{ "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/v0.9?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": [ "'4.10.1'" ] }, "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/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/arf_obs23559.fits\n", "read RMF file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/rmf_obs23559.fits\n", "read background file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/bkg_obs23559.fits\n", "read ARF file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/arf_obs23523.fits\n", "read RMF file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/rmf_obs23523.fits\n", "read background file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/bkg_obs23523.fits\n", "read ARF file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/arf_obs23592.fits\n", "read RMF file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/rmf_obs23592.fits\n", "read background file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/bkg_obs23592.fits\n", "read ARF file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/arf_obs23526.fits\n", "read RMF file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/rmf_obs23526.fits\n", "read background file /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/bkg_obs23526.fits\n", "1: /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/pha_obs23559.fits OBS_ID: 23559 MJD_OBS: N/A\n", "2: /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/pha_obs23523.fits OBS_ID: 23523 MJD_OBS: N/A\n", "3: /Users/deil/work/gammapy-tutorials/datasets/joint-crab/spectra/hess/pha_obs23592.fits OBS_ID: 23592 MJD_OBS: N/A\n", "4: /Users/deil/work/gammapy-tutorials/datasets/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 = 641.651\n", "Final fit statistic = 135.43 at function evaluation 193\n", "Data points = 112\n", "Degrees of freedom = 110\n", "Probability [Q-value] = 0.0503069\n", "Reduced statistic = 1.23118\n", "Change in statistic = 506.222\n", " p1.gamma 2.6166 +/- 0 \n", " p1.ampl 4.34008e-20 +/- 1.94647e-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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/M7NuZcz/KDNrFR3Uv48Grya6+yY6MySad18z62VmdQnVfPFlGwlcZWabROW2MrODylr2SLqztpcJGWdpC+zJKe8hVN9fHM2nJeGyULm3vrn7ZMLB9zEz2zFDDA8Bx5vZVlGS9X8pxRQTMvYVZtadcADKlWzmfUl0JrINodpxfGzcPYTLJwdS/vf1LaFKv9S/gW3MbHszq0846yhr+jcJCcufzayOme1OWI8fLGe+pYoJO9zvzaw54awiLTM73MxKE8ZFhI13NaGK9j13XxKNeypahkMs3HFxBqExbnyey6J5tgX+VM4yFgM/EWoBGxLO3ktjqmtmA82sqbuvZM22CmHHONRCTZZFCf8BsR3xWvMxsz3MbNvoTHIJoRZ0tZk1IDREm5zhq3kAONvMNjWzxlF841POrrMxn1CF3LGMaSYCG5nZWWZWz8yKY9tYOsXRsiwzsy2Jzj5T/MnMmplZe+BM1qzLa22zZfz+5ZUzErgg2lYws6ZmdngWy/Mt0MHKvqOlLqEd0HxglZntR2jDVaqsGqu1RPvn24EbzWzDKNa2ZrZv9DrtMcFCXxJ7RonQCsL2VPq9pJt/l9i2cRZh3Z6aRYhZb6uxZfqSUHN1jZnVN7PtCGf348qLp6zlMrM9ozIOLa3ZLWWhZrO/mTWOjo/7Ei6vvRiN3zEqu8jMWgC3EG6HXRyNPyz6bJGF25OPIpwMEi3D76LteWPCZcCb3X1RFt9fqm+BFmbWNBZ7pmN1RuXVfKRbAS4n/CivETak64CB7l6aQW0GPE/YSU4hNHKZHJ0F9CNcb/sfIVseQzgjy6QP8KGZLQNuBvpHZ8hrXQpy9w+BUwln3t9EccWrk24m/AjPmdlSwgpb1o6n9KwztdoRQpJRTKi6S/ceQsPM6YSW1+8Tamiy6mPC3f9FOCg/aeEWq9RlfYaw0r1EqMqcEo36Kfp/CnB5tJz/x5qz2VzIZt4vE+J+Abje3Z8rHeHurxMOIm+7++flzOtS4G4L1bxHuPt/COvm88BnhPUz7g7CZZ3vzexxd/+ZkOTsR1gXhwPHpCTaZbmJcO10AWF9KutW9G7Am9F6/CThuvr/+PVvuwA4nFAdu5CwLb0eK+cywnXdxYRE5dGU+VxDSHq/N7PzCMncbMLZ2kf8ekd9NPC5harooYSdFe4+HTiRcIfHIsLvdVwZ89mIcAKwhHBJ82VC8rgXoVF46iXGUncSarheIewTVgCnZ5g2I3f/kehOjiimHmmmWUrYnvsRais/A/Yoo9jzCMnzUsKBdXyaaZ4gNOx7l/B73JFhv5Hp989YThTzY4SzyAej3+gDwvpa3vI8HP1faGaltcPpvo8zCNvoomhZn4xNUtFbbP9CWE+mRrE+T6htgAzHBELycy1hG5pLOGu+MDqobcWay1alniC0DVtEWHcPiRLn8lRkW40bQKgt/Bp4DPhrtH8uL560yxV95hLCMe9pW3NZ8JlonBOS3DlRmdcT2t89EY3vGMW+lLAu/BTFWOpMwrb+PeFS14nR9wyh0ez9hN/gLcLvEO8j5MJYHGWK9pEPALOi7a0NmY/VGdmayoM0I82eJtx6mTf3qkdZ5lxC46bF5U2/HvPpTlj27lU1jyxiaE3YIbXxDD+UmW1FWBHrrcMZY85YaBfzP0LjqYxxmtmLwP3uPiZHoSXGzD4CDnP3j5KOpSqY2XDgA3cfHhv2CjDG3e9JLrKqU9H9hpk54dLOzKqNLHvZ7HeqeP5HELaLI2LDLiU0WD0q1/FI1Siv5mMy4Qw7nzQn3OVSZYlHTLnVc1WsKaF/hLV2AGb2BwvV5s0IZ0YT8jnxyJaFS3CdSX+WWVAsXB68p1ATj8i7hDNG4Je2WB0JSWghS3q/sb7S7ndy6HvCrfZSwMqs+ZD8ZGbPEm5lW02o4j7F3b9JNqqylVfzYWZ3E24dPdPdx+Y0OKlyUVuAmYT+H45K8MCWV/Kx5iMfVVXNR3SZIJ39PHPjdqkESj5EREQkpwr2qbYiIiKSn5R8iIiISE7l1SOo5ddatmzpHTp0SDoMEZFqZcaMGQvcvVX5U0oSlHzkuQ4dOjB9+vSkwxARqVbMbHb5U0lSdNlFREREckrJh4iIiOSUkg8RERHJKSUfecrM+pnZ6MWLc9GRq4iISO4o+chT7j7B3Yc0bVrWc/dERESqHyUfIiIiklNKPkRERCSnlHwUqCNHTeHIUVOSDkNERORXlHwIoGRFRERyR8mHiIiI5JSSDxEREckpJR8FaumKlXz1/XJmzF5UJdOLiIisKyUfBWjG7EV8MncpcxYtZ+CYqeUmFBWdXkREZH0o+ShAU2ctpMTD65WrSpg6a2GlTi8iIrI+aicdgFS+Hh1bUJsSHKeOQY8Wy8udvsigxKFO7SJ6dGyRo0hFRKQmUvJRgLps0ozOrYtovfR9jvMJdHnsI/jsMNj1T9Bq87TTb7lRMUtWrOLm/jvQZZNmCUQtIiI1hS67VCEz62hmd5jZI7FhjczsbjO73cwGVtm8GzZnXuvd6HLuY9DzVPhkIgzrDo8Mgvmf/mr64vp1aLtBAyUeIiJS5ZR8ZGBmd5rZPDP7IGV4HzP71Mxmmtn5ZZXh7rPcfVDK4EOAR9z9RODASg771xpvCL2vhDPfg53PgE+fgWE7wsPHw7yPq3z2IiIiqZR8ZDYW6BMfYGa1gGHAfsDWwAAz29rMtjWziSl/G2Yotx3wZfR6dRXF/muNW8E+l8NZ70Ovs+Cz52B4T3j4OPj2o5yFISIiojYfGbj7K2bWIWVwd2Cmu88CMLMHgYPc/Rqgb5ZFzyEkIO+SIfkzsyHAEICNN964wrGXqVEL2PtS6Hk6TB0Gb46CDx/j7Pq9eKD4+Mqdl4iISBrm7knHkLei5GOiu/8uen8Y0MfdB0fvjwZ2dPfTMny+BXAVsA8wxt2vMbNGwG3ACuA1dx9XVgxdu3b16dOnV9ISpfHjdzB1OEwdCat/hl3Pg53PhNr1Mn6k9Bkw40/qWXVxiYisBzOb4e5dk45D0lPNR8VYmmEZszd3XwgMTRn2A5A/VQwNm8OeF0O3wfDs+fDSVfD+w9D3RujQa72LV6IiIiKp1OajYuYA7WPv2wFfV8WMzKyfmY1evHhxVRT/a8UbweFjYeA/YdVPMPYAePxU+EEdjomISOVS8lEx04DNzGxTM6sL9AeerIoZufsEdx/StGnTqig+s832hlOmQq+z4b0H4bau8O79oMtzIiJSSZR8ZGBmDwBTgC3MbI6ZDXL3VcBpwCTgY+Ahd/8wyTirRN2GoVHqSa9Ay83g8ZPh7n6w4DNAD6ETEZH1owanecrM+gH9OnXqdOJnn32WXCAlJfD23fD8X2Hlcmb87mIOf6sTJQ716xQxbnCPMjsm2//mV9RzqojknBqc5jfVfOSpxC67pCoqgq7Hw2nTYeuDmDpjOnjonqS8h9DpabkiIpKOkg/JTuMN4dAx9Nj/GOqwmlqspo6tpkebzDdM6Wm5IiKSjpKPPJXzu12y1GWnfdhioybs2/BTxtW5ii6P7wHvjEvbILX0abmQ3dNyjxw15Zdbc0VEpHAp+chTeXPZJY0GDRqysHUvupw6NjRIfeKUtRqklip9Wm67Zg3KbRsiIiI1h5IPWXcbbgXHPwt9b4K578GInWDy30I/IRE9LVdERFIp+ZD1U9og9dRpsFU/mHw1jNgZPn8t6chERCRPKfnIU/na5iOj4tZw2J2hh9TVP4ceUp84lcYlS7IuQv2HiIjUDOrnI89V+YPlqsLPP8LLf4M3bmWJNeae4hM57ayLwdI9GieYMXsRh498I+v+Q0REyqJ+PvKbaj6k8tVtCPtcBie9QpM2m3Pa4utDTci3mTuD1W25IiI1h5IPqTob/Q5OeA763QLzPoaRu8CzF8KKX1+KqehtuSIiUn0p+chT1a7NRyZFRdDlWDh9BnQ+BqYODw+re++htfoG0W25IiI1h5KPPJXP/Xysk4bNod9NcOKL0KQtPHriry7FVPS2XHVKJiJSPSn5kNxq2xkGvwD9bi73UoyIiBQmJR+Se0VF0OW4X12K6bX8xbTdtIuISGFR8iHJSbkUc/r31/HX7/4M336UdGQiIlKFlHxI8qJLMaOankn7lZ/DyF4w6SJdihERKVBKPvJUwdztkq2iIk46+3KK//QedD4apgyD27rB+49kvBSjHlFFRKonJR95quDudslWw+ahMergF6DJb+Cfg8ITc+d9stZkM2Yv4pO5S5mzaDkDx0wtNwGpyJ0xuotGRKRqKfmQ/NSuS0hA+t4Ic9+HkTvDcxfDT0sB9YgqIlKdKfmQ/FVUC7qeAKe/Db8fAG/cCrd1hw8epcemzdUjqohINaXkQ/JfoxZw0G0w6Hlo1BIeOZ4uLx9Hr1bL1SOqiEg1pORDqo/23WDIZNj/evjmXe5cchJ/LhpHl9a1ko5MREQqQMmHVC9FtaD7iXDaDF5tsCcH/vBIeFbMuw9ASUnGj1XkzhjdRSMiUrWUfOSpGnerbUU1bsXIDc7hwhY3Q9P28PhQuLM3fDXjV5NW5M6Yit5FIyIiFafkI0/V2FttK+i/dbeAQf+Cg0fCotlw+17wxKmwbN4v01TkzhjdRSMiUvWUfEj1V1QE2w8Iz4rZ6XT493i4tUvoqGz1Snp0bJH1nTEVmVZERNaNkg8pHPWbQO8r4JSp0H5HmHQhjNiZLivfZsuNirO6M6bLJs2ynraqqbMzESlUSj6k8LTsBEc9An98CEpWwn2HcNmPV7ND4++zSiaK69eh7QYNdPuuiEgVqZ10ACLravxJPcueYPN9oePuMHUEv3vhWq6fPxRe+wJ6ngq16uQiRBERSUM1H1LYateDXmdxdqsxvFu/Kzz/Vxi9R9q7YnJBl1JERJR8SA2xqFYLbmh2CRw5Dn5cCGP2hmfOh5+WJR2aiEiNo+RDapat+sKpb0LXQfDmSBjeA/4zKemoRERqFCUfUvPUbwIHXA8nTIK6jeH+I+Dh42Dpt0lHthb1tCoihUrJR55SD6c5sPGOcNIrsMfF8MlTMKwbzLgb3CtUTFW041BPqyJSyJR85Cn1cJojtevCbn+Ck9+A1tvChDMYX+8qxh/aMtGw1NOqiBQyJR9SI4w/qWfZt+a23AyOnQAH3grfvh/agjx2Msz/tFLjyPZSinpaFZFCpuRDpFRREXQ+Bk6dBt0Gw4ePwbAd4cGBlXJrbkUupeRTT6siIpVNyYdIquLWsN/f4OwPYNfz4PNX4fY94e4DYdbkCrcJKVXRSynqaVVECpWSD5FMGrWEPS+Gsz6AfS6H+Z/APQfBmL3g44lQUlKh4nQpRUQkUPIhUp76TWDnM+HM96DvjaGTsvEDYURPePcBavmqrIrRpRQRkUDJh0i26tSHrifAaTPg0DugqDY8PpRBcy+n1vyPmPH5d+UWoUspIiJKPkQqrlZt2PYwGPoaM/YezykrTmHqslYMHPUqMz74KOnoRETynpIPkXVlxtSVnfiJupRQi5VuTH3oenjtJli9MunoRETyVu2kAxCpzkobkZY41Kldmx6bFIcn577/MPS9Cdp3W+eyy+yXRESkGivYmg8z+8jMLjKz3yYdixSutRqRntiTLoNuDk/OXb4I7tgHnjoXVqiLfBGRuIJNPoABQGPgOTN708zOMrM2SQclhedXjUhLn5y741CYfifc1i10WLaO/YOIiBSagk0+3P3f7n6Bu/8WOBPYBJhqZi+a2Ym5iMHMOprZHWb2SFnDpADVK4b9roXBL0DxRuGpufcfQatVc5OOTEQkcQWbfMS5+1R3Pxs4BmgG3FbeZ8zsTjObZ2YfpAzvY2afmtlMMzu/nPnOcvdB5Q2TAta2Mwx+Efa9Bj5/nesXnMTByx6ElSuSjkxEJDEFn3yYWTczu8HMZgOXAaOBtll8dCzQJ6WsWsAwYD9ga2CAmW1tZtua2cSUvw0rd0mk2qpVG3qeAqe9Rf0t9mHA0rEwfEf4eIIuxYhIjVSwd7uY2dXAkcAi4EFgZ3efk+3n3f0VM+uQMrg7MNPdZ0XzeBA4yN2vAfpWRtxSwJq2g/7j4L8vwbMXwPijYNNdoc+10HqbpKMTEcmZQq75+AnYz927uvv1FUk8ytAW+DL2fg5l1KKYWQszGwnsYGaJ6ppqAAAeyklEQVQXZBqW5nNDzGy6mU2fP39+JYQteeW3e8DQ12D/62Hu+zCyF0w8B34o+0FzIiKFomBrPtz9MgAzawicC2zs7iea2WbAFu4+cR2KtXSzKiOGhcDQ8oal+dxowuUhunbtqnr5QlSrNnQ/EX53KEy+FqaNgQ8egd0vgG6DoVadpCMUEakyhVzzUeouQi1IaY9Nc4Ar17GsOUD72Pt2wNfrHlpmZtbPzEYvXqw+Igpaw+aw/3Vw8uvQpjM8ez6M2Ak+ez7pyEREqkxNSD5+6+7XASsB3H056WswsjEN2MzMNjWzukB/4MnKCXNt7j7B3Yc0bdq0KoqXfLPhVnD0YzDgQShZBeMOhXGHw4LPko5MRKTS1YTk42cza0B0eSTq8fSn8j5kZg8AU4AtzGyOmQ1y91XAacAk4GPgIXf/sOpClxrFDLbYD06ZCvtcAbOnwPAeMOkiWP590tGJiFSagm3zEfNX4FmgvZmNA3YGjivvQ+4+IMPwp4GnKzPAdMysH9CvU6dOVT0ryTe168HOZ8Dv+8OLV8CUYfDvB2HPi6HzMVBUK+kIRUTWi3kN6GfAzFoAPQiXW6a6+4KEQ8pa165dffr06UmHIUn6+t3QFuSLKdB629BzaodeSUclktfMbIa7d006Dkmv4C+7mNnl7r7Q3Z+K7nD5LqoBEake2mwPxz8Dh90FK76HsQfAQ8fCotlJRyYisk4KPvkANo71sVEPeBzI+1Z8uttF1mIGvzsETn0Ldr8Q/jMJhnWHF6+En39IOjoRkQop+MsuZmbAOOB9YA/gGXe/MdmosqfLLpLW4jnw/KXw/sNQ3Ab2uQy2PTwkKSKiyy55rmBrPsyss5l1BnYAbiZ0tf4Z8HI0XKT6atoODh0DJzwHxa3h0RP59Oqe8O1HSUcmIlKugq35MLOXyhjt7r5nzoJZD6r5kHKVlDD85isYuPQOmvIj7PZn6HW2ekmVGk01H/mtkG+1vQiY4tU0u9KttpK1oiJebtibt+t1Z8yGj8BLV8FHT8JBt4XGqjl05KgpAIw/qWc5U4pITVawl12AY4AZZvagmR1nZhslHVBFqIdTqailtTaAw+6A/vfDD/Ph9j3h+ctg5YpfTXvkqCm/JAoiIrlWsMmHuw91987ApUAzYKyZTTGzq81sVzNTT01SmLY8AE6dCtsPgNdugFG7wJdvJR2ViMgvCjb5KOXun7j7je7eB9gTeA04HHgz2chEqlCDZnDQMDjqUVi5HO7oDc9eCD//mHRkIiKFn3yYWQ8zK4ZfHir3KnCfGiJJjdBpLzhlCnQbBFOHhSfm/u/VpKMSkRqu4JMPYASwLPb+h2hYXlMnY1Jp6hXDAf+A454K7+/uy6DFt9GgRJ2TiUgyakLyYfE7Xty9hGpwl48anEql69ALTn4Dep7G3j8+xT/mnwQfT0w6KhGpgWpC8jHLzM4wszrR35nArKSDEklE3Yaw71Vc0uJGlhUVw/iB8OBAWPJ10pGJSA1SE5KPocBOwFfAHGBHYEiiEYkkbGbdLbmg5a2w92Uw8wW4rTu8dTuUrE46NBGpAQo++XD3ee7e3903dPfW7v5Hd5+XdFwilWnpipV89f1yZsxelPVnVltt6HVWaJDavhs8fV64K2buBzmNQ0RqnoJPPsxsczN7wcw+iN5vZ2YXJx1XedTgVLI1Y/YiPpm7lDmLljNwzNSKH/ibbxpuyT3kdlj0OYzeDf711wrflrvecYhIjVHwyQdwO3ABsBLA3d8D+icaURbU4FSyNXXWQkqiJtUrV5UwddbCihdiBtsdAadNg9/3h9dvghE94b8v5jYOEakRakLy0dDdU7t3XJVIJCJVoEfHFhRZeF2ndhE9OrZY98IaNg+dkx07EYpqw71/gH+eCMvm5zYOESloNSH5WGBmvwUcwMwOA75JNiSRytNlk2ZsuVEx7Zo1YNzgHnTZpFm5nym3bcamu8DQ12HXP8OHj8FtXWHG3VBSUqlxiEjNVBOSj1OBUcCWZvYVcBZwcrIhiVSu4vp1aLtBg6wO+Fm3zahTH/a8CIa+BhtuDRPOgLH7w7yPKyUOEam5Cj75cPdZ7r430ArY0t17ufvnCYclkpgKt83YcMvQO+qBt8H8T2BkL3j+Uj0nRkTWWcEnH2Z2ppk1AX4EbjSzt82sd9JxiSRlndpmFBVB56PhtOmw7RHw2o0wvAd89nzVBisiBangkw/gBHdfAvQGNgSOB65NNqTy6VZbqSrr1TajUUv4w4jQILVWXRh3KDx8HCydW2XxikjhqQnJR3SOx/7AXe7+79iwvKVbbaUqVaRtxpGjpnDkqClrD9x0Fzj5ddjjIvjkabitG7x1O+bqIVVEylcTko8ZZvYcIfmYZGbFQOYm+yKSndr1YLc/hx5S23aGp8/jyoVn02HlzKQjE5E8V7DJh5mVPrl2EHA+0M3dfwTqEi69iEhlaPFbOPpxOGQMLVfP45oFZ8CzF8BPS5OOTETyVMEmH8BUM3uc8BC579z9ewB3Xxj1cioilcUMtjucc1rdzvMN94OpI8LD6j56EtyTjk5E8kzBJh/u3hU4M3p7k5lNM7Mbzay3mdVLMjaRQvVDUTF3ND0dBv0LGraAh46G+4+ERbOTDk1E8kjBJh8A7j7b3Ue6+8HATsAEYG/gVTN7KtnoRApY+24wZDL0vgo+fw2G7Rhuz129MunIRCQPFHTyEefuK4F3gPvcvTvhcoyIVJVatWGn0+C0t6DTXqFjspG7wOwp5X5URApbwScfZjbZzJqYWXPg38BdZnaDu3+VdGwiNULTdtB/HPR/AH5eBnf1gSdOgx+/SzoyEUlIwScfQNOok7FDCP18dCFcehGRXNpyfzj1TdjpDHj3fri1C7wzTg1SRWqgmpB81Daz3wBHABOTDiZb6uFUClLdRtD7Chj6KrTcDJ44Be7uBwvUN4hITVITko/LgEnATHefZmYdgc8Sjqlc6uFUClrrbeD4Z6HvjfDNezBiJ3jl77Dq56QjE5EcqAnJxzfuvp27nwLhKbfADQnHJCJFRdD1hNAgdYv94MUrYdSu8OVbSUcmIlWsJiQft2Y5TESSULwRHHE3DHgw9Ip6R2946lxYse6XHNM+j0ZE8kbt8iepnsysJ6Fvj1Zmdk5sVBOgVjJRiUhGW+wHHXrBi1fBmyPhk6dg/7/DVv2SjkxEKlnBJh+EZ7g0JixjcWz4EuCwRCISKXDjT+q5fgXUK4b9roXtDocnz4TxR8GWfWG/66Bp28oJUkQSV7DJh7u/DLxsZmPdXX07i6yjpStWsmTFKmbMXkSXTZrlZqZtu8CQl2DqcHjpmtBD6t5/DW1Eiiq34rL08sx6J045VB1jFomrCW0+6pnZaDN7zsxeLP1LOiiR6mDG7EV8MncpcxYtZ+CYqcyYvSh3M69VB3Y+E06ZAu26wtPnwZ37wryPcxeDiFSJmpB8PEzoVv1i4E+xPxEpx9RZCymJ+gBbuaqEqbMW5j6I5pvC0Y/BH0bDwv+GLtpfuhpW/ZT7WESkUhTsZZeYVe4+IukgRKqjHh1bUGRQ4lCndhE9OrZIJhAz+P2R4Rkxky6El/8GHz4OB94CG/dIJiYRWWc1oeZjgpmdYma/MbPmpX9JByVSHXTZpBlbblRMu2YNGDe4R+7afGTSqCUcMhoG/hNWLg+XYZ46F1YsSTYuEamQmlDzcWz0P36pxYGOCcQiUu0U169Dcf06yScecZvtHdqCvHQ1vDkCPnkaDvhHeH6MiOS9gq/5cPdN0/wp8RCp7uo1hj5Xw6DnoUEzeHAAPHQsLP026chEpBwFX/NhZsekG+7u9+Rg3h2BiwhP1j0sGnYwcACwITDM3Z+r6jhEClq7LnDSy/D6zfDydTDrJXavdwKTG/ROOjIRyaDgkw+gW+x1fWAv4G2gzOTDzO4E+gLz3P13seF9gJsJvaSOcfdrM5URPUdmkJk9Ehv2OPC4mTUDrgeUfMh6q479PVRqXxW16sCu58HWB8GEMzl59o30Wv4SLB4HTdutf/kiUqlqwmWX02N/JwI7EHo/Lc9YoE98gJnVAoYB+wFbAwPMbGsz29bMJqb8bVhO+RdHZYlIZWm5GRw7kdubnM5mKz+B4TvBv8eDe9KRiUhMTaj5SPUjsFl5E7n7K2bWIWVwd2BmVKOBmT0IHOTu1xBqScplZgZcCzzj7m9XIG4RyUZREc83OoD36+3ALfVHw2ND4NOnoO9N0DB/b3RTr6VSkxR88mFmEwh3t0C4VLIV8NA6FtcW+DL2fg6wYxnzbgFcBexgZhdEScrpwN5AUzPr5O4j03xuCDAEYOONN17HUEUyq44HuIoenL+t3QaOfzq0BXnpavhiKhw0DDbbpyrDzIlEurwXqUQFn3wQ2lWUWgXMdvc561iWpRmWsT7X3RcCQ1OG3QLcUtZM3H00MBqga9euqi8WqaBfDs5fLqHLLudAp73hsZNg3GHQ5XjofWW4W6YaKu3yvsRh4Jip+dH/ikgF1YQ2Hy8DnxCebNsM+Hk9ipsDtI+9bwd8vR7liUglS/s8mt9sB0Mmw05nwIyxMLIXfPlWwpGum7zo8l5kPRV88mFmRwBvAYcDRwBvmtlh61jcNGAzM9vUzOoC/YEnKyfStZlZPzMbvXjx4qooXqRgZTw4164Hva+A456CktWhd9QXLqeWr0wu2HVQ2uU9JNzlvch6KPjkg9DPRjd3P9bdjyE0Gr2kvA+Z2QPAFGALM5tjZoPcfRVwGjAJ+Bh4yN0/rIqg3X2Cuw9p2rRpVRQvUrDKPTh32BlOfh1+/0d49R/88dt/sGzh17l9Yu96yLsu70XWQU1o81Hk7vNi7xeSRdLl7gMyDH8aeLqSYsvIzPoB/Tp16lTVsxIpKKUH5yUrVnFz/x3SH5zrN4GDhzGj+f6c+/TPrFxei4GjXmPc4B3p0rG8u+STV9Eu73UnjeSbmlDz8ayZTTKz48zsOOApcpA8rC/VfIisu+L6dWi7QYNyD85TV2/BT9SlhFqsLHGmjv9btW0LIlKdFGzyYWadzGxnd/8TMArYDvg94VLK6ESDE5G8sNYlmlq16FH0EdzRW0/KFaliBZt8ADcBSwHc/VF3P8fdzybUetyUaGQikhfWaj8xZCe6nPUQ7HgSTLsDhu0InzyVdIg5d+SoKb9cphGpKoWcfHRw9/dSB7r7dKBD7sOpGN3tIpIba12iqVcM+/0NBpc+KfePMP5oWDr3l+l1cBZZf4WcfNQvY1yDnEWxjtTmQyRB7bqGJ+XueQn8ZxLc1h2m3wUlJUlHJlIQCjn5mGZmJ6YONLNBwIwE4hGR6qT0SbknvxE6KZt4Ftzdlzarviz/syJSpkK+1fYs4DEzG8iaZKMr4Ym2f0gsqizpVluRPNGyExw7Ad65F567mOtWnMIjxQNhddeQoJRBt7iKpFewNR/u/q277wRcBnwe/V3m7j3dfW5Zn80HuuwikkfMoPMxcOo0ptfvwYClY+H2PeCbXzUrE5EsFGzyUcrdX3L3W6O/F5OOR0SqseLW3NTsIv6xwcWw9NuQgLxwBaz6ab2LXrpiJV99v7za9LQqsj4KPvkQEalsbzXoBae+CdseDq9eD6N2hTnT17m8tA/DEylgSj5ERNZFw+bwh5Ew8BH4aRncsQ9Mugh+/rHCRelJtVLTKPnIU+rnQ6Sa2GwfOGUKdDkOptwGI3aCz1+rUBF6Uq3UNEo+8pQanIpUI/WbQN8b4diJgMPYA2DiOdQvya4WRE+qlZpGyYeISGXZdJfQL0iPU2H6nVy/YCjb/ZRdt0LZPgxPpBAo+RARqUx1G0Gfq2HQc/xMPS767iKYcBb8tDSxkHQnjeQbJR8iIlWhfXf+0moYTzY6FGaMhRE7V7gtSGXQnTSSj5R85Ck1OBWp/lZaXcY1ORGOfwasKLQFeeb8dbojZl3pThrJR0o+8pQanIoUkE16wsmvQ/ch8OYIGLULfDktJ7Ou6J00ukQjuaDkQ0QkF+o2gv3/Dsc8EXpEvbM3/OuvldI7alkqcieNLtFIrij5EBHJpY67hztith8Ir98Eo3eHr9+t0llmeyeNLtFIrij5EBHJtfpN4KDb4I8Pw4/fwZi9OGzpfdTyVYmGpc7OJFeUfIiIJGXz3qF31G0O4fBl93HVgjNh7geJhaPOziRXlHyIiCSpYXM49Haub3YJzUoWwujdYPK1sOrnRMKpSGdnR46awpGjpuQgKik0Sj7ylG61FalZptXfmXNbjYZt/gCTr4Hb94Bv/p10WCJVQslHntKttiI1z7KiJnDoGOj/APwwH0bvAS9eWeV3xIjkmpIPEZF8s+X+cMpU2O4IeOXv4Y6Yr95OOqr1oks0EqfkQ0QkHzVsDn8YCX98CJYvgjF7w/OXwcoVjD+pJ+NP6pl0hCLrTMmHiEg+23zfUAuy/QB47QYYtSvMmZ50VCLrRcmHiEi+a7ABHDQMBv4Tfl4Gd+wDz10MK1ckHVmV0WWawqbkQ0Skuths71AL0vkYeOPWUAtSzduCSM2k5ENEpDqp3wT63QxH/RN+Whragrx4VWL9goisCyUfIiLVUae9Q++o2x0Br1wHY/aEbz9MOiqRrCj5EBGprhpsEO6IOXIcLJ0Lo3aDV2+A1ck+I0akPEo+8pR6OBWRrG3VN7QF2WI/eOEyuKsPLJiZdFQiGSn5yFPq4VREKqRRSzjiHjj0DljwGYzsBVNHQklJ0pGJ/IqSDxGRQmEG2x4WakE23QWe/QvccyCtVs1NOjKRtSj5EBEpNE1+E3pGPfBW+Pod/r7gZPb68WlwTzoyEUDJh4hIYTIL/YGc/Ab/rbM5QxbfAvceDN9/kXRkIko+REQKWrNNuLL5NYxpchp8OQ2G7wTT71ItiCRKyYeISIFzK+JfjfqGfkHa7gATz1ItiCRKyYeISE3RbBM4+gk44IbwcDrVgkhClHyIiNQkRUXQbRCc/IZqQSQxSj5ERGqiZpvAMU9C3xujWpCeMP1O1YJITij5EBGpqcyg6wlRLUgXmHg2F313IS1XfZt0ZFLglHyIiNR0zTaBY56Avjey2cpPuH7BUNWCSJVS8iEiIr/UgpzXciQz62wBE8+Ge/8A33+ZdGRSgJR8iIjILxbUbs2Vza+J7oiZFtqCzBirWhCpVEo+qpCZdTSzO8zskdiwrcxspJk9YmYnJxmfiEhaZmvfETPhTLjvENWCSKVR8pGBmd1pZvPM7IOU4X3M7FMzm2lm55dVhrvPcvdBKcM+dvehwBFA18qPXESkkvzSL8g/4Is3o1qQu1ULIutNyUdmY4E+8QFmVgsYBuwHbA0MMLOtzWxbM5uY8rdhpoLN7EDgNeCFqgtfRKQSFBVBt8FwyhvQZnuYcAbcdygsnpN0ZFKNKfnIwN1fAb5LGdwdmBnVaPwMPAgc5O7vu3vflL95ZZT9pLvvBAxMN97MhpjZdDObPn/+/MpaJBGRddesQ+gXZP/r4YspMLwne/z4rGpBZJ0o+aiYtkD8ouecaFhaZtbCzEYCO5jZBdGw3c3sFjMbBTyd7nPuPtrdu7p711atWlVi+CIi66GoCLqfGNqCbLQdQxffxAWLLobv/pd0ZFLN1E46gGrG0gzLmPa7+0JgaMqwycDkSo1KRCSXmm8Kx07gzpsuYsDSu2B4D9j1PNjpDKhdL+nopBpQzUfFzAHax963A76uihmZWT8zG7148eKqKF5EZP0UFTGp0YGc02o0bL4vvHgljOwF/3s16cikGlDyUTHTgM3MbFMzqwv0B56sihm5+wR3H9K0adOqKF5EpFJ8V6sVHHEP/PFhWPUT3N0XHj0Jlqm9mmSm5CMDM3sAmAJsYWZzzGyQu68CTgMmAR8DD7n7h0nGKSKSFzbvDadMhV3Ogw/+Cbd1hel3QUlJ0pFJHlKbjwzcfUCG4U+ToaFoZTKzfkC/Tp06VfWsREQqR92GsNclsN0R8NS5MPEseHdceHKuSIxqPvKULruISLXVags4dgL8YVS4E2bUbhy9ZBT1S35MOjLJE0o+RESk8pnB7/vDadOg8zH0/eEx/jF/CMyanHRkkgeUfOQp3e0iIgWhYXPodxMXt7iRFUUN4Z6D4V9/hdUrk45MEqTkI0/psouIFJLP6m7FBS1vgS7Hwus3wZ37wnezkg5LEqLkQ0REcuJnqw/9bobD74aFM2HkrvDew0mHJQlQ8iEiIrm1zcEw9DVovQ08OhgeOxl+Wpp0VJJDSj7ylNp8iEhB22BjOO4p2O0v8N6DMGpX+PqdpKOSHFHykafU5kNECl6t2rDHhXDsxNA76ph94I1b1TFZDaDkQ0REktVh53AZZvN94bmLYdyhNF39XdJRSRVS8iEiIslr2ByOvA8OuAFmv8F1C07h9z9NTzoqqSJKPvKU2nyISI1jBt0GwYkvsaRoA1qt+jbpiKSKKPnIU2rzISI1VuutuaDlLTzfcP+kI5EqogfLiYhI3llldZMOQaqQaj5EREQkp5R8iIiISE4p+RAREZGcUvKRp3S3i4iIFColH3lKd7uIiEihUvIhIiIiOaXkQ0RERHLK3D3pGKQMZjYfmJ3FpE2BijYQqehnsp0+2+laAgsqMP9CsC6/U1XJVSyVOZ/KKEvbSvWwvr/1Ju7eqrKCkcql5KNAmNlodx9SlZ/JdvoKTDfd3btmO/9CsC6/U1XJVSyVOZ/KKEvbSvWQT9uKVD5ddikcE3LwmWynX5dYaop8+m5yFUtlzqcyytK2Uj3ouylgqvmQxNTEszmRdaFtRQqNaj4kSaOTDkCkmtC2IgVFNR8iIiKSU6r5EBERkZxS8iEiIiI5peRDREREckrJh+QFM9vazB4ysxFmdljS8YjkGzPraGZ3mNkjsWGNzOxuM7vdzAYmGZ9IRSj5kCpjZnea2Twz+yBleB8z+9TMZprZ+dHg/YBb3f1k4JicByuSgIpsI+4+y90HpRRxCPCIu58IHJijsEXWm5IPqUpjgT7xAWZWCxhGSDa2BgaY2dbAvUB/M/s70CLHcYokZSzZbyPptAO+jF6vrqIYRSqdkg+pMu7+CvBdyuDuwMzoLO5n4EHgIHef5+6nAudT855hITVURbaRDEXMISQgoP25VCNaWSXX2rLmTA3CzrOtmXUws9HAPcDfE4lMJD9k2kZamNlIYAczuyAa9yhwqJmNQN2RSzVSO+kApMaxNMPc3T8H9BApkczbyEJgaMrAH4DjcxKVSCVSzYfk2hygfex9O+DrhGIRyUfaRqTgKfmQXJsGbGZmm5pZXaA/8GTCMYnkE20jUvCUfEiVMbMHgCnAFmY2x8wGufsq4DRgEvAx8JC7f5hknCJJ0TYiNZUeLCciIiI5pZoPERERySklHyIiIpJTSj5EREQkp5R8iIiISE4p+RAREZGcUvIhIiIiOaXkQ6QaM7PVZvZu7O/8pGMqZWaPmFnH6PWyCnxuspntmzLsLDMbbmatzOzZyo5VRHJLz3YRqd6Wu/v2lVmgmdWOOrpanzK2AWq5+6x1+PgDhF49J8WG9Qf+5O7zzewbM9vZ3V9fnxhFJDmq+RApQGb2uZldZmZvm9n7ZrZlNLyRmd1pZtPM7B0zOygafpyZPWxmE4DnzKwoqmn40MwmmtnTZnaYme1lZo/F5rOPmT2aJoSBwBNp4mppZlPM7IDo/Z+iWN4zs8uiyR4B+ppZvWiaDkAb4LVo/ONR+SJSTSn5EKneGqRcdjkyNm6Bu3cGRgDnRcMuAl50927AHsDfzaxRNK4ncKy77wkcAnQAtgUGR+MAXgS2MrNW0fvjgbvSxLUzMCM+wMxaA08B/+fuT5lZb2AzoDuwPdDFzHaNnt76FtAn+mh/YLyv6Y55OrBLlt+PiOQhXXYRqd7KuuxSWiMxg5BMAPQGDjSz0mSkPrBx9Ppf7v5d9LoX8LC7lwBzzewlCM91N7N7gaPM7C5CUnJMmnn/Bpgfe18HeAE41d1fjsXSG3gnet+YkIy8wppLL09E/0+IlTWPUBMiItWUkg+RwvVT9H81a7Z1Aw5190/jE5rZjsAP8UFllHsXMAFYQUhQ0rUPWU5IbEqtIiRB+wKlyYcB17j7qDSffxy4wcw6Aw3c/e3YuPpR+SJSTemyi0jNMgk43cwMwMx2yDDda8ChUduP1sDupSPc/Wvga+BiYGyGz38MdIq9d0LtxZaxO3ImASeYWeMolrZmtmE0j2XAZOBOQi1I3ObAB+UtqIjkL9V8iFRvDczs3dj7Z929rNttrwBuAt6LEpDPgb5ppvsnsBfhIP8f4E1gcWz8OKCVu3+UYT5PERKW50sHuPtqM+sPTDCzJe4+3My2AqZEudAy4CjCZRUIScejhMsucXtE5YtINWVr2nCJiKxhZo3dfZmZtSA0AN3Z3edG424D3nH3OzJ8tgHwUvSZ1ZUc1yvAQe6+qDLLFZHcUfIhImmZ2WRgA6AucJ27j42GzyC0D9nH3X8q4/P7Ah+7+xeVGFMrQkLzeGWVKSK5p+RDREREckoNTkVERCSnlHyIiIhITin5EBERkZxS8iEiIiI5peRDREREckrJh4iIiOTU/wML89PblzNbLgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "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-2.08218e-21\n", "p1.gamma lower bound:\t-0.0643092\n", "p1.ampl upper bound:\t2.14828e-21\n", "p1.gamma upper bound:\t0.0655594\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 2.6166 -0.0643092 0.0655594\n", " p1.ampl 4.34008e-20 -2.08218e-21 2.14828e-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 }