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katdal

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.

Quick Tutorial

Open any data set through a single function to obtain a data set object:

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:

- v1 : Fringe Finder (HDF5 file)
- v2 : KAT-7 (HDF5 file)
- v3 : MeerKAT (HDF5 file)
- v4 : MeerKAT (RDB file + chunk store based on objects in Ceph)

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:

# This is just for illustration - the real URL looks a bit different
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):

d = katdal.open(['1234567890.h5', '1234567891.h5'])

Inspect the contents of the data set by printing the object:

print(d)

Here is a typical output:

===============================================================================
Name: 1313067732.h5 (version 2.0)
===============================================================================
Observer: someone  Experiment ID: 2118d346-c41a-11e0-b2df-a4badb44fe9f
Description: 'Track on Hyd A,Vir A, 3C 286 and 3C 273'
Observed from 2011-08-11 15:02:14.072 SAST to 2011-08-11 15:19:47.810 SAST
Dump rate: 1.00025 Hz
Subarrays: 1
ID  Antennas                            Inputs  Corrprods
 0  ant1,ant2,ant3,ant4,ant5,ant6,ant7  14      112
Spectral Windows: 1
ID  CentreFreq(MHz)  Bandwidth(MHz)  Channels  ChannelWidth(kHz)
 0  1822.000         400.000          1024      390.625
-------------------------------------------------------------------------------
Data selected according to the following criteria:
subarray=0
ants=['ant1', 'ant2', 'ant3', 'ant4', 'ant5', 'ant6', 'ant7']
spw=0
-------------------------------------------------------------------------------
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:

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:

d.select(scans='track', channels=slice(200, 300), ants='ant4')
print(d)

This results in the following printout:

===============================================================================
Name: /Users/schwardt/Downloads/1313067732.h5 (version 2.0)
===============================================================================
Observer: siphelele  Experiment ID: 2118d346-c41a-11e0-b2df-a4badb44fe9f
Description: 'track on Hyd A,Vir A, 3C 286 and 3C 273 for Lud'
Observed from 2011-08-11 15:02:14.072 SAST to 2011-08-11 15:19:47.810 SAST
Dump rate: 1.00025 Hz
Subarrays: 1
ID  Antennas                            Inputs  Corrprods
 0  ant1,ant2,ant3,ant4,ant5,ant6,ant7  14      112
Spectral Windows: 1
ID  CentreFreq(MHz)  Bandwidth(MHz)  Channels  ChannelWidth(kHz)
 0  1822.000         400.000          1024      390.625
-------------------------------------------------------------------------------
Data selected according to the following criteria:
channels=slice(200, 300, None)
subarray=0
scans='track'
ants='ant4'
spw=0
-------------------------------------------------------------------------------
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:

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:

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:

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:

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.

	def populate_pointing_dict(num_antennas, observation_duration, start_time, phase_center, pointing_name='default'):
	"""Construct a dictionary containing the columns of the POINTING subtable.

	The POINTING subtable contains data on individual antennas tracking a target.
	It has one row per pointing/antenna?

	Parameters
	----------
	num_antennas : integer
	Number of antennas
	observation_duration : float
	Length of observation, in seconds
	start_time : float
	Start time of observation, as a Modified Julian Date in seconds
	phase_center : array of float, shape (2,)
	Direction of phase center, in ra-dec coordinates as 2-element array
	pointing_name : string, optional
	Name for pointing

	Returns
	-------
	pointing_dict : dict
	Dictionary containing columns of POINTING subtable

	"""
	phase_center = phase_center.reshape((2, 1, 1))
	pointing_dict = {}
	# Antenna Id (integer)
	pointing_dict['ANTENNA_ID'] = np.arange(num_antennas, dtype=np.int32)
	# Antenna pointing direction as polynomial in time (double, 2-dim)
	pointing_dict['DIRECTION'] = np.repeat(phase_center, num_antennas)
	# Time interval (double)
	pointing_dict['INTERVAL'] = np.tile(np.float64(observation_duration), num_antennas)
	# Pointing position name (string)
	pointing_dict['NAME'] = np.array([pointing_name] * num_antennas)
	# Series order (integer)
	pointing_dict['NUM_POLY'] = np.zeros(num_antennas, dtype=np.int32)
	# Target direction as polynomial in time (double, -1-dim)
	pointing_dict['TARGET'] = np.repeat(phase_center, num_antennas)
	# Time interval midpoint (double)
	pointing_dict['TIME'] = np.tile(np.float64(start_time), num_antennas)
	# Time origin for direction (double)
	pointing_dict['TIME_ORIGIN'] = np.tile(np.float64(start_time), num_antennas)
	# Tracking flag - True if on position (boolean)
	pointing_dict['TRACKING'] = np.ones(num_antennas, dtype=np.uint8)
	return pointing_dict

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