hsdn_importdata

hsdn_importdata(vis: list[str] | None = None, session: list[str] | None = None, hm_rasterscan: str | None = None, datacolumns: dict | None = None, overwrite: bool | None = None, nocopy: bool | None = None, createmms: str | None = None) → ResultsList[NROImportDataResults][source]

Imports Nobeyama data into the single dish pipeline.

Imports Nobeyama data into the single dish pipeline. The hsdn_importdata task loads the specified visibility data into the pipeline context unpacking and / or converting it as necessary.

If the overwrite input parameter is set to False, then when the output MS already exists in the output directory, the existing MS will be imported instead.

Parameters:

  • vis --

    List of visibility data files. These may be MSes, or tar files of MSes.

    Example: vis=['X227.ms', 'anyms.tar.gz']

  • session --

    List of sessions to which the visibility files belong. Defaults to a single session containing all the visibility files, otherwise a session must be assigned to each vis file.

    Example: session=['Session_1', 'Sessions_2']

  • hm_rasterscan --

    Heuristics method for raster scan analysis. Two analysis modes, time-domain analysis ('time') and direction analysis ('direction'), are available.

    Default: None (equivalent to 'time')

  • datacolumns --

    Dictionary defining the data types of existing columns. The format is:

    {'data': 'data type 1'}

    or

    {'data': 'data type 1', 'corrected': 'data type 2'}

    For MSes one can define two different data types for the DATA and CORRECTED_DATA columns and they can be any of the known data types (RAW, REGCAL_CONTLINE_ALL, REGCAL_CONTLINE_SCIENCE, SELFCAL_CONTLINE_SCIENCE, REGCAL_LINE_SCIENCE, SELFCAL_LINE_SCIENCE, BASELINED, ATMCORR). The intent selection strings _ALL or _SCIENCE can be skipped. In that case the task determines this automatically by inspecting the existing intents in the dataset. Usually, a single datacolumns dictionary is used for all datasets. If necessary, one can define a list of dictionaries, one for each MS, with different setups per MS. If no type is specified, {'data':'raw'} will be assumed.

  • overwrite -- Overwrite existing files on import. If overwrite=False and the MS already exists in output directory, then this existing MS dataset will be used instead.

  • nocopy -- Disable copying of MS to working directory.

  • createmms -- Create an MMS

Returns:

The results object for the pipeline task is returned.

Examples

  1. Load MS list in the ../rawdata subdirectory into the context:

>>> hsdn_importdata (vis=['../rawdata/mg2-1.ms', '../rawdata/mg2-2.ms'])

  1. Load an MS in the current directory into the context:

>>> hsdn_importdata (vis=['mg2.ms'])

  1. Load a tarred MS in ../rawdata into the context:

>>> hsdn_importdata (vis=['../rawdata/mg2.tar.gz'])

  1. Import a list of MeasurementSets:

>>> myvislist = ['mg2-1.ms', 'mg2-2.ms']
>>> hsdn_importdata(vis=myvislist)