{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Configuring the model and running g-tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial shows how to configure the model and how to run Fermipy-LAT g-tools with Fermipy.\n", "Many parts of this tutorial are taken directly from the documentation page of Fermipy: [fermipy.readthedocs](http://fermipy.readthedocs.io/en/latest/). \n", "I suggest to visit it to find further informations.\n", "\n", "This tutorial uses the same data as the SMC.ipynb tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configuring the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model can be configured with a configuration file or directly running Fermipy with your script." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The configuration file defines the data selection and analysis parameters. The configuration file has a hierarchical structure that groups parameters into dictionaries that are keyed to a section name (data, binning, etc.). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When creating a class instance, the configuration is initialized by passing either a configuration dictionary or configuration file path to the class constructor. \n", "Keyword arguments can be passed to the constructor to override configuration parameters in the input dictionary. \n", "In the following example the config dictionary defines values for the parameters emin and emax. By passing a dictionary for the selection keyword argument, the value of emax in the keyword argument (10000) overrides the value of emax in the input dictionary." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# config = {\n", "'selection' : { 'emin' : 100,\n", " 'emax' : 1000 }\n", "}\n", "\n", "gta = GTAnalysis(config,selection={'emax' : 10000})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The configuration file can be created also with a yaml file.\n", "Below I report a sample of configuration applied for an analysis of the SMC:" ] }, { "cell_type": "raw", "metadata": { "vscode": { "languageId": "raw" } }, "source": [ "data:\n", " evfile : 'ft1.fits'\n", " scfile : '/u/gl/mdimauro/kipac/workdir/files/SC/P8_104months_ft2.fits'\n", " ltcube : '/u/gl/mdimauro/dmcat/workdir/mattia/LogNLogS_Ebins/files_bins/bin13/P8_SOURCE_zmax105_1_3_ltcube.fits'\n", "\n", "binning:\n", " roiwidth : 12.0\n", " binsz : 0.08\n", " binsperdec : 8\n", " coordsys : 'GAL'\n", "\n", "selection :\n", " emin : 1000\n", " emax : 500000\n", " tmin : 239557417\n", " tmax : 512994417\n", " zmax : 105\n", " evclass : 128\n", " evtype : 3\n", " glon: 302.25\n", " glat : -44.37\n", " \n", "gtlike:\n", " edisp : True\n", " irfs : 'P8R2_SOURCE_V6'\n", " edisp_disable : ['isodiff','galdiff']\n", "\n", "model:\n", " src_roiwidth : 12.0\n", " galdiff : '$FERMI_DIFFUSE_DIR/gll_iem_v07.fits'\n", " isodiff : '$FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_v06.txt' \n", " catalogs: gll_psc_v16.fit\n", "\n", "fileio:\n", " usescratch: False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The configuration file has the same structure as the configuration dictionary such that one can read/write configurations using the load/dump methods of the yaml module:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DATA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data section defines the input data files for the analysis (FT1, FT2, and livetime cube). \n", "evfile and scfile can either be individual files or group of files. The optional ltcube option can be used to choose a pre-generated livetime cube. \n", "If ltcube is null a livetime cube will be generated at runtime with gtltcube." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below an example of the data part:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "data :\n", " evfile : ft1.lst\n", " scfile : ft2.fits\n", " ltcube : null" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The options for the data component are the following:\n", "* cacheft1: Cache FT1 files when performing binned analysis. If false then only the counts cube is retained.\n", "* evfile: Path to FT1 file or list of FT1 files.\n", "* ltcube: Path to livetime cube. If none a livetime cube will be generated with gtmktime.\n", "* scfile: Path to FT2 (spacecraft) file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BINNING" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Options in the binning section control the spatial and spectral binning of the data. " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "binning:\n", "\n", " # Binning\n", " roiwidth : 10.0\n", " npix : null\n", " binsz : 0.1 # spatial bin size in deg\n", " binsperdec : 8 # nb energy bins per decade\n", " projtype : WCS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The roiwidth is the ROI width, npix specifies the number of pixels, binsz indicates the pixel size, binsperdec is the number of energy bins per decade and projtype is the type of projection and can be WCS and HEALPIX." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other options are: \n", "* coordsys: for the coordinate system Galactic or Celestial (CEL or GAL), \n", "* hpx_order: is the healpix order, hpx_ordering_scheme is the HEALPix Ordering Scheme, \n", "* proj: is the Spatial projection for WCS mode, Width of the ROI in degrees. \n", "* roiwidth: is the number of pixels in each spatial dimension will be set from roiwidth / binsz (rounded up)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### COMPONENTS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The components section can be used to define analysis configurations for independent subselections of the data. Each subselection will have its own binned likelihood instance that is combined in a global likelihood function for the ROI (implemented with the SummedLikelihood class in pyLikelihood). The components section is optional and when set to null (the default) only a single likelihood component will be created with the parameters of the root analysis configuration.\n", "\n", "The component section is defined as a list of dictionaries where each element sets analysis parameters for a different subcomponent of the analysis. The component configurations follow the same structure and accept the same parameters as the root analysis configuration. Parameters not defined in a given element will default to the values set in the root analysis configuration.\n", "\n", "The following example illustrates how to define a Front/Back analysis with two components. Files associated to each component will be given a suffix according to their order in the list (e.g. file_00.fits, file_01.fits, etc.)." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Component section for Front/Back analysis\n", " - { selection : { evtype : 1 } } # Front\n", " - { selection : { evtype : 2 } } # Back" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example illustrates how to define an analysis divided in four components. Each of them has a different zmax and uses a different evtype and isotropic template." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "components:\n", " - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF0_v06.txt}\n", " selection: {evtype: 4, zmax: 80}\n", " data: {ltcube: ltcube_00.fits}\n", " - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF1_v06.txt}\n", " selection: {evtype: 8, zmax: 85}\n", " data: {ltcube: ltcube_01.fits}\n", " - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF2_v06.txt}\n", " selection: {evtype: 16, zmax: 95}\n", " data: {ltcube: ltcube_02.fits}\n", " - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF3_v06.txt}\n", " selection: {evtype: 32, zmax: 100}\n", " data: {ltcube: ltcube_03.fits}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EXTENSION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The options in extension control the default behavior of the extension method. For more information about using this method see the Extension Fitting page. The main options are the following:\n", "* fit_ebin: Perform a fit for the angular extension in each analysis energy bin.\n", "* fit_position: Perform a simultaneous fit to the source position and extension.\n", "* fix_shape: Fix spectral shape parameters of the source of interest. If True then only the normalization parameter will be fit.\n", "* free_background: Leave background parameters free when performing the fit. If True then any parameters that are currently free in the model will be fit simultaneously with the source of interest.