{ "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_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](http://docs.gammapy.org/dev/spectrum/index.html) 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](http://docs.gammapy.org/dev/api/gammapy.data.DataStore.html)\n", "* [gammapy.data.DataStoreObservation](http://docs.gammapy.org/dev/api/gammapy.data.DataStoreObservation.html)\n", "* [gammapy.data.ObservationStats](http://docs.gammapy.org/dev/api/gammapy.data.ObservationStats.html)\n", "* [gammapy.data.ObservationSummary](http://docs.gammapy.org/dev/api/gammapy.data.ObservationSummary.html)\n", "\n", "To extract the 1-dim spectral information:\n", "\n", "* [gammapy.spectrum.SpectrumObservation](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumObservation.html)\n", "* [gammapy.spectrum.SpectrumExtraction](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumExtraction.html)\n", "* [gammapy.background.ReflectedRegionsBackgroundEstimator](http://docs.gammapy.org/dev/api/gammapy.background.ReflectedRegionsBackgroundEstimator.html)\n", "\n", "\n", "For the global fit (using Sherpa and WSTAT in the background):\n", "\n", "* [gammapy.spectrum.SpectrumFit](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumFit.html)\n", "* [gammapy.spectrum.models.PowerLaw](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.PowerLaw.html)\n", "* [gammapy.spectrum.models.ExponentialCutoffPowerLaw](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.ExponentialCutoffPowerLaw.html)\n", "* [gammapy.spectrum.models.LogParabola](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.LogParabola.html)\n", "\n", "To compute flux points (a.k.a. \"SED\" = \"spectral energy distribution\")\n", "\n", "* [gammapy.spectrum.SpectrumResult](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumResult.html)\n", "* [gammapy.spectrum.FluxPoints](http://docs.gammapy.org/dev/api/gammapy.spectrum.FluxPoints.html)\n", "* [gammapy.spectrum.SpectrumEnergyGroupMaker](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumEnergyGroupMaker.html)\n", "* [gammapy.spectrum.FluxPointEstimator](http://docs.gammapy.org/dev/api/gammapy.spectrum.FluxPointEstimator.html)\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: 0.7.dev5129\n", "numpy: 1.13.3\n", "astropy 3.0.dev20290\n", "regions 0.2\n", "sherpa 4.9.1+12.g3626715\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 SpectrumExtraction, SpectrumObservation, SpectrumFit, SpectrumResult\n", "from gammapy.spectrum.models import PowerLaw, ExponentialCutoffPowerLaw, LogParabola\n", "from gammapy.spectrum import FluxPoints, SpectrumEnergyGroupMaker, FluxPointEstimator\n", "from gammapy.image import SkyImage" ] }, { "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", "logging.basicConfig()\n", "log = logging.getLogger('gammapy.spectrum')\n", "log.setLevel(logging.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": [ "DATA_DIR = '$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2'\n", "\n", "datastore = DataStore.from_dir(DATA_DIR)\n", "obs_ids = [23523, 23526, 23559, 23592]\n", "\n", "obs_list = datastore.obs_list(obs_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Target Region\n", "\n", "The next step is to define a signal extraction region, also known as on region. In the simplest case this is just a [CircleSkyRegion](http://astropy-regions.readthedocs.io/en/latest/api/regions.CircleSkyRegion.html#regions.CircleSkyRegion), but here we will use the ``Target`` class in gammapy that is useful for book-keeping if you run several analysis in a script." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "target_position = SkyCoord(ra=83.63, dec=22.01, unit='deg', frame='icrs')\n", "on_region_radius = Angle('0.11 deg')\n", "on_region = CircleSkyRegion(center=target_position, radius=on_region_radius)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load exclusion mask\n", "\n", "Most analysis will require a mask to exclude regions with possible gamma-ray signal from the background estimation procedure. For simplicity, we will use a pre-cooked exclusion mask from gammapy-extra which includes (or rather excludes) all source listed in the [TeVCat](http://tevcat.uchicago.edu/) and cutout only the region around the crab.