km3io 0.8.2

KM3NeT I/O without ROOT

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 arrays.

It’s very easy to use and according to the 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 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

  • Overview of daq files

  • Overview of offline files

• DAQ files reader

  • Reading Events

  • Reading SummarySlices

  • Reading Timeslices

• Offline files reader

  • reading events data

  • reading hits data

  • reading tracks data

  • reading mc hits data

  • reading mc tracks data

Introduction

Most of km3net data is stored in root files. These root files are either created with Jpp or aanet software. A root file created with Jpp is often referred to as “a Jpp root file”. Similarly, a root file created with 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” 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. Here is how a lazyarray looks like:

# <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:

# <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

DAQ files, or also called online files, are written by the DataWriter and contain events, timeslics and summary slices.

Overview of offline files

Offline files contain data about events, hits and tracks. Based on aanet version 2.0.0 documentation, the following tables show the definitions, the types and the units of the branches founds in the events, hits and tracks trees. A description of the file header are also displayed.

events keys definitions and units

type	name	definition
int	id	offline event identifier
int	det_id	detector identifier from DAQ
int	mc_id	identifier of the MC event (as found in ascii or antcc file)
int	run_id	DAQ run identifier
int	mc_run_id	MC run identifier
int	frame_index	from the raw data
ULong64_t	trigger_mask	trigger mask from raw data (i.e. the trigger bits)
ULong64_t	trigger_counter	trigger counter
unsigned int	overlays	number of overlaying triggered events
TTimeStamp	t	UTC time of the start of the timeslice the event came from
vec Hit	hits	list of hits
vec Trk	trks	list of reconstructed tracks (can be several because of prefits,showers, etc)
vec double	w	MC: Weights w[0]=w1 & w[1]=w2 & w[2]]=w3
vec double	w2list	MC: factors that make up w[1]=w2
vec double	w3list	MC: atmospheric flux information
double	mc_t	MC: time of the mc event
vec Hit	mc_hits	MC: list of MC truth hits
vec Trk	mc_trks	MC: list of MC truth tracks
string	comment	user can use this as he/she likes
int	index	user can use this as he/she likes

hits keys definitions and units

type	name	definition
int	id	hit id
int	dom_id	module identifier from the data (unique in the detector)
unsigned int	channel_id	PMT channel id {0,1, .., 31} local to module
unsigned int	tdc	hit tdc (=time in ns)
unsigned int	tot	tot value as stored in raw data (int for pyroot)
int	trig	non-zero if the hit is a trigger hit
int	pmt_id	global PMT identifier as found in evt files
double	t	hit time (from calibration or MC truth)
double	a	hit amplitude (in p.e.)
vec	pos	hit position
vec	dir	hit direction i.e. direction of the PMT
double	pure_t	photon time before pmt simultion (MC only)
double	pure_a	amptitude before pmt simution (MC only)
int	type	particle type or parametrisation used for hit (mc only)
int	origin	track id of the track that created this hit
unsigned	pattern_flags	some number that you can use to flag the hit

tracks keys definitions and units

type	name	definition
int	id	track identifier
vec	pos	position of the track at time t
vec	dir	track direction
double	t	track time (when particle is at pos)
double	E	Energy (either MC truth or reconstructed)
double	len	length if applicable
double	lik	likelihood or lambda value (for aafit: lambda)
int	type	MC: particle type in PDG encoding
int	rec_type	identifyer for the overall fitting algorithm/chain/strategy
vec int	rec_stages	list of identifyers of succesfull fitting stages resulting in this track
int	status	MC status code
int	mother_id	MC id of the parent particle
vec double	fitinf	place to store additional fit info for jgandalf see FitParameters.csv
vec int	hit_ids	list of associated hit-ids (corresponds to Hit::id)
vec double	error_matrix	(5x5) error covariance matrix (stored as linear vector)
string	comment	user comment

offline file header definitions

name	definition
DAQ	livetime
cut_primary cut_seamuon cut_in cut_nu	Emin Emax cosTmin cosTmax
generator physics simul	program version date time
seed	program level iseed
PM1_type_area	type area TTS
PDF	i1 i2
model	interaction muon scattering numberOfEnergyBins
can	zmin zmax r
genvol	zmin zmax r volume numberOfEvents
merge	time gain
coord_origin	x y z
translate	x y z
genhencut	gDir Emin
k40	rate time
norma	primaryFlux numberOfPrimaries
livetime	numberOfSeconds errorOfSeconds
flux	type key file_1 file_2
spectrum	alpha
fixedcan	xcenter ycenter zmin zmax radius
start_run	run_id

DAQ files reader

km3io is able to read events, summary slices and timeslices (except the L0 slices, which is work in progress).

Let’s have a look at some ORCA data (KM3NeT_00000044_00005404.root)

Reading Events

To get a lazy ragged array of the events:

import km3io
f = km3io.DAQReader("KM3NeT_00000044_00005404.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)! Now let’s have a look at the hits data:

>>> f.events
Number of events: 17023
>>> f.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)

The resulting arrays are numpy arrays.

Reading SummarySlices

The following example shows how to access summary slices, in particular the DOM IDs of the slice with the index 23:

>>> f.summaryslices
<km3io.daq.SummarySlices at 0x7effcc0e52b0>
>>> f.summaryslices.slices[23].dom_id
array([806451572, 806455814, 806465101, 806483369, 806487219, 806487226,
     806487231, 808432835, 808435278, 808447180, 808447186, 808451904,
     808451907, 808469129, 808472260, 808472265, 808488895, 808488990,
     808489014, 808489117, 808493910, 808946818, 808949744, 808951460,
     808956908, 808959411, 808961448, 808961480, 808961504, 808961655,
     808964815, 808964852, 808964883, 808964908, 808969848, 808969857,
     808972593, 808972598, 808972698, 808974758, 808974773, 808974811,
     808974972, 808976377, 808979567, 808979721, 808979729, 808981510,
     808981523, 808981672, 808981812, 808981864, 808982005, 808982018,
     808982041, 808982066, 808982077, 808982547, 808984711, 808996773,
     808997793, 809006037, 809007627, 809503416, 809521500, 809524432,
     809526097, 809544058, 809544061], dtype=int32)

The .dtype attribute (or in general, <TAB> completion) is useful to find out more about the field structure:

>>> f.summaryslices.headers.dtype
dtype([(' cnt', '<u4'), (' vers', '<u2'), (' cnt2', '<u4'), (' vers2',
'<u2'), (' cnt3', '<u4'), (' vers3', '<u2'), ('detector_id', '<i4'), ('run',
'<i4'), ('frame_index', '<i4'), (' cnt4', '<u4'), (' vers4', '<u2'),
('UTC_seconds', '<u4'), ('UTC_16nanosecondcycles', '<u4')])
>>> f.summaryslices.headers.frame_index
<ChunkedArray [162 163 173 ... 36001 36002 36003] at 0x7effccd4af10>

The resulting array is a ChunkedArray which is an extended version of a numpy array and behaves like one.

Reading Timeslices

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:

>>> f.timeslices
Available timeslice streams: L1, SN
>>> f.timeslices.stream("L1", 24).frames
{806451572: <Table [<Row 1577843> <Row 1577844> ... <Row 1578147>],
 806455814: <Table [<Row 1578148> <Row 1578149> ... <Row 1579446>],
 806465101: <Table [<Row 1579447> <Row 1579448> ... <Row 1580885>],
 ...
}

The frames are represented by a dictionary where the key is the DOM ID and the value a numpy array of hits, with the usual fields to access the PMT channel, time and ToT:

>>> f.timeslices.stream("L1", 24).frames[806451572].dtype
dtype([('pmt', 'u1'), ('tdc', '<u4'), ('tot', 'u1')])
>>> f.timeslices.stream("L1", 24).frames[806451572].tot
array([29, 21,  8, 29, 22, 20,  1, 37, 11, 22, 11, 22, 12, 20, 29, 94, 26,
       26, 18, 16, 13, 22,  6, 29, 24, 30, 14, 26, 12, 23,  4, 25,  6, 27,
        5, 13, 21, 28, 30,  4, 25, 10,  5,  6,  5, 17,  4, 27, 24, 25, 27,
       28, 32,  6,  3, 15,  3, 20, 33, 30, 30, 20, 28,  6,  7,  3, 14, 12,
       25, 27, 26, 25, 22, 21, 23,  6, 20, 21,  4,  4, 10, 24, 29, 12, 30,
        5,  3, 24, 15, 14, 25,  5, 27, 23, 26,  4, 28, 15, 34, 22,  4, 29,
       24, 26, 29, 23, 25, 28, 14, 31, 27, 26, 27, 28, 23, 54,  4, 25, 11,
       28, 25, 24,  7, 27, 28, 28, 18,  3, 13, 14, 38, 28,  4, 21, 16, 16,
        4, 21, 26, 21, 28, 64, 21,  1, 24, 21, 26, 26, 25,  4, 28, 11, 31,
       10, 24, 24, 28, 10,  6,  4, 20, 26, 18,  5, 18, 24,  5, 27, 23, 20,
       29, 20,  6, 18,  5, 24, 17, 28, 24, 15, 26, 27, 25,  9,  3, 18,  3,
       34, 29, 10, 25, 30, 28, 19, 26, 34, 27, 14, 17, 15, 26,  8, 19,  5,
       27, 13,  5, 27, 46,  3, 25, 13, 30,  9, 21, 12,  1, 32, 25,  8, 30,
        4, 24, 11,  3, 11, 27,  5, 13,  5, 16, 18,  3, 22, 10,  7, 32, 29,
       15, 20, 18, 16, 27,  5, 22,  4, 33,  5, 29, 24, 30,  7,  7, 25, 33,
        7, 20,  8, 30,  4,  4,  6, 26,  8, 24, 22, 12,  6,  3, 21, 28, 11,
       24, 27, 27,  6, 29,  5, 18, 11, 26,  5, 19, 32, 25,  4, 20, 35, 30,
        5,  3, 26, 30, 23, 28,  6, 25, 25,  5, 45, 23, 18, 29, 28, 23],
      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).

Note: this file was cropped to 10 events only, so don’t be surprised in this tutorial if you see few events in the file.

First, let’s read our file:

>>> import km3io as ki
>>> file = 'my_file.root'
>>> r = ki.OfflineReader(file)
<km3io.offline.OfflineReader at 0x7f24cc2bd550>

and that’s it! Note that file can be either an str of your file path, or a path-like object.

To read the file header:

>>> r.header
DAQ             394
PDF             4      58
XSecFile
can             0 1027 888.4
can_user        0.00 1027.00  888.40
coord_origin    0 0 0
cut_in          0 0 0 0
cut_nu          100 1e+08 -1 1
cut_primary     0 0 0 0
cut_seamuon     0 0 0 0
decay           doesnt happen
detector        NOT
drawing         Volume
end_event
genhencut       2000 0
genvol          0 1027 888.4 2.649e+09 100000
kcut            2
livetime        0 0
model           1       2       0       1      12
muon_desc_file
ngen            0.1000E+06
norma           0 0
nuflux          0       3       0 0.500E+00 0.000E+00 0.100E+01 0.300E+01
physics         GENHEN 7.2-220514 181116 1138
seed            GENHEN 3  305765867         0         0
simul           JSirene 11012 11/17/18 07
sourcemode      diffuse
spectrum        -1.4
start_run       1
target          isoscalar
usedetfile      false
xlat_user       0.63297
xparam          OFF
zed_user        0.00 3450.00

Note: not all file header types are supported, so don’t be surprised when you get the following warning

/home/zineb/km3net/km3net/km3io/km3io/offline.py:341: UserWarning: Your file header has an unsupported format
warnings.warn("Your file header has an unsupported format")

To explore all the available branches in our offline file:

>>> r.keys
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.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
Mc hits keys are:
      mc_hits.id
      mc_hits.dom_id
      mc_hits.channel_id
      mc_hits.tdc
      mc_hits.tot
      mc_hits.trig
      mc_hits.pmt_id
      mc_hits.t
      mc_hits.a
      mc_hits.pos.x
      mc_hits.pos.y
      mc_hits.pos.z
      mc_hits.dir.x
      mc_hits.dir.y
      mc_hits.dir.z
      mc_hits.pure_t
      mc_hits.pure_a
      mc_hits.type
      mc_hits.origin
      mc_hits.pattern_flags
Mc tracks keys are:
      mc_trks.fUniqueID
      mc_trks.fBits
      mc_trks.id
      mc_trks.pos.x
      mc_trks.pos.y
      mc_trks.pos.z
      mc_trks.dir.x
      mc_trks.dir.y
      mc_trks.dir.z
      mc_trks.t
      mc_trks.E
      mc_trks.len
      mc_trks.lik
      mc_trks.type
      mc_trks.rec_type
      mc_trks.rec_stages
      mc_trks.status
      mc_trks.mother_id
      mc_trks.fitinf
      mc_trks.hit_ids
      mc_trks.error_matrix
      mc_trks.comment

In an offline file, there are 5 main trees with data:

• events tree

• hits tree

• tracks tree

• mc hits tree

• mc tracks tree

with km3io, these trees can be accessed with a simple tab completion:

In the following, we will explore each tree using km3io package.

reading events data

to read data in events tree with km3io:

>>> r.events
<OfflineEvents: 10 parsed events>

to get the total number of events in the events tree:

>>> len(r.events)
10

the branches stored in the events tree in an offline file can be easily accessed with a tab completion as seen below:

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:

>>> r.events.id
<ChunkedArray [1 2 3 ... 8 9 10] at 0x7f249eeb6f10>

to get detector ids:

>>> r.events.det_id
<ChunkedArray [44 44 44 ... 44 44 44] at 0x7f249eeba050>

to get frame_index:

>>> r.events.frame_index
<ChunkedArray [182 183 202 ... 185 185 204] at 0x7f249eeba410>

to get snapshot hits:

>>> 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.

>>> 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):

>>> 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:

>>> r.events[0].overlays
60

or the number of hits:

>>> r.events[0].hits
176

reading hits data

to read data in hits tree with km3io:

>>> r.hits
<OfflineHits: 10 parsed elements>

this shows that in our offline file, there are 10 events, with each event is associated a hits trees.

to have access to all data in a specific branche from the hits tree, you can use the tab completion:

to get ALL the dom ids in all hits trees in our offline file:

>>> r.hits.dom_id
<ChunkedArray [[806451572 806451572 806451572 ... 809544061 809544061 809544061] [806451572 806451572 806451572 ... 809524432 809526097 809544061] [806451572 806451572 806451572 ... 809544061 809544061 809544061] ... [806451572 806455814 806465101 ... 809526097 809544058 809544061] [806455814 806455814 806455814 ... 809544061 809544061 809544061] [806455814 806455814 806455814 ... 809544058 809544058 809544061]] at 0x7f249eebac50>

to get ALL the time over threshold (tot) in all hits trees in our offline file:

>>> r.hits.tot
<ChunkedArray [[24 30 22 ... 38 26 23] [29 26 22 ... 26 28 24] [27 19 13 ... 27 24 16] ... [22 22 9 ... 27 32 27] [30 32 17 ... 30 24 29] [27 41 36 ... 29 24 28]] at 0x7f249eec9050>

if you are interested in a specific event (let’s say event 0), you can access the corresponding hits tree by doing the following:

>>> 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:

>>> r[0].hits.dom_id
array([806451572, 806451572, 806451572, 806451572, 806455814, 806455814,
   806455814, 806483369, 806483369, 806483369, 806483369, 806483369,
   806483369, 806483369, 806483369, 806483369, 806483369, 806487219,
   806487226, 806487231, 806487231, 808432835, 808435278, 808435278,
   808435278, 808435278, 808435278, 808447180, 808447180, 808447180,
   808447180, 808447180, 808447180, 808447180, 808447180, 808447186,
   808451904, 808451904, 808472265, 808472265, 808472265, 808472265,
   808472265, 808472265, 808472265, 808472265, 808488895, 808488990,
   808488990, 808488990, 808488990, 808488990, 808489014, 808489014,
   808489117, 808489117, 808489117, 808489117, 808493910, 808946818,
   808949744, 808951460, 808951460, 808951460, 808951460, 808951460,
   808956908, 808956908, 808959411, 808959411, 808959411, 808961448,
   808961448, 808961504, 808961504, 808961655, 808961655, 808961655,
   808964815, 808964815, 808964852, 808964908, 808969857, 808969857,
   808969857, 808969857, 808969857, 808972593, 808972698, 808972698,
   808972698, 808974758, 808974758, 808974758, 808974758, 808974758,
   808974758, 808974758, 808974758, 808974758, 808974758, 808974758,
   808974773, 808974773, 808974773, 808974773, 808974773, 808974972,
   808974972, 808976377, 808976377, 808976377, 808979567, 808979567,
   808979567, 808979721, 808979721, 808979721, 808979721, 808979721,
   808979721, 808979721, 808979729, 808979729, 808979729, 808981510,
   808981510, 808981510, 808981510, 808981672, 808981672, 808981672,
   808981672, 808981672, 808981672, 808981672, 808981672, 808981672,
   808981672, 808981672, 808981672, 808981672, 808981672, 808981672,
   808981672, 808981672, 808981812, 808981812, 808981812, 808981864,
   808981864, 808982005, 808982005, 808982005, 808982018, 808982018,
   808982018, 808982041, 808982041, 808982077, 808982077, 808982547,
   808982547, 808982547, 808997793, 809006037, 809524432, 809526097,
   809526097, 809544061, 809544061, 809544061, 809544061, 809544061,
   809544061, 809544061], dtype=int32

