MultiExposureModel

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

Bases: jwst.datamodels.JwstDataModel

A data model for multi-slit images derived from numerous exposures. The intent is that all slits in this model are of the same source, with each slit representing a separate exposure of that source.

This model has a special member exposures that can be used to deal with an entire slit at a time. It behaves like a list:

>>> from .image import ImageModel
>>> multiexposure_model = MultiExposureModel()
>>> multiexposure_model.exposures.append(ImageModel())
>>> multiexposure_model.exposures[0]      
<ImageModel>

Also, there is an extra attribute, meta. This will contain the meta attribute from the exposure from which each slit has been taken.

See the module exp_to_source for the initial creation of these models. This is part of the Level 3 processing of multi-objection observations.

Parameters

  • exposures.items.data (numpy float32 array) –

  • exposures.items.dq (numpy uint32 array) –

  • exposures.items.err (numpy float32 array) –

  • exposures.items.area (numpy float32 array) –

  • 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

core_schema_url

schema_url

Attributes Documentation

core_schema_url = 'http://stsci.edu/schemas/jwst_datamodel/core.schema'
schema_url = 'http://stsci.edu/schemas/jwst_datamodel/multiexposure.schema'