{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", "**This is a fixed-text formatted version of a Jupyter notebook.**\n", "\n", " You can contribute with your own notebooks in this\n", " [GitHub repository](https://github.com/gammapy/gammapy-extra/tree/master/notebooks).\n", "\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 this [tutorial](http://nbviewer.jupyter.org/github/sherpa/sherpa/blob/master/docs/SherpaQuickStart.ipynb)\n", "\n", "Tu run this notebook, one should first run spectrum_analysis to extract the relevant OGIP files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing sherpa high level UI\n", "\n", "### loading the data as a sherpa DataStack\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). 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", "plt.style.use('ggplot')\n", "\n", "import glob # to list files\n", "from sherpa.astro.datastack import DataStack\n", "import sherpa.astro.datastack as sh" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "read ARF file crab_analysis/ogip_data/arf_obs23523.fits\n", "read RMF file crab_analysis/ogip_data/rmf_obs23523.fits\n", "read background file crab_analysis/ogip_data/bkg_obs23523.fits\n", "read ARF file crab_analysis/ogip_data/arf_obs23526.fits\n", "read RMF file crab_analysis/ogip_data/rmf_obs23526.fits\n", "read background file crab_analysis/ogip_data/bkg_obs23526.fits\n", "read ARF file crab_analysis/ogip_data/arf_obs23559.fits\n", "read RMF file crab_analysis/ogip_data/rmf_obs23559.fits\n", "read background file crab_analysis/ogip_data/bkg_obs23559.fits\n", "read ARF file crab_analysis/ogip_data/arf_obs23592.fits\n", "read RMF file crab_analysis/ogip_data/rmf_obs23592.fits\n", "read background file crab_analysis/ogip_data/bkg_obs23592.fits\n", "1: crab_analysis/ogip_data/pha_obs23523.fits OBS_ID: 23523 MJD_OBS: N/A\n", "2: crab_analysis/ogip_data/pha_obs23526.fits OBS_ID: 23526 MJD_OBS: N/A\n", "3: crab_analysis/ogip_data/pha_obs23559.fits OBS_ID: 23559 MJD_OBS: N/A\n", "4: crab_analysis/ogip_data/pha_obs23592.fits OBS_ID: 23592 MJD_OBS: N/A\n" ] } ], "source": [ "ANALYSIS_DIR = 'crab_analysis'\n", "\n", "ds = DataStack()\n", "pha_list = glob.glob(ANALYSIS_DIR + '/ogip_data/pha_obs*.fits')\n", "for pha in pha_list:\n", " sh.load_data(ds,pha)\n", " \n", "# see what is stored\n", "ds.show_stack()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating the 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": 3, "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", "# View parameters \n", "print(p1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform the fit and compute errors\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": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datasets = 1, 2, 3, 4\n", "Method = levmar\n", "Statistic = wstat\n", "Initial fit statistic = 358.208\n", "Final fit statistic = 139.463 at function evaluation 197\n", "Data points = 112\n", "Degrees of freedom = 110\n", "Probability [Q-value] = 0.0302949\n", "Reduced statistic = 1.26784\n", "Change in statistic = 218.746\n", " p1.gamma 2.43652 \n", " p1.ampl 2.3608e-20 \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": [ "## Plot the result\n", "\n", "Note that sherpa does not provide flux points. It also only provides plot for each individual spectrum." ] }, { "cell_type": "code", "execution_count": 5, "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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1McJXo5zxeg8LxN8rxvTdyfgluajdzCNGCYsQ7UJJhUCXz8Nr3hZYk7cXntW5\niL6Uh8hf05BSUKHp0Agh7UynSSqdsfeXsqxry7Cw6HfMHWiDh5W1mPvbPaz/IwdlVbWaDo0Q0k50\nmob6ztj7qyU4AP0cjOHTxRA7/yzA/pQHuJBRird9rTDI2Rhc/dLHhBDSgE5zp0KUI9Th4x0/CVYN\nc4K1SAdR57Ox6FgGMh5WKV9YRTlQlA92J1n1gRJCtAollQ6OP2dZq2ZB7mqujxVDHDE9wBppDyox\n+2AqticpPt0Lu5MMZKYCBbmQrlqk+sRCCYsQrUJJhfyjkS9oHsdh6HOmWDe6KwY6GuPn64V4b8cl\n/LF2XbNF1o/qBwDU1tQ9VhG1JyxCiNIoqRAAin1Bm+oLMLufDZaGOkCX1WKZ+AUsOZGJ7NLGp3vh\n3L2B+nYYvqDusapiVmPCIoS0DCUVAkC5L+juEiFW5e/HWw8TcDW3HDP2pzY6A7JsVL+FBLzIJc8M\nwmwNdSYsQkjLUFIhAJT/gtaBFP96dAPrRzujn4MIP10rxPR9d3E2vQTPLNHTyKj+VsesxoRFCGmZ\nTpNUaJxK01r6BS0W6uCD/jZYNtgBIj0+vjidhf8cy0B6S3qJ/U2pUfJqSliEkJahcSrkHwZCwEDY\noi9oLyshVg1zwm+3i7HjSj5mH0jFSHczvOZtAX01hEoI0U6dJqkQ9ePzOIxwM8MABxFirhRgX/ID\nnEorwQSDrgiquAu+pgMkhKhdp6n+IirWxPgQY30Bpve2xpfDnCAx0sFXZgOw0GIYzSVGSCdASYUo\nTdHxIa7iuoGT7z04i1y+CB/9dg9fnL7fZBdkQkj7RtVfRGkNdT9urB2Gx3EIqbiDvpX3sG/IDMTd\nKMIfmaUY/pwZxnpbwFivdZVirZktgBCiepRUiNKeXqa4ue7H/DnLYATgdQBDnzPDzj/zceDmA/x+\n9yFe8RJjpLsZ9ARP3DRXlAMVj8DuJFOvLkLaGar+IkprzfgQcwMB3u3dBWtGOMPTygDbkvIxfd9d\nHL/7EFLGaOoVQtq5ZpNKTEwMMjMz2yIW0p60cnyIg6keFg2yx2cv2MNEX4Co89mIPJSGrIRLNPUK\nIe1Ys9Vf2dnZmDt3Luzt7REYGIgBAwbA2Ni4LWIjbUwT7RM9rA3x5TAhTqeVIOZKPqIKLfAZTwCB\ntAYcTb1CSLvTbFKZM2cOysrKcPbsWZw+fRo7duzA888/j6CgIPj5+UEgoGYZ0jo8jkOQswn6Oohw\nIMUMy1g1XB+mw8zHFy84PAehpgMkhChMoTYVIyMjDB06FEuWLMGXX34JR0dHxMTEIDw8HNHR0eqO\nUSVomhbtp8vnIcxTjNmViSif7gTtAAAgAElEQVS2cMCWB2Z4d18qTqU1MJ8YIUQrKd1Q36VLF7z8\n8st4/fXXoa+vjyNHjqgjLpXz9/dHeHi4psMgCjCRVuHdh+fxxVBHmBnwsepsFv7dyvnECCFtQ6m6\nq5SUFJw8eRLnz5+HSCRCcHAwAgMD1RUb6eTcLQywcqgTDt8uRszf84mN9jDHOG8xhDo06Qsh2qjZ\npJKXl4dTp07h1KlTKC0tRe/evTF37lx4eND4AaJ+fB6H4W5m6OcgwvdJ+djzVxFOpZXgbV8rDHQU\ngaufrl/D6mdVpsGYpLNrNqnMmjUL3t7eGDt2LAICAqCrq9sWcREix0RfgBl9umCIqyk2XszBqrNZ\nOHxbiKm9JHAw0dN0eISQvzWbVNatWwdzc/O2iIWQZjVUJTbS3Qyvdm/9lC+EkNZrNqnUJ5THjx/j\n559/xtmzZ1FaWopt27bhypUryM7OxrBhw9QeKNEumqzmqa8S6/93ldj+lAc4duchxniJMfrpKV8I\nIW1K4atv27ZtyMjIwMyZM2X12Pb29jh8+LDagiOkKcb6ArzXpwui/p7yZXtSPiJ+uYujd4pRK6Uu\nyIRogsK9v+Lj47F27Vro6+vLkoq5uTmKiorUFhwhinD8e8qX67nl2Ho5D19dyMGev4rwlo8letka\naU1jPiGdgcJJRSAQQCqVym0rKSmBSCRSeVCkc2tp1ZqXRIgvhjrifEYptiflY+nJ+/C0NMBEXyu4\nWxioOMqn0MzKhABQovqrT58++Prrr5GXlwcAePDgAaKjo9GvXz+1BUeIsjiOQz8HY3w1qium9ZIg\nq7QaH/12DytOZSKzRD2DJ2lmZUL+oXBSeeONN2BlZYXIyEiUl5dj5syZMDMzwyuvvKLO+AhpEcHf\njfnfvOiC13tY4HJ2OWbsT8WG+BwUV9So9FgNLVpGSGfFsRZMqlRf7cVxHGpqatrdpJJZWVmaDkEt\nLCwsUFBQoOkwtFJxZQ1+3LYfhw3doKMjwMte5njJw1wlPcXYnWRIP59bl1h0dJVeY4a0PbpWlGdj\nY6PQfgpfUVu3bpX929jYGBzHobq6Gp9//rnSwRHS1kz1BZhSEo81eXvh00WIHVcKELHvLn7/e3Gw\n1mjNomWEdDQKJ5V79+7hp59+kj2uqqrC8uXLYWpqqpbACFEHm9pSzA+0w7LBDjA3EGDN34uD/Znz\nqHUFt3LRMkI6CoWTykcffYSkpCTs378flZWVWLZsGSQSCaZPn67O+OTk5uZiw4YNWLVqVZPbCGmO\nl1VdT7HI/jYoq67Fv49lYMmJDGTQTMiEtIrCScXAwAALFizA8ePH8dFHH8HR0RHTpk1TeAzA+vXr\nMXnyZERGRsptT0pKwqxZszBjxgzs2bOnyTIkEgkiIiKa3UaIIngch0AnY6wb3RX/52OJ63kVmHkg\nFd/E56C4UrWN+YR0Fk22sO/ateuZba6urkhMTIShoaHs+XHjxjV7oEGDBmHYsGFYt26dbJtUKkV0\ndDQWLVoEsViM+fPnw9/fH1KpFD/88IPc6yMiImBiYqLQmyJEGbp8HsZ4ifGCiwl2XS3AoVvFOJFa\nUteY380cunya9oUQRTWZVAoLCxvc3rNnz0afa4ynp6dsjEu927dvw9raGhKJBADQr18/XLx4EWFh\nYZg3b55S5Tfl6NGjOHr0KABgxYoVsLCwUFnZ2kQgEHTY96YKRTo6AADzRj4jCwAL7Kwxvk85NpxJ\nQ8yVApxIK8MHwS7o7WjWqrKJdqFrRX2aTCrTp09HTk4OrK2t1XLwoqIiiMVi2WOxWIxbt241un9p\naSl27tyJtLQ0xMXFISwsrMFtTwsNDUVoaKjscUftSkjdJJtW+/gxgOb//oYAPuxrhVAnQ2y8mIMP\n9lzHQEcRJvlJYG7Q8CWjaNnapDOvAUPXivIU7VLc7ACT5cuXA6i7O/H19YWnp6fKxqU0NESmqTYa\nkUiEqVOnNruNkAYpOZWKTxdDrBnpjNjrRfjf9UJcynqEN5+3xLDnTMHntc18Yp35i5+0T81WFq9Z\nswYLFiyARCLB/v37ER4eji+++AJHjx5VugrsaWKxWK6MwsJCmJk1Xc3QUgkJCdi4caNayibar6VT\nqejyeXithwW+GumM58T62JSQi49+u4fbhZVqjpiQ9kmhWw6JRILhw4dj+PDhqK6uxtWrV3H58mXE\nxcXBwMAAPXv2RHBwsMK3R/VcXFyQnZ2NvLw8mJub49y5c5g5c2aL3khz/P394e/vr5ayifZraCoV\nZcaU2Bjr4pMQe5y+V4pvL+Vizm9pGO5mhvE9LGCoy6c7CUL+pnQ9lq6uLvz8/ODn5wcAyMjIwOXL\nl5Gent5kUomKisKNGzdQWlqKadOmYezYsQgJCcGkSZOwdOlSSKVSBAcHw97evuXvhpBGcO7eYBxX\nl1j4AnDu3sqX8XcXZF8bQ+y4ko+DKQ9wLr0Uk/2s0N9BRFPsEwIlksrJkyfh5OQER0dH2ba0tDSk\np6fjxRdfbPb1s2fPbnC7r68vfH19FQ2jxRISEnDp0iWEh4er/VhE+8imUql4BN7kyFaNfDfS5SO8\nlzVCuppgQ3wOVp7JwpEuhpjWS4IuIl0VRq08aoMhmqZwB/xdu3bJ9dQC6npQ/PjjjyoPSh38/f0p\noXR2Kp5K5TmxAVYOdcIUfyuk5Fdgxv5U/PBnPqpqpM2/mJAOSuGkUlFRAaFQKLdNKBTi0aNWzplE\nSDvG53EY5W6OdaOd0ddBhF1XCzHjQCouZpZpOrTmVZQDRfm0/gtRKYWTip2dHS5cuCC3LT4+HnZ2\ndioPipD2RizUQWR/G3z2gj10eByWnMzEkhOZyC2r1nRoDaKFxYi6KNymMn78eCxfvhznzp2DtbU1\ncnJycPXqVcyfP1+d8akMtamQttDD2hBRI5yxP6UIP14twHv7U/GKlxhhni2c7kVNyxS3tjccIY1R\n+Cz38PDAqlWr4OrqisrKSri6umLVqlXw8GgfJyK1qZC2osPnEOYpxrrRXdHL1gg//FmAmQdSkZil\nXJWYOu8mOHdvoL63Wgt7wxHSEKW6FFtYWODFF1/Ew4cP1TZIkRBt0dqeVBZCHXw00BZJ2Y+w8WIu\nPjmeiT72RpjsJ4GloU6zr1fn3YQqe8MR8iSF71QePXqENWvWYPz48bIBigkJCe2m9xchmuLTxRBr\nRzphwvOWSMx6hOn77uLna4V4XNv0ipMtuptQpvGdFhYjaqBwUtm8eTOEQiHWr18vm/vLzc0N586d\nU1twqkTTtBBN0uHz8Ep3MdaP7gpfG0Nsv5KP2QdTcT23vNHXKLtMMTW+E22gcFK5evUq3n77bblq\nL2NjYzx8+FAtgakatakQbWBpqIP5gXZYFGSH6lopFhxNx5rz2ShpbFEwJe4mGqouI6StKZxUhEIh\nSktL5bYVFBRQ2wohLdDLzghfj+qKlz3NcTL1Iabvu4sjt4shbWDmbkVR4zvRBgonlRdeeAGrVq3C\ntWvXwBjDzZs3sW7dOgwePFid8RHSYekJeHirpxWiRjjD3kQPX/+Rg4VH0nGvuKpF5SlbXUaIOiic\nVF566SX07dsX0dHRqK2txYYNG+Dv748RI0aoMz5COjwHUz0sG+yAmX2skVFSjfcPpmLb5TxUtmS6\nF2p8JxqmcJdijuMwcuRIjBw5Up3xqA0NfiTaoqGuyhzH4QUXU/SyE2Hb5TzE3ijCmXslmKxnC/+q\n+5oKlRClKZxUrl27BisrK1hZWaG4uBgxMTHg8Xh44403YGpqqs4YVYLWUyHtgbEeHzP6dMELf8+A\nvEz8AnpX3EN4+WOIhc2PbSFE0xSu/oqOjgaPV7f7tm3bUFtbC47jqJsuIWrgaSXE6hHOeLPkEi7r\n2eK9/ak4fLu4wSW4CdEmCieVoqIiWFhYoLa2FleuXEF4eDimTJmCmzdvqjM+QjotAY/DmLLriMr/\nBS7m+lj3Rw7+fSwD2aXaOUklIYAS1V8GBgYoLi5GRkYG7OzsoK+vj5qaGtTUNNK/nhAt014XrrKu\nLcNnL9jjyJ2H2JqYh5kHUvFGDwu86GEOPq/lq02218+DaDeFk8qwYcMwf/581NTUYOLEiQCA5ORk\n2Nraqis2QjRLTTMEtwTHcRjiago/G0NsvJiLrZfzcfpeKWb0sYazmb5GY2stWq2yY1E4qfzrX/9C\nQEAAeDwerK2tAQDm5uaYNm2a2oJTJer9RZQhm/KEMUhXLdKacR9ioQ7mB9riXEYpNl7MReShNLzs\nJcbY7mLotGRqfUJUrNmkEhERAR8fH/Ts2RM9evSAvv4/v4psbGzUGpwqUe8vogxtXm+E4zj0dzCG\nt8QQ3yXm4qdrhTiXXor3+ljDTdPBkU6v2aSybNkyXL58GadOncLGjRvh5OSEnj17wtfXt10lFUKU\nwbl7g3FcXWLR0ilPjPX4mNXXBgMdjbEhPgfzD6djmHEA3ixNhJGmgyOdVrNJxczMDCEhIQgJCUFt\nbS3++usvJCYmYuXKlaipqZElGC8vL+joUD960jFoy3ojirQz+NoYYe3Iroi5ko8Dye74w8ABk9JK\nMMBRBI5reUM+IS2h1CJdfD4f3bt3R/fu3fHWW28hLy8PiYmJOHToENLT0/Hiiy+qK05C2p6BEDAQ\nak21V1MMdHiY4i/BwDMx2GTSG1+ezcLhO0KE+0tgZ6Kn6fBIJ6JUUnnStWvXwOPxMGzYMAwbNkyV\nMRFCWui5x4VYUXAIx176ENuv5GPWwVS86GGOcd4W0BdQQz5RP4WTyuLFi/H666/Dw8MDe/bswYED\nB8Dj8TB06FCMGTNGnTES0rGosasyf84y8AEMB9DPQYRtl/MRe6MIp9JK8I6fFfraU5UYUS+Ff7pk\nZGTAza2ub8mxY8ewePFiLF26FEeOHFFbcKpEKz8SbdCWqzOa6Asws28XrBjsAJEeH5+fzsLHxzOR\nVUIj8on6KJxU6uccysnJAQDY2dnBwsICjx49Uk9kKkYrPxJtoInVGbtZCbFqmBMm+1nhZkEFZhxI\nRUxSPqpaMrW+OlSUA0X5tPxxB6Fw9Ze7uzu+/fZbPHjwAL169QJQl2BEIpHagiOko9FUV2U+j8No\nD3MMcDTG1sQ8/O96IU6mPcQ7fhL0tjPSWJWYtg4yJS2n8J3Ku+++C6FQCEdHR4wdOxYAkJWVRYt0\nEaIETa/OaGYgwPv9bbAs1AH6Ah6Wn7qPT49n4r6GqsQ0cedG1Eup9VTeeOMNuW2+vr64cOGCyoMi\npEPTgq7KXpK6qfUP3nyAnX8WYOaBu3jJwxyvdreAgU7b9RJrD4NMiXIUPnu++eabBrdT4zch7ZOA\nx+FFD3OsH90VgU7G2H2jCO/uu4vTaSVttm6Lpu/ciOo1e6eSm5sLAJBKpcjLy5M72XJzc6Grq6u+\n6AghamdmIMCsvjYY4mqKTRdz8eXZLPx6W4ip/hI4mrbBwEktuHMjqtNsUpk5c6bs3zNmzJB7ztTU\nFK+++qrqoyKEtLlulkJ8OcwJh28XI+ZKPmYfTMUodzO85m0BQ12+psMj7USzSWXXrl0A6gY/fvLJ\nJ2oPiBCiOXweh+FuZujvIELMlQLsS36AU2kl+L+eVhjkbAweDZwkzVC4TYUSCiGdh7G+ANN7W+PL\nYU6wMtTBmvPZWHgkHXlljzUdGtFyCvf+ysvLw86dO5GWlobKykq55zZs2KDywAghmucq1sfnQx3x\n+92H2JKQh/cPpWJmny7obU/j00jDFE4qa9asgUQiwVtvvQU9vfY36ymt/EhIy/A4DqEupvCyEmLl\nmftYduo+RnuY4f98rKDDp+owIk/hpJKZmYnPPvsMPF77nOmUVn4kpHW6iHTx+RBHfHc5H/uSHyA5\nvwJzBthAYkQ9QMk/FM4Q3bp1Q1pamhpDIYRoOx0+D1P9JZg30BZZJdV4/2AazqWXaDosokUUvlOx\ntLTE0qVLERAQAFNTU7nnxo0bp/LACCHaq6+DCF3N9bDyTBY+P52FEW7leNvXCrr89lmTQVRH4aRS\nVVUFPz8/1NbWorCwUJ0xEULaAYmRLpYPdsT2pDzs/bs67KOBtugiouqwzkzhpDJ9+nR1xkEIaYd0\n+Bwm+UnQXSLEmvPZeP9gGqb3tkagk7GmQyMaonBSqZ+upSESiUQlwRBC2qcAOxGiRuhj5ZksrDqb\nhWu55ZjoawmhDo3E72wUTipPTtfytPpR94SQzsvSUAfLBjtgx5W6JYwv3i/D275WGOhISxh3Jgon\nlacTR3FxMf73v/+hW7duKg+KENI+CXgc/q+nFfrYi7DxYg5Wnc3C4dtCTO0lgYNJ+xvfRpTX4q4a\npqammDhxIn744QdVxkMI6QDcLQywcqgTpvWSIPVBJWYfSMV3iXkof1yr6dCImil8p9KQrKwsVFVV\nqSoWQkgH8uTklN8n5WPvX0U4mVaCSVQl1qEpnFT+85//yJ0EVVVVyMjIwCuvvKKWwAghHYOxvgDv\n9emCIa6m2HgxF6vOZuG3WwYI72UNB1M98Ocs03SIRIUUTiohISFyj/X19eHo6IguXbqoPChCSMfj\nZmGAL4Y64sidYsQk5WPWwVSMdjfDaz0sqJdYB6JwUhk0aJAawyCEdAZ8Hodhz5mhn4MxYpLy8cvf\n67WE97JGXwea+bgjUDip1NTUIDY2FqdOncKDBw9gZmaGwMBAjBkzBgJBq5pmCCGdjLEeH9N7W2Ow\nqwk2xOdgxen7COlqjMl+Elplsp1TOBvExMTgzp07mDJlCiwtLZGfn4/du3ejvLwcEydOVGOI8nJz\ncxEbG4vy8nJERkYCAOLj45GYmIiSkhIMHToUzz//fJvFQwhpuefEBvhiqBN2XS3Az9cLcS23HLP7\n2sBLItR0aKSFFE4qFy5cwMqVKyES1d2i2tjYwNnZGXPmzFE4qaxfvx6JiYkwMTHBqlWrZNuTkpLw\n3XffQSqV4oUXXsC//vWvRsuQSCSIiIiQe31AQAACAgJQVlaG7du3U1IhKkENyG1DwOMw/nlL+Nsa\n4b9ns7DwaDrCPM3xRg8L6NAEle2OwkmFMdbqgw0aNAjDhg3DunXrZNukUimio6OxaNEiiMVizJ8/\nH/7+/pBKpc+MgYmIiICJiUmj5cfGxmLo0KGtjpMQ0vbcLQwQNcIZ3yXmIfZGERKzHuH9fl3gZKav\n6dCIEhROKn379sXnn3+OV155BRYWFigoKMDu3bvRp08fhQ/m6emJvLw8uW23b9+GtbW1bP6wfv36\n4eLFiwgLC8O8efMUKpcxhh07dsDHxwddu3ZVOB5CiHYx0OFhem9r9LI1wld/ZCPy13t4y8cSoz3M\nwKNxLe2CwknlzTffxO7duxEdHY0HDx7A3Nwc/fv3x8svv9yqAIqKiiAWi2WPxWIxbt261ej+paWl\n2LlzJ9LS0hAXF4ewsDAcOnQIV69eRXl5OXJycjBkyBC51xw9ehRHjx4FAKxYsQIWFhatillbCQSC\nDvveOpIiHR0AgDn9rRo13MICfdxs8fmxW/g2MQ9JeVVYOPg5WBur5q6FrhX1aTapJCcnIyEhAW++\n+SbGjRsntyBXTEwM7t69Czc3txYH0FC1WlMjbUUiEaZOnSq3bcSIERgxYkSjrwkNDUVoaKjscUFB\nQQsi1X71d5BEu9U+fgygfZ2H7E4yWMpVcO7e4Fw82uy4kX0s4WOli80JeZgQk4jwXhIEORm3ejQ+\nXSvKs7GxUWi/ZlvB4uLi4Onp2eBz3bt3R2xsrHKRPUUsFsst+lVYWAgzM7NWlUkIUR12JxnSVYvA\n9uyo+/+d5DY7NsdxCHUxxZoRTnA01cPqc9n48mwWzSGmxZpNKmlpafDx8WnwOW9vb6SmprYqABcX\nF2RnZyMvLw81NTU4d+4c/P39W1VmQxISErBx40aVl0tIR8dSrgI1NQCTArU1dY/bmLVIF0tDHfDm\n8xY4l16K9w+m4W5RZZvHQZrXbPVXRUUFampqoKv77BKhtbW1qKioUPhgUVFRuHHjBkpLSzFt2jSM\nHTsWISEhmDRpEpYuXQqpVIrg4GDY29sr9y4U4O/vr5ZkRYiy2ltXZc7dG0wgAGprAL4AnLu3RuLg\n8zi82t0CXlZCfHkmC3N+u4d3/Kww/DlTmpxSizSbVGxtbXHlyhX06tXrmeeuXLkCW1tbhQ82e/bs\nBrf7+vrC19dX4XIIIW2Hc/EAL3KJwm0q6m5/8bQSImqEE6LOZ2PjxVxczS3He72taSS+lmg2qYwc\nORKbNm2CVCpFr169wOPxIJVKcfHiRURHR+Ott95qizhbLSEhAZcuXUJ4eLimQyGk3eFcPBRKEPXt\nL6ipARMIwItcopbEYqwvwKJBdthzowjbr+TjblEl5gywhauYxrRoWrNJZcCAASguLsa6devw+PFj\nGBsbo6SkBLq6unj11VcxYMCAtoiz1aj6ixD1a6j9RV29xXgchzFeYnSzNMDKs1mYe/geJvlaYYQb\nVYdpkkLjVEaNGoWQkBDcvHkTZWVlMDIygpubG4RCmp+HEPIPTbS/dLMSImqEM9acy8KmhLrqsBl9\nqDpMUzimivlX2oEnq7+ysrI0HY5aUN97og00NaZFyhj2/lWE7Un5sDTUwYcDbPCc2KDBfelaUZ6i\n41Q6TVJ5EiUVQjqu5PwKrDxzH8WVNZjY0woj3Z+d4oWuFeWpbPAjIYS0Jx6WdRNT9uxihC2X8rDg\nSDrSi6s0HVanQUmFENLhiPT4WBhki1l9uyCzpBrvH0rFjiv5qK6Vajq0Dq/TJBUaUU9I58JxHEK6\nmmDdKGcMcDTGT9cKMetAKv7MeaTp0Do0alPpQKiemJDGJWU/wob4HOSUPcZITyu85mkCYz3qIaYo\naqhvAiUVQjq+hnqhVdVIsetqAfYkP4ChDg/v+FmpZNbjzoCSShMoqRDSsT05sh8NjOwvZgZY+ttf\nuFlYCZ8uhojoJYG16Nn5Dck/qPcXIaTTam5mZVdLQ6wY4oip/hKk5FdgxoFU7L5eiFppp/uNrXKd\nJqlQQz0hnQfn7g0IBACP1+jIfj6Pw0h3M6wb7QxfG0N8n5SPfx9LR2H5Yw1E3HFQ9VcHQtVfhPyj\nqZH9DV0rJ1IfYkN8DvT4PHzQ3wY+XQzbMlytR20qTaCkQkjn1ti1kvGwCp+fvo/Mh9V4zdsCr3YX\ng8+jRnyA2lQIIURp9iZ6+HKYE4KcjbHzagE+OZ6B4soaTYfVrlBSIYSQJ+gLeJjdtwve622Nv/Ir\nMPtgGq7nlms6rHaDkgohhDyF4zgMdjXFF0MdYSDgsOhYOnZfL4S087UWKK3TJBXq/UUIUZazmT5W\nDXdCX3sRvk/Kx5ITmSipqtV0WFqNGuo7EGqoJ0Qxyl4rjDEcvFmMbxNzYaovwEcDbeFu0fBaLR0V\nNdQTQoiKcFzdmJYVQxzB4zjMP3wPe/8qQif8Td4sSiqEEKKg58QGWD3cCX62Rvg2MQ8rTt9HWTVV\nhz2JkgohhCjBSI+PBYG2mORrhYuZZfjgUBpuFVZoOiytQUmFEEKUxHEcXupmjmWDHVErZZh3OB0H\nUh5QdRgoqRBCSIt5WBpg9QhnPG8txKaEXKw8k4Xyx527OoySCiGEtIKxHh+LBtnhLR9LnM8oxQeH\n0nC3qFLTYWlMp0kqNE6FEKIuPI7Dy15iLAl1QFUNw0e/3cNvt4o7ZXUYjVPpQGicCiGKUee18rCy\nBv89l42k7EcIdDLG9ABrGOi0/9/vNE6FEEI0wERfgMXBdhjfwwJn7pUg8tc0pD3oPNVhlFQIIUTF\neByHsd4W+CTEHuXVtfjw17rBkpqcO4zdSYb04P/A7iSr9TiUVAghRE16WBsiaoQzetoY4tvEPPz7\nWAbyytp+ZUl2JxnSVYvA9uyo+78aEwslFUIIUSNTAwEWBNpiRh9r3CmsxMwDqTh2p20b8VnKVaCm\nBmBSoLam7rGaUFIhhBA14zgOoS6mWDPSCS7melh7IQfLT91vswXAOHdvQCAAeDyAL6h7rK5jUe+v\njoN6fxGiGE1eK1LGsC/5AbYn5UOow8O7va3R216k9uOyO8lgKVfBuXuDc/FQ+vW0Rn0TKKkQ0rlp\nw7Vyr7gKUeeycPdBFUK6mmCynxUMdfkajakp1KWYEEK0mKOpHr4Y6oSx3cU4kfoQsw6k4s+cR5oO\nq9UoqRBCiIbo8DmMf94SK4Y4QofP4d/HMhB9KRdVNVJNh9ZinSap0DQthBBt5W5RNzHlCDdT/JL8\nALMPpuGvvHJNh9Ui1KbSgWhDPTEh7YE2Xyt/5jzCVxdykP/oMUZ5mGHC85bQE2j+9z+1qRBCSDvU\nw9oQa0c6Y7ibKfYlP8DMA6m4ntt+7looqRBCiJYx0OEhvJc1loTaAwAWHE3HpoRcVLaDthZKKoQQ\noqW8JYZYM9IZo9zNcCDlAWYdSMXVXO3uIUZJhRBCtJi+gIcp/hIsG+wAjgMWHc3AN/E5qHisnXct\nlFQIIaQd8LISYs0IZ7zoYYZfbxVjppaOa6GkQggh7YSegId3/CRYPtgBAl7duJbvL+dpdEr9p1FS\nIYSQdqablRBRI5ww1NUUu28UYfmp+yh/XKvpsABQUiGEkHZJT8BDRIAEU/ytkHC/DPMOp2tkrZan\nUVIhhJB2iuM4jHI3x3+C7VHw6DE+/FXzI/EpqRBCSDvXs4shvhjmCENdHhYdS8exO8Uai4WSCiGE\ndAB2xnWzHntaCbH2Qg6+S8xDrbTtG/ApqRBCSAch0uNjcbA9hj9nij1/FWH5qcw2b8CnpEIIIR2I\ngMdhWoA1wntJcCnrEeb+dg85pdVtd/w2O5IK5ObmIjY2FuXl5YiMjAQAZGZm4uDBgygtLYW3tzeG\nDBmi4SgJIUTzRriZwdZYF1+cvo8Pf7uH+QNt4SURqv24bXansn79ekyePFmWDOolJSVh1qxZmDFj\nBvbs2dNkGRKJBBEREXLb7OzsMHXqVLz//vu4c+eOyuMmhJD26nlrQ6wc6gRjPT7+83s6jtxWfwN+\nm92pDBo0CMOGDcO6dbcPji4AAAquSURBVOtk26RSKaKjo7Fo0SKIxWLMnz8f/v7+kEql+OGHH+Re\nHxERARMTkwbLTkhIwJ49ezBs2DC1vgdCCGlvbIx18cVQR6w8k4U7RZUYrObjtVlS8fT0RF5enty2\n27dvw9raGhKJBADQr18/XLx4EWFhYZg3b57CZfv7+8Pf3x/Lly/HgAEDnnn+6NGjOHr0KABgxYoV\nsLCwaMU70V4CgaDDvjdCVKmzXSsWANa8YgUwBgFfvRVUGm1TKSoqglgslj0Wi8W4detWo/uXlpZi\n586dSEtLQ1xcHMLCwnD9+nX88ccfqKmpQc+ePRt8XWhoKEJDQ2WPtXXFt9bS5tXsCNEmdK0oT9GV\nHzWaVBpayZjjuEb3F4lEmDp1qtw2Ly8veHl5qTw2QgghytNol2KxWIzCwkLZ48LCQpiZmanlWAkJ\nCdi4caNayiaEEFJHo0nFxcUF2dnZyMvLQ01NDc6dOwd/f3+1HMvf3x/h4eFqKZsQQkidNqv+ioqK\nwo0bN1BaWopp06Zh7NixCAkJwaRJk7B06VJIpVIEBwfD3t6+rUIihBCiYhxrqGGjA0pISMClS5cQ\nHh6OrKwsTYejFtT4SIhi6FpRXrtoqG9L9d2OCSGEqA/N/UUIIURlOk31FyGEEPWjO5VGtKT7sbKv\nUWZ/RfZVZhaCjkLbuom3RTyqPkZry9Oma0XR/ehaUR9KKo3w8/NT+2uU2b8l8XQG2va5tEU8qj5G\na8vTpmtF284HbdJWnw1Vf3Ug8+bNw4oVKzQdBiFaj64V9aE7lQ7kyfnNCCGNo2tFfehOhRBCiMrQ\nnQohhBCVoaRCCCFEZSipEEIIUZlOM01LZ5OZmYmffvoJIpEI3t7e6NOnj6ZDIkSr5ObmIjY2FuXl\n5YiMjAQAVFZWYsuWLRAIBPDy8sLAgQM1HGX7Q3cq7cj69esxefJk2QVQLykpCbNmzcKMGTOwZ88e\nAMDly5cxfPhwTJkyBSdPntREuIS0OWWuEYlEgoiICLn94uPj0adPH0ybNg0JCQltFndHQkmlHRk0\naBAWLFggt00qlSI6OhoLFizA6tWrcfbsWWRmZiIwMBBnz57F9u3bUVZWpqGICWlbylwjDSksLJSt\nXc/j0ddjS9Cn1o54enrCyMhIbtvt27dhbW0NiUQCgUCAfv364eLFizAxMcHkyZMxfvx4iEQiDUVM\nSNtS5hppyJOr0dJoi5ahNpV2rqioCGKxWPZYLBbj1q1byMvLQ1xcHKqqqvDiiy9qMEJCNKuxa6S0\ntBQ7d+5EWloa4uLiEBYWhoCAAHz77bdITEykKV9aiJJKO9fQrymO42BlZUXLJxOCxq8RkUiEqVOn\nym3X19fH9OnT2yq0Domqv9q5J2/Xgbo6YTMzMw1GRIh2oWukbVFSaedcXFyQnZ2NvLw81NTU4Ny5\nc7TCJSFPoGukbdHcX+1IVFQUbty4gdLSUpiYmGDs2LEICQlBYmIitm3bBqlUiuDgYIwZM0bToRKi\nEXSNaB4lFUIIISpD1V+EEEJUhpIKIYQQlaGkQgghRGUoqRBCCFEZSiqEEEJUhpIKIYQQlaGkQkg7\nc+TIEWzduhUAkJeXh7Fjx6K2tlblxzl48CB27Nih8nJJx0ZJhXRK7777LsaPH48JEybI/ouOjtZ0\nWM2qqalBbGxsqycJ3bRpE77++utntt+7dw9vvPEGysrKEBoaitOnT+Phw4etOhbpXGhCSdJpzZ07\nFz169FDrMWpra8Hn81VW3sWLF2FjYwNzc/NWlTNo0CB89tlnmDx5MvT19WXbT548CV9fX9n08T4+\nPjh58iTNdE0URkmFkKecOHECx44dw3PPPYfj/9/e/YU0+YUBHP+2TdssaMvKMjJpIdVAm0Vm5F4i\noVaEoDC6KNiFQVAQRFoUQd3EZgQZEUFtlCkRdmFJBbESt/7YH5tFTaguguwPpTMjtxjpfhfRW+9P\nzfqh8SOfDwzcOe8559l787hzXvY0NZGWlkZ5eTl2ux2AWCzG6dOnCYfDjBs3jhUrVuByudDpdOpY\nq9VKc3Mzq1atwuVyUVtbS3NzM0ajkXXr1uH3+zl79ix3796loaEBr9errt/Y2Eh7ezuVlZUDYguH\nwyxYsGDI2FtaWjhz5gw7d+4kKyuLp0+fUlNTQ0dHB1OnTsXtdmOz2cjJyWHy5MncuXMHRVGAr8Ws\nbt68yaZNm9T5bDYb169fl6QifplsfwkxiOfPn5OZmYnP56OkpITjx4+rP6F+9OhR9Ho9R44coaqq\niocPH3Lt2jV17LNnz8jIyODkyZOUlpYSCAQIh8N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8Ad7dm45TWTS/hRDSfpRUOhF2MxXITgcK8yBbGtXmxAIA9iZCRA1xxKehTtDj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\nMZvNstO1GDdJKkL8ze3bt6mrq2PRokXU19fj5+d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thPC2EmBTUg7irhbg1P1ivOdnhQBbE3DqFvl6Co2+J4SoAtVU2ikTfR6m9+qA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\nZaSlpfHs2TNqamro7Oxk2rRpWK1WMjMzycjIICk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aK+2Z24wQ0vVRSUWHmBvwsTzQAWEDJbiWX4mI/Wk4n1mm/gtTV2VCdAaVVLq5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\n8g9YbS0ABkbT2XdaSo1TefrppxESEoK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"text/plain": [ "" ] }, "metadata": {}, "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": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p1.gamma lower bound:\t-0.057005\n", "p1.ampl lower bound:\t-1.12614e-21\n", "p1.ampl upper bound:\t1.16189e-21\n", "p1.gamma upper bound:\t0.0582552\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 2.43652 -0.057005 0.0582552\n", " p1.ampl 2.3608e-20 -1.12614e-21 1.16189e-21\n" ] } ], "source": [ "### Compute confidnce intervals\n", "ds.conf()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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acTDYMCmiFe1q+7NgYxQT/xfJyNYNGdoiDH0lmOVZqEW+qIuDtIFsUTEGUVoq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\nQc/GSYG/yxO9jj4hFI53H02g0biHNi5GE0pTiYvzRQLiAnI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Compute confidence contours for amplitude and index\n", "sh.reg_unc(p1.gamma,p1.ampl)" ] } ], "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": "3.6.3" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 1 }