\n", "\n", "TODO: Change to [gamma-cat](https://gammapy.github.io/gamma-cat/)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "EXCLUSION_FILE = '$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits'\n", "\n", "allsky_mask = SkyImage.read(EXCLUSION_FILE)\n", "exclusion_mask = allsky_mask.cutout(\n", " position=on_region.center,\n", " size=Angle('6 deg'),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate background\n", "\n", "Next we will manually perform a background estimate by placing [reflected regions](http://docs.gammapy.org/dev/background/reflected.html) around the pointing position and looking at the source statistics. This will result in a [gammapy.background.BackgroundEstimate](http://docs.gammapy.org/dev/api/gammapy.background.BackgroundEstimate.html) that serves as input for other classes in gammapy." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BackgroundEstimate\n", " Method: Reflected Regions\n", " on region\n", " Region: CircleSkyRegion\n", "center: \n", "radius: 0.11 deg\n", " EventList info:\n", "- Number of events: 172\n", "- Median energy: 1.2035582065582275 TeV\n", "- Median azimuth: 0.0\n", "- Median altitude: 0.0\n", "\n", " off region\n", " [, radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>, , radius=0.11040258105103296 deg)>]\n", " EventList info:\n", "- Number of events: 51\n", "- Median energy: 1.2129952907562256 TeV\n", "- Median azimuth: 0.0\n", "- Median altitude: 0.0\n", "\n" ] } ], "source": [ "background_estimator = ReflectedRegionsBackgroundEstimator(\n", " obs_list=obs_list,\n", " on_region=on_region,\n", " exclusion_mask = exclusion_mask)\n", "\n", "background_estimator.run()\n", "print(background_estimator.result[0])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(,\n", " ,\n", " None)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YPpWonSCcimJ/7DiOdgmnojTYcU4bMp7vTb4YyzAxlMGNE84DGlcHdCzuJB7DZHHJ7E6V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\njZN3yqqWB9+xG6bBMWfP6CXruo8FPA0cQWkz1cLAUOAlYAbQ2vnDu40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OvQrJ7xZzL/H2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a5/lz7wH3OdFnuUjZsZ0yZHn3YYS2++n6ceeIbMUHrKM3ERZdNthln2aIbFG/WxzatGCHwlN5pA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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](http://docs.gammapy.org/dev/api/gammapy.data.ObservationSummary.html#gammapy.data.ObservationSummary) class." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** Observation summary report ***\n", "Observation Id: 23526\n", "Livetime: 0.437 h\n", "On events: 168\n", "Off events: 58\n", "Alpha: 0.091\n", "Bkg events in On region: 5.27\n", "Excess: 162.73\n", "Excess / Background: 30.86\n", "Gamma rate: 6.41 1 / min\n", "Bkg rate: 0.20 1 / min\n", "Sigma: 24.24\n", "\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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": { "collapsed": true }, "source": [ "## Extract spectrum\n", "\n", "Now, we're going to extract a spectrum using the [SpectrumExtraction](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumExtraction.html) 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": 11, "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](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumExtraction.html) 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": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/deil/Library/Python/3.6/lib/python/site-packages/astropy/units/quantity.py:634: RuntimeWarning: invalid value encountered in true_divide\n", " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "*** Observation summary report ***\n", "Observation Id: 23523\n", "Livetime: 0.439 h\n", "On events: 139\n", "Off events: 44\n", "Alpha: 0.091\n", "Bkg events in On region: 4.00\n", "Excess: 135.00\n", "Excess / Background: 33.75\n", "Gamma rate: 0.13 1 / min\n", "Bkg rate: 0.00 1 / min\n", "Sigma: 22.28\n", "energy range: 0.68 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](http://docs.gammapy.org/dev/api/gammapy.spectrum.SpectrumObservation.html) 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 at https://gamma-astro-data-formats.readthedocs.io/en/latest/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": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/local/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/colors.py:1238: UserWarning: Power-law scaling on negative values is ill-defined, clamping to 0.\n", " warnings.warn(\"Power-law scaling on negative values is \"\n", "/opt/local/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/colors.py:1199: RuntimeWarning: invalid value encountered in power\n", " np.power(resdat, gamma, resdat)\n" ] }, { "data": { "image/png": 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0HbgXmAvIzPYzs3srcX6epM8kvRa+7yjpI0kzJT1bvOq+pDrh+1nh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by5qd89c68L94XQ2fSUtK+ZPQ2QfinL/g0U+wUmhBDaTbWFyUeZWZ/c4+psh5mNAd7Efwm4Pfee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\nEWb7F+MBbTGRmiJ0Z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"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": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:161: RuntimeWarning: divide by zero encountered in log\n", " term2_ = - n_on * np.log(mu_sig + alpha * mu_bkg)\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:166: RuntimeWarning: divide by zero encountered in log\n", " term3_ = - n_off * np.log(mu_bkg)\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:203: RuntimeWarning: divide by zero encountered in log\n", " term1 = - n_on * (1 - np.log(n_on))\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:204: RuntimeWarning: divide by zero encountered in log\n", " term2 = - n_off * (1 - np.log(n_off))\n" ] } ], "source": [ "model = PowerLaw(\n", " index=2 * u.Unit(''),\n", " amplitude=2e-11 * u.Unit('cm-2 s-1 TeV-1'),\n", " reference=1 * u.TeV,\n", ")\n", "\n", "joint_fit = SpectrumFit(obs_list=extraction.observations, model=model)\n", "\n", "joint_fit.fit()\n", "joint_fit.est_errors()\n", "#fit.run(outdir = ANALYSIS_DIR)\n", "\n", "joint_result = joint_fit.result" ] }, { "cell_type": "code", "execution_count": 15, "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 frozen\n", "\t--------- --------- --------- --------------- --- --- ------\n", "\t index 2.178e+00 4.441e-02 nan nan False\n", "\tamplitude 2.012e-11 1.020e-12 1 / (cm2 s TeV) nan nan False\n", "\treference 1.000e+00 0.000e+00 TeV nan nan True\n", "\n", "Covariance: \n", "\n", "\tname/name index amplitude\n", "\t--------- -------- ---------\n", "\t index 0.00197 2.21e-14\n", "\tamplitude 2.21e-14 1.04e-24 \n", "\n", "Statistic: 34.777 (wstat)\n", "Fit Range: [ 0.68129207 100. ] TeV\n", "\n" ] }, { "data": { "image/png": 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cr8ZsP3mWGK++GJWReYeldy+EEKLqOe3WO631Q2Us/rScbZOBO4qfHwPaOyuu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\nIUScJlyjYIBx0VFnMrFuHBPqxzG5vonmhiYas3X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"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": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SpectrumEnergyGroups:\n", "\n", "Info including underflow- and overflow bins:\n", "Number of groups: 6\n", "Bin range: (0, 71)\n", "Energy range: EnergyRange(min=0.01 TeV, max=100.0 TeV)\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/deil/code/gammapy/gammapy/stats/poisson.py:383: RuntimeWarning: divide by zero encountered in double_scalars\n", " temp = (alpha + 1) / (n_on + n_off)\n", "/Users/deil/code/gammapy/gammapy/stats/poisson.py:385: RuntimeWarning: divide by zero encountered in log\n", " m = n_off * log(n_off * temp)\n", "/Users/deil/code/gammapy/gammapy/stats/poisson.py:384: RuntimeWarning: divide by zero encountered in log\n", " l = n_on * log(n_on * temp / alpha)\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_range_safe()\n", "seg.compute_groups_fixed(ebounds=ebounds)\n", "\n", "print(seg.groups)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:161: RuntimeWarning: divide by zero encountered in log\n", " term2_ = - n_on * np.log(mu_sig + alpha * mu_bkg)\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:166: RuntimeWarning: divide by zero encountered in log\n", " term3_ = - n_off * np.log(mu_bkg)\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:203: RuntimeWarning: divide by zero encountered in log\n", " term1 = - n_on * (1 - np.log(n_on))\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:204: RuntimeWarning: divide by zero encountered in log\n", " term2 = - n_off * (1 - np.log(n_off))\n" ] } ], "source": [ "fpe = FluxPointEstimator(\n", " obs=stacked_obs,\n", " groups=seg.groups,\n", " model=joint_result[0].model,\n", ")\n", "fpe.compute_points()" ] }, { "cell_type": "code", "execution_count": 18, "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
