{ "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/v0.12?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.\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.SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst)\n", "* [gammapy.background.ReflectedRegionsBackgroundEstimator](..\/api/gammapy.background.ReflectedRegionsBackgroundEstimator.rst)\n", "\n", "To perform the joint fit:\n", "\n", "* [gammapy.spectrum.SpectrumDatasetOnOff](..\/api/gammapy.spectrum.SpectrumDatasetOnOff.rst)\n", "* [gammapy.spectrum.SpectrumDatasetOnOffStacker](..\/api/gammapy.spectrum.SpectrumDatasetOnOffStacker.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.FluxPoints](..\/api/gammapy.spectrum.FluxPoints.rst)\n", "* [gammapy.spectrum.FluxPointsEstimator](..\/api/gammapy.spectrum.FluxPointsEstimator.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: 0.12\n", "numpy: 1.16.2\n", "astropy 3.1.1\n", "regions 0.3\n" ] } ], "source": [ "# Check package versions\n", "import gammapy\n", "import numpy as np\n", "import astropy\n", "import regions\n", "\n", "print(\"gammapy:\", gammapy.__version__)\n", "print(\"numpy:\", np.__version__)\n", "print(\"astropy\", astropy.__version__)\n", "print(\"regions\", regions.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import astropy.units as u\n", "from astropy.coordinates import SkyCoord, Angle\n", "from regions import CircleSkyRegion\n", "from gammapy.maps import Map\n", "from gammapy.utils.fitting import Fit\n", "from gammapy.data import ObservationStats, ObservationSummary, DataStore\n", "from gammapy.background import ReflectedRegionsBackgroundEstimator\n", "from gammapy.spectrum.models import PowerLaw\n", "from gammapy.spectrum import (\n", " SpectrumExtraction,\n", " SpectrumDatasetOnOff,\n", " SpectrumDatasetOnOffStacker,\n", " CrabSpectrum,\n", " FluxPointsEstimator,\n", " FluxPointsDataset,\n", ")" ] }, { "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": 4, "metadata": {}, "outputs": [], "source": [ "datastore = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1/\")\n", "obs_ids = [23523, 23526, 23559, 23592]\n", "observations = datastore.get_observations(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": 5, "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": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 7, "metadata": {}, "outputs": [], "source": [ "background_estimator = ReflectedRegionsBackgroundEstimator(\n", " observations=observations,\n", " on_region=on_region,\n", " exclusion_mask=exclusion_mask,\n", ")\n", "\n", "background_estimator.run()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "background_estimator.plot(add_legend=True);" ] }, { "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": 9, "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: 6.95 1 / min\n", "Bkg rate: 0.72 1 / min\n", "Sigma: 21.86\n", "\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stats = []\n", "for obs, bkg in zip(observations, background_estimator.result):\n", " stats.append(ObservationStats.from_observation(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 also provide the true energy binning to use." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "e_reco = np.logspace(-1, np.log10(40), 40) * u.TeV\n", "e_true = np.logspace(np.log10(0.05), 2, 200) * u.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": 11, "metadata": {}, "outputs": [], "source": [ "extraction = SpectrumExtraction(\n", " observations=observations,\n", " bkg_estimate=background_estimator.result,\n", " containment_correction=False,\n", " e_reco=e_reco,\n", " e_true=e_true,\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.8 s, sys: 32.3 ms, total: 1.83 s\n", "Wall time: 1.85 s\n" ] } ], "source": [ "%%time\n", "extraction.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can (optionally) compute the energy thresholds for the analysis, acoording to different methods. Here we choose the energy where the effective area drops below 10% of the maximum:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# 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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the datasets, we just extracted:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Requires IPython widgets\n", "# extraction.spectrum_observations.peek()\n", "\n", "extraction.spectrum_observations[0].peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally you can write the extrated datasets to disk using the OGIP format (PHA, ARF, RMF, BKG, see [here](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/ogip/index.html) for details):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# ANALYSIS_DIR = \"crab_analysis\"\n", "# extraction.write(outdir=ANALYSIS_DIR, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to read back the datasets from disk you can use:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# datasets = []\n", "# for obs_id in obs_ids:\n", "# filename = ANALYSIS_DIR + \"/ogip_data/pha_obs{}.fits\".format(obs_id)\n", "# datasets.append(SpectrumDatasetOnOff.from_ogip_files(filename))" ] }, { "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": 17, "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", "datasets_joint = extraction.spectrum_observations\n", "\n", "for dataset in datasets_joint:\n", " dataset.model = model\n", "\n", "fit_joint = Fit(datasets_joint)\n", "result_joint = fit_joint.run()\n", "\n", "# we make a copy here to compare it later\n", "model_best_joint = model.copy()\n", "model_best_joint.parameters.covariance = result_joint.parameters.covariance" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 47\n", "\ttotal stat : 113.58\n", "\n" ] } ], "source": [ "print(result_joint)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 25)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "ax_spectrum, ax_residual = datasets_joint[0].plot_fit()\n", "ax_spectrum.set_ylim(0, 25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute Flux Points\n", "\n", "To round up our 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": 20, "metadata": {}, "outputs": [], "source": [ "e_min, e_max = 0.7, 30\n", "e_edges = np.logspace(np.log10(e_min), np.log10(e_max), 11) * u.TeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create an instance of the `FluxPointsEstimator`, by passing the dataset and the energy binning:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "fpe = FluxPointsEstimator(datasets=datasets_joint, e_edges=e_edges)\n", "flux_points = fpe.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a the table of the resulting flux points:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
e_refe_mine_maxref_dnderef_fluxref_efluxref_e2dndenormloglikenorm_errcounts [4]norm_errpnorm_errnnorm_ulsqrt_tstsnorm_scan [11]dloglike_scan [11]dndednde_uldnde_errdnde_errpdnde_errn
TeVTeVTeV1 / (cm2 s TeV)1 / (cm2 s)TeV / (cm2 s)TeV / (cm2 s)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)
float64float64float64float64float64float64float64float64float64float64int64float64float64float64float64float64float64float64float64float64float64float64float64
0.8590.7371.0024.198e-111.120e-119.535e-123.099e-110.87814.1170.1180 .. 00.1230.1131.13413.903193.2950.200 .. 5.00082.99951168352825 .. 358.00905501914033.685e-114.760e-114.959e-125.165e-124.758e-12
1.2611.0021.5881.527e-119.087e-121.124e-112.429e-110.95013.1450.08831 .. 360.0910.0851.13720.656426.6580.200 .. 5.000171.90332333110035 .. 642.11689264819371.450e-111.737e-111.347e-121.394e-121.302e-12
1.8521.5882.1605.552e-123.193e-125.861e-121.905e-111.1669.8270.14727 .. 120.1530.1411.48414.824219.7580.200 .. 5.000109.57521292555477 .. 250.398546512042176.474e-128.239e-128.169e-138.499e-137.847e-13
2.5182.1602.9362.472e-121.933e-124.825e-121.568e-111.2309.2510.17610 .. 110.1840.1671.61313.446180.8070.200 .. 5.00092.00048036311878 .. 178.38996076818663.040e-123.989e-124.341e-134.547e-134.141e-13
3.6972.9364.6568.991e-131.569e-125.685e-121.229e-110.94414.6390.15917 .. 110.1680.1511.29710.654113.4980.200 .. 5.00060.11957248672836 .. 211.01492975742748.489e-131.166e-121.433e-131.512e-131.357e-13
5.4294.6566.3303.270e-135.512e-132.966e-129.637e-121.1498.5200.2719 .. 50.2920.2511.7748.12666.0240.200 .. 5.00037.34928358492572 .. 78.596705887476573.757e-135.801e-138.859e-149.533e-148.213e-14
7.9716.33010.0371.189e-134.473e-133.495e-127.556e-121.09612.5320.2835 .. 40.3080.2591.7587.17251.4400.200 .. 5.00035.53849334782444 .. 76.418262853901751.303e-132.091e-133.363e-143.663e-143.081e-14
11.70310.03713.6474.325e-141.572e-131.823e-125.924e-120.97610.5980.4490 .. 10.5110.3902.1313.66013.3960.200 .. 5.00016.653005609307925 .. 36.155554215391194.223e-149.216e-141.940e-142.211e-141.689e-14
17.18313.64721.6361.573e-141.275e-132.148e-124.645e-120.3287.1920.2981 .. 00.3780.3281.2530.9410.8850.200 .. 5.0007.405550778656064 .. 40.4902502003477955.159e-151.971e-144.683e-155.951e-155.159e-15
25.22921.63629.4195.721e-154.482e-141.121e-123.642e-120.0000.5410.0010 .. 00.2980.0001.1920.0020.0000.200 .. 5.0001.2117459554950087 .. 17.320097638362711.021e-206.819e-158.345e-181.705e-151.021e-20
" ], "text/plain": [ "\n", " e_ref e_min e_max ... dnde_err dnde_errp dnde_errn \n", " TeV TeV TeV ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n", "float64 float64 float64 ... float64 float64 float64 \n", "------- ------- ------- ... --------------- --------------- ---------------\n", " 0.859 0.737 1.002 ... 4.959e-12 5.165e-12 4.758e-12\n", " 1.261 1.002 1.588 ... 1.347e-12 1.394e-12 1.302e-12\n", " 1.852 1.588 2.160 ... 8.169e-13 8.499e-13 7.847e-13\n", " 2.518 2.160 2.936 ... 4.341e-13 4.547e-13 4.141e-13\n", " 3.697 2.936 4.656 ... 1.433e-13 1.512e-13 1.357e-13\n", " 5.429 4.656 6.330 ... 8.859e-14 9.533e-14 8.213e-14\n", " 7.971 6.330 10.037 ... 3.363e-14 3.663e-14 3.081e-14\n", " 11.703 10.037 13.647 ... 1.940e-14 2.211e-14 1.689e-14\n", " 17.183 13.647 21.636 ... 4.683e-15 5.951e-15 5.159e-15\n", " 25.229 21.636 29.419 ... 8.345e-18 1.705e-15 1.021e-20" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flux_points.table_formatted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we plot the flux points and their likelihood profiles. For the plotting of upper limits we choose a threshold of TS < 4." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "flux_points.table[\"is_ul\"] = flux_points.table[\"ts\"] < 4\n", "ax = flux_points.plot(\n", " energy_power=2, flux_unit=\"erg-1 cm-2 s-1\", color=\"darkorange\"\n", ")\n", "flux_points.to_sed_type(\"e2dnde\").plot_likelihood(ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final plot with the best fit model, flux points and residuals can be quickly made like this: " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "flux_points_dataset = FluxPointsDataset(\n", " data=flux_points, model=model_best_joint\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "flux_points_dataset.peek();" ] }, { "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. For this we first stack the individual datasets using the `SpectrumDatasetOnOffStacker` class:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "stacker = SpectrumDatasetOnOffStacker(datasets_joint)\n", "dataset_stacked = stacker.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again we set the model on the dataset we would like to fit (in this case it's only a singel one) and pass it to the `Fit` object:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "dataset_stacked.model = model\n", "stacked_fit = Fit([dataset_stacked])\n", "result_stacked = stacked_fit.run()\n", "\n", "# make a copy to compare later\n", "model_best_stacked = model.copy()\n", "model_best_stacked.parameters.covariance = result_stacked.parameters.covariance" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 26\n", "\ttotal stat : 28.85\n", "\n" ] } ], "source": [ "print(result_stacked)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=3\n", "
\n", "\n", "\n", "\n", "\n", "\n", "
namevalueerrorunitminmaxfrozen
str9float64float64str14float64float64bool
index2.633e+007.598e-02nannanFalse
amplitude2.814e-111.891e-12cm-2 s-1 TeV-1nannanFalse
reference1.000e+000.000e+00TeVnannanTrue
" ], "text/plain": [ "\n", " name value error unit min max frozen\n", " str9 float64 float64 str14 float64 float64 bool \n", "--------- --------- --------- -------------- ------- ------- ------\n", " index 2.633e+00 7.598e-02 nan nan False\n", "amplitude 2.814e-11 1.891e-12 cm-2 s-1 TeV-1 nan nan False\n", "reference 1.000e+00 0.000e+00 TeV nan nan True" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_best_joint.parameters.to_table()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=3\n", "
\n", "\n", "\n", "\n", "\n", "\n", "
namevalueerrorunitminmaxfrozen
str9float64float64str14float64float64bool
index2.634e+007.601e-02nannanFalse
amplitude2.814e-111.892e-12cm-2 s-1 TeV-1nannanFalse
reference1.000e+000.000e+00TeVnannanTrue
" ], "text/plain": [ "\n", " name value error unit min max frozen\n", " str9 float64 float64 str14 float64 float64 bool \n", "--------- --------- --------- -------------- ------- ------- ------\n", " index 2.634e+00 7.601e-02 nan nan False\n", "amplitude 2.814e-11 1.892e-12 cm-2 s-1 TeV-1 nan nan False\n", "reference 1.000e+00 0.000e+00 TeV nan nan True" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_best_stacked.parameters.to_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we compare the results of our stacked analysis to a previously published Crab Nebula Spectrum for reference. This is available in `gammapy.spectrum`." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_kwargs = {\n", " \"energy_range\": [0.1, 30] * u.TeV,\n", " \"energy_power\": 2,\n", " \"flux_unit\": \"erg-1 cm-2 s-1\",\n", "}\n", "\n", "# plot stacked model\n", "model_best_stacked.plot(**plot_kwargs, label=\"Stacked analysis result\")\n", "model_best_stacked.plot_error(**plot_kwargs)\n", "\n", "# plot joint model\n", "model_best_joint.plot(**plot_kwargs, label=\"Joint analysis result\", ls=\"--\")\n", "model_best_joint.plot_error(**plot_kwargs)\n", "\n", "CrabSpectrum().model.plot(**plot_kwargs, label=\"Crab reference\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "Now you have learned the basics of a spectral analysis with Gammapy. To practice you can continue with the following exercises:\n", "\n", "- Fit a different spectral model to the data. You could try e.g. an `ExponentialCutoffPowerLaw` or `LogParabola` model.\n", "- Compute flux points for the stacked dataset.\n", "- Create a `FluxPointsDataset` with the flux points you have computed for the stacked dataset and fit the flux points again with obe of the spectral models. How does the result compare to the best fit model, that was directly fitted to the counts data?" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }