This package serves as a data access library to interact with the chunk stores and HDF5 files produced by the MeerKAT radio telescope and its predecessors (KAT-7 and Fringe Finder), which are collectively known as MeerKAT Visibility Format (MVF) data sets. It uses memory carefully, allowing data sets to be inspected and partially loaded into memory. Data sets may be concatenated and split via a flexible selection mechanism. In addition, it provides a script to convert these data sets to CASA MeasurementSets.
Open any data set through a single function to obtain a data set object:
.. code:: python
import katdal d = katdal.open('1234567890.h5')
The open function automatically determines the version and storage location
of the data set. The versions roughly map to the various instruments::
Each MVFv4 data set is split into a Redis dump (aka RDB) file containing the metadata in the form of a telescope state database, and a chunk store containing the visibility data split into many small blocks or chunks (typically served by a Ceph object store over the network). The RDB file is the main entry point to the data set and it can be accessed directly from the MeerKAT SDP archive if you have the appropriate permissions:
.. code:: python
d = katdal.open('https://archive/1234567890/1234567890_sdp_l0.rdb?token=AsD3')
Multiple data sets (even of different versions) may also be concatenated together (as long as they have the same dump rate):
.. code:: python
d = katdal.open(['1234567890.h5', '1234567891.h5'])
Inspect the contents of the data set by printing the object:
.. code:: python
print(d)
Here is a typical output::
Shape: (1054 dumps, 1024 channels, 112 correlation products) => Size: 967.049 MB Antennas: *ant1,ant2,ant3,ant4,ant5,ant6,ant7 Inputs: 14 Autocorr: yes Crosscorr: yes Channels: 1024 (index 0 - 1023, 2021.805 MHz - 1622.195 MHz), each 390.625 kHz wide Targets: 4 selected out of 4 in catalogue ID Name Type RA(J2000) DEC(J2000) Tags Dumps ModelFlux(Jy) 0 Hyd A radec 9:18:05.28 -12:05:48.9 333 33.63 1 Vir A radec 12:30:49.42 12:23:28.0 251 166.50 2 3C 286 radec 13:31:08.29 30:30:33.0 230 12.97 3 3C 273 radec 12:29:06.70 2:03:08.6 240 39.96 Scans: 8 selected out of 8 total Compscans: 1 selected out of 1 total Date Timerange(UTC) ScanState CompScanLabel Dumps Target 11-Aug-2011/13:02:14 - 13:04:26 0:slew 0: 133 0:Hyd A 13:04:27 - 13:07:46 1:track 0: 200 0:Hyd A 13:07:47 - 13:08:37 2:slew 0: 51 1:Vir A 13:08:38 - 13:11:57 3:track 0: 200 1:Vir A 13:11:58 - 13:12:27 4:slew 0: 30 2:3C 286 13:12:28 - 13:15:47 5:track 0: 200 2:3C 286 13:15:48 - 13:16:27 6:slew 0: 40 3:3C 273 13:16:28 - 13:19:47 7:track 0: 200 3:3C 273
The first segment of the printout displays the static information of the data set, including observer, dump rate and all the available subarrays and spectral windows in the data set. The second segment (between the dashed lines) highlights the active selection criteria. The last segment displays dynamic information that is influenced by the selection, including the overall visibility array shape, antennas, channel frequencies, targets and scan info.
The data set is built around the concept of a three-dimensional visibility array with dimensions of time, frequency and correlation product. This is reflected in the shape of the dataset:
.. code:: python
d.shape
which returns (1054, 1024, 112), meaning 1054 dumps by 1024 channels by 112
correlation products.
Let's select a subset of the data set:
.. code:: python
d.select(scans='track', channels=slice(200, 300), ants='ant4') print(d)
This results in the following printout::
Shape: (800 dumps, 100 channels, 4 correlation products) => Size: 2.560 MB Antennas: ant4 Inputs: 2 Autocorr: yes Crosscorr: no Channels: 100 (index 200 - 299, 1943.680 MHz - 1905.008 MHz), each 390.625 kHz wide Targets: 4 selected out of 4 in catalogue ID Name Type RA(J2000) DEC(J2000) Tags Dumps ModelFlux(Jy) 0 Hyd A radec 9:18:05.28 -12:05:48.9 200 31.83 1 Vir A radec 12:30:49.42 12:23:28.0 200 159.06 2 3C 286 radec 13:31:08.29 30:30:33.0 200 12.61 3 3C 273 radec 12:29:06.70 2:03:08.6 200 39.32 Scans: 4 selected out of 8 total Compscans: 1 selected out of 1 total Date Timerange(UTC) ScanState CompScanLabel Dumps Target 11-Aug-2011/13:04:27 - 13:07:46 1:track 0: 200 0:Hyd A 13:08:38 - 13:11:57 3:track 0: 200 1:Vir A 13:12:28 - 13:15:47 5:track 0: 200 2:3C 286 13:16:28 - 13:19:47 7:track 0: 200 3:3C 273
Compared to the first printout, the static information has remained the same while the dynamic information now reflects the selected subset. There are many possible selection criteria, as illustrated below:
.. code:: python
d.select(timerange=('2011-08-11 13:10:00', '2011-08-11 13:15:00'), targets=[1, 2]) d.select(spw=0, subarray=0) d.select(ants='ant1,ant2', pol='H', scans=(0,1,2), freqrange=(1700e6, 1800e6))
See the docstring of DataSet.select for more detailed information (i.e.
do d.select? in IPython). Take note that only one subarray and one spectral
window must be selected.
Once a subset of the data has been selected, you can access the data and timestamps on the data set object:
.. code:: python
vis = d.vis[:] timestamps = d.timestamps[:]
Note the [:] indexing, as the vis and timestamps properties are
special LazyIndexer objects that only give you the actual data when
you use indexing, in order not to inadvertently load the entire array into memory.
For the example dataset and no selection the vis array will have a shape of
(1054, 1024, 112). The time dimension is labelled by d.timestamps, the
frequency dimension by d.channel_freqs and the correlation product dimension
by d.corr_products.
Another key concept in the data set object is that of sensors. These are named
time series of arbitrary data that are either loaded from the data set
(actual sensors) or calculated on the fly (virtual sensors). Both variants
are accessed through the sensor cache (available as d.sensor) and cached
there after the first access. The data set object also provides convenient
properties to expose commonly-used sensors, as shown in the plot example below:
.. code:: python
import matplotlib.pyplot as plt plt.plot(d.az, d.el, 'o') plt.xlabel('Azimuth (degrees)') plt.ylabel('Elevation (degrees)')
Other useful attributes include ra, dec, lst, mjd, u,
v, w, target_x and target_y. These are all one-dimensional
NumPy arrays that dynamically change length depending on the active selection.
As in katdal's predecessor (scape) there is a DataSet.scans generator
that allows you to step through the scans in the data set. It returns the
scan index, scan state and target object on each iteration, and updates
the active selection on the data set to include only the current scan.
It is also possible to iterate through the compound scans with the
DataSet.compscans generator, which yields the compound scan index, label
and first target on each iteration for convenience. These two iterators may also
be used together to traverse the data set structure:
.. code:: python
for compscan, label, target in d.compscans(): plt.figure() for scan, state, target in d.scans(): if state in ('scan', 'track'): plt.plot(d.ra, d.dec, 'o') plt.xlabel('Right ascension (J2000 degrees)') plt.ylabel('Declination (J2000 degrees)') plt.title(target.name)
Finally, all the targets (or fields) in the data set are stored in a catalogue
available at d.catalogue, and the original HDF5 file is still accessible via
a back door installed at d.file in the case of a single-file data set (v3
or older). On a v4 data set, d.source provides access to the underlying
telstate for metadata and the chunk store for data.