{ "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/v0.13?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: 0.13\n", "numpy: 1.15.0\n", "astropy 3.2.1\n", "regions 0.4\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", " create_crab_spectral_model,\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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iDvAl4G8j4uLulLjRRm2jpLcA3wbOiYi7u1Tb+LqdWG2m9b9TnJP4FfAz4CPAQcD9wDLgPmDftO1WFGfUV1B8gXrif91225m2/+PUzgeB87tdfxVtLL3v8/TIIUmb/14/B6wBlpZeO3S7DSNfvjTczLL1wyGJmXWIA8PMsjkwzCybA8PMsjkwzCybA8PMsjkweoikt0r6uqTH0y3Q90g6bpz3zCnfXt3m/k6WtGNp/jJJ78x873xJiyey31ySfpB+zpF04gTef7KkXr0ArCscGD1CkoDrgTsjYreI2JfisvCdKtztycDrgRERH42I2lxFGhHNG7fmUAxnYBVzYPSOQ4HXIuLS5oKIWBkRX4bX/5f9vqQl6fWmuyBbbSPp02nwlmWSvijpBGAecHUa7GVLSbdLmpe2PzJ9xjJJ38tthKTDJP047evy5i3ckp6QdG76zOWS9kjLt5d0a1r+VUkrJc1O615MH/tF4OBU55kjew6SFkuan6Y/LOlRSXdQ3KBIaT/XSfpRer2+zkq6fampX3kv4JPAP7ZYPwOYnqZ/ExhM03NINz+12OYoitunZ6T5menn7cC80j5upwiR7YGfAo3y9iPqmQ8sHrFsenrf7mn+X4Ez0vQTwOlp+s+Ay9L0xRT3VwAcSTEuxOw0/+Jo+6LoGV1cml+ctvl1inuMtgc2B+5ubgd8HTgoTe8CPNztv/M6vvphPIwpSdIlFPclvBYR+1Hcxn+xpLnAOopxMkYaa5v3AVdExEsAsWHckLHsT3FoNJS5fdM7gKGIeDTNXwUspLihDGBR+nk/8Ptp+iDguLSfmyS9kLmv0bybYoSr5wAkXcMb/wzeWRz5AbCNpK0jYvVG7K/vODB6xwqKgVUAiIiFqWs+mBadCTwD7E1xqPnKKJ8x1jai+J87V7vbl9/XSnPAmHVs+Lc5kRGm1vLGw+3ppemx6t4EeE9EvDyB/U0ZPofRO26jGLbtE6VlM0rT2wJPR8R64CRg2iifMdY2twCnSJoBIGlmWr4a2HqUz7kHOERSY8T243kEmCPpN9L8ScAd47znLoqBZpB0BLDdKNuMrPMJYK6kTSTtDDQHE74PmC9plqTNgA+U3nMLcFpzJvXCbAQHRo+I4uD6WIov6pCkH1J06c9Om3wF+BNJ91J0s9eM8jGjbhMRNwE3AIOSlrJhGLwrgUubJz1LtTwHfAxYJGkZxSC1ozlM0s+aL4qBbj8MXCtpOcUYlZeO8d6mc4EjJC2hONfyNEVAlD0ArE0nYM+kODcxBCynGK1rSar7aYrb4+8BvttcnnwSmCfpAUkPAX86Tl1Tkm9vt1pLv0VZFxFrJb0H+OeI8P/+XeJzGFZ3uwDflLQJxcjvp3a5ninNPQwzy+ZzGGaWzYFhZtkcGGaWzYFhZtkcGGaW7f8BSfnx5UIl1UYAAAAASUVORK5CYII=\n", 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" ] }, "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": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "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: 228\n", "Alpha: 0.083\n", "Bkg events in On region: 19.00\n", "Excess: 182.00\n", "Excess / Background: 9.58\n", "Gamma rate: 6.94 1 / min\n", "Bkg rate: 0.72 1 / min\n", "Sigma: 21.79\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "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 1.6 s, sys: 20.7 ms, total: 1.62 s\n", "Wall time: 1.62 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 : 113.85\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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8cUv0lLhFil/U5TyKXujPAp/vtZXy+cWwRYpQzVj9CRMmxByJiIQl6nKumdiktK1ZDKuegZ5HN90nIpt966jpp6AELlK8oi7nRZHAGxqaJQ0Lu+9Dwao7qc/7L6eGFNad1KeuxvZVk4yIxCDxq5G1adOGDRs2lG5CagZ3Z8OGDbRp0ybuUOJVtTmVkGHXpD752FdEJAKJr4F369aNtWvXkpcx4iWkTZs2dOvWLe4wold3Up87x8LOz6DFHo1P6pPNviIiEUh8Am/VqlXttKQiWek+NDWZT5B27Wz2FRGJQOjjwPOpvnHgIkmwfv16ADp16hRzJCISljDKedzjwEVKnhK3SPGLupwnvhObSBLMnj2b2bNnxx2GiIQo6nKuBC4SASVwkeKnBC4iIiJNUgIXERFJICVwERGRBFICFxERSSANIxOJwPz58+MOQURCFnU5VwIXicCee+4ZdwgiErKoy7luoYtE4Oabb+bmm2+OOwwRCVHU5VwJXCQC9913H/fdd1/cYYhIiKIu50rgIiIiCaQELiIikkBK4CIiIgmkBC4iIpJAGkYmEoGnnnoq7hBEJGRRl3PVwEVERBJICVwkAjNmzGDGjBlxhyEiIYq6nCuBi0Rg3rx5zJs3L+4wRCREUZdzJXAREZEEUgIXiVHl6k38+sk3qVy9Ke5QRCRh1AtdJCLL39nChNueq329tWo7r763lWqHMoND9yunvE0rAOacPzyuMEUkIZTARSLQtm1bWuzx6W7btlTtoNpTz6s99bomgYtI8rRt2zbS85m7R3rCXFRUVPiSJUviDkMkLypXb+KsWc+zfUc1rVqWcc95wxjSo33cYYlIATGzSnevqO891cBFYjKkR3vuOW8Yz6/cwLCDOip5i0hWlMBFInDllVcCcNlll+22fUiP9krcIkWioXIeFvVCF4nAE088wRNPPBF3GCISoqjLuRK4iIhIAimBi4iIJJASuIiISAKpE5tIBDp27Bh3CCISsqjLeejjwM3sduBk4AN375vedgXwLWBderfp7j6/qWNpHLiIiJSSxsaBR3ELfTZwYj3bf+nuA9OPJpO3iIiI7BJ6Anf3p4GNYZ9HpJBNmzaNadOmxR2GiIQo6nIeZxv4d83sP4AlwA/dXcsxSdF67rnnmt5JRBIt6nIeVy/0W4B/AwYC7wK/aGhHM5tsZkvMbMm6desa2k1ERKSkxJLA3f19d9/p7tXAb4Chjew7090r3L2ic+fO0QUpIiJSwGJJ4Ga2f8bLccDLccQhIiKSVKG3gZvZvcAooJOZrQV+Aowys4GAA6uA88OOQyRO3bp1izsEEQlZ1OVc64GLSOPWLIZVz0DPo6F7g61dIhICrQcuIsHdcdKu559ugfdfBq8GK4MufaH13qn3vvFwPPGJCKC50EUiMWXKFKZMmRJ3GNmr2pxK3pD6t2pzvPGIFLCoy7lq4CIRWLZsWdwhBJdZs16zGO4cCzs/gxZ7wPhZuo0u0oCoy3mzE7iZtQe6u/tLeYxHRApJ96Fw7ly1gYsUoKwSuJk9BYxNf24ZsM7MFrr7f4UQm4gUgu5DlbhFClC2beD7uPsW4FTgDncfAozJf1gikqly9SZ+/eSbVK7WjMMikpLtLfSW6UlYzgB+HEI8IkXpkEMOyWr/CbftmlN5a9V2Xn1vK9UOZQaH7ldOeZtWAMw5f3he4xSR5su2nOcq2wT+U+AvwLPu/oKZHQS8kf+wRIrLzJkzm/3ZLVU7qE5P11Dtqdc1CVxECkcu5bw5sk3g77p7/5oX7r7SzP43zzGJlLzMmnXl6k2cNet5tu+oplXLMq4/cxBDerSPMToRKQTZJvAbgcEBtolIhsmTJwPN+wt9SI/23HPeMJ5fuYFhB3VU8hYpULmU8+YIlMDNbDgwAuhsZpk9zvcGWoQRmEguKldvKqiE9/rrr+f0+SE92hfE9xCRhuVazrMVtAa+B9AuvX95xvYtwGn5DkokW+r0JSKlJlACd/eFwEIzm+3uq0OOSSQn6vQlIqUg2zbw1mY2E+iZ+Vl3Py6fQYlkS52+RKTUZJvA7wduBWYBO/MfjkjuCrHT18CBA+MOQURCFnU5z2o98PS6pENCjKdRWg9cRERKSWPrgWc7lepDZvZtM9vfzDrUPPIQo4iIiGQh21vo56b/vThjmwMH5ScckeJ09tlnA3D33XfHHImIhCXqcp5VAnf3XmEFIlLM1q5dG3cIIhKyqMt5tsuJ/kd92939d/kJR0RERILI9hb6ERnP2wCjgRcBJXAREZEIZXsL/XuZr81sH+CuvEYkIiIiTcq2Bl7XNuDgfAQiUsyGD9cUriLFLupynu048IdI9TqH1CImhwH3ufvUEGL7HI0DF2lcoS3iIiK5aWwceLY18BkZz3cAq91d3WtFYhJ0ERfQQi4ixSariVzSi5q8SmpFsvbAZ2EEJVJsxo8fz/jx40M9R32LuIhIdKIo55myHUZ2BnAd8BRgwI1mdrG7/18IsYkUjQ0bNoRyXC3iIlI4wirnDcn2FvqPgSPc/QMAM+sMLACUwEViVoiLuIhIeLJN4GU1yTttA9nPpy4iIRnSo33wxL1mMax6BnoeDd2HhhuYiORdtgn8UTP7C3Bv+vUEYH5+QxIpIVEm0TtO2vX80y3w/svg1WBl0KUvtN479d43Hg43DhHJi0AJ3Mz+H9DF3S82s1OBo0i1gT8H3BNifCJFYfTo0fDiXYWTRKs2p84LqX+rNu86t4g0y+jRoyM9X6Bx4GY2D5ju7i/V2V4B/MTdvxpSfLvROHBJtMzkDbB5DXy4etfrfXvAPt1Tz8NO4GsWw51jYedn0GIPOHeubqOLFKB8jAPvWTd5A7j7EjPrmUNsIqWjblKum0THz4ouiXYfmkraagMXSaygCbxNI++1zUcgIsXsy1/+MgCPPPLIro1xJ9HuQ5W4RfKo3nIeoqAJ/AUz+5a7/yZzo5l9E6jMf1gixeWTTz6p/w0lUZGi0WA5D0nQBD4F+JOZncWuhF0B7AGMCyMwERERaVigBO7u7wMjzOxYoG9688Pu/tfQIhMREZEGZbse+JPAkyHFIiIR0splIsmW63rgTTKz24GTgQ/cvW96WwdgDtATWAWc4e6bwo5FJC4nn3xybgfIw4QvQVcu06plIs2TcznPUugJHJgN3AT8LmPbVOAJd7/WzKamX18SQSwisbjooouy+0DIE77Ut3JZ5tKjIpK9rMt5jkJP4O7+dD1jxU8BRqWf30lqdTMlcJH65GnWNK1cJlJcAs3ElvNJUgl8XsYt9A/dfd+M9ze5e72/PcxsMjAZ4MADDxyyevXq+nYTKWijRo0C4Kmnnsr+wyHNmqY2cJH8yqmcNyAfM7HFxt1nAjMhNZVqzOGIRC+kCV+yWrlMRApOXAn8fTPb393fNbP9gQ+a/IRIKdOELyJSR1xrec8Fzk0/Pxf4c0xxiIiIJFLoCdzM7iW17GhvM1ubnn71WuBLZvYG8KX0axEREQkoil7oX2/grWgXThWJ0RlnnBF3CCISsqjLeSS90PNF64GLiEgpaawXelxt4CLhWrMYnvlF6t8CsG3bNrZt2xZ3GCISoqjLecEPIxMJJOSZy3L1la98Bcjv+NCoaLy4SDBRl3MlcCk+eZq5rFRpznSRZFACl+KQWbOuO3PZ+FkaQ91MmjNdpHApgUvxCWnmslKhOdNFkkEJXIqTZi7LiyE92nPPecPUBi5SgJTARSIwceLEuENoNs2ZLhJM1OVc48BFREQKlMaBi8Rs/fr1rF+/Pu4wRCREUZdz3UIXicBpp50GJHMcuIgEE3U5Vw1cREQkgZTARUREEkgJXEREJIGUwEVERBJIndhEIvCf//mfcYcgIiGLupxrHLiI5I1WLhPJr8bGgasGLhKBNWvWANC9e/eYI8mfzFXLQCuXiURdzpXARSJwzjnnABGMD12zOLZFXLRymZS6yMp5mhK4SJLdcdKu559ugfdfTq2BbmXQpe/u66BnLrmaB3Vr1Vq5TCRaSuAixaJqcyp5Q+rfqs27J/CQaeUykWgpgYskWWates1iuHMs7PwMWuwB42dFfhu96FYui7FJQqQpSuAixaL7UDh3rhJOc2U2R0DjTRJ5bo4QaQ4lcJEI/PCHP4zmRN2HKnHnS8xNEpI8kZXzNI0DF5HIJWK8eN0miXPn6o8jiZzGgYvE7LXXXgOgd+/eMUcSj8wx44kZL64mCclS1OVcCVwkAueffz6g9cAhYePF1SQhWYi6nCuBi0joMmvWGi8ukh9K4CISKY0XF8kPJXARiVzRjRcXiYHWAxcREUkg1cAlORI8K9all14adwjRSPDPSCRXUZdzJXApXDEu1JFvY8aMiTuE/Itg5rJQxovrjwwJSdTlXAlckiHhs2ItW7YMgIEDB8YcSYjy8DMKOl4cshgzHvQPwQL/I1AKX9TlXAlcCleBLdSRiylTpgBFNg68bsLL888olPHiCf9DUApb1OVcCVySQbNiFb48/IxyGi/e0K3xIvpDUCSTErgkh2bFKnx5/Bk1OV68ObfG9YegFJFYE7iZrQK2AjuBHQ1N2C4ipSnwePFsbo3rD0EpEoVQAz/W3dfHHYSIJIxujUuJK4QELlL0rrnmmrhDKG7dh/LqCXezaflfad/nOA5V8pYYRF3OY10P3Mz+BWwCHLjN3WfWs89kYDLAgQceOGT16tXRBikiBSmRS5SKZKmx9cDjnkp1pLsPBr4MfMfMjqm7g7vPdPcKd6/o3Llz9BGK5MGiRYtYtGhR3GEUrfqGnIlELepyHmsNPJOZXQF85O4zGtqnoqLClyxZEl1QInkyatQooMjGgReQukPO7jlvmBZLkciFUc4bq4HH1gZuZnsBZe6+Nf38eOBnccUjIgUo4LSnWqJUSlGcndi6AH8ys5o4fu/uj8YYj4jELYf577VEqZSa2BK4u68EBsR1fhEpcJr2VKRRGkYmIoVDY7tFAlMCF4nAr371q7hDSB5NeyoJE3U5L5he6EGoF7qI5CqUNcZFQlKQvdBFSsmCBQsAGDNmTMyRlB5N+CJRibqcK4FLYiS55nTVVVcBSuBxC2WNcZG0qMu5ErgUrKA1J1DtKXECju/Oh5zWGBcpYErgkgiqOSVcc9buDoEmfJFiogQuBUs1pyIV8/huTfgixUIJXBJBNaeE0/hukbzTMDKRCLz22msA9O7dO+ZICkSEbeAiUQmjnGsYmUjMlLjr6D40EYk7ySMfJHpRl3MlcJEIPPTQQwB89atfjTkSaUjmqAfQmHHJXtTlXAlcJAK/+MUvACXwJAll5IOaDopa1OVcCVxEhM/XqvMy8iGH5VFFmqIELiJSj7yPfNDyqJJnSuAiIg3Iecy4hs9JiJTARURyFKi3upZHlTxTAheJwF133RV3CJJHzV7hLCHD56R5oi7nSuAiEejevXvcIUhINE+/1Ii6nCuBi0Rgzpw5AEyYMCHmSCQfNE+/1Cfqcq6pVEUiMGrUKACeeuqpWOOQcASdsU0zuxW3MMq5plIVEQnRkLI3GNLyGSg7GtjVxq017SVMSuAiItkKPEHLpbW7ZdtWrtq6NEUJXEQkF41M0JJNW3mze7ZLyVICFxHJVjMmaMlmZjf1bJcg1IlNJALr168HoFOnTjFHIqHI8yIldWvr95w3TLfREyCMct5YJzYlcBGRAqSe7QLqhS4Su9mzZwMwceLEWOOQ5GhoHna1lReuqMt5WSRnESlxs2fPri3cIvlSX1u5xCfqcq4auIhIgmgWOKmhBC4iklDZrlmeiPbyPHcILGZK4CIiCRZkFjjIrr080kQfdFKczKF7AiiBi4gUpoZqos2YBQ4aH1teMB3jGpkURz4vUAI3swrgaOAA4BPgZWCBu28MMTaRojF//vy4Q5BCFzgxZwg4Cxw03l6emcCzmUQmLzX1ZkyKU6iiLueNjgM3s4nA94F/AZXAB0Ab4BBgJKlEfpm7vxV6pGgcuIgUscwEvnkNfLh61+t9e8A+6bWmG0t4585tNOEFSbiNTSKTy+IsQZP9qy8sYNPyv9K+z3EcesSYBvcrFc2eyMXMvgPc7u6fNPD+QKCjuz+Rl0iboARenBLRsSZHN998MwDf/va3Y45EEiGbxBxCp6+GymRmAn/7w6Ht05oAABMmSURBVE9Yu2lXaujWvi1d921b7/GaSvZB9kvCuPYwyrlmYpOCUjDtbRHSeuCStQLvjd3UdK/NSfaN7Vff74NCm62uoNYDN7MbGnvf3b+fY2AnAtcDLYBZ7n5tLseT5NGiDSIN6D60IBN3jaaGsDVnvHrQdnoIp1afTaIvhDuHTXViqwzrxGbWAvg18CVgLfCCmc119+VhnVOi1dAFrokoRIpDQ9O91rdfkPHqYazYlo/e93U1tO/yd7bQ54Does03msDd/c7M12a2l7t/nKdzDwXedPeV6WP/ATgFiC2BZ9N5Iui+cR4zynPX99dx2/crOdJWcI0fxiddhtT7V++QHu15cGyrXcdU8hYJT9Db8vnej4bHq39uvwb+KKivtnzdrN8xxF+h0g7n4jP/o8FafUP71e19P5DXGdZiBc9XH8YHVQMaTODZ7BumQG3gZjYc+C3Qzt0PNLMBwPnu3uyWejM7DTjR3c9Lvz4HONLdv9vQZ/LdBv7KNUfVPn9/+54Mr15KK3awnZY8VzaILq221fu5oPvGecy4v8+iTw/ibB6p3fduvsyI1isBOHz/fXbtWCITN6gNXCKX2asdgg9Ny/d+Te0btJzX832q33sFqAbKKNvv8AbjDLLfq1ta0mPjotrfWas7jODQveufW76hfUf9/HnYr19kbeBBE/jfgNOAue4+KL3tZXfvm0NQpwMn1EngQ939e3X2mwxMTr/sDbxW51D7AJsbOVVT7wPQteOeB3fZ49O9Dccx3v+s9Za3N2x7I5d94zxmCOfep2vHPb8Q+Jj77nFgl7Y7O9fu+0mLdW9/+FnNcMPan0nXcttvv3bWFcDB3//I33l7q79X3zHrfjYLQT8TZL+8XG8JFPX3yuf5cjlW0VxvQctavvfLdt+g3yvPce7TtdzadmlnXS21H+9/5G839n0a2Tff11sPd+9c797u3uQD+Fv636UZ2/4e5LONHHM48JeM19OAac04zsxc3k/qI+rvlc/z5XKs5nw26GeC7KfrLXnn0/WWvIeut2CPoMuJrjGzEYCb2R5mdhGwIuBnG/ICcLCZ9TKzPYAzgbnNOM5DOb6fVFF/r3yeL5djNeezQT8TZD9db8k7n6635NH1FkDQW+idSA33GgMY8BhwobtvaE6EGcf9CvArUsPIbnf3q3M5noiISKlI1EQuIiIiktLURC43kmqjr5fnOJGLiIiINE9TbeBLSE3m0gYYDLyRfgwEdoYbmoiIiDQkaBv4k8Dx7r49/boV8Ji7HxtyfLvp1KmT9+zZM8pTioiIxKaysnK9NzCMLNB64KTWAS8Hatb/bpfeFqmePXuixUxERKRUmNnqht4LmsCvBZama+IAXwSuyDEuERERaaZACdzd7zCzR4AjSXVqm+re6Cw6IiIiEqKgNXBIzT5/dPq5U7wTCIiIiBS8QDOxmdm1wIWkVgpbDnzfzP47zMBERESkYUFr4F8BBrp7NYCZ3QksJTV/uYiIiEQsm1vo+7KrF/o+je0oUkpqlgptipYSFZF8CprA/5tdvdANOAbVvkVERGITtBf6vWb2FHAEqQR+iXqhi6SoZi0icQi6nChAzUwwLYARZnZqCPGIiIhIAIFq4GZ2O9AfeAWoTm924I8hxSUiIiKNCNoGPszd+4QaiYiIiAQW9Bb6c2amBC4iIlIggtbA7ySVxN8DPiXVkc3dvX9okYlILDQsTiQZgibw24FzgH+wqw1cREREYhI0gb/l7nPzfXIzWwVsBXYCO9y9It/nEJHsqGYtkgxBE/irZvZ7UguYfFqz0d3z0Qv9WHdfn4fjiIiIlIygCbwtqcR9fMY2DSMTERGJSdCZ2L4R0vkdeMzMHLjN3WeGdB4REZGiks1iJmEY6e7vmNkXgMfN7FV3fzpzBzObDEwGOPDAA+OIUUREpOBkM5Vq3rn7O+l/PwD+BAytZ5+Z7l7h7hWdO3eu+7aIiEhJii2Bm9leZlZe85xU+/rLccUjIiKSJFkncDObl6dzdwGeNbO/A4uBh9390TwdW0REpKg1pw28az5O7O4rgQH5OJaISK6CzkAHGisvhaE5t9CX5j0KERERyUrWNXB3nxRGICIicVKtWpIm7mFkIg3SLU0RkYbFOoxMREREmkc1cClYqlWLiDQsUAI3s87AJUAfoE3Ndnc/LqS4REREpBFBb6HfA6wAegE/BVYBL4QUk4iIiDQhaALv6O6/Bba7+8J0T/RhIcYlIiIijQjaBr49/e+7ZnYS8A7QLZyQRETyJ+hoBvW5kKQJmsCvMrN9gB8CNwJ7A1NCi0pEREQaFTSBb3L3zcBm4FgAMxsZWlQiknelWhMttu8jUiNoG/iNAbeJiIhIBBqtgZvZcGAE0NnM/ivjrb2BFmEGJiL5pZqoSHFp6hb6HkC79H7lGdu3AKeFFZSISFOS0CSg6YAlTI0mcHdfCCw0s9nuvjqimERERKQJQTuxbTOz64DD0UxsIlIAklBjTUKMklxBE/g9wBzgZOAC4FxgXa4nN7MTgetJtafPcvdrcz2miIgkRxKaQgpVbDOxmVkL4NfAl0nNsf51M+uTyzFFRERKRZwzsQ0F3nT3lQBm9gfgFGB5jscNLJsOJpI/+ktaRGro90Hz5TIT2w9yPHdXYE3G67XAkXV3MrPJwGSAjh07csUVV+R42l1WrVqVt2NJcPn8GdaYPXt2oP0mTpyY93OLSDIF/b2RjSh/x5i7R3ay3U5sdjpwgrufl359DjDU3b/X0GcqKip8yZIlUYUoIQmjzUvtaFIqSvVaT8od03z/v5tZpbtX1PdeUxO53Ag0mOHd/fs5xLUW6J7xuhupW/MiWSu2X1ZS2JRE8yfO/6Ok/3yauoVeU90dSaqj2Zz069OByhzP/QJwsJn1At4GzgT+PcdjSgIkvdBIeEp54pM4/yhIQu222H7e+dDURC53ApjZROBYd9+efn0r8FguJ3b3HWb2XeAvpIaR3e7ur+RyTBGRKCQhmRRbbVk+L2gntgNITaW6Mf26XXpbTtx9PjA/1+OIBFXKNbwkKOX/c91KlmwFTeDXAkvN7Mn06y8CV4QSkYgkgv4YKmz6Py9+gRK4u99hZo+wa5jXVHd/L7ywRMKhX2oiUiya6oXe091XAaQT9p/rvG9AV3dfG1qEIlKQ9MeQSLyaqoFfZ2ZlpBJ3Jan5z9sA/w84FhgN/ITUkDARERGJSFO90E9Pz09+FjAJ2B/YBqwg1fnsanevCj1KERER2U2TbeDuvhz4cQSxiIiISEBBe6GLSISKbaavYvs+IoUg6HKiIiIiUkBUAxcpQMVWEy227yNSCALVwM1spJntlX5+tpn9r5n1CDc0ERERaUjQW+i3ANvMbADwI2A18LvQohIREZFGBU3gOzy1cPgpwPXufj2pudFFREQkBkHbwLea2TTgbOAYM2sBtAovLBEJQvORi5SuoDXwCcCnwDfTU6p2Ba4LLSoRERFplKXujCdDRUWFL1myJO4wREREImFmle5eUd97TS1mshWoL8Mb4O6+dzMDugL4Fqm51QGmp9cGFylaut0tIvnU1FzoYXZU+6W7zwjx+CIiIkUrq4lczOwLpFYjA8Dd38p7RCIFIt/Tf6pWLSL5FHQil7Fm9gbwL2AhsAp4JMdzf9fMXjKz282sfY7HEhERKSmBOrGZ2d+B44AF7j7IzI4Fvu7ukxv5zAJgv3re+jHwPLCeVPv6lcD+7j6pgeNMBiYDHHjggUNWr17dZLwiIiLFoNmd2DJsd/cNZlZmZmXu/qSZ/byxD7j7mIDB/QaY18hxZgIzIdULPWC8IiIiRS1oAv/QzNoBTwP3mNkHwI7mntTM9nf3d9MvxwEvN/dYIiIipShoAj8FqAJ+AJwF7AP8LIfz/o+ZDSR1C30VcH4OxxIRESk5gRK4u3+c8fLOXE/q7ufkegwREZFSFiiB15nQZQ9S86B/3NyJXERERCQ3QWvgu03oYmZfA4aGEpGIiIg0KehiJrtx9wdJDSsTERGRGAS9hX5qxssyoIL650gXERGRCATthf7VjOc7SPUcPyXv0YiIiEggQdvAvxF2ICIiIhJcU8uJ3kgjt8rd/ft5j0hERESa1FQntiVAJakVyAYDb6QfA4Gd4YYmIiIiDWlqPfA7AcxsInCsu29Pv74VeCz06ERERKReQYeRHQBkjgVvl94mIiIiMQjaC/1aYKmZPZl+/UXgilAiEhERkSYF7YV+h5k9AhyZ3jTV3d8LLywRERFpTKO30M3s0PS/g0ndMl+TfhyQ3iYiIiIxaKoG/l/AZOAX9bznaDpVERGRWJh7cmZENbN1wOo6m/cBNjfyscbe7wSsz0NocWjqexfy+XI5VnM+G/QzQfbT9Za88+l6Sx5db7v0cPfO9e7t7k0+gNOB8vTzS4E/AoOCfDbsBzCzue8DS+KOP6zvXcjny+VYzfls0M8E2U/XW/LOp+steQ9db8EeQYeRXebuW83sKOAE4E7g1oCfDdtDOb6fVFF/r3yeL5djNeezQT8TZD9db8k7n6635NH1FkCgW+hmttTdB5nZfwP/cPff12xrbpSFwMyWuHtF3HFIadD1JlHS9Vb8gtbA3zaz24AzgPlm1jqLzxaymXEHICVF15tESddbkQtaA98TOJFU7fsNM9sf6Ofumk5VREQkBoFq0e6+DfgAOCq9aQepRU1EREQkBkFr4D8BKoDe7n6ImR0A3O/uI8MOUERERD4vaDv2OGAs8DGAu7/D7oubiIiISISCJvDPPFVVdwAz2yu8kAqDmR1kZr81s/+LOxYpTma2l5ndaWa/MbOz4o5Hipt+pxWfoAn8vnQv9H3N7FvAAmBWeGHlxsxuN7MPzOzlOttPNLPXzOxNM5va2DHcfaW7fzPcSKXYZHntnQr8n7t/i9QdLpGsZHO96Xda8QnaiW0G8H/AA0Bv4HJ3vyHMwHI0m1Sv+Vpm1gL4NfBloA/wdTPrY2b9zGxenccXog9ZisRsAl57QDdSiwMB7IwwRikeswl+vUmRCboeOO7+OPA4pC4QMzvL3e8JLbIcuPvTZtazzuahwJvuvhLAzP4AnOLu/w2cHG2EUqyyufaAtaSS+DKKY14FiViW19vyaKOTsDW1nOjeZjbNzG4ys+Mt5bvASlKTuiRJV3bVdiD1y7NrQzubWUczuxUYZGbTwg5OilpD194fgfFmdgvFOyWmRK/e602/04pPUzXwu4BNwHPAecDFwB6kaq7LQo4t36yebQ2OoXP3DcAF4YUjJaTea8/dPwa+EXUwUvQaut70O63INJXAD3L3fgBmNovU0nQHuvvW0CPLv7VA94zX3YB3YopFSouuPYmSrrcS0VS72/aaJ+6+E/hXQpM3wAvAwWbWy8z2AM4E5sYck5QGXXsSJV1vJaKpBD7AzLakH1uB/jXPzWxLFAE2h5ndS+q2f28zW2tm33T3HcB3gb8AK4D73P2VOOOU4qNrT6Kk6620BZpKVURERAqLhq6IiIgkkBK4iIhIAimBi4iIJJASuIiISAIpgYuIiCSQEriIiEgCKYGLJISZ7TSzZRmPRpfEjYqZrTKzf5hZhZn9KR3bm2a2OSPWEQ189jwzu6vOti7pJTJbmdkcM9toZl+L5tuIJIfGgYskhJl95O7t8nzMlumJP3I5xiqgwt3XZ2wbBVzk7o2u9Gdm7YE3gG7uXpXe9l2gn7ufn359N6l10x/MJU6RYqMauEjCpWvAPzWzF9M14UPT2/cys9vN7AUzW2pmp6S3TzSz+83sIeAxMyszs5vN7BUzm2dm883sNDMbbWZ/yjjPl8zsjznEeYSZLTSzSjN7xMy6uPsmYBFwUsauZwL3Nvc8IqVCCVwkOdrWuYU+IeO99e4+GLgFuCi97cfAX939COBY4Doz2yv93nDgXHc/DjgV6An0I7Xq4PD0Pn8FDjOzzunX3wDuaE7gZtYauB4Y7+5DgLuBK9Nv30sqaWNm3dOxPN2c84iUkqZWIxORwvGJuw9s4L2amnElqYQMcDww1sxqEnob4MD088fdfWP6+VHA/e5eDbxnZk9Cav3JdPv02WZ2B6nE/h/NjP0w4HBggZkBtCC1ahakFtq4wczaARNIzd1d3czziJQMJXCR4vBp+t+d7CrXRqrG+1rmjmZ2JPBx5qZGjnsH8BBQRSrJN7e93ICX3P3oum+4+8dmtgA4hVRN/D+beQ6RkqJb6CLF6y/A9yxd5TWzQQ3s9ywwPt0W3gUYVfOGu79Dai3pS4HZOcSyHOhqZkPTsexhZodnvH8vcDGwr7u/kMN5REqGErhIctRtA7+2if2vBFoBL5nZy+xqc67rAVK3s18GbgP+BmzOeP8eYI27L29u4O7+KXAa8L9m9ndgKXBkxi6Pkrq9/4fmnkOk1GgYmYhgZu3c/SMz6wgsBka6+3vp924Clrr7bxv47CrqDCPLc2waRiZSD9XARQRgnpktA54BrsxI3pVAf1K9xhuyDnjCzCryHZSZzQFGkmqDF5EMqoGLiIgkkGrgIiIiCaQELiIikkBK4CIiIgmkBC4iIpJASuAiIiIJpAQuIiKSQP8f432EYmRxREUAAAAASUVORK5CYII=\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
0.8590.7371.0024.210e-111.123e-119.564e-123.108e-110.87513.9200.1180 .. 00.1230.1131.13013.866192.2630.200 .. 5.00082.4500622221552 .. 359.5182156709263.683e-114.759e-114.959e-125.164e-124.758e-12
1.2611.0021.5881.530e-119.108e-121.126e-112.435e-110.94412.7660.08831 .. 360.0910.0851.13120.509420.6060.200 .. 5.000168.41051974880432 .. 644.96956634885161.444e-111.731e-111.347e-121.396e-121.300e-12
1.8521.5882.1605.561e-123.198e-125.871e-121.908e-111.1769.8880.14727 .. 120.1530.1411.49315.288233.7300.200 .. 5.000114.55102237143302 .. 250.195243880715966.539e-128.303e-128.164e-138.497e-137.840e-13
2.5182.1602.9362.475e-121.935e-124.829e-121.569e-111.2409.2080.17510 .. 110.1840.1671.62313.944194.4370.200 .. 5.00095.99339801166462 .. 177.99017049331183.068e-124.017e-124.338e-134.548e-134.136e-13
3.6972.9364.6568.993e-131.569e-125.687e-121.230e-110.94614.8940.15917 .. 110.1680.1511.29910.705114.5950.200 .. 5.00060.74745727751254 .. 211.276859567896088.508e-131.168e-121.433e-131.511e-131.357e-13
5.4294.6566.3303.269e-135.510e-132.964e-129.633e-121.1568.7580.2719 .. 50.2910.2511.7818.14166.2720.200 .. 5.00038.677280196058746 .. 78.638020302402113.780e-135.823e-138.851e-149.527e-148.203e-14
7.9716.33010.0371.188e-134.468e-133.491e-127.547e-121.07412.4200.2835 .. 40.3080.2601.7366.85546.9910.200 .. 5.00033.38530403863936 .. 76.720794002795741.276e-132.062e-133.365e-143.654e-143.092e-14
11.70310.03713.6474.317e-141.569e-131.820e-125.913e-120.94510.5010.4500 .. 10.5130.3922.1023.36911.3490.200 .. 5.00015.70778970425867 .. 36.257102123650344.079e-149.076e-141.943e-142.214e-141.693e-14
17.18313.64721.6361.569e-141.272e-132.143e-124.633e-120.3297.2300.2981 .. 00.3790.3291.2560.9400.8840.200 .. 5.0007.444575923963111 .. 40.429207085122315.157e-151.971e-144.682e-155.950e-155.157e-15
25.22921.63629.4195.702e-154.467e-141.117e-123.630e-120.0000.5240.0010 .. 00.2990.0001.1960.0020.0000.200 .. 5.0001.1929670243726282 .. 17.251475169483331.018e-206.818e-158.331e-181.704e-151.018e-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 5.164e-12 4.758e-12\n", " 1.261 1.002 1.588 ... 1.347e-12 1.396e-12 1.300e-12\n", " 1.852 1.588 2.160 ... 8.164e-13 8.497e-13 7.840e-13\n", " 2.518 2.160 2.936 ... 4.338e-13 4.548e-13 4.136e-13\n", " 3.697 2.936 4.656 ... 1.433e-13 1.511e-13 1.357e-13\n", " 5.429 4.656 6.330 ... 8.851e-14 9.527e-14 8.203e-14\n", " 7.971 6.330 10.037 ... 3.365e-14 3.654e-14 3.092e-14\n", " 11.703 10.037 13.647 ... 1.943e-14 2.214e-14 1.693e-14\n", " 17.183 13.647 21.636 ... 4.682e-15 5.950e-15 5.157e-15\n", " 25.229 21.636 29.419 ... 8.331e-18 1.704e-15 1.018e-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", 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" ] }, "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 : 35\n", "\ttotal stat : 29.38\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
index2.635e+007.617e-02nannanFalse
amplitude2.822e-111.894e-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 2.635e+00 7.617e-02 nan nan False\n", "amplitude 2.822e-11 1.894e-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
index2.637e+007.619e-02nannanFalse
amplitude2.825e-111.895e-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 2.637e+00 7.619e-02 nan nan False\n", "amplitude 2.825e-11 1.895e-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", 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" ] }, "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", "create_crab_spectral_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": null, "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 }