0.6812920690580.4641588833611.03.7087890856e-112.98925024997e-124.33476015219e-11False24.86675438242.9833525432e-122.94494024816e-12
1.66810053721.02.782559402218.1570462151e-125.40511435651e-139.29533532901e-12False32.6952521875.65789940379e-135.12020688833e-13
5.27499706372.7825594022110.05.95234607581e-135.74566970764e-147.18471748878e-13False21.82821927485.8807378136e-145.52229297328e-14
16.68100537210.027.82559402213.18839971318e-147.40994324674e-154.89510875816e-14False8.580470383847.9064762535e-156.85090500174e-15
" ], "text/plain": [ "\n", " e_ref e_min ... dnde_errp dnde_errn \n", " TeV TeV ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) \n", " float64 float64 ... float64 float64 \n", "-------------- -------------- ... ----------------- -----------------\n", "0.681292069058 0.464158883361 ... 2.9833525432e-12 2.94494024816e-12\n", " 1.6681005372 1.0 ... 5.65789940379e-13 5.12020688833e-13\n", " 5.2749970637 2.78255940221 ... 5.8807378136e-14 5.52229297328e-14\n", " 16.681005372 10.0 ... 7.9064762535e-15 6.85090500174e-15" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"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": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.4, 50)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vrLVvzEukFULJhMiFW60tmJiYIB6PF/x+k8tJHhhJt/EemU/gNHDNNi8DfX72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\nuSDWWo7PpQeP3T8SZTaaosa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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectrum_result = SpectrumResult(\n", " points=fpe.flux_points,\n", " 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, flux_unit='erg-1 cm-2 s-1',\n", " fig_kwargs=dict(figsize=(8,8)),\n", " point_kwargs=dict(color='navy')\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": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:161: RuntimeWarning: divide by zero encountered in log\n", " term2_ = - n_on * np.log(mu_sig + alpha * mu_bkg)\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:166: RuntimeWarning: divide by zero encountered in log\n", " term3_ = - n_off * np.log(mu_bkg)\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:203: RuntimeWarning: divide by zero encountered in log\n", " term1 = - n_on * (1 - np.log(n_on))\n", "/Users/deil/code/gammapy/gammapy/stats/fit_statistics.py:204: RuntimeWarning: divide by zero encountered in log\n", " term2 = - n_off * (1 - np.log(n_off))\n" ] }, { "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 frozen\n", "\t--------- --------- --------- --------------- --- --- ------\n", "\t index 2.177e+00 4.432e-02 nan nan False\n", "\tamplitude 2.012e-11 1.020e-12 1 / (cm2 s TeV) nan nan False\n", "\treference 1.000e+00 0.000e+00 TeV nan nan True\n", "\n", "Covariance: \n", "\n", "\tname/name index amplitude\n", "\t--------- -------- ---------\n", "\t index 0.00196 2.21e-14\n", "\tamplitude 2.21e-14 1.04e-24 \n", "\n", "Statistic: 81.604 (wstat)\n", "Fit Range: [ 0.46415888 100. ] TeV\n", "\n", " method index index_err amplitude amplitude_err \n", " 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n", "------- ----- --------- --------------- ---------------\n", "stacked 2.18 0.0443 2.01e-11 1.02e-12\n", " joint 2.18 0.0444 2.01e-11 1.02e-12\n" ] } ], "source": [ "stacked_obs = extraction.observations.stack()\n", "\n", "stacked_fit = SpectrumFit(obs_list=stacked_obs, model=model)\n", "stacked_fit.fit()\n", "stacked_fit.est_errors()\n", "\n", "\n", "stacked_result = stacked_fit.result\n", "print(stacked_result[0])\n", "\n", "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(total_table['method', 'index', 'index_err', 'amplitude', 'amplitude_err'])" ] }, { "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": 21, "metadata": {}, "outputs": [], "source": [ "# Start exercises here" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "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](http://docs.gammapy.org/dev/stats/index.html)\n", "* if you want to simulate spectral look at [this tutorial](http://docs.gammapy.org/dev/spectrum/simulation.html)\n", "* if you want to compare your spectra to e.g. Fermi spectra published in catalogs have a look at [this](http://docs.gammapy.org/dev/spectrum/plotting_fermi_spectra.html)" ] } ], "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 }