{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.10?urlpath=lab/tree/spectrum_fitting_with_sherpa.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[spectrum_fitting_with_sherpa.ipynb](../_static/notebooks/spectrum_fitting_with_sherpa.ipynb) |\n", "[spectrum_fitting_with_sherpa.py](../_static/notebooks/spectrum_fitting_with_sherpa.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting gammapy spectra with sherpa\n", "\n", "Once we have exported the spectral files (PHA, ARF, RMF and BKG) in the OGIP format, it becomes possible to fit them later with gammapy or with any existing OGIP compliant tool such as XSpec or sherpa.\n", "\n", "We show here how to do so with sherpa using the high-level user interface. For a general view on how to use stand-alone sherpa, see https://sherpa.readthedocs.io." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data stack\n", "\n", "- We first need to import the user interface and load the data with [load_data](http://cxc.harvard.edu/sherpa/ahelp/load_data.html).\n", "- One can load files one by one, or more simply load them all at once through a [DataStack](http://cxc.harvard.edu/sherpa/ahelp/datastack.html)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: imaging routines will not be available, \n", "failed to import sherpa.image.ds9_backend due to \n", "'RuntimeErr: DS9Win unusable: Could not find ds9 on your PATH'\n", "WARNING: failed to import sherpa.astro.xspec; XSPEC models will not be available\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import glob # to list files\n", "from os.path import expandvars\n", "from sherpa.astro.datastack import DataStack\n", "import sherpa.astro.datastack as sh" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'4.10.2'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sherpa\n", "\n", "sherpa.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "read ARF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/arf_obs23559.fits\n", "read RMF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/rmf_obs23559.fits\n", "read background file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/bkg_obs23559.fits\n", "read ARF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/arf_obs23523.fits\n", "read RMF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/rmf_obs23523.fits\n", "read background file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/bkg_obs23523.fits\n", "read ARF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/arf_obs23592.fits\n", "read RMF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/rmf_obs23592.fits\n", "read background file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/bkg_obs23592.fits\n", "read ARF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/arf_obs23526.fits\n", "read RMF file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/rmf_obs23526.fits\n", "read background file /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/bkg_obs23526.fits\n", "1: /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/pha_obs23559.fits OBS_ID: 23559 MJD_OBS: N/A\n", "2: /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/pha_obs23523.fits OBS_ID: 23523 MJD_OBS: N/A\n", "3: /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/pha_obs23592.fits OBS_ID: 23592 MJD_OBS: N/A\n", "4: /Users/jer/DATA/GAMMAPY/joint-crab/spectra/hess/pha_obs23526.fits OBS_ID: 23526 MJD_OBS: N/A\n" ] } ], "source": [ "ds = DataStack()\n", "ANALYSIS_DIR = expandvars(\"$GAMMAPY_DATA/joint-crab/spectra/hess/\")\n", "filenames = glob.glob(ANALYSIS_DIR + \"pha_obs*.fits\")\n", "for filename in filenames:\n", " sh.load_data(ds, filename)\n", "\n", "# see what is stored\n", "ds.show_stack()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define source model\n", "\n", "We can now use sherpa models. We need to remember that they were designed for X-ray astronomy and energy is written in keV. \n", "\n", "Here we start with a simple PL." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "powlaw1d.p1\n", " Param Type Value Min Max Units\n", " ----- ---- ----- --- --- -----\n", " p1.gamma thawed 2 -10 10 \n", " p1.ref frozen 1e+09 -3.40282e+38 3.40282e+38 \n", " p1.ampl thawed 1e-20 0 3.40282e+38 \n" ] } ], "source": [ "# Define the source model\n", "ds.set_source(\"powlaw1d.p1\")\n", "\n", "# Change reference energy of the model\n", "p1.ref = 1e9 # 1 TeV = 1e9 keV\n", "p1.gamma = 2.0\n", "p1.ampl = 1e-20 # in cm**-2 s**-1 keV**-1\n", "\n", "# View parameters\n", "print(p1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit and error estimation\n", "\n", "We need to set the correct statistic: [WSTAT](http://cxc.harvard.edu/sherpa/ahelp/wstat.html). We use functions [set_stat](http://cxc.harvard.edu/sherpa/ahelp/set_stat.html) to define the fit statistic, [notice](http://cxc.harvard.edu/sherpa/ahelp/notice.html) to set the energy range, and [fit](http://cxc.harvard.edu/sherpa/ahelp/fit.html)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datasets = 1, 2, 3, 4\n", "Method = levmar\n", "Statistic = wstat\n", "Initial fit statistic = 687.716\n", "Final fit statistic = 158.732 at function evaluation 195\n", "Data points = 128\n", "Degrees of freedom = 126\n", "Probability [Q-value] = 0.0257379\n", "Reduced statistic = 1.25977\n", "Change in statistic = 528.985\n", " p1.gamma 2.61709 +/- 0 \n", " p1.ampl 4.33145e-20 +/- 1.9118e-21 \n", "WARNING: parameter value p1.ampl is at its minimum boundary 0.0\n" ] } ], "source": [ "### Define the statistic\n", "sh.set_stat(\"wstat\")\n", "\n", "### Define the fit range\n", "ds.notice(0.6e9, 20e9)\n", "\n", "### Do the fit\n", "ds.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results plot\n", "\n", "Note that sherpa does not provide flux points. It also only provides plot for each individual spectrum." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: The displayed errorbars have been supplied with the data or calculated using chi2xspecvar; the errors are not used in fits with wstat\n", "WARNING: The displayed errorbars have been supplied with the data or calculated using chi2xspecvar; the errors are not used in fits with wstat\n", "WARNING: The displayed errorbars have been supplied with the data or calculated using chi2xspecvar; the errors are not used in fits with wstat\n", "WARNING: The displayed errorbars have been supplied with the data or calculated using chi2xspecvar; the errors are not used in fits with wstat\n" ] }, { "data": { "image/png": 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\n", 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FqNQ23N1HJK7QzAaZWcvo/46EYsiXo/etzWyfaPl1zOwSwlXo69H4AwmlMMe5+1sJy21pZn3NrEG0Lx1GOJ9MTuFzSGaj4y2FfXwT6bqCS7ajXEc4ibxGKIu+hVAD6P1ofAfCL4wVhEoid7v7lOig6004AX5B+JU5inC5X5yewAdmtoJQk6lvVBRUWvXfAYSKLB9GMY4lVrwXFyXixOIMgG7RepcR7nU0AvZw9/diCeoud/8+9vqCcDVUWjHlMYQT4IPx+Qk1P/Oj7d5oG6PtPiDapheiuD4h/FoteobuFMLn/X3Cckcki8ndv42KG0oUFWu85O6r4sOjA+4g4PaEdc4ifJ4pFdcmE51gd6KYhOXunxDuI95F2Jd6E355/lyO1d1EOBktMbOLi5nmZMKB9zGhdt4FJSzvdsK9jgWEigbJHrF5iHDv53tCLbKi6tyJ+/YWhP13GaEix1Q2PlEmXU5UrHs04UfMfMJV0iX8em5Iuj3Rj9nHgDnR5xEvVou7mFAMvZzwq/+J2LgyVc9392cIJTSPWyjufJ9ffyQ2ipa/mFBKtBD4v9g2fBnNM4SwPxS3/u+jZXxLOJEPSfjhXpyJhB8Dn0brX82mt1KSuZ5QA/y/hApks6NhqcRT3HYNIiSGqy32nG1smfsA75nZSsL2TyB8/xAS9T3ROr8hnGMOd/eF0firCOfiCbFl/ysa54QfMPOi+f+PUHfgOdiooY3WKXwusOnxVto+vomimksVYuHh5WHuXmWeJYmubr4nVONdGg1rR6iwUZDsqq6U5e1J2MY9S524kpjZ5sC7wFZl3Z5cYeHB9z7ufkJs2HWE6vanZy+yzEm2b5cy/RRCpZuUWnepLBYaGOjj7ps0NFAJ697k2LHQeMHD7r51ZccjmZGuK7gpwCtpWla6bEaobRQ/AfyGUOZe3mSQrAgpmxoTHhmokcktsoTw/BPwy5V2R8LVf65Ktm9XK2ZWi1AyUenJLaJjpwZIyxVcdWBmFxKegTrX3VMpU5dqyMzeIdxjOyYqdq3xquoVXFWTqSu4qAive5JRN0aVkyRDakyCExGRmqVG9SYgIiI1R04nODPrbeGBz97ZjkVERCpXjSiibN68ubdt2zbbYYiIVCuzZs1a4O4tsh1HeaWry44qrW3btsycOTPbYYiIVCtmNrf0qaqunC6iFBGRmksJTkREcpISnIiI5CQlOBERyUlKcCIikpOU4EREJCcpwYmISE5SgkuDE++dzon3Ts92GCIiEpPTCa6oqa6lS6ttryIiIlJOOZ3g3H2cuw9u3LikzsBFRCQX5XSCExGRmksJLg2Wr17LN0tWMWvu4myHIiIiESW4Cpo1dzEff7+ceYtX0X/UjEpNcqrcIiJSPCW4CpoxZyEboh6H1q7bwIw5C7MbkIiIAEpwFda1XTPyLPxfWJBH13bNshuQiIgASnAV1rlNU3bcoiFbN63LI4O60rlN02KnTXeRou79iYgUTwkuDRrWKaRVk7olJrd0y+a9PxGR6kAJrprSvT8RkZIpwVVTuvcnIlKygmwHkAueOLNbStMtX72WZavXMWvu4goXZxbd+1u2eh139N29UotHRUSqA13BlSJdFUNmzX6Lz79bzLeLV9J/5GvM+vSrCi8zG/f+RESqiyqf4MysnZndb2ZjY8Pqm9k/zOw+M+ufzfhKtGE9fDQeRh/BjKfvYh3GBvJYu96Z8fDV8M8z4ItXwT3bkYqI5JyMJjgze8DMfjSz9xOG9zSzT8zsczO7tKRluPscdx+YMPhYYKy7nwEcleawK271Mph+N9y5OzzRH5Z8Tdeu+4HlA1BYUEDXndrCpxPhH73CdK/+DZZ9V6bVPHFmt5SLR0VEappM34MbAwwDHiwaYGb5wHDgEGAe8LaZPQ/kAzclzH+6u/+YZLlbA+9F/69Pc8zlt2gOvDkS3nkYfl4OrbvBoX+BHY6kc34BO/5vWuye2RGw9o/w4fMw+0F4+TqYfAN0OBQ6DQh/83WLVESkvDJ6BnX3aWbWNmHwnsDn7j4HwMweB45295uAXikueh4hyb1LVShmXfYdvHARfDIB8vJh52Oh61nQqtNGkzWsU0jDOoW/3jMrrAu/PTG8Fv4P3nkI3n0UPv0XNN8ejh0JW+2ehQ0SEan+spEcWgFfx97Pi4YlZWbNzGwEsLuZXRYNfho4zszuAcYVM99gM5tpZjPnz59f7mBLbS1k/ToYexrMeQW6XwQXvA/H3bdJcitVs+3g4GvgDx/ytyZXsnDRIhh1cCi63FB1LlJFRKqLbJSBWZJhxdaycPeFwJCEYSuB00paibuPBEYCdOnSpVy1OIpaC9ng0H/UjORNcU27Fb6aDsfeB7ueUJ7VbCy/gLfq