The km3io Python package
========================
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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 package and gives you access to the data via numpy 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 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
To get the latest (stable) development release::
pip install git+https://git.km3net.de/km3py/km3io.git
**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.
Tutorial
========
**Table of contents:**
* `Introduction <#introduction>`__
* `Overview of daq files <#overview-of-daq-files>`__
* `Overview of offline files <#overview-of-offline-files>`__
* `Daq files reader <#daq-files-reader>`__
* `Offline files reader <#offline-file-reader>`__
Introduction
------------
Most of km3net data is stored in root files. These root files are either created with `Jpp <https://git.km3net.de/common/jpp>`__ or `aanet <https://git.km3net.de/common/aanet>`__ software. A root file created with
`Jpp <https://git.km3net.de/common/jpp>`__ is often referred to as "a Jpp root file". Similarly, a root file created with `aanet <https://git.km3net.de/common/aanet>`__ is often referred to as "an aanet file". In km3io, an aanet root file will always be reffered to as an ``offline file``, while a Jpp root file will always be referred to as a ``daq file``.
km3io is a Python package that provides a set of classes (``DaqReader`` and ``OfflineReader``) to read both daq root files and offline root files without any dependency to aanet, Jpp or ROOT.
Data in km3io is often returned as a "lazyarray", a "jagged lazyarray", "a jagged array" or a Numpy array. A lazyarray is an array-like object that reads data on demand! In a lazyarray, only the first and the last chunks of data are read in memory. A lazyarray can be used with all Numpy's universal `functions <https://docs.scipy.org/doc/numpy/referenceufuncs.html>`__. Here is how a lazyarray looks like:
.. code-block:: python3
# <ChunkedArray [5971 5971 5971 ... 5971 5971 5971] at 0x7fb2341ad810>
A jagged array, is a 2+ dimentional array with different arrays lengths. In other words, a jagged array is an array of arrays of different sizes. So a jagged lazyarray is simply a jagged array of lazyarrays with different sizes. Here is how a jagged lazyarray looks like:
.. code-block:: python3
# <JaggedArray [[102 102 102 ... 11517 11518 11518] [] [101 101 102 ... 11518 11518 11518] ... [101 101 102 ... 11516 11516 11517] [] [101 101 101 ... 11517 11517 11518]] at 0x7f74b0ef8810>
Overview of daq files
"""""""""""""""""""""
# info needed here
Overview of offline files
"""""""""""""""""""""""""
# info needed here
Daq files reader
----------------
# an update is needed here?
Currently only events (the ``KM3NET_EVENT`` tree) are supported but timeslices and summaryslices will be implemented very soon.
Let's have a look at some ORCA data (``KM3NeT_00000044_00005404.root``)
To get a lazy ragged array of the events:
.. code-block:: python3
import km3io as ki
events = ki.JppReader("KM3NeT_00000044_00005404.root").events
That's it! Now let's have a look at the hits data:
.. code-block:: python3
events
# Number of events: 17023
events[23].snapshot_hits.tot
# array([28, 22, 17, 29, 5, 27, 24, 26, 21, 28, 26, 21, 26, 24, 17, 28, 23,29, 27, 24, 23, 26, 29, 25, 18, 28, 24, 28, 26, 20, 25, 31, 28, 23, 26, 21, 30, 33, 27, 16, 23, 24, 19, 24, 27, 22, 23, 21, 25, 16, 28, 22, 22, 29, 24, 29, 24, 24, 25, 25, 21, 31, 26, 28, 30, 42, 28], dtype=uint8)
Offline files reader
--------------------
Let's have a look at some muons data from ORCA 4 lines simulations - run id 5971 (``datav6.0test.jchain.aanet.00005971.root``).
To get a lazy ragged array of all data::
>>> import km3io as ki
>>> reader = ki.AanetReader('datav6.0test.jchain.aanet.00005971.root')
That's it! Now let's take a look at all the available branches in our file::
>>> reader
Number of events: 145028
Events keys are:
id
det_id
mc_id
run_id
mc_run_id
frame_index
trigger_mask
trigger_counter
overlays
hits
trks
w
w2list
w3list
mc_t
mc_hits
mc_trks
comment
index
flags
t.fSec
t.fNanoSec
Hits keys are:
hits.id
hits.dom_id
hits.channel_id
hits.tdc
hits.tot
hits.trig
hits.pmt_id
hits.t
hits.a
hits.pos.x
hits.pos.y
hits.pos.z
hits.dir.x
hits.dir.y
hits.dir.z
hits.pure_t
hits.pure_a
hits.type
hits.origin
hits.pattern_flags
Tracks keys are:
trks.fUniqueID
trks.fBits
trks.usr_data
trks.usr_names
trks.id
trks.pos.x
trks.pos.y
trks.pos.z
trks.dir.x
trks.dir.y
trks.dir.z
trks.t
trks.E
trks.len
trks.lik
trks.type
trks.rec_type
trks.rec_stages
trks.status
trks.mother_id
trks.fitinf
trks.hit_ids
trks.error_matrix
trks.comment
Now that you have seen all the available branches, you can choose any key from
the above (key refers to a branch name) and display the corresponding data. For
example, we will check that we are indeed reading data from the run 5971::
>>> reader['run_id']
<ChunkedArray [5971 5971 5971 ... 5971 5971 5971] at 0x7fb2341ad810>
Let's look at the number of hits and tracks in the event number 5::
>>> reader[5]['hits']
60
>>> reader[5]['trks']
56
So event 5 has exactly 60 hits and 56 tracks. Let's explore in more details
hits and tracks data in event 5::
>>> reader['hits.dom_id'][5]
array([806455814, 806487219, 806487219, 806487219, 806487226, 808432835,
808432835, 808432835, 808432835, 808432835, 808432835, 808432835,
808451904, 808451904, 808451907, 808451907, 808469129, 808469129,
808469129, 808493910, 808949744, 808949744, 808951460, 808951460,
808956908, 808961655, 808964908, 808969848, 808969857, 808972593,
808972593, 808972598, 808972598, 808972698, 808972698, 808974758,
808974811, 808976377, 808981510, 808981523, 808981812, 808982005,
808982005, 808982018, 808982077, 808982077, 808982547, 809007627,
809521500, 809521500, 809521500, 809524432, 809526097, 809526097,
809526097, 809526097, 809526097, 809526097, 809526097, 809544058],
dtype=int32)
One can access the dom_id for the first hit in event 5 as follows::
>>> reader['hits.dom_id'][5][0]
806455814
Now let's read tracks data in event 5::
>>> reader['trks.dir.z'][5]
array([-0.60246049, -0.60246049, -0.60246049, -0.51420541, -0.5475772 ,
-0.5772408 , -0.56068238, -0.64907684, -0.67781799, -0.66565114,
-0.63014839, -0.64566464, -0.62691012, -0.58465493, -0.59287533,
-0.63655091, -0.63771247, -0.73446841, -0.7456636 , -0.70941246,
-0.66312268, -0.66312268, -0.56806477, -0.56806477, -0.66312268,
-0.66312268, -0.74851077, -0.74851077, -0.66312268, -0.74851077,
-0.56806477, -0.74851077, -0.66312268, -0.74851077, -0.56806477,
-0.66312268, -0.56806477, -0.66312268, -0.56806477, -0.56806477,
-0.66312268, -0.74851077, -0.66312268, -0.93501626, -0.56806477,
-0.74851077, -0.66312268, -0.56806477, -0.82298389, -0.74851077,
-0.66312268, -0.56806477, -0.82298389, -0.56806477, -0.66312268,
-0.97094183])
One can access the 'trks.dir.z' for the first track in event 5 as follows::
>>> reader['trks.dir.z'][5][0]
-0.60246049