.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorials/starting/overview.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorials_starting_overview.py: Data structures =============== Introduction to basic data structures handling. Introduction ------------ This is a getting started tutorial for Gammapy. In this tutorial we will use the `Third Fermi-LAT Catalog of High-Energy Sources (3FHL) catalog `__, corresponding event list and images to learn how to work with some of the central Gammapy data structures. We will cover the following topics: - **Sky maps** - We will learn how to handle image based data with gammapy using a Fermi-LAT 3FHL example image. We will work with the following classes: - `~gammapy.maps.WcsNDMap` - `~astropy.coordinates.SkyCoord` - `~numpy.ndarray` - **Event lists** - We will learn how to handle event lists with Gammapy. Important for this are the following classes: - `~gammapy.data.EventList` - `~astropy.table.Table` - **Source catalogs** - We will show how to load source catalogs with Gammapy and explore the data using the following classes: - `~gammapy.catalog.SourceCatalog`, specifically `~gammapy.catalog.SourceCatalog3FHL` - `~astropy.table.Table` - **Spectral models and flux points** - We will pick an example source and show how to plot its spectral model and flux points. For this we will use the following classes: - `~gammapy.modeling.models.SpectralModel`, specifically the `~gammapy.modeling.models.PowerLaw2SpectralModel` - `~gammapy.estimators.FluxPoints` - `~astropy.table.Table` .. GENERATED FROM PYTHON SOURCE LINES 60-68 Setup ----- **Important**: to run this tutorial the environment variable ``GAMMAPY_DATA`` must be defined and point to the directory on your machine where the datasets needed are placed. To check whether your setup is correct you can execute the following cell: .. GENERATED FROM PYTHON SOURCE LINES 68-74 .. code-block:: Python import astropy.units as u from astropy.coordinates import SkyCoord import matplotlib.pyplot as plt .. GENERATED FROM PYTHON SOURCE LINES 75-77 Check setup ----------- .. GENERATED FROM PYTHON SOURCE LINES 77-85 .. code-block:: Python from gammapy.utils.check import check_tutorials_setup # %matplotlib inline check_tutorials_setup() .. rst-class:: sphx-glr-script-out .. code-block:: none System: python_executable : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/bin/python python_version : 3.9.20 machine : x86_64 system : Linux Gammapy package: version : 1.3 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy Other packages: numpy : 1.26.4 scipy : 1.13.1 astropy : 5.2.2 regions : 0.8 click : 8.1.7 yaml : 6.0.2 IPython : 8.18.1 jupyterlab : not installed matplotlib : 3.9.2 pandas : not installed healpy : 1.17.3 iminuit : 2.30.1 sherpa : 4.16.1 naima : 0.10.0 emcee : 3.1.6 corner : 2.2.3 ray : 2.39.0 Gammapy environment variables: GAMMAPY_DATA : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/1.3 .. GENERATED FROM PYTHON SOURCE LINES 86-100 Maps ---- The `~gammapy.maps` package contains classes to work with sky images and cubes. In this section, we will use a simple 2D sky image and will learn how to: - Read sky images from FITS files - Smooth images - Plot images - Cutout parts from images .. GENERATED FROM PYTHON SOURCE LINES 100-106 .. code-block:: Python from gammapy.maps import Map gc_3fhl = Map.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz") .. GENERATED FROM PYTHON SOURCE LINES 107-109 The image is a `~gammapy.maps.WcsNDMap` object: .. GENERATED FROM PYTHON SOURCE LINES 109-113 .. code-block:: Python print(gc_3fhl) .. rst-class:: sphx-glr-script-out .. code-block:: none WcsNDMap geom : WcsGeom axes : ['lon', 'lat'] shape : (400, 200) ndim : 2 unit : dtype : >i8 .. GENERATED FROM PYTHON SOURCE LINES 114-119 The shape of the image is 400 x 200 pixel and it is defined using a cartesian projection in galactic coordinates. The ``geom`` attribute is a `~gammapy.maps.WcsGeom` object: .. GENERATED FROM PYTHON SOURCE LINES 119-123 .. code-block:: Python print(gc_3fhl.geom) .. rst-class:: sphx-glr-script-out .. code-block:: none WcsGeom axes : ['lon', 'lat'] shape : (400, 200) ndim : 2 frame : galactic projection : CAR center : 0.0 deg, 0.0 deg width : 20.0 deg x 10.0 deg wcs ref : 0.0 deg, 0.0 deg .. GENERATED FROM PYTHON SOURCE LINES 124-126 Let’s take a closer look a the ``.data`` attribute: .. GENERATED FROM PYTHON SOURCE LINES 126-130 .. code-block:: Python print(gc_3fhl.data) .. rst-class:: sphx-glr-script-out .. code-block:: none [[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 1] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 1]] .. GENERATED FROM PYTHON SOURCE LINES 131-134 That looks familiar! It just an *ordinary* 2 dimensional numpy array, which means you can apply any known numpy method to it: .. GENERATED FROM PYTHON SOURCE LINES 134-138 .. code-block:: Python print(f"Total number of counts in the image: {gc_3fhl.data.sum():.0f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Total number of counts in the image: 32684 .. GENERATED FROM PYTHON SOURCE LINES 139-147 To show the image on the screen we can use the ``plot`` method. It basically calls `plt.imshow `__, passing the ``gc_3fhl.data`` attribute but in addition handles axis with world coordinates using `astropy.visualization.wcsaxes `__ and defines some defaults for nicer plots (e.g. the colormap ‘afmhot’): .. GENERATED FROM PYTHON SOURCE LINES 147-151 .. code-block:: Python gc_3fhl.plot(stretch="sqrt") plt.show() .. image-sg:: /tutorials/starting/images/sphx_glr_overview_001.png :alt: overview :srcset: /tutorials/starting/images/sphx_glr_overview_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 152-155 To make the structures in the image more visible we will smooth the data using a Gaussian kernel. .. GENERATED FROM PYTHON SOURCE LINES 155-161 .. code-block:: Python gc_3fhl_smoothed = gc_3fhl.smooth(kernel="gauss", width=0.2 * u.deg) gc_3fhl_smoothed.plot(stretch="sqrt") plt.show() .. image-sg:: /tutorials/starting/images/sphx_glr_overview_002.png :alt: overview :srcset: /tutorials/starting/images/sphx_glr_overview_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 162-168 The smoothed plot already looks much nicer, but still the image is rather large. As we are mostly interested in the inner part of the image, we will cut out a quadratic region of the size 9 deg x 9 deg around Vela. Therefore we use `~gammapy.maps.Map.cutout` to make a cutout map: .. GENERATED FROM PYTHON SOURCE LINES 168-176 .. code-block:: Python # define center and size of the cutout region center = SkyCoord(0, 0, unit="deg", frame="galactic") gc_3fhl_cutout = gc_3fhl_smoothed.cutout(center, 9 * u.deg) gc_3fhl_cutout.plot(stretch="sqrt") plt.show() .. image-sg:: /tutorials/starting/images/sphx_glr_overview_003.png :alt: overview :srcset: /tutorials/starting/images/sphx_glr_overview_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 177-187 For a more detailed introduction to `~gammapy.maps`, take a look a the :doc:`/tutorials/api/maps` notebook. Exercises ~~~~~~~~~ - Add a marker and circle at the position of ``Sgr A*`` (you can find examples in `astropy.visualization.wcsaxes `__). .. GENERATED FROM PYTHON SOURCE LINES 190-206 Event lists ----------- Almost any high level gamma-ray data analysis starts with the raw measured counts data, which is stored in event lists. In Gammapy event lists are represented by the `~gammapy.data.EventList` class. In this section we will learn how to: - Read event lists from FITS files - Access and work with the ``EventList`` attributes such as ``.table`` and ``.energy`` - Filter events lists using convenience methods Let’s start with the import from the `~gammapy.data` submodule: .. GENERATED FROM PYTHON SOURCE LINES 206-209 .. code-block:: Python from gammapy.data import EventList .. GENERATED FROM PYTHON SOURCE LINES 210-213 Very similar to the sky map class an event list can be created, by passing a filename to the `~gammapy.data.EventList.read()` method: .. GENERATED FROM PYTHON SOURCE LINES 213-217 .. code-block:: Python events_3fhl = EventList.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-events.fits.gz") .. GENERATED FROM PYTHON SOURCE LINES 218-222 This time the actual data is stored as an `~astropy.table.Table ` object. It can be accessed with ``.table`` attribute: .. GENERATED FROM PYTHON SOURCE LINES 222-226 .. code-block:: Python print(events_3fhl.table) .. rst-class:: sphx-glr-script-out .. code-block:: none ENERGY RA DEC L ... DIFRSP1 DIFRSP2 DIFRSP3 DIFRSP4 MeV deg deg deg ... --------- --------- ---------- ---------- ... ------- ------- ------- ------- 12186.642 260.45935 -33.553337 353.36273 ... 0.0 0.0 0.0 0.0 25496.598 261.37506 -34.395004 353.09607 ... 0.0 0.0 0.0 0.0 15621.498 259.56973 -33.409416 353.05673 ... 0.0 0.0 0.0 0.0 12816.32 273.95883 -25.340391 6.45856 ... 0.0 0.0 0.0 0.0 18988.387 260.8568 -36.355804 351.23734 ... 0.0 0.0 0.0 0.0 11610.23 266.15518 -26.224436 2.1986027 ... 0.0 0.0 0.0 0.0 13960.802 271.44742 -29.615316 1.6267247 ... 0.0 0.0 0.0 0.0 10477.372 266.3981 -28.96814 359.97003 ... 0.0 0.0 0.0 0.0 13030.88 271.70428 -20.632627 9.59348 ... 0.0 0.0 0.0 0.0 11517.904 265.00894 -30.065119 358.40112 ... 0.0 0.0 0.0 0.0 ... ... ... ... ... ... ... ... ... 387834.72 270.3779 -21.56711 8.171749 ... 0.0 0.0 0.0 0.0 20559.74 268.5538 -26.345692 3.200638 ... 0.0 0.0 0.0 0.0 27209.146 266.59344 -30.52607 358.72775 ... 0.0 0.0 0.0 0.0 13911.061 269.30997 -27.239439 2.7684028 ... 0.0 0.0 0.0 0.0 13226.425 265.16287 -27.344238 0.7796942 ... 0.0 0.0 0.0 0.0 17445.463 266.63342 -28.807201 0.21464892 ... 0.0 0.0 0.0 0.0 13133.864 270.42474 -22.651058 7.251185 ... 0.0 0.0 0.0 0.0 32095.705 266.0002 -29.77206 359.1034 ... 0.0 0.0 0.0 0.0 18465.783 266.39728 -29.105953 359.85202 ... 0.0 0.0 0.0 0.0 14457.25 262.72217 -34.388405 353.7184 ... 0.0 0.0 0.0 0.0 Length = 32843 rows .. GENERATED FROM PYTHON SOURCE LINES 227-230 You can do *len* over event_3fhl.table to find the total number of events. .. GENERATED FROM PYTHON SOURCE LINES 230-234 .. code-block:: Python print(len(events_3fhl.table)) .. rst-class:: sphx-glr-script-out .. code-block:: none 32843 .. GENERATED FROM PYTHON SOURCE LINES 235-237 And we can access any other attribute of the ``Table`` object as well: .. GENERATED FROM PYTHON SOURCE LINES 237-241 .. code-block:: Python print(events_3fhl.table.colnames) .. rst-class:: sphx-glr-script-out .. code-block:: none ['ENERGY', 'RA', 'DEC', 'L', 'B', 'THETA', 'PHI', 'ZENITH_ANGLE', 'EARTH_AZIMUTH_ANGLE', 'TIME', 'EVENT_ID', 'RUN_ID', 'RECON_VERSION', 'CALIB_VERSION', 'EVENT_CLASS', 'EVENT_TYPE', 'CONVERSION_TYPE', 'LIVETIME', 'DIFRSP0', 'DIFRSP1', 'DIFRSP2', 'DIFRSP3', 'DIFRSP4'] .. GENERATED FROM PYTHON SOURCE LINES 242-251 For convenience we can access the most important event parameters as properties on the ``EventList`` objects. The attributes will return corresponding Astropy objects to represent the data, such as `~astropy.units.Quantity`, `~astropy.coordinates.SkyCoord` or `~astropy.time.Time` objects: .. GENERATED FROM PYTHON SOURCE LINES 251-254 .. code-block:: Python print(events_3fhl.energy.to("GeV")) .. rst-class:: sphx-glr-script-out .. code-block:: none [12.186643 25.4966 15.621499 ... 32.095707 18.465784 14.457251] GeV .. GENERATED FROM PYTHON SOURCE LINES 255-258 .. code-block:: Python print(events_3fhl.galactic) # events_3fhl.radec .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 259-262 .. code-block:: Python print(events_3fhl.time) .. rst-class:: sphx-glr-script-out .. code-block:: none [54682.82946153 54682.89243456 54682.89709472 ... 