\n", "* free_radius: Free normalizations of background sources within this angular distance in degrees from the source of interest. If None then no sources will be freed.\n", "* sqrt_ts_threshold: Threshold on sqrt(TS_ext) that will be applied when update is True. If None then nothreshold is applied.\n", "* width: Sequence of values in degrees for the likelihood scan over spatial extension (68% containment radius). If this argument is None then the scan points will be determined from width_min/width_max/width_nstep.\n", "* width_max: Maximum value in degrees for the likelihood scan over spatial extent.\n", "* width_min: Minimum value in degrees for the likelihood scan over spatial extent.\n", "* width_nstep: Number of scan points between width_min and width_max. Scan points will be spaced evenly on a logarithmic scale between width_min and width_max." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a notebook dedicated to this analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FILE I/O" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fileio section collects options related to file bookkeeping. The outdir option sets the root directory of the analysis instance where all output files will be written. If outdir is null then the output directory will be automatically set to the directory in which the configuration file is located. Enabling the usescratch option will stage all output data files to a temporary scratch directory created under scratchdir." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sample fileio Configuration" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "fileio:\n", " outdir : null\n", " logfile : null\n", " usescratch : False\n", " scratchdir : '/scratch'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The options for the fileio are the following:\n", "* logfile: Path to log file. If None then log will be written to fermipy.log.\n", "* outdir: Path of the output directory. If none this will default to the directory containing the configuration file.\n", "* outdir_regex: Stage files to the output directory that match at least one of the regular expressions in this list. This option only takes effect when usescratch is True.\n", "* savefits: Save intermediate FITS files.\n", "* scratchdir: Path to the scratch directory. If usescratch is True then a temporary working directory will be created under this directory.\n", "* usescratch: Run analysis in a temporary working directory under scratchdir.\n", "* workdir: Path to the working directory.\n", "* workdir_regex: Stage files to the working directory that match at least one of the regular expressions in this list. This option only takes effect when usescratch is True." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GTLIKE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Options in the gtlike section control the setup of the likelihood analysis include the IRF name (irfs)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A subsample of options for the gtlike section is listed below:\n", "* edisp: Enable the correction for energy dispersion.\n", "* edisp_disable: Provide a list of sources for which the edisp correction should be disabled.\n", "* use_external_srcmap: Use an external precomputed source map file.\n", "* use_scaled_srcmap: Generate source map by scaling an external srcmap file.\n", "* wmap: Likelihood weights map." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Likelihood wights map is created and used in order to account for example for the uncertainty in the interstellar emission." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MODEL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model section collects options that control the inclusion of point-source and diffuse components in the model. galdiff and isodiff set the templates for the Galactic IEM and isotropic diffuse respectively. catalogs defines a list of catalogs that will be merged to form a master analysis catalog from which sources will be drawn. Valid entries in this list can be FITS files or XML model files. sources can be used to insert additional point-source or extended components beyond those defined in the master catalog. src_radius and src_roiwidth set the maximum distance from the ROI center at which sources in the master catalog will be included in the ROI model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sample for the Model section:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "model :\n", "\n", " # Diffuse components\n", " galdiff : '$FERMI_DIR/refdata/fermi/galdiffuse/gll_iem_v06.fits'\n", " isodiff : '$FERMI_DIR/refdata/fermi/galdiffuse/iso_P8R2_SOURCE_V6_v06.txt'\n", "\n", " # List of catalogs to be used in the model.\n", " catalogs :\n", " - '3FGL'\n", " - 'extra_sources.xml'\n", "\n", " sources :\n", " - { 'name' : 'SourceA', 'ra' : 60.0, 'dec' : 30.0, 'SpectrumType' : PowerLaw }\n", " - { 'name' : 'SourceB', 'ra' : 58.0, 'dec' : 35.0, 'SpectrumType' : PowerLaw }\n", "\n", " # Include catalog sources within this distance from the ROI center\n", " src_radius : null\n", "\n", " # Include catalog sources within a box of width roisrc.\n", " src_roiwidth : 15.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the sample above the official IEM and isotropic template are used together with the 3FGL catalog given with a fits file and by an additional catalog given with an xml file following the notation of the Fermi Science Tools [fermi.fssc.spectralmodel](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/xml_model_defs.html#xmlModelDefinitions).\n", "Then two sources named as SourceA and SourceB are added." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To define two or more galactic diffuse components you can optionally define the galdiff and isodiff parameters as lists. A separate component will be generated for each element in the list with the name galdiffXX or isodiffXX where XX is an integer position in the list." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "model:\n", " galdiff :\n", " - '$FERMI_DIFFUSE_DIR/diffuse_component0.fits'\n", " - '$FERMI_DIFFUSE_DIR/diffuse_component1.fits'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To explicitly set the name of a component you can define any element as a dictionary containing name and file fields:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "model:\n", " galdiff :\n", " - { 'name' : 'component0', 'file' : '$FERMI_DIFFUSE_DIR/diffuse_component0.fits' }\n", " - { 'name' : 'component1', 'file' : '$FERMI_DIFFUSE_DIR/diffuse_component1.fits' }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The list of sources for inclusion in the ROI model is set by defining a list of catalogs with the catalogs parameter. Catalog files can be in either XML or FITS format. Sources from the catalogs in this list that satisfy either the src_roiwidth or src_radius selections are added to the ROI model. If a source is defined in multiple catalogs the source definition from the last file in the catalogs list takes precedence." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "model:\n", "\n", " src_radius: 5.0\n", " src_roiwidth: 10.0\n", " catalogs :\n", " - 'gll_psc_v16.fit'\n", " - 'extra_sources.xml'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fermipy supports four spatial models which are defined with the SpatialModel property:\n", "\n", "* PointSource : A point source (SkyDirFunction).\n", "* RadialGaussian : A symmetric 2D Gaussian with width parameter ‘Sigma’.\n", "* RadialDisk : A symmetric 2D Disk with radius ‘Radius’.\n", "* SpatialMap : An arbitrary 2D shape with morphology defined by a FITS template.\n", "\n", "The spatial extension of RadialDisk and RadialGaussian can be controlled with the SpatialWidth parameter which sets the 68% containment radius in degrees. Note for ST releases prior to 11-01-01, RadialDisk and RadialGaussian sources will be represented with the SpatialMap type." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "model:\n", " sources :\n", " - { name: 'PointSource', glon : 120.0, glat : 0.0,\n", " SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n", " SpatialModel: 'PointSource' }\n", " - { name: 'DiskSource', glon : 120.0, glat : 0.0,\n", " SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n", " SpatialModel: 'RadialDisk', SpatialWidth: 1.0 }\n", " - { name: 'GaussSource', glon : 120.0, glat : 0.0,\n", " SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n", " SpatialModel: 'RadialGaussian', SpatialWidth: 1.0 }\n", " - { name: 'MapSource', glon : 120.0, glat : 0.0,\n", " SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n", " SpatialModel: 'SpatialMap', Spatial_Filename : 'template.fits' }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### OPTIMIZER" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section provides informations for the setup used for the optimization. The list of options are the following:\n", "* init_lambda: Initial value of damping parameter for step size calculation when using the NEWTON fitter. A value of zero disables damping.