{ "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/v0.8?urlpath=lab/tree/spectrum_analysis.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_analysis.ipynb](../_static/notebooks/spectrum_analysis.ipynb) |\n", "[spectrum_analysis.py](../_static/notebooks/spectrum_analysis.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectral analysis with Gammapy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This notebook explains in detail how to use the classes in [gammapy.spectrum](..\/spectrum/index.rst) and related ones. Note, that there is also [spectrum_pipe.ipynb](spectrum_pipe.ipynb) which explains how to do the analysis using a high-level interface. This notebook is aimed at advanced users who want to script taylor-made analysis pipelines and implement new methods.\n", "\n", "Based on a datasets of 4 Crab observations with H.E.S.S. (simulated events for now) we will perform a full region based spectral analysis, i.e. extracting source and background counts from certain \n", "regions, and fitting them using the forward-folding approach. We will use the following classes\n", "\n", "Data handling:\n", "\n", "* [gammapy.data.DataStore](..\/api/gammapy.data.DataStore.rst)\n", "* [gammapy.data.DataStoreObservation](..\/api/gammapy.data.DataStoreObservation.rst)\n", "* [gammapy.data.ObservationStats](..\/api/gammapy.data.ObservationStats.rst)\n", "* [gammapy.data.ObservationSummary](..\/api/gammapy.data.ObservationSummary.rst)\n", "\n", "To extract the 1-dim spectral information:\n", "\n", "* [gammapy.spectrum.SpectrumObservation](..\/api/gammapy.spectrum.SpectrumObservation.rst)\n", "* [gammapy.spectrum.SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst)\n", "* [gammapy.background.ReflectedRegionsBackgroundEstimator](..\/api/gammapy.background.ReflectedRegionsBackgroundEstimator.rst)\n", "\n", "\n", "For the global fit (using Sherpa and WSTAT in the background):\n", "\n", "* [gammapy.spectrum.SpectrumFit](..\/api/gammapy.spectrum.SpectrumFit.rst)\n", "* [gammapy.spectrum.models.PowerLaw](..\/api/gammapy.spectrum.models.PowerLaw.rst)\n", "* [gammapy.spectrum.models.ExponentialCutoffPowerLaw](..\/api/gammapy.spectrum.models.ExponentialCutoffPowerLaw.rst)\n", "* [gammapy.spectrum.models.LogParabola](..\/api/gammapy.spectrum.models.LogParabola.rst)\n", "\n", "To compute flux points (a.k.a. \"SED\" = \"spectral energy distribution\")\n", "\n", "* [gammapy.spectrum.SpectrumResult](..\/api/gammapy.spectrum.SpectrumResult.rst)\n", "* [gammapy.spectrum.FluxPoints](..\/api/gammapy.spectrum.FluxPoints.rst)\n", "* [gammapy.spectrum.SpectrumEnergyGroupMaker](..\/api/gammapy.spectrum.SpectrumEnergyGroupMaker.rst)\n", "* [gammapy.spectrum.FluxPointEstimator](..\/api/gammapy.spectrum.FluxPointEstimator.rst)\n", "\n", "Feedback welcome!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "As usual, we'll start with some setup ..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gammapy: 0.8\n", "numpy: 1.13.0\n", "astropy 3.0.4\n", "regions 0.3\n", "sherpa 4.10.0\n" ] } ], "source": [ "# Check package versions\n", "import gammapy\n", "import numpy as np\n", "import astropy\n", "import regions\n", "import sherpa\n", "\n", "print(\"gammapy:\", gammapy.__version__)\n", "print(\"numpy:\", np.__version__)\n", "print(\"astropy\", astropy.__version__)\n", "print(\"regions\", regions.__version__)\n", "print(\"sherpa\", sherpa.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import astropy.units as u\n", "from astropy.coordinates import SkyCoord, Angle\n", "from astropy.table import vstack as vstack_table\n", "from regions import CircleSkyRegion\n", "from gammapy.data import DataStore, ObservationList\n", "from gammapy.data import ObservationStats, ObservationSummary\n", "from gammapy.background.reflected import ReflectedRegionsBackgroundEstimator\n", "from gammapy.utils.energy import EnergyBounds\n", "from gammapy.spectrum import (\n", " SpectrumExtraction,\n", " SpectrumObservation,\n", " SpectrumFit,\n", " SpectrumResult,\n", ")\n", "from gammapy.spectrum.models import (\n", " PowerLaw,\n", " ExponentialCutoffPowerLaw,\n", " LogParabola,\n", ")\n", "from gammapy.spectrum import (\n", " FluxPoints,\n", " SpectrumEnergyGroupMaker,\n", " FluxPointEstimator,\n", ")\n", "from gammapy.maps import Map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configure logger\n", "\n", "Most high level classes in gammapy have the possibility to turn on logging or debug output. We well configure the logger in the following. For more info see https://docs.python.org/2/howto/logging.html#logging-basic-tutorial" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Setup the logger\n", "import logging\n", "\n", "logging.basicConfig()\n", "logging.getLogger(\"gammapy.spectrum\").setLevel(\"WARNING\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data\n", "\n", "First, we select and load some H.E.S.S. observations of the Crab nebula (simulated events for now).