getting_started.rst

Getting started with OrcaSong

• Introduction
• Preprocessing
  • Conversion from .root to .h5
  • Calibrating the .h5 file
• Usage of OrcaSong

Introduction

On this page, you can find a step by step introduction into the usage of OrcaSong. The guide starts with some exemplary root simulation files made with jpp and ends with hdf5 event 'images' that can be used for deep neural networks.

Preprocessing

Let's suppose you have some KM3NeT simulation files in the ROOT dataformat, e.g.:

/sps/km3net/users/kmcprod/JTE_NEMOWATER/withMX/muon-CC/3-100GeV/JTE.KM3Sim.gseagen.muon-CC.3-100GeV-9.1E7-1bin-3.0gspec.ORCA115_9m_2016.99.root

The file above contains simulated charged-current muon neutrinos from the official 2016 23m ORCA production. Now, we want to produce neutrino event images based on this data using OrcaSong.

Conversion from .root to .h5

At first, we have to convert the files from the .root dataformat to a more usable one: hdf5. For this purpose, we can use a tool called tohdf5 which is contained in the collaboration framework km3pipe. In order to use tohdf5, you need to have loaded a jpp version first. A ready to use bash script for doing this can be found at:

/sps/km3net/users/mmoser/setenvAA_jpp9_cent_os7.sh

Additionally, you need to have a python environment on Lyon, where you have installed km3pipe (e.g. use a pyenv). Then, the usage of tohdf5 is quite easy:

~$: tohdf5 -o testfile.h5 /sps/km3net/users/kmcprod/JTE_NEMOWATER/withMX/muon-CC/3-100GeV/JTE.KM3Sim.gseagen.muon-CC.3-100GeV-9.1E7-1bin-3.0gspec.ORCA115_9m_2016.99.root
++ tohdf5: Converting '/sps/km3net/users/kmcprod/JTE_NEMOWATER/withMX/muon-CC/3-100GeV/JTE.KM3Sim.gseagen.muon-CC.3-100GeV-9.1E7-1bin-3.0gspec.ORCA115_9m_2016.99.root'...
Pipeline and module initialisation took 0.002s (CPU 0.000s).
loading root....  /afs/.in2p3.fr/system/amd64_sl7/usr/local/root/v5.34.23/
loading aalib...  /pbs/throng/km3net/src/Jpp/v9.0.8454//externals/aanet//libaa.so
++ km3pipe.io.aanet.AanetPump: Reading metadata using 'JPrintMeta'
WARNING ++ km3pipe.io.aanet.MetaParser: Empty metadata
WARNING ++ km3pipe.io.aanet.AanetPump: No metadata found, this means no data provenance!
--------------------------[ Blob     250 ]---------------------------
--------------------------[ Blob     500 ]---------------------------
--------------------------[ Blob     750 ]---------------------------
--------------------------[ Blob    1000 ]---------------------------
--------------------------[ Blob    1250 ]---------------------------
--------------------------[ Blob    1500 ]---------------------------
--------------------------[ Blob    1750 ]---------------------------
--------------------------[ Blob    2000 ]---------------------------
--------------------------[ Blob    2250 ]---------------------------
--------------------------[ Blob    2500 ]---------------------------
--------------------------[ Blob    2750 ]---------------------------
--------------------------[ Blob    3000 ]---------------------------
--------------------------[ Blob    3250 ]---------------------------
EventFile io / wall time = 6.27259 / 73.9881 (8.47784 % spent on io.)
================================[ . ]================================
++ km3pipe.io.hdf5.HDF5Sink: HDF5 file written to: testfile.h5
============================================================
3457 cycles drained in 75.842898s (CPU 70.390000s). Memory peak: 177.71 MB
  wall  mean: 0.021790s  medi: 0.019272s  min: 0.015304s  max: 2.823921s  std: 0.049242s
  CPU   mean: 0.020330s  medi: 0.020000s  min: 0.010000s  max: 1.030000s  std: 0.018179s
++ tohdf5: File '/sps/km3net/users/kmcprod/JTE_NEMOWATER/withMX/muon-CC/3-100GeV/JTE.KM3Sim.gseagen.muon-CC.3-100GeV-9.1E7-1bin-3.0gspec.ORCA115_9m_2016.99.root' was converted.

