+JS1EaGtfA+IZ3gWACB4EUAAAD5VUBoyPHt/lHxNDwgAoAQBBADgU84KNiK05oAwCR0Agh4BBADgM6ZpqrCifUDKbERommZA2gYAqBsIIAAAnyk7xKrsKlhlh2MVFjMMCwCCGQEEAOAzZZfZragHRGIeCAAEOwIIAMBnPHM8QgzJFmJYx8NCDRmld1kJCwCCGwEEAOAzZTchNIzDAcQwDDlKe0HYjBAAgluDCCBr1qzRmDFjlJKSosjISLVq1UqXXXaZNm/e7FXuhhtukGEY5W4pKSkV1rtw4UKlpKTIbrerXbt2mjVrloqKisqV27dvn2644QYlJCSoUaNG6tWrl1avXl0rzxUA6jJP70bZ4Vce9jBWwgIASLZAN8AXHn/8cWVkZOj2229Xp06dtH//fj388MPq2bOnPvroI51//vlW2YiICK1Zs8br8REREeXqnDt3rqZPn67Jkydr4MCB2rRpk6ZNm6bdu3frqaeessoVFBToggsu0MGDB/Xoo4+qWbNmWrRokQYPHqxVq1apT58+tffEAaCOKXCV34TQ4/BmhPSAAEAwaxABZNGiRWrWrJnXscGDB6tDhw6aN2+eVwAJCQlRz549K60vIyNDc+bM0Y033qh58+ZJkvr27auioiJNmzZNEydOVKdOnSRJzz77rLZt26bPPvtMvXr1kiT169dPp556qiZNmqQvv/zSl08VAOo0T+9G2VWvPNiMEAAgNZAhWEeGD0mKiopSp06dtGvXrmrXt3LlSjmdTo0ePdrr+OjRo2Wapt555x3r2LJly5ScnGyFD0my2Wy67rrr9NVXX2n37t3Vvj4A1Fee3o3KekAKmIQOAEGtQQSQimRlZWnLli3q3Lmz1/H8/HwlJiYqNDRUJ5xwgsaPH6/MzEyvMtu2bZMkdenSxet4ixYtlJCQYJ33lO3atWu563uO/fDDDz55PgBQH1TWA+IJJfSAAEBwaxBDsCoybtw45ebmaurUqdaxU089VaeeeqpOOeUUSdL69ev173//W6tXr9amTZsUFRUlqWQIlt1uV2RkZLl64+LilJGRYd3PyMhQXFxcheU854+moKBABQUF1v3s7OxqPksAqFsKKukBcTAJHQCgBhpApk+frpdfflkLFy5U9+7dreN33HGHV7kBAwbotNNO0/Dhw/X00097nS+7fOSRjjxXnbJl3X///Zo1a9ZRzwNAfeMsDRf2SnpAmIQOAMGtwQ3BmjVrlubMmaO5c+dq/Pjxxyw/dOhQRUZG6osvvrCOxcfHy+l0Ki8vr1z5zMxMrx6P+Pj4Cns5PMO6Kuod8ZgyZYqysrKs2/HMVwGAuqSyHhBrDgg9IAAQ1BpUAJk1a5ZmzpypmTNn6t57763y40zTVEjI4ZfCM/dj69atXuX27t2r9PR0awiXp+yR5co+tmzZI9ntdsXExHjdAKA+s/YBYRUsAMBRNJgAMnv2bM2cOVPTpk3TfffdV+XHvfnmm8rLy/Namnfw4MFyOBxavHixV9nFixfLMAxdfvnl1rGhQ4dq+/btXsvtulwuLVmyRGeddZZatmx53M8JAOqbw/uAVDQEy7MPCD0gABDMGsQckIcfflgzZszQ4MGDddFFF3kNp5Kknj176o8//tA111yjkSNHqkOHDjIMQ+vXr9cjjzyizp07a+zYsVb5uLg4TZs2TdOnT1dcXJy1EeHMmTM1duxYaw8QSRozZowWLVqkESNGaP78+WrWrJkee+wxpaamatWqVX57DQCgLvAEEE9vR1l2Tw8Ic0AAIKg1iADy/vvvSyrZv2PlypXlzpumqZiYGDVv3lz/+te/9Ndff6m4uFht2rTRhAkTdO+995Zb8Wrq1KmKjo7WokWLtGDBAiUmJmry5Mleq2pJJcOoVq9erUmTJum2225TXl6eunXrpg8//JBd0AEEHWsIViU9IAzBAoDg1iACyLp1645ZpkmTJnr77berVe+ECRM0YcKEY5Zr3ry5XnjhhWrVDQAN0eEhWBX1gDAECwDQgOaAAAACr7KNCB1sRAgAEAEEAOBDzsqW4aUHBAAgAggAwIcqXwWLHhAAAAEEAOBDBUWeIVjle0A8w7LYiBAAghsBBADgM84q9IA4WYYXAIIaAQQA4DMFleyEfngZXnpAACCYEUAAAD7j6QFxVDAJ3TMsizkgABDcCCAAAJ+pSg8IQ7AAILgRQAAAPlPo6QGpYBI6Q7AAABIBBADgQ549PiqahG4NwaIHBACCGgEEAOAzh/cBOfpGhPSAAEBwI4AAAHymwBqCVclGhPSAAEBQI4AAAHzm8BCsyjYiJIAAQDAjgAAAfMJV7JbLbUqqfCPCwmK3ikvLAQCCDwEEAOAThcWHezYqWwVLOrxaFgAg+BBAAAA+UXZ/j/AKe0BCypRlIjoABCsCCACgRnJzc2UYhpq1aClJCgs1FBpilCtnCw2RrfQ480AAIHjZAt0AAED9lJaWpvT0dOXn50uSjNBwSVJYiLRlyxYlJCQoKSnJ6zGOsFAdKnCxFC8ABDECCACg2tLS0pScnCyn02kdM2wlASTnYKa6d79IDodDqampXiHEbgvRoQKpY0pnFaX/oUOHDikyMtLv7QcABA4BBABQbenp6V7hQzocQExXkSTJ6XQqPT3dCiBpaWkyTFdp2TBJ0rfffquIiAhJqrDHBADQ8BBAAAA+cTiAFJQ75+kxibvuEYXFn2AN1+rdu7dVpqIeEwBAw8MkdACATxzZA1KWp8fELC45Z4SFlyvj6TEBADRsBBAAgE94hlWZxYVHLWO6Ss55ekAAAMGHAAIA8AnDZpckmUVVCCClYQUAEHwIIAAAn6hWD0hpWAEABB8CCADAJzzDqugBAQBUhgACAKi2hIQEORwOr2OeieWeHhCHw6GEhASvMocDCHNAACBYEUAAANWWlJSk1NRUbd68WRs2bJB0uAfkkiEXavPmzRUuqesJJwQQAAhe7AMCADguSUlJVsAwTVP//uRnPbr6F7VKbKbTTz/Fq6ynx8SzRG9FAaSiHhMAQMNDAAEA+ITTVSxJstvKd657ekweWvW73v851wogGzZsYCd0AAgyBBAAgE8UFLklSfawikf3JiUlqXXLPOnn3zTxH3frvnWL/dg6AEBdwRwQAIBPFJT2gDhsoUctYy89V+By+6VNAIC6hwACAPCJY/WASJKj9JyzqNgvbQIA1D0EEACAT3h6NeyV9oCEeJUFAAQfAggAwCc8vRqOSntASodgFRFAACBYEUAAAD5RpR6QME8PCEOwACBYEUAAAD5RUMkyvB7WJHR6QAAgaBFAAAA+4SwNFZ5hVhVx0AMCAEGPAAIA8Inq9IA46QEBgKBFAAEA+IQ1B6SSHpDDq2DRAwIAwYoAAgDwCc8qWJX1gFirYLEMLwAELQIIAMAnPKGismV4PeGEjQgBIHgRQAAAPmHthF7pRoT0gABAsCOAAABqzDRNOT2T0CvdiPBwD4hpmn5pGwCgbiGAAABqrKjYlCdPVKUHxG1KLjcBBACCEQEEAFBjzjKrWlU6B6TMOYZhAUBwIoAAAGqs7M7m4aHHnoQuMREdAIIVAQQAUGNlNyE0DOOo5QzDULi1Fwg9IAAQjAggAIAa8+xs7qhkE0IPazNCekAAICgRQAAANVa2B+RYPCHFWUQPCAAEIwIIAKDGPMOpKluC18PqAXHRAwIAwYgAAgCoMc+EckclS/B6eHpAmAMCAMGJAAIAqLHj6QFhFSwACE4NIoCsWbNGY8aMUUpKiiIjI9WqVStddtll2rx5c7myW7ZsUf/+/RUVFaXGjRtr2LBh+v333yusd+HChUpJSZHdble7du00a9YsFRUVlSu3b98+3XDDDUpISFCjRo3Uq1cvrV692ufPEwDqKs8yvJVtQuhhZxUsAAhqDSKAPP7449q5c6duv/12rVixQo8++qj27dunnj17as2aNVa57du3q2/fviosLNTSpUv13HPP6eeff9a5556r/fv3e9U5d+5c3X777Ro2bJg++ugj3XrrrZo3b57GjRvnVa6goEAXXHCBVq9erUcffVTvvvuumjdvrsGDB2v9+vV+ef4AEGie+RyVbULocXgSOj0gABCMbIFugC8sWrRIzZo18zo2ePBgdejQQfPmzdP5558vSZoxY4bsdruWL1+umJgYSVL37t3VsWNHLViwQA888IAkKSMjQ3PmzNGNN96oefPmSZL69u2roqIiTZs2TRMnTlSnTp0kSc8++6y2bdumzz77TL169ZIk9evXT6eeeqomTZqkL7/80i+vAQAEEj0gAICqahA9IEeGD0mKiopSp06dtGvXLkmSy+XS8uXLdcUVV1jhQ5LatGmjfv36admyZdaxlStXyul0avTo0V51jh49WqZp6p133rGOLVu2TMnJyVb4kCSbzabrrrtOX331lXbv3u2rpwkAdZazGj0gnpBCAAGA4NQgAkhFsrKytGXLFnXu3FmS9Ntvvyk/P19du3YtV7Zr16769ddf5XQ6JUnbtm2TJHXp0sWrXIsWLZSQkGCd95Q9Wp2S9MMPP/jmCQFAHVadHhBPSGEjQgAITg1iCFZFxo0bp9zcXE2dOlVSybAqSYqLiytXNi4uTqZp6sCBA2rRooUyMjJkt9sVGRlZYVlPXZ56j1Zn2etWpKCgQAUFBdb97OzsKj47AKhbqrMRIT0gABDcGmQPyPTp0/Xyyy/r3//+t7p37+51zjCMoz6u7Lmqlqtu2bLuv/9+xcbGWrfWrVsftSwA1GWeXc09E8wrY6cHBACCWoMLILNmzdKcOXM0d+5cjR8/3joeHx8vqeIeiczMTBmGocaNG1tlnU6n8vLyKixbtscjPj7+qHVKFfe4eEyZMkVZWVnWzTNfBQDqm+r0gFirYNEDAgBBqUEFkFmzZmnmzJmaOXOm7r33Xq9z7du3V0REhLZu3VrucVu3blWHDh3kcDgkHZ77cWTZvXv3Kj09Xaeccop1rEuXLketU5JX2SPZ7XbFxMR43QCgPrI2IqzSECx6QAAgmDWYADJ79mzNnDlT06ZN03333VfuvM1m0yWXXKK3335bOTk51vG0tDStXbtWw4YNs44NHjxYDodDixcv9qpj8eLFMgxDl19+uXVs6NCh2r59u9dyuy6XS0uWLNFZZ52lli1b+u5JAkAd5dnTw16VIVgswwsAQa1BTEJ/+OGHNWPGDA0ePFgXXXSRvvjiC6/zPXv2lFTSQ9KjRw9dfPHFmjx5spxOp2bMmKGEhAT94x//sMrHxcVp2rRpmj59uuLi4jRw4EBt2rRJM2fO1NixY609QCRpzJgxWrRokUaMGKH58+erWbNmeuyxx5SamqpVq1b55wUAgACrTg8IGxECQHBrEAHk/fffl1Syf8fKlSvLnTdNU5KUkpKidevW6Z577tHw4cNls9l0/vnna8GCBWratKnXY6ZOnaro6GgtWrRICxYsUGJioiZPnmytquVht9u1evVqTZo0Sbfddpvy8vLUrVs3ffjhh+rTp08tPWMAqFuOpwfEM3EdABBcGkQAWbduXZXLdu/evco9ExMmTNCECROOWa558+Z64YUXqtwGAGho8kvDREQVAsjhSej0gABAMGowc0AAAIHjLCwJE43Cjx1AIkrL5BUSQAAgGBFAAAA1ll86BKsqPSCekMIcEAAITgQQAECNeQJIVTYi9JTJpwcEAIISAQQAUGP51RmC5Qkg9IAAQFAigAAAaswaglWFANIovGT9E3pAACA4EUAAADXmCRNVmQNCDwgABDcCCACgRtxus1o9II7wkj89+UXF1j5NAIDgQQABANSIZxd0qaqrYJUMwTJN78cCAIIDAQQAUCNlh1JVaRUs2+E/PcwDAYDgQwABANRIXqFLkmS3hSg0xDhmeVtoiMJDDw/DAgAEFwIIAKBGnNWY/+HhCCOAAECwIoAAAGokv7BkHkdV5n94sBQvAAQvAggAoEasFbCqEUA8vSX0gABA8CGAAABqxDMHpHpDsEoDCD0gABB0CCAAgBpxHkcPSKPSsJJHAAGAoEMAAQDUSHU2IfTwhBUnQ7AAIOgQQAAANeLpxahOD4g1BIsAAgBBhwACAKgRzzyO6vSAMAQLAIIXAQQAUCPHMweEIVgAELwIIACAGsk7jh4QaxleekAAIOgQQAAANXI8+4AwBwQAghcBBABQIyzDCwCojjoVQHbt2qWVK1cqIyMj0E0BAFTR8UxCZw4IAASvgAWQadOm6Y477rDur1q1SieddJIuuuginXTSSfrhhx8C1TQAQDUczxwQB3NAACBoBSyAvPXWW+rUqZN1f9q0aeratauWLVumNm3aaM6cOYFqGgCgGo5nDkij0rJ59IAAQNCxBerCu3fvVocOHSRJGRkZ2rRpk1asWKFBgwbJ6XTqH//4R6CaBgCohuNahre0B8RJDwgABJ2A9YCYpim32y1J2rhxo0JDQ3XeeedJklq0aKH09PRANQ0AUA3HtQwvq2ABQNAKWABp3769li9fLkl67bXXdOaZZyoiIkKS9Oeff6pJkyaBahoAoBqOZwiWtQ8IAQQAgk7AhmDdfPPNGjdunF588UUdPHhQzz33nHVu48aNXvNDAAB1l7MmPSAMwQKAoBOwAPL3v/9dTZo00WeffaYzzzxT1113nXUuPz9fo0aNClTTAADV4JlI3uh4dkKnBwQAgk7AAogkjRw5UiNHjix3/KmnngpAawAAx8PTi+GozhAsekAAIGgFbA5IaGiovvrqqwrPbd68WaGhVf9DBgAIDLfbVIGrZEGR450D4nabtdI2AEDdFNBVsI7GszoWAKBuc7oO92A0Cq96p3rZsOIJMACA4BCwACJJhmFUeHzz5s1q3LixfxsDAKi2vDJDqOy2qv9JKTtci3kgABBc/DoH5NFHH9Wjjz4qqSR8XH755bLb7V5l8vPztW/fPg0fPtyfTQMAHIfD8z9CFBJS8YdKFQkNMWS3hajA5VZeoUtxkeG11UQAQB3j1wDSrFkzde7cWZK0c+dOnXjiieV6Oux2u7p06aLbb7/dn00DAByH49kF3SMiPFQFLrdVBwAgOPg1gFx99dW6+uqrJUn9+vXT448/rpSUFH82AQDgQ54hWNWZ/+ERERaqgypSfiFzQAAgmARsGd61a9cG6tIAAB/xzN9whFV/SiF7gQBAcAroPiCmaWrTpk36448/lJ+fX+789ddfH4BWAQCqyhMeqrMLuodn2FZeocunbQIA1G0BCyA///yzLr30Uv3yyy8VLslrGAYBBADqOKdnCFbY8Q3BksQcEAAIMgELIOPGjZPT6dTrr7+url27llsNCwBQ93nmgDiOpweEIVgAEJQCFkC++uorPf300yy3CwD1mDUE63jmgFhDsAggABBMArYRYVRUlGJiYgJ1eQCAD3iGTx3XKlieHhACCAAElYAFkNGjR+uVV14J1OUBAD5gDcE6nn1AmAMCAEEpYEOwTjnlFL366qu69NJLdckllyg+Pr5cmWHDhgWgZQCAqsqv4UaEZesAAASHgAWQa665RpK0Y8cOLV++vNx5wzBUXMwfJQCoyzzDpyLCmQMCAKgaNiIEABy3/BruhC4xBAsAgk3AAkifPn0CdWkAgI8c3gm9BkOw6AEBgKASsEnoAID6zxdzQBiCBQDBJWA9IOeff36l5w3D0OrVq/3UGgDA8Ti8DO/xr4LFJHQACC4BCyBut1uGYXgdS09PV2pqqpo1a6aTTjopQC0DAFRVTZbh9YQW5oAAQHAJWABZt25dhcd//vlnXXbZZbrvvvv82yAAQLUdXgWr+gHEwSpYABCU6twckJNOOkl33323Jk2aFOimAACOgSFYAIDqqnMBRJLatm2rbdu2BboZAIBj8PRe1GQSupMeEAAIKnUygLz11ltq2bJltR6Tk5OjSZMmaeDAgWratKkMw9DMmTPLlbvhhhtkGEa5W0pKSoX1Lly4UCkpKbLb7WrXrp1mzZqloqKicuX27dunG264QQkJCWrUqJF69erFJHoADV5NluFtxE7oABCUAjYHZMyYMeWOFRQU6Pvvv9ePP/6oBx98sFr1ZWRk6KmnntKpp56qyy+/XM8888xRy0ZERGjNmjXljh1p7ty5mj59uiZPnqyBAwdq06ZNmjZtmnbv3q2nnnrKq90XXHCBDh48qEcffVTNmjXTokWLNHjwYK1atYo9TwA0WPk1GILFHBAACE4BCyBr1qwptwqWw+FQ27ZtNWXKFF1zzTXVqq9NmzY6cOCADMNQenp6pQEkJCREPXv2rLS+jIwMzZkzRzfeeKPmzZsnSerbt6+Kioo0bdo0TZw4UZ06dZIkPfvss9q2bZs+++wz9erVS5LUr18/nXrqqZo0aZK+/PLLaj0XAKgPit2mCl1uScc5BKv0MQUut9xuUyEhxjEeAQBoCAIWQHbu3OnT+o4MMzW1cuVKOZ1OjR492uv46NGjNXXqVL3zzjtWAFm2bJmSk5Ot8CFJNptN1113ne69917t3r1brVq18mn7ACDQyg6dOp5VsBqFH/4T5HQVe90HADRcdXIOSG3Lz89XYmKiQkNDdcIJJ2j8+PHKzMz0KuOZBN+lSxev4y1atFBCQoLXJPlt27apa9eu5a7jOfbDDz/4+ikAQMB5luA1DMluq96fk7S0NP2w9VvrfnSTBG3cuFFbtmzRli1blJaW5sumAgDqkIB+3JSZmal///vfWr16tTIyMpSQkKD+/ftr4sSJatKkSa1c89RTT9Wpp56qU045RZK0fv16qw2bNm1SVFSUpJIhWHa7XZGRkeXqiIuLU0ZGhnU/IyNDcXFxFZbznK9IQUGBCgoKrPvZ2dnH/8QAwM88S/BGhIVWqxc6LS1NycnJcjqdan3nmwoJc8iw2dW7d2+rjMPhUGpqqpKSknzebgBAYAUsgOzevVvnnHOO0tLSdPLJJyspKUl79uzR7Nmz9eKLL2rjxo3VXgmrKu644w6v+wMGDNBpp52m4cOH6+mnn/Y6X9kf1CPPVaesx/33369Zs2ZVpdkAUOcc7xK86enpcjqdkiSzqEAKcygkzK6yU9GdTqfS09MJIADQAAVsCNa9996r/Px8ffnll/rhhx/0ySef6IcfftCXX36p/Px83XvvvX5ry9ChQxUZGakvvvjCOhYfHy+n06m8vLxy5TMzM716POLj4yvs5fAM66qod0SSpkyZoqysLOu2a9eumj4VAPCbmizB62EWlfQCG2F2n7QJAFD3BSyArFy5UnPmzFGPHj28jvfo0UP//Oc/9eGHH/q1PaZpKiTk8MvhmfuxdetWr3J79+5Venq6NYTLU/bIcmUfW7ZsWXa7XTExMV43AKgvPHNAjmcJXg/T5QkgDp+0CQBQ9wUsgGRlZalt27YVnmvXrp2ysrL81pY333xTeXl5XkvzDh48WA6HQ4sXL/Yqu3jxYhmGocsvv9w6NnToUG3fvt1ruV2Xy6UlS5borLPOqpWhZAAQaPlFLknHtwKWh5seEAAIOgGbA9KuXTt98MEHGjBgQLlzH374odq1a1ftOj/88EPl5uYqJydHkvTjjz/qzTfflCQNGTJE+/fv1zXXXKORI0eqQ4cOMgxD69ev1yOPPKLOnTtr7NixVl1xcXGaNm2apk+frri4OGsjwpkzZ2rs2LHWErxSyaaKixYt0ogRIzR//nw1a9ZMjz32mFJTU7Vq1apqPw8AqA/yC0v2AKnZEKySuSAhNgIIAASLgAWQ0aNHa/LkyXK73Ro1apRatGihP//8U0uWLNHChQs1f/78atf597//XX/88Yd1/4033tAbb7whSdqxY4diY2PVvHlz/etf/9Jff/2l4uJitWnTRhMmTNC9995bbsWrqVOnKjo6WosWLdKCBQuUmJioyZMna+rUqV7l7Ha7Vq9erUmTJum2225TXl6eunXrpg8//JBd0AE0WDXZBd3j8BwQhmABQLAIWAC5++679dtvv+n//b//p0WLFlnHTdPUTTfdpLvuuqvadVZlc8O33367WnVOmDBBEyZMOGa55s2b64UXXqhW3QBQn+UXlg7BYhI6AKAaAhZADMPQk08+qTvvvFNr165VRkaG4uPjdf755+ukk04KVLMAAFWUX3R8y/AmJCTI4XDI6XTKXToE68gA4nA4lJCQ4JuGAgDqFL8GkAMHDmjs2LEaPXq0Lr74YklScnKykpOTrTLLly/XlClT9NRTTyk+Pt6fzQMAVINnDkh1J6EnJSUpNTVV6enp+n9fZGhNWqFCwhzasGGDIiIiJJWEFPYAAYCGya8B5JlnntF3332nwYMHH7XM4MGDdeedd2rRokWaMWOGH1sHADiWtLQ0paenKz8/XzPnPK3YniOUuf8vbdlSJKnqwSEpKUlJSUlqu/tHKW2Hpky/T+eck1LbzQcA1AF+XYb3tdde04033iib7ei5x2az6cYbb9R7773nx5YBAI4lLS1NycnJ6t69u3r37m1NHH91yQvq3r27unfvruTkZKWlpVW5Ts8Eds+eIgCAhs+vAeTnn3/WGWecccxyp59+un7++Wc/tAgAUFXp6elyOp3W/ZDSeRueieSS5HQ6lZ6eXuU6PcO3nEUEEAAIFn4NIC6XS2FhYccsFxYWpqKiIj+0CABwvAybJ4A4j1Hy6Dx7iOTRAwIAQcOvAaRFixb68ccfj1nuhx9+UGJioh9aBAA4Xp4hWGV7QKrLs4JWPj0gABA0/BpA+vTpo8cee6zS3o2ioiI9/vjj6tevnx9bBgCoLs/Sue4aBJBGDMECgKDj1wByxx13aPv27Ro6dKj27NlT7vyePXt0+eWXKzU1VXfccYc/mwYAqCYjjCFYAIDq8+syvF27dtWiRYt06623ql27durevbvatWsnSdqxY4c2b94st9utxx9/XF26dPFn0wAA1RTimQPiqsEQLFbBAoCg4/ed0G+88UadcsopmjdvntauXasvvvhCktSoUSMNHjxYU6ZMUc+ePf3dLABANfliDghDsAAg+Pg9gEhSr1699P7778vtdlvLNSYkJCgkxK8jwgAA1ZCQkCCHw2EtxXt4DsjhIVgOh0MJCQlVrjOCIVgAEHQCEkA8QkJC1KxZs0A2AQBQRUlJSUpNTbU+OPq/ZXuVW2Tq7aWvqWV0yZ+Tqu6E7uFgFSwACDoBDSAAgPolKSnJChiFb62QJHXv1kUtYiOOqz5rJ3QCCAAEDcY8AQCqrajYraJiU9LhYVTHw/PYQpdbxW7TJ20DANRtBBAAQLWVnTTuWcnqeJR9LL0gABAcCCAAgGrzLJsbYkjhocf/p8RuC5FheNcJAGjYCCAAgGrz9FZEhIXK8CSI42AYhjUMi6V4ASA4EEAAANVmBZDwmq9lwlK8ABBcCCAAgGrzDJeKCK/5nxGW4gWA4EIAAQBUmxVAarACloe1FC89IAAQFAggAIBq8+kQrHDmgABAMCGAAACq7fAkdN8NwWIOCAAEBwIIAKDa8mpjCBY9IAAQFAggAIBq8wyXauTDVbAIIAAQHAggAIBqy3G6JB3uvaiJSHtJiDlUWicAoGEjgAAAqi07v0iSFBsRVuO6YhwldWSV1gkAaNgIIACAast2+i6AeOrw1AkAaNgIIACAavP0VsQ28kUAsXnVCQBo2AggAIBq84QFz/Cpmojx9IAQQAAgKBBAAADVluXDOSCeOugBAYDgQAABAFSb1QPiyzkgBBAACAoEEABAtWXnlyyZ65NVsOgBAYCgQgABAFSL221aK1bFRNR8I8LDq2C5ZJpmjesDANRtBBAAQLXkFLjkyQm+nANS7DZ1qIDNCAGgoSOAAACqxTNXwxEWIrut5juhO8JCFW4r+XOUzW7oANDgEUAAANXiyyV4Pazd0POYBwIADR0BBABQLdk+XILXg80IASB4EEAAANXiyz1APNgLBACCBwEEAFAttRlAPKtrAQAaLgIIAKBaDi/B68M5IGxGCABBgwACAKgWhmABAGqCAAIAqBZrFSwCCADgOBBAAADVkpVfsleHL3tAPMvwMgQLABo+AggAoFqyrX1AbD6rkx4QAAgeBBAAQLXUxhyQGAIIAAQNAggAoFpqZyNCAggABAsCCACgWqwekEa+7AEpGc6V7XT5rE4AQN1EAAEAVJlpmof3AXHQAwIAqD4CCACgyvKLilVUbEqqnSFYhS63nEXFPqsXAFD3EEAAAFXm6aGwhRhqFB7qs3qj7DaFGN7XAAA0TAQQAECVld2E0DAMn9VrGIa1EhZ7gQBAw0YAAQBUWXYtbELowTwQAAgODSaA5OTkaNKkSRo4cKCaNm0qwzA0c+bMCstu2bJF/fv3V1RUlBo3bqxhw4bp999/r7DswoULlZKSIrvdrnbt2mnWrFkqKir/x3Hfvn264YYblJCQoEaNGqlXr15avXq1L58iAPhdWlqatmzZoo0bN8owDPUdeKEkyeYu1JYtW5SWluazaxFAACA4NJgAkpGRoaeeekoFBQW6/PLLj1pu+/bt6tu3rwoLC7V06VI999xz+vnnn3Xuuedq//79XmXnzp2r22+/XcOGDdNHH32kW2+9VfPmzdO4ceO8yhUUFOiCCy7Q6tWr9eijj+rdd99V8+bNNXjwYK1fv742ni4A1Lq0tDQlJyere/fu6t27tyQpxBElSfr+6y/UvXt3JScn+yyEeFbVIoAAQMNmC3QDfKVNmzY6cOCADMNQenq6nnnmmQrLzZgxQ3a7XcuXL1dMTIwkqXv37urYsaMWLFigBx54QFJJoJkzZ45uvPFGzZs3T5LUt29fFRUVadq0aZo4caI6deokSXr22We1bds2ffbZZ+rVq5ckqV+/fjr11FM1adIkffnll7X99AHA59LT0+V0Or2OeQKIuyBXkuR0OpWenq6kpKQaXy+WOSAAEBQaTA+IYRjHnBDpcrm0fPlyXXHFFVb4kErCS79+/bRs2TLr2MqVK+V0OjV69GivOkaPHi3TNPXOO+9Yx5YtW6bk5GQrfEiSzWbTddddp6+++kq7d++u4bMDgLohxF4aQJyHfF53jDUEi80IAaAhazABpCp+++035efnq2vXruXOde3aVb/++qv1ad+2bdskSV26dPEq16JFCyUkJFjnPWWPVqck/fDDDz57DgAQSFYPSC0EEOaAAEBwaDBDsKoiIyNDkhQXF1fuXFxcnEzT1IEDB9SiRQtlZGTIbrcrMjKywrKeujz1Hq3Ostc9UkFBgQoKCqz72dnZ1XtCAOBntRlAYiJK/iQRQACgYQuqHhCPyoZqlT1X1XLVLetx//33KzY21rq1bt36qHUAQF1w5BwQX7LmgDgJIADQkAVVAImPj5dUcY9EZmamDMNQ48aNrbJOp1N5eXkVli3b4xEfH3/UOqWKe1wkacqUKcrKyrJuu3btqvZzAgB/CrGX9AozBAsAcLyCKoC0b99eERER2rp1a7lzW7duVYcOHeRwOCQdnvtxZNm9e/cqPT1dp5xyinWsS5cuR61TklfZsux2u2JiYrxuAFCX+WMOCKtgAUDDFlQBxGaz6ZJLLtHbb7+tnJwc63haWprWrl2rYcOGWccGDx4sh8OhxYsXe9WxePFiGYbhtdfI0KFDtX37dq/ldl0ul5YsWaKzzjpLLVu2rLXnBAC1JSEhwfpQxuPIAOJwOJSQkOCT67EPCAAEhwY1Cf3DDz9Ubm6uFS5+/PFHvfnmm5KkIUOGqFGjRpo1a5Z69Oihiy++WJMnT5bT6dSMGTOUkJCgf/zjH1ZdcXFxmjZtmqZPn664uDgNHDhQmzZt0syZMzV27FhrDxBJGjNmjBYtWqQRI0Zo/vz5atasmR577DGlpqZq1apV/n0RAMBHkpKSlJqaqvT0dOvYyLf+VGGxtGzpK0qMsikhIcEne4BI9IAAQLAwTNM0A90IX2nbtq3++OOPCs/t2LFDbdu2lSRt3rxZ99xzjz7//HPZbDadf/75WrBggdq3b1/ucf/5z3+0aNEi7dy5U4mJiRo9erSmTp2qsLAwr3J//fWXJk2apOXLlysvL0/dunXT7Nmz1b9//yq3Pzs7W7GxscrKymI4FoA6p8BVrORpKyVJ380YqNhGYcd4RPUcyC3UabM/kST9MvdChYUGVSc9gHqC92s116ACSH3HDzSAumx/ToF6zF0lw5B+mztEISGVb/5aXa5itzpM/VCStHlaf8VH2X1aPwD4Au/Xao6PlwAAVeKZmxFtt/k8fEiSLTREUXb2AgGAho4AAgCoEk8oiInw7dCrsg7vBeKqtWsAAAKLAAIAqBLP5PDYWgwgMewFAgANHgEEAFAlnh3KazWAOBiCBQANXYNahhcAUHusIVgO3weQtLQ0paenyyzMlSRtS/1NJxTvtc77crlfAEBgEUAAAFWSlVc7PSBpaWlKTk6W0+lU/IW3K6rrAD3w74Wa+sUbVhmHw6HU1FRCCAA0AAzBAgBUiacHxNf7f6Snp8vpdEo6vMO6Z8d1D6fT6bUhIgCg/iKAAACqxB9zQNwFJUOwjgwgAICGgwACAKiSw3NAam/07tF6QAAADQcBBABQJf7YB8QKIPbIWrsGACCwCCAAgCrJyi/ZHLBWh2DRAwIADR4BBABQJf7YiNBdQAABgIaOAAIAqJJsPwzBKqYHBAAaPAIIAOCYit2mcgpqZwhWQkKCHA6HpLJzQBpJMqwyDodDCQkJPr0uACAw2IgQAHBMnt4PyfcBJCkpSampqUpPT1dhsamRb+2VYYTov59/pcjwks/J2AkdABoOAggA4Jg8e4A0Cg9VWKjvO8+TkpKsgGF/90MVuNxqe1IntY5r5PNrAQACiyFYAIBjOpBX+xPQPRqX7rR+IK+w1q8FAPA/AggA4Jj2ZjklSc1jHLV+Lc81/souqPVrAQD8jwACADimvVn5kqQWsf4LIJ5rAgAaFgIIAOCY/swu6QFJ9EMA8YScP0t7XQAADQsBBABwTJ4hWP7oAfGEnL3ZBBAAaIgIIACAY/L0RiTGRtT6tTwhZy89IADQIBFAAADH5NcekJgIr2sCABoWAggAoFKmaVphINEPq2CVnQNimmatXw8A4F8EEABApTJzC1VY7Jbkn2V4PXNA8ouKlZ3vqvXrAQD8iwACAKiUZzJ4QlS4wm21/2fDERaqJqWbEf6ZzVK8ANDQEEAAAJWyhl/5Yf6Hh2eyO0vxAkDDQwABAFTKWgErpvZXwPJgJSwAaLgIIACASvlzBSyPRDYjBIAGiwACAKjUnwEYgtWidLL7XwQQAGhwCCAAgErtLZ0I7s8ekOaeHhB2QweABocAAgCoVEB6QKw5IKyCBQANjS3QDQAA1F1lNyFsEeu/Sehm7gFJ0v8yc7Vly5Zy5xMSEpSUlOS39gAAfIcAAgA4qmynS3mFxZL8swu6JKWlpWnweWep2a1LlFdk6oxevWUWeveEOBwOpaamEkIAoB5iCBYA4Kg8vR+NG4UpIjzUL9dMT09Xfs5BuQtyJUmhUfHlyjidTqWnp/ulPQAA3yKAAACO6s/SORj+6v0oy5VTEjBs0eUDCACg/iKAAACO6q9s/+8B4lGckyFJCo1O8Pu1AQC1hwACADiqwytg+W8Cukex1QNCAAGAhoQAAgA4qkDsgu7hGYIVyhAsAGhQWAULAGBJS0vzmtyduitTklRw8C9t2ZLj1+VvGYIFAA0TAQQAIKkkfCQnJ8vpPLz7eIsx/0/hTdtqxl23adLOb/26/C2T0AGgYWIIFgBAUsnyt2XDh3S498HTG+GP5W8TEhLkcDgq7QFxOBxKSKBnBADqI3pAAAAVMsIcCnVESTrcG+EPSUlJSk1N1Y7d+zTq3b8U2ihWn3/1tcJDDasMO6EDQP1FAAEAVMgz+dtdkFduJ/LalpSUpNatW8uxYqWcRW61ODFFbeIj/doGAEDtYAgWAKBCnuVv/dn7UZZhGGpRuvyvZzlgAED9RwABAFTI0wNSHKAAIh3egX0vAQQAGgwCCACgQkdOQA8Ez/4j9IAAQMNBAAEAVCjQQ7AkKbE0gPyVTQABgIaCAAIAkHR4+VuP0KjyQ7D8vfzt4R4Q/06CBwDUHlbBAgBIOrz8rWefj398vF87Drr0r7kz1L3FPEn+X/42sXQSOnNAAKDhIIAAACxJSUlWwMj64BNJUu/TT9HJLWIC0h5PD8geAggANBgMwQIAlJOZW6jM3EJJUuu4RgFrR+smJdfen1OgHGdRwNoBAPAdAggAoJzte7MlSa3jIhRlD1xneWyjMGsp3p//yglYOwAAvkMAAQCUk7q35M1+SmJghl6VlZwYLUn66U8CCAA0BEEXQNatWyfDMCq8ffHFF15lt2zZov79+ysqKkqNGzfWsGHD9Pvvv1dY78KFC5WSkiK73a527dpp1qxZKipiuACAui0tLU1btmwpd9u4bYckqUWEO8AtlFJalAQQTygCANRvQTsJfd68eerXr5/XsVNOOcX6evv27erbt6+6deumpUuXyul0asaMGTr33HP17bffqmnTplbZuXPnavr06Zo8ebIGDhyoTZs2adq0adq9e7eeeuopvz0nAKiOtLQ0JScny+ksP8E78f8WyN4yRf+ZPVljz1rs15WvjpSSSAABgIYkaANIx44d1bNnz6OenzFjhux2u5YvX66YmJIhCN27d1fHjh21YMECPfDAA5KkjIwMzZkzRzfeeKPmzStZprJv374qKirStGnTNHHiRHXq1Kn2nxAAVFN6enqF4UMyFJbQRpKUu+cXpaenBzSAJDcv+R28fW+2TNOUYRgBawsAoOaCbghWVbhcLi1fvlxXXHGFFT4kqU2bNurXr5+WLVtmHVu5cqWcTqdGjx7tVcfo0aNlmqbeeecdfzUbAHzC1ri5QsIjZLoK5crcE+jmqH2zSIWGGMp2urSXHdEBoN4L2gAybtw42Ww2xcTEaNCgQdqwYYN17rffflN+fr66du1a7nFdu3bVr7/+an1quG3bNklSly5dvMq1aNFCCQkJ1nkAqC/CmraVJBWmp0lm4OeA2G2hOjEhUpK0nYnoAFDvBV0AiY2N1e23364nn3xSa9eu1aOPPqpdu3apb9+++uijjySVDKuSpLi4uHKPj4uLk2maOnDggFXWbrcrMjKywrKeuipSUFCg7OxsrxsABFp4aQAp2v9HYBtSRkoLzzAsAggA1HdBNwfktNNO02mnnWbdP/fcczV06FB16dJFkyZN0qBBg6xzlY0zLnuuquWOdP/992vWrFlVbToA+IWnB6QofWdA21FWSmK03v9OSt3LBzUAUN8FXQ9IRRo3bqyLL75Y33//vfLz8xUfHy9JFfZeZGZmyjAMNW7cWJIUHx8vp9OpvLy8CstW1IviMWXKFGVlZVm3Xbt2+eYJAUANhDctmYBeuG9nYBtSRlxoybDXb3bsq3DZ4LS0tAC3EABQVUHXA3I0pmlKKumxaN++vSIiIrR169Zy5bZu3aoOHTrI4SjZmdcz92Pr1q0666yzrHJ79+5Venq619K+R7Lb7bLb7b58GgBQI4YtXLYmLSVJRft3BrYxpdLS0jR2+BA1HfO4dmbmq3uPMyV3sVcZh8Oh1NTUgK7WBQCoGnpAJB04cEDLly9Xt27d5HA4ZLPZdMkll+jtt99WTs7h8cZpaWlau3athg0bZh0bPHiwHA6HFi9e7FXn4sWLZRiGLr/8cj89CwConoSEBOvDFI+w+NYyQkJVnJel4twDcjgcSkhICFALS6Snpytv/y65C/JkhIYpLO6EcmWcTqfS09MD0DoAQHUFXQ/INddco6SkJJ1xxhlKSEjQL7/8oocfflh//fWXV4iYNWuWevTooYsvvliTJ0+2NiJMSEjQP/7xD6tcXFycpk2bpunTpysuLs7aiHDmzJkaO3Yse4AAqLOSkpKUmprq9cZ9zY48/b9NWTq1TVO9u3mzEhIS6kyvQuH+nXKc0ElhTduoKL3uTJAHAFRP0AWQrl276vXXX9cTTzyhQ4cOKS4uTr1799ZLL72kHj16WOVSUlK0bt063XPPPRo+fLhsNpvOP/98LViwwGsXdEmaOnWqoqOjtWjRIi1YsECJiYmaPHmypk6d6u+nBwDVkpSU5BUwVuz5UVKWzujYUqef3jlwDatA0f4/5Dihk8KbtlXeT/8NdHMAAMcp6ALI5MmTNXny5CqV7d69u1atWlWlshMmTNCECRNq0jQACLjUv0qGnaYkRge4JeUVls5J8azSBQCon5gDAgCw/FS60V9yHQwgnknxnlW6AAD1EwEEACBJyjhUoPRDBTIM6aTmdTeA2GKbywhvFNjGAACOGwEEACBJSi3dZTwprpEi7XVvhK67IFeu7P2S6AUBgPqMAAIAkCRtLw0gyXWs96PscsFF+0tWvzpyHkhdWC4YAFA1de8jLgBAQGzfmy2p7k1AL7tc8IvfZeud1Fxde+vduqn7HKtMXVouGABQOQIIAECS9PUfByRJnVvFBrgl5XmWC/5f6B69k/qNduXbdPrppwe6WQCA48AQLACA/ncgT7/vz1VoiKFe7eMD3Zyj6nViSdt+2JOtjEMFAW4NAOB40AMCAEEmLS3Na/dzSfrktzxJUsc4mw7u+1MxdXQ4U9Nouzq1iNGPf2Zrw6/puqxbq0A3CQBQTQQQAAgiaWlpSk5OltPp9DqecNlkRab01hfLnlfy9HeUmppaZ+dUnHtSgn78M1v//ZkAAgD1EQEEAIJIenp6ufAhI0SOtt0kSfk7tqjQ6VR6enqdDSApsaYkac2Pe7R5s0uGYXidZ0I6ANRtBBAACHLhLToq1BGlYuchFe79NdDNqVRaWpquGdhTTW9+XgfkUK/BV6go/Q+vMg6Ho0734ABAsGMSOgAEuYi2p0mSnDu/lUx3YBtzDOnp6XLm5qggbZskydGu/EpYztIeHABA3UQAAYAg53kT79z5TYBbUnX5O7dIkiLanRbglgAAqosAAgBBzLBHyt4yWVLJ/I/6wrmjJCzZT+gswxYe4NYAAKqDAAIAQcyR1FVGSKiKMnapOHt/oJtTZUUZu+TKSVdImF32EzoHujkAgGoggABAEIsoHX6Vv6P+DL/y8PSCOBiGBQD1CgEEAIJIQkKCHA6Hdd8zh8JZZviVw+FQQkKC39tWXZ4hYxEVTEQHANRdLMMLAA3ckTufv/nmmzp48KAyCkL0r59jFGpIn7zyuCLCSj6Tqi/7aDj/+E6m6VZ407YKjYpT8aHMQDcJAFAFBBAAaMCOtvO5JEV3v1Rx/W9Sfto2tW7RpV6EDk8PjtPplDs/W4V7f5W9xUlytOuu3K2fSKo/PTgAEKwIIADQgFW483mpqFMHSZIObd+g9PQB9SKAJCUlKTU11erRWfpDjl774ZDOvf4uzT1/vqT604MDAMGKAAIAQch+QmeFN20jd6FTh7atkTQr0E2qsqSkJCtgtGzv1Bs/rtZP6UX6I6tIbWLDlJ6e7jXkjEACAHULAQQAglD0aRdKknJ/Wi+zMC/ArTl+hVn7lPvLF3J06Km/zXteB1Y9Wa6Mw+FQamoqIQQA6ghWwQKAIBPSKFaNks+RJB36ZkWAW1Mz6enpytq8XJIUdcr5MsIc5co4nU6vHhEAQGDRAwIADUzZVa9++umncuejuvSXERqmgj2pKvzrN383z+ecO79TUeYehcW1VOTJ5+nQ9x8HukkAgEoQQACgAals1asShqK6lQy/yqnnvR+HmTr03Ydq0u9viup2IQEEAOo4AggANCCVrXolSY4TT1dY40QVOw8pb/unfmxZ7Tq0dbUan/t/srfoqPDEjirc+4vX+bI9QUxKB4DAIoAAQBCJ7jZEkpS7dZVMV6GkhrFvhjs/W7nbNyjqlPMVfdqFyvjQO4Bcd9111tdMSgeAwGISOgAECVtsc0W0P0OSNOPa87V582Zt3ry5wbwZP/RtyZCyRiefpxBH1FHLMSkdAAKLAAIAQaJJv7/JCAlV/u9f67xuyTr99NN1+umnN4jwIUkFu7er8K/fFBLmUOw51wS6OQCAoyCAAEAQcLQ5VY2Sz5bpLtaBtc8Fujk+k5CQIIfj8NK7nucWffpFCktoGMEKABoaAggANHQhoWrS/yZJUs6WD1SUnhbgBvlOUlKSUlNTreFkz8y9W3mpn8kICVWTC24KdPMAABVgEjoA1HPH2vcj+rSLFJ7QRsV5Wcra8LK/m1frkpKSvIaRHVj7D0W0P0MRbbspomMv5f/yeQBbBwA4EgEEAOqxY+37ERIRo8a9S+ZDHPzvi3IX5DaIVa8q48r6S9lfva3Ys0cq7vy/ac+OzdaKXx4rVqywwlrjxo3VokULSSzRCwD+QAABgHrsWPt+NO4zSiGOKLVwuPTGM3MVGjIvKN5kZ33xhiK79JetcaJiegxV1ueve52fPn16hY9jiV4AqH3MAQGAeigtLU1btmypcMiVR6OUcxV96iBJ0vizm6vHGd0b1KpXFfFMSjeLCqwJ6bFnX6XwxI5VejxL9AJA7aMHBADqmWMNu5KksGbtFD/kdklS1hdv6uQrb/NX8wLKMyk9PT1dpmlq6spd2p4TrqZD79WfL9whd97BQDcRAIIePSAAUM8ca9hVSESMmg2bppAwh/J//1oH//uiH1sXeElJSTr99NPVvXt3Tet/gooydskW01RNL58ihfC5GwAEGgEEAOoBz5CrYw27Ukioml42WbbY5irK3KP09x6STLf/GlrHNAoL0b6355RMvm/dWXEXjA10kwAg6PFREADUcVUZciVJMkIUP2i8HG26yl2Qp/1vz5a7INc/jazDXJm7lf7eQ2o6fIaiT79YhelpOvTNikA3CwCCFj0gAFDHHWvIlSQp1KaEy+5RVNcBMt3FSl/+sIoydvmngfVA/u9f6+CnSyRJ8QNvVXSPoQFuEQAELwIIANRzRniEmg2fpcjkc2S6irT/3fnK//VL63xD3/ejqrI/X6qsL9+SJMWd/zc1Pu/6ALcIAIITQ7AAoA461u7mHqGRTdT0ihmyt+hYOuxqjpxp30uSlixZopNPPjko9v04Gs+yvJ4epIPrnpc7P1tN+o5WbK8rFeKIVuYnj1vzZMLCwvTpp59WuEmhp75gfS0BwFcM0zTNQDcCJbKzsxUbG6usrCzFxMQEujkA/Khs4Pjzzz81bNgwFRYWVvqYiJN6KX7QeIU2ilVxXpb2vXGfCvf+ap3fvHmzTj/99Fptd31w5Gt78OBBfZUZrnd3R0gy5Pzje6V/8C8V5xx7/w82KgTA+7WaowcEAAKsypPMSxnhEYrrf7OiuvSXJBX+9Zv2v/uAXAf2WGUYdnVYUlJSucBwraSO727Ug+v/lKNNV7UY8/+U+dH/U972DZXW5dmokAACAMePAAIAAValSeaSJEONTj5XTfqMki22uUx3sbK/fEsHN7wiuV3WkCuJoUJVcXbrCP25+HYlXPwP2Vsmq+llk3Wo/RodWPus3HlZR33cihUrKhyixWsOAFVDAAEAPyk7FKisSvf1KOVod7oan3e97IkdJElFB/cqY/m/VLD7R6vMySefzJCranId2KO9L09S7DlXK7bnCEWdcr4adeyprC/fVM6md2W6Cso9Zvr06RXWxfAsAKgaAggA+EF1h1lJkkJsanRSL0WffrEcrTtLktwFecr+8i1lf/2OzKLDb44ZclUD7mJlfbpEzt+/VpMLbpK9xUlqct71ij5tiLI2vqpD29ZIxUXHrMbpdOrTTz+lFwoAjoFJ6HUIk5qAhmvLli3q3r17lcraGicqskt/RXcdpNCoJpIk01WknG8+UNbnS+XOz5YkhlzVUMXfE0ONTj5PTfpcL1tsc0lSce5B5Xy7QjlbVsidd7DK9YeHh+vtt99miBbQwPB+reboAQGAOiCsaTs1OqmXGp3US+HN2lnHXYcydei7lTr07UcqPpTh9RiGXNUGU3k/rVfez58p+rQhijnjMtlim6nxOdco9qwRyvvlc+X+sFb5O7ZI7uJKayosLNTFF19s3SeQAEAJAggAVOLIeRueZVw9yk5C/vPPPyXJ676n7I4dO6zHGDa7whKSZD/hZDlOOEX21p0V2ijWOm+6i+VM+16Hvl2pvF++OOYbXdSC4iLlfP2ucja/r0bJZyvmjMtlb5WiyJPPU+TJ56k4L0t52zco75cv5Ny1VSp2HbPKYwWSsggnABoyhmDVIXWpS+9ok2U9qvrH8Vj1FBQUyG63V3iusjdzUvkNwiqr68g2V9au6l6XNwoN13HN2ygV4ohWaExT2WISFBrdVLaYpgqLa6WwhDayNUmUYYR4lXcXFci58xvl/fyZ8n/dJLczp9L6mfBcc9X9/oY3b6/Izv0UeXIfa2icJLkL8+Xc+a3yf/9azrStXsshHy+73a633nqL30NAHVSX3q/VVwSQOqSu/EBX5Y+yzWbTggULlJCQoMLCQoWHh1vnPPfT09N19913q6jo2JM3/cHzaaMkXXHFFSooKL+6zfGo7I1gdT49l6oeko4sW1M1CWRlv/+eOjyToY+8f+TPSmW9B0c68vlWtLlcWWXrPjKcli1fWZt37Nhx1BWPFGJTWFwr2Zq0VFiTFrI1biFb4+ayxTRVaExThYQ5Kn5cqeLcgyr482cV/O+HktveXyv9FJ3hO7Wjsv+jR/3+GyFytDlVjVJ6K+LEM2SLjvc67crJKP2+/qiCvb+o8K/fqzSJvSbK/h7y5+8OIBjVlfdr9RkBpA7x5w/0cf3RxVHNnj1b7dq183rzejwBLCwsTA899JAkHfOxnrIVvbk/VhA48o2/LwNZbSn7BtyfbTbCI0qDRqvSHozWCktoo7AmLWWEVj6KtTj3gFzZ+1WcnS5XTrpcB/eqaP9OFab/Uek+E2V5JprzptH/qrpwQHjz9opo30OOtt1kb5Eswxbmdd4sdqko/Q8V7tupooxd1s11cK9kun3W3tmzZys6OvqYvzvKfoBU2e8Gz/Hj/bkjCKGhIoDUHAHERw4dOqRp06Zp6dKlyszMVEpKiiZPnqyRI0dWuQ5//UDXZFgJ0NCEOKIUGtlEoZFNFBLZRLboeNniWimsSUvZ4lrJFhV31Me6C3JVlLFbroN7VHRwr1wH/pQre58VOHzxqffmzZuZaB4gx/W7MjRM9hYnyZHUReEtTpK9RUeFRjapsKjpKlLRgT0lgSTzfyrO2idX9v6S0Jqz32uZ5UAp+0FHdXo4q/IBTFV60j2qOteqonZUtc01uc6x7le3Hcf7gZLk3eN7ZJt9+fx9OQy6qr3SFV03EEGWAFJzTEL3kWHDhmnTpk2aP3++TjrpJL3yyiu6+uqr5Xa7dc011wS6eV6O3HV55jVnKDREMkrvG4YhQ6YkQ4Zhyig9Yxhly3h/HWpIIUe7qex9U6FGyWNCZJYpc/jrUMNUiMySeiUZhlmmjFlan3n4Mdbxw4/zHAuVWyGldZimIbcMuSWZMuQuvS+p5HjpfVOS2zRklvm67PFiGSo2DbnchorMELnMkvtFbkMu05DLlFyer92GitySyy25zDJfu6Uit1TsNlVUrNJypordpgrdkqvYlKvYVFGxKZe75FbkMkseU+xWYbG75LzbLZfLraJit1yukvtFLrdcxW4VutxyFRfLdLslt1umu1gyiw9/7S6Wabq97kt1/POIUJuM0DAZtvAj/g2z7is0TCG2cBnhEQqxRyrE3qj060YKCW9UciwiRqFRjRXaqEm5T6srUnzogIoO7FZR5m65Mv+nwv1/qCj9DxXnZBzzsai/kpKSlJqaWuFQv6P2FBcXWcPqPEKjExSe2LGk9yzecztBIWEOhTdto/CmbSq8fnF+too9geTQARXnZ8mde1DFeVkqzjsod152yb/5OT7tSSmrqKhIEydOrJW6XS5XrdWNuscTOKVj9/BXB8NT6yd6QHxgxYoVuuiii6zQ4TFw4ED98MMPSktLU2ho6DHr8VeiPnJYQdGMxrIZtfPHC4FXbBpyySaXQlSsUBUpVMUKlUshcpmhcln3PcdCSr8OVbEZUlq+zHHTU0+IikuPFZkhFZQJUZEZWhLQFFJSxgw5fF3TKKnHsMklm9xGqIpkU7FRcjv8dZhcsqk4NLykvFmm/Srb/pIIelyvUX6OinMPyJ17QMW5B1R0YI9cmXtUlLlbRQf2yCzI9fF3pWqYaF531bwn2VBoTNPDoaRJy9JFC5rKFp2gEEdUtWpzF+TJXZBr/WsW5MldcKji40VOmUUFJTdXgdyer4ucMl0FMl2Fx/mcgMDzx+9NekBqjh4QH1i2bJmioqI0YsQIr+OjR4/WNddcoy+//FJnn312gFp3bC/9daJCDMk0D3/+Xf5rQ5Ips/S+h7v062Kz5Hhx6c1t3TflNiW3+4j7pbdid9n7ptye+17H3XK7PfWW9BKUnD9ctrj0saYpud1u635J+ZJjhmGU9NSEGAoxjJKeEsNQaIgkGSW9KSEl/1ply5QLCSk5bjOk0BBDYaGGbKGGbCGGwkJV8m+IIVuIqbDQknrDQ6TQEMkWIoWFSDZDsoWYJfeNkn9tIaZshln6r2Qz3Nb9MMNUqGHKZrgVVno81HArTG6Fesqp2DpfkVDDVKiKVGHHuFHRwfqrJGyVBB136c+w6fl5lGRaP3+lPy9ut0zTLXeoZEaX3Dw/u96Pj6ygrsNfS+X/z0jy+v/i/bVplevQsaMiIhqVnDCsfkVJki0sTI7Vtxw+ZhzxDTMMq2z5r498jFGu/mM/vpK6fH79I4/7ov1He7xqXFeSYWjvqxOUl5cvSco5dEivvvaaiouLy33fpcM/L+4yvyOL3QflNg+o2Pxe7gzJna6S31em5A4Nl+GIlumIkRyxkj1SCo+UGR4l2SNl2qMke7RkjyrpwY0IkdtREvZLfis0lltxpb2+IXLLc86wbm4d7ukt+R1vlLRTISouKpDpKpLb7ZK7uFju4pJ/zeJiud3FMotdKi49XvaYp5zb7ZLbVSx3cZHM4mIVF7tkut0l5dzukp5Zs0xvrNstmcWl991lemg9vbjFMo/owfV6/BG9uZ7HlpQp6fcu+U9qlvmuoCFyOp1KT0/ng5s6jgDiA9u2bdPJJ58sm8375ezatat1vi4HkDFPbgl0E+AjoYZKQ42sUGSFnxCVCUuSLTREYbYQhYWGlnxt3Q9RaKjnMYcfHxZaEr7CQksCmFVvqKzjtnJhq/S4oSPaIa/wFeYJX6XhzGYc/rfsY8JKz4eHVpycSsJWsWRUZ9+M4+s18RnnDonpWPVSbOlNklpIuu+8MEnHHtJXdXmlt70+rLOKKl/EzWeKTaNMKJJXQCp7zO0JSF5lSsJSSc/nkccOl5NkDcH1vk6ZukyVeXzZY7LqcB9Rziytt+QjhdJ/zdLrlT4/s+TiZT7EM7zuq0z9h0Or59zhusq2Q6bXP2X+Pfx7sVwA9rpu+cd4f3hilmvT4TIVPEevMkc+R+/6Kmq/zMPP0ev4EXXryPOljys2pTFPfF2+IOo0AogPZGRk6MQTTyx3PC4uzjpfkYKCAq9VfLKzs2ungQgaxaZUXCwVFEsqksp/0lf2fv0edhdiHBF4PKGoNAgZpWVK5jR5vi79V8f62vsxR/s6pMyH90aZr6XD15WOPn/KHh6mxx57TE0TEnT4r7J5xNeS9eltua+PKFPh44+n3soecxx1VauNnseo4sfXyvM92vOq2Wt36NCh0iFapg4ePKD/rl9vzZcLLe1tLZn35jmmMseMCo755rGen9cjf64P/2t4/X8o+3/E10INP/VI1ELbUTcUmaEaE+hGoNoIID5iHDk0ogrn7r//fs2aNau2mgQ0aG5TKiwuuZU42psY37+5qepyp0culVzR6i1NGSbQYEWV3iQpLy1N46Y2jNUHjx1gdJQAY1Sp3NHq9QyJPXYdhkJCQhQSGiIjJNR6bEiIIcMIKSlneIbiHh5ea92k0rKem+faIQoxTKuOEMMo88nD4bJG6XA9z9/+kNIhfYbhKSd5el4Nwyz917Be29Lqytw3yl5GpVcoGRWokoVijDIPtt5xeMoaZpnHeBaZqfhaRgXXqvi6nro8ZY8oV+ZtT0Vtq7i9h+sred2OOH+U51nybA4I9QsBxAfi4+Mr7OXIzMyUdLgn5EhTpkzRnXfead3Pzs5W69ata6eRDURtraJR1evm5OSwR0qQczgcuv7665WUlKShQ4eyzwGq5MgVtcr6888/NWzYMBUWHn3ytyfM1oXfQSVDj0q+Lq5Wvq/phwHVeXx1hmEC8DcCiA906dJFr776qlwul9c8kK1bt0qSTjnllAofZ7fbK10zu7YkJCTI4XBU65O4IzeBq85a6NXZ7bo664hX9uav7HWq8sfdbrfrrbfeOuYu3GlpaZo7d+5xf4p55Jr6dWmn+KqqyQaI1d0noLqOp2eiJmvOJyUlETBQZZX9vPzyyy9VCrM1/R0EAHUBy/D6wIcffqghQ4botdde01VXXWUdv/DCC/X999/XuWV4pco3AZLqxkY/vuTLHXmrs4GS5P1aHnmd6nwffLX5VlXe+B+5rnpZvvxZqOz7Ut0QWXbpRXZgRkNW3d/fVd2Yrr5+KAIcqbY3cGUZ3pojgPjIwIED9fXXX+uBBx5Qhw4d9Oqrr+rpp5/WkiVLdO2111apDn6g4S/18Q16fWwzUN/UZIfq2thVvCqhyJ+9stXZvd0fO6FXt82+ev6Sb4dBV7VXuiptZB+Q+oEA4iOHDh3S1KlTtXTpUmVmZiolJUVTpkzRyJEjq1wHP9AAAHjz54cP9fGDjtrs4a+srqr2YHvu+2qURV34HvF+reYIIHUIP9AAAAB1G+/Xai7AO3ABAAAACCYEEAAAAAB+QwABAAAA4DcEEAAAAAB+QwABAAAA4DcEEAAAAAB+QwABAAAA4DcEEAAAAAB+QwABAAAA4DcEEAAAAAB+QwABAAAA4DcEEAAAAAB+QwABAAAA4De2QDcAh5mmKUnKzs4OcEsAAABQEc/7NM/7NlQfAaQOycnJkSS1bt06wC0BAABAZXJychQbGxvoZtRLhkl8qzPcbrf27Nmj6OhoGYZR69fLzs5W69attWvXLsXExNT69VCC1z0weN0Dg9c9MHjdA4PXPTD8/bqbpqmcnBy1bNlSISHMZjge9IDUISEhITrhhBP8ft2YmBh+UQYAr3tg8LoHBq97YPC6Bwave2D483Wn56NmiG0AAAAA/IYAAgAAAMBvCCBBzG6367777pPdbg90U4IKr3tg8LoHBq97YPC6Bwave2Dwutc/TEIHAAAA4Df0gAAAAADwGwIIAAAAAL8hgAAAAADwGwIIytmwYYOGDBmiJk2aKCIiQh07dtTs2bMD3ayg8cwzz8gwDEVFRQW6KQ3WmjVrNGbMGKWkpCgyMlKtWrXSZZddps2bNwe6aQ3GoUOHNHHiRLVs2VIOh0PdunXTa6+9FuhmNWj8XNcN/A73L96z1E8EEHh55ZVX1KdPH8XGxurFF1/UihUrdM8994i1Cvxj9+7duuuuu9SyZctAN6VBe/zxx7Vz507dfvvtWrFihR599FHt27dPPXv21Jo1awLdvAZh2LBheuGFF3Tffffpww8/VI8ePXT11VfrlVdeCXTTGix+rgOP3+H+xXuW+otVsGDZvXu3kpOTdf311+uxxx4LdHOC0iWXXCLDMBQXF6c333xThw4dCnSTGqR9+/apWbNmXscOHTqkDh066JRTTtGqVasC1LKGYcWKFbrooov0yiuv6Oqrr7aODxw4UD/88IPS0tIUGhoawBY2TPxcBx6/w/2H9yz1Gz0gsDzzzDPKzc3VPffcE+imBKUlS5Zo/fr1/CL1gyPfpElSVFSUOnXqpF27dgWgRQ3LsmXLFBUVpREjRngdHz16tPbs2aMvv/wyQC1r2Pi5Dix+h/sX71nqNwIILP/9738VFxen7du3q1u3brLZbGrWrJluueUWZWdnB7p5Ddq+ffs0ceJEzZ8/XyeccEKgmxOUsrKytGXLFnXu3DnQTan3tm3bppNPPlk2m83reNeuXa3z8A9+rv2D3+H+x3uW+o0AAsvu3buVl5enESNG6KqrrtKqVat0991368UXX9SQIUMYU1mLbr31ViUnJ+vvf/97oJsStMaNG6fc3FxNnTo10E2p9zIyMhQXF1fuuOdYRkaGv5sUtPi59g9+h/sf71nqN9uxi6A+Wrdunfr161elst988426desmt9stp9Op++67T5MnT5Yk9e3bV+Hh4Zo4caJWr16t/v3712az673jed3feustvf/++/rmm29kGEYtt7DhOZ7X/EjTp0/Xyy+/rIULF6p79+4+bmFwquxnmZ9z/+Dn2j/4HR4YvGep3wggDVRycrKefvrpKpVNSkqSJMXHx+uXX37RoEGDvM5feOGFmjhxorZs2cJ/5mOo7ut+6NAhjRs3TrfddptatmypgwcPSpIKCwslSQcPHlRYWJgiIyNrq8n13vH8rJc1a9YszZkzR3PnztX48eN93bygFB8fX2EvR2ZmpiRV2DsC3+Ln2j/4HR44vGep50yg1E033WRKMrds2eJ1fPv27aYk86GHHgpQyxquHTt2mJIqvV122WWBbmaDNXPmTFOSOXPmzEA3pUG58cYbzaioKLOoqMjr+KuvvmpKMjdu3BiglgUHfq79h9/hgcN7lvqNHhBYrrjiCj311FP68MMPddppp1nHV6xYIUnq2bNnoJrWYCUmJmrt2rXljs+fP1/r16/Xhx9+qISEhAC0rOGbPXu2Zs6cqWnTpum+++4LdHMalKFDh+rpp5/WW2+9pauuuso6/sILL6hly5Y666yzAti6ho2fa//id3jg8J6lfmMfEHi59NJL9fHHH2vatGnq2bOnvv76a82aNUv9+/fX+++/H+jmBY0bbriBNeRr0cMPP6y77rpLgwcPrvBNGn+4am7gwIH6+uuv9cADD6hDhw569dVX9fTTT2vJkiW69tprA928Bomf67qD3+H+wXuW+osAAi/5+fmaNWuWXnnlFf35559q2bKlrr32Wt13332y2+2Bbl7Q4I9X7erbt6/Wr19/1PP8Wqy5Q4cOaerUqVq6dKkyMzOVkpKiKVOmaOTIkYFuWoPFz3Xdwe9w/+A9S/1FAAEAAADgN+wDAgAAAMBvCCAAAAAA/IYAAgAAAMBvCCAAAAAA/IYAAgAAAMBvCCAAAAAA/IYAAgAAAMBvCCAAUAsWL14swzD09ddfV/kxp59+ugzD0IIFC6xj69atk2EYVbody65duzR+/Hi1b99eDodDTZo0Ud++ffXyyy9XuEndN998oz59+ig2NlaGYeiRRx6RJK1evVpnnHGGIiMjZRiG3nnnnSo/x6r47LPPNHPmTB08eLBK5fv27Wu9BhdffPExy+/cuVOGYWjx4sU1a+gxXH755Va7TjnllFq9FgDUJ7ZANwAAIH377bf65ptvJEnPPvus7rrrLkkloeTzzz/3Kjt06FC1b9/eK6gcy8aNG3XxxRcrKipKd999t7p27aqsrCwtXbpU1113nd5//3298sorCgk5/LnUmDFjlJubq9dee01NmjRR27ZtZZqmrrzySp100kl67733FBkZqeTkZB+8Aod99tlnmjVrlm644QY1bty4So857bTT9Nhjj6lJkyY+bUtNPPjgg5o8ebJuvfVWFRYWBro5AFBnEEAAoA545plnJEkXXXSRPvjgA3322Wc6++yzFRMTo549e3qVtdvtaty4cbnjR3Pw4EENGzZMsbGx+vLLL9W8eXPr3GWXXaauXbtq8uTJ6tatmyZPnmyd27Ztm2688UZdeOGF1rHdu3crMzNTQ4cO1QUXXFCTp+xTFb1OgXbSSSdJKmlbenp6gFsDAHUHQ7AAIMCcTqdeeeUVde/eXf/+978lSc8995zP6n/mmWe0b98+zZ8/3yt8eEyaNEkpKSl66KGHVFRUZA0fc7lcevzxx61hRDNnztQJJ5wgSbrnnntkGIbatm0rSdq/f79uuukmtW7dWna7XU2bNtU555yjVatWeV1r1apVuuCCCxQTE6NGjRrpnHPO0erVq63zM2fO1N133y1JateunXXtdevWHddz37Nnj6688kpFR0crNjZWV111lfbu3Vth2a+//lqXXnqp4uLi5HA4dNppp2np0qXlym3YsEG9evWSw+FQq1atNH36dD3zzDMyDEM7d+48rnYCQDAhgABAgL399ts6cOCAxowZo44dO6p37956/fXXdejQIZ/U/8knnyg0NFSXXHJJhecNw9Cll16qzMxMbd68WRdddJE17Gv48OH6/PPP9fnnn2vs2LF6++23JUm33XabPv/8cy1btkyS9H//93965513NGPGDH388cd65pln1L9/f2VkZFjXWbJkiQYOHKiYmBi98MILWrp0qeLi4jRo0CArhIwdO1a33Xab9bp4rn366adX+3nn5+erf//++vjjj3X//ffrjTfeUGJioq666qpyZdeuXatzzjlHBw8e1BNPPKF3331X3bp101VXXeU1V+T777/XgAEDlJeXpxdeeEFPPPGEtmzZorlz51a7fQAQrBiCBQAB9uyzz8rhcOiaa66RJP3tb3/T6NGjtXTpUo0ZM6bG9aelpalp06aKjIw8apl27dpZZXv27KmmTZtKkpo3b+41tMnlckmSkpKSvI5v3LhRY8eO1Y033mgdu+yyy6yv8/LydPvtt+viiy+2QoskDRkyRKeffrruvfdeffnllzrhhBOUlJQkqWReh6eH5Xi88MIL+umnn/Tuu+/q0ksvlSQNHDhQ+fn5evrpp73K3nrrrercubPWrFkjm63kT+OgQYOUnp6ue++9V9dff71CQkI0Z84chYaGavXq1UpISJBUMmyuS5cux91OAAg29IAAQADt2LFDa9eu1bBhw6wJ1yNGjFB0dLRPh2Edi2cVrKqsplWRM888U4sXL9acOXP0xRdfqKioyOv8Z599pszMTI0aNUoul8u6ud1uDR48WJs2bVJubm6Nn0dZa9euVXR0tBU+PDxBz+PXX3/V9u3bde2110qSV/uGDBmiP//8U6mpqZKk9evX6/zzz7fChySFhIToyiuv9GnbAaAhI4AAQAA999xzMk1Tw4cP18GDB3Xw4EEVFRXp0ksv1caNG7V9+/YaXyMpKUn79++v9A2+Z+5C69atj+sar7/+ukaNGqVnnnlGvXr1UlxcnK6//nprvsVff/0lqWRIV1hYmNftgQcekGmayszMPK5rH01GRkaFc14SExO97nvadtddd5Vr26233ipJ1iTyo9VZ0TEAQMUYggUAAeJ2u635BcOGDauwzHPPPacHH3ywRtcZMGCAPv74Y73//vsaOXJkufOmaeq9995TXFycunfvflzXSEhI0COPPKJHHnlEaWlpeu+99zR58mTt27dPK1eutHoMFi5ceNTVqnz9Jj4+Pl5fffVVueNHTkL3tG3KlClH/T54lhqOj4+3AktldQIAjo4AAgAB8tFHH+l///ufxo0bp+HDh5c7P378eL344ouaN2+eNS/heIwdO1YPPfSQpkyZovPPP1/NmjXzOv/ggw9q+/btmj9/vsLCwo77Oh5JSUkaP368Vq9erY0bN0qSzjnnHDVu3Fg//vijxo8fX+nj7Xa7pJJJ5DXRr18/LV26VO+9957XMKxXXnnFq1xycrI6duyo7777TvPmzau0zj59+mjFihVKT0+3govb7dYbb7xRo7YCQDAhgABALVqzZk2FS7MOGTJEzz77rGw2m+699161bNmyXJmbb75ZEyZM0AcffOA1obu6GjdurLffflsXX3yxunfvrrvvvlunnnqqsrOz9frrr+vll1/WVVddZS1/W11ZWVnq16+frrnmGqWkpCg6OlqbNm3SypUrrR6FqKgoLVy4UKNGjVJmZqaGDx+uZs2aaf/+/fruu++0f/9+Pf7445JkTeh+9NFHNWrUKIWFhSk5OVnR0dHVatf111+vf//737r++us1d+5cdezYUStWrNBHH31UruyTTz6pCy+8UIMGDdINN9ygVq1aKTMzUz/99JO2bNliBYypU6fq/fff1wUXXKCpU6cqIiJCTzzxhDW8rexGjgCAozABAD73/PPPm5KOetu0aZMZHh5uXn755Uet48CBA2ZERIR5ySWXeB1v06aNedFFF1W7TWlpaea4cePME0880QwPDzdjY2PN8847z1yyZInpdrvLlZdkjhs3zuvYjh07TEnmQw89ZB1zOp3mLbfcYnbt2tWMiYkxIyIizOTkZPO+++4zc3NzvR6/fv1686KLLjLj4uLMsLAws1WrVuZFF11kvvHGG17lpkyZYrZs2dIMCQkxJZlr16496vPq06eP2adPnwrP/e9//zOvuOIKMyoqyoyOjjavuOIK87PPPjMlmc8//7xX2e+++8688sorzWbNmplhYWFmYmKief7555tPPPGEV7lPP/3UPOuss0y73W4mJiaad999t/nAAw+YksyDBw9W2L7OnTsftf0AEGwM0yxd+gQAgHqob9++Mk1Tq1evVkhISEB6IQYOHKidO3fq559/to653W653W5dcMEFysjI0LZt2/zeLgCoixiCBQCo9/773/8qLCxMF110kZYvX16r17rzzjt12mmnqXXr1srMzNTLL7+sTz75RM8++6xXuWHDhundd9+VJHXu3LlW2wQA9Qk9IACAei01NVU5OTmSSua7dOjQoVavd/vtt+u9997T3r17ZRiGOnXqpIkTJ+q6667zKvfbb7/pwIEDkqSIiAhCCACUIoAAAAAA8BuW6wAAAADgNwQQAAAAAH5DAAEAAADgNwQQAAAAAH5DAAEAAADgNwQQAAAAAH5DAAEAAADgNwQQAAAAAH5DAAEAAMD/b7+OBQAAAAAG+VvPYldZBBsBAQAANgICAABsBAQAANgICAAAsBEQAABgIyAAAMBGQAAAgI2AAAAAGwEBAAA2AgIAAGwEBAAA2ASrgLgsq6wzegAAAABJRU5ErkJggg==",
