.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorials/data/hawc.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_data_hawc.py: HAWC with Gammapy ===================== Explore HAWC event lists and IRFs and perform the data reduction steps. `HAWC `__ is an array of water Cherenkov detectors located in Mexico that detects gamma-rays in the range between hundreds of GeV and hundreds of TeV. Gammapy recently added support of HAWC high level data analysis, after export to the current `open data level 3 format `__. The HAWC data is largely private. However, in 2022, a small sub-set of HAWC Pass4 event lists from the Crab nebula region was publicly `released `__. This dataset is 1 GB in size, so only a subset will be used here as an example. This notebook is a quick introduction to HAWC data analysis with Gammapy. It briefly describes the HAWC data and how to access it, and then uses a subset of it to produce a MapDataset, to show how the data reduction is performed. The HAWC data release contains events where energy is estimated using two different algorithms, referred to as "NN" and "GP" (see this `paper `__ for a detailed description). These two event classes are not independent, meaning that events are repeated between the NN and GP datasets. So these data should never be analyzed jointly, and one of the two estimators needs to be chosen before proceeding. Once the data has been reduced to a MapDataset, there are no differences in the way that HAWC data is handled with respect to data from any other observatory, such as H.E.S.S. or CTA. HAWC data access and reduction ------------------------------ This is how to access data and IRFs from the HAWC Crab event data release. .. GENERATED FROM PYTHON SOURCE LINES 40-52 .. code-block:: Python import numpy as np import astropy.units as u from astropy.coordinates import SkyCoord import matplotlib.pyplot as plt from IPython.display import display from gammapy.data import DataStore, HDUIndexTable, ObservationTable from gammapy.datasets import MapDataset from gammapy.estimators import ExcessMapEstimator from gammapy.makers import MapDatasetMaker, SafeMaskMaker from gammapy.maps import Map, MapAxis, WcsGeom .. GENERATED FROM PYTHON SOURCE LINES 53-55 Check setup ----------- .. GENERATED FROM PYTHON SOURCE LINES 55-60 .. code-block:: Python from gammapy.utils.check import check_tutorials_setup from gammapy.visualization import plot_theta_squared_table 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 : 3.9.19 machine : x86_64 system : Linux Gammapy package: version : 1.3.dev241+g0271bebfc 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 : 1.13.0 astropy : 5.2.2 regions : 0.8 click : 8.1.7 yaml : 6.0.1 IPython : 8.18.1 jupyterlab : not installed matplotlib : 3.8.4 pandas : not installed healpy : 1.16.6 iminuit : 2.25.2 sherpa : 4.16.0 naima : 0.10.0 emcee : 3.1.6 corner : 2.2.2 ray : 2.20.0 Gammapy environment variables: GAMMAPY_DATA : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev .. GENERATED FROM PYTHON SOURCE LINES 61-62 Chose which estimator we will use .. GENERATED FROM PYTHON SOURCE LINES 62-66 .. code-block:: Python energy_estimator = "NN" .. GENERATED FROM PYTHON SOURCE LINES 67-82 A useful way to organize the relevant files are the index tables. The observation index table contains information on each particular observation, such as the run ID. The HDU index table has a row per relevant file (i.e., events, effective area, psf…) and contains the path to said file. The HAWC data is divided into different event types, classified using the fraction of the array that was triggered by an event, a quantity usually referred to as "fHit". These event types are fully independent, meaning that an event will have a unique event type identifier, which is usually a number indicating which fHit bin the event corresponds to. For this reason, a single HAWC observation has several HDU index tables associated to it, one per event type. In each table, there will be paths to a distinct event list file and IRFs. In the HAWC event data release, all the HDU tables are saved into the same FITS file, and can be accesses through the choice of the hdu index. .. GENERATED FROM PYTHON SOURCE LINES 85-86 Load the tables .. GENERATED FROM PYTHON SOURCE LINES 86-99 .. code-block:: Python data_path = "$GAMMAPY_DATA/hawc/crab_events_pass4/" hdu_filename = f"hdu-index-table-{energy_estimator}-Crab.fits.gz" obs_filename = f"obs-index-table-{energy_estimator}-Crab.fits.gz" # there is only one observation table obs_table = ObservationTable.read(data_path + obs_filename) # we read the hdu index table of fHit bin number 6 fHit = 6 hdu_table = HDUIndexTable.read(data_path + hdu_filename, hdu=fHit) .. GENERATED FROM PYTHON SOURCE LINES 100-101 From the tables, we can create a Datastore .. GENERATED FROM PYTHON SOURCE LINES 101-107 .. code-block:: Python data_store = DataStore(hdu_table=hdu_table, obs_table=obs_table) data_store.info() .. rst-class:: sphx-glr-script-out .. code-block:: none Data store: HDU index table: BASE_DIR: /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev/hawc/crab_events_pass4 Rows: 6 OBS_ID: 103000133 -- 103000133 HDU_TYPE: ['aeff', 'bkg', 'edisp', 'events', 'gti', 'psf'] HDU_CLASS: ['edisp_kernel_map', 'events', 'gti', 'map', 'psf_map_reco'] Observation table: Observatory name: 'N/A' Number of observations: 1 .. GENERATED FROM PYTHON SOURCE LINES 108-109 There is only one observation .. GENERATED FROM PYTHON SOURCE LINES 109-112 .. code-block:: Python obs = data_store.get_observations()[0] .. GENERATED FROM PYTHON SOURCE LINES 113-114 Select and peek events .. GENERATED FROM PYTHON SOURCE LINES 114-119 .. code-block:: Python obs.events.peek() plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_001.png :alt: hawc :srcset: /tutorials/data/images/sphx_glr_hawc_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 120-121 Peek the energy dispersion .. GENERATED FROM PYTHON SOURCE LINES 121-125 .. code-block:: Python obs.edisp.peek() plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_002.png :alt: hawc :srcset: /tutorials/data/images/sphx_glr_hawc_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 126-127 Peek the psf .. GENERATED FROM PYTHON SOURCE LINES 127-130 .. code-block:: Python obs.psf.peek() plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_003.png :alt: Exposure, Containment radius at 1 TeV, Containment radius at center of map, PSF at center of map :srcset: /tutorials/data/images/sphx_glr_hawc_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 131-132 Peek the background for one transit .. GENERATED FROM PYTHON SOURCE LINES 132-136 .. code-block:: Python plt.figure() obs.bkg.reduce_over_axes().plot(add_cbar=True) plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_004.png :alt: hawc :srcset: /tutorials/data/images/sphx_glr_hawc_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 137-138 Peek the effective exposure for one transit .. GENERATED FROM PYTHON SOURCE LINES 138-143 .. code-block:: Python plt.figure() obs.aeff.reduce_over_axes().plot(add_cbar=True) plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_005.png :alt: hawc :srcset: /tutorials/data/images/sphx_glr_hawc_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 144-150 Data reduction into a MapDataset -------------------------------- We will now produce a MapDataset using the data from one of the fHit bins. In order to use all bins, one just needs to repeat this process inside of a for loop that modifies the variable fHit. .. GENERATED FROM PYTHON SOURCE LINES 153-154 First we define the geometry and axes .. GENERATED FROM PYTHON SOURCE LINES 154-181 .. code-block:: Python # create the energy reco axis # Note that this axis is the one used to create the background model map. If # different edges are used, the MapDatasetMaker will interpolate between # them, which might lead to unexpected behavior. energy_axis = MapAxis.from_edges( [1.00, 1.78, 3.16, 5.62, 10.0, 17.8, 31.6, 56.2, 100, 177, 316] * u.TeV, name="energy", interp="log", ) # and energy true axis energy_axis_true = MapAxis.from_energy_bounds( 1e-3, 1e4, nbin=140, unit="TeV", name="energy_true" ) # create a geometry around the Crab location geom = WcsGeom.create( skydir=SkyCoord(ra=83.63, dec=22.01, unit="deg", frame="icrs"), width=6 * u.deg, axes=[energy_axis], binsz=0.05, ) .. GENERATED FROM PYTHON SOURCE LINES 182-183 Define the makers we will use .. GENERATED FROM PYTHON SOURCE LINES 183-188 .. code-block:: Python maker = MapDatasetMaker(selection=["counts", "background", "exposure", "edisp", "psf"]) safemask_maker = SafeMaskMaker(methods=["aeff-max"], aeff_percent=10) .. GENERATED FROM PYTHON SOURCE LINES 189-192 Create empty Mapdataset The keyword reco_psf=True is needed because the HAWC PSF is derived in reconstructed energy. .. GENERATED FROM PYTHON SOURCE LINES 192-208 .. code-block:: Python dataset_empty = MapDataset.create( geom, energy_axis_true=energy_axis_true, name="fHit " + str(fHit), reco_psf=True ) # run the map dataset maker dataset = maker.run(dataset_empty, obs) # The livetime info is used by the SafeMask maker to retrieve the # effective area from the exposure. The HAWC effective area is computed # for one source transit above 45º zenith, which is around 6h # Note that since the effective area condition used here is relative to # the maximum, this value does not actually impact the result dataset.exposure.meta["livetime"] = "6 h" dataset = safemask_maker.run(dataset) .. GENERATED FROM PYTHON SOURCE LINES 209-214 Now we have a dataset that has background and exposure quantities for one single transit, but our dataset might comprise more. The number of transits can be derived using the good time intervals (GTI) stored with the event list. For convenience, the HAWC data release already included this quantity as a map .. GENERATED FROM PYTHON SOURCE LINES 214-223 .. code-block:: Python transit_map = Map.read(data_path + "irfs/TransitsMap_Crab.fits.gz") transit_number = transit_map.get_by_coord(geom.center_skydir) # Correct the quantities dataset.background.data *= transit_number dataset.exposure.data *= transit_number .. GENERATED FROM PYTHON SOURCE LINES 224-228 Check the dataset we produced ----------------------------- We will now check the contents of the dataset .. GENERATED FROM PYTHON SOURCE LINES 231-232 We can use the .peek() method to quickly get a glimpse of the contents .. GENERATED FROM PYTHON SOURCE LINES 232-236 .. code-block:: Python dataset.peek() plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_006.png :alt: Counts, Excess counts, Exposure, Background :srcset: /tutorials/data/images/sphx_glr_hawc_006.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 237-238 And make significance maps to check that the Crab is visible .. GENERATED FROM PYTHON SOURCE LINES 238-249 .. code-block:: Python excess_estimator = ExcessMapEstimator( correlation_radius="0.2 deg", selection_optional=[], energy_edges=energy_axis.edges ) excess = excess_estimator.run(dataset) (dataset.mask * excess["sqrt_ts"]).plot_grid( add_cbar=True, cmap="coolwarm", vmin=-5, vmax=5 ) plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_007.png :alt: Energy 1.00 TeV - 1.78 TeV, Energy 1.78 TeV - 3.16 TeV, Energy 3.16 TeV - 5.62 TeV, Energy 5.62 TeV - 10.0 TeV, Energy 10.0 TeV - 17.8 TeV, Energy 17.8 TeV - 31.6 TeV, Energy 31.6 TeV - 56.2 TeV, Energy 56.2 TeV - 100 TeV, Energy 100 TeV - 177 TeV, Energy 177 TeV - 316 TeV :srcset: /tutorials/data/images/sphx_glr_hawc_007.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 250-251 Combining all energies .. GENERATED FROM PYTHON SOURCE LINES 251-260 .. code-block:: Python excess_estimator_integrated = ExcessMapEstimator( correlation_radius="0.2 deg", selection_optional=[] ) excess_integrated = excess_estimator_integrated.run(dataset) excess_integrated["sqrt_ts"].plot(add_cbar=True) plt.show() .. image-sg:: /tutorials/data/images/sphx_glr_hawc_008.png :alt: hawc :srcset: /tutorials/data/images/sphx_glr_hawc_008.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 261-268 Exercises --------- - Repeat the process for a different fHit bin - Repeat the process for all the fHit bins provided in the data release and fit a model to the result. .. GENERATED FROM PYTHON SOURCE LINES 271-278 Next steps ---------- Now you know how to access and work with HAWC data. All other tutorials and documentation concerning 3D analysis and MapDatasets can be used from this step. .. _sphx_glr_download_tutorials_data_hawc.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/main?urlpath=lab/tree/notebooks/dev/tutorials/data/hawc.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: hawc.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: hawc.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_