protozfits Python Bindings

Table of Contents

• Usage
  • Open a file
  • Get an event
  • RunHeader
  • Table header
• Performance
• Installation
• Where does this come from?

Usage

If you are just starting with proto-z-fits files and would like to explore the file contents, try this:

Open a file

>>> from protozfits import File
>>> example_path = 'protozfits/tests/resources/example_9evts_NectarCAM.fits.fz'
>>> file = File(example_path)
>>> file
File({
    'RunHeader': Table(1xDataModel.CameraRunHeader),
    'Events': Table(9xDataModel.CameraEvent)
})

From this we learn, the file contains two Table named RunHeader and Events which contains 9 rows of type CameraEvent. There might be more tables with other types of rows in other files. For instance LST has its RunHeader called CameraConfig.

Getting an event

Usually people just iterate over a whole Table like this:

for event in file.Events:
    # do something with the event
    pass

But if you happen to know exactly which event you want, you can also directly get an event, like this:

event_17 = file.Events[17]

You can also get a range of events, like this:

for event in file.Events[100:200]:
    # do something events 100 until 200
    pass

It is not yet possible to specify negative indices, like file.Events[:-10] does not work.

If you happen to have a list or any iterable or a generator with event ids you are interested in you can get the events in question like this:

interesting_event_ids = range(100, 200, 3)
for event in file.Events[interesting_event_ids]:
    # do something with intesting events
    pass

RunHeader

Even though there is usually only one run header per file, technically this single run header is stored in a Table. This table could contain multiple "rows" and to me it is not clear what this would mean... but technically it is possible.

At the moment I would recommend getting the run header out of the file we opened above like this (replace RunHeader with CameraConfig for LST data):

assert len(file.RunHeader) == 1
header = file.RunHeader[0]

For now, I will just get the next event

event = file.Events[0]
type(event)
<class 'protozfits.CameraEvent'>
event._fields
('telescopeID', 'dateMJD', 'eventType', 'eventNumber', 'arrayEvtNum', 'hiGain', 'loGain', 'trig', 'head', 'muon', 'geometry', 'hilo_offset', 'hilo_scale', 'cameraCounters', 'moduleStatus', 'pixelPresence', 'acquisitionMode', 'uctsDataPresence', 'uctsData', 'tibDataPresence', 'tibData', 'swatDataPresence', 'swatData', 'chipsFlags', 'firstCapacitorIds', 'drsTagsHiGain', 'drsTagsLoGain', 'local_time_nanosec', 'local_time_sec', 'pixels_flags', 'trigger_map', 'event_type', 'trigger_input_traces', 'trigger_output_patch7', 'trigger_output_patch19', 'trigger_output_muon', 'gps_status', 'time_utc', 'time_ns', 'time_s', 'flags', 'ssc', 'pkt_len', 'muon_tag', 'trpdm', 'pdmdt', 'pdmt', 'daqtime', 'ptm', 'trpxlid', 'pdmdac', 'pdmpc', 'pdmhi', 'pdmlo', 'daqmode', 'varsamp', 'pdmsum', 'pdmsumsq', 'pulser', 'ftimeoffset', 'ftimestamp', 'num_gains')
event.hiGain.waveforms.samples
array([241, 245, 248, ..., 218, 214, 215], dtype=int16)

An LST event will look something like so:

>>> event
CameraEvent(
    configuration_id=1
    event_id=1
    tel_event_id=1
    trigger_time_s=0
    trigger_time_qns=0
    trigger_type=0
    waveform=array([  0,   0, ..., 288, 263], dtype=uint16)
    pixel_status=array([ 0,  0,  0,  0,  0,  0,  0, 12, 12, 12, 12, 12, 12, 12], dtype=uint8)
    ped_id=0
    nectarcam=NectarCamEvent(
        module_status=array([], dtype=float64)
        extdevices_presence=0
        tib_data=array([], dtype=float64)
        cdts_data=array([], dtype=float64)
        swat_data=array([], dtype=float64)
        counters=array([], dtype=float64))
    lstcam=LstCamEvent(
        module_status=array([0, 1], dtype=uint8)
        extdevices_presence=0
        tib_data=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint8)
        cdts_data=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
               0, 0, 0, 0, 0, 0, 0, 0], dtype=uint8)
        swat_data=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
               0, 0, 0, 0], dtype=uint8)
        counters=array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
                 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
                 0,   0,   1,   0,   0,   0,  31,   0,   0,   0, 243, 170, 204,
                 0,   0,   0,   0,   0], dtype=uint8)
        chips_flags=array([    0,     0,     0,     0,     0,     0,     0,     0, 61440,
                 245, 61440,   250, 61440,   253, 61440,   249], dtype=uint16)
        first_capacitor_id=array([    0,     0,     0,     0,     0,     0,     0,     0, 61440,
                 251, 61440,   251, 61440,   241, 61440,   245], dtype=uint16)
        drs_tag_status=array([ 0, 12], dtype=uint8)
        drs_tag=array([   0,    0, ..., 2021, 2360], dtype=uint16))
    digicam=DigiCamEvent(
        ))
>>> event.waveform
array([  0,   0,   0, ..., 292, 288, 263], dtype=uint16)

event supports tab-completion, which I regard as very important while exploring. It is implemented using collections.namedtuple. I tried to create a useful string represenation, it is very long, yes ... but I hope you can still enjoy it:

>>> event
CameraEvent(
    telescopeID=1
    dateMJD=0.0
    eventType=<eventType.NONE: 0>
    eventNumber=97750287
    arrayEvtNum=0
    hiGain=PixelsChannel(
        waveforms=WaveFormData(
            samples=array([241, 245, ..., 214, 215], dtype=int16)
            pixelsIndices=array([425, 461, ..., 727, 728], dtype=uint16)
            firstSplIdx=array([], dtype=float64)
            num_samples=0
            baselines=array([232, 245, ..., 279, 220], dtype=int16)
            peak_time_pos=array([], dtype=float64)
            time_over_threshold=array([], dtype=float64))
        integrals=IntegralData(
            gains=array([], dtype=float64)
            maximumTimes=array([], dtype=float64)
            tailTimes=array([], dtype=float64)
            raiseTimes=array([], dtype=float64)
            pixelsIndices=array([], dtype=float64)
            firstSplIdx=array([], dtype=float64)))
# [...]

Table header

fits.fz files are still normal FITS files and each Table in the file corresponds to a so called "BINTABLE" extension, which has a header. You can access this header like this:

>>> file.Events
Table(100xDataModel.CameraEvent)
>>> file.Events.header
# this is just a sulection of all the contents of the header
XTENSION= 'BINTABLE'           / binary table extension
BITPIX  =                    8 / 8-bit bytes
NAXIS   =                    2 / 2-dimensional binary table
NAXIS1  =                  192 / width of table in bytes
NAXIS2  =                    1 / number of rows in table
TFIELDS =                   12 / number of fields in each row
EXTNAME = 'Events'             / name of extension table
CHECKSUM= 'BnaGDmS9BmYGBmY9'   / Checksum for the whole HDU
DATASUM = '1046602664'         / Checksum for the data block
DATE    = '2017-10-31T02:04:55' / File creation date
ORIGIN  = 'CTA'                / Institution that wrote the file
WORKPKG = 'ACTL'               / Workpackage that wrote the file
DATEEND = '1970-01-01T00:00:00' / File closing date
PBFHEAD = 'DataModel.CameraEvent' / Written message name
CREATOR = 'N4ACTL2IO14ProtobufZOFitsE' / Class that wrote this file
COMPILED= 'Oct 26 2017 16:02:50' / Compile time
TIMESYS = 'UTC'                / Time system
>>> file.Events.header['DATE']
'2017-10-31T02:04:55'
>>> type(file.Events.header)
<class 'astropy.io.fits.header.Header'>

The header is provided by astropy.

Isn't this a little slow?

Well, indeed, converting the original google protobuf instances into namedtuples full of "useful" Python values takes time. And in case you for example know exactly what you want from the file, then you can get a speed up doing it like this:

>>> from protozfits import File
>>> file = File(example_path, pure_protobuf=True)
>>> event = next(file.Events)
>>> type(event)
<class 'L0_pb2.CameraEvent'>

Now iterating over the file is much faster then before. But you have no tab-completion and some contents are useless for you, but some are just fine:

>>> event.eventNumber
97750288   # <--- just fine
>>> event.hiGain.waveforms.samples

type: S16
data: "\362\000\355\000 ... "   # <---- goes on "forever" .. utterly useless
>>> type(event.hiGain.waveforms.samples)
<class 'CoreMessages_pb2.AnyArray'>

You can convert these AnyArrays into numpy arrays like this:

>>> from protozfits import any_array_to_numpy
>>> any_array_to_numpy(event.hiGain.waveforms.samples)
array([242, 237, 234, ..., 218, 225, 229], dtype=int16)

So ... I hope based on this little example you can implement your own reader, which is optimized for your telescope.

If you have questions, please open an issue or a pull request to improve this documentation.

Installation

We all use Anaconda and this package is tested against Anaconda and python 3.5, 3.6 and 3.7. You can download anaconda for your system for free.

You do not have to use a conda environment to use this package. It cleanly installs and uninstalls with pip. If you plan to play around with different versions of this package your might want to use environments though.

Installing from source (including from PyPI on macOS) requires a C++11 compiler, protobuf and zeromq with development headers.

On macOS, we recommend using brew and then brew install zeromq protobuf.

Linux / OSX (with anaconda)

pip install protozfits

Most common issues and possible remedies

• Cannot import _message, message along the lines of:

    from google.protobuf.pyext import _message
    ImportError: cannot import name _message

Try uninstalling conda-protobuf and reinstalling from pypi, like this:

conda uninstall protobuf --yes
pip install protobuf

Editable installs

Editable installs pip installa -e . are possible, but remember that we have compiled components, so editable only works for the python part of the bindings. Any changes to the C++ code require running pip install -e . to compile again.