\n", "\n", "We will access the events, effective area, energy dispersion, livetime and PSF for containement correction." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "datastore = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1/\")\n", "obs_ids = [23523, 23526, 23559, 23592]\n", "obs_list = datastore.obs_list(obs_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Target Region\n", "\n", "The next step is to define a signal extraction region, also known as on region. In the simplest case this is just a [CircleSkyRegion](http://astropy-regions.readthedocs.io/en/latest/api/regions.CircleSkyRegion.html#regions.CircleSkyRegion), but here we will use the ``Target`` class in gammapy that is useful for book-keeping if you run several analysis in a script." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "target_position = SkyCoord(ra=83.63, dec=22.01, unit=\"deg\", frame=\"icrs\")\n", "on_region_radius = Angle(\"0.11 deg\")\n", "on_region = CircleSkyRegion(center=target_position, radius=on_region_radius)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create exclusion mask\n", "\n", "We will use the reflected regions method to place off regions to estimate the background level in the on region.\n", "To make sure the off regions don't contain gamma-ray emission, we create an exclusion mask.\n", "\n", "Using http://gamma-sky.net/ we find that there's only one known gamma-ray source near the Crab nebula: the AGN called [RGB J0521+212](http://gamma-sky.net/#/cat/tev/23) at GLON = 183.604 deg and GLAT = -8.708 deg." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exclusion_region = CircleSkyRegion(\n", " center=SkyCoord(183.604, -8.708, unit=\"deg\", frame=\"galactic\"),\n", " radius=0.5 * u.deg,\n", ")\n", "\n", "skydir = target_position.galactic\n", "exclusion_mask = Map.create(\n", " npix=(150, 150), binsz=0.05, skydir=skydir, proj=\"TAN\", coordsys=\"GAL\"\n", ")\n", "\n", "mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)\n", "exclusion_mask.data = mask\n", "exclusion_mask.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate background\n", "\n", "Next we will manually perform a background estimate by placing [reflected regions](..\/background/reflected.rst) around the pointing position and looking at the source statistics. This will result in a [gammapy.background.BackgroundEstimate](..\/api/gammapy.background.BackgroundEstimate.rst) that serves as input for other classes in gammapy." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "background_estimator = ReflectedRegionsBackgroundEstimator(\n", " obs_list=obs_list, on_region=on_region, exclusion_mask=exclusion_mask\n", ")\n", "\n", "background_estimator.run()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# print(background_estimator.result[0])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jer/anaconda/envs/gammapy-dev/lib/python3.6/site-packages/matplotlib/patches.py:83: UserWarning: Setting the 'color' property will overridethe edgecolor or facecolor properties. \n", " warnings.warn(\"Setting the 'color' property will override\"\n" ] }, { "data": { "text/plain": [ "(
,\n", " ,\n", " None)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "background_estimator.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source statistic\n", "\n", "Next we're going to look at the overall source statistics in our signal region. For more info about what debug plots you can create check out the [ObservationSummary](..\/api/gammapy.data.ObservationSummary.rst#gammapy.data.ObservationSummary) class." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** Observation summary report ***\n", "Observation Id: 23526\n", "Livetime: 0.437 h\n", "On events: 201\n", "Off events: 225\n", "Alpha: 0.083\n", "Bkg events in On region: 18.75\n", "Excess: 182.25\n", "Excess / Background: 9.72\n", "Gamma rate: 7.67 1 / min\n", "Bkg rate: 0.72 1 / min\n", "Sigma: 21.86\n", "\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MzsQrnSTNfDO5DTM5k2ax5UsWzoiGTNLsNFPbME9rSpIkdYjJmSRJUoeYnEmSJHWIyZkkSVKHmJxJkiR1iMmZJElSh5icSZIkdchAk7Mk85KcmeTqJFcleWaSPZOcn+Sa9n2Ptm6SvC/JtUmuTHLIIGOTJEnqokH3nP0f4PNV9UvAU4GrgLcAF1TVgcAF7TzAEcCB7etE4OQBxyZJktQ5A0vOkjwSeA5wKkBV/ayqtgBHAqvaaquA5e30kcBp1bgYmJdk30HFJ0mS1EWD7Dl7HLAZ+Kcklyf5UJKHAftU1SaA9n3vtv5C4Iae9de3ZZLUKUnmtO3aee38Y5Nc0g7X+GSSBw87RknT1yCTs92AQ4CTq2oJ8BPuPYU5noxTVveplJyYZE2SNZs3b56cSCVpYl5PM0xjzLuB97TDNW4BThhKVJJmhEEmZ+uB9VV1STt/Jk2yduPY6cr2/aae+vv1rL8I2Lj9RqvqlKpaWlVL58+fP7DgJWk8SRYB/w34UDsf4Hk0bRxsO1xDkiZsYMlZVf0AuCHJ4rbocOBbwLnAcW3ZccA57fS5wLHtVZuHAbeOnf6UpA55L/Bm4Oft/KOBLVV1dzu/wyEZ9vxL6sduA97+64CPteMvrgOOp0kIz0hyAvB94Oi27meBFwHXAne0dSWpM5L8d+Cmqro0yXPHisepep8hGdD0/AOnACxdunTcOpI00OSsqq4Alo6z6PBx6hbw2kHGI0kP0LOAFyd5EfAQ4JE0PWnzkuzW9p6NOyRDkvrlEwIkqU9VdVJVLaqqA4CXA1+sqlcCFwIvaav1DteQpAkzOZOkB+6PgDcmuZZmDNqpQ45H0jQ26DFnkjQjVdVFwEXt9HXAocOMR9LMYc+ZJElSh5icSZIkdYjJmSRJUoc45kySpFng7Ms3sHL1OjZuGWXBvBFWLFvM8iU+wrqLTM4kSZrhzr58AyedtZbRu7YCsGHLKCedtRbABK2DPK0pSdIMt3L1unsSszGjd21l5ep1Q4pIO2NyJknSDLdxy+iEyjVcJmeSJM1wC+aNTKhcw2VyJknSDLdi2WJG5s7Zpmxk7hxWLFs8pIi0M14QIEnSDDc26N+rNacHkzNJkmaB5UsWmoxNE57WlCRJ6hCTM0mSpA4xOZMkSeoQkzNJkqQOMTmTJEnqEJMzSZKkDjE5kyRJ6hCTM0mSpA4xOZMkSeoQkzNJkqQOMTmTJEnqEJMzSZKkDjE5kyRJ6hCTM0mSpA4xOZMkSeoQkzNJkqQO2W3YAUjTydmXb2Dl6nVs3DLKgnkjrFi2mOVLFg47LEnSDGJyJvXp7Ms3cNJZaxm9aysAG7aMctJZawFM0CRJk8bTmlKfVq5ed09iNmb0rq2sXL1uSBFJkmYikzOpTxu3jE6oXJKk+2OgyVmS65OsTXJFkjVt2Z5Jzk9yTfu+R1ueJO9Lcm2SK5McMsjYpIlaMG9kQuWSJN0fU9Fz9qtVdXBVLW3n3wJcUFUHAhe08wBHAAe2rxOBk6cgNqlvK5YtZmTunG3KRubOYcWyxUOKSJI0Ew3jtOaRwKp2ehWwvKf8tGpcDMxLsu8Q4pPGtXzJQt551EEsnDdCgIXzRnjnUQd5MYAkaVIN+mrNAr6QpIB/qKpTgH2qahNAVW1KsndbdyFwQ8+669uyTb0bTHIiTc8a+++//4DDl7a1fMlCkzFJ0kANOjl7VlVtbBOw85NcvZO6Gaes7lPQJHinACxduvQ+yyVJkqazgZ7WrKqN7ftNwKeBQ4Ebx05Xtu83tdXXA/v1rL4I2DjI+CRJkrpmYMlZkoclecTYNPAC4BvAucBxbbXjgHPa6XOBY9urNg8Dbh07/SlJkjRbDPK05j7Ap5OMfc7Hq+rzSb4GnJHkBOD7wNFt/c8CLwKuBe4Ajh9gbJIkSZ00sOSsqq4DnjpO+Q+Bw8cpL+C1g4pHkiRpOvAJAZIkSR1iciZJktQhJmeSJEkdYnImSZLUISZnkiRJHWJyJkmS1CEmZ5IkSR1iciZJktQhJmeSZqX2EXNzhh2HJG3P5EzSrJDkQUlekeRfk9wEXA1sSvLNJCuTHDjsGCUJTM4kzR4XAr8InAT8QlXtV1V7A78CXAy8K8lvDTNASYLBPvhckrrk+VV1V5K3VtWVY4VV9SPgU8CnkswdXniS1DA5kzQrVNVd7eRbkzwU2BO4DPhEVd2yXR1JGhqTM0mzTQE/BVYDhwBfTfLyqvr6cMNSl5x9+QZWrl7Hxi2jLJg3wopli1m+ZOGww9IsYXImaba5uqre2k6fmeQjwAeB5/WzcpKHAF8GdqdpQ8+sqrcmeSzwCe7tkXtVVf1ssoPX4J19+QZOOmsto3dtBWDDllFOOmstgAmapoQXBEiabW5O8rSxmar6NjB/AuvfCTyvqp4KHAy8MMlhwLuB91TVgcAtwAmTGLOm0MrV6+5JzMaM3rWVlavXDSkizTb2nEmabf4A+ESSS4G1wFOA7/a7clUV8ON2dm77Kpqet1e05auAtwEnT07Imkobt4xOqFyabPacSZpV2rFlBwOnt0UXAsdMZBtJ5iS5ArgJOB/4DrClqu5uq6wHPP81TS2YNzKhcmmymZxJmhWSZGy6qu6sqn+tqndX1Yeq6ifb19mZqtpaVQcDi4BDgSeOV22cGE5MsibJms2bN9+/HdHArVi2mJG52z48YmTuHFYsWzykiDTbmJxJmi0uTPK6JPv3FiZ5cJLnJVkFHDeRDVbVFuAi4DBgXpKxoSKLgI3j1D+lqpZW1dL58ycyzE1TafmShbzzqINYOG+EAAvnjfDOow7yYgBNGcecSZotXgj8NnB6e2XlFmCE5iD1CzSD+a/Y1UaSzAfuqqotSUaA59NcDHAh8BKaKzaPA84ZyF5oSixfstBkTENjciZpVqiqnwIfAD7QPglgL2C07f2aiH2BVe1D0x8EnFFV5yX5Fs2FBu8ALgdOncTwJc0iJmeSZp32SQCb7ue6VwJLxim/jmb8mSQ9II45kyRJ6hCTM0mSpA4xOZM0q6TxW0n+vJ3fP4mnIyV1hsmZpNnmA8AzuffGs7cD7x9eOJK0LS8IkDTbPKOqDklyOUBV3ZLkwcMOSpLG2HMmaba5q70NRsE99y37+XBDkqR7mZxJmm3eB3wa2DvJXwJfAf5quCFJ0r36Oq2Z5Gjg81V1e5I/BQ4B3lFVlw00OkmaZFX1sSSXAocDAZZX1VVDDkuS7tHvmLM/q6p/SfJsYBnwN8DJwDMGFpkkDUhVXQ1cPew4JGk8/Z7W3Nq+/zfg5Ko6B3AAraRpJ8mqJPN65vdI8uFhxiRJvfpNzjYk+QfgpcBnk+w+gXUlqUue0vs8zaq6hXEexyRJw9JvgvVSYDXwwrZR2xNY0c+KSeYkuTzJee38Y5NckuSaJJ8cu4Q9ye7t/LXt8gMmvDeStGsPSrLH2EySPfG2QpI6pN/kbF/gX6vqmiTPBY4G/qvPdV8P9A62fTfwnqo6ELgFOKEtPwG4paoeD7ynrSdJk+1vgf9M8vYkbwe+CqwcckySdI9+k7NPAVuTPB44FXgs8PFdrZRkEc04tQ+18wGeB5zZVlkFLG+nj2znaZcf3taXpElTVacBRwE3tq+j2jJJ6oR+k7OfV9XdNA3ae6vqD2l603blvcCbufcGj48GtrTbAlgPLGynFwI3ALTLb23rbyPJiUnWJFmzefPmPsOXpEY7ZvZg4JE0QzReMvacTUnqgn6Ts7uSHAMcC5zXls3d2QpJ/jtwU1Vd2ls8TtXqY9m9BVWnVNXSqlo6f/78XUcuSds6h6an/m7gJz0vSeqEfgfBHg/8HvCXVfXdJI8F/nkX6zwLeHGSFwEPoTlKfS8wL8lube/YImBjW389sB+wPsluwKOAH01obyRp1xZV1QuHHYQk7UhfPWdV9S3gj4DL2vnvVtW7drHOSVW1qKoOAF4OfLGqXglcCLykrXYczVEswLntPO3yL1bVfXrOJOkB+mqSg4YdhCTtSF/JWZJfB64APt/OH5zk3Pv5mX8EvDHJtTRjyk5ty08FHt2WvxF4y/3cviTtzLOBS5OsS3JlkrVJrhx2UJI0pt/Tmm8DDgUuAqiqK9pTm32pqot61r2u3db2dX5Kc4sOSRqkI4YdgCTtTL/J2d1Vdet2d7bwlKOkaaeqvtfehPZAmvGwY743pJAkaRv9JmffSPIKYE6SA4E/oLlxoyRNK0leQ3Nz7EU0wzUOA/6T5h6MkjR0/d5K43XAk4E7aW4+eyvwhkEFJUkD9Hrg6cD3qupXaZ6r6U0TJXVGXz1nVXUH8CftS5Kms59W1U+TkGT3qro6yeJhByVJY/q9WvP8JPN65vdIsnpwYUnSwKxv27OzgfOTnMO991uUpKHrd8zZXlW1ZWymqm5JsveAYpKkgamq32gn35bkQpobXn9+iCFJ0jb6Tc5+nmT/qvo+QJLH4NWakqa5qvrSsGOQpO31e0HAnwBfSfLRJB8FvgycNLiwJGkwkqwaZ5jGh4cZkyT16veCgM8nOYTmkvMAf1hVNw80MkkajKeMM0xjyTADkqRe/V4QcEJV3VxV51XVZ4Bbkrx1wLFJ0iA8qL0JLQBJ9qT/IR6SNHD9NkiHJ/lN4ASa52H+E+BYDe3U2ZdvYOXqdWzcMsqCeSOsWLaY5UsWDjss6W+B/0zyL+380cBfDTEeSdpGv6c1X5HkZcBa4A7gmKr6j4FGpmnt7Ms3cNJZaxm9aysAG7aMctJZawFM0DRUVXVakjXAr9IM0/iNqrpqyGFJ0j36Ss7aRza9HvgU8ETgVUkub29OK93HytXr7knMxozetZWVq9eZnGkoknylqp6d5Haaq83Ts6yq6pHDi06S7tXvac3PAK+tqgvSPP38jcDXaB7pJN3Hxi2jEyqXBq2qnt2+P2LYsUjSzvR7K41Dq+oCgGr8LbB8cGFpulswb2RC5dKgtbcBIsnrhx2LJO3MTpOzJG8GqKrbkhy93eLjBxaVpr0VyxYzMnfONmUjc+ewYpmPMNTQPK29gfZvt/c227P3NezgJGnMrnrOXt4zvf1NZ184ybFoBlm+ZCHvPOogFs4bIcDCeSO886iDHG+mYfogzWOafgm4dLvXmiHGJUnb2NWYs+xgerx5aRvLlyw0GVNnVNX7gPclObmqfn/Y8UjSjuyq56x2MD3evCR1nomZpK7bVc/ZU5PcRtNLNtJO084/ZKCRSdIk2tmtNGiudfJWGpI6YafJWVXN2dlySZouvJWGpOnC58lJmlWS7A78JnAAPW1gVf3FsGKSpF4mZ5Jmm3OAW2mu0rxzyLFI0n2YnEmabRZVlbcCktRZ/T4hQJJmiq8mOWjYQUjSjthzJmm2eTZwfJLraE5rhuZqzacMNyxJapicSZptjhh2AJK0MyZnkmab3xyn7NYkl1bVFVMejSRtxzFnkmabpcDvAQvb14nAc4F/TPLmIcYlSYA9Z5Jmn0cDh1TVjwGSvBU4E3gOze01/nqIsU1LZ1++gZWr17FxyygL5o2wYtlin6srPQAmZ5Jmm/2Bn/XM3wU8pqpGk3jfswk6+/INnHTWWkbv2grAhi2jnHTWWgATNOl+MjmTNNt8HLg4yTnt/K8Dpyd5GPCt4YU1Pa1cve6exGzM6F1bWbl6ncmZdD+ZnEmaVarq7Uk+BzyL5jYav1dVa9rFrxxeZNPTxi2jEyqXtGsDS86SPAT4MrB7+zlnVtVbkzwW+ASwJ3AZ8Kqq+ln7vLvTgKcBPwReVlXXDyo+SbNXm4yt2WVF7dKCeSNsGCcRWzBvZAjRSDPDIK/WvBN4XlU9FTgYeGGSw4B3A++pqgOBW4AT2vonALdU1eOB97T1JGlSJPlK+357ktt6XrcnuW3Y8U1XK5YtZmTunG3KRubOYcWyxUOKSJr+BpacVePH7ezc9lXA82iujAJYBSxvp49s52mXH54kg4pP0uxSVc9u3x9RVY/seT2iqh7Z73aS7JfkwiRXJflmkte35XsmOT/JNe37HoPaly5ZvmQh7zzqIBbOGyHAwnkjvPOogxxvJj0AAx1zlmQOzaXpjwfeD3wH2FJVd7dV1tPcZ4j2/QaAqro7ya00l7zfPMgYJc0OSZ4O3FBVP2jnj6W5Ie31wP+uqh/1uam7gTdV1WVJHgFqq0MrAAAPj0lEQVRcmuR84NXABVX1riRvAd4C/NEk70YnLV+y0GRMmkQDvQltVW2tqoOBRcChwBPHq9a+j9dLVtsXJDkxyZokazZv3jx5wUqa6f6B9hYaSZ4DvItmnOttwCn9bqSqNlXVZe307cBVNAeXvb3/vWcFJGlCpuQJAVW1BbgIOAyYl2Ssx24RsLGdXg/sB9AufxRwnyPZqjqlqpZW1dL58+cPOnRJM8ecnt6xlwGnVNWnqurPaHr3JyzJAcAS4BJgn6raBE0CB+w9Tn0PLiXt0sCSsyTzk8xrp0eA59McYV4IvKStdhwwdq+hc9t52uVfrKr79JxJ0v00p+fA8HDgiz3LJjzEI8nDgU8Bb6iqvi4o8OBSUj8GOeZsX2BVO+7sQcAZVXVekm8Bn0jyDuBy4NS2/qnAR5NcS9Nj9vIBxiZp9jkd+FKSm4FR4N8BkjweuHUiG0oylyYx+1hVndUW35hk36ralGRf4KbJC13SbDKw5KyqrqTp7t++/Dqa8Wfbl/8UOHpQ8Uia3arqL5NcQHPg+IWenvkHAa/rdzvtVeSnAldV1d/1LBrr/X8X254VkKQJ8QkBkmaNqrp4nLJvT3AzzwJeBaxNckVb9sc0SdkZSU4Avo8Hm5LuJ5MzSZqAqvoK419dDs1YNkl6QKbkak1JkiT1x+RMkiSpQ0zOJEmSOsTkTJIkqUNMziRJkjrE5EySJKlDTM4kSZI6xORMkiSpQ0zOJEmSOsTkTJIkqUNMziRJkjrE5EySJKlDTM4kSZI6xORMkiSpQ0zOJEmSOsTkTJIkqUNMziRJkjrE5EySJKlDTM4kSZI6xORMkiSpQ0zOJEmSOsTkTJIkqUNMziRJkjrE5EySJKlDTM4kSZI6xORMkiSpQ0zOJEmSOsTkTJIkqUNMziRJkjrE5EySJKlDTM4kSZI6xORMkiSpQwaWnCXZL8mFSa5K8s0kr2/L90xyfpJr2vc92vIkeV+Sa5NcmeSQQcUmSZLUVYPsObsbeFNVPRE4DHhtkicBbwEuqKoDgQvaeYAjgAPb14nAyQOMTZIkqZMGlpxV1aaquqydvh24ClgIHAmsaqutApa300cCp1XjYmBekn0HFZ8kSVIXTcmYsyQHAEuAS4B9qmoTNAkcsHdbbSFwQ89q69uy7bd1YpI1SdZs3rx5kGFLkiRNuYEnZ0keDnwKeENV3bazquOU1X0Kqk6pqqVVtXT+/PmTFaYkSVInDDQ5SzKXJjH7WFWd1RbfOHa6sn2/qS1fD+zXs/oiYOMg45MkSeqaQV6tGeBU4Kqq+rueRecCx7XTxwHn9JQf2161eRhw69jpT0mSpNlitwFu+1nAq4C1Sa5oy/4YeBdwRpITgO8DR7fLPgu8CLgWuAM4foCxSZIkddLAkrOq+grjjyMDOHyc+gW8dlDxSJIkTQc+IUCSJKlDTM4kSZI6xORMkiSpQwZ5QYAm4OzLN7By9To2bhllwbwRVixbzPIl97kHryRJmuFMzjrg7Ms3cNJZaxm9aysAG7aMctJZawFM0CRJmmU8rdkBK1evuycxGzN611ZWrl43pIgkSdKwmJx1wMYtoxMqlyRJM5fJWQcsmDcyoXJJkjRzmZx1wIplixmZO2ebspG5c1ixbPGQIpIkScPiBQEdMDbo36s1JUmSyVlHLF+y0GRMkiR5WlOSJKlLTM4kSZI6xORMkiYgyYeT3JTkGz1leyY5P8k17fsew4xR0vRmciZJE/MR4IXblb0FuKCqDgQuaOcl6X4xOZOkCaiqLwM/2q74SGBVO70KWD6lQUmaUUzOJOmB26eqNgG073uPVynJiUnWJFmzefPmKQ1Q0vRhciZJU6SqTqmqpVW1dP78+cMOR1JHmZxJ0gN3Y5J9Adr3m4Ycj6RpzORMkh64c4Hj2unjgHOGGIukac7kTJImIMnpwH8Ci5OsT3IC8C7g15JcA/xaOy9J94uPb5KkCaiqY3aw6PApDUTSjGXPmSRJUoeYnEmSJHWIyZkkSVKHmJxJkiR1iMmZJElSh5icSZIkdYjJmSRJUoeYnEmSJHWIyZkkSVKHmJxJkiR1iMmZJElSh5icSZIkdcjAkrMkH05yU5Jv9JTtmeT8JNe073u05UnyviTXJrkyySGDikuSJKnLBtlz9hHghduVvQW4oKoOBC5o5wGOAA5sXycCJw8wLkmSpM4aWHJWVV8GfrRd8ZHAqnZ6FbC8p/y0alwMzEuy76BikyRJ6qqpHnO2T1VtAmjf927LFwI39NRb35bdR5ITk6xJsmbz5s0DDVaSJGmqdeWCgIxTVuNVrKpTqmppVS2dP3/+gMOSJEmaWlOdnN04drqyfb+pLV8P7NdTbxGwcYpjkyRJGrqpTs7OBY5rp48DzukpP7a9avMw4Nax05+SJEmzyW6D2nCS04HnAnslWQ+8FXgXcEaSE4DvA0e31T8LvAi4FrgDOH5QcUmSJHXZwJKzqjpmB4sOH6duAa8dVCySJEnTRVcuCJAkSRImZ5IkSZ1iciZJktQhJmeSJEkdYnImSZLUISZnkiRJHWJyJkmS1CEmZ5IkSR1iciZJktQhJmeSJEkdYnImSZLUISZnkiRJHWJyJkmS1CEmZ5IkSR1iciZJktQhJmeSJEkdYnImSZLUISZnkiRJHWJyJkmS1CEmZ5IkSR1iciZJktQhJmeSJEkdYnImSZLUISZnkiRJHWJyJkmS1CEmZ5IkSR1iciZJktQhJmeSJEkdYnImSZLUISZnkiRJHWJyJkmS1CEmZ5IkSR1iciZJktQhnUrOkrwwybok1yZ5y7DjkaSJsA2TNBk6k5wlmQO8HzgCeBJwTJInDTcqSeqPbZikydKZ5Aw4FLi2qq6rqp8BnwCOHHJMktQv2zBJk6JLydlC4Iae+fVtmSRNB7ZhkibFbsMOoEfGKav7VEpOBE5sZ+9M8o2BRtUtewE3DzuIKTbb9tn93bXHDCKQSbDLNmyWt1/gv++Zzv3dtb7ary4lZ+uB/XrmFwEbt69UVacApwAkWVNVS6cmvOGbbfsLs2+f3d9pbZdt2Gxuv2D27bP7O7MNcn+7dFrza8CBSR6b5MHAy4FzhxyTJPXLNkzSpOhMz1lV3Z3kfwKrgTnAh6vqm0MOS5L6YhsmabJ0JjkDqKrPAp+dwCqnDCqWjppt+wuzb5/d32lsgm3YjNr3Ps22fXZ/Z7aB7W+q7jPmXpIkSUPSpTFnkiRJs960SM529UiUJLsn+WS7/JIkB0x9lJOnj/19dZLNSa5oX68ZRpyTJcmHk9y0o9sKpPG+9vu4MskhUx3jZOpjf5+b5Naev++fT3WMkynJfkkuTHJVkm8mef04dWbU37iX7dd9ltt+TWO2X1PUflVVp180A2u/AzwOeDDwdeBJ29X5H8AH2+mXA58cdtwD3t9XA/932LFO4j4/BzgE+MYOlr8I+BzNfaQOAy4ZdswD3t/nAucNO85J3N99gUPa6UcA3x7n3/SM+hv37Jftl+3XjPq3bfs1Ne3XdOg56+eRKEcCq9rpM4HDk4x3Q8jpYNY9Aqaqvgz8aCdVjgROq8bFwLwk+05NdJOvj/2dUapqU1Vd1k7fDlzFfe+cP6P+xj1sv2y/ZtS/bduvqWm/pkNy1s8jUe6pU1V3A7cCj56S6CZfv4+A+c22+/TMJPuNs3wmmY2PxXlmkq8n+VySJw87mMnSnrJbAlyy3aKZ+je2/bL9mqn/tnfG9usB/o2nQ3LWz2Od+nr00zTRz758Bjigqp4C/Bv3HnXPVDPp79uPy4DHVNVTgb8Hzh5yPJMiycOBTwFvqKrbtl88zioz4W9s+2X7NZP+vv2w/Wo8oL/xdEjO+nms0z11kuwGPIrp2+3azyNgflhVd7az/wg8bYpiG5a+Hu01U1TVbVX143b6s8DcJHsNOawHJMlcmobtY1V11jhVZurf2PbL9mum/tsel+0XMAl/4+mQnPXzSJRzgePa6ZcAX6x2lN40tMv93e5c9otpzoHPZOcCx7ZXxBwG3FpVm4Yd1KAk+YWxMUdJDqX5nf5wuFHdf+2+nApcVVV/t4NqM/VvbPtl+zVT/22Py/Zrcv7GnXpCwHhqB49ESfIXwJqqOpfmi/tokmtpjjhfPryIH5g+9/cPkrwYuJtmf189tIAnQZLTaa7w2SvJeuCtwFyAqvogzR3XXwRcC9wBHD+cSCdHH/v7EuD3k9wNjAIvn8b/WQM8C3gVsDbJFW3ZHwP7w8z8G4+x/bL9Yob927b9Aqag/fIJAZIkSR0yHU5rSpIkzRomZ5IkSR1iciZJktQhJmeSJEkdYnImSZLUISZnkiRJHWJypgcsyY/HKfu9JMfez+29OsmCnvkPJXnSA4lxB58zkuRLSeYkeW6S83ZQ7xNJDpzsz5c0fLZf6iKTMw1EVX2wqk67n6u/Grincauq11TVtyYlsG39NnBWVW3dRb2TgTcP4PMldZDtl4bN5EwDkeRtSf5Xkicm+a+e8gOSXNlOP6098rs0yeok+yZ5CbAU+FiSK9qjw4uSLG3X+XGSd7fr/FuSQ9vl17V3Hac9klyZ5GtJrkzyuzsI85XAOT3zD09yZpKrk3xs7BEkwL8Dz0/z3ENJM5ztl4bN5EwDVVVXAQ9O8ri26GXAGWkeJPv3wEuq6mnAh4G/rKozgTXAK6vq4Koa3W6TDwMuate5HXgH8GvAbwB/0dY5gebZZk8Hng78TpLH9m4kzXP/HldV1/cULwHeADwJeBzNYzuoqp/TPJbjqQ/oy5A0rdh+aVjMpDUVzgBeCryLpnF7GbAY+GXg/PYAbw7Qz4NifwZ8vp1eC9xZVXclWQsc0Ja/AHhKexQL8CjgQOC7PdvZC9iy3bb/q6rWA7TPUDsA+Eq77CaaUxWX9hGjpJnD9ktTzuRMU+GTwL8kOQuoqromyUHAN6vqmRPc1l09D9H9OXAnzUZ/3tNtH+B1VbV6J9sZBR6yXdmdPdNb2fb38ZB2HUmzi+2XppynNTVwVfUdmsbiz2gaOoB1wPwkzwRIMjfJk9tltwOPeAAfuRr4/fbUA0mekORh28V0CzAnyfYN3I48AfjmA4hJ0jRk+6VhsOdMk+GhSdb3zP/dOHU+CawEHgtQVT9ru+3fl+RRNP8W30vTgHwE+GCSUWCiR6YAH6Lp0r+sHRS7GVg+Tr0vAM8G/m1nG0uyDzBaVf2ctpA0vdh+qXNybw+rNLskWQK8sapetYt6fwjcVlWnTk1kkrRztl8zm6c1NWtV1eXAhUnm7KLqFmDVFIQkSX2x/ZrZ7DmTJEnqEHvOJEmSOsTkTJIkqUNMziRJkjrE5EySJKlDTM4kSZI65P8B7WrNGqGX6sIAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stats = []\n", "for obs, bkg in zip(obs_list, background_estimator.result):\n", " stats.append(ObservationStats.from_obs(obs, bkg))\n", "\n", "print(stats[1])\n", "\n", "obs_summary = ObservationSummary(stats)\n", "fig = plt.figure(figsize=(10, 6))\n", "ax1 = fig.add_subplot(121)\n", "\n", "obs_summary.plot_excess_vs_livetime(ax=ax1)\n", "ax2 = fig.add_subplot(122)\n", "obs_summary.plot_significance_vs_livetime(ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract spectrum\n", "\n", "Now, we're going to extract a spectrum using the [SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst) class. We provide the reconstructed energy binning we want to use. It is expected to be a Quantity with unit energy, i.e. an array with an energy unit. We use a utility function to create it. We also provide the true energy binning to use." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "e_reco = EnergyBounds.equal_log_spacing(0.1, 40, 40, unit=\"TeV\")\n", "e_true = EnergyBounds.equal_log_spacing(0.05, 100., 200, unit=\"TeV\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a [SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst) object that will do the extraction. The containment_correction parameter is there to allow for PSF leakage correction if one is working with full enclosure IRFs. We also compute a threshold energy and store the result in OGIP compliant files (pha, rmf, arf). This last step might be omitted though." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** Observation summary report ***\n", "Observation Id: 23523\n", "Livetime: 0.439 h\n", "On events: 125\n", "Off events: 98\n", "Alpha: 0.083\n", "Bkg events in On region: 8.17\n", "Excess: 116.83\n", "Excess / Background: 14.31\n", "Gamma rate: 0.07 1 / s\n", "Bkg rate: 0.01 1 / min\n", "Sigma: 18.74\n", "energy range: 0.88 TeV - 100.00 TeV\n" ] } ], "source": [ "ANALYSIS_DIR = \"crab_analysis\"\n", "\n", "extraction = SpectrumExtraction(\n", " obs_list=obs_list,\n", " bkg_estimate=background_estimator.result,\n", " containment_correction=False,\n", ")\n", "extraction.run()\n", "\n", "# Add a condition on correct energy range in case it is not set by default\n", "extraction.compute_energy_threshold(method_lo=\"area_max\", area_percent_lo=10.0)\n", "\n", "print(extraction.observations[0])\n", "# Write output in the form of OGIP files: PHA, ARF, RMF, BKG\n", "# extraction.run(obs_list=obs_list, bkg_estimate=background_estimator.result, outdir=ANALYSIS_DIR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Look at observations\n", "\n", "Now we will look at the files we just created. We will use the [SpectrumObservation](..\/api/gammapy.spectrum.SpectrumObservation.rst) object that are still in memory from the extraction step. Note, however, that you could also read them from disk if you have written them in the step above. The ``ANALYSIS_DIR`` folder contains 4 ``FITS`` files for each observation. These files are described in detail [here](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/ogip/index.html). In short, they correspond to the on vector, the off vector, the effectie area, and the energy dispersion." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# filename = ANALYSIS_DIR + '/ogip_data/pha_obs23523.fits'\n", "# obs = SpectrumObservation.read(filename)\n", "\n", "# Requires IPython widgets\n", "# _ = extraction.observations.peek()\n", "\n", "extraction.observations[0].peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit spectrum\n", "\n", "Now we'll fit a global model to the spectrum. First we do a joint likelihood fit to all observations. If you want to stack the observations see below. We will also produce a debug plot in order to show how the global fit matches one of the individual observations." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model = PowerLaw(\n", " index=2, amplitude=2e-11 * u.Unit(\"cm-2 s-1 TeV-1\"), reference=1 * u.TeV\n", ")\n", "\n", "joint_fit = SpectrumFit(obs_list=extraction.observations, model=model)\n", "joint_fit.run()\n", "joint_result = joint_fit.result" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Fit result info \n", "--------------- \n", "Model: PowerLaw\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max\n", "\t--------- --------- --------- --------------- --------- ---\n", "\t index 2.663e+00 7.100e-02 nan nan\n", "\tamplitude 2.912e-11 1.782e-12 1 / (cm2 s TeV) nan nan\n", "\treference 1.000e+00 0.000e+00 TeV 0.000e+00 nan\n", "\n", "Covariance: \n", "\n", "\t name index amplitude reference\n", "\t--------- --------- --------- ---------\n", "\t index 5.042e-03 7.189e-14 0.000e+00\n", "\tamplitude 7.189e-14 3.176e-24 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 \n", "\n", "Statistic: 42.226 (wstat)\n", "Fit Range: [ 0.87992254 100. ] TeV\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax0, ax1 = joint_result[0].plot(figsize=(8, 8))\n", "ax0.set_ylim(0, 20)\n", "print(joint_result[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute Flux Points\n", "\n", "To round up out analysis we can compute flux points by fitting the norm of the global model in energy bands. We'll use a fixed energy binning for now." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SpectrumEnergyGroups:\n", "energy_group_idx bin_idx_min bin_idx_max bin_type energy_min energy_max \n", " TeV TeV \n", "---------------- ----------- ----------- --------- -------------- --------------\n", " 0 0 26 underflow 0.01 0.316227766017\n", " 1 27 36 normal 0.316227766017 1.13646366639\n", " 2 37 44 normal 1.13646366639 3.16227766017\n", " 3 45 53 normal 3.16227766017 10.0\n", " 4 54 62 normal 10.0 31.6227766017\n", " 5 63 71 overflow 31.6227766017 100.0\n", "\n" ] } ], "source": [ "# Define energy binning\n", "ebounds = [0.3, 1.1, 3, 10.1, 30] * u.TeV\n", "\n", "stacked_obs = extraction.observations.stack()\n", "\n", "seg = SpectrumEnergyGroupMaker(obs=stacked_obs)\n", "seg.compute_groups_fixed(ebounds=ebounds)\n", "\n", "print(seg.groups)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "fpe = FluxPointEstimator(\n", " obs=stacked_obs, groups=seg.groups, model=joint_result[0].model\n", ")\n", "fpe.compute_points()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=4\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
e_refe_mine_maxdndednde_errdnde_ulis_ulsqrt_tsdnde_errpdnde_errn
TeVTeVTeV1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)
float64float64float64float64float64float64boolfloat64float64float64
0.5994842503190.3162277660171.136463666391.05447669983e-109.39217622316e-121.25252134295e-10False21.6226349672nannan
1.895735652411.136463666393.162277660175.7321929928e-124.02988917232e-136.58095470908e-12False27.2776214305nannan
5.62341325193.1622776601710.02.86912523535e-133.80109405297e-143.68795252279e-13False14.1556677919nannan
17.782794100410.031.62277660178.18648343536e-153.32077285631e-151.6435757112e-14False4.27104998385nannan
" ], "text/plain": [ "\n", " e_ref e_min e_max ... dnde_errp dnde_errn \n", " TeV TeV TeV ... 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n", " float64 float64 float64 ... float64 float64 \n", "-------------- -------------- ------------- ... --------------- ---------------\n", "0.599484250319 0.316227766017 1.13646366639 ... nan nan\n", " 1.89573565241 1.13646366639 3.16227766017 ... nan nan\n", " 5.6234132519 3.16227766017 10.0 ... nan nan\n", " 17.7827941004 10.0 31.6227766017 ... nan nan" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Kkqp6qKpOrarXdj+nVtVDvfZLcnaSu5LcMKv9sCS3JNmS5MQex95aVcfMaru5qo4D/oDOTQCSpCHp96GNBya5Msl3k2yd/vSx6znAYbP62gk4HXglsC/wxiT7JvmNJJfM+jx9gZpeA1wDXNXP7yBJake/YyQbgXfSeW/7I/12XlVXJ1k9q3l/YEtVbQVIch5weFWtB/p+onBVXQRclORS4LP97idJGqx+g+T+qrpsQMdcAdwxY3kKeNF8GyfZHfgAsF+Sk6pqfZKDgNcBjwe+NM9+xwLHAqxatWowlUuSfkW/QfLVJB8CPs+MhzVu58z2uZ4aXPNtXFX3AMfNavsa8LWFDlJVG4ANABMTE/P2L0lqpt8gmT5jmDmwXcDvbMcxp4C9ZiyvBO7cjn56SrIOWLdmzZo2upck0f9DGw8e4DE3AWuT7A38ADgSeNMA+39UVV0MXDwxMfG2NvqXJPV+aON/WWh9VX24x/7nAgcBeySZAk6uqo1JjgeuAHYCzq6qGxdVtSRp2eh1RvLk7p/PAX4LuKi7vA64ulfnVfXGedq/xDyD5IPkpS1Jal+qeo9DJ/ky8Pqq+n/d5ScDn6uqwxbec3mYmJioycnJYZchSSMlyeaq6jnpu99HpKwCHp6x/DCwejvqWlJJ1iXZcP/99w+7FEkaW/0Gyd8C/5TkvyU5mc67SD7VXlmDUVUXV9Wxu+2227BLkaSx1e9dWx9Ichnw0m7TUVX1jfbKkiSNin7nkUxPPvTVupKkX9Lvpa2R5BiJJLVvrIPEMRJJat9YB4kkqX1jHSRe2pKk9o11kHhpS5LaN9ZBIklqn0EiSWrEIJEkNTLWQeJguyS1b6yDxMF2SWrfWAeJJKl9BokkqRGDRJLUiEEiSWpkrIPEu7YkqX1jHSTetSVJ7RvrIJEktc8gkSQ1YpBIkhoxSCRJjRgkkqRGDBJJUiNjHSTOI5Gk9o11kDiPRJLaN9ZBIklqn0EiSWrEIJEkNWKQSJIaMUgkSY0YJJKkRgwSSVIjBokkqRGDRJLUyLIPkiT7JNmY5MJZ7bsm2Zzk1cOqTZLUcpAkOTvJXUlumNV+WJJbkmxJcuJCfVTV1qo6Zo5VfwFcMMh6JUmL99iW+z8H+Bjw6emGJDsBpwOHAlPApiQXATsB62ftf3RV3TW70yQvB24CdmmnbElSv1oNkqq6OsnqWc37A1uqaitAkvOAw6tqPdDvZaqDgV2BfYEHk3ypqn4+mKolSYsxjDGSFcAdM5anum1zSrJ7kjOA/ZKcBFBV76qqPwE+C5w1V4gkOTbJZJLJbdu2DfY3kCQ9ahhBkjnaar6Nq+qeqjquqp7VPWuZue6cqrpknv02VNVEVU3sueeeDUuWpIWdeuV3h13C0AwjSKaAvWYsrwTubONAvthK0lL56FW3DruEoWl7sH0um4C1SfYGfgAcCbypjQNV1cXAxRMTE29ro39JS++IM68ddgnzWo61nf/2A1o/RqtBkuRc4CBgjyRTwMlVtTHJ8cAVdO7UOruqbmyzDklqw9R9D/CDHz306PJ1t90LwIqn7sLKpz1xWGUtuVTNOzwx8pKsA9atWbPmbbfeuuOedkpq3+oTL+X2U1417DIGKsnmqprotd2yn9nehO9sl6T2jXWQONguaamccMjaYZcwNGN9aWvaxMRETU5ODrsMSRopXtqSJC0Jg0SS1MhYB4ljJJLUvrEOEu/akqT2jXWQSJLaN4xHpCyZ6QmJwP9NspgZibsBg7oe1qSvxe7b7/b9bNdrm4XW7wHc3Ucdy8kg/50v5bH8fo2OpfqODfI4/d3TXFV+Zn2ADcuhr8Xu2+/2/WzXa5uF1gOTw/53OMx/50t5LL9fo/NZqu/YML5fXtqa28XLpK/F7tvv9v1s12ubQf4zWg6W8vfx+7Xjfb9g6X6nJf9+7RATErW0kkxWH5OYpO3h92v58YxEbdgw7AI01vx+LTOekUiSGvGMRJLUiEEiSWrEIJEkNWKQqFVJdk3yqSRnJXnzsOvR+EmyT5KNSS4cdi07KoNEi5bk7CR3JblhVvthSW5JsiXJid3m1wEXVtXbgNcsebEaSYv5jlXV1qo6ZjiVCgwSbZ9zgMNmNiTZCTgdeCWwL/DGJPsCK4E7ups9soQ1arSdQ//fMQ2ZQaJFq6qrgXtnNe8PbOn+7fBh4DzgcGCKTpiA3zf1aZHfMQ2Z/2FrUFbwizMP6ATICuDzwOuTfJzxfOyFls6c37Ekuyc5A9gvyUnDKW3HNtZP/9WSyhxtVVU/Bo5a6mI0lub7jt0DHLfUxegXPCPRoEwBe81YXgncOaRaNJ78ji1TBokGZROwNsneSXYGjgQuGnJNGi9+x5Ypg0SLluRc4FrgOUmmkhxTVT8DjgeuAG4GLqiqG4dZp0aX37HR4kMbJUmNeEYiSWrEIJEkNWKQSJIaMUgkSY0YJJKkRgwSSVIjBol2eEkeSfLNGZ8Te++1NJJc2H3fxnXd2r6fZNuMWlfPs9/7k7xvVttEkm93f74qyW7t/wbaETiPRDu8JP9aVU8acJ+P7U6ga9LHc4H3V9VrZ7S9FZioquP72PcLVfXsGW3/A7inqtYnOQbYo6r+e5MaJfCMRJpXktuTvCfJ9Um+k+TXuu27dl+8tCnJN5Ic3m1/a5LPJbkY+HKSxyT5myQ3JrkkyZeS/H6SQ5J8YcZxDk3y+TlKeDPwxT7qfGWSa7t1np9k1+6M74eSvLC7TYA30Hn0Ot1+39Tkn480zSCR4AmzLm0dMWPd3VX1AuDjwH/ttr0L+EpV/RZwMPChJLt21x0A/GFV/Q6dt0OuBn4D+M/ddQBfAX49yZ7d5aOAT85R14HA5oUKT/J04ETgkG6d3wZO6K4+l87zqKb7urOqbgOoqruBJyd56kL9S/3wMfISPFhVz59n3fSZwmY6wQDwCuA1SaaDZRdgVffnK6tq+oVMLwE+V1U/B/4lyVeh89zzJH8LvCXJJ+kEzH+a49jPALb1qP236bwt8B86Jx3sDFzTXXcu8PUkf04nUM6dte+27jF+1OMY0oIMEmlhP+n++Qi/+O8lwOur6paZGyZ5EfDjmU0L9PtJOi/6eohO2Mw1nvIgnZBaSIDLq+o/zl5RVbcnuRN4KfBa4IWzNtmlewypES9tSYt3BfDH3XEHkuw3z3bX0Hk75GOS/FvgoOkVVXUnnXdpvJvO+8nncjOwpkct/wC8LMk+3Vp2TbJ2xvpzgdOAm6vqX6YbkzwG2INffuOgtF0MEulXx0hO6bH9+4DHAd9OckN3eS5/T+dlTDcAZwLXAffPWP8Z4I6qumme/S9lRvjMpap+CBwDnJ/kW3SC5dkzNrkAeB6/GGSftj9wTVU9slD/Uj+8/VdqUZInVdW/Jtkd+CfgwOkzgyQfA75RVRvn2fcJwFe7+wz0f/hJTqfzPo+vD7Jf7ZgcI5HadUn3zqidgffNCJHNdMZT/nS+HavqwSQnAyuA7w+4rm8YIhoUz0gkSY04RiJJasQgkSQ1YpBIkhoxSCRJjRgkkqRGDBJJUiP/H8S+bwp7mtH8AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fpe.flux_points.plot()\n", "fpe.flux_points.table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final plot with the best fit model and the flux points can be quickly made like this" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.4, 50)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectrum_result = SpectrumResult(\n", " points=fpe.flux_points, model=joint_result[0].model\n", ")\n", "ax0, ax1 = spectrum_result.plot(\n", " energy_range=joint_fit.result[0].fit_range,\n", " energy_power=2,\n", " flux_unit=\"erg-1 cm-2 s-1\",\n", " fig_kwargs=dict(figsize=(8, 8)),\n", " point_kwargs=dict(color=\"red\"),\n", ")\n", "\n", "ax0.set_xlim(0.4, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stack observations\n", "\n", "And alternative approach to fitting the spectrum is stacking all observations first and the fitting a model to the stacked observation. This works as follows. A comparison to the joint likelihood fit is also printed." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "FitResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tnfev : 58\n", "\ttotal stat : 30.31\n", "\tmessage : Optimization terminated successfully." ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stacked_obs = extraction.observations.stack()\n", "\n", "stacked_fit = SpectrumFit(obs_list=stacked_obs, model=model)\n", "stacked_fit.run()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Fit result info \n", "--------------- \n", "Model: PowerLaw\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max\n", "\t--------- --------- --------- --------------- --------- ---\n", "\t index 2.667e+00 7.147e-02 nan nan\n", "\tamplitude 2.909e-11 1.784e-12 1 / (cm2 s TeV) nan nan\n", "\treference 1.000e+00 0.000e+00 TeV 0.000e+00 nan\n", "\n", "Covariance: \n", "\n", "\t name index amplitude reference\n", "\t--------- --------- --------- ---------\n", "\t index 5.108e-03 7.241e-14 0.000e+00\n", "\tamplitude 7.241e-14 3.183e-24 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 \n", "\n", "Statistic: 30.311 (wstat)\n", "Fit Range: [ 0.68129207 100. ] TeV\n", "\n" ] } ], "source": [ "stacked_result = stacked_fit.result\n", "print(stacked_result[0])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " method index index_err amplitude amplitude_err \n", " 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n", "------- ----- --------- --------------- ---------------\n", "stacked 2.67 0.0715 2.91e-11 1.78e-12\n", " joint 2.66 0.071 2.91e-11 1.78e-12\n" ] } ], "source": [ "stacked_table = stacked_result[0].to_table(format=\".3g\")\n", "stacked_table[\"method\"] = \"stacked\"\n", "joint_table = joint_result[0].to_table(format=\".3g\")\n", "joint_table[\"method\"] = \"joint\"\n", "total_table = vstack_table([stacked_table, joint_table])\n", "print(\n", " total_table[\"method\", \"index\", \"index_err\", \"amplitude\", \"amplitude_err\"]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "Some things we might do:\n", "\n", "- Fit a different spectral model (ECPL or CPL or ...)\n", "- Use different method or parameters to compute the flux points\n", "- Do a chi^2 fit to the flux points and compare\n", "\n", "TODO: give pointers how to do this (and maybe write a notebook with solutions)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# Start exercises here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What next?\n", "\n", "In this tutorial we learned how to extract counts spectra from an event list and generate the corresponding IRFs. Then we fitted a model to the observations and also computed flux points.\n", "\n", "Here's some suggestions where to go next:\n", "\n", "* if you want think this is way too complicated and just want to run a quick analysis check out [this notebook](spectrum_pipe.ipynb)\n", "* if you interested in available fit statistics checkout [gammapy.stats](..\/stats/index.rst)" ] } ], "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 }