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Commit 3338fab8 authored by Tamas Gal's avatar Tamas Gal :speech_balloon:
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Add premiere talk

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#+OPTIONS: num:nil toc:nil reveal_single_file:t
#+REVEAL_ROOT: ~/opt/reveal.js-3.9.2
#+REVEAL_TRANS: none
#+REVEAL_THEME: white
#+REVEAL_MIN_SCALE: 1.0
#+REVEAL_MAX_SCALE: 1.0
#+REVEAL_TITLE_SLIDE: <h1>%t</h1><h3>%s</h3><p>%A %a</p><p><a href="%u">%u</a></p>
#+Title: km3io
#+Subtitle: Reading KM3NeT ROOT files without ROOT
#+Author: Zineb Aly (CPPM) and Tamas Gal (ECAP)
#+Email: zaly@km3et.de tgal@km3net.de
#+REVEAL_TALK_URL: https://indico.cern.ch/event/878692/
* Prerequisites
#+BEGIN_SRC bash :results silent :async t
python3 -m venv ~/tmp/km3io-premiere-venv
. ~/tmp/km3io-permiere-venv/bin/activate
pip install km3io==0.8.1
#+END_SRC
#+BEGIN_SRC elisp
(setq org-babel-python-command "~/tmp/km3io-permiere-venv/bin/python")
#+END_SRC
#+RESULTS:
: ~/tmp/km3io-permiere-venv/bin/python
* km3io
#+ATTR_REVEAL: :frag (appear)
- [[https://git.km3net.de/km3py/km3io][km3io]] tiny Python package with minimal dependencies to read KM3NeT ROOT files
- Uses the [[https://github.com/scikit-hep/uproot][uproot]] library to access ROOT data
- Provides convenient wrapper classes
- Maximum performance due to numpy and numba
- Data are read lazily
- only loaded into memory when directly accessed
- apply several cut masks on huge datasets without reading them into the memory
** Installation
- Dependencies:
- Python 3.5+
- uproot (installed automatically)
- Releases are published on the official Python package repository:
- ~pip install km3io~
* Online (DAQ) Data
** km3io supports the following DAQ datatypes
#+ATTR_REVEAL: :frag (appear)
- ~JDAQEvent~ (the event dataformat)
- header information
- snapshot hits
- triggered hits
- ~JDAQSummaryslices~
- header information
- accessor methods for bit flags (HRV, FIFO, UDP status)
- ~JDAQTimeslices~
- header information
- frame hits
- currently only L1, L2 and SN streams, the L0 stream is work in progress
** Examples
*** Opening a run file
#+BEGIN_SRC python :results output replace :session km3io :exports both
import km3io
f = km3io.DAQReader("KM3NeT_00000044_00006880.root")
print(f.events)
print(f.timeslices)
print(f.summaryslices)
#+END_SRC
#+RESULTS:
: Number of events: 115038
: Available timeslice streams: SN, L1
: <km3io.daq.SummmarySlices object at 0x7f8ee90cc970>
*** Investigating timeslice frames
#+BEGIN_SRC python :results output replace :session km3io :exports both
a_timeslice = f.timeslices.stream("L1", 23)
print(a_timeslice.frames.keys())
#+END_SRC
#+RESULTS:
: dict_keys([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])
*** Reading the first 100 ToTs and channel IDs of a frame sent by the DOM 806451572
#+BEGIN_SRC python :results output replace :session km3io :exports both
print(a_timeslice.frames[806451572].tot[:100])
print(a_timeslice.frames[806451572].pmt[:100])
#+END_SRC
#+RESULTS:
#+begin_example
[33 16 22 24 7 27 4 31 31 15 5 26 30 24 7 26 26 26 27 15 7 3 63 28
26 30 25 24 20 7 23 6 22 22 26 15 29 25 24 22 23 21 26 20 27 27 25 12
27 15 21 9 17 27 21 30 29 5 25 24 22 27 34 10 13 17 25 10 15 28 15 20
29 24 24 5 16 26 25 33 26 26 26 14 30 6 27 10 18 24 26 49 59 25 31 30
23 25 28 11]
[ 0 9 4 12 13 2 10 10 8 9 27 27 28 18 27 2 6 15 4 2 2 12 27 3
15 10 23 14 19 9 9 24 24 6 7 20 7 20 27 22 24 25 22 14 18 27 30 29
29 11 28 1 1 11 12 18 29 20 20 19 10 23 21 2 2 16 1 3 3 24 26 9
13 10 29 30 30 10 22 0 22 30 6 19 21 25 25 27 27 26 27 25 29 9 10 5
13 6 13 11]
#+end_example
*** Checking the number of UDP packets in summary slices
#+BEGIN_SRC python :results output replace :session km3io :exports both
f = km3io.DAQReader("KM3NeT_00000044_00006880.root")
sumslice = f.summaryslices.slices[23]
print(sumslice.dom_id)
print(km3io.daq.get_number_udp_packets(sumslice.dq_status))
#+END_SRC
#+RESULTS:
#+begin_example
[806451572 806483369 806487231 808435278 808447180 808451907 808472265
808488895 808489014 808489117 808493910 808946818 808949744 808951460
808956908 808959411 808961448 808961504 808961655 808964815 808964883
808964908 808969848 808969857 808972593 808972598 808972698 808974972
808976377 808979721 808979729 808981510 808981523 808981672 808981812
808981864 808982005 808982018 808982041 808982066 808982547 808984711
808996773 808997793 809006037 809007627 809521500 809524432 809544058]
[17 17 16 16 25 16 27 17 18 17 21 16 16 16 34 18 18 18 17 18 16 18 15 17
20 18 15 17 17 19 16 18 16 17 17 16 18 18 17 27 18 20 16 17 15 18 17 17
17]
#+end_example
* Offline (MC/reco) Data
** Reading offline files (aka aanet-ROOT files)
#+ATTR_REVEAL: :frag (appear)
- Events
- header information
- hits
- tracks (from reconstruction)
- MC tracks
- MC information
- Reco information
** Opening a reconstructed MUPAGE file
#+BEGIN_SRC python :results output replace :session km3io :exports both
f = km3io.OfflineReader("mc.root")
print(f)
#+END_SRC
#+RESULTS:
: <km3io.offline.OfflineReader object at 0x7f8eeb436e20>
** Investigating events and tracks
#+BEGIN_SRC python :results output replace :session km3io :exports both
print(f.events)
#+END_SRC
#+RESULTS:
: Number of events: 10
#+BEGIN_SRC python :results output replace :session km3io :exports both
print(f.tracks.lik)
print(f.tracks.dir_z)
#+END_SRC
#+RESULTS:
: [[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]]
: [[-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]]
** Some pretty print features for single objects
*** Hits
#+BEGIN_SRC python :results output replace :session km3io :exports both
print(f[0].hits[1])
#+END_SRC
#+RESULTS:
#+begin_example
offline hit:
id : 0
dom_id : 806451572
channel_id : 9
tdc : 0
tot : 30
trig : 1
pmt_id : 0
t : 70104016.0
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
#+end_example
*** Tracks
#+BEGIN_SRC python :results output replace :session km3io :exports both
print(f[0].tracks[0])
#+END_SRC
#+RESULTS:
#+begin_example
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
#+end_example
** Extracting the energy of every first reco track in each event
#+BEGIN_SRC python :results output replace :session km3io :exports both
f = km3io.OfflineReader("mupage.root")
print(f.mc_tracks)
print(f.mc_tracks.id.counts)
mask = f.mc_tracks.id.counts > 0
print(f.mc_tracks.E[mask, 0])
#+END_SRC
#+RESULTS:
: Number of tracks: 12236
: [11 2 3 ... 10 1 4]
: [17.72 73.213 10884.78 1694.332 1221.061 22945.123 11019.418 ...]
* ORCA DU4 RBR Analysis Example
** A tiny function to extract track attributes from a list of files
#+BEGIN_SRC python
def extract_features(files, features):
"""Return a dict with the features from every best reco track"""
data = defaultdict(list)
for f in tqdm(files):
tracks = km3io.OfflineReader(f).tracks
mask = tracks.len.counts > 0
for feature in features:
data[feature].append(getattr(tracks, feature)[mask, 0])
return {k: np.hstack(v) for k, v in data.items()}
#+END_SRC
** Extracting ~E~, ~lik~, ~pos[xyz]~ and ~dir[xyz]~
- Only takes a few seconds per file
- Results are numpy arrays
#+BEGIN_SRC python
sea_files = glob("data/reco-sea/*aanet*.root")
features = ['E', 'lik', *[e + '_' + q for q in 'xyz' for e in ['pos', 'dir']]]
sea_data = extract_features(sea_files, features)
#+END_SRC
** Plotting some data with ~matplotlib~
#+BEGIN_SRC python
fig, ax = plt.subplots()
plot_options = {
'histtype': 'step',
'bins': 100,
'log': True,
'linewidth': 2
}
ax.hist(sea_data['E'], label="sea data", **plot_options)
ax.hist(mc_data['E'], label="atm. muons MC (JSirene)", **plot_options)
ax.set_xlabel("energy / GeV")
ax.legend(); ax.grid();
#+END_SRC
#+REVEAL: split
[[file:./images/orca-du4.png]]
* Command line tool(s)
- We are working on some counter parts to the Jpp tools
- ~KPrintTree -f FILENAME~: similar to ~JPrintTree~
- more to come (feel free to request or contribute)
* Thanks
- Zineb Aly (CPPM)
- Tamas Gal (ECAP)
- Johannes Schumann (ECAP)
** Important links
- Docs: [[https://km3py.pages.km3net.de/km3io]]
- Source: [[https://git.km3net.de/km3py/km3io]]
- uproot: [[https://github.com/scikit-hep/uproot]]
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