      "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": "2026-02-25T23:46:06.322499Z",
     "iopub.status.busy": "2026-02-25T23:46:06.322335Z",
     "iopub.status.idle": "2026-02-25T23:46:15.703273Z",
     "shell.execute_reply": "2026-02-25T23:46:15.702743Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 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": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Fixing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:06 INFO    GTAnalysis.localize(): Running localization for 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:13 INFO    GTAnalysis._localize(): Localization succeeded.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:13 INFO    GTAnalysis._localize(): Updating source 3FGL J0633.9+1746 to localized position.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:13 INFO    GTAnalysis.delete_source(): Deleting source 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:13 INFO    GTAnalysis.add_source(): Adding source 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:14 WARNING GTBinnedAnalysis._scale_srcmap(): The expscale parameter was zero, setting it to 1e-8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:14 INFO    GTAnalysis._localize(): Localization completed with new position:\n",
      "(  ra, dec) = (   98.4767 +/-   0.0015,   17.7719 +/-   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": [
      "2026-02-25 23:46:14 INFO    GTAnalysis._localize(): LogLike:   -26228.501 DeltaLogLike:       -0.191\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:14 INFO    GTAnalysis.localize(): Finished localization.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:15 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": [
      "2026-02-25 23:46:15 INFO    GTAnalysis.localize(): Execution time: 9.36 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:15 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": "2026-02-25T23:46:15.705208Z",
     "iopub.status.busy": "2026-02-25T23:46:15.705014Z",
     "iopub.status.idle": "2026-02-25T23:47:03.994463Z",
     "shell.execute_reply": "2026-02-25T23:47:03.993880Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:15 INFO    GTAnalysis.find_sources(): Starting.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:15 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:16 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:29 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:29 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": [
      "2026-02-25 23:46:29 INFO    GTAnalysis.tsmap(): Execution time: 13.79 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:29 INFO    GTAnalysis._build_src_dicts_from_peaks(): Found source\n",
      "name: PS J0633.9+1746\n",
      "ts: 10107.000935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:29 INFO    GTAnalysis._build_src_dicts_from_peaks(): Found source\n",
      "name: PS J0617.2+2224\n",
      "ts: 905.764875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:29 INFO    GTAnalysis.add_source(): Adding source PS J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:31 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:31 INFO    GTAnalysis.add_source(): Adding source PS J0617.2+2224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:32 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0617.2+2224       : ['Prefactor']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:32 INFO    GTAnalysis._find_sources_iterate(): Performing spectral fit for PS J0633.9+1746.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:32 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:32 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:793: 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",
      "2026-02-25 23:46:33 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.fit(): LogLike:   -19915.528 DeltaLogLike:      783.891 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis._find_sources_iterate(): {'Index': {'error': 0.038711225866265145, 'value': -2.938259075077796},\n",
      " 'Prefactor': {'error': 6.324420094491686e-12, 'value': 2.1839883984371711e-10},\n",
      " 'Scale': {'error': nan, 'value': 1000.0}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis._find_sources_iterate(): Performing spectral fit for PS J0617.2+2224.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.fit(): LogLike:   -19895.015 DeltaLogLike:       20.513 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis._find_sources_iterate(): {'Index': {'error': 0.06463374008790608, 'value': -2.378966256416391},\n",
      " 'Prefactor': {'error': 2.941838802992821e-12, 'value': 3.671385621748616e-11},\n",
      " 'Scale': {'error': nan, 'value': 1000.0}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.find_sources(): Found 2 sources in iteration 0.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:33 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:34 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:47 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:47 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": [
      "2026-02-25 23:46:47 INFO    GTAnalysis.tsmap(): Execution time: 14.05 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:47 INFO    GTAnalysis._build_src_dicts_from_peaks(): Found source\n",
      "name: PS J0616.3+2219\n",
      "ts: 27.518989\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:47 INFO    GTAnalysis.add_source(): Adding source PS J0616.3+2219\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": [
      "2026-02-25 23:46:49 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0616.3+2219       : ['Prefactor']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis._find_sources_iterate(): Performing spectral fit for PS J0616.3+2219.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0616.3+2219       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:793: 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",
      "2026-02-25 23:46:49 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status:   0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis.fit(): LogLike:   -19880.695 DeltaLogLike:        0.086 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis._find_sources_iterate(): {'Index': {'error': 0.19367335656495255, 'value': -2.003930035996887},\n",
      " 'Prefactor': {'error': 1.3773391947844156e-12, 'value': 3.169479686926172e-12},\n",
      " 'Scale': {'error': nan, 'value': 1000.0}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0616.3+2219       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis.find_sources(): Found 1 sources in iteration 1.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:49 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:46:51 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:03 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:03 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": [
      "2026-02-25 23:47:03 INFO    GTAnalysis.tsmap(): Execution time: 14.21 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:03 INFO    GTAnalysis.find_sources(): Found 0 sources in iteration 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:03 INFO    GTAnalysis.find_sources(): Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:03 INFO    GTAnalysis.find_sources(): Execution time: 48.28 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:03 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  78089.04     19242.0    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12044.70      3626.1     \n",
      "3FGL J0626.8+1743      1.684   0.000  3.99e-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        6.068   3.671  8.59e-05   2.38   1087.18       543.8     \n",
      "PS J0616.3+2219        6.135   0.317   1.8e-05   2.00     28.68        63.3     \n",
      "isodiff                  ---   0.498     0.015   2.12     19.73       339.6    *\n",
      "galdiff                  ---   0.994      0.13  -0.00  36746.87      9720.6    *\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": "2026-02-25T23:47:03.996368Z",