There are also some options that can be used with tohdf5:

~$: tohdf5 -h
Convert ROOT and EVT files to HDF5.

Usage:
    tohdf5 [options] FILE...
    tohdf5 (-h | --help)
    tohdf5 --version

Options:
    -h --help                       Show this screen.
    --verbose                       Print more output.
    --debug                         Print everything.
    -n EVENTS                       Number of events/runs.
    -o OUTFILE                      Output file (only if one file is converted).
    -j --jppy                       (Jpp): Use jppy (not aanet) for Jpp readout.
    --ignore-hits                   Don't read the hits.
    -e --expected-rows NROWS        Approximate number of events.  Providing a
                                    rough estimate for this (100, 1000000, ...)
                                    will greatly improve reading/writing speed
                                    and memory usage.
                                    Strongly recommended if the table/array
                                    size is >= 100 MB. [default: 10000]
    -t --conv-times-to-jte          Converts all MC times in the file to JTE

For now though, we will just stick to the standard conversion without any options.

After this conversion, you can investigate the data structure of the hdf5 file with the command ptdump:

ptdump -v testfile.h5
/ (RootGroup) 'KM3NeT'
/event_info (Table(3457,), fletcher32, shuffle, zlib(5)) 'EventInfo'
  description := {
  "weight_w4": Float64Col(shape=(), dflt=0.0, pos=0),
  "weight_w3": Float64Col(shape=(), dflt=0.0, pos=1),
  "weight_w2": Float64Col(shape=(), dflt=0.0, pos=2),
  "weight_w1": Float64Col(shape=(), dflt=0.0, pos=3),
  "run_id": Int64Col(shape=(), dflt=0, pos=4),
  "timestamp": Int64Col(shape=(), dflt=0, pos=5),
  "nanoseconds": Int64Col(shape=(), dflt=0, pos=6),
  "mc_time": Float64Col(shape=(), dflt=0.0, pos=7),
  "event_id": Int64Col(shape=(), dflt=0, pos=8),
  "mc_id": Int64Col(shape=(), dflt=0, pos=9),
  "group_id": Int64Col(shape=(), dflt=0, pos=10)}
...

Hdf5 files are structured into "folders", in example the folder that is shown above is called "event_info". The event_info is just a two dimensional numpy recarray with the shape (3457, 11), where for each event important information is stored, e.g. the event_id or the run_id.

There is also a folder called "hits", which contains the photon hits of the detector for all events. If you dig a little bit into the subfolders you can see that a lot of information is contained about these hits, e.g. the hit time, but there is no XYZ position of the hits. The only information that you have is the dom_id and the channel_id of a hit.

Calibrating the .h5 file

In order to fix this, we can run another tool, calibrate, that will add the pos_xyz information to the hdf5 datafile:

calibrate /sps/km3net/users/mmoser/det_files/orca_115strings_av23min20mhorizontal_18OMs_alt9mvertical_v1.detx testfile.h5

As you can see, you need a .detx geometry file for this "calibration". Typically, you can find the path of this detx file on the wiki page of the simulation production that you are using. This calibration step is optional, since OrcaSong can also do it on the fly, using a .detx file.

At this point, we are now ready to start using OrcaSong for the generation of event images.

Usage of OrcaSong

In order to use OrcaSong, you can just install it with pip:

~/$: pip install orcasong

Before you can start to use OrcaSong, you need a .detx detector geometry file that corresponds to your input files. OrcaSong is currently producing event "images" based on a 1 DOM / XYZ-bin assumption. This image generation is done automatically, based on the number of bins (n_bins) for each dimension XYZ that you supply as an input and based on the .detx file which contains the DOM positions.

If your .detx file is not contained in the OrcaSong/detx_files folder, please add it to the repository! Currently, only the 115l ORCA 2016 detx file is available.

