HDF5

HDF5 files are the prefered input and primary output type in KM3Pipe. It is a general format for hierarchical storage of large amount of numerical data. Unlike e.g. ROOT, HDF5 is a general-purpose dataformat and not specifically designed for HEP experiments. HDF5 also requires only a tiny library (hdf5lib) and is accessible with almost all popular programming languages (Python, C, C++, Java, Go, Julia, R, Matlab, Rust…).

In KM3NeT, it is used to store event information like PMT hits, reconstructed particles and all kind of other analysis results.

Data Hierarchy

HDF5 has an internal structure line a Unix file system: There are groups (“folders”) containing tables (“files”) which hold the data. Every table/group is identified by a “filepath”. This is the output of ptdump for a file which contains MUPAGE simulation data for the ORCA detector:

/ (RootGroup) 'KM3NeT'
/event_info (Table(3476,), fletcher32, shuffle, zlib(5)) 'EventInfo'
/mc_tracks (Table(25651,), fletcher32, shuffle, zlib(5)) 'McTracks'
/hits (Group) 'RawHitSeries'
/hits/_indices (Table(3476,), fletcher32, shuffle, zlib(5)) 'Indices'
/hits/channel_id (EArray(7373599,), fletcher32, shuffle, zlib(5)) 'Channel_id'
/hits/dom_id (EArray(7373599,), fletcher32, shuffle, zlib(5)) 'Dom_id'
/hits/event_id (EArray(7373599,), fletcher32, shuffle, zlib(5)) 'Event_id'
/hits/time (EArray(7373599,), fletcher32, shuffle, zlib(5)) 'Time'
/hits/tot (EArray(7373599,), fletcher32, shuffle, zlib(5)) 'Tot'
/hits/triggered (EArray(7373599,), fletcher32, shuffle, zlib(5)) 'Triggered'
/mc_hits (Group) 'McHitSeries'
/mc_hits/_indices (Table(3476,), fletcher32, shuffle, zlib(5)) 'Indices'
/mc_hits/a (EArray(240744,), fletcher32, shuffle, zlib(5)) 'A'
/mc_hits/event_id (EArray(240744,), fletcher32, shuffle, zlib(5)) 'Event_id'
/mc_hits/origin (EArray(240744,), fletcher32, shuffle, zlib(5)) 'Origin'
/mc_hits/pmt_id (EArray(240744,), fletcher32, shuffle, zlib(5)) 'Pmt_id'
/mc_hits/time (EArray(240744,), fletcher32, shuffle, zlib(5)) 'Time'

All nodes with the type Table are 2D tables, which is a list of HDF5Compund data (similar to C-structs). This format is supported by all HDF5 wrappers and can be read e.g. with the Pandas framework, which is written in Python and designed for high level statistical analysis. Other nodes are of the type EArray, which is an “Extensible Arrays” in the HDF5 context. It represents a 1D array. The hits and mc_hits are stored this way to get the maximum performance when doing event-by-event analysis. The _indices array holds the index information, which you need to split up the huge 1D arrays into the corresponding events.

A typical km3net h5 file looks like this: The reconstruction tables, for example, have columns called “energy” or “zenith”, and each row of the table corresponds to a single event:

├── event_info        # 2D table
├── mc_tracks         # 2D table
├── hits              # group
│   ├── _indices      # 2D table with the index information (index, n_items)
│   ├── tot           # 1D int array
│   ├── time          # 1D float array
│   └── ...
└── reco              # group
    ├── aashowerfit   # 2D table
    │   ├── E         # 1D float array
    │   ├── phi
    │   └── ...
    └── ...

(Experts) Data Substructure

Since HDF5 is a general-purpose table based format, there are some minor tweaks done by km3pipe to emulate an event-by-event based workflow:

For example, the event Hit storage: each hit in the whole file (!) is split up into its “components” time, tot etc. and stored under the /hits group. In order to get the hits for the event #23, you first have to read in the /hits/_indices table (keep that in memory if you want to look at multiple events, it’s 1-2 MB or so!) and look at its entry at index 23. You will see two numbers, the first is the index of the first hit (let’s call it idx) and the second is the number of hits (n_items). Now you can read /hit/time[idx:idx+n_items], /hit/time[idx:idx+n_item, etc. Of course KM3Pipe provides the km3pipe.io.hdf5.HDF5Pump(filename=...) instance which does this for you:

p = km3pipe.io.hdf5.HDF5Pump(filename="path/to/file.h5")
blob = p[23]
hits = blob["Hits"]

Conversion Utils

To convert a ROOT/EVT file to KM3NeT HDF5 use tohdf5 which comes with KM3Pipe:

tohdf5 filename.root

See the HDF5 CLI Utils on how to convert & inspect HDF5 files from the shell.