Note
Go to the end to download the full example code. or to run this example in your browser via Binder
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
Event lists
Source catalogs
We will show how to load source catalogs with Gammapy and explore the data using the following classes:
SourceCatalog, specificallySourceCatalog3FHL
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:
SpectralModel, specifically thePowerLaw2SpectralModel
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:
import astropy.units as u
from astropy.coordinates import SkyCoord
import matplotlib.pyplot as plt
Check setup#
from gammapy.utils.check import check_tutorials_setup
# %matplotlib inline
check_tutorials_setup()
System:
python_executable : /Users/mregeard/Workspace/dev/code/gammapy/gammapy/.tox/build_docs/bin/python
python_version : 3.11.10
machine : x86_64
system : Darwin
Gammapy package:
version : 1.3.dev1205+g00f44f94ac
path : /Users/mregeard/Workspace/dev/code/gammapy/gammapy/.tox/build_docs/lib/python3.11/site-packages/gammapy
Other packages:
numpy : 1.26.4
scipy : 1.14.1
astropy : 5.2.2
regions : 0.10
click : 8.1.7
yaml : 6.0.2
IPython : 8.28.0
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 : 2.2.2
ray : 2.37.0
Gammapy environment variables:
GAMMAPY_DATA : /Users/mregeard/Workspace/dev/code/gammapy/gammapy-data/
Maps#
The 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
The image is a WcsNDMap object:
print(gc_3fhl)
WcsNDMap
geom : WcsGeom
axes : ['lon', 'lat']
shape : (400, 200)
ndim : 2
unit :
dtype : >i8
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 WcsGeom object:
print(gc_3fhl.geom)
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
Let’s take a closer look a the .data attribute:
print(gc_3fhl.data)
[[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]]
That looks familiar! It just an ordinary 2 dimensional numpy array, which means you can apply any known numpy method to it:
print(f"Total number of counts in the image: {gc_3fhl.data.sum():.0f}")
Total number of counts in the image: 32684
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’):
gc_3fhl.plot(stretch="sqrt")
plt.show()
To make the structures in the image more visible we will smooth the data using a Gaussian kernel.
gc_3fhl_smoothed = gc_3fhl.smooth(kernel="gauss", width=0.2 * u.deg)
gc_3fhl_smoothed.plot(stretch="sqrt")
plt.show()
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 cutout to make a
cutout map:
# 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()
For a more detailed introduction to maps, take a look a the
Maps notebook.
Exercises#
Add a marker and circle at the position of
Sgr A*(you can find examples in astropy.visualization.wcsaxes).
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 EventList class.
In this section we will learn how to:
Read event lists from FITS files
Access and work with the
EventListattributes such as.tableand.energyFilter events lists using convenience methods
Let’s start with the import from the data submodule:
from gammapy.data import EventList
Very similar to the sky map class an event list can be created, by
passing a filename to the read() method:
events_3fhl = EventList.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-events.fits.gz")
This time the actual data is stored as an
It can be accessed with `.table`` attribute:
print(events_3fhl.table)
ENERGY RA DEC L B THETA PHI ... CONVERSION_TYPE LIVETIME DIFRSP0 DIFRSP1 DIFRSP2 DIFRSP3 DIFRSP4
MeV deg deg deg deg deg deg ... s
