Data model attributes

The purpose of the data model is to abstract away the peculiarities of the underlying file format. The same data model may be used for data created from scratch in memory, loaded from FITS or ASDF files, or from some other future format.

Calling sequences of models

List of current models

The current models are as follows:

‘ABVegaOffsetModel’, ‘AmiLgModel’, ‘FgsImgApcorrModel’, ‘MirImgApcorrModel’, ‘NrcImgApcorrModel’, ‘NisImgApcorrModel’, ‘MirLrsApcorrModel’, ‘MirMrsApcorrModel’, ‘NrcWfssApcorrModel’, ‘NisWfssApcorrModel’, ‘NrsMosApcorrModel’, ‘NrsFsApcorrModel’, ‘NrsIfuApcorrModel’, ‘AsnModel’, ‘BarshadowModel’, ‘CameraModel’, ‘CollimatorModel’, ‘CombinedSpecModel’, ‘ContrastModel’, ‘CubeModel’, ‘DarkModel’, ‘DarkMIRIModel’, ‘DisperserModel’, ‘DistortionModel’, ‘DistortionMRSModel’, ‘DrizParsModel’, ‘Extract1dImageModel’, ‘Extract1dIFUModel’, ‘FilteroffsetModel’, ‘FlatModel’, ‘NirspecFlatModel’, ‘NirspecQuadFlatModel’, ‘FOREModel’, ‘FPAModel’, ‘FringeModel’, ‘GainModel’, ‘GLS_RampFitModel’, ‘GuiderRawModel’, ‘GuiderCalModel’, ‘IFUCubeModel’, ‘NirspecIFUCubeParsModel’, ‘MiriIFUCubeParsModel’, ‘IFUFOREModel’, ‘IFUImageModel’, ‘IFUPostModel’, ‘IFUSlicerModel’, ‘ImageModel’, ‘IPCModel’, ‘IRS2Model’, ‘LastFrameModel’, ‘Level1bModel’, ‘LinearityModel’, ‘MaskModel’, ‘ModelContainer’, ‘MSAModel’, ‘MultiCombinedSpecModel’, ‘MultiExposureModel’, ‘MultiExtract1dImageModel’, ‘MultiSlitModel’, ‘MultiSpecModel’, ‘NIRCAMGrismModel’, ‘NIRISSGrismModel’, ‘OTEModel’, ‘OutlierParsModel’, ‘PathlossModel’, ‘PersistenceSatModel’, ‘PixelAreaModel’, ‘NirspecSlitAreaModel’, ‘NirspecMosAreaModel’, ‘NirspecIfuAreaModel’, ‘FgsImgPhotomModel’, ‘MirImgPhotomModel’, ‘MirLrsPhotomModel’, ‘MirMrsPhotomModel’, ‘NrcImgPhotomModel’, ‘NrcWfssPhotomModel’, ‘NisImgPhotomModel’, ‘NisSossPhotomModel’, ‘NisWfssPhotomModel’, ‘NrsFsPhotomModel’, ‘NrsMosPhotomModel’, ‘PsfMaskModel’, ‘QuadModel’, ‘RampModel’, ‘MIRIRampModel’, ‘RampFitOutputModel’, ‘ReadnoiseModel’, ‘ReferenceFileModel’, ‘ReferenceCubeModel’, ‘ReferenceImageModel’, ‘ReferenceQuadModel’, ‘RegionsModel’, ‘ResetModel’, ‘ResolutionModel’, ‘MiriResolutionModel’, ‘RSCDModel’, ‘SaturationModel’, ‘SlitDataModel’, ‘SlitModel’, ‘SpecModel’, ‘SegmentationMapModel’, ‘SourceModelContainer’, ‘SpecKernelModel’, ‘SpecProfileModel’, ‘SpecProfileSingleModel’, ‘SpecTraceModel’, ‘SpecTraceSingleModel’, ‘SpecwcsModel’, ‘StrayLightModel’, ‘SuperBiasModel’, ‘ThroughputModel’, ‘TrapDensityModel’, ‘TrapParsModel’, ‘TrapsFilledModel’, ‘TsoPhotModel’, ‘WavelengthrangeModel’, ‘WaveCorrModel’, ‘WaveMapModel’, ‘WaveMapSingleModel’, ‘WfssBkgModel’

Commonly used attributes

Here are a few model attributes that are used by some of the pipeline steps.

For uncalibrated data _uncal.fits. Getting the number of integrations and the number of groups from the first and second axes assumes that the input data array is 4-D data. Pixel coordinates in the data extensions are 1-indexed as in FORTRAN and FITS headers, not 0-indexed as in Python.

  • input_model.data.shape[0]: number of integrations

  • input_model.data.shape[1]: number of groups

  • input_model.meta.exposure.nframes: number of frames per group

  • input_model.meta.exposure.groupgap: number of frames dropped

    between groups

  • input_model.meta.subarray.xstart: starting pixel in X (1-based)

  • input_model.meta.subarray.ystart: starting pixel in Y (1-based)

  • input_model.meta.subarray.xsize: number of columns

  • input_model.meta.subarray.ysize: number of rows

The data, err, dq, etc., attributes of most models are assumed to be numpy.ndarray arrays, or at least objects that have some of the attributes of these arrays. numpy is used explicitly to create these arrays in some cases (e.g. when a default value is needed). The data and err arrays are a floating point type, and the data quality arrays are an integer type.

Some of the step code makes assumptions about image array sizes. For example, full-frame MIRI data have 1032 columns and 1024 rows, and all other detectors have 2048 columns and rows; anything smaller must be a subarray. Also, full-frame MIRI data are assumed to have four columns of reference pixels on the left and right sides (the reference output array is stored in a separate image extension). Full-frame data for all other instruments have four columns or rows of reference pixels on each edge of the image.

DataModel Base Class

class jwst.datamodels.DataModel(init=None, schema=None, memmap=False, pass_invalid_values=None, strict_validation=None, validate_on_assignment=None, cast_fits_arrays=True, validate_arrays=False, ignore_missing_extensions=True, **kwargs)[source]
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.

  • cast_fits_arrays (bool) – If True, arrays will be cast to the dtype described by the schema when read from a FITS file. If False, arrays will be read without casting.

  • validate_arrays (bool) – If True, arrays will be validated against ndim, max_ndim, and datatype validators in the schemas.

  • 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.