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This software provides a set of Python classes to read KM3NeT ROOT files
without having ROOT, Jpp or aanet installed. It only depends on Python 3.5+ and the amazing `uproot <https://github.com/scikit-hep/uproot>`__ package and gives you access to the data via `numpy <https://www.numpy.org>`__ and `awkward <https://awkward-array.readthedocs.io>`__ arrays.
It's very easy to use and according to the `uproot <https://github.com/scikit-hep/uproot>`__ benchmarks, it is able to outperform the original ROOT I/O performance.
**Note:** Beware that this package is in the development phase, so the API will change until version ``1.0.0`` is released!
Installation
============
Install km3io using pip::
pip install km3io
docker run -it docker.km3net.de/km3io
wget https://sftp.km3net.de/singularity/km3io_v0.27.2.sif # pick the version you like
singularity shell km3io_v0.27.2.sif
**Reminder:** km3io is **not** dependent on aanet, ROOT or Jpp!
Questions
=========
If you have a question about km3io, please proceed as follows:
- Read the documentation below.
- Explore the `examples <https://km3py.pages.km3net.de/km3io/examples.html>`__ in the documentation.
- Haven't you found an answer to your question in the documentation, post a git issue with your question showing us an example of what you have tried first, and what you would like to do.
- Have you noticed a bug, please post it in a git issue, we appreciate your contribution.
Most of km3net data is stored in root files. These root files are created using the `KM3NeT Dataformat library <https://git.km3net.de/common/km3net-dataformat>`__
A ROOT file created with
`Jpp <https://git.km3net.de/common/jpp>`__ is an "online" file and all other software usually produces "offline" files.
km3io is a Python package that provides a set of classes: ``OnlineReader``, ``OfflineReader`` and a special class to read gSeaGen files. All of these ROOT files can be read installing any other software like Jpp, aanet or ROOT.
Data in km3io is returned as ``awkward.Array`` which is an advance Numpy-like container type to store
contiguous data for high performance computations.
Such an ``awkward.Array`` supports any level of nested arrays and records which can have different lengths, in contrast to Numpy where everything has to be rectangular.
The example is shown below shows the array which contains the ``dir_z`` values
of each track of the first 4 events. The type ``4 * var * float64`` means that
it has 4 subarrays with variable lengths of type ``float64``:
>>> import km3io
>>> from km3net_testdata import data_path
>>> f = km3io.OfflineReader(data_path("offline/numucc.root"))
>>> f[:4].tracks.dir_z
<Array [[0.213, 0.213, ... 0.229, 0.323]] type='4 * var * float64'>
The same concept applies to all other branches, including ``hits``, ``mc_hits``,
In general an offline file has two attributes to access data: the header and the events. Let's start with the header.
To read an offline file start with opening it with the ``OfflineReader``:
>>> import km3io
>>> from km3net_testdata import data_path
>>> f = km3io.OfflineReader(data_path("offline/numucc.root"))
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
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>>> print(f.header)
MC Header:
DAQ(livetime=394)
PDF(i1=4, i2=58)
can(zmin=0, zmax=1027, r=888.4)
can_user: can_user(field_0=0.0, field_1=1027.0, field_2=888.4)
coord_origin(x=0, y=0, z=0)
cut_in(Emin=0, Emax=0, cosTmin=0, cosTmax=0)
cut_nu(Emin=100, Emax=100000000.0, cosTmin=-1, cosTmax=1)
cut_primary(Emin=0, Emax=0, cosTmin=0, cosTmax=0)
cut_seamuon(Emin=0, Emax=0, cosTmin=0, cosTmax=0)
decay: decay(field_0='doesnt', field_1='happen')
detector: NOT
drawing: Volume
genhencut(gDir=2000, Emin=0)
genvol(zmin=0, zmax=1027, r=888.4, volume=2649000000.0, numberOfEvents=100000)
kcut: 2
livetime(numberOfSeconds=0, errorOfSeconds=0)
model(interaction=1, muon=2, scattering=0, numberOfEnergyBins=1, field_4=12)
ngen: 100000.0
norma(primaryFlux=0, numberOfPrimaries=0)
nuflux: nuflux(field_0=0, field_1=3, field_2=0, field_3=0.5, field_4=0.0, field_5=1.0, field_6=3.0)
physics(program='GENHEN', version='7.2-220514', date=181116, time=1138)
seed(program='GENHEN', level=3, iseed=305765867, field_3=0, field_4=0)
simul(program='JSirene', version=11012, date='11/17/18', time=7)
sourcemode: diffuse
spectrum(alpha=-1.4)
start_run(run_id=1)
target: isoscalar
usedetfile: false
xlat_user: 0.63297
xparam: OFF
zed_user: zed_user(field_0=0.0, field_1=3450.0)
To read the values in the header one can call them directly, as the structures
are simple ``namedtuple``-like objects:
>>> f.header.DAQ.livetime
394
>>> f.header.cut_nu.Emin
100
>>> f.header.genvol.numberOfEvents
100000
Events are at the top level of an offline file, so that each branch of an event
is directly accessible at the ``OfflineReader`` instance. The ``.keys()`` method
can be used to list the available attributes. Notice that some of them are aliases
for backwards compatibility (like ``mc_tracks`` and ``mc_trks``). Another
backwards compatibility feature is the ``f.events`` attribute which is simply
mapping everything to ``f``, so that ``f.events.mc_tracks`` is the same as
``f.mc_tracks``.
>>> f
OfflineReader (10 events)
>>> f.keys()
{'comment', 'det_id', 'flags', 'frame_index', 'hits', 'id', 'index',
'mc_hits', 'mc_id', 'mc_run_id', 'mc_t', 'mc_tracks', 'mc_trks',
'n_hits', 'n_mc_hits', 'n_mc_tracks', 'n_mc_trks', 'n_tracks',
'n_trks', 'overlays', 'run_id', 't_ns', 't_sec', 'tracks',
'trigger_counter', 'trigger_mask', 'trks', 'usr', 'usr_names',
'w', 'w2list', 'w3list'}
>>> f.tracks
<Branch [10] path='trks'>
>>> f.events.tracks
<Branch [10] path='trks'>
The ``[10]`` denotes that there are ``10`` events available, each containing a sub-array of ``tracks``.
Using <TAB> completion gives an overview of available data. Alternatively the attribute `fields`
can be used on event-branches and to see what is available for reading.
.. code-block:: python3
>>> f.tracks.fields
['id',
'pos_x',
'pos_y',
'pos_z',
'dir_x',
'dir_y',
'dir_z',
't',
'E',
'len',
'lik',
'rec_type',
'rec_stages',
'fitinf']
Reading the reconstructed values like energy and direction of an event can be done with:
.. code-block:: python3
>>> f.events.tracks.E
<Array [[117, 117, 0, 0, 0, ... 0, 0, 0, 0, 0]] type='10 * var * float64'>
The ``Array`` in this case is an `awkward <https://awkward-array.readthedocs.io>`__ array with the data type
``10 * var * float64`` which means that there are ``10`` sub-arrays with ``var``iable lengths of type ``float64``.
Awkward arrays allow high-performance access to arrays which are not rectangular (in contrast to ``numpy``).
Read the documention of AwkwardArray to learn how to work with these structures efficiently. One example
to retrieve the energy of the very first reconstructed track for the first three events is:
.. code-block:: python3
>>> f.events.tracks.E[:3, 0]
<Array [117, 4.4e+03, 8.37] type='3 * float64'>
``km3io`` is able to read events, summary slices and timeslices. Timeslices are
currently only supported with split level of 2 or more, which means that reading
L0 timeslices is not working at the moment (but is in progress).
Now we use the ``OnlineReader`` to create our file object.
f = km3io.OnlineReader(data_path("online/km3net_online.root"))
That's it, we created an object which gives access to all the events, but the
relevant data is still not loaded into the memory (lazy access)!
The structure is different compared to the ``OfflineReader``
because online files contain additional branches at the top level
(summaryslices and timeslices).
Number of events: 3
>>> f.events.snapshot_hits[1].tot[:10]
array([27, 24, 21, 17, 22, 15, 24, 30, 19, 15], dtype=uint8)
>>> f.events.triggered_hits[1].channel_id[:10]
array([ 2, 3, 16, 22, 23, 0, 2, 3, 4, 5], dtype=uint8)
The resulting arrays are numpy arrays. The indexing convention is: the first indexing
corresponds to the event, the second to the branch and consecutive ones to the
optional dimensions of the arrays. In the last step we accessed the PMT channel IDs
of the first 10 hits of the second event.
The following example shows how to access summary slices. The summary slices are
returned in chunks to be more efficient with the I/O. The default chunk-size is
1000. In the example file we only have three summaryslices, so there is only a single
chunk. The first index passed to the summaryslices reader is corresponding to the
chunk and the second to the index of the summaryslice in that chunk.
<SummarysliceReader 3 items, step_size=1000 (1 chunk)>
>>> f.summaryslices[0]
SummarysliceChunk(headers=<Array [{' cnt': 671088704, ... ] type='3 * {" cnt": uint32, " vers": uint16, " ...'>, slices=<Array [[{dom_id: 806451572, ... ch30: 48}]] type='3 * var * {"dom_id": int32, "...'>)
>>> f.summaryslices[0].headers
<Array [{' cnt': 671088704, ... ] type='3 * {" cnt": uint32, " vers": uint16, " ...'>
>>> f.summaryslices[0].slices[2]
<Array [{dom_id: 806451572, ... ch30: 48}] type='68 * {"dom_id": int32, "dq_stat...'>
>>> f.summaryslices[0].slices[2].dom_id
<Array [806451572, 806455814, ... 809544061] type='68 * int32'>
>>> f.summaryslices[0].slices[2].ch23
<Array [48, 43, 46, 54, 83, ... 51, 51, 52, 50] type='68 * uint8'>
Timeslices are split into different streams since 2017 and ``km3io`` currently
supports everything except L0, i.e. L1, L2 and SN streams. The API is
work-in-progress and will be improved in future, however, all the data is
already accessible (although in ugly ways ;-)
To access the timeslice data, you need to specify which timeslice stream
to read:
Available timeslice streams: SN, L1
>>> f.timeslices.stream("L1", 0).frames
{806451572: <Table [<Row 0> <Row 1> <Row 2> ... <Row 981> <Row 982> <Row 983>] at 0x00014c167340>,
806455814: <Table [<Row 984> <Row 985> <Row 986> ... <Row 1985> <Row 1986> <Row 1987>] at 0x00014c5f4760>,
806465101: <Table [<Row 1988> <Row 1989> <Row 1990> ... <Row 2236> <Row 2237> <Row 2238>] at 0x00014c5f45e0>,
806483369: <Table [<Row 2239> <Row 2240> <Row 2241> ... <Row 2965> <Row 2966> <Row 2967>] at 0x00014c12b910>,
...
809544061: <Table [<Row 48517> <Row 48518> <Row 48519> ... <Row 49240> <Row 49241> <Row 49242>] at 0x00014ca57100>}
The frames are represented by a dictionary where the key is the ``DOM ID`` and
the value an awkward array of hits, with the usual fields to access the PMT
>>> f.timeslices.stream("L1", 0).frames[809524432].tot
array([25, 27, 28, ..., 29, 22, 28], dtype=uint8)