Overview

The RcalEmulatorDevice is a Tango Device designed to provide real-time calibration (RCal) emulator for the Square Kilometre Array (SKA) LOW CBF testing purpose.

It emulates the generation of Jones calibration matrices for multiple beams, stations, and frequencies, and streams the results with associated metadata.

This device supports configuration of calibration parameters, execution of the calibration emulator, and integration with Kafka for data transport.

Attributes

Attribute Name	Type	Description
`kafka_address`	String	URL and port number of the Kafka server to which calibration data is sent.
`list_of_frequencies`	Array[int]	List of SPS frequencies used during calibration. Configurable via `configure_rcal`.
`list_of_stations`	Array[String]	List of stations/substations included in calibration. Configurable via `configure_rcal`.
`rotate_degrees`	Float	Rotation angle (in degrees) applied to both X and Y polarisations.
`list_of_beams`	Array[Int]	List of PST beams to be calibrated. Configurable via `configure_rcal`.
`updating_period`	Float	Period (in seconds) at which calibration matrices are generated and sent.
`max_message`	Integer	Max Message size proxy where Kafka message_max_bytes=max_message * 2^20.

Commands

configure_rcal

Input: JSON string

Output: Tango Result → Ok if configuration done, else FAILED.

Description:

Configures the lists of SPS frequencies, stations, and PST beams.

Example JSON:

{
    "frequencies": [64, 65],
    "stations": ["1/1", "2/1"],
    "beams": [1, 2]
}

start_calibration

Input: Topic name

Output: Tango Result

Description:

Starts the calibration emulator. Once started, the device will generate and send Jones matrices every updating_period seconds.

Workflow:

Prepare one Jones matrix per beam:
- If cal_count % 2 == 0 → use unity matrix.
- Else → apply rotation matrix with rotate_degrees.
Attach metadata to the calibration data.
Send calibration results to Kafka as an xarray.DataArray.

stop_calibration

Input: None

Output: Tango Result

Description:

Stops the calibration process. Allow for one full updating period before restarting.

Data Format

The calibration results are sent as an xarray.DataArray object:

xarray_to_return = xarray.DataArray(
    data,
    coords={
        "antenna": list_of_stations,
        "frequency": list_of_frequencies,
        "polarisation": polarisations.split(","),  # e.g., ["XX", "XY", "YX", "YY"]
    },
    attrs={
        "cal_interval_start": interval_start_time,
        "cal_interval_end": interval_end_time,
        "cal_count": count,
    },
)

data contains the generated Jones matrices.
coords provide mapping to beams, stations, frequencies, and polarisations.
attrs carry timing and counter metadata for traceability.

Example Workflow

Configure the device:

json_config = '{"frequencies": [64, 65], "stations": ["1/1", "2/1"], "beams": [1, 2]}'
rcal = tango.DeviceProxy(f"{TANGO_DB_HOST}/low-cbf/rcal/0")
rcal.configure_rcal(json_config)

Adjust attributes if necessary:

rcal.rotate_degrees = 45.0
rcal.kafka_address = "test-kafka.rcal-jou008.svc.cluster.local:9092"
rcal.updating_period = 10.0

Start calibration:
```
rcal.start_calibration("my-topic")
```
Receive results:

Jones matrices are received every updating_period seconds in the Kafka stream.