to get data from the events tree, chose any branch of interest with the tab completion, the following is a non exaustive set of examples.
to get event ids:
.. code-block:: python3
>>> r.events.id
<ChunkedArray [1 2 3 ... 8 9 10] at 0x7f249eeb6f10>
to get detector ids:
.. code-block:: python3
>>> r.events.det_id
<ChunkedArray [44 44 44 ... 44 44 44] at 0x7f249eeba050>
to get frame_index:
.. code-block:: python3
>>> r.events.frame_index
<ChunkedArray [182 183 202 ... 185 185 204] at 0x7f249eeba410>
to get snapshot hits:
.. code-block:: python3
>>> r.events.hits
<ChunkedArray [176 125 318 ... 84 255 105] at 0x7f249eebaa10>
to illustrate the strength of this data structure, we will play around with `r.events.hits` using Numpy universal `functions <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`__.
.. code-block:: python3
>>> import numpy as np
>>> np.log(r.events.hits)
<ChunkedArray [5.170483995038151 4.8283137373023015 5.762051382780177 ... 4.430816798843313 5.541263545158426 4.653960350157523] at 0x7f249b8ebb90>
to get all data from one specific event (for example event 0):
.. code-block:: python3
>>> r.events[0]
offline event:
id : 1
det_id : 44
mc_id : 0
run_id : 5971
mc_run_id : 0
frame_index : 182
trigger_mask : 22
trigger_counter : 0
overlays : 60
hits : 176
trks : 56
w : []
w2list : []
w3list : []
mc_t : 0.0
mc_hits : 0
mc_trks : 0
comment : b''
index : 0
flags : 0
t_fSec : 1567036818
t_fNanoSec : 200000000
to get a specific value from event 0, for example the number of overlays:
.. code-block:: python3
>>> r.events[0].overlays
60
or the number of hits:
.. code-block:: python3
>>> r.events[0].hits
176
reading usr data of events
""""""""""""""""""""""""""
To access the ``usr`` data of events, use the ``.usr`` property which behaves
like a dictionary and returns ``lazyarray``, compatible to the ``numpy.array``
interface. The available keys can be accessed either as attributes or via a
dictionary lookup:
.. code-block:: python3
>>> import km3io
>>> f = km3io.OfflineReader("tests/samples/usr-sample.root")
if you are interested in a specific event (let's say event 0), you can access the corresponding hits tree by doing the following:
.. code-block:: python3
>>> r[0].hits
<OfflineHits: 176 parsed elements>
notice that now there are 176 parsed elements (as opposed to 10 elements parsed when r.hits is called). This means that in event 0 there are 176 hits! To get the dom ids from this event:
if you are interested in a specific event (let's say event 0), you can access the corresponding tracks tree by doing the following:
.. code-block:: python3
>>> r[0].tracks
<OfflineTracks: 56 parsed elements>
notice that now there are 56 parsed elements (as opposed to 10 elements parsed when r.tracks is called). This means that in event 0 there is data about 56 possible tracks! To get the tracks likelihood from this event:
to get a numpy `recarray <https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html>`__ of all fit data of the best reconstructed track:
.. code-block:: python3
>>> r.best_reco
to get an array of a parameter of interest, let's say `'JENERGY_ENERGY'`:
**Note**: In km3io, the best fit is defined as the track fit with the maximum reconstruction stages. When "nan" is returned, it means that the reconstruction parameter of interest is not found. for example, in the case of muon simulations: if `[1, 2]` are the reconstruction stages, then only the fit parameters corresponding to the stages `[1, 2]` are found in the Offline files, the remaining fit parameters corresponding to the stages `[3, 4, 5]` are all filled with nan.
to get a numpy recarray of the fit data of tracks with specific reconstruction stages, let's say `[1, 2, 3, 4, 5]` in the case of a muon track reconstruction:
.. code-block:: python3
>>> r.get_reco_fit([1, 2, 3, 4, 5])
again, to get the reconstruction parameters names:
.. code-block:: python3
>>> r.get_reco_fit([1, 2, 3, 4, 5]).dtype.names
('JGANDALF_BETA0_RAD',
'JGANDALF_BETA1_RAD',
'JGANDALF_CHI2',
'JGANDALF_NUMBER_OF_HITS',
'JENERGY_ENERGY',
'JENERGY_CHI2',
'JGANDALF_LAMBDA',
'JGANDALF_NUMBER_OF_ITERATIONS',
'JSTART_NPE_MIP',
'JSTART_NPE_MIP_TOTAL',
'JSTART_LENGTH_METRES',
'JVETO_NPE',
'JVETO_NUMBER_OF_HITS',
'JENERGY_MUON_RANGE_METRES',
'JENERGY_NOISE_LIKELIHOOD',
'JENERGY_NDF',
'JENERGY_NUMBER_OF_HITS')
to get the reconstruction data of interest, for example ['JENERGY_ENERGY']:
**Note**: When the reconstruction stages of interest are not found in all your data file, an error is raised.
reading mc hits data
""""""""""""""""""""
to read mc hits data:
.. code-block:: python3
>>> r.mc_hits
<OfflineHits: 10 parsed elements>
that's it! All branches in mc hits tree can be accessed in the exact same way described in the section `reading hits data <#reading-hits-data>`__ . All data is easily accesible and if you are stuck, hit tab key to see all the available branches:
that's it! All branches in mc tracks tree can be accessed in the exact same way described in the section `reading tracks data <#reading-tracks-data>`__ . All data is easily accesible and if you are stuck, hit tab key to see all the available branches:
