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 package and gives you access to the data via numpy and awkward arrays.
It's very easy to use and according to the uproot benchmarks, it is able to outperform the original ROOT I/O performance.
Install km3io using pip:
pip install km3io
or conda:
conda install km3io
To get the latest (stable) development release:
pip install git+https://git.km3net.de/km3py/km3io.git
Docker:
docker run -it docker.km3net.de/km3io
Singularity:
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!
If you have a question about km3io, please proceed as follows:
- Read the documentation below.
- Explore the examples 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 A ROOT file created with Jpp is an "online" file and all other software usually produces "offline" files.
km3io is a Python package that provides access to offline files with its
OfflineReader class and a special one to read gSeaGen files. All of these
ROOT files can be read without installing any other software like Jpp, aanet or
ROOT. km3io v1.1 and earlier also support the access to online files (events,
summaryslices and timeslices). This feature has been dropped due to a lack of
mainteinance power and inf favour of the KM3io.jl <https://git.km3net.de/common/KM3io.jl>`__ Julia Package, which
provides high-performances access to all ROOT files and should also be
prioritised over km3io when performance matters (which does, most of the
time).
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,
mc_tracks, t_sec etc.
km3io utilises uproot behind the scenes and creates a lazy and thin
wrapper which offers convenient slicing and iterations by delaying the access to
the actual ROOT data branches to the very last moment. When using the iteration
functionality, the data is loaded in chunks and the iteration is done over e.g.
events in each chunk or a bunch of frames in case of the summaryslice reader.
The base class for the event-based readout is the km3io.rootio.EventReader
class. When subclassing this class, the branches, aliases and nested branches
need to be defined in the static variables which are then used to mask unwanted
attributes. Especially in case of the Offline ROOT format, where the "one class
fits all" design was chosen, it is distracting that e.g. a Hit has many
attributes which make no sense depending on the context (MC hit, raw hit etc.).
By specifing the branches explicitely, the user API will only expose the
meaningful fields.
The online ROOT format support is partly still based on uproot3.
Many of the utility functions are using Numba to achieve the best possible
performance. km3io does not offer alternative implementations, so Numba is a
strict dependency and an integral part of the implementation.
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"))Accessing is as easy as typing:
>>> f.header
<km3io.offline.Header at 0x7fcd81025990>Printing it will give an overview of the structure:
>>> 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
100000Events 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.
>>> 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:
>>> 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 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:
>>> f.events.tracks.E[:3, 0]
<Array [117, 4.4e+03, 8.37] type='3 * float64'>The support to read online ROOT files has been dropped in km3io v1.2.