The Structure of DataModels¶
Datamodels allows for the creation of a new model though the usual method of calling the __init__ method. Each type of model has its own class and schema. The schema is specified through the class variable schema_url. The schema gives the binding between the FITS header keyword and/or extension and the datamodels attribute name. The chief distinction between the two is that FITS has a flat model, datamodels supports a hierarchical data model. The typical structure of a datamodels class is that it first calls the __init__ method of the base class and then initializes the required arrays of the models with lines that look like they shouldn’t do anything, for example
self.dq = self.dq
The reason why a line like the above initializes the array is that the access to the array on the right side of the assignment will initialize the array to a default value if it is not already defined and one is found in the schema. The sequence of calls is that the dot notation invokes __getattr__, which calls _make_default if the attribute is not defined, which in turn calls _make_default_array if the schema says the attribute is an array. All these methods can be found in properties.py.
The base class for Datamodels isDataModel, in model_base.py. It takes several arguments, the most important of which is init, which as the name suggests, specifies how to initialize the primary data array of the model. Init is most usually the name of a file, but can be an already opened fits or asdf file, a numpy array, a shape tuple, or None. If init is a shape tuple the primary data array is initialized to its default value.
Optional arguments to __init__ can give a schema which overrides the class schema, extensions to the schema, two flages pass_invalid_values and strict_validation, which control the data validation, and numpy arrays which are used to initialized the model arrays by using parameters of the same name.
As an alternative to creating a model by initializing an object of the specific class, you can call the open function, which is in util.py. This function takes the same arguments as the __init_ method. If it is called with the name of a FITS file, it looks in the primary header for a keyword named DATAMODL that conains the name of the class to use to open the model. If that keyword is not found, checks the dimensionality of the image and uses a generic model type to open the image.
The base class for Datamodels loads the schema from the a file in the schemas subdirectory. If the base class is passed a descriptor of an already open model, it returns a shallow copy of the already open image. This is done to speed the code, as re-opening already open models is a common operation in the pipeline. If it is passed the name of a file, it peeks at the first several bytes of the file to determine the file type. This test is in filetype.py.
If the file type is a FITS file, it calls from_fits in fits_support.py to open the file. From_fits first reads the serialized version of the asdf tree stored in the asdf extension of the FITS file. It then walks through the schema, which has a tree structure and uses the fields fits_keyword and fits_hdu to locate and read items in the fits file into the asdf tree. So items in the rest of the FITS file override items in the asdf extension. It keeps track of the names of these items and then makes a pass over the FITS file and writes any keywords and hdus to another area of the asdf tree called extra_fits. Extra_fits has subtrees for each hdu. Keywords in each hdu not found in the schema are placed in the header subtree and data is placed in the data subtree. Finally, it reads the history keywords and places them in a history structure.
To write a model back to a file, call the save method on the file. It first calls validate_required to check the schema to see if all the required fields are present in the model. Then it calls the function to_fits in fits_support.py. It first creates an empty fits file and then calls a custom validator to write the contents of the asdf tree into this file. The functions called are defined by the dictionary FITS_VALIDATORS found in fits_support.py. Since the validator uses the schema as a guide, only items in the schema are added to the FITS file at this time. After saving items mentioned in the schema, to_fits then saves the contents of extra_fits, and then the history. Finally, it serializes the asdf tree and writes it to the asdf extension.
Items within a model are accessed as attribute, that is, with dot motation. The code which handles getting and setting attributes is found in properties.py. Datamodels distinguishes between items at the endpoints of the asdf tree and subtrees within the asdf tree. The former are returned as scalars or numpy arrays, depending on whether the endpoint represents a FITS keyword or data array. Subtrees are returned as nodes. A node is an object containing the subtree as well as the subschema which describes the subtree. If one tries to get an attribute that does not exist in the asdf tree, one of several things may happen. If the attribute is not mentioned in the schema, the value of the attribute is set to None. If it is in the schema and the schema has a default value, the code creates the item with the default value and then returns it. The functions that do this are _make_default and _make_default_array, which it calls. If not only the item, but the subtree containg the item is missing, the code throws an AttributeError. When an attribute representing an array is accessed, the type of the array is compared to the type in the schema and if they are different, the array is cast to the type in the schema. The same is true for numpy records, which represent rows in a FITS table. The casting is done by the function gentle_asarray in util.py.
When setting or deleting an attribute, the code validates the change. The code which does the validation can be found in validate.py. The validator checks the values of pass_invalid_values, which allows values inconsistent with the schema to be set, and strict_validation, which throws an exception if the value does not match the schema.