{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**This is a fixed-text formatted version of a Jupyter notebook**\n",
    "\n",
    "- Try online [![Binder](https://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.16?urlpath=lab/tree/analysis_2.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",
    "[analysis_2.ipynb](../_static/notebooks/analysis_2.ipynb) |\n",
    "[analysis_2.py](../_static/notebooks/analysis_2.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First analysis with gammapy library API\n",
    "\n",
    "## Prerequisites:\n",
    "\n",
    "- Understanding the gammapy data workflow, in particular what are DL3 events and intrument response functions (IRF).\n",
    "- Understanding of the data reduction and modeling fitting process as shown in the [first gammapy analysis with the high level interface tutorial](analysis_1.ipynb)\n",
    "\n",
    "## Context\n",
    "\n",
    "This notebook is an introduction to gammapy analysis this time using the lower level classes and functions\n",
    "the library.\n",
    "This allows to understand what happens during two main gammapy analysis steps, data reduction and modeling/fitting. \n",
    "\n",
    "**Objective: Create a 3D dataset of the Crab using the H.E.S.S. DL3 data release 1 and perform a simple model fitting of the Crab nebula using the lower level gammapy API.**\n",
    "\n",
    "## Proposed approach:\n",
    "\n",
    "Here, we have to interact with the data archive (with the `~gammapy.data.DataStore`) to retrieve a list of selected observations (`~gammapy.data.Observations`). Then, we define the geometry of the `~gammapy.cube.MapDataset` object we want to produce and the maker object that reduce an observation\n",
    "to a dataset. \n",
    "\n",
    "We can then proceed with data reduction with a loop over all selected observations to produce datasets in the relevant geometry and stack them together (i.e. sum them all).\n",
    "\n",
    "In practice, we have to:\n",
    "- Create a `~gammapy.data.DataStore` poiting to the relevant data \n",
    "- Apply an observation selection to produce a list of observations, a `~gammapy.data.Observations` object.\n",
    "- Define a geometry of the Map we want to produce, with a sky projection and an energy range.\n",
    "    - Create a `~gammapy.maps.MapAxis` for the energy\n",
    "    - Create a `~gammapy.maps.WcsGeom` for the geometry\n",
    "- Create the necessary makers : \n",
    "    - the map dataset maker : `~gammapy.cube.MapDatasetMaker`\n",
    "    - the background normalization maker, here a `~gammapy.cube.FoVBackgroundMaker`\n",
    "    - and usually the safe range maker : `~gammapy.cube.SafeRangeMaker`\n",
    "- Perform the data reduction loop. And for every observation:\n",
    "    - Apply the makers sequentially to produce the current `~gammapy.maps.MapDataset`\n",
    "    - Stack it on the target one.\n",
    "- Define the `~gammapy.modeling.models.SkyModel` to apply to the dataset.\n",
    "- Create a `~gammapy.modeling.Fit` object and run it to fit the model parameters\n",
    "- Apply a `~gammapy.spectrum.FluxPointsEstimator` to compute flux points for the spectral part of the fit.\n",
    "\n",
    "## Setup\n",
    "First, we setup the analysis by performing required imports.\n"
   ]
  },
  {
   "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": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from regions import CircleSkyRegion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.data import DataStore\n",
    "from gammapy.maps import WcsGeom, MapAxis, Map\n",
    "from gammapy.cube import (\n",
    "    MapDatasetMaker,\n",
    "    MapDataset,\n",
    "    SafeMaskMaker,\n",
    "    FoVBackgroundMaker,\n",
    ")\n",
    "from gammapy.modeling.models import (\n",
    "    SkyModel,\n",
    "    PowerLawSpectralModel,\n",
    "    PointSpatialModel,\n",
    ")\n",
    "from gammapy.modeling import Fit\n",
    "from gammapy.spectrum import FluxPointsEstimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the datastore and selecting observations\n",
    "\n",
    "We first use the `~gammapy.data.DataStore` object to access the observations we want to analyse. Here the H.E.S.S. DL3 DR1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now define an observation filter to select only the relevant observations. \n",
    "Here we use a cone search which we define with a python dict.\n",
    "\n",
    "We then filter the `ObservationTable` with `~gammapy.data.ObservationTable.select_observations()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "selection = dict(\n",
    "    type=\"sky_circle\",\n",
    "    frame=\"icrs\",\n",
    "    lon=\"83.633 deg\",\n",
    "    lat=\"22.014 deg\",\n",
    "    radius=\"5 deg\",\n",
    ")\n",
    "selected_obs_table = data_store.obs_table.select_observations(selection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now retrieve the relevant observations by passing their `obs_id` to the`~gammapy.data.DataStore.get_observations()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = data_store.get_observations(selected_obs_table[\"OBS_ID\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing reduced datasets geometry\n",
    "\n",
    "Now we define a reference geometry for our analysis, We choose a WCS based geometry with a binsize of 0.02 deg and also define an energy axis: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit=\"TeV\")\n",
    "\n",
    "geom = WcsGeom.create(\n",
    "    skydir=(83.633, 22.014),\n",
    "    binsz=0.02,\n",
    "    width=(2, 2),\n",
    "    frame=\"icrs\",\n",
    "    proj=\"CAR\",\n",
    "    axes=[energy_axis],\n",
    ")\n",
    "\n",
    "# Reduced IRFs are defined in true energy (i.e. not measured energy).\n",
    "energy_axis_true = MapAxis.from_energy_bounds(0.5, 20, 10, unit=\"TeV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can define the target dataset with this geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked = MapDataset.create(\n",
    "    geom=geom, energy_axis_true=energy_axis_true, name=\"crab-stacked\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data reduction\n",
    "\n",
    "### Create the maker classes to be used\n",
    "\n",
    "The `~gammapy.cube.MapDatasetMaker` object is initialized as well as the `~gammapy.cube.SafeMaskMaker` that carries here a maximum offset selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset_max = 2.5 * u.deg\n",
    "maker = MapDatasetMaker()\n",
    "maker_safe_mask = SafeMaskMaker(methods=[\"offset-max\"], offset_max=offset_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "circle = CircleSkyRegion(\n",
    "    center=SkyCoord(\"83.63 deg\", \"22.14 deg\"), radius=0.2 * u.deg\n",
    ")\n",
    "data = geom.region_mask(regions=[circle], inside=False)\n",
    "exclusion_mask = Map.from_geom(geom=geom, data=data)\n",
    "maker_fov = FoVBackgroundMaker(method=\"fit\", exclusion_mask=exclusion_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform the data reduction loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background norm obs 23523: 0.99\n",
      "Background norm obs 23526: 1.08\n",
      "Background norm obs 23559: 0.98\n",
      "Background norm obs 23592: 1.09\n",
      "CPU times: user 1.78 s, sys: 102 ms, total: 1.88 s\n",
      "Wall time: 1.91 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "for obs in observations:\n",
    "    # First a cutout of the target map is produced\n",
    "    cutout = stacked.cutout(obs.pointing_radec, width=2 * offset_max)\n",
    "    # A MapDataset is filled in this cutout geometry\n",
    "    dataset = maker.run(cutout, obs)\n",
    "    # fit background model\n",
    "    dataset = maker_fov.run(dataset)\n",
    "    print(\n",
    "        f\"Background norm obs {obs.obs_id}: {dataset.background_model.norm.value:.2f}\"\n",
    "    )\n",
    "    # The data quality cut is applied\n",
    "    dataset = maker_safe_mask.run(dataset, obs)\n",
    "    # The resulting dataset cutout is stacked onto the final one\n",
    "    stacked.stack(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect the reduced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.WCSAxesSubplot at 0x1c1d3c9c88>,\n",
       " <matplotlib.colorbar.Colorbar at 0x1c1d4e3da0>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked.counts.sum_over_axes().smooth(0.05 * u.deg).plot(\n",
    "    stretch=\"sqrt\", add_cbar=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save dataset to disk\n",
    "\n",
    "It is common to run the preparation step independent of the likelihood fit, because often the preparation of maps, PSF and energy dispersion is slow if you have a lot of data. We first create a folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path(\"analysis_2\")\n",
    "path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then write the maps and IRFs to disk by calling the dedicated `~gammapy.cube.MapDataset.write()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = path / \"crab-stacked-dataset.fits.gz\"\n",
    "stacked.write(filename, overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the model\n",
    "We first define the model, a `SkyModel`, as the combination of a point source `SpatialModel` with a powerlaw `SpectralModel`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_position = SkyCoord(ra=83.63308, dec=22.01450, unit=\"deg\")\n",
    "spatial_model = PointSpatialModel(\n",
    "    lon_0=target_position.ra, lat_0=target_position.dec, frame=\"icrs\"\n",
    ")\n",
    "\n",
    "spectral_model = PowerLawSpectralModel(\n",
    "    index=2.702,\n",
    "    amplitude=4.712e-11 * u.Unit(\"1 / (cm2 s TeV)\"),\n",
    "    reference=1 * u.TeV,\n",
    ")\n",
    "\n",
    "sky_model = SkyModel(\n",
    "    spatial_model=spatial_model, spectral_model=spectral_model, name=\"crab\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we assign this model to our reduced dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked.models = sky_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the model\n",
    "\n",
    "The `~gammapy.modeling.Fit` class is orchestrating the fit, connecting the `stats` method of the dataset to the minimizer. By default, it uses `iminuit`.\n",
    "\n",
    "Its contructor takes a list of dataset as argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------\n",
      "| FCN = 1.624E+04               |     Ncalls=137 (137 total)     |\n",
      "| EDM = 5.25E-05 (Goal: 1E-05)  |            up = 1.0            |\n",
      "------------------------------------------------------------------\n",
      "|  Valid Min.   | Valid Param.  | Above EDM | Reached call limit |\n",
      "------------------------------------------------------------------\n",
      "|     True      |     True      |   False   |       False        |\n",
      "------------------------------------------------------------------\n",
      "| Hesse failed  |   Has cov.    | Accurate  | Pos. def. | Forced |\n",
      "------------------------------------------------------------------\n",
      "|     False     |     True      |   True    |   True    | False  |\n",
      "------------------------------------------------------------------\n",
      "CPU times: user 3.08 s, sys: 134 ms, total: 3.22 s\n",
      "Wall time: 3.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fit = Fit([stacked])\n",
    "result = fit.run(optimize_opts={\"print_level\": 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `FitResult` contains information on the fitted parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=8</i>\n",
       "<table id=\"table120753327912\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>value</th><th>error</th><th>unit</th><th>min</th><th>max</th><th>frozen</th></tr></thead>\n",
       "<thead><tr><th>str9</th><th>float64</th><th>float64</th><th>str14</th><th>float64</th><th>float64</th><th>bool</th></tr></thead>\n",
       "<tr><td>lon_0</td><td>8.362e+01</td><td>3.116e-03</td><td>deg</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>lat_0</td><td>2.202e+01</td><td>2.965e-03</td><td>deg</td><td>-9.000e+01</td><td>9.000e+01</td><td>False</td></tr>\n",
       "<tr><td>index</td><td>2.595e+00</td><td>1.000e-01</td><td></td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>amplitude</td><td>4.590e-11</td><td>3.757e-12</td><td>cm-2 s-1 TeV-1</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>reference</td><td>1.000e+00</td><td>0.000e+00</td><td>TeV</td><td>nan</td><td>nan</td><td>True</td></tr>\n",
       "<tr><td>norm</td><td>9.370e-01</td><td>2.198e-02</td><td></td><td>0.000e+00</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>tilt</td><td>0.000e+00</td><td>0.000e+00</td><td></td><td>nan</td><td>nan</td><td>True</td></tr>\n",
       "<tr><td>reference</td><td>1.000e+00</td><td>0.000e+00</td><td>TeV</td><td>nan</td><td>nan</td><td>True</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=8>\n",
       "   name     value     error        unit         min        max    frozen\n",
       "   str9    float64   float64      str14       float64    float64   bool \n",
       "--------- --------- --------- -------------- ---------- --------- ------\n",
       "    lon_0 8.362e+01 3.116e-03            deg        nan       nan  False\n",
       "    lat_0 2.202e+01 2.965e-03            deg -9.000e+01 9.000e+01  False\n",
       "    index 2.595e+00 1.000e-01                       nan       nan  False\n",
       "amplitude 4.590e-11 3.757e-12 cm-2 s-1 TeV-1        nan       nan  False\n",
       "reference 1.000e+00 0.000e+00            TeV        nan       nan   True\n",
       "     norm 9.370e-01 2.198e-02                 0.000e+00       nan  False\n",
       "     tilt 0.000e+00 0.000e+00                       nan       nan   True\n",
       "reference 1.000e+00 0.000e+00            TeV        nan       nan   True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.parameters.to_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting residuals\n",
    "\n",
    "For any fit it is usefull to inspect the residual images. We have a few option on the dataset object to handle this. First we can use `.plot_residuals()` to plot a residual image, summed over all energies: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.axes._subplots.WCSAxesSubplot at 0x1c1d761400>, None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked.plot_residuals(method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition we can aslo specify a region in the map to show the spectral residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "region = CircleSkyRegion(\n",
    "    center=SkyCoord(\"83.63 deg\", \"22.14 deg\"), radius=0.5 * u.deg\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.axes._subplots.WCSAxesSubplot at 0x1c1fd84780>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1c1fc93160>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked.plot_residuals(\n",
    "    region=region, method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also directly access the `.residuals()` to get a map, that we can plot interactively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = stacked.residuals(method=\"diff\")\n",
    "residuals.smooth(\"0.08 deg\").plot_interactive(\n",
    "    cmap=\"coolwarm\", vmin=-0.1, vmax=0.1, stretch=\"linear\", add_cbar=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting fit statistic profiles\n",
    "\n",
    "To check the quality of the fit it is also useful to plot fit statistic profiles for specific parameters.\n",
    "For this we use `~gammapy.modeling.Fit.stat_profile()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile = fit.stat_profile(parameter=\"lon_0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a good fit and error estimate the profile should be parabolic, if we plot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Delta TS')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_stat = result.total_stat\n",
    "plt.plot(profile[\"values\"], profile[\"stat\"] - total_stat)\n",
    "plt.xlabel(\"Lon (deg)\")\n",
    "plt.ylabel(\"Delta TS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the fitted spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a butterfly plot \n",
    "\n",
    "The `SpectralModel` component can be used to produce a, so-called, butterfly plot showing the enveloppe of the model taking into account parameter uncertainties.\n",
    "\n",
    "To do so, we have to copy the part of the covariance matrix stored on the `FitResult` on the model parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec = sky_model.spectral_model\n",
    "\n",
    "# set covariance on the spectral model\n",
    "covar = result.parameters.get_subcovariance(spec.parameters)\n",
    "spec.parameters.covariance = covar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can actually do the plot using the `plot_error` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_range = [1, 10] * u.TeV\n",
    "spec.plot(energy_range=energy_range, energy_power=2)\n",
    "ax = spec.plot_error(energy_range=energy_range, energy_power=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing flux points\n",
    "\n",
    "We can now compute some flux points using the `~gammapy.spectrum.FluxPointsEstimator`. \n",
    "\n",
    "Besides the list of datasets to use, we must provide it the energy intervals on which to compute flux points as well as the model component name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_edges = [1, 2, 4, 10] * u.TeV\n",
    "fpe = FluxPointsEstimator(datasets=[stacked], e_edges=e_edges, source=\"crab\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.78 s, sys: 42.7 ms, total: 1.83 s\n",
      "Wall time: 1.85 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "flux_points = fpe.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1bbbc710>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4irs/knRMFfAqd78Qv633BmJLPiS39SDwONBtZmEzuw/A3ReJ9Zi+BJwGPuXu/Ru9Jyk8dXV19PT0sHfv3oyU3pmamqK/v5/Lly+zsLDh3WcRSYGtNyHPzF4J/AEwSezZyhvc/Vvx177l7s/MSpQ54Pjx4/7EEyr+nQ8S1Q9GR0fTWmMuwcxoaWkhFApRVlaW9uuLFBIze9Ldj6/22kY9oP8GHHf3Y8DPAg+a2csT101jjCJpU1RURGdnJ319fTQ3b6+y9mrcnfHxcU6ePMng4GBal5YQ2Uk2GoRQ5O5DAO7+dTN7MfCIme1mndFmIrkgMVChtbWVcDjMtWubGWC5scQS4RMTE7d7RKq8LZK6jXpAc2a2P7ERT0YvBF5FrPKASM6rrq6mu7ubAwcOZOSW2fLyMmNjY5w8eZJwOJzWibIihWyjHtDPJx/j7lEz+zHgtRmLSiQDEhUVRkdHGRkZYWlpKa3XX15eZnR0lPHxcVpbWwmFQpSUZGWmg0he2mg5hm+t3I5Xw06c89lMBSWSKWZGKBSiubmZSCTCxMRE2ttQIhJJTUo/FWb2M8D7gSViRUiN2DOgPZkLTSRzSktL2bt3L62trQwODqb9+RDcmYja2tpob29XIhJZIdWfhv8C/IC7j2UyGJFsq6qqoru7m+npacLhcEbm+CSGhY+NjSkRiayQ6k/BRSC9tU5EckhjYyP19fW3nw9lYv7QykSkW3MiqSegdwH/ZGb/TGydHwDcPe9WRRVZS1FRER0dHTQ3NzM0NJSxBeuSnxG1t7dr+LbsSKkmoAeAfwJOsPFCdCJ5raysjP37999+PnT9+vWMtLMyEWkekexEqSagZXf/xYxGIpJjampq6OnpYXx8nEgkkrH5PYl5RJrQKjtNqgno783szcDD3HkLTs+FpOC1trbS1NTE0NAQExMTrFc/cTsSiWhlj0i15qSQrVuM9PZBZoOr7HZ33zHDsFWMVCC2hPfg4CCzs7MZb8vMaG5upqOjQ4lI8tZ6xUhT6gG5++6NjxIpfJWVlRw5coSpqSnC4XBGC5G6OxMTE0xOTtLU1ERHRwfl5eUZa08k21JakM7M3pZYXC6+3Whmb81cWCK5rampiWPHjhEKhTDLbGF4d2dycvL2wng3btzIaHsi2ZLqiqhvc/eZxIa7TwM/l5mQRPJDUVERXV1d9PX1UV9fn/H23P32wngXL15kfn4+422KZFKqgxCKV26YWRGgYToiQHl5OYcOHWJmZoZwOJyVpbunp6eZnp6moaGBjo4OqqqqMt6mSLqlmoC+El8W+wFiNeB+Dvi7jEUlkocaGhqoq6vL6GqsyWZmZpiZmaG+vp6Ojg6qq6sz3qZIuqSagP4zsaTzy8QKkX4Z+HCmghLJV4nVWJubmxkcHCQajWal3Wg0SjQapba2lo6ODmpra7PSrsh2pDQMWzQMW7YmGo0yODiYldtyK9XU1NDR0UFdXV1W2xVJtt4w7HUHIZjZ35jZS8zsaT0lM9trZu+JT1AVkVXU19fT29tLR0cHRUWpjvnZvmvXrvHUU09x+vRpZmZmNj5BJACprIj6TuB+MxsFxoEK4AAwANzv7n+V2RBF8ltQt+UArl+/zoULF6isrCQUCtHU1JS1tkU2kvItODM7BHQA88BZd8/8VPAcoltwki4zMzMMDg5mZO2hjVRUVNxORJmevyQCaaiEAODu54HzaYtKZIdKjJYbHh5mdHQ0Y7XlVnPjxg0uX75MJBIhFArR0tKiRCSByd5NaRG5LTGJtbe3N5ARawsLCwwMDHDixImsDRkXSaYEJBKgiooKjhw5wv79+wNZguHWrVuEw2FOnDjB8PAwS0tLWY9Bdq6NRsH9oZm9zMw0zVokg5qamujr66OtrS2QW2KLi4tEIhFOnDjB0NBQxtY+Ellpox7QXwI/BPydmX3ZzN5pZn1ZiEtkxykuLmb37t309PQEVtFgaWmJkZERTpw4weDgYEarfYtsZhRcG/DvgJcAR4EngC+6+19nLrzcoVFwkm0TExOB90YSaxKFQiEtBSFbkq5RcGPAJ4BPWOwewbOBe4AdkYBEsq2lpYWGhgbC4TCTk5OBxLByTaLGxkZCoRCVlZWBxCKFJ+UEtJLHuk3fiH+JSIaUlJSwb98+WlpaGBgYCGwJhsRSEFNTU6rALWmzpQQkItlVU1NDT08Po6OjDA8PBzpsOlGBu66ujlAopMKnsmXrJiAzK3Z3jcsUyQFmdruKweDgYOA13q5evcrVq1epqakhFAplZVE+KSwbjYKLmNmHzOzfZCUaEdlQWVkZBw8e5ODBg5SVlQUdDteuXeP8+fOcPn2a6enpoMORPLJRAroLOAn8TzMbMLPfMbNnZSEuEdlAQ0MDfX19tLe350Q5nevXr3Px4kX6+/uZnJzMaokhyU/rJiB3H3P3+939+cDzgGHgATM7Z2bvy0qEIrKmoqIidu3aFejcoWSJenMnT55kbGxMZX5kTSmX4nH3QeBDwO8Bc8SWahCRHFBZWcnRo0fZs2cPxcXFQYcDxOrNDQ4OcuLECUZGRlTmR55mwwRkZmVm9goz+xRwCfgJ4D3ElmYQkRzS2tpKX19fTq37s7i4yNDQECdOnCASiajMj9y20Si4PyM22fTrwEPAG939ejYCE5GtKS0tZf/+/TQ3NzMwMJD15cDXsrS0dHsJipaWFtrb23NiEIUEZ6N5QF8DftHdtaavSJ6pq6ujt7eXkZERRkZGcmZQwPLyMmNjY4yPj9Pc3Ex7ezsVFRVBhyUB2GgQwkfdfcbMWs3sw2b2twBm1mtmb8xKhGlkZgfM7KNm9pnVtkUKTWI58KDWHVpPosxPf38/Fy9e5Pp13VzZaVIdhPBxYr2hXfHtp4B3rneCme02s6+a2Wkz6zezd2w1SDP7mJmNmdnJVV67x8zOmtl5M3vXetdx94vuft9a2yKFKrHu0L59+ygpyb0CKNPT05w+fZrz589z7dq1oMORLEk1AbW5+18CywDufgvYaEjLIvBOd+8BngP8vJn1rjzAzNrMrDZp36FVrvVxYs+i7mBmxcD9xCp09wKvjffO7jKzR5K+2lJ6pyIFrLm5mb6+Ppqbm4MOZVXRaJSzZ89y9uxZotFo0OFIhqWagObMrAlwADP7QWB2vRPcfdjdvxX/+yxwGuhKOuwFwOfMrCJ+3bcAv7/KtR4DplZp5tnA+XhPZoHYQIl73f2Eu7806Wssxfd6h/iCfB/RD4MUikSB0yNHjuTssxdVV9gZUk1Avwo8DBwws68BDwK/kGojZrYPuJuk6tnu/mngi8BDZvY64M3AT6V6XWIJbXDFdpinJ7mVcTSb2QPA3Wb27uTt1c5x94fd/a2qcyWFpra2lt7eXjo6OnKiksJqVlZXmJiYyJmBFJIeGw3Dfo67/7O7P2FmLwJ6AANOxXscGzKzGuCvgF9y96vJr7v7B83sIWKTXA+6+2ZuAK/2U7Pm/1B3nwTelrQ7eVtkxzAzOjs7aWpqYmBggNnZdW9sBObGjRtcuXKF4eFh2tvbaWlpoago5Xn0kqM2+g7+YeIv7r7g7t919+9sIvmUEks+f7HWyqlm9nzgGPBZ4L2phX1bGNi9YnsXENnkNUR2vMQghb179+bkIIWEldUVhoeHVV0hz2XsV4j4qqkfBU67+++ucczdwB8B9wJvAprM7AObaOabwGEz229mZcBrgM9vL3KRnaulpSXnKimsZnFxkUgkwokTJwiHw9y6dSvokGQLNvpV54CZrfmB7u4vX+fc5wE/DZwws+/E9/1Xd//CimOqgFe5+wUAM3sD8MbkC5nZg8ALgRYzCwPvjc9RWjSztwNfAoqBj7l7/wbvSUTWUVJSkpOVFFaztLTE6OjoHZNay8vLgw5LUmTrPdQzs6eAn1nrdXf/WiaCykXHjx/3J554IugwRLJqeXn5dvmcfBgAYGY0NjYSCoWorKwMOhwBzOxJdz++2msb9YCu7aQkIyJ3Kioqoqur6/YghVyfJOruTE1NMTU1RUNDA6FQKGeWqZCn2ygBXcpKFCKS0yorK+nu7mZ8fJyhoaG8ePg/MzPDzMwMtbW1hEIh6urqgg5JkmyUgFYdPJBgZnXAHnd/WokcESk8ra2tNDQ0MDg4mDcTRGdnZ5mdnaWqqopQKERjY2PQIUncRgnolWb2QWKTRZ8ExoEK4BDwImAvG9SEE5HCUlpayoEDB4hGowwMDLCwkNKsjMAlJrVWVFQQCoVoamrK2Qm4O8W6gxAAzKwR+Elio9o6gHliZXX+1t3/MeMR5ggNQhB5uuXlZYaGhhgb21Klq0CVlZVpUmsWrDcIYcMEJDFKQCJrm5ub48qVK8zPzwcdyqaVlJTQ1tZGW1tbzixnXkjWS0BK+yKybdXV1fT09NDV1ZV3vQlNag1O7tbcEJG8Yma3H/IPDAxw9erTSj/mNE1qzb78+lVFRHJeeXk5hw8fztnF7zayvLzM+Pg4/f39XLp0KS9vK+aLlP53mFkVsdFue9z9LWZ2GOh290cyGp2I5K3m5mbq6+sJh8NMTk4GHc6mrZzUWl9fT0dHhya1plmqPaA/AW4Cz41vh4HNFA0VkR0osfjd4cOH8/p2VjQa5cyZM5w7dy7vbi3mslQT0EF3/yBwC8Dd51l9LR4Rkaepq6ujt7eXUCiU13NvZmdneeqpp7RSa5qkeoN2wcwq+f6S3AeJ9YhERFKSqCvX2NjIlStXuH79etAhbdnKSa3t7e00NzfndWINSqo9oPcSq4aw28z+Avh74NcyFpWIFKyqqiqOHj3K7t27827IdrLESq0nT55kdHSU5eXloEPKKyn1gNz9K2b2LeA5xG69vcPdJzIamYgULDOjra2NhoYGBgYGiEajQYe0LQsLC4TDYUZGRjSpdRPWTUBm9sykXcPxP/eY2R53/1ZmwhKRnaCsrIxDhw4xNTXF4OAgi4uLQYe0LYlJrSMjI7S2ttLe3k5paWnQYeWsjXpA/zv+ZwVwHPgusR7QvwK+Afxw5kITkZ2iqamJurq6vB2ynWx5eZnR0VHGxsZobm4mFArl9SjATFn3Bqy7v8jdXwRcAZ7p7sfd/VnA3cD5bAQoIjtDYsj2kSNHCubD2t2ZmJigv7+fixcvalJrklSfAB519xOJjfj6P8/ITEgispPV1tZmdMj2ex6d5D2PZreX5e5MT09z6tQpzp8/n/Mry2ZLqsOwT5vZHwN/Tmwo9uuJLckgIpJ2hTRkO1k0GiUajVJTU0MoFKK+vj7okAKTag/oTUA/8A7gl4BT8X0iIhmTGLK9a9euvB+ynezatWucP3+eU6dOMTU1xU5cGifVYdg3gN+Lf4mIZI2Z0d7efnvIdqGVwpmfn+fSpUtEIpHbC+TtlEmtKf1KYWbPM7OvmNk5M7uY+Mp0cCIiCfleZXsjN2/eZGBggBMnTjA6OsrS0lLQIWVcqt/FjwK/DDwJFP6/iojkrESV7cHBQaampoIOJ+1u3bpFOBxmeHj49qTWQky4kHoCirr7/8toJCIiKSopKWH//v00Nzdz5coVFhYWgg4p7ZaWlhgeHmZ0dJSWlhba29spKysLOqy0SjUBfdXMfhv4a1YUIVUlBBEJUl1dHX19fQwNDTE2NhZ0OBmxvLzM2NgY4+PjNDU1EQqFqKioCDqstEg1Af1Q/M/jK/Y58OL0hiMisjlFRUXs3r2bpqYmrly5UrCTPd2dyclJJicnaWxsJBQKUVVVFXRY25LqKLgXZToQEZHtqK6upqenh5GREYaHhwt6WPP09DTT09PU1dURCoWora0NOqQt2agY6a+s97q7/256wxER2Tozo6Oj4/YE1kKvOHD16lWuXr1KdXU1oVCIhoaGoEPalI16QIm02g38IPD5+PbLgMcyFZSIyHZUVFTQ3d3N+Pg4Q0NDTxvSPDZXWIN55+bmuHDhAhUVFYRCIZqamvJiLtG6Ccjd3wdgZl8mVox0Nr7934FPZzw6EZFtaG1tvT2BdWZm5vb+8euFuXDcjRs3uHz5MpFIhFAoRHNzc05XkEh1EMIeYOU4xwVgX9qjEZEtefWHHw86hJy3uLjIzZs3uTQTW3Mo2wVJs28YM6O0tIyyslJiK+lszSd/9rnpC2uFVBPQJ4B/MbPPEhv99lkVk68AAAq8SURBVArgTzMSkYhIBozMLjA0c3sWCf3jtwBorSqirbowVy91dxYWbrKwsEBZWSmlpWU5dWvOUh0pEl8d9fnxzcfc/dsZiyoHHT9+3J944omgwxCRbXr1hx/nG5emeOT1e7l58+bGJxSQoqKi2wvkZWtSq5k96e7HV3st5foO8UmnmngqIgWht7eXSCTC2NhYQQ/ZXml5eZnx8XEmJiZuzyWqrKwMLJ7CLDAkIrKOroYKioqK2LVrF01NTVy+fLlgJ7Cuxt2ZmppiamqKhoYGQqEQ1dXVWY9DCUhEdpxdjd+vIFBVVUVPTw+jo6NEIpEd0xtKmJmZYWZmhtraWkKhEHV1dVlrWwlIRHY8M7s9kXMnTGBdzezsLLOzs1RVVdHV1ZWVRJS7A8RFRLIsMYF1z549FBcX5si4jVy/fp3R0dGstKUEJCKSpLW1ld7eXurr64MOpaApAYmIrKKsrIxDhw6xf//+gl0QLmj6VxURWUdTUxN1dXWEw2EmJwu9ekJ2qQckIrKBkpIS9u3bx+HDhwtuVdIgKQGJiKQosQJrW1tb0KEUBCUgEZFNSKzA2t3dXTBLYwdFCUhEZAtqamro7e2lo6Mjpwp85hMlIBGRLTIzOjs76enpCaSUTb7TKDgR2VEysbZNZWUl3d3djI2NEYlEWF4uzAXv0k09IBGRNDAz2tvb6e3tpba2Nuhw8oISkIhIGpWXl3PkyBH27t27Y8v5pEoJSEQkA1paWujr66OhoSHoUHKWEpCISIaUlpZy8OBBDhw4QGlpadDh5BwNQhARybDGxkZqa2tVzieJekAiIlmgcj5PpwQkIpJFKufzfUpAIiJZpnI+MUpAIiIB2enlfJSAREQCtLKcT1VVVdDhZJUSkIhIDqisrOTo0aN0dXVRVLQzPpp3xrsUEckDZkYoFKK3t5eampqgw8k4JSARkRxTXl5Od3c3e/bsKehyPkpAIiI5qrW1ld7eXurr64MOJSOUgEREclhZWRmHDh1i//79lJQUVvEaJSARkTzQ1NREX18fjY2NQYeSNkpAIiJ5oqSkhAMHDnDw4MGCKG5aWP05EZEdoKGh4XZx04mJiaDD2TL1gERE8lBxcTF79+7N6+KmSkAiInksn4ubKgGJiOS5fC1uqgQkIlIgEsVNQ6FQXhQ3VQISESkgZkZXVxdHjx7N+eKmSkAiIgWoqqqKo0eP0tnZmbO9ISUgEZECZWZ0dHTQ29tLdXV10OE8jRKQiEiBq6io4OjRo+zevTunlnrInUgyyMwOmNlHzewz6+0TESlkbW1t9Pb2UltbG3QoQB4kIDP7mJmNmdnJpP33mNlZMztvZu9a7xruftHd79ton4hIoSsvL+fIkSPs3bs38KUe8qEUz8eBPwD+LLHDzIqB+4EfBcLAN83s80Ax8JtJ57/Z3ceyE6qISH5oaWmhvr6eK1euEI1GA4kh5xOQuz9mZvuSdj8bOO/uFwHM7CHgXnf/TeCl6WrbzN4KvBVgz5496bqsiEhOKC0t5dChQ0xNTTE4OMji4mJW28/5W3Br6AIGV2yH4/tWZWbNZvYAcLeZvXutfcnc/SPuftzdj7e2tqYxfBGR3BHUUg853wNaw2qD2n2tg919EnjbRvtERHaqxFIPMzMzzMzMZKfNrLSSfmFg94rtXUAkoFhERApGQ0MDDQ0NWWkrX2/BfRM4bGb7zawMeA3w+YBjEhGRTcj5BGRmDwKPA91mFjaz+9x9EXg78CXgNPApd+8PMk4REdmcnL8F5+6vXWP/F4AvZDkcERFJk5zvAYmISGFSAhIRkUAoAYmISCCUgEREJBBKQCIiEgglIBERCYS5r1nBRlYws3FgBthK2dgWYCK9Eck66tna9ynX5er7CiKuTLeZieun45rbvcZWz9/OZ9hed1+1mKYS0CaY2Ufc/a1bOO8Jdz+eiZjk6bb6fcp1ufq+gogr021m4vrpuOZ2r5Frn2G6Bbc5DwcdgKSkUL9Pufq+gogr021m4vrpuOZ2r5FT/4fUA8oC9YBEJJ+pB5TfPhJ0ACIi25CRzzD1gEREJBDqAYmISCCUgEREJBBKQCIiEggloACYWbWZ/amZ/ZGZvS7oeEREUmVmB8zso2b2me1eSwkoTczsY2Y2ZmYnk/bfY2Znzey8mb0rvvs/AJ9x97cAL896sCIiK2zm88vdL7r7feloVwkofT4O3LNyh5kVA/cDLwF6gdeaWS+wCxiMH7aUxRhFRFbzcVL//EobJaA0cffHgKmk3c8Gzsd/Y1gAHgLuBcLEkhDoeyAiAdvk51fa6MMvs7r4fk8HYomnC/hr4JVm9iFyrDSGiEjcqp9fZtZsZg8Ad5vZu7fTQMl2TpYN2Sr73N3ngDdlOxgRkU1Y6/NrEnhbOhpQDyizwsDuFdu7gEhAsYiIbEbGP7+UgDLrm8BhM9tvZmXAa4DPBxyTiEgqMv75pQSUJmb2IPA40G1mYTO7z90XgbcDXwJOA59y9/4g4xQRSRbU55eKkYqISCDUAxIRkUAoAYmISCCUgEREJBBKQCIiEgglIBERCYQSkIiIBEIJSCQNzGzJzL6z4utdG5+VHWb2mfgaLt+IxzZgZuMrYt23xnkfMLP3J+07bmbfi//9782sPvPvQAqV5gGJpIGZXXP3mjRfsyQ+GXA71+gDPuDur1ix743AcXd/ewrnftbdj6zY9zvApLv/ppndB7S4+//aToyyc6kHJJJBZnbZzN5nZt8ysxNmdjS+vzq+CNg3zezbZnZvfP8bzezTZvYw8GUzKzKzPzSzfjN7xMy+YGY/aWY/YmafXdHOj5rZX68SwuuAz6UQ50vM7PF4nJ80s+r4rPcbZvas+DEGvIpYWX7i1/2P2/n3kZ1NCUgkPSqTbsG9esVrE+7+TOBDwK/G9/068A/u/oPAi4DfNrPq+GvPBd7g7i8mtnruPuAu4GfirwH8A9BjZq3x7TcBf7JKXM8DnlwvcDNrA94F/Eg8zu8B74i//CCxGmCJa0Xc/RKAu08AtWbWsN71Rdai5RhE0mPe3Z+xxmuJnsmTxBIKwI8BLzezREKqAPbE//4Vd08sDvbDwKfdfRkYMbOvQqwmvpl9Ani9mf0JscT0n1ZpuwMY3yD2f01sxcuvxzo5lAH/GH/tQeBrZvZrxBLRg0nnjsfbmNmgDZGnUQISybyb8T+X+P7PnAGvdPezKw80sx8C5lbuWue6f0JsQcMbxJLUas+L5oklt/UY8EV3/+nkF9z9splFgOcDrwCelXRIRbwNkU3TLTiRYHwJ+IX4cxXM7O41jvtHYqvnFplZO/DCxAvuHiG2PstvAB9f4/zTwKENYvk68AIzOxCPpdrMDq94/UHg94HT7j6S2GlmRUALd66aKZIyJSCR9Eh+BvRbGxz/fqAU+J6ZnYxvr+aviC0MdhL4MPANILri9b8ABt391Brn/y0rktZq3H0UuA/4pJl9l1hCOrLikE8Bx/j+4IOEZwP/6O5L611fZC0ahi2S48ysxt2vmVkz8C/A8xI9ETP7A+Db7v7RNc6tBL4aPyeticLM7ie2RszX0nld2Tn0DEgk9z0SH2lWBrx/RfJ5ktjzoneudaK7z5vZe4EuYCDNcX1byUe2Qz0gEREJhJ4BiYhIIJSAREQkEEpAIiISCCUgEREJhBKQiIgEQglIREQC8f8Bvt9/m85c/+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = spec.plot_error(energy_range=energy_range, energy_power=2)\n",
    "flux_points.plot(ax=ax, energy_power=2)"
   ]
  }
 ],
 "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
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "01965b3508404ac1ac56d86eedf08b01": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
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      "state": {
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       "_model_module_version": "1.2.0",
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       "_view_module": "@jupyter-widgets/base",
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       "grid_template_areas": null,
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