The km3io Python package

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.

Installation

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!

Questions

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.

Introduction

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 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

The same concept applies to all other branches, including hits, mc_hits, mc_tracks, t_sec etc.

Architecture overview

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.

Offline files reader

In general an offline file has two attributes to access data: the header and the events. Let's start with the header.

Reading the file 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

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
100000

Reading offline events

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

>>> f.events.tracks

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

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 variable 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]

Online files reader

The support to read online ROOT files has been dropped in km3io v1.2.