SourceModelContainer

class jwst.datamodels.SourceModelContainer(init=None, **kwargs)[source]

Bases: jwst.datamodels.ModelContainer

A container to make MultiExposureModel look like ModelContainer

The MultiExposureModel.exposures list contains the data for each exposure from a common slit id. Though the information is the same, the structures are not true SlitModel instances. This container creates a SlitModel wrapper around each exposure, such that pipeline code can treat each as a DataModel.

Parameters

  • init (str, tuple, HDUList, ndarray, dict, None) –

    • None : Create a default data model with no shape.

    • tuple : Shape of the data array. Initialize with empty data array with shape specified by the.

    • file path: Initialize from the given file (FITS or ASDF)

    • readable file object: Initialize from the given file object

    • HDUList : Initialize from the given HDUList.

    • A numpy array: Used to initialize the data array

    • dict: The object model tree for the data model

  • schema (dict, str (optional)) – Tree of objects representing a JSON schema, or string naming a schema. The schema to use to understand the elements on the model. If not provided, the schema associated with this class will be used.

  • memmap (bool) – Turn memmap of FITS file on or off. (default: False). Ignored for ASDF files.

  • pass_invalid_values (bool or None) – If True, values that do not validate the schema will be added to the metadata. If False, they will be set to None. If None, value will be taken from the environmental PASS_INVALID_VALUES. Otherwise the default value is False.

  • strict_validation (bool or None) – If True, schema validation errors will generate an exception. If False, they will generate a warning. If None, value will be taken from the environmental STRICT_VALIDATION. Otherwise, the default value is False.

  • validate_on_assignment (bool or None) – Defaults to ‘None’. If None, value will be taken from the environmental VALIDATE_ON_ASSIGNMENT, defaulting to ‘True’ if no environment variable is set. If ‘True’, attribute assignments are validated at the time of assignment. Validation errors generate warnings and values will be set to None. If ‘False’, schema validation occurs only once at the time of write. Validation errors generate warnings.

  • ignore_missing_extensions (bool) – When False, raise warnings when a file is read that contains metadata about extensions that are not available. Defaults to True.

  • kwargs (dict) –

    Additional keyword arguments passed to lower level functions. These arguments are generally file format-specific. Arguments of note are:

    • FITS

      skip_fits_update - bool or None

      True to skip updating the ASDF tree from the FITS headers, if possible. If None, value will be taken from the environmental SKIP_FITS_UPDATE. Otherwise, the default value is True.

  • available built-in formats are (The) –

  • ==== ===== ============= (=========) – Format Read Write Auto-identify

  • ==== ===== ============= –

  • Yes Yes Yes (datamodel) –

  • ==== ===== ============= –

Attributes Summary

multiexposure

Return the MultiExposureModel that is being wrapped

Methods Summary

save([path, dir_path, save_model_func])

Save out the container as a MultiExposureModel

Attributes Documentation

multiexposure

Return the MultiExposureModel that is being wrapped

The result will be updated with any new data in the container.

Methods Documentation

save(path=None, dir_path=None, save_model_func=None, *args, **kwargs)[source]

Save out the container as a MultiExposureModel