     "iopub.status.busy": "2026-02-25T23:47:03.996174Z",
     "iopub.status.idle": "2026-02-25T23:47:06.037438Z",
     "shell.execute_reply": "2026-02-25T23:47:06.036965Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:03 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:04 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:04 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:04 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:04 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:04 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0616.3+2219       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:04 INFO    GTAnalysis.fit(): Starting fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:793: 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",
      "2026-02-25 23:47:06 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   2 Status: 102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:06 INFO    GTAnalysis.fit(): LogLike:   -19823.592 DeltaLogLike:       57.103 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:06 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  59675.98     19239.7    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.69      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.69e-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   1.288  2.27e-05   2.67    134.44       171.3    *\n",
      "PS J0617.2+2224        6.068   4.217  7.59e-05   2.53    890.02       558.1    *\n",
      "PS J0616.3+2219        6.135   0.410  2.08e-05   2.04     39.24        78.5    *\n",
      "isodiff                  ---   0.459    0.0139   2.12     15.99       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36762.71      9808.3    *\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": "2026-02-25T23:47:06.039135Z",
     "iopub.status.busy": "2026-02-25T23:47:06.038966Z",
     "iopub.status.idle": "2026-02-25T23:47:21.033626Z",
     "shell.execute_reply": "2026-02-25T23:47:21.032932Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:06 INFO    GTAnalysis.tsmap(): Generating TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:07 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:20 INFO    GTAnalysis.tsmap(): Finished TS map\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:21 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": [
      "2026-02-25 23:47:21 INFO    GTAnalysis.tsmap(): Execution time: 14.99 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": "2026-02-25T23:47:21.035702Z",
     "iopub.status.busy": "2026-02-25T23:47:21.035469Z",
     "iopub.status.idle": "2026-02-25T23:47:21.311637Z",
     "shell.execute_reply": "2026-02-25T23:47:21.310995Z"
    }
   },
   "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": "2026-02-25T23:47:21.313675Z",
     "iopub.status.busy": "2026-02-25T23:47:21.313458Z",
     "iopub.status.idle": "2026-02-25T23:47:25.074935Z",
     "shell.execute_reply": "2026-02-25T23:47:25.073973Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:21 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": [
      "2026-02-25 23:47:21 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": [
      "2026-02-25 23:47:21 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 %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": [
      "2026-02-25 23:47:21 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 00.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:21 INFO    GTBinnedAnalysis.write_model_map(): Generating model map for component 01.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:22 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": "2026-02-25T23:47:25.077370Z",
     "iopub.status.busy": "2026-02-25T23:47:25.077133Z",
     "iopub.status.idle": "2026-02-25T23:47:25.082485Z",
     "shell.execute_reply": "2026-02-25T23:47:25.081869Z"
    }
   },
   "outputs": [
    {
     "data": {
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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": "2026-02-25T23:47:25.084104Z",
     "iopub.status.busy": "2026-02-25T23:47:25.083925Z",
     "iopub.status.idle": "2026-02-25T23:47:25.088246Z",
     "shell.execute_reply": "2026-02-25T23:47:25.087665Z"
    }
   },
   "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": "2026-02-25T23:47:25.089831Z",
     "iopub.status.busy": "2026-02-25T23:47:25.089658Z",
     "iopub.status.idle": "2026-02-25T23:47:25.093879Z",
     "shell.execute_reply": "2026-02-25T23:47:25.093271Z"
    }
   },
   "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": "2026-02-25T23:47:25.095537Z",
     "iopub.status.busy": "2026-02-25T23:47:25.095361Z",
     "iopub.status.idle": "2026-02-25T23:47:25.117803Z",
     "shell.execute_reply": "2026-02-25T23:47:25.117199Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 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  59675.98     19239.7    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.69      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.69e-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   1.288  2.27e-05   2.67    134.44       171.3    *\n",
      "PS J0617.2+2224        6.068   4.217  7.59e-05   2.53    890.02       558.1    *\n",
      "PS J0616.3+2219        6.135   0.410  2.08e-05   2.04     39.24        78.5    *\n",
      "isodiff                  ---   0.459    0.0139   2.12     15.99       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36762.71      9808.3    *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0648.8+1516     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0650.5+2055     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0619.4+2242     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0617.2+2224       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0616.3+2219       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 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  59675.98     19239.7    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.69      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.69e-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   1.288  2.27e-05   2.67    134.44       171.3     \n",
      "PS J0617.2+2224        6.068   4.217  7.59e-05   2.53    890.02       558.1     \n",
      "PS J0616.3+2219        6.135   0.410  2.08e-05   2.04     39.24        78.5     \n",
      "isodiff                  ---   0.459    0.0139   2.12     15.99       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36762.71      9808.3    *\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": "2026-02-25T23:47:25.119702Z",
     "iopub.status.busy": "2026-02-25T23:47:25.119496Z",
     "iopub.status.idle": "2026-02-25T23:47:27.136062Z",
     "shell.execute_reply": "2026-02-25T23:47:27.135329Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.sed(): Computing SED for 3FGL J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis._make_sed(): Fitting SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:25 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:793: 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": [
      "2026-02-25 23:47:26 INFO    GTAnalysis.sed(): Finished SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:27 INFO    GTAnalysis.sed(): Execution time: 2.01 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": "2026-02-25T23:47:27.138177Z",
     "iopub.status.busy": "2026-02-25T23:47:27.137967Z",
     "iopub.status.idle": "2026-02-25T23:47:29.316949Z",
     "shell.execute_reply": "2026-02-25T23:47:29.316328Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:27 INFO    GTAnalysis.sed(): Computing SED for PS J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:27 INFO    GTAnalysis._make_sed(): Fitting SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:27 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:27 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:27 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:27 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/fermipy/fermipy/fermipy/utils.py:793: 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": [
      "2026-02-25 23:47:28 INFO    GTAnalysis.sed(): Finished SED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.sed(): Execution time: 2.17 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": "2026-02-25T23:47:29.318861Z",
     "iopub.status.busy": "2026-02-25T23:47:29.318665Z",
     "iopub.status.idle": "2026-02-25T23:47:29.323461Z",
     "shell.execute_reply": "2026-02-25T23:47:29.322894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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": "2026-02-25T23:47:29.325113Z",
     "iopub.status.busy": "2026-02-25T23:47:29.324915Z",
     "iopub.status.idle": "2026-02-25T23:47:29.329737Z",
     "shell.execute_reply": "2026-02-25T23:47:29.329107Z"
    }
   },
   "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": "2026-02-25T23:47:29.331458Z",
     "iopub.status.busy": "2026-02-25T23:47:29.331267Z",
     "iopub.status.idle": "2026-02-25T23:47:50.488978Z",
     "shell.execute_reply": "2026-02-25T23:47:50.488266Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Fixing parameters for 3FGL J0626.8+1743     : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Fixing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 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  59675.98     19239.7     \n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.69      3625.7     \n",
      "3FGL J0626.8+1743      1.684   0.000  8.69e-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   1.288  2.27e-05   2.67    134.44       171.3     \n",
      "PS J0617.2+2224        6.068   4.217  7.59e-05   2.53    890.02       558.1     \n",
      "PS J0616.3+2219        6.135   0.410  2.08e-05   2.04     39.24        78.5     \n",
      "isodiff                  ---   0.459    0.0139   2.12     15.99       312.8     \n",
      "galdiff                  ---   1.002     0.132  -0.00  36762.71      9808.3     \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Freeing parameters for 3FGL J0633.9+1746     : ['Prefactor', 'Index1', 'Cutoff']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0633.9+1746       : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 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  59675.98     19239.7    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.69      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.69e-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   1.288  2.27e-05   2.67    134.44       171.3     \n",
      "PS J0617.2+2224        6.068   4.217  7.59e-05   2.53    890.02       558.1     \n",
      "PS J0616.3+2219        6.135   0.410  2.08e-05   2.04     39.24        78.5     \n",
      "isodiff                  ---   0.459    0.0139   2.12     15.99       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36762.71      9808.3    *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:29 INFO    GTAnalysis.extension(): Running extension fit for PS J0633.9+1746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:41 INFO    GTAnalysis._extension(): Fitting extended-source model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:47 INFO    GTAnalysis._extension(): Generating TS map.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:48 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": [
      "2026-02-25 23:47:50 INFO    GTAnalysis._extension(): Best-fit extension: 0.0032 + 0.0355 -    nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:50 INFO    GTAnalysis._extension(): TS_ext:        -0.004\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:50 INFO    GTAnalysis._extension(): Extension UL: 0.0487\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:50 INFO    GTAnalysis._extension(): LogLike:   -19823.547 DeltaLogLike:        0.045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:50 INFO    GTAnalysis.extension(): Finished extension fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:50 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": [
      "2026-02-25 23:47:50 INFO    GTAnalysis.extension(): Execution time: 21.14 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-25 23:47:50 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  59675.98     19239.7    *\n",
      "PS J0633.9+1746        0.006   2.184  0.000232   2.94  12053.69      3625.7    *\n",
      "3FGL J0626.8+1743      1.684   0.000  8.69e-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   1.288  2.27e-05   2.67    134.44       171.3     \n",
      "PS J0617.2+2224        6.068   4.217  7.59e-05   2.53    890.02       558.1     \n",
      "PS J0616.3+2219        6.135   0.410  2.08e-05   2.04     39.24        78.5     \n",
      "isodiff                  ---   0.459    0.0139   2.12     15.99       312.8    *\n",
      "galdiff                  ---   1.002     0.132  -0.00  36762.71      9808.3    *\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([-2.04666689e-03, -2.13639701e-02, -3.56450121e-02, -6.07263384e-02,\n",
      "       -1.06489222e-01, -1.93779998e-01, -3.68014969e-01, -7.29011353e-01,\n",
      "       -1.49467855e+00, -3.13189880e+00, -6.58404228e+00, -1.36507556e+01,\n",
      "       -2.74625189e+01, -5.36804741e+01, -9.97982142e+01, -1.72427487e+02,\n",
      "       -2.87718389e+02, -4.53614789e+02, -6.80720630e+02, -9.78332018e+02,\n",
      "       -1.35056834e+03, -1.79255568e+03]), 'loglike': array([-19823.54718137, -19823.56649867, -19823.58077972, -19823.60586104,\n",
      "       -19823.65162393, -19823.7389147 , -19823.91314967, -19824.27414606,\n",
      "       -19825.03981325, -19826.6770335 , -19830.12917698, -19837.19589035,\n",
      "       -19851.00765365, -19877.22560877, -19923.34334887, -19995.97262134,\n",
      "       -20111.26352399, -20277.15992397, -20504.26576437, -20801.87715258,\n",
      "       -21174.11346998, -21616.10081646]), 'loglike_ptsrc': -19823.545134704145, 'loglike_ext': -19823.54718310932, 'loglike_init': -19823.592187568098, 'loglike_base': -19823.592186976275, 'ext': 0.0031622776601683794, 'ext_err_hi': 0.03553568631895012, 'ext_err_lo': nan, 'ext_err': 0.03553568631895012, 'ext_ul95': 0.04867742777048674, 'ts_ext': -0.004096810349437874, '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': 98.48241809672234, 'dec': 17.76825047457844, 'glon': 195.13858599343803, 'glat': 4.270645947412207, '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 0x7ff9b07676d0>, '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([ 2.17917780e-10, -2.93927962e+00,  1.00000000e+03,             nan,\n",
      "                   nan,             nan,             nan,             nan,\n",
      "                   nan,             nan]), 'param_errors': array([6.44569908e-12, 4.00507114e-02,            nan,            nan,\n",
      "                  nan,            nan,            nan,            nan,\n",
      "                  nan,            nan]), 'ts': 12472.17350611194, 'loglike': -19823.54718310932, 'loglike_scan': array([-26059.63393617, -19826.27396667, -19825.62704997, -19825.06955703,\n",
      "       -19824.60047823, -19824.21882178, -19823.92361325, -19823.7138952 ,\n",
      "       -19823.58872671, -19823.54718311, -19823.57495188, -19823.65798736,\n",
      "       -19823.79580115, -19823.98791163, -19824.23384378, -19824.53312914,\n",
      "       -19824.88530559, -19825.28991732, -19825.74651467, -19826.25465401]), 'dloglike_scan': array([-6.23608675e+03, -2.72678356e+00, -2.07986686e+00, -1.52237392e+00,\n",
      "       -1.05329512e+00, -6.71638670e-01, -3.76430145e-01, -1.66712086e-01,\n",
      "       -4.15436050e-02,  0.00000000e+00, -2.77687746e-02, -1.10804254e-01,\n",
      "       -2.48618046e-01, -4.40728518e-01, -6.86660674e-01, -9.85946027e-01,\n",
      "       -1.33812248e+00, -1.74273421e+00, -2.19933156e+00, -2.70747090e+00]), 'eflux_scan': array([0.        , 0.00021979, 0.00022119, 0.0002226 , 0.000224  ,\n",
      "       0.0002254 , 0.00022681, 0.00022821, 0.00022962, 0.00023102,\n",
      "       0.00023218, 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, 1.07017183e-07, 1.07701026e-07, 1.08384869e-07,\n",
      "       1.09068712e-07, 1.09752554e-07, 1.10436397e-07, 1.11120240e-07,\n",
      "       1.11804083e-07, 1.12487926e-07, 1.13049403e-07, 1.13610879e-07,\n",
      "       1.14172356e-07, 1.14733833e-07, 1.15295310e-07, 1.15856786e-07,\n",
      "       1.16418263e-07, 1.16979740e-07, 1.17541217e-07, 1.18102693e-07]), 'norm_scan': array([0.        , 2.07319557, 2.08644335, 2.09969113, 2.11293891,\n",
      "       2.12618669, 2.13943447, 2.15268225, 2.16593002, 2.1791778 ,\n",
      "       2.19005504, 2.20093227, 2.21180951, 2.22268675, 2.23356398,\n",
      "       2.24444122, 2.25531845, 2.26619569, 2.27707293, 2.28795016]), 'npred': 3620.7973021213293, 'npred_wt': 3620.7973021213293, 'pivot_energy': 1675.943804731014, 'flux': 1.1248792573749016e-07, 'flux100': 9.791121090520684e-06, 'flux1000': 1.1248792573749016e-07, 'flux10000': 1.2912260217689346e-09, 'flux_err': 2.3752578470244713e-09, 'flux100_err': 9.248234127511655e-07, 'flux1000_err': 2.3752578470244713e-09, 'flux10000_err': 1.2185614659900904e-10, 'flux_ul95': 1.1643613935527828e-07, 'flux100_ul95': 1.013477964204576e-05, 'flux1000_ul95': 1.1643613935527828e-07, 'flux10000_ul95': 1.3365467629006328e-09, 'eflux': 0.0002310219977221619, 'eflux100': 0.00201727365109177, 'eflux1000': 0.0002310219977221619, 'eflux10000': 2.5643644343049865e-05, 'eflux_err': 6.867312745314218e-06, 'eflux100_err': 0.0001482805737881156, 'eflux1000_err': 6.867312745314218e-06, 'eflux10000_err': 2.8357929726827678e-06, 'eflux_ul95': 0.00023913063864015567, 'eflux100_ul95': 0.0020880779373991954, 'eflux1000_ul95': 0.00023913063864015567, 'eflux10000_ul95': 2.6543710595860155e-05, 'dnde': 4.776738073987628e-11, 'dnde100': 1.8948388841844216e-07, 'dnde1000': 2.1791778026611987e-10, 'dnde10000': 2.506184528535934e-13, 'dnde_err': 1.0096963735514904e-12, 'dnde100_err': 2.176638907415179e-08, 'dnde1000_err': 6.445699081999604e-12, 'dnde10000_err': 1.869306858835905e-14, 'dnde_index': 2.9392796218690163, 'dnde100_index': 2.9392796218690163, 'dnde1000_index': 2.9392796218690163, 'dnde10000_index': 2.939279621857915, 'name': 'PS J0633.9+1746', 'spectral_pars': {'Prefactor': {'name': 'Prefactor', 'value': 2.1791778026611985, 'error': 0.06445699081999604, 'min': 1e-05, 'max': 1000.0, 'free': True, 'scale': 1e-10}, 'Index': {'name': 'Index', 'value': 2.9392796218642236, 'error': 0.04005071144191184, '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([1.48948442e+03, 8.98718925e+02, 5.28726612e+02, 3.04527727e+02,\n",
      "       1.72524281e+02, 9.71622569e+01, 5.52350871e+01, 3.18053991e+01,\n",
      "       1.82024047e+01, 1.03764777e+01, 5.93396659e+00, 3.44361787e+00,\n",
      "       2.00895453e+00, 1.15859657e+00, 6.62312890e-01, 3.77832758e-01,\n",
      "       2.15360295e-01, 1.22812088e-01, 7.01745345e-02, 4.00804155e-02]), 'model_counts_wt': array([1.48948442e+03, 8.98718925e+02, 5.28726612e+02, 3.04527727e+02,\n",
      "       1.72524281e+02, 9.71622569e+01, 5.52350871e+01, 3.18053991e+01,\n",
      "       1.82024047e+01, 1.03764777e+01, 5.93396659e+00, 3.44361787e+00,\n",
      "       2.00895453e+00, 1.15859657e+00, 6.62312890e-01, 3.77832758e-01,\n",
      "       2.15360295e-01, 1.22812088e-01, 7.01745345e-02, 4.00804155e-02]), 'covar': array([[0.0041547 , 0.0018058 ],\n",
      "       [0.0018058 , 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([2.17917780e-10, 1.54287031e-10, 1.09236097e-10, 7.73397793e-11,\n",
      "       5.47570046e-11, 3.87682713e-11, 2.74481571e-11, 1.94334517e-11,\n",
      "       1.37589945e-11, 9.74144656e-12, 6.89699969e-12, 4.88311509e-12,\n",
      "       3.45727332e-12, 2.44776921e-12, 1.73303456e-12, 1.22699834e-12,\n",
      "       8.68721821e-13, 6.15060001e-13, 4.35465986e-13, 3.08312400e-13,\n",
      "       2.18286937e-13, 1.54548395e-13, 1.09421145e-13, 7.74707942e-14,\n",
      "       5.48497639e-14, 3.88339454e-14, 2.74946547e-14, 1.94663723e-14,\n",
      "       1.37823025e-14, 9.75794873e-15, 6.90868332e-15, 4.89138717e-15,\n",
      "       3.46313000e-15, 2.45191578e-15, 1.73597035e-15, 1.22907690e-15,\n",
      "       8.70193450e-16, 6.16101923e-16, 4.36203673e-16, 3.08834687e-16,\n",
      "       2.18656718e-16, 1.54810203e-16, 1.09606506e-16, 7.76020310e-17,\n",
      "       5.49426803e-17, 3.88997308e-17, 2.75412311e-17, 1.94993486e-17,\n",
      "       1.38056500e-17, 9.77447886e-18]), 'dnde_lo': array([2.11657259e-10, 1.50303752e-10, 1.06680123e-10, 7.56648070e-11,\n",
      "       5.36191884e-11, 3.79587225e-11, 2.68456745e-11, 1.89700395e-11,\n",
      "       1.33960315e-11, 9.45531180e-12, 6.67157356e-12, 4.70627561e-12,\n",
      "       3.31935521e-12, 2.34087729e-12, 1.65069554e-12, 1.16393642e-12,\n",
      "       8.20678547e-13, 5.78634099e-13, 4.07968125e-13, 2.87635740e-13,\n",
      "       2.02794547e-13, 1.42977724e-13, 1.00804664e-13, 7.10712869e-14,\n",
      "       5.01083342e-14, 3.53287874e-14, 2.49087011e-14, 1.75621431e-14,\n",
      "       1.23825024e-14, 8.73060109e-15, 6.15580643e-15, 4.34041318e-15,\n",
      "       3.06043166e-15, 2.15794288e-15, 1.52160867e-15, 1.07293121e-15,\n",
      "       7.56565848e-16, 5.33491628e-16, 3.76196332e-16, 2.65281898e-16,\n",
      "       1.87071151e-16, 1.31920467e-16, 9.30301502e-17, 6.56056808e-17,\n",
      "       4.62663625e-17, 3.26283753e-17, 2.30107999e-17, 1.62283418e-17,\n",
      "       1.14451826e-17, 8.07193095e-18]), 'dnde_hi': array([2.24363479e-10, 1.58375872e-10, 1.11853310e-10, 7.90518300e-11,\n",
      "       5.59189656e-11, 3.95950855e-11, 2.80641608e-11, 1.99081844e-11,\n",
      "       1.41317920e-11, 1.00362403e-11, 7.13004275e-12, 5.06659936e-12,\n",
      "       3.60092188e-12, 2.55954216e-12, 1.81948075e-12, 1.29347696e-12,\n",
      "       9.19577591e-13, 6.53778968e-13, 4.64817258e-13, 3.30475400e-13,\n",
      "       2.34962860e-13, 1.67055440e-13, 1.18774137e-13, 8.44465355e-14,\n",
      "       6.00398446e-14, 4.26868690e-14, 3.03490750e-14, 2.15770733e-14,\n",
      "       1.53403453e-14, 1.09061865e-14, 7.75363972e-15, 5.51230205e-15,\n",
      "       3.91881626e-15, 2.78593610e-15, 1.98053093e-15, 1.40794677e-15,\n",
      "       1.00088663e-15, 7.11504284e-16, 5.05782827e-16, 3.59537776e-16,\n",
      "       2.55575274e-16, 1.81671574e-16, 1.29136480e-16, 9.17919781e-17,\n",
      "       6.52460654e-17, 4.63764758e-17, 3.29636265e-17, 2.34296641e-17,\n",
      "       1.66529428e-17, 1.18361316e-17]), 'dnde_err': array([6.44569908e-12, 4.08884117e-12, 2.61721332e-12, 1.71205073e-12,\n",
      "       1.16196102e-12, 8.26814202e-13, 6.16003756e-13, 4.74732724e-13,\n",
      "       3.72797499e-13, 2.94793709e-13, 2.33043066e-13, 1.83484267e-13,\n",
      "       1.43648561e-13, 1.11772945e-13, 8.64461991e-14, 6.64786141e-14,\n",
      "       5.08557705e-14, 3.87189676e-14, 2.93512715e-14, 2.21630000e-14,\n",
      "       1.66759229e-14, 1.25070446e-14, 9.35299180e-15, 6.97574132e-15,\n",
      "       5.19008070e-15, 3.85292358e-15, 2.85442027e-15, 2.11070108e-15,\n",
      "       1.55804276e-15, 1.14823773e-15, 8.44956401e-16, 6.20914875e-16,\n",
      "       4.55686265e-16, 3.34020320e-16, 2.44560582e-16, 1.78869868e-16,\n",
      "       1.30693178e-16, 9.54023612e-17, 6.95791542e-17, 5.07030896e-17,\n",
      "       3.69185554e-17, 2.68613709e-17, 1.95299744e-17, 1.41899471e-17,\n",
      "       1.03033851e-17, 7.47674499e-18, 5.42239532e-18, 3.93031544e-18,\n",
      "       2.84729281e-18, 2.06165275e-18]), 'dnde_ferr': array([0.0291537 , 0.02615942, 0.02367893, 0.02189703, 0.02099984,\n",
      "       0.02110441, 0.02219614, 0.02413737, 0.02673744, 0.02981736,\n",
      "       0.03323686, 0.03689487, 0.04072092, 0.04466615, 0.04869644,\n",
      "       0.05278758, 0.05692216, 0.06108743, 0.06527391, 0.06947444,\n",
      "       0.07368355, 0.07789701, 0.08211152, 0.08632446, 0.09053376,\n",
      "       0.09473776, 0.09893512, 0.10312477, 0.10730583, 0.1114776 ,\n",
      "       0.11563949, 0.11979106, 0.12393191, 0.12806174, 0.13218033,\n",
      "       0.13628747, 0.14038303, 0.14446689, 0.14853898, 0.15259924,\n",
      "       0.15664765, 0.1606842 , 0.16470888, 0.16872173, 0.17272276,\n",
      "       0.17671203, 0.18068957, 0.18465546, 0.18860974, 0.1925525 ]), 'pivot_energy': 1675.943804731014}}}\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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