to get all data of a specific hit (let’s say hit 0) from event 0:

>>> r[0].hits[0]
offline hit:
      id                  :               0
      dom_id              :       806451572
      channel_id          :               8
      tdc                 :               0
      tot                 :              24
      trig                :               1
      pmt_id              :               0
      t                   :      70104010.0
      a                   :             0.0
      pos_x               :             0.0
      pos_y               :             0.0
      pos_z               :             0.0
      dir_x               :             0.0
      dir_y               :             0.0
      dir_z               :             0.0
      pure_t              :             0.0
      pure_a              :             0.0
      type                :               0
      origin              :               0
      pattern_flags       :               0

to get a specific value from hit 0 in event 0, let’s say for example the dom id:

>>> r[0].hits[0].dom_id
806451572

reading tracks data

to read data in tracks tree with km3io:

>>> r.tracks
<OfflineTracks: 10 parsed elements>

this shows that in our offline file, there are 10 parsed elements (events), each event is associated with tracks data.

to have access to all data in a specific branche from the tracks tree, you can use the tab completion:

to get ALL the cos(zenith angle) in all tracks tree in our offline file:

>>> r.tracks.dir_z
<ChunkedArray [[-0.872885221293917 -0.872885221293917 -0.872885221293917 ... -0.6631226836266504 -0.5680647731737454 -0.5680647731737454] [-0.8351996698137462 -0.8351996698137462 -0.8351996698137462 ... -0.7485107718446855 -0.8229838871876581 -0.239315690284641] [-0.989148723802379 -0.989148723802379 -0.989148723802379 ... -0.9350162572437829 -0.88545604390297 -0.88545604390297] ... [-0.5704611045902105 -0.5704611045902105 -0.5704611045902105 ... -0.9350162572437829 -0.4647231989130516 -0.4647231989130516] [-0.9779941383490359 -0.9779941383490359 -0.9779941383490359 ... -0.88545604390297 -0.88545604390297 -0.8229838871876581] [-0.7396916780974963 -0.7396916780974963 -0.7396916780974963 ... -0.6631226836266504 -0.7485107718446855 -0.7485107718446855]] at 0x7f249eed2090>

to get ALL the tracks likelihood in our offline file:

>>> r.tracks.lik
<ChunkedArray [[294.6407542676734 294.6407542676734 294.6407542676734 ... 67.81221253265059 67.7756405143316 67.77250505700384] [96.75133289411137 96.75133289411137 96.75133289411137 ... 39.21916536442286 39.184645826013806 38.870325146341884] [560.2775306614813 560.2775306614813 560.2775306614813 ... 118.88577278801066 118.72271313687405 117.80785995187605] ... [71.03251451148226 71.03251451148226 71.03251451148226 ... 16.714140573909347 16.444395245214945 16.34639241716669] [326.440133294878 326.440133294878 326.440133294878 ... 87.79818671079849 87.75488082571873 87.74839444768625] [159.77779654216795 159.77779654216795 159.77779654216795 ... 33.8669134999348 33.821631538334984 33.77240735670646]] at 0x7f249eed2590>

if you are interested in a specific event (let’s say event 0), you can access the corresponding tracks tree by doing the following:

>>> 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:

>>> r[0].tracks.lik
array([294.64075427, 294.64075427, 294.64075427, 291.64653113,
   291.27392663, 290.69031512, 289.19290546, 289.08449217,
   289.03373947, 288.19030836, 282.92343367, 282.71527118,
   282.10762402, 280.20553861, 275.93183966, 273.01809111,
   257.46433694, 220.94357656, 194.99426403, 190.47809685,
    79.95235686,  78.94389763,  78.90791169,  77.96122466,
    77.9579604 ,  76.90769883,  75.97546175,  74.91530508,
    74.9059469 ,  72.94007716,  72.90467038,  72.8629316 ,
    72.81280833,  72.80229533,  72.78899435,  71.82404165,
    71.80085542,  71.71028058,  70.91130096,  70.89150223,
    70.85845637,  70.79081796,  70.76929743,  69.80667603,
    69.64058976,  68.93085058,  68.84304037,  68.83154232,
    68.79944298,  68.79019375,  68.78581291,  68.72340328,
    67.86628937,  67.81221253,  67.77564051,  67.77250506])

to get all data of a specific track (let’s say track 0) from event 0:

>>> r[0].tracks[0]
offline track:
      fUniqueID                      :                           0
      fBits                          :                    33554432
      id                             :                           1
      pos_x                          :            445.835395997812
      pos_y                          :           615.1089636184813
      pos_z                          :           125.1448339836911
      dir_x                          :          0.0368711082700674
      dir_y                          :        -0.48653048395923415
      dir_z                          :          -0.872885221293917
      t                              :           70311446.46401498
      E                              :           99.10458562488608
      len                            :                         0.0
      lik                            :           294.6407542676734
      type                           :                           0
      rec_type                       :                        4000
      rec_stages                     :                [1, 3, 5, 4]
      status                         :                           0
      mother_id                      :                          -1
      hit_ids                        :                          []
      error_matrix                   :                          []
      comment                        :                           0
      JGANDALF_BETA0_RAD             :        0.004957442219414389
      JGANDALF_BETA1_RAD             :        0.003417848024252858
      JGANDALF_CHI2                  :          -294.6407542676734
      JGANDALF_NUMBER_OF_HITS        :                       142.0
      JENERGY_ENERGY                 :           99.10458562488608
      JENERGY_CHI2                   :     1.7976931348623157e+308
      JGANDALF_LAMBDA                :      4.2409761837248484e-12
      JGANDALF_NUMBER_OF_ITERATIONS  :                        10.0
      JSTART_NPE_MIP                 :           24.88469697331908
      JSTART_NPE_MIP_TOTAL           :           55.88169412579765
      JSTART_LENGTH_METRES           :           98.89582506402911
      JVETO_NPE                      :                         0.0
      JVETO_NUMBER_OF_HITS           :                         0.0
      JENERGY_MUON_RANGE_METRES      :           344.9767431592819
      JENERGY_NOISE_LIKELIHOOD       :         -333.87773581129136
      JENERGY_NDF                    :                      1471.0
      JENERGY_NUMBER_OF_HITS         :                       101.0

to get a specific value from track 0 in event 0, let’s say for example the liklihood:

>>> r[0].tracks[0].lik
294.6407542676734

to get the reconstruction parameters, first take a look at the available reconstruction keys:

>>> r.best_reco.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']

the keys above can also be accessed with a tab completion:

to get a numpy recarray of all fit data of the best reconstructed track:

>>> r.best_reco

to get an array of a parameter of interest, let’s say ‘JENERGY_ENERGY’:

>>> r.best_reco['JENERGY_ENERGY']
array([1141.87137899, 4708.16378575,  499.7243005 ,  103.54680875,
    208.6103912 , 1336.52338666,  998.87632267, 1206.54345674,
     16.28973662])

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:

>>> r.get_reco_fit([1, 2, 3, 4, 5])

again, to get the reconstruction parameters names:

>>> 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’]:

>>> r.get_reco_fit([1, 2, 3, 4, 5])['JENERGY_ENERGY']
array([1141.87137899, 4708.16378575,  499.7243005 ,  103.54680875,
    208.6103912 , 1336.52338666,  998.87632267, 1206.54345674,
     16.28973662])

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:

>>> 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 . All data is easily accesible and if you are stuck, hit tab key to see all the available branches:

reading mc tracks data

to read mc tracks data:

>>> r.mc_tracks
<OfflineTracks: 10 parsed elements>

that’s it! All branches in mc tracks tree can be accessed in the exact same way described in the section reading tracks data . All data is easily accesible and if you are stuck, hit tab key to see all the available branches: