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)

<class ‘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.testing.factories import create_wfi_image
import numpy as np

image_node = create_wfi_image()
image_model = rdm.ImageModel(image_node)
#<class 'roman_datamodels.datamodels.ImageModel'>


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.

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.

from roman_datamodels import datamodels
with datamodels.open("myimage.asdf") as im:
    assert isinstance(im, datamodels.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:

from roman_datamodels.datamodels import ImageModel
with ImageModel("myimage.asdf") as im:
    # raises exception if myimage.asdf is not an image file

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

Saving a data model to a file

Simply call the save method on the model instance:



This save always clobbers the output file.

For now the only format supported is ASDF.

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 = old_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 im.items()), "\n")

which will list the contents of the ImageModel im:

meta.aperture.name  Aperture name c1d861ddaebdb859f619fb2b79ea7bdf
meta.aperture.position_angle        115.33996998457596
meta.cal_step.flat_field    SKIPPED

area        <array (unloaded) shape: [4096, 4096] dtype: float32>
history.description HISTORY of this file
history.time        2021-12-29 14:03:57.465551
history.software.name       roman_datamodels
history.software.author     STSCI
history.software.homepage   https://github.com/spacetelescope/roman_datamodels
history.software.version    0.8

or you can print specifics:

print("\n".join("{: >20}\t{}".format(k, v) for k, v in im.meta.wcsinfo.items()), "\n")
            v2_ref  1312.9491452484797
            v3_ref  -1040.7853726755036
           vparity  -1
          v3yangle  -60.0
            ra_ref  84.49289366006334
           dec_ref  -69.14101326380924
          roll_ref  0.0
          s_region  NONE

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