About datamodels¶

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, or loaded from ASDF files or some future file format.

The detailed datamodel structure and specifics are contained in the documentation included with the roman_datamodels package found here.

Each model instance is created to contain a variety of attributes and data that are needed for analysis or to propagate information about the file and the contents of the file. For example, the ImageModel class has the following arrays associated with it:

data: The science data

dq: The data quality array

err: The error array

Along with data arrays the datamodel also contains information about the observation that can include the observation program, exposure information, pointing information and processing steps.

Working with models¶

Reading a data model¶

If you have an existing data file it is straightforward to access the file using python.

>>> from roman_datamodels import datamodels as rdm
>>> fn = 'r0019106003005004023_03203_0034_WFI01_cal.asdf'
>>> data_file = rdm.open(fn)  
>>> type(data_file) 
roman_datamodels.datamodels.ImageModel

Where the output of the type command tells you that you have imported an ImageModel from roman_datamodels,

Creating a data model from scratch¶

To create a new ImageModel, you can just

>>> from roman_datamodels import datamodels as rdm
>>> from roman_datamodels.maker_utils import mk_datamodel

>>> image_model = mk_datamodel(rdm.ImageModel)
>>> type(image_model)
<class 'roman_datamodels.datamodels._datamodels.ImageModel'>

Warning

The values in the file generated by create_wfi_image are intended to be clearly incorrect and should be replaced if the file is intended to be used for anything besides a demonstration.

Saving a data model to a file¶

Simply call the save method on the model instance:

>>> image_model.save("myimage.asdf")
PosixPath('myimage.asdf')

Note

This save always clobbers the output file. For now the only format supported is ASDF.

Creating a data model from a file¶

The roman_datamodels.open function is a convenient way to create a model from a file on disk. It may be passed any of the following:

a path to an ASDF file

a readable file-like object

The file will be opened, and based on the nature of the data in the file, the correct data model class will be returned. For example, if the file contains 2-dimensional data, an ImageModel instance will be returned. You will generally want to instantiate a model using a with statement so that the file will be closed automatically when exiting the with block.

>>> with rdm.open("myimage.asdf") as im:
...    assert isinstance(im, rdm.ImageModel)

If you know the type of data stored in the file, or you want to ensure that what is being loaded is of a particular type, use the constructor of the desired concrete class. For example, if you want to ensure that the file being opened contains 2-dimensional image data

>>> with rdm.ImageModel("myimage.asdf") as im:
...     pass  # raises exception if myimage.asdf is not an image file

This will raise an exception if the file contains data of the wrong type.

Copying a model¶

To create a new model based on another model, simply use its copy method. This will perform a deep-copy: that is, no changes to the original model will propagate to the new model

>>> new_model = image_model.copy()

Looking at the contents of a model¶

You can examine the contents of your model from within python using

>>> print("\n".join("{: >20}\t{}".format(k, v) for k, v in image_model.items()), "\n") 
meta.calibration_software_version   9.9.0
meta.sdf_software_version   7.7.7
       meta.filename        dummy value
      meta.file_date        2020-01-01T00:00:00.000
     meta.model_type        ImageModel
         meta.origin        STSCI
meta.prd_software_version   8.8.8
      meta.telescope        ROMAN
  meta.aperture.name        WFI_06_FULL
meta.aperture.position_angle        30.0
...

or you can print specifics

>>> print("\n".join("{: >20}\t{}".format(k, v) for k, v in image_model.meta.wcsinfo.items()), "\n")
              v2_ref        -999999
              v3_ref        -999999
             vparity        -999999
            v3yangle        -999999
              ra_ref        -999999
             dec_ref        -999999
            roll_ref        -999999
            s_region        dummy value

Note

These will be incorporated as methods in the data models in a future release.