At this point, you're finally ready to use OrcaSong. OrcaSong can be called from every directory by using the make_nn_images command:

~/$: make_nn_images testfile.h5 geofile.detx configfile.toml

OrcaSong will then generate a hdf5 file with images that will be put in a "Results" folder at the path that you've specified in the configfile current path. Please checkout the default_config.toml file in the orcasong folder of the OrcaSong repo in order to get an idea about the structure of the config files.

All available configuration options of OrcaSong can be found in /orcasong/default_config:

--- Documentation for every config parameter that is available ---

None arguments should be written as string: 'None'

Parameters
----------
output_dirpath : str
    Full path to the directory, where the orcasong output should be stored.
chunksize : int
    Chunksize (along axis_0) that is used for saving the OrcaSong output to a .h5 file.
complib : str
    Compression library that is used for saving the OrcaSong output to a .h5 file.
    All PyTables compression filters are available, e.g. 'zlib', 'lzf', 'blosc', ... .
complevel : int
    Compression level for the compression filter that is used for saving the OrcaSong output to a .h5 file.
n_bins : tuple of int
    Declares the number of bins that should be used for each dimension, e.g. (x,y,z,t).
    The option should be written as string, e.g. '11,13,18,60'.
det_geo : str
    Declares what detector geometry should be used for the binning. E.g. 'Orca_115l_23m_h_9m_v'.
do2d : bool
    Declares if 2D histograms, 'images', should be created.
do2d_plots : bool
    Declares if pdf visualizations of the 2D histograms should be created, cannot be called if do2d=False.
do2d_plots_n: int
    After how many events the event loop will be stopped (making the 2d plots in do2d_plots takes long time).
do3d : bool
    Declares if 3D histograms should be created.
do4d : bool
    Declares if 4D histograms should be created.
do4d_mode : str
    If do4d is True, what should be used as the 4th dim after xyz.
    Currently, only 'time' and 'channel_id' are available.
prod_ident : int
    Optional int identifier for the used mc production.
    This is e.g. useful, if you use events from two different mc productions, e.g. the 1-5GeV & 3-100GeV Orca 2016 MC.
    In this case, the events are not fully distinguishable with only the run_id and the event_id!
    In order to keep a separation, an integer can be set in the event_track for all events, such that they stay distinguishable.
timecut_mode : str
    Defines what timecut should be used in hits_to_histograms.py.
    Currently available:
    'timeslice_relative': Cuts out the central 30% of the snapshot. The value of timecut_timespan doesn't matter in this case.
    'trigger_cluster': Cuts based on the mean of the triggered hits.
    'None': No timecut. The value of timecut_timespan doesn't matter in this case.
timecut_timespan : str/None
    Defines what timespan should be used if a timecut is applied. Only relevant for timecut_mode = 'trigger_cluster'.
    Currently available:
    'all': [-350ns, 850ns] -> 20ns / bin (if e.g. 60 timebins)
    'tight-0': [-450ns, 500ns] -> 15.8ns / bin (if e.g. 60 timebins)
    'tight-1': [-250ns, 500ns] -> 12.5ns / bin (if e.g. 60 timebins)
    'tight-2': [-150ns, 200ns] -> 5.8ns / bin (if e.g. 60 timebins)
do_mc_hits : bool
    Declares if hits (False, mc_hits + BG) or mc_hits (True) should be processed.
data_cut_triggered : bool
    Cuts away hits that haven't been triggered.
data_cut_e_low : float
    Cuts away events that have an energy lower than data_cut_e_low.
data_cut_e_high : float
    Cuts away events that have an energy higher than data_cut_e_high.
data_cut_throw_away : float
    Cuts away random events with a certain probability (1: 100%, 0: 0%).
flush_freq : int
    After how many events the accumulated output should be flushed to the harddisk.
    A larger value leads to a faster orcasong execution, but it increases the RAM usage as well.

--- Documentation for every config parameter that is available ---

If anything is still unclear after this introduction just tell me in the deep_learning channel on chat.km3net.de or write me an email at michael.m.moser@fau.de, such that I can improve this guide!