57236.75267735 57233.37455141 57233.44802852] .. GENERATED FROM PYTHON SOURCE LINES 263-267 In addition ``EventList`` provides convenience methods to filter the event lists. One possible use case is to find the highest energy event within a radius of 0.5 deg around the vela position: .. GENERATED FROM PYTHON SOURCE LINES 267-281 .. code-block:: Python # select all events within a radius of 0.5 deg around center from gammapy.utils.regions import SphericalCircleSkyRegion region = SphericalCircleSkyRegion(center, radius=0.5 * u.deg) events_gc_3fhl = events_3fhl.select_region(region) # sort events by energy events_gc_3fhl.table.sort("ENERGY") # and show highest energy photon print("highest energy photon: ", events_gc_3fhl.energy[-1].to("GeV")) .. rst-class:: sphx-glr-script-out .. code-block:: none highest energy photon: 1917.85925 GeV .. GENERATED FROM PYTHON SOURCE LINES 282-288 Exercises ~~~~~~~~~ - Make a counts energy spectrum for the galactic center region, within a radius of 10 deg. .. GENERATED FROM PYTHON SOURCE LINES 291-306 Source catalogs --------------- Gammapy provides a convenient interface to access and work with catalog based data. In this section we will learn how to: - Load builtins catalogs from `~gammapy.catalog` - Sort and index the underlying Astropy tables - Access data from individual sources Let’s start with importing the 3FHL catalog object from the `~gammapy.catalog` submodule: .. GENERATED FROM PYTHON SOURCE LINES 306-309 .. code-block:: Python from gammapy.catalog import SourceCatalog3FHL .. GENERATED FROM PYTHON SOURCE LINES 310-313 First we initialize the Fermi-LAT 3FHL catalog and directly take a look at the ``.table`` attribute: .. GENERATED FROM PYTHON SOURCE LINES 313-318 .. code-block:: Python fermi_3fhl = SourceCatalog3FHL() print(fermi_3fhl.table) .. rst-class:: sphx-glr-script-out .. code-block:: none Source_Name RAJ2000 DEJ2000 ... Redshift NuPeak_obs deg deg ... Hz ------------------ -------- -------- ... -------- ------------------ 3FHL J0001.2-0748 0.3107 -7.8075 ... -- 306196370000000.0 3FHL J0001.9-4155 0.4849 -41.9303 ... -- 6309576500000000.0 3FHL J0002.1-6728 0.5283 -67.4825 ... -- 4466832000000000.0 3FHL J0003.3-5248 0.8300 -52.8150 ... -- 7.079464e+16 3FHL J0007.0+7303 1.7647 73.0560 ... -- -- 3FHL J0007.9+4711 1.9931 47.1920 ... 0.2800 2511884200000000.0 3FHL J0008.4-2339 2.1243 -23.6514 ... 0.1470 524807800000000.0 3FHL J0009.1+0628 2.2874 6.4814 ... -- 663742400000000.0 3FHL J0009.4+5030 2.3504 50.5049 ... -- 1412536400000000.0 3FHL J0009.7-4319 2.4450 -43.3195 ... -- -- ... ... ... ... ... ... 3FHL J2347.9-1630 356.9978 -16.5106 ... 0.5760 9332549000000.0 3FHL J2350.5-3006 357.6354 -30.1070 ... 0.2237 3981075200000000.0 3FHL J2351.5-7559 357.8926 -75.9890 ... -- -- 3FHL J2352.1+1753 358.0415 17.8865 ... -- 1737799900000000.0 3FHL J2356.2+4035 359.0746 40.5985 ... 0.1310 6309576500000000.0 3FHL J2357.4-1717 359.3690 -17.2996 ... -- 8.912525e+16 3FHL J2358.4-1808 359.6205 -18.1408 ... -- -- 3FHL J2358.5+3829 359.6266 38.4963 ... -- -- 3FHL J2359.1-3038 359.7760 -30.6397 ... 0.1650 2.818388e+17 3FHL J2359.3-2049 359.8293 -20.8256 ... 0.0960 4073799600000000.0 Length = 1556 rows .. GENERATED FROM PYTHON SOURCE LINES 319-325 This looks very familiar again. The data is just stored as an `~astropy.table.Table` object. We have all the methods and attributes of the ``Table`` object available. E.g. we can sort the underlying table by ``Signif_Avg`` to find the top 5 most significant sources: .. GENERATED FROM PYTHON SOURCE LINES 325-336 .. code-block:: Python # sort table by significance fermi_3fhl.table.sort("Signif_Avg") # invert the order to find the highest values and take the top 5 top_five_TS_3fhl = fermi_3fhl.table[::-1][:5] # print the top five significant sources with association and source class print(top_five_TS_3fhl[["Source_Name", "ASSOC1", "ASSOC2", "CLASS", "Signif_Avg"]]) .. rst-class:: sphx-glr-script-out .. code-block:: none Source_Name ASSOC1 ... CLASS Signif_Avg ------------------ -------------------------- ... ------- ---------- 3FHL J0534.5+2201 Crab Nebula ... PWN 168.641 3FHL J1104.4+3812 Mkn 421 ... BLL 144.406 3FHL J0835.3-4510 PSR J0835-4510 ... PSR 138.801 3FHL J0633.9+1746 PSR J0633+1746 ... PSR 99.734 3FHL J1555.7+1111 PG 1553+113 ... BLL 94.411 .. GENERATED FROM PYTHON SOURCE LINES 337-341 If you are interested in the data of an individual source you can access the information from catalog using the name of the source or any alias source name that is defined in the catalog: .. GENERATED FROM PYTHON SOURCE LINES 341-349 .. code-block:: Python mkn_421_3fhl = fermi_3fhl["3FHL J1104.4+3812"] # or use any alias source name that is defined in the catalog mkn_421_3fhl = fermi_3fhl["Mkn 421"] print(mkn_421_3fhl.data["Signif_Avg"]) .. rst-class:: sphx-glr-script-out .. code-block:: none 144.40611 .. GENERATED FROM PYTHON SOURCE LINES 350-364 Exercises ~~~~~~~~~ - Try to load the Fermi-LAT 2FHL catalog and check the total number of sources it contains. - Select all the sources from the 2FHL catalog which are contained in the Galactic Center region. The methods `~gammapy.maps.WcsGeom.contains()` and `~gammapy.catalog.SourceCatalog.positions` might be helpful for this. Add markers for all these sources and try to add labels with the source names. - Try to find the source class of the object at position ra=68.6803, dec=9.3331 .. GENERATED FROM PYTHON SOURCE LINES 367-380 Spectral models and flux points ------------------------------- In the previous section we learned how access basic data from individual sources in the catalog. Now we will go one step further and explore the full spectral information of sources. We will learn how to: - Plot spectral models - Compute integral and energy fluxes - Read and plot flux points As a first example we will start with the Crab Nebula: .. GENERATED FROM PYTHON SOURCE LINES 380-386 .. code-block:: Python crab_3fhl = fermi_3fhl["Crab Nebula"] crab_3fhl_spec = crab_3fhl.spectral_model() print(crab_3fhl_spec) .. rst-class:: sphx-glr-script-out .. code-block:: none PowerLawSpectralModel type name value unit error min max frozen link prior ---- --------- ---------- -------------- --------- --- --- ------ ---- ----- index 2.2202e+00 2.498e-02 nan nan False amplitude 1.7132e-10 cm-2 GeV-1 s-1 3.389e-12 nan nan False reference 2.2726e+01 GeV 0.000e+00 nan nan True .. GENERATED FROM PYTHON SOURCE LINES 387-394 The ``crab_3fhl_spec`` is an instance of the `~gammapy.modeling.models.PowerLaw2SpectralModel` model, with the parameter values and errors taken from the 3FHL catalog. Let’s plot the spectral model in the energy range between 10 GeV and 2000 GeV: .. GENERATED FROM PYTHON SOURCE LINES 394-399 .. code-block:: Python ax_crab_3fhl = crab_3fhl_spec.plot(energy_bounds=[10, 2000] * u.GeV, energy_power=0) plt.show() .. image-sg:: /tutorials/starting/images/sphx_glr_overview_004.png :alt: overview :srcset: /tutorials/starting/images/sphx_glr_overview_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 400-406 We assign the return axes object to variable called ``ax_crab_3fhl``, because we will re-use it later to plot the flux points on top. To compute the differential flux at 100 GeV we can simply call the model like normal Python function and convert to the desired units: .. GENERATED FROM PYTHON SOURCE LINES 406-410 .. code-block:: Python print(crab_3fhl_spec(100 * u.GeV).to("cm-2 s-1 GeV-1")) .. rst-class:: sphx-glr-script-out .. code-block:: none 6.3848912826152664e-12 1 / (cm2 GeV s) .. GENERATED FROM PYTHON SOURCE LINES 411-414 Next we can compute the integral flux of the Crab between 10 GeV and 2000 GeV: .. GENERATED FROM PYTHON SOURCE LINES 414-422 .. code-block:: Python print( crab_3fhl_spec.integral(energy_min=10 * u.GeV, energy_max=2000 * u.GeV).to( "cm-2 s-1" ) ) .. rst-class:: sphx-glr-script-out .. code-block:: none 8.67457342435522e-09 1 / (cm2 s) .. GENERATED FROM PYTHON SOURCE LINES 423-426 We can easily convince ourself, that it corresponds to the value given in the Fermi-LAT 3FHL catalog: .. GENERATED FROM PYTHON SOURCE LINES 426-430 .. code-block:: Python print(crab_3fhl.data["Flux"]) .. rst-class:: sphx-glr-script-out .. code-block:: none 8.658909145253801e-09 1 / (cm2 s) .. GENERATED FROM PYTHON SOURCE LINES 431-433 In addition we can compute the energy flux between 10 GeV and 2000 GeV: .. GENERATED FROM PYTHON SOURCE LINES 433-441 .. code-block:: Python print( crab_3fhl_spec.energy_flux(energy_min=10 * u.GeV, energy_max=2000 * u.GeV).to( "erg cm-2 s-1" ) ) .. rst-class:: sphx-glr-script-out .. code-block:: none 5.311489174710791e-10 erg / (cm2 s) .. GENERATED FROM PYTHON SOURCE LINES 442-444 Next we will access the flux points data of the Crab: .. GENERATED FROM PYTHON SOURCE LINES 444-448 .. code-block:: Python print(crab_3fhl.flux_points) .. rst-class:: sphx-glr-script-out .. code-block:: none FluxPoints ---------- geom : RegionGeom axes : ['lon', 'lat', 'energy'] shape : (1, 1, 5) quantities : ['norm', 'norm_errp', 'norm_errn', 'norm_ul', 'sqrt_ts', 'is_ul'] ref. model : pl n_sigma : 1 n_sigma_ul : 2 sqrt_ts_threshold_ul : 1 sed type init : flux .. GENERATED FROM PYTHON SOURCE LINES 449-456 If you want to learn more about the different flux point formats you can read the specification `here `__. No we can check again the underlying astropy data structure by accessing the ``.table`` attribute: .. GENERATED FROM PYTHON SOURCE LINES 456-460 .. code-block:: Python print(crab_3fhl.flux_points.to_table(sed_type="dnde", formatted=True)) .. rst-class:: sphx-glr-script-out .. code-block:: none e_ref e_min e_max dnde ... dnde_ul sqrt_ts is_ul GeV GeV GeV 1 / (cm2 GeV s) ... 1 / (cm2 GeV s) -------- ------- -------- --------------- ... --------------- ------- ----- 14.142 10.000 20.000 5.120e-10 ... nan 125.157 False 31.623 20.000 50.000 7.359e-11 ... nan 88.715 False 86.603 50.000 150.000 9.024e-12 ... nan 59.087 False 273.861 150.000 500.000 7.660e-13 ... nan 33.076 False 1000.000 500.000 2000.000 4.291e-14 ... nan 15.573 False .. GENERATED FROM PYTHON SOURCE LINES 461-465 Finally let’s combine spectral model and flux points in a single plot and scale with ``energy_power=2`` to obtain the spectral energy distribution: .. GENERATED FROM PYTHON SOURCE LINES 465-470 .. code-block:: Python ax = crab_3fhl_spec.plot(energy_bounds=[10, 2000] * u.GeV, sed_type="e2dnde") crab_3fhl.flux_points.plot(ax=ax, sed_type="e2dnde") plt.show() .. image-sg:: /tutorials/starting/images/sphx_glr_overview_005.png :alt: overview :srcset: /tutorials/starting/images/sphx_glr_overview_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 471-477 Exercises ~~~~~~~~~ - Plot the spectral model and flux points for PKS 2155-304 for the 3FGL and 2FHL catalogs. Try to plot the error of the model (aka “Butterfly”) as well. .. GENERATED FROM PYTHON SOURCE LINES 480-494 What next? ---------- This was a quick introduction to some of the high level classes in Astropy and Gammapy. - To learn more about those classes, go to the API docs (links are in the introduction at the top). - To learn more about other parts of Gammapy (e.g. Fermi-LAT and TeV data analysis), check out the other tutorial notebooks. - To see what’s available in Gammapy, browse the Gammapy docs or use the full-text search. - If you have any questions, ask on the mailing list. .. _sphx_glr_download_tutorials_starting_overview.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v1.3?urlpath=lab/tree/notebooks/1.3/tutorials/starting/overview.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: overview.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: overview.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: overview.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_