Warning

Page is out-dated and needs revision.

SKAMPI is currently in a state of flux and that these commands are unlikely to work exactly as described! They remain for reference purposes only and will be revised when SKAMPI is fully stable.

Running the SDP in SKAMPI

The Science Data Processor (SDP) is integrated with the other telescope subsystems as part of the SKA evolutionary prototype, also known as the Minimum Viable Product (MVP). The integration is done in the SKA MVP Prototype Integration (SKAMPI) repository.

Instructions for installing and running the MVP can be found in the SKAMPI documentation. TODO: this needs exact, better links (probably need to wait for skampi docs to be updated)

The default namespace into which SKAMPI deploys (in a non-GitLab CI environment) is integration. You can change this by setting the KUBE_NAMESPACE and KUBE_NAMESPACE_SDP environment variables before deploying SKAMPI. E.g:

$ export KUBE_NAMESPACE=test-skampi-deployment
$ export KUBE_NAMESPACE_SDP=test-skampi-deployment-sdp

If you are deploying on a shared machine, make sure your namespace doesn’t clash with existing SKAMPI deployments.

SDP is integrated into the SKAMPI charts for SKA-Mid and SKA-Low. All of the standard SDP pods will start up upon SKAMPI deployment.

Interacting with SDP within SKAMPI

Using the SDP console

Follow the “Testing it out” steps in Running the SDP stand-alone. Remember to specify the namespace in which the pod is running when you want to access one. E.g. to start the console pod, run:

$ kubectl -n <namespace> exec -it sdp-console-0 -- bash

Using the iTango pod

If your deployment doesn’t have the ska-tango-base-itango-console running, add the following to the pipeline.yml file in the SKAMPI root directory:

tango-base:
  itango:
    enabled: true

And then upgrade your SKAMPI installation via its Makefile:

$ make upgrade-chart VALUES=pipeline.yml

Once you have a running iTango console, follow the steps to test it out in Running the SDP stand-alone at “Accessing the Tango interface” section. Again, you will have to specify the namespace you deployed in, when interacting with the iTango pod.

Using the Observation Execution Tool

Observation Execution Tool (OET), is an application, which provides on-demand Python script execution for the SKA. For interactive telescope control (and hence SDP control), and experimentation, one can use OET scripts:

OET scripts

Using the OET Jupyter Notebooks

Warning

Links no longer work; this section needs revision and updating.

An OET Jupyter Notebook server is automatically deployed when SKAMPI is started. The pod running it has the name oet-jupyter-test-..., where ... will be a multi-character string, specific to the deployment.

The webserver running in this pod can be accessed via a web link of the following structure:

http://<ingress_host>/<namespace>/jupyter

<namespace> is the KUBE_NAMESPACE, while the <ingress_host> is either an environment variable called INGRESS_HOST, or the default one can be found in the Makefile of SKAMPI under the same variable name. You can also find this value by running this command:

$ kubectl describe ingress <pod_name> -n <namespace>

Depending on how you access the website (i.e. with port forwarding or directly), you may need to replace the <ingress_host> with your localhost or similar. More information on how to access the Notebooks on SKAMPI (including the required password) can be found in the OET docs.

You can access the existing Notebooks in scripts/notebooks. Based on these examples, you may also create your own version of an SKA control Notebook. You may also use the OET scripts as a starting point for your own development. For example, the steps in the script at Control using static JSON can be copy-pasted into a Jupyter Notebook. You will need to specify a JSON file, which contains the necessary configuration string to control the subarray device and hence, SDP. Example JSON files can be found in the OET Scripts repository. Here is an example how you can update the lines which require JSON files:

# Allocate resources, provide a path to a file with allocation JSON
subarray.allocate_from_file('../data/example_allocate.json')

# Configure sub-array, provide a path to a file with configuration JSON
subarray.configure_from_file('../data/example_configure.json', scan_duration=10.0)

The above assumes that your Notebook is started from the scripts/notebooks directory.

Using the OET Rest Client

The OET Rest Client provides a command line interface to communicate with a backend, which allows one to run SKA control scripts.

The easiest to do this is through the terminal window of the Jupyter server deployed in SKAMPI. Please follow the above links to learn more about OET and how to use Python scripts to control an SKA telescope via this interface.