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Commit 56997d55 authored by Tamas Gal's avatar Tamas Gal :speech_balloon:
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Update DAQ data docs

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1 merge request!12Update DAQ data docs
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......@@ -92,9 +92,11 @@ A jagged array, is a 2+ dimentional array with different arrays lengths. In othe
# <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
Overview of DAQ files
"""""""""""""""""""""
# info needed here
DAQ files, or also called online files, are written by the DataWriter and
contain events, timeslics and summary slices.
Overview of offline files
"""""""""""""""""""""""""
......@@ -104,29 +106,121 @@ Overview of offline files
DAQ files reader
----------------
# an update is needed here?
Currently only events (the ``KM3NET_EVENT`` tree) are supported but timeslices and summaryslices will be implemented very soon.
``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:
.. code-block:: python3
import km3io as ki
events = ki.DAQReader("KM3NeT_00000044_00005404.root").events
import km3io
f = km3io.DAQReader("KM3NeT_00000044_00005404.root")
That's it! Now let's have a look at the hits data:
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:
.. code-block:: python3
>>> events
>>> f.events
Number of events: 17023
>>> events[23].snapshot_hits.tot
>>> 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``:
.. code-block:: python3
>>> f.summaryslices
<km3io.daq.SummmarySlices 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:
.. code-block:: python3
>>> 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:
.. code-block:: python3
>>> 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:
.. code-block:: python3
>>> 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
--------------------
......
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