\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/fermi_lat.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",
"[fermi_lat.ipynb](../_static/notebooks/fermi_lat.ipynb) |\n",
"[fermi_lat.py](../_static/notebooks/fermi_lat.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fermi-LAT data with Gammapy\n",
"\n",
"## Introduction\n",
"\n",
"This tutorial will show you how to work with Fermi-LAT data with Gammapy. As an example, we will look at the Galactic center region using the high-energy dataset that was used for the 3FHL catalog, in the energy range 10 GeV to 2 TeV.\n",
"\n",
"We note that support for Fermi-LAT data analysis in Gammapy is very limited. For most tasks, we recommend you use \n",
"[Fermipy](http://fermipy.readthedocs.io/), which is based on the [Fermi Science Tools](https://fermi.gsfc.nasa.gov/ssc/data/analysis/software/) (Fermi ST).\n",
"\n",
"Using Gammapy with Fermi-LAT data could be an option for you if you want to do an analysis that is not easily possible with Fermipy and the Fermi Science Tools. For example a joint likelihood fit of Fermi-LAT data with data e.g. from H.E.S.S., MAGIC, VERITAS or some other instrument, or analysis of Fermi-LAT data with a complex spatial or spectral model that is not available in Fermipy or the Fermi ST.\n",
"\n",
"Besides Gammapy, you might want to look at are [Sherpa](http://cxc.harvard.edu/sherpa/) or [3ML](https://threeml.readthedocs.io/). Or just using Python to roll your own analyis using several existing analysis packages. E.g. it it possible to use Fermipy and the Fermi ST to evaluate the likelihood on Fermi-LAT data, and Gammapy to evaluate it e.g. for IACT data, and to do a joint likelihood fit using e.g. [iminuit](http://iminuit.readthedocs.io/) or [emcee](http://dfm.io/emcee).\n",
"\n",
"To use Fermi-LAT data with Gammapy, you first have to use the Fermi ST to prepare an event list (using ``gtselect`` and ``gtmktime``, exposure cube (using ``gtexpcube2`` and PSF (using ``gtpsf``). You can then use [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst), [gammapy.maps](..\/maps/index.rst) and the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) to read the Fermi-LAT maps and PSF, i.e. support for these high-level analysis products from the Fermi ST is built in. To do a 3D map analyis, you can use Fit for Fermi-LAT data in the same way that it's use for IACT data. This is illustrated in this notebook. A 1D region-based spectral analysis is also possible, this will be illustrated in a future tutorial.\n",
"\n",
"## Setup\n",
"\n",
"**IMPORTANT**: For this notebook you have to get the prepared ``3fhl`` dataset provided in your $GAMMAPY_DATA.\n",
"\n",
"Note that the ``3fhl`` dataset is high-energy only, ranging from 10 GeV to 2 TeV."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fermi_3fhl_events_selected.fits.gz\r\n",
"fermi_3fhl_exposure_cube_hpx.fits.gz\r\n",
"fermi_3fhl_psf_gc.fits.gz\r\n",
"gll_iem_v06_cutout.fits\r\n",
"iso_P8R2_SOURCE_V6_v06.txt\r\n"
]
}
],
"source": [
"# Check that you have the prepared Fermi-LAT dataset\n",
"# We will use diffuse models from here\n",
"!ls -1 $GAMMAPY_DATA/fermi_3fhl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from astropy import units as u\n",
"from astropy.coordinates import SkyCoord\n",
"from gammapy.data import EventList\n",
"from gammapy.irf import EnergyDependentTablePSF, EnergyDispersion\n",
"from gammapy.maps import Map, MapAxis, WcsNDMap, WcsGeom\n",
"from gammapy.spectrum.models import TableModel, PowerLaw\n",
"from gammapy.image.models import SkyPointSource, SkyDiffuseConstant\n",
"from gammapy.cube.models import SkyModel, SkyDiffuseCube, BackgroundModel\n",
"from gammapy.cube import MapDataset, PSFKernel\n",
"from gammapy.utils.fitting import Fit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Events\n",
"\n",
"To load up the Fermi-LAT event list, use the [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst) class:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EventList info:\n",
"- Number of events: 697317\n",
"- Median energy: 1.59e+04 MeV\n",
"\n"
]
}
],
"source": [
"events = EventList.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz\"\n",
")\n",
"print(events)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The event data is stored in a [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html) object. In case of the Fermi-LAT event list this contains all the additional information on positon, zenith angle, earth azimuth angle, event class, event type etc."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ENERGY',\n",
" 'RA',\n",
" 'DEC',\n",
" 'L',\n",
" 'B',\n",
" 'THETA',\n",
" 'PHI',\n",
" 'ZENITH_ANGLE',\n",
" 'EARTH_AZIMUTH_ANGLE',\n",
" 'TIME',\n",
" 'EVENT_ID',\n",
" 'RUN_ID',\n",
" 'RECON_VERSION',\n",
" 'CALIB_VERSION',\n",
" 'EVENT_CLASS',\n",
" 'EVENT_TYPE',\n",
" 'CONVERSION_TYPE',\n",
" 'LIVETIME',\n",
" 'DIFRSP0',\n",
" 'DIFRSP1',\n",
" 'DIFRSP2',\n",
" 'DIFRSP3',\n",
" 'DIFRSP4']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.table.colnames"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=5\n",
"
\n",
"
ENERGY
RA
DEC
\n",
"
MeV
deg
deg
\n",
"
float32
float32
float32
\n",
"
12856.5205
139.64438
-9.93702
\n",
"
14773.319
177.04454
60.55275
\n",
"
23273.527
110.21325
37.002018
\n",
"
41866.125
334.85287
17.577398
\n",
"
42463.074
316.86676
48.152477
\n",
"
"
],
"text/plain": [
"
\n",
" ENERGY RA DEC \n",
" MeV deg deg \n",
" float32 float32 float32 \n",
"---------- --------- ---------\n",
"12856.5205 139.64438 -9.93702\n",
" 14773.319 177.04454 60.55275\n",
" 23273.527 110.21325 37.002018\n",
" 41866.125 334.85287 17.577398\n",
" 42463.074 316.86676 48.152477"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.table[:5][[\"ENERGY\", \"RA\", \"DEC\"]]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2008-08-04 15:49:26.782\n",
"2015-07-30 11:00:41.226\n"
]
}
],
"source": [
"print(events.time[0].iso)\n",
"print(events.time[-1].iso)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean = 28905.451171875 MeV\n",
"std = 61051.7421875 MeV\n",
"min = 10000.03125 MeV\n",
"max = 1998482.75 MeV\n",
"n_bad = 0\n",
"length = 697317\n"
]
}
],
"source": [
"energy = events.energy\n",
"energy.info(\"stats\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a short analysis example we will count the number of events above a certain minimum energy: "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Events above 10 GeV: 697317\n",
"Events above 100 GeV: 23628\n",
"Events above 1000 GeV: 544\n"
]
}
],
"source": [
"for e_min in [10, 100, 1000] * u.GeV:\n",
" n = (events.energy > e_min).sum()\n",
" print(\"Events above {0:4.0f}: {1:5.0f}\".format(e_min, n))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Counts\n",
"\n",
"Let us start to prepare things for an 3D map analysis of the Galactic center region with Gammapy. The first thing we do is to define the map geometry. We chose a TAN projection centered on position ``(glon, glat) = (0, 0)`` with pixel size 0.1 deg, and four energy bins."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"gc_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n",
"energy_axis = MapAxis.from_edges(\n",
" [10, 30, 100, 300, 2000], name=\"energy\", unit=\"GeV\", interp=\"log\"\n",
")\n",
"counts = Map.create(\n",
" skydir=gc_pos,\n",
" npix=(100, 80),\n",
" proj=\"TAN\",\n",
" coordsys=\"GAL\",\n",
" binsz=0.1,\n",
" axes=[energy_axis],\n",
")\n",
"# We put this call into the same Jupyter cell as the Map.create\n",
"# because otherwise we could accidentally fill the counts\n",
"# multiple times when executing the ``fill_by_coord`` multiple times.\n",
"counts.fill_by_coord(\n",
" {\n",
" \"skycoord\": events.radec,\n",
" # The coord-based interface doesn't use Quantity,\n",
" # so we need to pass energy in the same unit as\n",
" # used for the map axis\n",
" \"energy\": events.energy,\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'GeV' \n",
"\tnbins : 4 \n",
"\tnode type : edges \n",
"\tedges min : 1.0e+01 GeV\n",
"\tedges max : 2.0e+03 GeV\n",
"\tinterp : log "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts.geom.axes[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"counts.sum_over_axes().smooth(2).plot(stretch=\"sqrt\", vmax=30);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exposure\n",
"\n",
"The Fermi-LAT datatset contains the energy-dependent exposure for the whole sky as a HEALPix map computed with ``gtexpcube2``. This format is supported by ``gammapy.maps`` directly.\n",
"\n",
"Interpolating the exposure cube from the Fermi ST to get an exposure cube matching the spatial geometry and energy axis defined above with Gammapy is easy. The only point to watch out for is how exactly you want the energy axis and binning handled.\n",
"\n",
"Below we just use the default behaviour, which is linear interpolation in energy on the original exposure cube. Probably log interpolation would be better, but it doesn't matter much here, because the energy binning is fine. Finally, we just copy the counts map geometry, which contains an energy axis with `node_type=\"edges\"`. This is non-ideal for exposure cubes, but again, acceptable because exposure doesn't vary much from bin to bin, so the exact way interpolation occurs in later use of that exposure cube doesn't matter a lot. Of course you could define any energy axis for your exposure cube that you like."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HpxGeom\n",
"\n",
"\taxes : skycoord, energy\n",
"\tshape : (49152, 18)\n",
"\tndim : 3\n",
"\tnside : 64\n",
"\tnested : False\n",
"\tcoordsys : CEL\n",
"\tprojection : HPX\n",
"\tcenter : 0.0 deg, 0.0 deg\n",
"\n",
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : '' \n",
"\tnbins : 18 \n",
"\tnode type : center \n",
"\tcenter min : 1.0e+04 \n",
"\tcenter max : 2.0e+06 \n",
"\tinterp : log \n",
"\n"
]
}
],
"source": [
"exposure_hpx = Map.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz\"\n",
")\n",
"# Unit is not stored in the file, set it manually\n",
"exposure_hpx.unit = \"cm2 s\"\n",
"print(exposure_hpx.geom)\n",
"print(exposure_hpx.geom.axes[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Exposure varies very little with energy at these high energies\n",
"energy = np.logspace(1, 3, 10) * u.GeV\n",
"dnde = diffuse_galactic.interp_by_coord(\n",
" {\"skycoord\": gc_pos, \"energy\": energy}, interp=\"linear\", fill_value=None\n",
")\n",
"plt.plot(energy.value, dnde, \"*\")\n",
"plt.loglog()\n",
"plt.xlabel(\"Energy (GeV)\")\n",
"plt.ylabel(\"Flux (cm-2 s-1 MeV-1 sr-1)\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# TODO: show how one can fix the extrapolate to high energy\n",
"# by computing and padding an extra plane e.g. at 1e3 TeV\n",
"# that corresponds to a linear extrapolation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Isotropic diffuse background\n",
"\n",
"To load the isotropic diffuse model with Gammapy, use the [gammapy.spectrum.models.TableModel](..\/api/gammapy.spectrum.models.TableModel.rst). We are using `'fill_value': 'extrapolate'` to extrapolate the model above 500 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"filename = \"$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt\"\n",
"interp_kwargs = {\"fill_value\": None}\n",
"diffuse_iso = TableModel.read_fermi_isotropic_model(\n",
" filename=filename, interp_kwargs=interp_kwargs\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the model in the energy range between 50 GeV and 2000 GeV:"
]
},
{
"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": [
"erange = [50, 2000] * u.GeV\n",
"diffuse_iso.plot(erange, flux_unit=\"1 / (cm2 MeV s sr)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PSF\n",
"\n",
"Next we will tke a look at the PSF. It was computed using ``gtpsf``, in this case for the Galactic center position. Note that generally for Fermi-LAT, the PSF only varies little within a given regions of the sky, especially at high energies like what we have here. We use the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) class to load the PSF and use some of it's methods to get some information about it."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EnergyDependentTablePSF\n",
"-----------------------\n",
"\n",
"Axis info:\n",
" rad : size = 300, min = 0.000 deg, max = 9.933 deg\n",
" energy : size = 17, min = 10.000 GeV, max = 2000.000 GeV\n",
"\n",
"Containment info:\n",
" 68.0% containment radius at 10 GeV: 0.16 deg\n",
" 68.0% containment radius at 100 GeV: 0.10 deg\n",
" 95.0% containment radius at 10 GeV: 0.71 deg\n",
" 95.0% containment radius at 100 GeV: 0.43 deg\n",
"\n"
]
}
],
"source": [
"psf = EnergyDependentTablePSF.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz\"\n",
")\n",
"print(psf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get an idea of the size of the PSF we check how the containment radii of the Fermi-LAT PSF vari with energy and different containment fractions:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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C/4q7r819mCIiIpKuoZr4cne/o559/2lmA4GRLR+SiIiIZKPeJO7uzzR0orvvIdTORUREpA002CcOYGa/ATxt8yFgBfDf7l6Ri8BERESkYdmMTn8TOAo8GCuHgd2EvvEHcxeaiIiINKTRmjgw093PSXr/GzNb6u7nmJlGqIuIiLSRbGriJWZWO4At9vOA2NuTOYlKREREGpVNTfwrwMtm9gZgwGjg82bWHfhxLoMTERGR+jWaxN19iZmNByYSkvjrSYPZ7s5lcCIiIlK/RpvTzawbcCtws7uvBkaY2cdyHpmIiIg0KJs+8R8R+r7Pir0vA76bs4hEREQkK9kk8bHu/h9AJYC7lxOa1UVERKQNZZPET5pZV2ITvpjZWOBETqMSERGRRmUzOv1fgGcJfeE/A84G/iGXQYmIiEjjshmd/nszWwXMIzSjf8nd9+U8MhEREWlQQ0uRnpG2aWfsdaSZjXT3VbkLS0RERBrTUE38/8Vei4HZwBpCTbyUsL74+xu7uJldBNwD5AMPufudafvPA54C3optetzdv30K8YuIiHRaDS1Fej6AmT0K3ODu62LvpwJfbezCZpYP3A/MJzyWttzMFrv7xrRD/+Tueu5cRETkFGUzOn1iPIEDuPt6YEYW580Ftrn7m+5+EngUWNC0MEVERCRdNkl8k5k9ZGbnmdm5ZvYgsCmL84YB25Pel8W2pTvLzNaY2W/NbEoW1xURERGye8TsWuAm4Eux90uBH2ZxXqYJYTzt/SrgNHc/amYXA08C4+tcyOwG4AaAkSNHpu8WERHplBqtibt7hbt/390/GSvfT1oApSFlwIik98OBHWnXPuzuR2M/LwEKzWwAadx9obvPdvfZJSUlWXy0iIhIx1dvEjez35jZJWZWmGHfGDP7tpl9toFrLwfGm9loMysCrgAWp11nsJlZ7Oe5sXj2N+WLiIiIdDYNNadfD/wzcLeZHQD2Eh43Gw1sA+5z96fqO9ndq8zsZuA5wiNmD7v7BjO7Mbb/AeBy4CYzqwLKgSvcPb3JXURERDKwbHKmmY0ChhAS7RZ3P57bsOo3e/ZsX7FiRVt9vIiISKszs5XuPjt9ezYD23D3t4G3WzgmERERaYZsHjETERGRdkhJXEREJKJOKYmbWV8zK81VMCIiIpK9RpO4mb1kZr3MrB9hEZQfmdl/5j40ERERaUg2NfHe7n4YuBT4kbvPAj6U27AasHs9/Pp6WPUTeO9vbRaGiIhIW8tmdHqBmQ0BPgV8M8fxNK66Etb9IhSAPiNh9Dlw2tkw4kzoNwYs04yvIiIiHUs2SfzbhAlbXnb35WY2Btia27AaUDIJPvJFeGspvP0nOPgOvPbTUAC69Yfhc2HEnJDUh54BRd3aLFwREZFcyWqyl/YkZbKXmmrYtRbe+hNs/ytsXwbH9qSekFcAg6aGhD5ibii9R6i2LiIikVHfZC+NJnEz+xF1Vx/D3RuaNz1nGpyxzR3eexvKlieS+u714DWpx/UYnEjoI86EIdOhoEvOYxcREWmK5szY9nTSz8XAJ0lbjazdMIN+o0Mp/VTYduIo7FiVSOrbl8HRXbBpcSgA+UUwZEZqYu85uO2+h4iISBZOuTndzPKA5939gtyE1LBmz51eUwP7t0HZslhiXw57N9U9rvfIREIfMSc0yefXWdBNREQk55o1d3qa8cDI5ofURvLyoOT0UGb+fdhWfhDeXRGrqf8VylbCoXdCWf+rcExhtzBILl5bHz4Xuvdvu+8hIiKdXqNJ3MyOEPrELfa6C/h6juNqXV37wLgPhQJhwNyeTbHaeqwceAP+9nIocf3GJmrqI86EkomQl98230FERDqdaI9Ob03H9qUOmHt3FVSVpx7TpRcMm5VI7MNmhxsEERGRZjjl0elmNtHdXzezMzLtd/dVLRxjVtrNeuLVlbBrXVJiXx6a31NYqJ2PmBtGwJdMgAEToPsAPeImIiJZa0oSf9DdrzezFzPs9sgObMulwztCLT2e2HeugeqTdY/r2jck9wGnJxJ7yenQa3josxcREUnS5OfE25t2ncTTVVaERF62DHZvhH2bYe8WOHkk8/GF3WDA+ERSHzAhJPl+YzQyXkSkEzvl0elmdmlDF3T3x1sisA6tsBhGnhlKnDsc2Ql7N8O+Lamvx/aEpL9zTep18grCILrkxD7g9JDwi7q37ncSEZF2o6HR6ZfEXgcC7wNeiL0/H3gJUBJvCjPoNTSUseen7jt+oG5i37c5zA+/L/Yzv0k9p/fIpOSelOS79Wu1ryQiIm2j3iTu7tcCmNnTwGR33xl7PwS4v3XC62S69YOR80JJdvI47N8amuL3bYa9r4efD7yReJ592/Op53QvSUvssddeQzWoTkSkg8hmspdR8QQesxs4PUfxSCZF3cLo9iHTU7dXV8KBt2KJPa15/tjeUJKfawco6hma4eNN8iUTwiC7vqP0jLuISMRkk8RfMrPngEcIk71cAWQasS6tLb8wMfvcpEsS22tq4HBZUs09KcGXHwhzye9Ie0KwoCsMmhK7WSiFwaUwcHLo1xcRkXYpq9HpsUFuH4i9XeruT+Q0qgZEanR6e3RsX6KvfW9Sgj/8bt1j8wpCLX1waVJynwZderZ+3CIinZgeMZOGHT8Q1mbfuTaMjt+1FvZtJcMqtGGkfLy2Hm/m7z6g1UMWEeksmrOe+DzgXmASUATkA8fcvVcuAm2MkngrOnEUdm+IJffYo297NkFNZd1jew5NbYofMh16D9cgOhGRFtCcVczuI/SD/xKYDVwNjGvZ8KRd6tKj7nPuVSfD6Ph4bX3n2jD97JEdoWz5beLYrn1Ta+uDS6H/WA2gExFpIVktReru28ws392rgR+Z2Ss5jkvaq4KiUNseUprYVlMNB95MSuxrQnIvPwBv/TGUuMLuMHhqaj97yaRwXREROSXZJPHjZlYErDaz/wB2ApomTBLy8mPTxY6HaZeHbe5hsFw8oceT++F3YwvG/DXp/EIYOCnWFB+vtU/VbHQiIo3Ipk/8NMKz4UXALUBv4L/cfVvuw6tLfeIRd2w/7FqTmtz3v0HdAXQG/cclauuDpoZn2nsNUz+7iHQ6TRrYZmb5wI/d/e+b+KEXAfcQBsM95O531nPcHOAvwKfd/VcNXVNJvAM6cQR2rU8dHb93E9RU1T22qEfqIjElE8PPfUdBfla9QyIikdOkgW3uXm1mJWZW5O4Z1tRs8APzCdOzzgfKgOVmttjdN2Y47t+B507l+tKBdOkJp50VSlzViTASPt7PvmdTeKb9+D7Y8VooyfKL6l8kprBr634fEZFWkk3V5W3gz2a2GDgW3+ju/9nIeXOBbe7+JoCZPQosADamHfdF4NfAnCxjls6goAsMnRFKsuMHMk9Wc2h7qL3v3ZR2IYO+p9VdIGbA6dC1T6t9HRGRXMgmie+IlTzgVKbqGgZsT3pfBpyZfICZDQM+CVyAkrhko1u/urV2CM+0pywSE0vu+9+A994OZWtaY0+PQanzx8d/7jFI/e4iEgmNJnF3/9cmXjvTX8H0Dvi7ga/Hmu3rv5DZDcANACNHjmxiONKhdekBQ2eGkqzqZHj8bd/mtAS/FY7uDuXtP6Vdq3eG5V1Phz6n6Rl3EWlXcjkSqAwYkfR+OKFGn2w28GgsgQ8ALjazKnd/Mvkgd18ILIQwsC1nEUvHU1AEAyeGkqymJjTBxxeG2ft64ueKg1C2PJSUaxVD//F1E3z/saH5X0SkleUyiS8HxpvZaOBdwqxvVyYf4O6j4z+b2SLg6fQELpITeXmhr7zvaTB+fmK7e1jCtbbffUvi9cgO2L0ulGSWH0bHl8T72+MJ/nQtFiMiOdVoEjezs939z41tS+fuVWZ2M2HUeT7wsLtvMLMbY/sfaEbcIrlhBj0GhjL6A6n7Kg6FZvj0BP/e23DgjVA2L0k9p9ewRF/7gNgjcSUTtGCMiLSIbCZ7WeXuZzS2rbXoOXFpdyorQgKvHS0fS/D7t0H1iczndO2XlNiTau+9hodWAhGRJKf8nLiZnQW8Dygxs39O2tWLULMWEYDCYhg0JZRkNdWhlh7va9+3JfS9790S5pV/59VQUq7VLTzbnjxafsAE6Dca8gtb7SuJSDQ01JxeBPSIHZPcsXcYuDyXQYl0CHn5YdBb/7Ew4SOJ7e5wZGdSYk96PbYnsexryrUKod+Y1GfdSyaEgXZF3Vr3e4lIu5HV3Onu/rdWiqdRak6XDu34gbqJfd9mOPhOPScY9BmROolN/LVbv1YNXURypznriXcxs4XAqOTj3f2ClgtPRICQeEfOCyXZyeNpk9nEmuUPvBES/MF3YNvvU8/pXpJI7gMnheVfB00Jz9SLSIeQTRL/JfAA8BBQndtwRCSjom6xFd2mp26vroQDb6XOUhd/PbY3lL+9nHRCfHW42Hru8XXdVWsXiaRsmtNXuvusVoqnUWpOF8lCTQ0cLkvU3HdvDEvA7qlndbjeI2IJPSm59xqq6WdF2onmNKf/xsw+DzwB1D4v4+4HWjA+EWlJeXnQZ2Qo4z+U2B5fHW7X2sSa7rvXh9nrDm2Hzc8kju3WP6m2XgpDZkDf0XoETqQdyaYm/laGze7uY3ITUsNUExdpYTXV4Zn2nWtDbT2e3CsO1j22qCcMnppohh9SGh6H0+NvIjlVX0280STe3iiJi7QC91Azjyf0eM39yM66x+YXwcDJIaEPjtXYB03Ro28iLajJzelm1g34Z2Cku99gZuOBCe7+dA7iFJH2wCzRHD/pksT2o3tTa+u71oZV4nauDqX2/LzwDHu8th5vku/at/W/i0gHlk1z+mPASuBqd59qZl2BV919RmsEmE41cZF2puIQ7Fqf2s++93XwDA+z9BmZqK3Hk3vPwRpAJ9KI5gxsG+vunzazzwC4e7k1tPi3iHQuxb1h1NmhxFVWwJ4NqU3xuzcknml/Pakhr/vApNp6rObed7QSu0gWskniJ2O1bwcws7EkjVIXEamjsBiGzQolrroqTFiT0s++Nkw1u+35UOK69Eo0wceT+4DTIT+XqyeLRE82/0f8C/AsMMLMfgacDfxDLoMSkQ4ovyDMHDdwEky/ImxzD4vE7FyTSOq71sLR3WGSmuSJagqKYwPo4v3s02HQZCjs2iZfR6Q9yGp0upn1B+YBBvzF3fflOrD6qE9cpBM4sqvuI28HMyzhYPlhWtnkpvjB00ITv0gH0qxHzMyslLpzpz/ekgFmS0lcpJMqfw92rUvtZ9+3Bbym7rF9R6c+8jakFHoMbP2YRVpIcx4xexgoBTYA8f9bHGiTJC4inVTXvjD6nFDiTh6HPRtjj7jFkvvuDfDeW6FsfCpxbI/BaY+8TQ+j5TWATiIsmz7xee4+OeeRiIicqqJuMHx2KHHVlWERmORH3natg6O7YOsu2Ppc4tjiPqH5Pb64zJDpYYGYvPzW/y4iTZBNEn/VzCa7+8acRyMi0lz5hbGpYafCjCvDtpqaUDOvHUAXS+7H98HbfwolrrBbmHEuuZ994GQo6NI230ekAdkk8R8TEvkuwqNlRpg7vTSnkYmItJS8POg/NpSpl4Zt7mEa2fRH3g69A2XLQ6k9vzAk9BFnwog54bXX0Lb5LiJJskniDwP/C1hHok9cRCTazEIi7jUUJnwksf34gURCj9fc922Fd1eE8pfYcb2Gw4i5iTK4VAvBSKvLJom/4+6Lcx6JiEh70K0fjDkvlLiKQ/DuSti+LJSy5WG99g1lsCE2xregGIaekUjqw+dCj5LWj186lWyS+Otm9nPgN6SuJ67R6SLSORT3hrEXhAKhj33fZtj+V9i+PLzu3wrvvBJKXN/RqU3wAydr0Jy0qGwWQPlRhs3u7p/NTUgN03PiItIuHT8Qaujb/xpq6++uhMrjqccU9QhT0Y6YG5L68Nla2U2yovXERURaU3VVWAQm3gS//a+ZZ50bMCGpb/3MsIRrXl7rxyvtWpOTuJmVANdTd8Y21cRFRE7Fkd1QtiyR2He8BtVp60kV9w796fHEPmwWdOnZNvFKu9GcJP4K8CfCmuK1CwS7+69bOshsKImLSIdRdTKMfo83wW9fBkd2pB5jeTBwSupIeC3V2uk0J4mvdvcZOYvsFCmJi0iHdqgsdcDcrrVQU5V6THFpE0sAABQESURBVLcBqQPmhs7Uam4dXJPnTgeeNrOL3X1JDuISEZFkvYeHMvWy8L6yPDS71z7etgyO7YXNz4QCkFcQ+taT54YfPA2Ke7Xd95BWkU1N/AjQnfB4WSWJGdsa/e0ws4uAe4B84CF3vzNt/wLgO4RJZKqAL7v7y3UulEQ1cRHp1NzDFLLbk/rW92zIvJpbvzFpy7RO17PrEdXqo9PNLB/YAswHyoDlwGeS52A3sx7AMXf32HKnv3D3iQ1dV0lcRCTNyeNh9bbk9df3bITqk3WP7Tk0dSW3IaXQe4T62Nu5U25ON7OJ7v66mZ2Rab+7r2rkM+cC29z9zdj1HgUWALVJ3N2PJh3fnbDEqYiInIqibrH+8TmJbdWVsPf11Oljd60LA+eO7IAtzyaO7do3ltRjtfUh08M885qYpt1rqE/8n4EbgP+XYZ8DFzRy7WHA9qT3ZcCZ6QeZ2SeB7wEDgY82ck0REclGfmHoFx88DWZeFbbV1MCBN2M19qTkfnw/vPXHUOIKu8Ggqan97AMnaTW3dqbeJO7uN8Rez2/itTO1zdSpabv7E8ATZnYOoX/8Q3UuZHYD4YaCkSNHNjEcEZFOLi8PBowLJT5wzh0O70hapjWW3A+XhUF0ZcuSzi+EgRNjtfVYc/ygqdClR9t8H8lqdDpmNhWYDBTHt7n7/zRyWhkwIun9cGBHPcfi7kvNbKyZDXD3fWn7FgILIfSJZxOziIhkwQx6Dwtl4sWJ7cf2J9Zejyf3/dtCk/yudbC69gKh6X3I9KR+9ulhIRnJuUaTuJn9C3AeIYkvAT4CvAw0lsSXA+PNbDTwLnAFcGXatccBb8QGtp0BFAH7T/E7iIhIS+veH8aeH0rciaOwe31SU/wa2PN6SO77t8H6pDnAeg1P1Nbj/e29hmkAXQvLpiZ+OTAdeM3drzWzQcBDjZ3k7lVmdjPwHOERs4fdfYOZ3Rjb/wBwGXC1mVUC5cCnPWqTuYuIdBZdesDIeaHEVZ2APZtSm+J3rw/N8YfLYHPSFCPd+icS+pDpoVm+3xjNFd8M2Twnvszd55rZSuB84Aiw3t2ntEaA6fSImYhIO1dTDfvfSNTW44+9VRyse2xRj9gAvKTkXjIxDMyTWs2ZsW2FmfUBHiTMn34UWNbwKSIi0mnl5UPJ6aGU/l3Y5g6HtqeOit+5Njzu9s6rocTlF4WR8Mn97IOmQFH3tvk+7dgpTfZiZqOAXu6+NlcBNUY1cRGRDuTo3rqPvB14s+5xlheWaU3vZ+8k67E3ZwGUP7j7Bxvb1lqUxEVEOriKw7FR8En97HtfB6+ue2yfkamj4geXQs/BHW4AXVNmbCsGugEDzKwviee+ewFDcxKliIhIcS8YdXYocZUVYSrZ5Kb43Rvg4DuhvP504tjuJam19SHTO+zyrQ31if8j8GVCwl5JIokfBu7PcVwiIiIJhcUw7IxQ4qqrYP/WkNDjz7TvXBtWedv2fChxXXqFAXTJyX3ABMjParqUdiub5vQvuvu9rRRPo9ScLiIi9XKHg39LJPR4cj+6u+6xBcUwcHJI6CWTwkC8AROg19B2V2tv1ipmZvY+YBRJNfcsZmzLCSVxERE5ZUd2xxL66kRyf+/tzMcW9YQB46FkAgw4PTzyVjIB+pzWZjX3Jj9iZmY/AcYSJtmLjypwGp+xTUREpH3oOQh6zofx8xPbyg8mppHdtxn2bgmvx/fDjlWhJMsvgv7jYol9QuK1/zgo7Nq63ycmm1uK2cBkzaQmIiIdStc+MPoDoSQ7tg/2bk5N7Hu3hBno9mwMJYVB39NCU3xJrAyYEJrni3vn9Ctkk8TXA4OBnTmNREREpD3oPiCU5NHxEOaO37cllL2vJxL8gbdC0/x7b8PW51LP6TE40deeXHvvMahF+t2zSeIDgI1mtgw4Ed/o7h9v9qeLiIhERZcedUfIQ5g//sCbsdr7lkQtft9WOLorlLeWpp5T3DtRW09O8H1OO6W55LNJ4ndkfTUREZHOpqBLmCZ24KTU7TXV4Rn25MQer71XHKq7XjuEEfP9xycl99jAuvo+urHY3P2PsZXL5sQ2LXP3Paf4FUVERDqXvHzoNzqU0z+c2O4OR/fEkvrm1AR/dBfsXhdKFrIZnf4p4C7gJcKEL/ea2a3u/qumfCcREZFOzSw2Wn4QjD4ndV/5wdAMH0/w8Vo8azJeKpvm9G8Cc+K1bzMrAZ4HlMRFRERaUtc+MGJOKMm+nHkQXDa953lpzef7szxPREREciibmvizZvYc8Ejs/aeB3+YuJBEREclGNgPbbjWzS4H3E/rEF7r7EzmPTERERBrU0FKk44BB7v5nd38ceDy2/RwzG+vub7RWkCIiIlJXQ33bdwNHMmw/HtsnIiIibaihJD7K3demb3T3FYQVzURERKQNNZTEixvY1zbLtYiIiEithpL4cjO7Pn2jmV0HrMxdSCIiIpKNhkanfxl4wsyuIpG0ZwNFwCdzHZiIiIg0rN4k7u67gfeZ2fnA1NjmZ9z9hVaJTERERBqUzXPiLwIvtkIsIiIicgo0faqIiEhEKYmLiIhElJK4iIhIRCmJi4iIRFROk7iZXWRmm81sm5ndlmH/VWa2NlZeMbPpuYxHRESkI8lZEjezfOB+4CPAZOAzZjY57bC3gHPdvRT4DrAwV/GIiIh0NLmsic8Ftrn7m+5+EngUWJB8gLu/4u7vxd7+BRiew3hEREQ6lFwm8WHA9qT3ZbFt9bkO+G0O4xEREelQGp3spRkswzbPeGCYFe464P317L8BuAFg5MiRLRWfiIhIpOWyJl4GjEh6PxzYkX6QmZUCDwEL3H1/pgu5+0J3n+3us0tKSnISrIiISNTkMokvB8ab2WgzKwKuABYnH2BmI4HHgf/l7ltyGIuIiEiHk7PmdHevMrObgeeAfOBhd99gZjfG9j8A/B+gP/BfZgZQ5e6zcxWTiIhIR2LuGbup263Zs2f7ihUr2joMERGRVmNmKzNVcjVjm4iISEQpiYuIiESUkriIiEhEKYmLiIhElJK4iIhIRCmJi4iIRJSSuIiISEQpiYuIiESUkriIiEhEKYmLiIhElJK4iIhIRCmJi4iIRJSSuIiISEQpiYuIiESUkriIiEhEKYmLiIhElJK4iIhIRCmJi4iIRJSSuIiISEQpiYuIiESUkriIiEhEKYmLiIhElJK4iIhIRCmJi4iIRJSSuIiISEQpiYuIiESUkriIiEhEKYmLiIhElJK4iIhIRCmJi4iIRFROk7iZXWRmm81sm5ndlmH/RDN71cxOmNlXcxmLiIhIR1OQqwubWT5wPzAfKAOWm9lid9+YdNgB4J+AT+QqDhERkY4qlzXxucA2d3/T3U8CjwILkg9w9z3uvhyozGEcIiIiHVLOauLAMGB70vsy4MzmXnTr7qMsuO9luhTk06Uwr/Y136y5lxZpsvw8o0tBXiiF+XQpyKM49hpK6u9rfFtxfFtBXu3+4sI8ivLzMP1Oi0gjcpnEM/0F8iZdyOwG4AaAosHjWFN2qDlxiURC8k1BSrJPvwHI4qYgeVtx8g1Fys1G2F6Un0denm4gRKIgl0m8DBiR9H44sKMpF3L3hcBCgKnTZ/pPv3A2JyqrqaiqqX11b9L9gUiLqKp2TlbXUFFZzYmqGk5U1nCiKvxcuy32+1pnW1V14vjKsO1kdU3tfiqqWv37FCW3IBTk1blhSEn8ye8z3RxkupnIcBNSXJivGwiRU5TLJL4cGG9mo4F3gSuAK5t70eLCfGaM6NPcy4i0azU1nkjwSTcFFUk3B/GkX5GU/FOOybSt3mslbixOJpUjtMENRH5e6g1BWitE4uYgw7bY8cUp+5NuKgrTb05SbzTydQMhEZOzJO7uVWZ2M/AckA887O4bzOzG2P4HzGwwsALoBdSY2ZeBye5+OFdxiURBXp7RtSifrkX5rf7ZNTWeaAmorE67EUjdFr8pqEhuTajKokUi7QakorI61pIRWiFOVtdw5ESrf3UK8ix1LEOGG4b6WhGa1zKRR0G+pu2QU5fLmjjuvgRYkrbtgaSfdxGa2UWkncjLM4rz8ikuzIeuha362e7JNxCZWxHSbxhOpHVXVKRvy9A6UZF2IxJ/X1XjHD1RxdE2uoFo7sDI9PEOjbZcxF4L8kwDKSMqp0lcRORUmFksAeVDcet+trtTWe3Z3TjUd8NQmdrVkemGob6Wi6oap+pkNcdOVrfuFwfyjAxdEo13P9RppWjgBiT9evEbkMJ83UA0h5K4iAjhBqKowCgqyKNnK99AAFSmdWFkOyCyInbTcbKB8Q/pNyCJ7o5EC8Txk9UcP1lNa0/bYcapd0nEn9qo5yai0a6O2L6O8CinkriISDtQmJ9HYX4ePbq0/p/lqqSnIRrtfmikSyJzN0im1opEC0RFZRgP0driNxBdCvLpWVxA766F9CouDK9dE+97da27LbwvDN1ObUhJXESkkyvIDwPrundp/c+uig1kbLD7oZEbhuY8yhm/gThUXknZe+WnHH9RQV4s0de9CUhO9pn29epa2OwnIpTERUSkzcRvILoVtf5nxx/lLK+s5mhFFYfKKzlcURleyyvT3lel7avicHklJ6tq2Hf0BPuaOBqyR5eQ/GtbAuppDaiPkriIiHRKyY9y9uvetLuIisrqlIR/uDzpZuB4PTcBsX1HTlTFnoZo+nwMSuIiIiJNVFwYHscc2OvUR0PW1DhHToQafUqyz9AicE8911ASFxERaQN5eUbvWH95Y+75TD3XaOGYREREpJUoiYuIiESUkriIiEhEKYmLiIhElJK4iIhIRCmJi4iIRJSSuIiISEQpiYuIiESUkriIiEhEKYmLiIhElLl7W8dwSszsELA1B5fuDRxqJ9dq6vlNOW8AsK8JnyWZteTvUVtpT9+hNWPJ1Wfpb4u0hPHu3rvOVnePVAEWtvfrNvdaTT2/KecBK9r637QjlVz9fnbW79CasehvS8uep78tLVvq+zeIYnP6byJw3eZeq6nn5+q/jWSvI/wbtKfv0Jqx6G9L7j5Xmi/jv0HkmtOlZZnZCnef3dZxiEjHor8trSOKNXFpWQvbOgAR6ZD0t6UVqCYuIiISUaqJi4iIRJSSuIiISEQpiYuIiESUkrikMLNPmNmDZvaUmV3Y1vGISPSZ2SQze8DMfmVmN7V1PB2JkngnYGYPm9keM1uftv0iM9tsZtvM7DYAd3/S3a8H/gH4dBuEKyIRcIp/Vza5+43ApwA9dtaClMQ7h0XARckbzCwfuB/4CDAZ+IyZTU465Fux/SIimSziFP6umNnHgZeBP7RumB2bkngn4O5LgQNpm+cC29z9TXc/CTwKLLDg34Hfuvuq1o5VRKLhVP6uxI5f7O7vA65q3Ug7toK2DkDazDBge9L7MuBM4IvAh4DeZjbO3R9oi+BEJJIy/l0xs/OAS4EuwJI2iKvDUhLvvCzDNnf3HwA/aO1gRKRDqO/vykvAS60bSueg5vTOqwwYkfR+OLCjjWIRkY5Bf1damZJ457UcGG9mo82sCLgCWNzGMYlItOnvSitTEu8EzOwR4FVggpmVmdl17l4F3Aw8B2wCfuHuG9oyThGJDv1daR+0AIqIiEhEqSYuIiISUUriIiIiEaUkLiIiElFK4iIiIhGlJC4iIhJRSuIiIiIRpSQuEhFmVm1mq5PKbW0dU1xsnegxsZ97mNkPzewNM3vNzFaa2fWNnP+SmX04bduXzey/zKzEzJ7NZfwiUaW500Wio9zdZ7TkBc2sIDZBR3OuMQXId/c3Y5seAt4Exrt7jZmVAJ9t5DKPEGb3ei5p2xXAre6+18x2mtnZ7v7n5sQq0tGoJi4ScWb2tpn9q5mtMrN1ZjYxtr27mT1sZstjNeIFse3/YGa/NLPfAL8zs7xYjXeDmT1tZkvM7HIz+6CZPZH0OfPN7PEMIVwFPBU7ZixhOcpvuXsNgLvvdfd/T7rOrbGY1prZv8Y2/wr4mJl1iR0zChhKWH8a4Em0hKVIHUriItHRNa05/dNJ+/a5+xnAD4GvxrZ9E3jB3ecA5wN3mVn32L6zgGvc/QLCEpGjgGnA52L7AF4AJsVq0gDXAj/KENfZwMrYz1OANfEEns7MLgTGExL9DGCWmZ3j7vuBZcBFsUOvAB7zxJSSK4APNPDfRqRTUhIXiY5yd5+RVB5L2hevIa8kJGSAC4HbzGw1YRnIYmBkbN/v3f1A7Of3A7909xp33wW8CGH9SOAnwN+bWR9Ccv9thriGAHszBWxm34zdcMRXsrowVl4DVgETCUkdEk3qxF4fSbrUHkLNXESSqE9cpGM4EXutJvH/tQGXufvm5APN7EzgWPKmBq77I+A3QAUh0WfqPy8n3CAAbASmm1le7Kbg/wL/18yOJn3W99z9vzNc50ngP83sDKCru69K2lcc+xwRSaKauEjH9RzwRTMzADObWc9xLwOXxfrGBwHnxXe4+w7CetDfAhbVc/4mYFzs+G2Epu/vmll+7HOLSdwoPAd81sx6xPYNM7OBsXOPEloMHia1Fg5wOrA+my8t0pkoiYtER3qf+J2NHP8doBBYa2brY+8z+TVQRkiS/w38FTiUtP9nwHZ331jP+c+QlPgJ/er9gW1mthJ4Hvg6gLv/Dvg58KqZrSMMaOuZdO4jwHTg0bTPOD/2OSKSREuRighm1sPdj5pZf8IAs7Nj/eOY2X3Aa+7+/9VzbldCP/rZ7l6do/iWAgvc/b1cXF8kqpTERQQzewnoAxQB/+Hui2LbVxL6z+e7+4kGzv8wsMnd38lBbCWEG4QnW/raIlGnJC4iIhJR6hMXERGJKCVxERGRiFISFxERiSglcRERkYhSEhcREYkoJXEREZGI+v8B3u5vKDE2CQkAAAAASUVORK5CYII=\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"psf.plot_containment_vs_energy(linewidth=2, fractions=[0.68, 0.95])\n",
"plt.xlim(50, 2000)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition we can check how the actual shape of the PSF varies with energy and compare it against the mean PSF between 50 GeV and 2000 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch=\"log\", add_cbar=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Energy Dispersion\n",
"For simplicity we assume a diagonal energy dispersion:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"e_true = exposure.geom.axes[0].edges\n",
"e_reco = counts.geom.axes[0].edges\n",
"edisp = EnergyDispersion.from_diagonal_response(e_true=e_true, e_reco=e_reco)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"\n",
"Let's compute a background cube, with predicted number of background events per pixel from the diffuse Galactic and isotropic model components. For this, we use the use the [gammapy.cube.MapEvaluator](..\/api/gammapy.cube.MapEvaluator.rst) to multiply with the exposure and apply the PSF. The Fermi-LAT energy dispersion at high energies is small, we neglect it here."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background counts from Galactic diffuse: 13332.024923183082\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
\n",
" name value error unit min max frozen\n",
" str9 float64 float64 str14 float64 float64 bool \n",
"--------- ---------- ------- -------------- ---------- --------- ------\n",
" lon_0 -2.497e-02 nan deg -1.800e+02 1.800e+02 False\n",
" lat_0 -3.975e-02 nan deg -9.000e+01 9.000e+01 False\n",
" index 2.683e+00 nan nan nan False\n",
"amplitude 6.034e-10 nan cm-2 s-1 TeV-1 nan nan False\n",
"reference 1.000e+02 nan GeV nan nan True\n",
" norm 3.585e+00 nan 0.000e+00 nan False\n",
" tilt 0.000e+00 nan nan nan True\n",
"reference 1.000e+00 nan TeV nan nan True\n",
" norm 1.019e+00 nan 0.000e+00 nan False\n",
" tilt 0.000e+00 nan nan nan True\n",
"reference 1.000e+00 nan TeV nan nan True"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.parameters.to_table()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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