--------- --------- ---------- ----------- ------------ --------- ---------- ... --------------- ------------------ ------- ------- ------- ------- -------
12186.642 260.45935 -33.553337 353.36273 1.7538676 71.977325 125.50694 ... 0 238.57837238907814 0.0 0.0 0.0 0.0 0.0
25496.598 261.37506 -34.395004 353.09607 0.6520652 42.49406 278.49347 ... 1 176.16850754618645 0.0 0.0 0.0 0.0 0.0
15621.498 259.56973 -33.409416 353.05673 2.4450684 64.32412 234.22194 ... 1 9.392075657844543 0.0 0.0 0.0 0.0 0.0
12816.32 273.95883 -25.340391 6.45856 -4.0548873 43.292503 142.87392 ... 1 4.034786552190781 0.0 0.0 0.0 0.0 0.0
18988.387 260.8568 -36.355804 351.23734 -0.101912394 26.916113 290.39337 ... 0 131.60132896900177 0.0 0.0 0.0 0.0 0.0
11610.23 266.15518 -26.224436 2.1986027 1.6034819 35.77363 274.53387 ... 1 74.98110938072205 0.0 0.0 0.0 0.0 0.0
13960.802 271.44742 -29.615316 1.6267247 -4.1431155 25.917883 238.0368 ... 1 106.37336817383766 0.0 0.0 0.0 0.0 0.0
10477.372 266.3981 -28.96814 359.97003 -0.011748177 39.091587 275.5457 ... 0 214.62817406654358 0.0 0.0 0.0 0.0 0.0
13030.88 271.70428 -20.632627 9.59348 0.026241468 52.622505 161.3205 ... 0 94.68753063678741 0.0 0.0 0.0 0.0 0.0
11517.904 265.00894 -30.065119 358.40112 0.43904436 41.812317 276.02448 ... 0 123.15007302165031 0.0 0.0 0.0 0.0 0.0
19958.182 263.31854 -37.094856 351.71606 -2.153713 52.544586 121.415764 ... 0 17.113530546426773 0.0 0.0 0.0 0.0 0.0
23760.14 265.4694 -31.55994 357.34256 -0.68805 52.70319 248.87813 ... 1 225.2544113099575 0.0 0.0 0.0 0.0 0.0
32168.988 266.35397 -30.096745 358.98682 -0.56723064 64.830696 212.93645 ... 1 45.64776709675789 0.0 0.0 0.0 0.0 0.0
10165.9 266.15427 -19.829279 7.668937 4.9320326 51.85829 207.0177 ... 0 204.40402576327324 0.0 0.0 0.0 0.0 0.0
25277.545 269.68344 -24.668644 5.1628966 -0.34179303 42.77833 129.13857 ... 0 85.29761010408401 0.0 0.0 0.0 0.0 0.0
223713.7 261.1885 -31.794796 355.1619 2.239802 25.89418 284.8728 ... 0 166.99824661016464 0.0 0.0 0.0 0.0 0.0
15236.758 264.52255 -31.730831 356.7694 -0.09604572 51.646976 141.44576 ... 0 5.334206968545914 0.0 0.0 0.0 0.0 0.0
36314.297 264.79855 -31.116806 357.41434 0.032732587 48.567314 122.824875 ... 1 34.09180650115013 0.0 0.0 0.0 0.0 0.0
76511.96 269.20724 -26.198639 3.6230083 -0.7352153 54.72039 123.64289 ... 1 136.5931620001793 0.0 0.0 0.0 0.0 0.0
12432.439 266.09735 -32.71295 356.64053 -1.7461345 29.718775 225.30495 ... 0 57.99910423159599 0.0 0.0 0.0 0.0 0.0
14717.576 265.82196 -26.15737 2.0991938 1.8933698 50.693916 254.33784 ... 0 8.511121690273285 0.0 0.0 0.0 0.0 0.0
13233.963 269.8945 -23.260023 6.480208 0.19310227 45.217796 147.76228 ... 1 133.37614086270332 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
10941.6 265.99457 -28.765705 359.95795 0.3953491 53.88634 132.83992 ... 0 170.03486621379852 0.0 0.0 0.0 0.0 0.0
45478.75 271.7975 -22.14616 8.313876 -0.7867925 64.49326 140.47731 ... 1 164.58107435703278 0.0 0.0 0.0 0.0 0.0
13280.59 260.05453 -36.33665 350.88586 0.4406751 42.26522 219.64531 ... 1 22.002596974372864 0.0 0.0 0.0 0.0 0.0
74033.305 268.76508 -24.43612 4.944724 0.4974049 51.1174 225.20499 ... 0 65.83160126209259 0.0 0.0 0.0 0.0 0.0
47114.324 266.99127 -28.421051 0.70769924 -0.1726071 60.81272 212.27025 ... 1 77.02012866735458 0.0 0.0 0.0 0.0 0.0
14473.36 259.55792 -32.497227 353.79837 2.9774146 69.47437 136.79776 ... 0 13.922565162181854 0.0 0.0 0.0 0.0 0.0
20056.527 267.50372 -21.494694 6.888726 2.9924572 46.228405 129.99919 ... 1 115.29787397384644 0.0 0.0 0.0 0.0 0.0
10021.135 266.58603 -29.006046 0.023192262 -0.17183326 58.983257 139.42741 ... 0 115.27116513252258 0.0 0.0 0.0 0.0 0.0
11217.242 260.67258 -32.080307 354.67896 2.4407566 76.96116 214.30273 ... 1 64.04110872745514 0.0 0.0 0.0 0.0 0.0
18494.64 267.2745 -36.705185 353.7175 -4.6370916 38.942596 226.92189 ... 1 111.12007641792297 0.0 0.0 0.0 0.0 0.0
13333.997 265.57535 -31.519987 357.42413 -0.7436878 43.89681 230.49913 ... 1 23.238793969154358 0.0 0.0 0.0 0.0 0.0
13160.466 269.90778 -24.671898 5.2616706 -0.52015543 60.9269 226.45708 ... 0 1.9516496658325195 0.0 0.0 0.0 0.0 0.0
387834.72 270.3779 -21.56711 8.171749 0.64531475 56.755512 221.84715 ... 0 34.214694023132324 0.0 0.0 0.0 0.0 0.0
20559.74 268.5538 -26.345692 3.200638 -0.30328986 49.523575 233.67285 ... 0 103.17629969120026 0.0 0.0 0.0 0.0 0.0
27209.146 266.59344 -30.52607 358.72775 -0.96718174 62.1856 140.27434 ... 0 43.22334712743759 0.0 0.0 0.0 0.0 0.0
13911.061 269.30997 -27.239439 2.7684028 -1.3365301 65.15399 224.52101 ... 0 95.46356403827667 0.0 0.0 0.0 0.0 0.0
13226.425 265.16287 -27.344238 0.7796942 1.7680178 59.38332 126.7019 ... 1 3.733097553253174 0.0 0.0 0.0 0.0 0.0
17445.463 266.63342 -28.807201 0.21464892 -0.1039705 55.48627 135.59155 ... 1 80.52235281467438 0.0 0.0 0.0 0.0 0.0
13133.864 270.42474 -22.651058 7.251185 0.071358204 48.704975 134.73102 ... 1 117.88173341751099 0.0 0.0 0.0 0.0 0.0
32095.705 266.0002 -29.77206 359.1034 -0.13615231 45.013103 236.72498 ... 0 108.92976492643356 0.0 0.0 0.0 0.0 0.0
18465.783 266.39728 -29.105953 359.85202 -0.08294058 55.97552 135.87787 ... 0 70.72638684511185 0.0 0.0 0.0 0.0 0.0
14457.25 262.72217 -34.388405 353.7184 -0.26906812 45.683174 237.74162 ... 0 147.4274787902832 0.0 0.0 0.0 0.0 0.0
Length = 32843 rows
You can do len over event_3fhl.table to find the total number of events.
print(len(events_3fhl.table))
32843
And we can access any other attribute of the Table object as well:
print(events_3fhl.table.colnames)
['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']
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
Quantity,
SkyCoord
or
Time
objects:
print(events_3fhl.energy.to("GeV"))
[12.186643 25.4966 15.621499 ... 32.095707 18.465784 14.457251] GeV
print(events_3fhl.galactic)
# events_3fhl.radec
<SkyCoord (Galactic): (l, b) in deg
[(353.36228879, 1.75408483), (353.09562941, 0.6522806 ),
(353.05628243, 2.44528685), ..., (359.10295505, -0.1359316 ),
(359.85157506, -0.08269984), (353.71795506, -0.26883694)]>
print(events_3fhl.time)
[54682.82946153 54682.89243456 54682.89709472 ... 57236.75267735
57233.37455141 57233.44802852]
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:
# 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"))
highest energy photon: 1917.85925 GeV
Exercises#
Make a counts energy spectrum for the galactic center region, within a radius of 10 deg.
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
catalogSort and index the underlying Astropy tables
Access data from individual sources
Let’s start with importing the 3FHL catalog object from the
catalog submodule:
from gammapy.catalog import SourceCatalog3FHL
First we initialize the Fermi-LAT 3FHL catalog and directly take a look
at the .table attribute:
fermi_3fhl = SourceCatalog3FHL()
print(fermi_3fhl.table)
Source_Name RAJ2000 DEJ2000 GLON GLAT Conf_95_SemiMajor ... ASSOC2 ASSOC_PROB_BAY ASSOC_PROB_LR Redshift NuPeak_obs
deg deg deg deg deg ... Hz
------------------ -------- -------- -------- -------- ----------------- ... -------------------------- -------------- ------------- -------- ------------------
3FHL J0001.2-0748 0.3107 -7.8075 89.0094 -67.3118 0.0424 ... 0.9974 0.9721 -- 306196370000000.0
3FHL J0001.9-4155 0.4849 -41.9303 334.1216 -72.0697 0.1018 ... 0.9960 0.0000 -- 6309576500000000.0
3FHL J0002.1-6728 0.5283 -67.4825 310.0868 -48.9549 0.0357 ... 0.0000 0.9395 -- 4466832000000000.0
3FHL J0003.3-5248 0.8300 -52.8150 318.9245 -62.7936 0.0425 ... 0.9996 0.9716 -- 7.079464e+16
3FHL J0007.0+7303 1.7647 73.0560 119.6625 10.4666 0.0101 ... 1.0000 0.0000 -- --
3FHL J0007.9+4711 1.9931 47.1920 115.3093 -15.0354 0.0196 ... 1.0000 0.9873 0.2800 2511884200000000.0
3FHL J0008.4-2339 2.1243 -23.6514 50.2908 -79.7021 0.0366 ... 0.9996 0.9673 0.1470 524807800000000.0
3FHL J0009.1+0628 2.2874 6.4814 104.4637 -54.8669 0.0385 ... 0.9993 0.9878 -- 663742400000000.0
3FHL J0009.4+5030 2.3504 50.5049 116.1257 -11.8105 0.0176 ... 1.0000 0.9698 -- 1412536400000000.0
3FHL J0009.7-4319 2.4450 -43.3195 327.7563 -71.7445 0.0496 ... 0.9985 0.9072 -- --
3FHL J0013.0-3956 3.2523 -39.9381 332.3835 -74.9150 0.0517 ... 0.9998 0.9962 -- 1862089000000.0
3FHL J0013.8-1855 3.4684 -18.9169 74.5314 -78.0876 0.0485 ... 0.9995 0.9772 0.0950 5.260181e+16
3FHL J0014.0-5024 3.5038 -50.4003 317.4913 -65.6568 0.0597 ... 0.9998 0.8923 -- 8.413936e+16
3FHL J0014.7+5801 3.6951 58.0244 118.0788 -4.5015 0.0266 ... 1.0000 0.9778 -- 4.3651522e+16
3FHL J0014.9+6118 3.7366 61.3025 118.5645 -1.2594 0.0339 ... 0.9994 0.9996 -- 12971790000000.0
3FHL J0015.7+5551 3.9337 55.8649 117.9031 -6.6579 0.0335 ... 1.0000 0.9832 -- 6180169000000000.0
3FHL J0018.6+2946 4.6525 29.7821 114.4798 -32.5503 0.0312 ... 0.9985 0.9711 0.1000 5.9156075e+16
3FHL J0019.1-8151 4.7762 -81.8621 304.3273 -35.1783 0.0439 ... 0.9998 0.9878 -- 1393156900000000.0
3FHL J0021.4-2550 5.3693 -25.8471 42.0637 -83.1759 0.0428 ... 0.9996 0.9477 -- 337287200000000.0
3FHL J0021.8-5140 5.4639 -51.6755 313.7480 -64.8270 0.0321 ... 0.9997 0.9760 -- 4786304000000000.0
3FHL J0022.0+0006 5.5032 0.1058 107.1781 -61.8626 0.0350 ... 0.9999 0.9408 -- 1.584896e+18
3FHL J0022.1-1855 5.5260 -18.9170 82.0109 -79.3770 0.0266 ... 0.9987 0.8679 -- 1348963200000000.0
... ... ... ... ... ... ... ... ... ... ... ...
3FHL J2334.8+1431 353.7183 14.5263 96.5607 -44.3897 0.0284 ... 0.9999 0.9674 -- 113501160000000.0
3FHL J2336.3-8427 354.0982 -84.4635 305.0370 -32.3548 0.0447 ... 0.0000 0.9733 -- --
3FHL J2336.5-7620 354.1298 -76.3392 308.6093 -39.9520 0.0469 ... 0.9991 0.9921 -- 1.0
3FHL J2338.9+2123 354.7450 21.3966 101.2522 -38.4057 0.0284 ... 0.9998 0.9769 0.2910 524807800000000.0
3FHL J2339.2-7404 354.8159 -74.0796 309.5087 -42.1144 0.0332 ... 0.9999 0.9847 -- 1.56675e+16
3FHL J2339.5-0533 354.8927 -5.5577 81.3085 -62.4702 0.0448 ... 1.0000 0.0000 -- --
3FHL J2340.8+8015 355.2244 80.2593 119.8457 17.8129 0.0167 ... 1.0000 0.9970 0.2740 3427676800000000.0
3FHL J2343.6+3439 355.9012 34.6513 107.4255 -26.1696 0.0286 ... 0.9998 0.9819 0.3660 1029200060000000.0
3FHL J2345.1-1554 356.2999 -15.9151 65.6780 -70.9813 0.0166 ... 0.9999 0.9978 0.6210 20535250000000.0
3FHL J2346.6+0705 356.6726 7.0844 95.9910 -52.3676 0.0232 ... 0.9999 0.9971 0.1720 568853050000000.0
3FHL J2347.0+5142 356.7688 51.7008 112.8895 -9.9116 0.0129 ... 1.0000 0.9985 0.0440 7079464000000000.0
3FHL J2347.9+5435 356.9805 54.5974 113.7400 -7.1368 0.0276 ... 0.0000 0.9766 -- 2.5118841e+16
3FHL J2347.9-1630 356.9978 -16.5106 65.5355 -71.8766 0.0288 ... 0.9994 0.9999 0.5760 9332549000000.0
3FHL J2350.5-3006 357.6354 -30.1070 16.7759 -76.3194 0.0491 ... 0.9998 0.9218 0.2237 3981075200000000.0
3FHL J2351.5-7559 357.8926 -75.9890 307.6546 -40.5855 0.0650 ... 0.0000 0.9625 -- --
3FHL J2352.1+1753 358.0415 17.8865 103.5764 -42.7466 0.0838 ... 0.9926 0.0000 -- 1737799900000000.0
3FHL J2356.2+4035 359.0746 40.5985 111.7521 -21.0732 0.0298 ... 0.9998 0.9199 0.1310 6309576500000000.0
3FHL J2357.4-1717 359.3690 -17.2996 68.4009 -74.1285 0.0327 ... 0.9999 0.9631 -- 8.912525e+16
3FHL J2358.4-1808 359.6205 -18.1408 66.5520 -74.8501 0.0511 ... 0.0000 0.0000 -- --
3FHL J2358.5+3829 359.6266 38.4963 111.6905 -23.2173 0.0584 ... 0.0000 0.9254 -- --
3FHL J2359.1-3038 359.7760 -30.6397 12.7909 -78.0268 0.0231 ... 0.9999 0.9975 0.1650 2.818388e+17
3FHL J2359.3-2049 359.8293 -20.8256 58.0522 -76.5411 0.0722 ... 0.9894 0.9906 0.0960 4073799600000000.0
Length = 1556 rows
This looks very familiar again. The data is just stored as an
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:
# 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"]])
Source_Name ASSOC1 ASSOC2 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 Vela X field PSR 138.801
3FHL J0633.9+1746 PSR J0633+1746 PSR 99.734
3FHL J1555.7+1111 PG 1553+113 BLL 94.411
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:
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"])
144.40611
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
contains()andpositionsmight 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
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:
crab_3fhl = fermi_3fhl["Crab Nebula"]
crab_3fhl_spec = crab_3fhl.spectral_model()
print(crab_3fhl_spec)
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
The crab_3fhl_spec is an instance of the
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:
ax_crab_3fhl = crab_3fhl_spec.plot(energy_bounds=[10, 2000] * u.GeV, energy_power=0)
plt.show()
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:
print(crab_3fhl_spec(100 * u.GeV).to("cm-2 s-1 GeV-1"))
6.3848912826152664e-12 1 / (cm2 GeV s)
Next we can compute the integral flux of the Crab between 10 GeV and 2000 GeV:
print(
crab_3fhl_spec.integral(energy_min=10 * u.GeV, energy_max=2000 * u.GeV).to(
"cm-2 s-1"
)
)
8.67457342435522e-09 1 / (cm2 s)
We can easily convince ourself, that it corresponds to the value given in the Fermi-LAT 3FHL catalog:
print(crab_3fhl.data["Flux"])
8.658909145253801e-09 1 / (cm2 s)
In addition we can compute the energy flux between 10 GeV and 2000 GeV:
print(
crab_3fhl_spec.energy_flux(energy_min=10 * u.GeV, energy_max=2000 * u.GeV).to(
"erg cm-2 s-1"
)
)
5.311489174710791e-10 erg / (cm2 s)
Next we will access the flux points data of the Crab:
print(crab_3fhl.flux_points)
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
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:
print(crab_3fhl.flux_points.to_table(sed_type="dnde", formatted=True))
e_ref e_min e_max dnde dnde_errp dnde_errn dnde_ul sqrt_ts is_ul
GeV GeV GeV 1 / (cm2 GeV s) 1 / (cm2 GeV s) 1 / (cm2 GeV s) 1 / (cm2 GeV s)
-------- ------- -------- --------------- --------------- --------------- --------------- ------- -----
14.142 10.000 20.000 5.120e-10 1.321e-11 1.321e-11 nan 125.157 False
31.623 20.000 50.000 7.359e-11 2.842e-12 2.842e-12 nan 88.715 False
86.603 50.000 150.000 9.024e-12 5.367e-13 5.367e-13 nan 59.087 False
273.861 150.000 500.000 7.660e-13 8.707e-14 8.097e-14 nan 33.076 False
1000.000 500.000 2000.000 4.291e-14 1.086e-14 9.393e-15 nan 15.573 False
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:
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()
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.
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.
