{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to generate likelihood profiles for a fitted spectrum?" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we will learn how to generate likelihood profiles for a spectrum using `csspec`. This is done by running `csspec` with non-zero values for the `dll_sigstep` and `dll_sigmax` parameters. As we'll see, the results can then be plotted using the `show_spectrum.py` example script." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The preliminary data/fit\n", "Before we can demonstrate how to generate the likelihood profiles, we need to first simulate some data to work with. For this example we will simulate four hours on a simple Crab source. Once we have the simulated Crab data we will fit the model back to the data. This fitted model will be used to generate the SED and likelihood profile in the next section." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Load the modules\n", "import gammalib\n", "import ctools\n", "import cscripts\n", "\n", "# Define the ctools install directory\n", "import os\n", "ct_dir = os.environ['CTOOLS']\n", "os.environ['CALDB'] = ct_dir + '/share/caldb/'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Configure some preliminary variables\n", "inmodel = ct_dir + '/share/models/crab.xml'\n", "caldb = 'prod2'\n", "irf = 'North_5h'\n", "emin = 0.1\n", "emax = 100" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Simulate some data\n", "sim = ctools.ctobssim()\n", "sim['inmodel'] = inmodel\n", "sim['caldb'] = caldb\n", "sim['irf'] = irf\n", "sim['edisp'] = False\n", "sim['outevents'] = 'outfile.fits'\n", "sim['prefix'] = 'sim_events_'\n", "sim['ra'] = 83.63\n", "sim['dec'] = 22.151\n", "sim['rad'] = 5\n", "sim['tmin'] = 0\n", "sim['tmax'] = 7200\n", "sim['emin'] = emin\n", "sim['emax'] = emax\n", "sim['deadc'] = 0.98\n", "\n", "# Run the simulation (prevents writing to disk)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Fit the data\n", "fitter = ctools.ctlike(sim.obs())\n", "fitter['edisp'] = False\n", "fitter.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate the likelhood profiles\n", "Now that we have a fitted source model and observations we can generate the sample of likelihood profiles." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Configure csspec\n", "sed = cscripts.csspec(fitter.obs())\n", "sed['outfile'] = 'sed_likelihood_profile.fits'\n", "sed['caldb'] = caldb\n", "sed['irf'] = irf\n", "sed['srcname'] = 'Crab'\n", "sed['emin'] = emin\n", "sed['emax'] = emax\n", "sed['enumbins'] = 10\n", "sed['debug'] = True\n", "\n", "# Parameters that control the likelihood profile generation\n", "sed['dll_sigstep'] = 1\n", "sed['dll_sigmax'] = 5\n", "\n", "# Execute csspec & save results to file\n", "sed.execute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have a fitted SED, we can generate a plot from the results. A simple way to accomplish this is by using the `show_spectrum.py` example script. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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HV7el0CFTRqNLF5QsU1ixCu2Kx+SVGvMOlMhZFRbZXJ6++l433WhUpF75FSgy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\nAAAAAElFTkSuQmCC\n", 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