7ssHtX/LA80fDUWXn70EvxsBTdtUfPkiIjVENq7g5gHbxN5vDXybhThKVWprIXPfgGm3wG/7pSe5xazMawh9RsMxI+D792DEvvCfJ1TjUkQkRdm4gnsb6GBm2wLfAH2BkzKxIjPrDfRu3759ueYvai1kgydpLeSnRaGaf9O2cMStKS2vzDUezWC3ftCmGzx9JjwzGD59EXrdBnX17JuISEky/ZjAY8B0YAczm2dmA919HTAUmAh8BDzp7h9kYv3uPs7dBzdu3Lhc8xfbU4A7jDsPVvwAx90PtRumMeok9/2atoXTJsCBV8FHz8M9+8AX09K6ThGRXJPRBOfu/dx9S3cvdPet3f3+aPgEd9/e3bdz9xsyGUNFJW0tZNZo+GgcHPTnslcmKUWxvQTk5cN+F8PASaH25T+OgklXwro1aV2/iEiuyH4V+wwys95mNnLp0qXpW+iPH8GLl8F2B0K3oelbbqTU+36tOsOZ06DLafDGXTCyB3z7btrjEBGp7nI6wVW0iHITa1fB2NNDkeQxIyAv/R9fSr0E1KoPvf4OJz0Z7gXed2B4SHzdz2mPR0SkusrpBJd2k66EHz8Mya3h5hlZRVl6CGf7w+CcGbDL8aE2530HwHf/yUhcIiLVTU4nuLQWUX78Arw9KhRLdji44ssrQZl6CajbFI69F/o9Divnh6u5V27S1ZyI1Hg5neDSVUS52fr58Nw5sOVucNDVaYouzXY4HM6eEZoJm3ozjDowPD8nIlJD5XSCSwfz9Qxdcku4IurzABTUynZIxau3WWgm7MRHYPkPoQLK1Ftg/dpsRyYiUumU4Erx+E7T2fnn9+DI/wvtRVYHO/WCc96EjsfAKzcw56au/HHYwyXOkq6OXUVEqoqcTnAVvgc3bxZMuSlU4vhtv/QGl2n1NoM+98MJD7HZhgXctOBcmHgFrFmR7chERCpFTie4Ct+D27wjdL8QjrwtNJtVHXU8igtb3Mcr9Q6D6cNg+J6hl3ERkRyX0wmuwgrrwoFXQp1GlbradPfUvTKvIfc1Ph9OnwR1moRexh/rB0u+Ln1mEZFqSgmuJmm9F5w5FQ65DuZMgeF7hdZQ1q/LdmQiImmX0wkuI011VUMbNd6cXwj7nB8qoWzbPTy8PrIHTZZ/XnLHrhmiyi0ikik5neDS3lRXNVRs481NWoeHw098mFlLGzB1QX2+WbyS/vfNqPQkJyKSCTmd4KSUxpvNYKfezOj8N9ZQiJPH2nVrmfH6K+pYVUSqPSW4HJdK481dt98KszwMp9A20PXjG2FML/jx40qOVkQkfZTgclwqjTcXTdOqaT0eObM7nY8eCj+8DyP2gX9fDT+vzFh8m3TuKiKSJkpwNUAqjTf/Mk3bZtD5VDh3FuzaF16/PdS2/Gh82osti70/KCKSBjmd4FSLsgLqN4djhsNpL4b+757oD4/1hcVfpm0VpXbuKiJSATmd4FSLMg3adAs9iB96PXzxKgzvCtNuhXVrKrzolDp3FREpp5xOcJIm+YWw97kw9G3ocAhMvj4UW37yYoUWW6bOXUVEykgJTlLXuBUnLjmb6ze7MSS9x06ER46Hhf/bZNJUH+AuU+euIiJloAQnZfZe7U4w5PVQbDl3OtzdFV66pkr0VKCWUUSkiBKclE9BrVBsee4s+E0feO3vMGwPeG+sHhIXkSqhINsBSOal0jNBuXsvaLg5/O4e6HIaTLgE/jkQZj5A67X9+aqwXfmWKSKSBjl9BafHBCrRNnvCGZOh9x3w40f8dcFQTl86DFaq6r+IZEdOJzg9JlDJ8vLDQ+LnzWZSvV4c/NMEuHP30CVPGh4rEBEpi5xOcJIldZtyZ+0zOKrW/cxq1it0yTN8T/jwuU3uz6XauWuqlUfU9JeIFFGCkzJJJYEUNcH1wbLa9P+6N7MOGQsFdeHJATD6CPhmdkZiU9NfIhKnBCcpSzWBbNIE18/bwpDXoNftsPAzuO8AePpMWPpNWuNT018iEqcEJylLNYEkbYIrvyDUtDx3Nux7IXzwDNzVGSbfkLbn59T0l4jEKcFJylJNICU2wVWnERx8dWj2a8cjYNotcFcnmDka1q+rUHxq+ktE4pTgJGVlSSClNsHVtA30eQAGvgRNt4XxF8A9e8Mn/6rQg+Jq+ktEiijBSZmkPYFsswec/iKc+Aj4+tAlz5gj4ZtZG02m2pEiUlZKcJJ9ZrBTLzh7Bhz5N1jwKdx3IDx1Giz6QrUjRaRccjrBqSWTaia/EPYYBOe9A/v9ET59EYbtwYwXH63StSPVwLNI1ZTTCU4tmVRTtRvCgVeEGpe7nUTXb8ZQh58xNlCYr9qRIpIaNbYsVVejLeGoO+nc9WP+fO+NLF7jdG24iM7LVsKGYyEvp3+fiUgF6QwhVV/LHXmu5VksbLk3nRsvCz0WjDoIvnx9k0lTbfpLRHKfEpxUGx/U3g0GT4VjRsDy72HMEfB4f1jwebZDE5EqqMQEZ2YfmtkVZrZdZQUkUqK8PNitX+ho9cCrYM4UuHuv0BeduuYRkZjSruD6AQ2ASWb2ppldYGZbVUJcUs1lvKiwVj3Y7+JQ47LTAHh7FNy5G7x2O6xdnbn1JqFn9ESqphITnLv/x90vc/ftgPOBNsAMM5tsZmdUSoQiJWnQEnr9Hc6aDq27wUtXw7A94P1/VqhFlFTpGT2Rqivle3DuPsPd/wAMAJoCwzIWlVRZVbYSR8sdof+TMOA5qNMYxp4O9x8CX7+V0dWqBwORqiulBGdme5jZbWY2F7gWGAm0ymhkIuXRrgecORWOGgZLvgpJ7qnTYPHcjKxOPRiIVF0lPgdnZjcCJwKLgceBfdx9XmUEJlJuefnQ6WTY+Xfwxp3w+p3w8QvQ9SzofmG4wkuTogaol61exx19d1cjzyJVSGlXcGuAw929i7v/n5KbVCu1G8ABl4cal785Fl6/He7sBG/fX+GueeLUg4FI1VRaJZNr3f1TM6tnZleZ2X0AZtbBzHpVTogiFbz317gV/G4EDJ4CLXaEFy6EEfvA5y+lM0QRqWJSrWQymnA1V3SGmQdcn5GIRDJlq93h1PGha571P8PDx8HDfWD+J9mOTEQyINUEt5273wKsBXD3VYBlLKoYM2tnZveb2diShomk5Jeued6EQ28ItSzv7gYT/gg/Lcp2dCKSRqkmuJ/NrC7gAFHLJmtKm8nMHjCzH83s/YThPc3sEzP73MwuLWkZ7j7H3QeWNkwkVSfeO50T758Few+F82ZD51Ph7fvgzt1hxghYvzbbIZZKXfSIlC7VBHc18CKwjZk9ArwM/DGF+cYAPeMDzCwfGA4cDnQE+plZRzPbxczGJ7xaprohIuVSvzn0ug2GvB6KMF/8U7ii+3RipTwoLiKZk1J3Oe7+bzObDXQlFE2e7+4LUphvmpm1TRi8J/C5u88BMLPHgaPd/SZAFVckOzbvCCc/A59NgomXw6MnwHYHwmE3Qsudsh2diJRDqg96X+fuC939BXcfDyyKruTKoxXwdez9PEp4aNzMmpnZCGB3M7usuGFJ5htsZjPNbOb8+fPLGarUKGaw/WGh2a+eN8M3s+CefWDSVfDzymxHJyJllGqHp63N7DJ3v8nMagNPAbPLuc5klVOKLQty94XAkNKGJZlvJKHFFbp06aKyJkldQa3wUPguJ4S2Ld+4Ez54Bo64FXY4fJPJq2TTZSKS8j2404BdoqulccAr7n5NOdc5D9gm9n5r4NtyLqtEZtbbzEYuXbo0E4uXXFe/GRw9DE7fDdI3AAAcCklEQVR7EWo1gMf6hv7nlqq9A5HqoLT+4DqZWSdgd+AOQrNdnwFTo+Hl8TbQwcy2NbNaQF/g+XIuq0TuPs7dBzdunL6mmaQGatMNzpwGB18Dn78Mw/eC6cPT2hpKnGpIiqRHaUWUf0t4v5hQ8/FvhGLFA0ua2cweA3oAzc1sHnC1u99vZkOBiUA+8IC7f1CO2EUqT0Et2PcPoX3LCZeEiij/eQx63QFbd670cJavXsuy1euYNXexmggTKUZpCe4KYLp7+epLu3u/YoZPACaUZ5llYWa9gd7t27fP9KqkpmjaFk56Ej56Hv71Jxh1EHQ5HQ76M9RtUikhFPVBt8Gh/6gZPDKoq5KcSBKl3YMbAMwys8fN7FQz26IygkoXFVFKRphBx6PhnLdgryEwazQM3zNURKmEZ+fUB51IakprbHmIu3cCriF0cjrGzKab2Y1mtl/00LZItbJ89Vq+WbKq4r1v12kEh98MZ7wCDbeAp06FR08M/dBlkPqgE0lNSrUo3f1jd/+7u/ck3Hd7DTgeeDOTwYmkW1Hx3rzFq+g/akbFkxzAVrvBoMnhofAvX4XhXTNaCaWoD7qtm9ZV8aRICVJ90LurmTWEXxpafhV42N27ZDK4itJjApIoY8V7+QXQ7Rw4501ou2+ohDLqIPjuP+lZfoJU+6BTjUypyVJ9Du4eYEXs/cpoWJWme3CSKOPFe01aw0lPQJ/RsOxbGNkDJl6hllBEsiDVBGfxmpTuvoHUW0ERqTIqpXjPLPQgPvQt6DQApg8LxZafTkpp9rTdIxSp4VJNcHPM7DwzK4xe5wNzMhmYSKakWrxXYXWbQu87QksohXXh0eNDRZRl3xU7S0buEYrUUKkmuCHA3sA3hKa29gIGZyqodNE9OKkS2nSDIa/CAVfAxxNg2B4w/e6klVD0CIBI+qRai/JHd+/r7i3dfXN3P8ndf8x0cBWle3BSZRTU5sSPu3PeZvdA671g4mXh/tzXb200mR4BEEmfVGtRbm9mLxf1zG1mu5rZlZkNTST3/FCwFfQfCyc8CD8thPsPgefPhZ8WAXoEQCSdUi2ivA+4DFgL4O7/JTSSLCJlVdQSytC3oNtQeOcRuKszzH4INmyovHuEIjku1QRXz93fShiWmadY00j34KRKq90QDrsh9FTQvAM8PxRGH07rtemrv6UamVKTpZrgFpjZdkQdk5pZH6D4qmBVhO7BSbWwxW9CTcujh8OCT7l5wVD6LxtV4WfnylIjUw+ESy5KNcGdA9wL7Ghm3wAXAGdlLCqRmiYvD3b/PZw7i6l1D+GolWPh7q7w2UtJJ3/izG6l9iSuGplS06Vai3KOux8MtAB2dPd93f3LjEYmUhPV24x7m/yBaza7FfJrwyPHwdiBsKLslZZVI1NqulRrUZ5vZo2An4C/m9lsMzs0s6GJ1Fwf1d4Fznod9r809D03bA+Y/WCZuuNRjUyp6VItojzd3ZcBhwItgdOAmzMWlUgVkPX7UgW14YDLYMhr0LJjeJxgTC9Y8FnKi1CNTKnJUm6LMvp7BDDa3f8TG1ZlqRal5IQWO8CpL0DvO+GH9+CevWHKX2HdmmxHJlKlpZrgZpnZJEKCmxh1nbMhc2Glh2pRSs7Iy4POp8A5b8NOvWHKjTCiO3ylLhlFilNigjOzoh4DBgKXAnu4+09ALUIxpYhUpoabQ58HQmsoa1fBA4fBv/6k7nhEkijtCm6GmT1LaFh5kbsvAXD3hVFrJiKSorQ+dN3hEDj7DdjzDHhzRHik4H+vVHy5lSzr9zklp5WY4KIeu8+P3t5uZm+b2d/N7FAzq5358ERyQ0a6wandEI64NTwknl8LHjoGnhsKq5aUeVFq8URyUan34Nx9rruPcPdjCF3mjAMOBl41sxcyHaBILsjoQ9dtuoWalvtcAO8+Eq7mPvlXyrOrDzrJValWMgHA3dcC7wAPu/ueVIM+4USqgow/dF1YFw65Fga9DHU3g8f6wtiBNFxf+tVcWZKvihSlOikofRIwsynAUdH07wLzzWyqu1+YwdgqzMx6A73bt2+f7VCkCimtiatMKHroetnqddzRd/fMPZfWqhMMngKv/R2m3cpt/JvRjc4C7xp6MUiiKPlucLV4Irkl1Su4xtGD3scSnoPrTCimrNL0mIBUJak+dJ1KO5MlKqgFPf4EZ07jh/wtOH/JzfDkybBiftLJ1eKJ5KpUE1yBmW0JnACMz2A8IpIum3fkqma38XDDgfDpJLh7L3j/6aSTqsUTyUWpJrhrgYnA5+7+tpm1A1JvL0hEssItn3ENjg99zjVpA2NPgydPgZULsh2aSMalmuC+c/dd3f1sCL0LALdlLiwRSauWO8LAf8NBf4aPX4Dhe8GHz2U7KpGMSjXB3ZXiMBGpqvILoPtF4Wqu8dbw5AAYezr8tCjbkYlkRIm1KM2sG+HZtxZmFq8x2QjIz2RgIlJxSSurbN4RBr0Er90OU/8KX7xKl1pnMbNO5dcuXb56LctWr2PW3MW6/ydpV9oVXC2gASERNoy9lgF9MhuaSHbldOse+YWw/yXhkYKGm3PJ4ms5Z8ktsKrytlUPmEumldZU11R3vxbo6u7Xxl63ubsqmUjOqjEn3y1+A2e8wlMN+rP3qqlw997w+UuVsuqMtu4iQur34Gqb2Ugzm2Rmk4teGY1MJItq1Mk3v5CxDU/myua3Q51G8PBxMP4PsGZFRleb8dZdpMZLqSUT4ClgBDAKWJ+5cNJLLZlIedXE1j2+KOwAp0+FyX+B6cND7wTH3BPauoyk855ZpbXuIjVWqldw69z9Hnd/y91nFb0yGlkaqCUTKa8a27pHYR047IbQg7hvgNGHw6SrYO3qjBTb6gFzyaRUE9w4MzvbzLY0s82KXhmNTCTLavTJt+0+cNbroRfxN+6EkT2Y8Z/3a06xreSEVBPcKcAlwBvArOg1M1NBiUgVULsh9L4DTnoKVi2m66yLKYzuUNSUYlup3lK6B+fu22Y6EBGporY/FM6eTucJF/P4f69jPPvR64RBNfPKVqqVVLvLGZBsuLs/mN5wRKRKqrcZ9HmA1+bdxCVL76TeuLFgd8DOv0s6eVGfcdnomkikSKq1KPeI/V8HOAiYDSjBidQgb9TtwWeFOzKs1l3w1KkwZyr0vCl0uCpSxaRaRHlu/L2ZNQYeykhEIlKlzS/YAk6fGB4neP0O+GoGHD8aWu6U7dBENpJqJZNEPwEd0hmIiFQj+YVwyHXw+3/CTwtg5AEwczS4ZzsykV+keg9uHFC05+YDOwFPZiooEakm2h8MQ16HZwbD+AtgzpRQ81KkCkj1Htz/xf5fB8x193kZiEdEqpuGm8Pvn4HXb4fJ18O3s+mQ/wc+q6UiS8muVO/BTTWzzfm1sokaWhbJIRWu7ZiXB90vhLb7wtiBXLPwYp5p0BfWdYKC2ukJUqSMUroHZ2YnAG8BxwMnAG+ambrLEZGNbbMnDHmVGXX24/gVj8CIfWHuG9mOSmqoVCuZXAHs4e6nuPsAYE/gqsyFJSLVVt0m3NX0T9zU9C+wdnVoz3Lc+bBqSbYjkxom1QSX5+4/xt4vLMO8FWZm7czsfjMbGxt2jJndZ2bPmdmhlRWLiKTm3Tp7wNnTodtQmP0gDN8TPngmYzUtT7x3+i8PmItA6knqRTObaGanmtmpwAvAhFRmNLMHzOxHM3s/YXhPM/vEzD43s0tLWoa7z3H3gQnDnnX3M4BTgRNT3A4RqUy1G4TeCc6YDA23CA+HP9YXlqqOmmReiQnOzNqb2T7ufglwL7Ar8FtgOjAyxXWMAXomLDcfGA4cDnQE+plZRzPbxczGJ7xalrL8K6NliUhVtdXuMGgyHHoDfDENhu8FM0ZgXm26l5RqqLQruNuB5QDu/rS7X+jufyBcvd2eygrcfRqwKGHwnsDn0ZXZz8DjwNHu/p6790p4/bjJQgEL/gr8y91nJxk/2MxmmtnM+fPnpxKqiGRSfgHsPRTOngGtu8KLf+L6hX9gq3VfZzsyyVGlJbi27v7fxIHuPhNoW4H1tgLie/W8aFhSZtbMzEYAu5vZZdHgc4GDgT5mNiRJjCPdvYu7d2nRokUFQhVJjyfO7KbGhwGatoH+Y+G4+2m5/gduWjAU3n0021FJDirtObg6JYyrSOuqlmRYsXee3X0hMCRh2J3AnRWIQUSyxQx26UOjNvvA02fAs2eFhpuP/Fu4byeSBqVdwb1tZmckDjSzgYROT8trHrBN7P3WwLcVWF5SZtbbzEYuXbo03YsWkRIsX72Wb5asYtbcxSVP2GhLGPAc9LgM3nsSRu4P321SaCRSLqUluAuA08xsipn9LXpNBQYB51dgvW8DHcxsWzOrBfQFnq/A8pJy93HuPrhx48bpXrSIFGPW3MV8/P1y5i1eRf9RM0pPcnn50ONSOGUc/LwSRh0Mb92nhpulwkpMcO7+g7vvDVwLfBm9rnX3bu7+fSorMLPHCLUudzCzeWY20N3XAUOBicBHwJPu/kH5N0NEqooZcxayIcpNa9dtYMachanN2HZfGPIatNsfJlwMT56sh8OlQlJti/IV4JXyrMDd+xUzfAIpPktXXmbWG+jdvn37TK5GRGK6tmtGnsEGh8KCPLq2a5b6zPWbQ78nYMZweOkaGNEd+jwA2+xR6qwiiSqtNZJsUBGlSOXr3KYpO27RkK2b1uWRQV3p3KZp2RaQlwd7nxs6VTVgdE947XbYsCEj8UruyukEJyLZ0bBOIa2a1C17covbuguc+SrseCS8dDU8dLRaQJEyyekEp1qUItVc3SZw/D/gqLtg3iy4Z294/5/ZjkqqiZxOcCqiFMkBZtBpAAx5FZpvD2NPh6cHw2r9cJWS5XSCE5Hq75deApptB6e9CD0uh/fGwj37wJevZTs8qcKU4ESk+sgvgB5/goGTIL8QxvSCf18N635O/eFyqTFyOsHpHpxIjiqqgNJpALx+O7OGncwn3y9L/eFyqRFyOsHpHpxIDqvdAI66E/o+xoylTXAPjxGU6eHyakqdu6YmpxOciNQAOx5B1xP+SAEbyGc9hayj61YptWEhOU57gYikrKp299N5p+1ov8U3bLH8v5yz/mE6j18CtUbCtvtlOzTJIl3BiUhOaFi3kJUtO9P5zJFQqwH84yiYfD2sX7fRdCreqzlyOsGpkolIDbTlrnDmVNi9P0y7FUYfDovnZjsqyYKcTnCqZCJSQ9WqD0cPh+Puh/kfh0abP3g221FJJcvpBCciNdwufeDMadC8Azx1Cow7n1q+OqVZVZRZ/SnBiUhu22xbOP1F2PcPMOsf3LTgPFqvnZPtqKQSKMGJSO7LL4SDr4GTn6H+huXcuOB8eP0O2LA+25FJBuV0glMlExHZyHYHcEnze3in9h7w7z/DmCNh0RcVWqSKMquunE5wqmQiIomW5zfhb02vgmNGwA8fwIh9YdY/wD3boUma5XSCExFJygx26wdnvQGtOsG48+DRE2H5D9mOTNJICU5Eaq4m28DJz0HPv8IXU+Hurhl5nEDFmNmhBCciNVteHnQdEh4naNI6PE7wzzOov2F5tiOTClKCExEBaLEDDHoJ9r8U3v8nt84/i53XvJvtqKQClOBERIrkF8IBl8Ggf7Pa6nDlostg8g2btGeZKakWZapz19QowYmIJGrVmcuaD2Na3YNh2i3w4FGw7NtsRwXArLmL+fj75ercNQU5neD0HJyIlNeavDrc0+Si8DjBt+/CPfvAp5OyHRYz5ixkQ/REQ03o3LUicjrB6Tk4Eamw3frB4CnQaCt49HiYdCWsX5u1cLq2a0aehf8LC/Lo2q5Z1mKp6nI6wYmIpEWL7UMFlC4D4Y274IGeWeuCp3Obpuy4RUO2blqXRwZ1pXObplmJozpQghMRSUVhXeh1Gxw/BhZ8Grrg+fD5rITSsE4hrZrULTW51fTn75TgRETKYuffhWfmmm0HT57MaUuHU+g/ZzsqSUIJTkSkrDbbFk6fCN2G0vOncVy/4AJY8Fm2o5IESnAiIuVRUAsOu4G/Nr2WzdYvgHv3h3cfVaPNVYgSnIhIBcyusxd/bHF3aLT52bPg6cGwRs18VQVKcCIiFbQ4vzkMeA4OuALeHwv37gffvpPtsGo8JTgRkXTIy4f9/winvgDr1sCoQ+CNYbBhQ7Yjq7FyOsGpJRMRqXRt9oYhr0GHQ2HSFfDYiTRcvyTbUdVIOZ3g1JKJiGRFvc2g7yNwxP/BnCncsuBsdl7zn2xHVePkdIITEckaM9jzDBj0MqusHlcuuhQmXgFrV2U7shpDCU5EJJO23JXLmg/jpXpHwvRhMGJf+OrNbEdVIyjBiYhk2Jq8OtzfeGioabnuZ3jgMF3NVQIlOBGRytKuB5z9BnQ+9derua/fynJQuUsJTkSkMtVuCL1vh5OfDY8TPHBY6IJHV3NppwQnIpIN2x0AZ70BnQaELnhGdKfDzx9mO6qcogQnIpItdRpB7zvg5Gdg7SquW3gx/ZeNCvfppMKU4EREsm27A+Hs6UyuexhHrRwLY46AJV9nO6pqTwlORKQqqNOI+5qcz9+bXA4/fhTas/z8pWxHVa0pwYmIVCEz6u4Hg6dAwy3g4T7wyo2wYX22w6qWlOBERKqa5h1g0Mvw234w9a/w8LGwYn62o6p2lOBERKqiWvXgmLvhqLtg7vRQZPnVjGxHVa1U+QRnZu3M7H4zGxsbtpOZjTCzsWZ2VjbjExHJGLPwGMGgl6CgNow5MnTBo17DU5LRBGdmD5jZj2b2fsLwnmb2iZl9bmaXlrQMd5/j7gMThn3k7kOAE4Au6Y9cRKQK2XJXOHMqbN8TJl3BRYv/Qt0NK7MdVZWX6Su4MUDP+AAzyweGA4cDHYF+ZtbRzHYxs/EJr5bFLdjMjgJeA17OXPgiIlVEncZw4sNw6A10XjODmxcMhW/fzXZUVVpGE5y7TwMWJQzeE/g8ujL7GXgcONrd33P3XgmvH0tY9vPuvjfQP9l4MxtsZjPNbOb8+bo5KyI5wAz2Hsq1zW6l0NfC/YfA26NUZFmMbNyDawXEn2CcFw1LysyamdkIYHczuywa1sPM7jSze4EJyeZz95Hu3sXdu7Ro0SKN4YuIZNcntXbmTy2Gw7b7wQsXwdjTYfWybIdV5RRkYZ2WZFixPz/cfSEwJGHYFGBKWqMSEalGluc1hpOegtdvh8nXw3fvwvH/CPfrBMjOFdw8YJvY+62BbzOxIjPrbWYjly5dmonFi4hkV14edL8QTh0feiMYdTDMfEBFlpFsJLi3gQ5mtq2Z1QL6As9nYkXuPs7dBzdu3DgTixcRqRra7A1DXoO2+8L4P8A/B8Ga5dmOKusy/ZjAY8B0YAczm2dmA919HTAUmAh8BDzp7h9kMg4RkZxXvzn0HwsHXgUfPA0je9B67ZxsR5VVGb0H5+79ihk+gWIqh6STmfUGerdv3z7TqxIRyb68PNjvYmjdFcYO5IYVFzC68VlAt2xHlhVVviWTilARpYjUSG33hSGv8VGt39CmBl/FZaMWpYiIZFqDFty02V/IY8PGrW3UIDl9BadalCJSk7nls94Ksx1G1uR0glMRpYhIzZXTCU5ERGouJTgREclJOZ3gdA9ORKTmyukEp3twIiI1V04nOBERqbmU4EREJCeZ14BWp81sKfBZBRbRGCjrjbyyzpPq9KlM1xxYUIZ1V2fl+W4yoTLiSOc6KrKs8s5blvl0PJRPuvfDNu5efTvUdPecfwEjK3v+ss6T6vSpTAfMzPZnXl2+2+oURzrXUZFllXfessyn4yH7+0guvGpKEeW4LMxf1nlSnb6i25JrqsrnURlxpHMdFVlWeecty3w6HspHn0dMjSiirGnMbKa7d8l2HCJVgY6HmqumXMHVNCOzHYBIFaLjoYbSFZyIiOQkXcGJiEhOUoITEZGcpAQnIiI5SQkux5lZRzN70szuMbM+2Y5HJBvMrJ2Z3W9mY2PD6pvZP8zsPjPrn834JDOU4KohM3vAzH40s/cThvc0s0/M7HMzuzQafDhwl7ufBQyo9GBFMqQsx4G7z3H3gQmLOBYY6+5nAEdVUthSiZTgqqcxQM/4ADPLB4YTElpHoJ+ZdQQeAvqa2a1As0qOUySTxpD6cZDM1sDX0f/rMxSjZJESXDXk7tOARQmD9wQ+j36p/gw8Dhzt7j+6+znApdSc9vikBijLcVDMIuYRkhzoXJiT9KXmjlb8+msUwsHbyszamtlI4EHg1qxEJlJ5ijsOmpnZCGB3M7ssGvc0cJyZ3YOauMpJBdkOQNLGkgxzd/8SGFzJsYhkS3HHwUJgSMLAlcBplRKVZIWu4HLHPGCb2PutgW+zFItItug4kF8oweWOt4EOZratmdUC+gLPZzkmkcqm40B+oQRXDZnZY8B0YAczm2dmA919HTAUmAh8BDzp7h9kM06RTNJxIKVRY8siIpKTdAUnIiI5SQlORERykhKciIjkJCU4ERHJSUpwIiKSk5TgREQkJynBSY1nZuvN7N3Y69LS56ocZjbWzNpF/68ow3xTzOywhGEXmNndZtbCzF5Md6wiVY3aohSBVe6+WzoXaGYF0UPHFVnGzkC+u88px+yPEVrxmBgb1he4xN3nm9l3ZraPu79ekRhFqjJdwYkUw8y+NLNrzWy2mb1nZjtGw+tHnW2+bWbvmNnR0fBTzewpMxsHTDKzvOiK6QMzG29mE8ysj5kdZGbPxNZziJk9nSSE/sBzSeJqbmbTzezI6P0lUSz/NbNro8nGAr3MrHY0TVtgK+C1aPyz0fJFcpYSnAjUTSiiPDE2boG7dwLuAS6Ohl0BTHb3PYADgFvNrH40rhtwirsfSOgxui2wCzAoGgcwGdjJzFpE708DRieJax9gVnyAmW0OvAD82d1fMLNDgQ6EftB2Azqb2X5R6/lv8WuHoH2BJ/zXpotmAt1T/HxEqiUVUYqUXERZdGU1i5CwAA4FjjKzooRXB2gd/f9vdy/qhHNf4Cl33wB8b2avQOi7xcweAn5vZqMJiW9AknVvCcyPvS8EXgbOcfepsVgOBd6J3jcgJLxp/FpM+Vz09/TYsn4kXNGJ5CwlOJGSrYn+rufX48WA49z9k/iEZrYXsDI+qITljiZ0srmakAST3a9bRUieRdYREu1hQFGCM+Amd783yfzPAreZWSegrrvPjo2rEy1fJGepiFKk7CYC55qZAZjZ7sVM9xqhx+i8qGixR9EId/+W0E/ZlcCYYub/CGgfe++Eq7AdYzU9JwKnm1mDKJZWZtYyWscKYArwAOFqLm574P3SNlSkOtMVnEh0Dy72/kV3L+lRgb8AtwP/jZLcl0CvJNP9EziIkEg+Bd4ElsbGPwK0cPcPi1nPC4Sk+FLRAHdfb2Z9gXFmtszd7zaznYDpUb5dAfyeUAQJIbE9TSiijDsgWr5IzlJ3OSIZZGYN3H2FmTUjVPrYx92/j8YNA95x9/uLmbcu8Eo0z/o0xzUNONrdF6dzuSJViRKcSAaZ2RSgCVALuMXdx0TDZxHu1x3i7mtKmP8w4CN3/yqNMbUgJM1n07VMkapICU5ERHKSKpmIiEhOUoITEZGcpAQnIiI5SQlORERykhKciIjkJCU4ERHJSf8PE7Ct65BB1tAAAAAASUVORK5CYII=\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sh.get_data_plot_prefs()[\"xlog\"] = True\n", "sh.get_data_plot_prefs()[\"ylog\"] = True\n", "ds.plot_fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Errors and confidence contours\n", "\n", "We use [conf](http://cxc.harvard.edu/sherpa/ahelp/conf.html) and [reg_proj](http://cxc.harvard.edu/sherpa/ahelp/reg_proj.html) functions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p1.ampl lower bound:\t-2.00854e-21\n", "p1.gamma lower bound:\t-0.0625337\n", "p1.ampl upper bound:\t2.0723e-21\n", "p1.gamma upper bound:\t0.0637839\n", "Datasets = 1, 2, 3, 4\n", "Confidence Method = confidence\n", "Iterative Fit Method = None\n", "Fitting Method = levmar\n", "Statistic = wstat\n", "confidence 1-sigma (68.2689%) bounds:\n", " Param Best-Fit Lower Bound Upper Bound\n", " ----- -------- ----------- -----------\n", " p1.gamma 2.61709 -0.0625337 0.0637839\n", " p1.ampl 4.33145e-20 -2.00854e-21 2.0723e-21\n" ] } ], "source": [ "# Compute confidence intervals\n", "ds.conf()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Compute confidence contours for amplitude and index\n", "sh.reg_unc(p1.gamma, p1.ampl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "- Change the energy range of the fit to be only 2 to 10 TeV\n", "- Fit the built-in Sherpa exponential cutoff powerlaw model\n", "- Define your own spectral model class (e.g. powerlaw again to practice) and fit it" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }