{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced model manipulation and fitting in Python" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial you will learn to perform some more advanced model manipulation and fitting from Python. You can use these methods in an interactive Python session to explore your dataset and improve the known model of the sky. You can also use them in a Python pipeline to test multiple model hypotheses and select the one that is most appropriate.\n", "\n", "As usual we start by importing the gammalib, ctools, and cscripts Python modules." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import gammalib\n", "import ctools\n", "import cscripts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this tutorial we will also use the matplotlib package for plotting." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulated dataset\n", "\n", "For the tutorial we will simulate a small dataset. We start by defining the Instrument Response Function and energy range that we will use all along." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "caldb = 'prod2'\n", "irf = 'South_0.5h'\n", "emin = 0.1 #TeV\n", "emax = 160. #TeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we proceed to simulate the dataset. It consists of an hour of observations of the Crab nebula region, as usual pointed at a slightly offset position from the target. The input model is different from the one you have been using so far and contains some surprises. Don't look at it until you have completed the exercises at the end of the tutorial." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "evfile = 'events.fits'\n", "\n", "obssim = ctools.ctobssim()\n", "\n", "obssim['ra'] = 83.63\n", "obssim['dec'] = 22.51\n", "obssim['rad'] = 5.0\n", "obssim['tmin'] = 0\n", "obssim['tmax'] = 3600\n", "obssim['emin'] = emin\n", "obssim['emax'] = emax\n", "obssim['caldb'] = caldb\n", "obssim['irf'] = irf\n", "obssim['inmodel'] = '$CTOOLS/share/models/crab_beyond.xml'\n", "obssim['outevents'] = evfile\n", "\n", "obssim.execute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have saved the events on disk in the file `events.fits`. In this way you can easily re-use the code below by substituting `events.fits` with your own event list of observation definition XML file.\n", "\n", "We will peek at the simulated dataset by displaying a skymap." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "skymap = ctools.ctskymap()\n", "\n", "skymap['inobs'] = evfile\n", "skymap['emin'] = emin\n", "skymap['emax'] = emax\n", "skymap['nxpix'] = 40\n", "skymap['nypix'] = 40\n", "skymap['binsz'] = 0.02\n", "skymap['proj'] = 'TAN'\n", "skymap['coordsys'] = 'CEL'\n", "skymap['xref'] = 83.63\n", "skymap['yref'] = 22.01\n", "skymap['bkgsubtract'] = 'IRF'\n", "skymap['caldb'] = caldb\n", "skymap['irf'] = irf\n", "\n", "skymap.run()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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BELSI/7X+/383Q6bAKVkOns/b4X3APxypTjxX6darG2+o12rzqABXqOq9ItIH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\nM/AbwDVN72Ww6Cx75auqu4E/A54FBoE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