{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Configuring the model, running g-tools and output files and infos" ] }, { "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." ] }, { "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": {}, "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 : '$FERMI_DIFFUSE_DIR/gll_iem_v06.fits'\n", " isodiff : '$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": [ "FILEIO" ] }, { "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": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "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": [ "Then we 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": {}, "outputs": [], "source": [ "if os.path.isfile('../data/SMC_data.tar.gz'):\n", " !tar xzf ../data/SMC_data.tar.gz\n", "else:\n", " !curl -OL https://raw.githubusercontent.com/fermiPy/fermipy-extras/master/data/SMC_data.tar.gz\n", " !tar xzf SMC_data.tar.gz" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:02:08 INFO GTAnalysis.__init__(): \n", "--------------------------------------------------------------------------------\n", "fermipy version 0.16.0+175.ge34f \n", "ScienceTools version ScienceTools-11-07-00\n", "WARNING: UnitsWarning: 'photon/cm**2/MeV/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n", "WARNING: UnitsWarning: 'photon/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n", "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n", "2018-03-31 09:02:11 INFO GTAnalysis.setup(): Running setup.\n", "2018-03-31 09:02:11 INFO GTBinnedAnalysis.setup(): Running setup for component 00\n", "2018-03-31 09:02:11 INFO GTBinnedAnalysis._select_data(): Skipping data selection.\n", "2018-03-31 09:02:11 INFO GTBinnedAnalysis.setup(): Using external LT cube.\n", "2018-03-31 09:02:13 INFO GTBinnedAnalysis._create_expcube(): Skipping gtexpcube.\n", "2018-03-31 09:02:13 INFO GTBinnedAnalysis._create_srcmaps(): Skipping gtsrcmaps.\n", "2018-03-31 09:02:13 INFO GTBinnedAnalysis.setup(): Finished setup for component 00\n", "2018-03-31 09:02:13 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n", "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n", "2018-03-31 09:02:35 INFO GTAnalysis.setup(): Initializing source properties\n", "2018-03-31 09:03:30 INFO GTAnalysis.setup(): Finished setup.\n" ] } ], "source": [ "gta = GTAnalysis('config.yaml')\n", "matplotlib.interactive(True)\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": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:03:55 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 1434.2 \n", "3FGL J0112.9-7506 2.572 1.172 1.31e-06 2.17 nan 120.0 \n", "3FGL J0023.9-7203 2.662 0.714 9.3e-06 2.65 nan 1675.8 \n", "3FGL J0029.1-7045 3.008 0.506 2.49e-06 2.28 nan 269.9 \n", "3FGL J0021.6-6835 5.122 1.859 5.27e-07 2.65 nan 84.0 \n", "3FGL J2351.9-7601 5.495 1.027 3.74e-06 1.69 nan 117.5 \n", "3FGL J2338.7-7401 5.777 0.677 4.38e-06 1.89 nan 213.2 \n", "3FGL J0146.4-6746 6.423 0.420 1.03e-06 2.39 nan 123.7 \n", "3FGL J2336.5-7620 6.454 0.557 1.66e-06 2.33 nan 186.9 \n", "3FGL J0002.0-6722 7.153 0.483 2.07e-06 1.95 nan 113.2 \n", "isodiff --- 1.000 0.0313 2.12 nan 9076.7 \n", "galdiff --- 1.000 0.126 0.00 nan 17968.1 \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": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n", "2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n", "2018-03-31 09:04:41 INFO GTAnalysis.fit(): Starting fit.\n", "/u/gl/mdimauro/kipac/software/anaconda/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py:226: 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", " warnings.warn(message)\n", "2018-03-31 09:04:57 INFO GTAnalysis.fit(): Fit returned successfully. Quality: 3 Status: 0\n", "2018-03-31 09:04:57 INFO GTAnalysis.fit(): LogLike: -77883.041 DeltaLogLike: 80.036 \n" ] } ], "source": [ "gta.free_sources()\n", "fitresult=gta.fit()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:04:57 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.662 0.590 9.68e-06 2.70 4889.10 1771.0 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": {}, "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 J2336.5-7620': 0.021540975517969452, '3FGL J0029.1-7045': -0.018737972377049942, 'isodiff': -0.011270554190343823, 'galdiff': -0.16130718991869766, '3FGL J2338.7-7401': 0.01187470654461815, '3FGL J0059.0-7242e': 0.9999999999999998, '3FGL J0112.9-7506': -0.010991866042965983, '3FGL J0146.4-6746': 0.01590697189126327, '3FGL J0023.9-7203': -0.02136376358912324, '3FGL J0021.6-6835': 0.01579921758235634, '3FGL J0002.0-6722': 0.011830993863816544, '3FGL J2351.9-7601': 0.00401017952234348}\n", "[4.95790377e+02 3.41435355e+02 2.31934703e+02 1.55836591e+02\n", " 1.03590847e+02 6.85066795e+01 4.54694907e+01 3.03664598e+01\n", " 2.00853639e+01 1.32773079e+01 8.77338782e+00 5.83038311e+00\n", " 3.90279960e+00 2.59778026e+00 1.71628875e+00 1.12902221e+00\n", " 7.43126007e-01 4.87775565e-01 3.21625630e-01 2.11836077e-01\n", " 1.38999521e-01 8.97531536e-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": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14.75\n", "-72.7\n", "302.14493\n", "-44.4167\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", "0.0750319866158955\n", "-0.046748398465821905\n", "0.0884039\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": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:06:10 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.662 0.590 9.68e-06 2.70 4889.10 1771.0 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n", "\n", "2018-03-31 09:06:10 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.662 0.590 9.68e-06 2.70 4889.10 1771.0 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n", "\n", "2018-03-31 09:06:10 INFO GTAnalysis.localize(): Running localization for 3FGL J0023.9-7203\n", "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n", "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n", "2018-03-31 09:06:28 INFO GTAnalysis._localize(): Localization succeeded.\n", "2018-03-31 09:06:28 INFO GTAnalysis._localize(): Updating source 3FGL J0023.9-7203 to localized position.\n", "2018-03-31 09:06:28 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0023.9-7203\n", "2018-03-31 09:06:28 INFO GTAnalysis.add_source(): Adding source 3FGL J0023.9-7203\n", "2018-03-31 09:06:31 INFO GTAnalysis._localize(): Localization completed with new position:\n", "( ra, dec) = ( 5.9919 +/- 0.0054, -72.0832 +/- 0.0053)\n", "(glon,glat) = ( 305.9080 +/- 0.0054, -44.8859 +/- 0.0053)\n", "offset = 0.0183 r68 = 0.0081 r95 = 0.0131 r99 = 0.0163\n", "2018-03-31 09:06:31 INFO GTAnalysis._localize(): LogLike: -77883.001 DeltaLogLike: 0.040\n", "2018-03-31 09:06:31 INFO GTAnalysis.localize(): Finished localization.\n", "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n", "2018-03-31 09:06:35 INFO GTAnalysis.localize(): Execution time: 25.06 s\n", "2018-03-31 09:06:35 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='3fgl_j0023.9-7203_localize_peak.png') " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.991891558666937\n", "-72.08324039663697\n", "305.90801515198206\n", "-44.88593302102324\n", "0.005397242484279103\n", "0.005337532530487048\n", "0.005397242484279103\n", "0.005337532530487048\n", "0.005366221368316228\n", "0.008138277648229967\n", "0.013133545877823873\n", "0.016285398847939087\n", "0.005448724558888491\n", "0.005284967419907161\n", "2.609705201380567\n", "0.48092218927044694\n", "-2.5908516158641124\n", "-0.573805547525784\n", "2.6537356812\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": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Prefactor' 'Index' 'Scale' '' '' '' '' '' '' '']\n", "[ 1.51191261e-11 -2.46682153e+00 6.65532043e+02 nan\n", " nan nan nan nan\n", " nan nan]\n", "[1.25024392e-12 6.17596151e-02 nan nan\n", " nan nan nan nan\n", " nan nan]\n", "753.08940408\n", "-77883.0461087\n", "[0.00000000e+00 3.45304615e-09 3.49502174e-09 3.53699734e-09\n", " 3.57897294e-09 3.62094854e-09 3.66292414e-09 3.70489974e-09\n", " 3.74687534e-09 3.78885093e-09 3.83082653e-09 3.87280213e-09\n", " 3.91477773e-09 3.95675333e-09 3.99872893e-09 4.04070453e-09\n", " 4.08268013e-09 4.12465572e-09 4.16663132e-09 4.20860692e-09]\n", "[0. 1.38385295 1.40067521 1.41749748 1.43431974 1.45114201\n", " 1.46796427 1.48478654 1.5016088 1.51843106 1.53525333 1.55207559\n", " 1.56889786 1.58572012 1.60254239 1.61936465 1.63618692 1.65300918\n", " 1.66983145 1.68665371]\n", "1532.23595176\n", "3.77258581354e-09\n", "1.6566462673e-10\n", "4.03860906082e-09\n", "9.1858070747e-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": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:09:31 INFO GTBinnedAnalysis.write_xml(): Writing /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test_00.xml...\n", "2018-03-31 09:09:31 INFO GTAnalysis.write_fits(): Writing /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.fits...\n", "2018-03-31 09:09:32 INFO GTBinnedAnalysis.write_model_map(): Generating model map for component 00.\n", "2018-03-31 09:09:34 INFO GTAnalysis.write_roi(): Writing /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.npy...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for isodiff : ['Normalization']\n", "2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for galdiff : ['Prefactor', 'Index']\n", "2018-03-31 09:10:09 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 \n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 \n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 \n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 \n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 \n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 \n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 \n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 \n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 \n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 \n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 \n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 \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": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n", "2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n", "2018-03-31 09:10:13 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for isodiff : ['Normalization']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for galdiff : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n", "2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n", "2018-03-31 09:10:19 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 \n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 \n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 \n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 \n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 \n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 \n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 \n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:10:23 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0021.6-6835\n", "2018-03-31 09:10:23 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 \n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 \n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 \n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 \n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 \n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 \n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method delete_sources in module fermipy.gtanalysis:\n", "\n", "delete_sources(self, 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": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0112.9-7506\n", "2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0029.1-7045\n", "2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2351.9-7601\n", "2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2338.7-7401\n", "2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0146.4-6746\n", "2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2336.5-7620\n", "2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0002.0-6722\n", "2018-03-31 09:10:36 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method add_source in module fermipy.gtanalysis:\n", "\n", "add_source(self, 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": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:10:44 INFO GTAnalysis.add_source(): Adding source Source_PL\n", "2018-03-31 09:10:47 INFO GTAnalysis.add_source(): Adding source Source_Gauss\n", "2018-03-31 09:11:00 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "Source_Gauss 0.655 1.000 0.00621 2.00 nan 410827.7 *\n", "Source_PL 2.274 1.000 0.00319 4.00 nan 471919.8 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:11:04 INFO GTAnalysis.load_roi(): Loading ROI file: /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.npy\n", "2018-03-31 09:11:04 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n", "2018-03-31 09:11:23 INFO GTAnalysis.load_roi(): Finished Loading ROI\n", "2018-03-31 09:11:23 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\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": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method set_norm in module fermipy.gtanalysis:\n", "\n", "set_norm(self, 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(self, 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": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Prefactor' 'Index' 'Scale' '' '' '' '' '' '' '']\n", "[ 1.51191261e-11 -2.46682153e+00 6.65532043e+02 nan\n", " nan nan nan nan\n", " nan nan]\n", "[ 1.25024392e-12 -6.17596151e-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": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/u/gl/mdimauro/kipac/software/anaconda/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py:226: 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", " warnings.warn(message)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['Prefactor' 'Index' 'Scale' '' '' '' '' '' '' '']\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": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method set_source_spectrum in module fermipy.gtanalysis:\n", "\n", "set_source_spectrum(self, 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": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:14:18 INFO GTAnalysis.load_roi(): Loading ROI file: /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.npy\n", "2018-03-31 09:14:18 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n", "2018-03-31 09:14:37 INFO GTAnalysis.load_roi(): Finished Loading ROI\n", "2018-03-31 09:14:37 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n", "3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n", "\n" ] }, { "data": { "text/plain": [ "'PowerLaw'" ] }, "execution_count": 33, "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": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'LogParabola'" ] }, "execution_count": 34, "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": 35, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:15:23 INFO GTAnalysis.fit(): Starting fit.\n", "2018-03-31 09:15:38 INFO GTAnalysis.fit(): Fit returned successfully. Quality: 3 Status: 0\n", "2018-03-31 09:15:38 INFO GTAnalysis.fit(): LogLike: -77883.001 DeltaLogLike: 0.000 \n" ] }, { "data": { "text/plain": [ "{'config': {'covar': True,\n", " 'init_lambda': 0.0001,\n", " 'max_iter': 100,\n", " 'min_fit_quality': 2,\n", " 'optimizer': 'MINUIT',\n", " 'reoptimize': False,\n", " 'retries': 3,\n", " 'tol': 0.001,\n", " 'verbosity': 0},\n", " 'correlation': array([[ 1.00000000e+00, 4.40010977e-01, -1.80903032e-03,\n", " -1.29673069e-03, 3.87817026e-03, 2.64332600e-03,\n", " -2.04198717e-03, 9.41038275e-03, 6.18965916e-04,\n", " 1.18313905e-02, 2.62916494e-03, 4.12849233e-03,\n", " 2.26000858e-03, 6.23891541e-03, 3.04105095e-03,\n", " 5.05645412e-03, 3.00353477e-03, 4.90626867e-03,\n", " 2.82449046e-03, 2.82031889e-03, 2.93014665e-03,\n", " -1.19680073e-02, -2.71265943e-02, -3.41242300e-02],\n", " [ 4.40010977e-01, 1.00000000e+00, -1.81621670e-03,\n", " -7.32754724e-04, 2.44740751e-03, 1.49292747e-03,\n", " -8.93745178e-04, 5.79304529e-03, 4.32350178e-03,\n", " 1.14652827e-02, 8.09005714e-03, 3.14397563e-03,\n", " 2.46443004e-03, 3.52454107e-03, 2.37096614e-03,\n", " 4.72386017e-03, 3.46125929e-03, 2.76468822e-03,\n", " 2.53278353e-03, 1.15894276e-03, 3.11172827e-03,\n", " -3.39077196e-02, -2.77540909e-02, -1.89906219e-03],\n", " [-1.80903032e-03, -1.81621670e-03, 1.00000000e+00,\n", " 8.61787939e-01, 5.57550572e-03, 3.44059022e-03,\n", " -2.20704334e-03, -1.68151354e-02, -1.15186150e-02,\n", " 1.56722554e-02, 1.05487303e-02, 2.77193258e-03,\n", " 2.56573839e-03, 5.95117489e-03, 3.78280704e-03,\n", " 4.66781225e-03, 4.01891459e-03, 3.48241472e-03,\n", " 3.20035094e-03, 6.91485427e-04, 3.48455951e-03,\n", " -1.95753025e-02, -6.10088327e-02, -3.71947727e-02],\n", " [-1.29673069e-03, -7.32754724e-04, 8.61787939e-01,\n", " 1.00000000e+00, 2.21844270e-03, 1.44347366e-03,\n", " -7.90470631e-04, -7.08738255e-03, -5.00629583e-03,\n", " 7.50301160e-03, 8.19997549e-03, 2.34604520e-04,\n", " 1.10416771e-03, 1.99669462e-03, 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3.91147434e-03,\n", " 1.61737564e-03, -4.80676171e-06, -1.14013776e-05,\n", " -4.01006133e-05, -1.68410463e-05, -2.65016650e-05],\n", " [ 1.92918544e-05, 3.37499405e-05, 4.33430504e-04,\n", " 7.39881765e-05, 6.33263525e-06, 1.10713502e-05,\n", " -1.93872461e-06, 7.42543294e-05, 6.68117146e-05,\n", " 1.41519592e-04, 4.84981354e-05, 6.78590034e-05,\n", " 2.80725782e-05, 3.06802352e-05, 2.82536688e-05,\n", " 1.29966509e-06, 3.69590592e-06, 1.61737564e-03,\n", " 9.22065526e-03, -1.46644176e-05, -9.90956897e-06,\n", " -7.20995589e-05, -6.13732656e-05, -3.61509901e-07],\n", " [ 3.28010369e-05, 2.62961650e-05, 1.59463170e-04,\n", " -8.90504456e-05, -1.77680276e-06, -2.17869566e-05,\n", " 7.67454464e-06, 1.00575222e-04, -2.02063064e-05,\n", " 8.19086320e-05, -2.53010597e-05, 1.26928454e-04,\n", " 2.66207011e-05, 3.23107931e-05, 1.24727264e-05,\n", " -5.40147300e-04, -4.39951314e-04, -4.80676171e-06,\n", " -1.46644176e-05, 2.67345433e-02, -5.93105459e-03,\n", " -7.24654550e-05, 8.78269177e-05, 9.14133333e-05],\n", " [ 2.09375000e-05, 4.33788672e-05, 4.93709356e-04,\n", " 8.87220428e-05, 1.05895478e-05, 1.64524547e-05,\n", " -2.19129640e-06, 9.07458515e-05, 9.72971897e-05,\n", " 1.79416308e-04, 6.65800617e-05, 9.07351465e-05,\n", " 3.77824505e-05, 3.33208285e-05, 3.31096474e-05,\n", " -5.36230459e-04, -6.15282067e-04, -1.14013776e-05,\n", " -9.90956897e-06, -5.93105459e-03, 1.00917153e-02,\n", " -1.12656551e-04, -6.29357702e-05, 6.70916578e-05],\n", " [-1.72406892e-05, -9.52955094e-05, -5.59151206e-04,\n", " -1.27753117e-04, -1.69414560e-06, 4.23916781e-05,\n", " -2.67778539e-05, -1.52597486e-04, -2.86363212e-04,\n", " -4.08297663e-04, -1.84930673e-04, -2.58635978e-04,\n", " -1.06328281e-04, -2.74743268e-05, -4.80071555e-05,\n", " -1.70395729e-04, -1.87940256e-04, -4.01006133e-05,\n", " -7.20995589e-05, -7.24654550e-05, -1.12656551e-04,\n", " 4.10165958e-04, 2.39654093e-06, -6.88413280e-04],\n", " [-4.56261686e-05, -9.10724058e-05, -2.03469561e-03,\n", " -5.51781708e-04, -8.62182873e-05, -2.49482848e-04,\n", " 7.98444672e-05, -1.70308857e-04, -2.55851744e-04,\n", " -4.36825230e-04, -2.20589951e-04, 1.69933897e-05,\n", " -4.45634152e-05, -1.10090874e-04, -1.18004254e-04,\n", " -3.94153516e-05, -1.28953803e-04, -1.68410463e-05,\n", " -6.13732656e-05, 8.78269177e-05, -6.29357702e-05,\n", " 2.39654093e-06, 5.59154099e-04, 7.05461730e-04],\n", " [-1.18965985e-04, -1.29163618e-05, -2.57116446e-03,\n", " -4.25022787e-04, -1.40851598e-04, -4.61006686e-04,\n", " 1.73313360e-04, -2.04585802e-04, 2.32516771e-04,\n", " -6.90639031e-05, 8.57776731e-05, 2.60413254e-04,\n", " 8.45897653e-05, -2.02608509e-04, -1.23835086e-04,\n", " 1.48978993e-04, 9.75446086e-05, -2.65016650e-05,\n", " -3.61509901e-07, 9.14133333e-05, 6.70916578e-05,\n", " -6.88413280e-04, 7.05461730e-04, 2.40222644e-03]]),\n", " 'dloglike': 0.0001458001061109826,\n", " 'edm': 0.0002503984850541394,\n", " 'errors': array([0.07113003, 0.13876951, 1.4103957 , 0.56327771, 0.05534538,\n", " 0.1889185 , 0.08061457, 0.10888297, 0.13831382, 0.12501587,\n", " 0.06175989, 0.20632529, 0.11160342, 0.08672306, 0.11969093,\n", " 0.09231346, 0.15323099, 0.06254178, 0.09602424, 0.16350701,\n", " 0.10045753, 0.02025255, 0.02364644, 0.04901251]),\n", " 'fit_quality': 3,\n", " 'fit_status': 0,\n", " 'fit_success': True,\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", " 'loglike': -77883.00088192237,\n", " 'niter': 1,\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", " '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", " 'values': array([ 0.39538392, 1.94362954, 2.07191975, 3.25877604, 0.59012212,\n", " 1.19264762, 0.57570313, 0.74811706, 2.37595198, 1.51185817,\n", " 2.46682951, 1.26822259, 1.99775289, 0.52935079, 2.27543711,\n", " 0.4487337 , 2.3544816 , 0.57386554, 2.01149183, 1.42865223,\n", " 2.03646131, 0.94353891, -0.02082148, 0.91325844])}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gta.fit()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['norm' 'alpha' 'beta' 'Eb' '' '' '' '' '' '']\n", "[1.51185817e-11 2.46682951e+00 0.00000000e+00 6.65532043e+02\n", " nan nan nan nan\n", " nan nan]\n", "[1.25015869e-12 6.17598948e-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": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method set_source_morphology in module fermipy.gtanalysis:\n", "\n", "set_source_morphology(self, 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": 38, "metadata": {}, "outputs": [], "source": [ "gta.set_source_morphology(name='3FGL J0029.1-7045',spatial_model='RadialGaussian',spatial_pars={'SpatialWidth': 1.0} )" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-03-31 09:17:12 INFO GTAnalysis.print_model(): \n", "sourcename offset norm eflux index ts npred free\n", "--------------------------------------------------------------------------------\n", "3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n", "3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.13 140.7 *\n", "3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4888.53 1770.7 *\n", "3FGL J0029.1-7045 3.008 0.748 3.1e-06 2.38 89.43 379.6 *\n", "3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n", "3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.30 198.5 *\n", "3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.49 179.2 *\n", "3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.11 170.3 *\n", "3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.62 148.0 *\n", "3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n", "isodiff --- 0.913 0.0286 2.12 1145.15 8289.4 *\n", "galdiff --- 0.944 0.122 -0.02 5108.78 17184.0 *\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']" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }