The ITER IO has installed and is officially supporting a public installation of the OMAS library on the ITER clusters.

  1. SSH to the server (use the X2GO for interactive/graphical session)

  2. Load the IMAS and OMAS unix modules:

    >> module load OMAS IMAS

Access ITER data

Although ITER experimental data is yet to be produced, OMAS can already be used to access the database of ITER plasma scenarios that is curated by the ITER IO.

  1. Find what is available in the ITER IMAS database:

    >> scenario_summary
  2. Access ITER scenario database via OMAS from python:

    # load OMAS package
    from omas import *
    # load data from a pulse chosen from the ITER scenario database
    ods = load_omas_iter_scenario(pulse=131034, run=0)
    # print nodes with data
    from pprint import pprint
    # save data in different format (eg. pickle file)
    save_omas_pkl(ods, '')

    For more information on how to manipulate OMAS data see the high-level OMAS overview page and the extensive list of OMAS examples.

Remotely access ITER data with OMFIT

OMFIT adds remote access capability to the IMAS functions within OMAS

  1. Set the MainSettings['SERVER']['ITER_username'] to your ITER username

  2. Remotely query ITER scenario database:

    # get the iter scenario data in OMFIT
    OMFIT['iter_scenarios'] = iter_scenario_summary_remote()
    # filter based on some criterion
    OMFIT['filter'] = OMFIT['iter_scenarios'].filter(
        {'List of IDSs': ['equilibrium',
         'Workflow': 'CORSICA',
         'Fuelling': 'D-T'})
    # sort based on a column
    # display filtered and sorted table
  3. Access ITER scenario database remotely from within OMFIT:

    OMFIT['ods'] = load_omas_iter_scenario_remote(pulse=131034, run=0)

ITER scenario requirements

There is a subset of IDS fields that are required to add datasets to the ITER scenario database

Tutorial for running predictive ITER modeling with OMFIT+OMAS

Tutorial for running the IMAS Python HCD workflow via OMFIT+OMAS