\n", "* max_iter: Maximum number of iterations for the Newtons method fitter.\n", "* min_fit_quality: Set the minimum fit quality.\n", "* optimizer: Set the optimization algorithm to use when maximizing the likelihood function.\n", "* retries: Set the number of times to retry the fit when the fit quality is less than min_fit_quality.\n", "* tol: Set the optimizer tolerance.\n", "* verbosity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PLOTTING" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section selects the options to be used to make the plots:\n", "* cmap: Set the colormap for 2D plots. Default magma\n", "* cmap_resid: Set the colormap for 2D residual plots.\n", "* figsize: Set the default figure size.\n", "* format: format of images\n", "* interactive: Enable interactive mode. If True then plots will be drawn after each plotting command.\n", "* label_ts_threshold: TS threshold for labeling sources in sky maps. If None then no sources will be labeled." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SELECTION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The selection section collects parameters related to the data selection and target definition. The majority of the parameters in this section are arguments to gtselect and gtmktime. The ROI center can be set with the target parameter by providing the name of a source defined in one of the input catalogs (defined in the model section). Alternatively the ROI center can be defined by giving explicit sky coordinates with ra and dec or glon and glat." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "selection:\n", "\n", " # gtselect parameters\n", " emin : 100\n", " emax : 100000\n", " zmax : 90\n", " evclass : 128\n", " evtype : 3\n", " tmin : 239557414\n", " tmax : 428903014\n", "\n", " # gtmktime parameters\n", " filter : 'DATA_QUAL>0 && LAT_CONFIG==1'\n", " roicut : 'no'\n", "\n", " # Set the ROI center to the coordinates of this source\n", " target : 'mkn421'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A sample of options for this section are the following:\n", "* emax/emin: Maximum/Minimum Energy (MeV)\n", "* evclass: Event class selection.\n", "* evtype: Event type selection.\n", "* filter: Filter string for gtmktime selection.\n", "* logemax/logemin: Maximum/Minimum Energy (log10(MeV))\n", "* phasemax/phasemin: Maximum/Minimum pulsar phase\n", "* radius: Radius of data selection. If none this will be automatically set from the ROI size.\n", "* target: Choose an object on which to center the ROI. This option takes precendence over ra/dec or glon/glat.\n", "* tmax/tmin: Maximum/Minimum time (MET).\n", "* zmax: Maximum zenith angle." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running g-tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First of all you need to load the configuration file, create the object gta and run the tool gta.setup that implements the ST gtselect, gtmktime, gtbin, gtexpcube, gtsrcmap tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the commands below we import the packages needed to run the script." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:26.938539Z", "iopub.status.busy": "2026-02-25T23:14:26.938355Z", "iopub.status.idle": "2026-02-25T23:14:28.211075Z", "shell.execute_reply": "2026-02-25T23:14:28.210331Z" } }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "from fermipy.gtanalysis import GTAnalysis\n", "from fermipy.plotting import ROIPlotter, SEDPlotter\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This script uses the same data as the SMC tutorial.\n", "\n", "Download the SMC data andd then untar the file ../data/SMC_data.tar.gz. This will copy the config.yaml and ft1 file in the notebook directory." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:28.213424Z", "iopub.status.busy": "2026-02-25T23:14:28.213156Z", "iopub.status.idle": "2026-02-25T23:14:28.625279Z", "shell.execute_reply": "2026-02-25T23:14:28.624502Z" } }, "outputs": [], "source": [ "import os\n", "if os.path.isfile('./../data/SMC_data.tar.gz'):\n", " !tar xzf ./../data/SMC_data.tar.gz\n", "else:\n", " !curl -OL --output-dir ./../data/ https://raw.githubusercontent.com/fermiPy/fermipy-extras/master/data/SMC_data.tar.gz\n", " !tar xzf ./../data/SMC_data.tar.gz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The configuration file has the same structure as the configuration dictionary such that one can read/write configurations using the load/dump methods of the yaml module:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:28.627588Z", "iopub.status.busy": "2026-02-25T23:14:28.627392Z", "iopub.status.idle": "2026-02-25T23:14:28.635893Z", "shell.execute_reply": "2026-02-25T23:14:28.635425Z" } }, "outputs": [], "source": [ "import yaml\n", "# Load a configuration\n", "config = yaml.load(open('./SMC_data/config.yaml'), Loader=yaml.FullLoader)\n", "# Update a parameter and write a new configuration\n", "config['selection']['emin'] = 1000.\n", "yaml.dump(config, open('new_config.yaml','w'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:28.637412Z", "iopub.status.busy": "2026-02-25T23:14:28.637244Z", "iopub.status.idle": "2026-02-25T23:14:38.520055Z", "shell.execute_reply": "2026-02-25T23:14:38.519471Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:28 INFO GTAnalysis.__init__(): \n", "--------------------------------------------------------------------------------\n", "fermipy version 1.4.1 \n", "ScienceTools version 2.2.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:29 INFO GTAnalysis.setup(): Running setup.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:29 INFO GTBinnedAnalysis.setup(): Running setup for component 00\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:29 INFO GTBinnedAnalysis._select_data(): Skipping data selection.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:29 INFO GTBinnedAnalysis.setup(): Using external 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 23:14:29 INFO GTBinnedAnalysis._create_expcube(): Skipping gtexpcube.\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 57847.435324 from DATE-END'. [astropy.wcs.wcs]\n", "2026-02-25 23:14:29 INFO GTBinnedAnalysis._create_srcmaps(): Skipping gtsrcmaps.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:29 INFO GTBinnedAnalysis.setup(): Finished setup for component 00\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:29 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:35 INFO GTAnalysis.setup(): Initializing source properties\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.setup(): Finished setup.\n" ] } ], "source": [ "gta = GTAnalysis('new_config.yaml')\n", "gta.setup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The setup() method performs the data preparation and response calculations needed for the analysis (selecting the data, creating counts and exposure maps, etc.). Depending on the data selection and binning of the analysis this will often be the slowest step in the analysis sequence. The output of setup() is cached in the analysis working directory so subsequent calls to setup() will run much faster." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The g-tools performed with gta.setup() are the following:\n", "* gtselect: [gtselect_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtselect.txt)\n", "* gtmktime: [gtmiktime_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtmktime.txt)\n", "* gtbin: [gtbin_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtbin.txt)\n", "* gtsrcmaps: [gtsrcmaps_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtsrcmaps.txt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to have a summary of the source model you can use the command:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:38.521974Z", "iopub.status.busy": "2026-02-25T23:14:38.521787Z", "iopub.status.idle": "2026-02-25T23:14:38.525631Z", "shell.execute_reply": "2026-02-25T23:14:38.525151Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.059 1.45e-05 2.22 nan 1437.6 \n", "3FGL J0112.9-7506 2.572 1.172 1.31e-06 2.17 nan 120.3 \n", "3FGL J0023.9-7203 2.662 0.714 9.3e-06 2.65 nan 1679.9 \n", "3FGL J0029.1-7045 3.008 0.506 2.49e-06 2.28 nan 270.6 \n", "3FGL J0021.6-6835 5.122 1.859 5.27e-07 2.65 nan 84.2 \n", "3FGL J2351.9-7601 5.495 1.027 3.74e-06 1.69 nan 117.8 \n", "3FGL J2338.7-7401 5.777 0.677 4.38e-06 1.89 nan 213.7 \n", "3FGL J0146.4-6746 6.423 0.420 1.03e-06 2.39 nan 124.0 \n", "3FGL J2336.5-7620 6.454 0.557 1.66e-06 2.33 nan 187.3 \n", "3FGL J0002.0-6722 7.153 0.483 2.07e-06 1.95 nan 113.5 \n", "isodiff --- 1.000 0.0313 2.12 nan 9099.9 \n", "galdiff --- 1.000 0.132 0.00 nan 16777.6 \n", "\n" ] } ], "source": [ "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The table above contains:\n", "* sourcename as given in the catalog\n", "* offset from the center of the roi. Sources are ranked with respect to this parameter\n", "* normalization of the SED\n", "* energy flux of the source\n", "* index of the SED with a PowerLaw SED\n", "* Test Statistics\n", "* npred is the number of sources associated to the source\n", "* free column that tells if the SED parameter of the sources are free (if the column is filled with an asterix) or fixed without no asterix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source dictionary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First of all, let's make a fit using gta.fit tool.\n", "Source fitting with fermipy is generally performed with the optimize and fit methods. fit is a wrapper on the pyLikelihood fit method and performs a likelihood fit of all free parameters of the model. This method can be used to manually optimize of the model by calling it after freeing one or more source parameters." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:38.527360Z", "iopub.status.busy": "2026-02-25T23:14:38.527190Z", "iopub.status.idle": "2026-02-25T23:14:41.954419Z", "shell.execute_reply": "2026-02-25T23:14:41.953814Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:38 INFO GTAnalysis.fit(): Starting fit.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Drm_Cache::update Measured counts < 0 3FGL J0023.9-7203 21 -3.92429e-11 6.86055e-11\n", "508.548 476.301 383.986 267.676 161.68 85.2989 39.7061 16.4355 5.9306 1.87763 0.52174 0.127727 0.0277026 0.00525312 0.00086478 0.000124394 1.56781e-05 1.73055e-06 1.67578e-07 1.42376e-08 1.05942e-09 6.86055e-11 \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 23:14:41 INFO GTAnalysis.fit(): Fit returned successfully. Quality: 3 Status: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:41 INFO GTAnalysis.fit(): LogLike: -73815.272 DeltaLogLike: 154.334 \n" ] } ], "source": [ "gta.free_sources()\n", "fitresult=gta.fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:41.956152Z", "iopub.status.busy": "2026-02-25T23:14:41.955977Z", "iopub.status.idle": "2026-02-25T23:14:41.959522Z", "shell.execute_reply": "2026-02-25T23:14:41.959063Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:41 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.662 0.594 9.79e-06 2.70 5206.25 1799.4 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 *\n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you see that the ts column is filled and the free column has all asterix because we have freed the sources." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fermipy gives the possibility to access many informations on the sources in the model.\n", "Below a few examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### NAME AND CHARACTERISTICS OF THE SOURCE" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:41.961119Z", "iopub.status.busy": "2026-02-25T23:14:41.960957Z", "iopub.status.idle": "2026-02-25T23:14:41.966090Z", "shell.execute_reply": "2026-02-25T23:14:41.965594Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3FGL J0059.0-7242e\n", "3FGL J0059.0-7242e\n", "SpatialMap\n", "None\n", "SpatialMap\n", "DiffuseSource\n", "PowerLaw\n", "$FERMIPY_DATA_DIR/catalogs/Extended_archive_v15/Templates/SMC.fits\n", "None\n", "{'3FGL J0002.0-6722': 0.011790148677603435, '3FGL J0021.6-6835': 0.014374005416110978, '3FGL J0023.9-7203': -0.022902679562838098, '3FGL J0029.1-7045': -0.00040861905612040296, '3FGL J0059.0-7242e': 0.9999999999999999, '3FGL J0112.9-7506': -0.006446519317548159, '3FGL J0146.4-6746': 0.014675271224211939, '3FGL J2336.5-7620': 0.022611347556478673, '3FGL J2338.7-7401': 0.012248514056312932, '3FGL J2351.9-7601': 0.006110082031862612, 'galdiff': -0.1891644465120756, 'isodiff': 0.02205283917272138}\n", "[4.46857316e+02 3.08420680e+02 2.09968151e+02 1.41385340e+02\n", " 9.41907773e+01 6.24273301e+01 4.15264943e+01 2.77945637e+01\n", " 1.84249832e+01 1.22067121e+01 8.08387970e+00 5.38413610e+00\n", " 3.61208539e+00 2.40960580e+00 1.59550056e+00 1.05189465e+00\n", " 6.93896609e-01 4.56475194e-01 3.01655045e-01 1.99122859e-01\n", " 1.30944968e-01 8.47352407e-02]\n" ] } ], "source": [ "print(gta.roi.sources[0]['name']) #name of the source\n", "print(gta.roi.sources[0]['Source_Name']) #name of the source\n", "print(gta.roi.sources[0]['SpatialModel']) #spatial model\n", "print(gta.roi.sources[0]['SpatialWidth']) #spatial size parameter\n", "print(gta.roi.sources[0]['SpatialType']) #spatial size parameter\n", "print(gta.roi.sources[0]['SourceType']) #Source type\n", "print(gta.roi.sources[0]['SpectrumType']) #Spectrum type string\n", "print(gta.roi.sources[0]['Spatial_Filename']) #Path to spatial template\n", "print(gta.roi.sources[0]['Spectrum_Filename']) #Path to the SED source template\n", "print(gta.roi.sources[0]['correlation']) #Dictionary of correlation coefficients.\n", "print(gta.roi.sources[0]['model_counts']) #Vector of predicted counts for this source" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CHARACTERISTICS OF THE POSITION OF THE SOURCE" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:41.967625Z", "iopub.status.busy": "2026-02-25T23:14:41.967430Z", "iopub.status.idle": "2026-02-25T23:14:41.973360Z", "shell.execute_reply": "2026-02-25T23:14:41.972778Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14.75\n", "-72.7\n", "302.14493\n", "-44.416702\n", "nan\n", "nan\n", "nan\n", "nan\n", "nan\n", "nan\n", "nan\n", "nan\n", "nan\n", "nan\n", "-0.07331403829880656\n", "0.04939684436004943\n", "0.07503178772129071\n", "-0.0467496716123047\n", "0.088404864\n" ] } ], "source": [ "print(gta.roi.sources[0]['ra']) #ra\n", "print(gta.roi.sources[0]['dec']) #dec\n", "print(gta.roi.sources[0]['glon']) #glon\n", "print(gta.roi.sources[0]['glat']) #glat\n", "print(gta.roi.sources[0]['ra_err']) #error for ra\n", "print(gta.roi.sources[0]['dec_err']) #error for dec\n", "print(gta.roi.sources[0]['glon_err']) #error for glon\n", "print(gta.roi.sources[0]['glat_err']) #error for glat\n", "print(gta.roi.sources[0]['pos_err']) #error for the position in deg\n", "print(gta.roi.sources[0]['pos_r68']) #68% CL error for the position\n", "print(gta.roi.sources[0]['pos_r95']) #95% CL error for the position\n", "print(gta.roi.sources[0]['pos_r99']) #99% CL error for the position\n", "print(gta.roi.sources[0]['pos_err_semimajor']) #1-sigma uncertainty (deg) along major axis of uncertainty ellipse.\n", "print(gta.roi.sources[0]['pos_err_semiminor']) #1-sigma uncertainty (deg) along minor axis of uncertainty ellipse.\n", "print(gta.roi.sources[0]['offset_ra']) #Right ascension offset from ROI center in local celestial projection (deg).\n", "print(gta.roi.sources[0]['offset_dec']) #Declination offset from ROI center in local celestial projection (deg).\n", "print(gta.roi.sources[0]['offset_glon']) #Galactic longitude offset from ROI center in local galactic projection (deg).\n", "print(gta.roi.sources[0]['offset_glat']) #Galactic latitude offset from ROI center in local galactic projection (deg).\n", "print(gta.roi.sources[0]['offset']) #Angular offset from ROI center (deg)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The error for the position is nan because we have not calculated yet the relocalization. \n", "Let's run the localization for a source with gta.localize tool." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:41.975010Z", "iopub.status.busy": "2026-02-25T23:14:41.974838Z", "iopub.status.idle": "2026-02-25T23:14:48.030239Z", "shell.execute_reply": "2026-02-25T23:14:48.029642Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:41 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.662 0.594 9.79e-06 2.70 5206.25 1799.4 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 *\n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:41 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.662 0.594 9.79e-06 2.70 5206.25 1799.4 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 *\n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:41 INFO GTAnalysis.localize(): Running localization for 3FGL J0023.9-7203\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:46 INFO GTAnalysis._localize(): Localization succeeded.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:46 INFO GTAnalysis._localize(): Updating source 3FGL J0023.9-7203 to localized position.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:46 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0023.9-7203\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:46 INFO GTAnalysis.add_source(): Adding source 3FGL J0023.9-7203\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 23:14:47 INFO GTAnalysis._localize(): Localization completed with new position:\n", "( ra, dec) = ( 5.9936 +/- 0.0054, -72.0814 +/- 0.0053)\n", "(glon,glat) = ( 305.9077 +/- 0.0053, -44.8878 +/- 0.0053)\n", "offset = 0.0166 r68 = 0.0081 r95 = 0.0130 r99 = 0.0162\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:47 INFO GTAnalysis._localize(): LogLike: -73810.560 DeltaLogLike: 4.712\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:47 INFO GTAnalysis.localize(): Finished localization.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:48 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/SMC_data/3fgl_j0023.9-7203_loc.npy\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:48 INFO GTAnalysis.localize(): Execution time: 6.04 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:48 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 *\n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.free_sources(free=True)\n", "gta.print_model()\n", "gta.free_sources(skydir=gta.roi[gta.roi.sources[2].name].skydir,distance=[3.0],free=True)\n", "gta.print_model()\n", "localsmc = gta.localize(gta.roi.sources[2].name, update=True, make_plots=True)\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the option make_plots=True a few control plots are created like 3fgl_j0023.9-7203_localize_peak.png with the result for the likelihood analysis for the besy fit position and error for the position." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:48.032261Z", "iopub.status.busy": "2026-02-25T23:14:48.032052Z", "iopub.status.idle": "2026-02-25T23:14:48.040588Z", "shell.execute_reply": "2026-02-25T23:14:48.040001Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='./SMC_data/3fgl_j0023.9-7203_localize_peak.png') " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:48.042305Z", "iopub.status.busy": "2026-02-25T23:14:48.042130Z", "iopub.status.idle": "2026-02-25T23:14:48.047873Z", "shell.execute_reply": "2026-02-25T23:14:48.047381Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.993615495562085\n", "-72.08144238813252\n", "305.907656201347\n", "-44.8877902901592\n", "0.005367762783276858\n", "0.005279521016511993\n", "0.005347152023248827\n", "0.005300394768466772\n", "0.005322709640514254\n", "0.008072288845774397\n", "0.01302705320186296\n", "0.016153349535549193\n", "0.005400312049049391\n", "0.005246222377503256\n", "2.609433773478435\n", "0.4827772080733077\n", "-2.59051407908765\n", "-0.5756497058745496\n", "2.653805814818249\n" ] } ], "source": [ "print(gta.roi.sources[2]['ra']) #ra\n", "print(gta.roi.sources[2]['dec']) #dec\n", "print(gta.roi.sources[2]['glon']) #glon\n", "print(gta.roi.sources[2]['glat']) #glat\n", "print(gta.roi.sources[2]['ra_err']) #error for ra\n", "print(gta.roi.sources[2]['dec_err']) #error for dec\n", "print(gta.roi.sources[2]['glon_err']) #error for glon\n", "print(gta.roi.sources[2]['glat_err']) #error for glat\n", "print(gta.roi.sources[2]['pos_err']) #error for the position in deg\n", "print(gta.roi.sources[2]['pos_r68']) #68% CL error for the position\n", "print(gta.roi.sources[2]['pos_r95']) #95% CL error for the position\n", "print(gta.roi.sources[2]['pos_r99']) #99% CL error for the position\n", "print(gta.roi.sources[2]['pos_err_semimajor']) #1-sigma uncertainty (deg) along major axis of uncertainty ellipse.\n", "print(gta.roi.sources[2]['pos_err_semiminor']) #1-sigma uncertainty (deg) along minor axis of uncertainty ellipse.\n", "print(gta.roi.sources[2]['offset_ra']) #Right ascension offset from ROI center in local celestial projection (deg).\n", "print(gta.roi.sources[2]['offset_dec']) #Declination offset from ROI center in local celestial projection (deg).\n", "print(gta.roi.sources[2]['offset_glon']) #Galactic longitude offset from ROI center in local galactic projection (deg).\n", "print(gta.roi.sources[2]['offset_glat']) #Galactic latitude offset from ROI center in local galactic projection (deg).\n", "print(gta.roi.sources[2]['offset']) #Angular offset from ROI center (deg)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SED PARAMETERS FLUX AND SCAN OF FLUX" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:48.049419Z", "iopub.status.busy": "2026-02-25T23:14:48.049252Z", "iopub.status.idle": "2026-02-25T23:14:48.055242Z", "shell.execute_reply": "2026-02-25T23:14:48.054754Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[b'Prefactor' b'Index' b'Scale' b'' b'' b'' b'' b'' b'' b'']\n", "[ 1.35369200e-11 -2.45898536e+00 6.65532043e+02 nan\n", " nan nan nan nan\n", " nan nan]\n", "[1.14134479e-12 6.10685033e-02 nan nan\n", " nan nan nan nan\n", " nan nan]\n", "631.4279068165924\n", "-73815.27398292236\n", "[1.54536854e-10 3.09073709e-09 3.13147393e-09 3.17221077e-09\n", " 3.21294762e-09 3.25368446e-09 3.29442130e-09 3.33515814e-09\n", " 3.37589499e-09 3.41663183e-09 3.45736867e-09 3.49810551e-09\n", " 3.53884235e-09 3.57957920e-09 3.62031604e-09 3.66105288e-09\n", " 3.70178972e-09 3.74252657e-09 3.78326341e-09 3.82400025e-09]\n", "[0.06140635 1.22812693 1.24431401 1.26050109 1.27668817 1.29287525\n", " 1.30906233 1.32524942 1.3414365 1.35762358 1.37381066 1.38999774\n", " 1.40618482 1.4223719 1.43855899 1.45474607 1.47093315 1.48712023\n", " 1.50330731 1.51949439]\n", "1387.206279433444\n", "3.406737511517394e-09\n", "1.6137540993611978e-10\n", "3.6651650732812706e-09\n", "8.143478440799105e-13\n" ] } ], "source": [ "print(gta.roi.sources[0]['param_names']) #Names of spectral parameters.\n", "print(gta.roi.sources[0]['param_values']) #Spectral parameter values.\n", "print(gta.roi.sources[0]['param_errors']) #Spectral parameters errors.\n", "print(gta.roi.sources[0]['ts']) #Source test statistic.\n", "print(gta.roi.sources[0]['loglike']) #Log-likelihood of the model evaluated at the best-fit normalization of the source.\n", "print(gta.roi.sources[0]['flux_scan']) #Flux values for scan of source normalization.\n", "print(gta.roi.sources[0]['norm_scan']) #Normalization parameters values for scan of source normalization.\n", "print(gta.roi.sources[0]['npred']) #Number of predicted counts from this source integrated over the analysis energy range.\n", "print(gta.roi.sources[0]['flux']) #Photon flux (cm−2 s−1) integrated over analysis energy range\n", "print(gta.roi.sources[0]['flux_err']) #Photon flux uncertainty (cm−2 s−1) integrated over analysis energy range\n", "print(gta.roi.sources[0]['flux_ul95']) #95% CL upper limit on the photon Differential photon flux (cm−2 s−1 MeV−1tegrated over analysis energy range\n", "print(gta.roi.sources[0]['dnde']) #Differential photon flux (cm−2 s−1 MeV−1) evaluated at the pivot energy." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:48.056849Z", "iopub.status.busy": "2026-02-25T23:14:48.056681Z", "iopub.status.idle": "2026-02-25T23:14:50.746423Z", "shell.execute_reply": "2026-02-25T23:14:50.745765Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:48 INFO GTBinnedAnalysis.write_xml(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/SMC_data/model_test_00.xml...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:48 INFO GTAnalysis.write_fits(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/SMC_data/model_test.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 23:14:48 INFO GTBinnedAnalysis.write_model_map(): Generating model map for component 00.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:48 INFO GTAnalysis.write_roi(): Writing /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/SMC_data/model_test.npy...\n" ] } ], "source": [ "gta.write_roi('model_test',make_plots=True,save_model_map=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customizing your model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we saw before you can customize your model directly in the config.yaml file.\n", "However, sometimes you want to change something in your model directly in your Python script." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can free or fix sources using gta.free_sources.\n", "First let's fix the SED parameter of all the sources." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.748665Z", "iopub.status.busy": "2026-02-25T23:14:50.748415Z", "iopub.status.idle": "2026-02-25T23:14:50.764143Z", "shell.execute_reply": "2026-02-25T23:14:50.763685Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for isodiff : ['Normalization']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for galdiff : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 \n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 \n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 \n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 \n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 \n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 \n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 \n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 \n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 \n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 \n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 \n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 \n", "\n" ] } ], "source": [ "gta.free_sources(free=False)\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we free all parameters:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.765832Z", "iopub.status.busy": "2026-02-25T23:14:50.765660Z", "iopub.status.idle": "2026-02-25T23:14:50.780349Z", "shell.execute_reply": "2026-02-25T23:14:50.779910Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 *\n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.free_sources(free=True)\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we free all the SED parameters of sources within 3 degrees from 3FGL J0059.0-7242e:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.781947Z", "iopub.status.busy": "2026-02-25T23:14:50.781776Z", "iopub.status.idle": "2026-02-25T23:14:50.803419Z", "shell.execute_reply": "2026-02-25T23:14:50.802975Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for isodiff : ['Normalization']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Fixing parameters for galdiff : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 \n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 \n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 \n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 \n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 \n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 \n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 \n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.free_sources(free=False)\n", "gta.free_sources(skydir=gta.roi['3FGL J0059.0-7242e'].skydir,distance=3.0)\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to delete the source 3FGL J0021.6-6835:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.805019Z", "iopub.status.busy": "2026-02-25T23:14:50.804837Z", "iopub.status.idle": "2026-02-25T23:14:50.818481Z", "shell.execute_reply": "2026-02-25T23:14:50.817858Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0021.6-6835\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 \n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 \n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 \n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 \n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 \n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 \n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.delete_source('3FGL J0021.6-6835')\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also delete all the sources as a function of the npred/ts or position using the options minmax_npred/minmax_ts/skydir options. Finally, you can delete sources in a given list using the option names." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.820267Z", "iopub.status.busy": "2026-02-25T23:14:50.820069Z", "iopub.status.idle": "2026-02-25T23:14:50.823970Z", "shell.execute_reply": "2026-02-25T23:14:50.823424Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method delete_sources in module fermipy.gtanalysis:\n", "\n", "delete_sources(cuts=None, distance=None, skydir=None, minmax_ts=None, minmax_npred=None, exclude=None, square=False, names=None) method of fermipy.gtanalysis.GTAnalysis instance\n", " Delete sources in the ROI model satisfying the given\n", " selection criteria.\n", " \n", " Parameters\n", " ----------\n", " cuts : dict\n", " Dictionary of [min,max] selections on source properties.\n", " \n", " distance : float\n", " Cut on angular distance from ``skydir``. If None then no\n", " selection will be applied.\n", " \n", " skydir : `~astropy.coordinates.SkyCoord`\n", " Reference sky coordinate for ``distance`` selection. If\n", " None then the distance selection will be applied with\n", " respect to the ROI center.\n", " \n", " minmax_ts : list\n", " Select sources that have TS in the range [min,max]. If\n", " either min or max are None then only a lower (upper) bound\n", " will be applied. If this parameter is none no selection\n", " will be applied.\n", " \n", " minmax_npred : list\n", " Select sources that have npred in the range [min,max]. If\n", " either min or max are None then only a lower (upper) bound\n", " will be applied. If this parameter is none no selection\n", " will be applied.\n", " \n", " square : bool\n", " Switch between applying a circular or square (ROI-like)\n", " selection on the maximum projected distance from the ROI\n", " center.\n", " \n", " names : list \n", " Select sources matching a name in this list.\n", " \n", " Returns\n", " -------\n", " srcs : list\n", " A list of `~fermipy.roi_model.Model` objects.\n", "\n" ] } ], "source": [ "help(gta.delete_sources)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example below we delete all the sources that have an npred=[0,500]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.825563Z", "iopub.status.busy": "2026-02-25T23:14:50.825392Z", "iopub.status.idle": "2026-02-25T23:14:50.876854Z", "shell.execute_reply": "2026-02-25T23:14:50.876215Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0112.9-7506\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0029.1-7045\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2351.9-7601\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2338.7-7401\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0146.4-6746\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2336.5-7620\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0002.0-6722\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.delete_sources(minmax_npred=[0,500])\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can aldo add a new source in the model using the gta.add_source function." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.878560Z", "iopub.status.busy": "2026-02-25T23:14:50.878372Z", "iopub.status.idle": "2026-02-25T23:14:50.881566Z", "shell.execute_reply": "2026-02-25T23:14:50.881008Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method add_source in module fermipy.gtanalysis:\n", "\n", "add_source(name, src_dict, free=None, init_source=True, save_source_maps=True, use_pylike=True, use_single_psf=False, **kwargs) method of fermipy.gtanalysis.GTAnalysis instance\n", " Add a source to the ROI model. This function may be called\n", " either before or after `~fermipy.gtanalysis.GTAnalysis.setup`.\n", " \n", " Parameters\n", " ----------\n", " name : str\n", " Source name.\n", " \n", " src_dict : dict or `~fermipy.roi_model.Source` object\n", " Dictionary or source object defining the source properties\n", " (coordinates, spectral parameters, etc.).\n", " \n", " free : bool\n", " Initialize the source with a free normalization parameter.\n", " \n", " use_pylike : bool\n", " Create source maps with pyLikelihood.\n", " \n", " use_single_psf : bool \n", " Use the PSF model calculated for the ROI center. If false\n", " then a new model will be generated using the position of\n", " the source.\n", "\n" ] } ], "source": [ "help(gta.add_source)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example below we add a Source called Source_PL with a PLSuperExpCutoff and pointlike and a source called Source_Gauss that is spatial extended with a PowerLaw SED and with a RadialGaussian template with an extension of 1 deg." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:50.883243Z", "iopub.status.busy": "2026-02-25T23:14:50.883073Z", "iopub.status.idle": "2026-02-25T23:14:56.750938Z", "shell.execute_reply": "2026-02-25T23:14:56.750320Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:50 INFO GTAnalysis.add_source(): Adding source Source_PL\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:51 INFO GTAnalysis.add_source(): Adding source Source_Gauss\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:56 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "Source_Gauss 0.655 1.000 0.00621 2.00 nan 427843.4 *\n", "Source_PL 2.274 1.000 0.00319 4.00 nan 483408.5 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.add_source('Source_PL',{ 'glon' : 300., 'glat' : -46.,'SpectrumType' : 'PLSuperExpCutoff', 'Index1':-1.5, 'Index2' : 1.0,'Scale' : 1000,'Prefactor':1e-9,'SpatialModel' : 'PointSource' })\n", "gta.add_source('Source_Gauss',{ 'glon' : 302., 'glat' : -45.,'SpectrumType' : 'PowerLaw', 'Index':2.0,'Scale' : 1000,'Prefactor':1e-9,'SpatialModel' : 'RadialGaussian', 'SpatialWidth': 1.0 })\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I load the model saved into model_test to have back the intial model." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:14:56.752895Z", "iopub.status.busy": "2026-02-25T23:14:56.752685Z", "iopub.status.idle": "2026-02-25T23:15:02.168064Z", "shell.execute_reply": "2026-02-25T23:15:02.167453Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:56 INFO GTAnalysis.load_roi(): Loading ROI file: /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/SMC_data/model_test.npy\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:14:56 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:02 INFO GTAnalysis.load_roi(): Finished Loading ROI\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:02 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 *\n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] } ], "source": [ "gta.load_roi('model_test')\n", "gta.print_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to modify the SED parameters using the functions gta.set_norm, gta.set_parameter, gta.set_parameter_bounds, gta.set_parameter_error ." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:02.169839Z", "iopub.status.busy": "2026-02-25T23:15:02.169651Z", "iopub.status.idle": "2026-02-25T23:15:02.174461Z", "shell.execute_reply": "2026-02-25T23:15:02.173873Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method set_norm in module fermipy.gtanalysis:\n", "\n", "set_norm(name, value, update_source=True) method of fermipy.gtanalysis.GTAnalysis instance\n", "\n", "Help on method set_parameter in module fermipy.gtanalysis:\n", "\n", "set_parameter(name, par, value, true_value=True, scale=None, bounds=None, error=None, update_source=True) method of fermipy.gtanalysis.GTAnalysis instance\n", " Update the value of a parameter. Parameter bounds will\n", " automatically be adjusted to encompass the new parameter\n", " value.\n", " \n", " Parameters\n", " ----------\n", " \n", " name : str\n", " Source name.\n", " \n", " par : str\n", " Parameter name.\n", " \n", " value : float\n", " Parameter value. By default this argument should be the\n", " unscaled (True) parameter value.\n", " \n", " scale : float\n", " Parameter scale (optional). Value argument is interpreted\n", " with respect to the scale parameter if it is provided.\n", " \n", " error : float\n", " Parameter error (optional). By default this argument should be the\n", " unscaled (True) parameter value.\n", " \n", " update_source : bool\n", " Update the source dictionary for the object.\n", "\n" ] } ], "source": [ "help(gta.set_norm)\n", "help(gta.set_parameter)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:02.176150Z", "iopub.status.busy": "2026-02-25T23:15:02.175963Z", "iopub.status.idle": "2026-02-25T23:15:02.179720Z", "shell.execute_reply": "2026-02-25T23:15:02.179204Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[b'Prefactor' b'Index' b'Scale' b'' b'' b'' b'' b'' b'' b'']\n", "[ 1.35369200e-11 -2.45898536e+00 6.65532043e+02 nan\n", " nan nan nan nan\n", " nan nan]\n", "[ 1.14134479e-12 -6.10685033e-02 nan nan\n", " nan nan nan nan\n", " nan nan]\n" ] } ], "source": [ "print(gta.roi['3FGL J0059.0-7242e']['param_names'])\n", "print(gta.roi['3FGL J0059.0-7242e']['param_values'])\n", "print(gta.roi['3FGL J0059.0-7242e']['param_errors'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example below we fix the normalization to 1e-11 and the slope to 2.0." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:02.181317Z", "iopub.status.busy": "2026-02-25T23:15:02.181149Z", "iopub.status.idle": "2026-02-25T23:15:02.396133Z", "shell.execute_reply": "2026-02-25T23:15:02.395574Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/fermipy/fermipy/fermipy/utils.py:793: UserWarning: \n", "A theoretically impossible result was found during the iteration\n", "process for finding a smoothing spline with fp = s: s 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": "stdout", "output_type": "stream", "text": [ "[b'Prefactor' b'Index' b'Scale' b'' b'' b'' b'' b'' b'' b'']\n", "[1.00000000e-11 2.00000000e+00 6.65532043e+02 nan\n", " nan nan nan nan\n", " nan nan]\n", "[ 0. 0. nan nan nan nan nan nan nan nan]\n" ] } ], "source": [ "gta.set_norm('3FGL J0059.0-7242e',value=1.)\n", "gta.set_parameter('3FGL J0059.0-7242e',par='Index',value=2.0)\n", "print(gta.roi['3FGL J0059.0-7242e']['param_names'])\n", "print(gta.roi['3FGL J0059.0-7242e']['param_values'])\n", "print(gta.roi['3FGL J0059.0-7242e']['param_errors'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to set the SED shape of a source using gta.set_source_spectrum tool." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:02.397852Z", "iopub.status.busy": "2026-02-25T23:15:02.397677Z", "iopub.status.idle": "2026-02-25T23:15:02.400997Z", "shell.execute_reply": "2026-02-25T23:15:02.400501Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method set_source_spectrum in module fermipy.gtanalysis:\n", "\n", "set_source_spectrum(name, spectrum_type='PowerLaw', spectrum_pars=None, update_source=True) method of fermipy.gtanalysis.GTAnalysis instance\n", " Set the spectral model of a source. This function can be\n", " used to change the spectral type of a source or modify its\n", " spectral parameters. If called with\n", " spectrum_type='FileFunction' and spectrum_pars=None, the\n", " source spectrum will be replaced with a FileFunction with the\n", " same differential flux distribution as the original spectrum.\n", " \n", " Parameters\n", " ----------\n", " \n", " name : str\n", " Source name.\n", " \n", " spectrum_type : str\n", " Spectrum type (PowerLaw, etc.).\n", " \n", " spectrum_pars : dict\n", " Dictionary of spectral parameters (optional).\n", " \n", " update_source : bool\n", " Recompute all source characteristics (flux, TS, NPred)\n", " using the new spectral model of the source.\n", "\n" ] } ], "source": [ "help(gta.set_source_spectrum)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:02.402477Z", "iopub.status.busy": "2026-02-25T23:15:02.402295Z", "iopub.status.idle": "2026-02-25T23:15:07.831849Z", "shell.execute_reply": "2026-02-25T23:15:07.831271Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:02 INFO GTAnalysis.load_roi(): Loading ROI file: /home/runner/work/fermipy/fermipy/fermipy-extra/notebooks/SMC_data/model_test.npy\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:02 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:07 INFO GTAnalysis.load_roi(): Finished Loading ROI\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:07 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.354 1.02e-05 2.46 631.43 1387.2 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.27 129.2 *\n", "3FGL J0023.9-7203 2.654 0.582 9.37e-06 2.69 5334.39 1799.9 *\n", "3FGL J0029.1-7045 3.008 0.458 2.07e-06 2.33 269.77 239.2 *\n", "3FGL J0021.6-6835 5.122 2.618 2.57e-07 3.29 16.47 57.1 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.25 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.33 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.39 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.14 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.57 6220.2 *\n", "galdiff --- 1.094 0.134 0.05 7041.53 17811.3 *\n", "\n" ] }, { "data": { "text/plain": [ "'PowerLaw'" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gta.load_roi('model_test')\n", "gta.print_model()\n", "gta.roi['3FGL J0059.0-7242e']['SpectrumType']" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:07.833576Z", "iopub.status.busy": "2026-02-25T23:15:07.833402Z", "iopub.status.idle": "2026-02-25T23:15:07.938789Z", "shell.execute_reply": "2026-02-25T23:15:07.938166Z" } }, "outputs": [ { "data": { "text/plain": [ "'LogParabola'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gta.set_source_spectrum('3FGL J0059.0-7242e',spectrum_type='LogParabola')\n", "gta.roi['3FGL J0059.0-7242e']['SpectrumType']" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:07.940493Z", "iopub.status.busy": "2026-02-25T23:15:07.940304Z", "iopub.status.idle": "2026-02-25T23:15:11.367385Z", "shell.execute_reply": "2026-02-25T23:15:11.366890Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:07 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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:11 INFO GTAnalysis.fit(): Fit returned successfully. Quality: 3 Status: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:11 INFO GTAnalysis.fit(): LogLike: -73810.277 DeltaLogLike: 1.496 \n" ] }, { "data": { "text/plain": [ "{'fit_quality': 3,\n", " 'fit_status': 0,\n", " 'fit_success': True,\n", " 'dloglike': 1.4957104738568887,\n", " 'edm': 0.0002737282842093268,\n", " 'loglike': -73810.27663600704,\n", " 'covariance': array([[ 4.98472198e-03, 4.07309749e-03, -2.59347204e-04,\n", " -5.94243239e-05, 1.76863307e-05, 3.73518577e-05,\n", " -1.20573086e-05, 1.69901941e-05, 1.31487584e-05,\n", " 9.48751375e-05, 1.01761367e-05, 4.96385447e-05,\n", " 1.55697474e-05, 4.74379588e-05, 2.57847320e-05,\n", " 2.75638658e-05, 2.82827052e-05, 2.24006836e-05,\n", " 1.78592009e-05, 3.26272350e-05, 1.88822266e-05,\n", " -1.86688310e-05, -3.89211213e-05, -1.07173572e-04],\n", " [ 4.07309749e-03, 1.88514620e-02, -5.02535361e-04,\n", " -7.82619448e-05, 2.37933218e-05, 4.83781547e-05,\n", " -1.25386172e-05, 2.10851407e-05, 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-6.80406989e-02, -5.58898241e-02,\n", " 4.02143025e-02, -2.97607419e-02, -3.16004981e-02,\n", " -1.20826781e-01, -1.46812420e-01, 1.00999952e-02,\n", " -1.26388876e-02, -6.62837863e-02, -4.48525409e-02,\n", " -1.11708378e-02, -2.85915707e-02, -1.67610502e-02,\n", " -2.62046645e-02, 2.14668526e-02, -2.30674646e-02,\n", " 3.25558136e-02, 1.00000000e+00, 5.83353076e-01],\n", " [-3.51719927e-02, -4.30743335e-03, -3.87517576e-02,\n", " -1.64301182e-02, -4.42308775e-02, -4.26159106e-02,\n", " 3.83516891e-02, -1.40660658e-02, -2.75600805e-03,\n", " 2.21103037e-02, 3.25823039e-02, 3.42654397e-02,\n", " 2.04966449e-02, -6.88752143e-02, -2.76759953e-02,\n", " 3.88940679e-02, 2.01088464e-02, -1.81025906e-02,\n", " -8.40109028e-04, 1.13496978e-02, 1.82163415e-02,\n", " -6.85553338e-01, 5.83353076e-01, 1.00000000e+00]]),\n", " 'values': array([0.40861579, 1.97715111, 2.61901273, 3.28583985, 0.59494962,\n", " 1.17663859, 0.58371813, 0.46191178, 2.3315159 , 1.35174521,\n", " 2.45868649, 1.13297256, 1.95249163, 0.66401781, 2.35316433,\n", " 0.3917108 , 2.28965538, 0.59772866, 2.01586615, 1.40373579,\n", " 2.01272488, 1.09410496, 0.0463696 , 0.68374566]),\n", " 'errors': array([0.07060256, 0.13730063, 1.57532258, 0.4996716 , 0.05462299,\n", " 0.18568864, 0.07920989, 0.05757237, 0.1085442 , 0.11404355,\n", " 0.06109538, 0.1931521 , 0.11083977, 0.09684777, 0.11456492,\n", " 0.08433046, 0.15078899, 0.06245894, 0.09285456, 0.15990415,\n", " 0.0991055 , 0.02116719, 0.0205832 , 0.04315889]),\n", " 'indices': array([ 0, 1, 3, 4, 6, 7, 8, 10, 11, 13, 14, 17, 18, 20, 21, 23, 24,\n", " 26, 27, 29, 30, 32, 33, 35]),\n", " 'is_norm': array([ True, False, True, False, True, False, False, True, False,\n", " True, False, True, False, True, False, True, False, True,\n", " False, True, False, True, False, True]),\n", " 'src_names': ['3FGL J0002.0-6722',\n", " '3FGL J0002.0-6722',\n", " '3FGL J0021.6-6835',\n", " '3FGL J0021.6-6835',\n", " '3FGL J0023.9-7203',\n", " '3FGL J0023.9-7203',\n", " '3FGL J0023.9-7203',\n", " '3FGL J0029.1-7045',\n", " '3FGL J0029.1-7045',\n", " '3FGL J0059.0-7242e',\n", " '3FGL J0059.0-7242e',\n", " '3FGL J0112.9-7506',\n", " '3FGL J0112.9-7506',\n", " '3FGL J0146.4-6746',\n", " '3FGL J0146.4-6746',\n", " '3FGL J2336.5-7620',\n", " '3FGL J2336.5-7620',\n", " '3FGL J2338.7-7401',\n", " '3FGL J2338.7-7401',\n", " '3FGL J2351.9-7601',\n", " '3FGL J2351.9-7601',\n", " 'galdiff',\n", " 'galdiff',\n", " 'isodiff'],\n", " 'par_names': ['Prefactor',\n", " 'Index',\n", " 'Prefactor',\n", " 'Index',\n", " 'norm',\n", " 'alpha',\n", " 'beta',\n", " 'Prefactor',\n", " 'Index',\n", " 'norm',\n", " 'alpha',\n", " 'Prefactor',\n", " 'Index',\n", " 'Prefactor',\n", " 'Index',\n", " 'Prefactor',\n", " 'Index',\n", " 'Prefactor',\n", " 'Index',\n", " 'Prefactor',\n", " 'Index',\n", " 'Prefactor',\n", " 'Index',\n", " 'Normalization'],\n", " 'config': {'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,\n", " 'covar': True,\n", " 'reoptimize': False},\n", " 'niter': 1}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gta.fit()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:11.369119Z", "iopub.status.busy": "2026-02-25T23:15:11.368946Z", "iopub.status.idle": "2026-02-25T23:15:11.372721Z", "shell.execute_reply": "2026-02-25T23:15:11.372221Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[b'norm' b'alpha' b'beta' b'Eb' b'' b'' b'' b'' b'' b'']\n", "[1.35174521e-11 2.45868649e+00 0.00000000e+00 6.65532043e+02\n", " nan nan nan nan\n", " nan nan]\n", "[1.14043554e-12 6.10953785e-02 nan nan\n", " nan nan nan nan\n", " nan nan]\n" ] } ], "source": [ "print(gta.roi['3FGL J0059.0-7242e']['param_names'])\n", "print(gta.roi['3FGL J0059.0-7242e']['param_values'])\n", "print(gta.roi['3FGL J0059.0-7242e']['param_errors'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see above the tool gta.set_source_spectrum has modified the SED shape from PowerLaw to LogParabola." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to set the spatial morphology of a source using gta.set_source_morphology" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:11.374316Z", "iopub.status.busy": "2026-02-25T23:15:11.374149Z", "iopub.status.idle": "2026-02-25T23:15:11.377040Z", "shell.execute_reply": "2026-02-25T23:15:11.376544Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method set_source_morphology in module fermipy.gtanalysis:\n", "\n", "set_source_morphology(name, **kwargs) method of fermipy.gtanalysis.GTAnalysis instance\n", " Set the spatial model of a source.\n", " \n", " Parameters\n", " ----------\n", " name : str\n", " Source name.\n", " \n", " spatial_model : str\n", " Spatial model name (PointSource, RadialGaussian, etc.).\n", " \n", " spatial_pars : dict\n", " Dictionary of spatial parameters (optional).\n", " \n", " use_cache : bool \n", " Generate the spatial model by interpolating the cached source\n", " map.\n", " \n", " use_pylike : bool\n", "\n" ] } ], "source": [ "help(gta.set_source_morphology)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:11.378620Z", "iopub.status.busy": "2026-02-25T23:15:11.378420Z", "iopub.status.idle": "2026-02-25T23:15:16.518898Z", "shell.execute_reply": "2026-02-25T23:15:16.518283Z" } }, "outputs": [], "source": [ "gta.set_source_morphology(name='3FGL J0029.1-7045',spatial_model='RadialGaussian',spatial_pars={'SpatialWidth': 1.0} )" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2026-02-25T23:15:16.520854Z", "iopub.status.busy": "2026-02-25T23:15:16.520679Z", "iopub.status.idle": "2026-02-25T23:15:16.524388Z", "shell.execute_reply": "2026-02-25T23:15:16.523895Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-25 23:15:16 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.352 1.02e-05 2.46 630.25 1385.7 *\n", "3FGL J0112.9-7506 2.572 1.133 2.16e-06 1.95 145.28 129.2 *\n", "3FGL J0023.9-7203 2.654 0.595 9.8e-06 2.70 5215.82 1799.6 *\n", "3FGL J0029.1-7045 3.008 0.462 2.07e-06 2.33 271.65 240.6 *\n", "3FGL J0021.6-6835 5.122 2.619 2.57e-07 3.29 16.48 57.2 *\n", "3FGL J2351.9-7601 5.495 1.404 2.85e-06 2.01 243.24 192.1 *\n", "3FGL J2338.7-7401 5.777 0.598 2.91e-06 2.02 294.32 187.1 *\n", "3FGL J0146.4-6746 6.423 0.664 1.76e-06 2.35 256.38 201.6 *\n", "3FGL J2336.5-7620 6.454 0.392 1.28e-06 2.29 112.83 135.9 *\n", "3FGL J0002.0-6722 7.153 0.409 1.63e-06 1.98 94.13 95.4 *\n", "isodiff --- 0.684 0.0214 2.12 713.90 6222.0 *\n", "galdiff --- 1.094 0.134 0.05 7038.55 17808.8 *\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "RadialGaussian\n", "1.0\n" ] } ], "source": [ "gta.print_model()\n", "print(gta.roi['3FGL J0029.1-7045']['SpatialType'])\n", "print(gta.roi['3FGL J0029.1-7045']['SpatialWidth'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.23" } }, "nbformat": 4, "nbformat_minor": 4 }