JwstDataModel

class stdatamodels.jwst.datamodels.JwstDataModel(init=None, schema=None, pass_invalid_values=None, strict_validation=None, validate_on_assignment=None, validate_arrays=False, ignore_missing_extensions=True, ignore_unrecognized_tag=False, **kwargs)

Bases: DataModel

Base class for JWST data models.

Initialize a data model.

Parameters:

initstr, tuple, astropy.io.fits.HDUList, ndarray, dict, None

None : Create a default data model with no shape.
tuple : Shape of the data array. Initialize with default data array with shape specified by the tuple.
file path: Initialize from the given file (FITS or ASDF)
readable file object: Initialize from the given file object
astropy.io.fits.HDUList : Initialize from the given astropy.io.fits.HDUList.
A numpy array: Used to initialize the data array
dict: The object model tree for the data model
DataModel: Initialize from an existing DataModel instance. This will perform a shallow copy, and will convert between model subtypes as long as their schemas are compatible.

schemadict, 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.

pass_invalid_valuesbool 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_validationbool 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_assignmentbool 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.

validate_arraysbool

If True, arrays will be validated against ndim, max_ndim, and datatype validators in the schemas.

ignore_missing_extensionsbool

When False, raise warnings when a file is read that contains metadata about extensions that are not available. Defaults to True.

ignore_unrecognized_tagbool

When False, raise warnings when an unrecognized tag is encountered. When True, ignore unrecognized tags.

**kwargs

Additional keyword arguments are expected to be array-like attributes of the data model. These will be initialized with the given values only if they are defined in the schema and the schema expects an array-like value. Kwargs are only allowed when init is None, a tuple, or a numpy array. Example usage:

model = ImageModel(data=np.ones((10, 10)), dq=np.zeros((10, 10)))

add_history_entry(description, software=None)

Add an entry to the history list.

Parameters:

descriptionstr

A description of the change.

softwaredict or list of dict

A description of the software used. It should not include asdf itself, as that is automatically notated in the asdf_library entry.

Each dict must have the following keys:

name: The name of the software
author: The author or institution that produced the software
homepage: A URI to the homepage of the software
version: The version of the software

add_schema_entry(position, new_schema)

Extend the model’s schema.

Place the given new_schema at the given dot-separated position in the tree.

Parameters:

positionstr: Dot separated string indicating the position, e.g. meta.instrument.name.
new_schemadict: Schema tree.

Returns:

selfDataModel: The datamodel with the schema entry added.

static clone(target, source, deepcopy=False, memo=None)

Clone the contents of one model into another.

Parameters:

targetDataModel: The model to clone into.
sourceDataModel: The model to clone from.
deepcopybool, optional: If True, perform a deep copy of the source model. If False, perform a shallow copy.
memodict, optional: A dictionary to use as a memoization table for deep copy.

close(): Close all file references.

copy(memo=None)

Return a deep copy of this model.

Parameters:

memodict, optional: A dictionary to use as a memoization table for deep copy.

property crds_observatory

Get CRDS observatory code for this model.

Returns:

str: The observatory code.

extend_schema(new_schema)

Extend the model’s schema using the given schema, by combining it in an “allOf” array.

Parameters:

new_schemadict: Schema tree.

Returns:

selfDataModel: The datamodel with its schema updated.

find_fits_keyword(keyword, return_result=True)

Find a reference to a FITS keyword in this model’s schema.

This is intended for interactive use, and not for use within library code.

Parameters:

keywordstr: A FITS keyword name.

Returns:

locationslist of str: If return_result is True, a list of the locations in the schema where this FITS keyword is used. Each element is a dot-separated path.

classmethod from_asdf(init, schema=None, **kwargs)

Load a data model from an ASDF file.

Parameters:

initstr, file object, ~asdf.AsdfFile

str : file path: initialize from the given file
readable file object: Initialize from the given file object
~asdf.AsdfFile : Initialize from the given`~asdf.AsdfFile`.

schemadict

Same as for __init__

**kwargs

Aadditional arguments passed to lower level functions

Returns:

model~jwst.datamodels.DataModel instance: A data model.

classmethod from_fits(init, schema=None, **kwargs)

Load a model from a FITS file.

Parameters:

initfile path, file object, astropy.io.fits.HDUList

file path: Initialize from the given file
readable file object: Initialize from the given file object
astropy.io.fits.HDUList: Initialize from the given ~astropy.io.fits.HDUList.

schemadict, str

Same as for __init__

**kwargs

Aadditional arguments passed to lower level functions.

Returns:

model~jwst.datamodels.DataModel: A data model.

get_crds_parameters()

Get parameters used by CRDS to select references for this model.

Returns:

dict: Dictionary of CRDS parameters

get_default(attr)

Retrieve the schema-defined default value of an attribute.

Parameters:

attrstr: Attribute to set to its default value.

Returns:

object or None: The default value for the given attribute. If the attribute is schema-defined but has no default value in the schema, this will return None.

Raises:

AttributeError: If the given attribute is not defined in the schema.

get_dtype(attr)

Retrieve the numpy dtype for an attribute, if defined.

Parameters:

attrstr: The attribute to retrieve the dtype for.

Returns:

numpy.dtype: The numpy dtype for the attribute.

Raises:

AttributeError: If the given attribute is not defined in the schema.
ValueError: If the given attribute is defined in the schema but has no datatype.

get_fits_wcs(hdu_name='SCI', hdu_ver=1, key=' ')

Get a astropy.wcs.WCS object created from the FITS WCS information in the model.

Note that modifying the returned WCS object will not modify the data in this model. To update the model, use set_fits_wcs.

This method is deprecated and will be removed in a future version. To get the SIP approximation, call to_fits_sip() on the model.meta.wcs attribute.

Parameters:

hdu_namestr, optional: The name of the HDU to get the WCS from. This must use named HDU’s, not numerical order HDUs. To get the primary HDU, pass 'PRIMARY'.
hdu_verint, optional: The extension version. Used when there is more than one extension with the same name. The default value, 1, is the first.
keystr, optional: The name of a particular WCS transform to use. This may be either ' ' or 'A'-'Z' and corresponds to the "a" part of the CTYPEia cards. key may only be provided if header is also provided.

Returns:

wcsastropy.wcs.WCS or pywcs.WCS object: The type will depend on what libraries are installed on this system.

get_primary_array_name()

Retrieve the name of the “primary” array for this model.

The primary array controls the size of other arrays that are implicitly created. If the schema has the “data” property, then this method returns “data”. Otherwise, it returns an empty string. This is intended to be overridden in the subclasses if the primary array’s name is not “data”.

Returns:

primary_array_namestr: The name of the primary array.

getarray_noinit(attribute)

Retrieve array but without initialization.

Arrays initialize when directly referenced if they had not previously been initialized. This circumvents the initialization and instead raises AttributeError.

Parameters:

attributestr: The attribute to retrieve.

Returns:

valueobject: The value of the attribute.

Raises:

AttributeError: If the attribute does not exist.

hasattr(attr)

Check if the node has an attribute in its instance.

Parameters:

attrstr: The name of the attribute to check for.

Returns:

bool: True if the attribute is in the instance, False otherwise.

property history

Get the history as a list of entries.

Returns:

historyHistoryList: A list of history entries.

info(max_rows=24, max_cols=120, show_values=True, show_blocks=False)

Print a rendering of this file’s tree to stdout.

Parameters:

max_rowsint, tuple, or None, optional: Maximum number of lines to print. Nodes that cannot be displayed will be elided with a message. If int, constrain total number of displayed lines. If tuple, constrain lines per node at the depth corresponding to the tuple index. If None, display all lines.
max_colsint or None, optional: Maximum length of line to print. Nodes that cannot be fully displayed will be truncated with a message. If int, constrain length of displayed lines. If None, line length is unconstrained.
show_valuesbool, optional: Set to False to disable display of primitive values in the rendered tree.
show_blocks: bool, optional: Set to True to also print a table of block header fields for each block in the file.

items()

Iterate over all of the datamodel contents in a flat way.

Each element is a pair (key, value). Each key is a dot-separated name. For example, the schema element meta.observation.date will end up in the result as:

("meta.observation.date": "2012-04-22T03:22:05.432")

keys()

Iterate over all of the datamodel contents in a flat way.

Yields:

keystr: The key of the schema element. Each key is a dot-separated name. For example, the schema element meta.observation.date will end up in the result as the string “meta.observation.date”.

on_init(init)

Run a hook before returning a newly created model instance.

Parameters:

initobject: First argument to __init__.

on_save(init)

Run a hook before writing a model to a file (FITS or ASDF).

Parameters:

initstr: The path to the file that we’re about to save to.

property override_handle

Identify in-memory models where a filepath would normally be used.

Returns:

str: A string that can be used to identify the model as an in-memory model.

save(path, dir_path=None, *args, **kwargs)

Save to either a FITS or ASDF file, depending on the path.

Parameters:

pathstr or func: File path to save to. If function, it takes one argument with is model.meta.filename and returns the full path string.
dir_pathstr: Directory to save to. If not None, this will override any directory information in the path

Returns:

output_pathstr: The file path the model was saved in.

property schema

Retrieve the schema for this model.

Returns:

dict: The datamodel schema.

schema_url = 'http://stsci.edu/schemas/jwst_datamodel/core.schema': The schema URI to validate the model against. If None, only basic validation of required metadata properties (filename, model_type) will occur.

search(key=NotSet, type_=NotSet, value=NotSet, filter_=None)

Search this file’s tree.

Parameters:

keyNotSet, str, or any other object

Search query that selects nodes by dict key or list index. If NotSet, the node key is unconstrained. If str, the input is searched among keys/indexes as a regular expression pattern. If any other object, node’s key or index must equal the queried key.

type_NotSet, str, or builtins.type

Search query that selects nodes by type. If NotSet, the node type is unconstrained. If str, the input is searched among (fully qualified) node type names as a regular expression pattern. If builtins.type, the node must be an instance of the input.

valueNotSet, str, or any other object

Search query that selects nodes by value. If NotSet, the node value is unconstrained. If str, the input is searched among values as a regular expression pattern. If any other object, node’s value must equal the queried value.

filter_callable

Callable that filters nodes by arbitrary criteria. The callable accepts one or two arguments:

the node
the node’s list index or dict key (optional)

and returns True to retain the node, or False to remove it from the search results.

Returns:

asdf.search.AsdfSearchResult: the result of the search

search_schema(substring)

Search the metadata schema for a particular phrase.

This is intended for interactive use, and not for use within library code.

The searching is case insensitive.

Parameters:

substringstr: The substring to search for.

Returns:

locationslist of tuples: The locations within the schema where the element is found.

set_fits_wcs(wcs, hdu_name='SCI')

Set the FITS WCS information on the model using the given astropy.wcs.WCS object.

Note that the “key” of the WCS is stored in the WCS object itself, so it can not be set as a parameter to this method.

This method is deprecated and will be removed in a future version. The WCS should only be modified by setting model.meta.wcs

Parameters:

wcsastropy.wcs.WCS or pywcs.WCS object: The object containing FITS WCS information
hdu_namestr, optional: The name of the HDU to set the WCS from. This must use named HDU’s, not numerical order HDUs. To set the primary HDU, pass 'PRIMARY'.

property shape: Return the shape of the primary array.

to_asdf(init, *args, **kwargs)

Write a data model to an ASDF file.

Parameters:

initfile path or file object: The file to write to.
*args: Additional positional arguments passed to ~asdf.AsdfFile.write_to.
**kwargs: Any additional keyword arguments are passed along to ~asdf.AsdfFile.write_to.

to_fits(init, *args, **kwargs)

Write a data model to a FITS file.

Parameters:

initfile path or file object: The file to write to.
*args: Additional positional arguments passed to astropy.io.fits.writeto.
**kwargs: Additional keyword arguments passed to astropy.io.fits.writeto.

to_flat_dict(include_arrays=True)

Return a dictionary of all of the datamodel contents as a flat dictionary.

Each dictionary key is a dot-separated name. For example, the schema element meta.observation.date will end up in the dictionary as:

{"meta.observation.date": "2012-04-22T03:22:05.432"}

Parameters:

include_arraysbool: If True, include arrays in the output. If False, exclude arrays. Default is True.

Returns:

flat_dictdict: A dictionary of all of the datamodel contents as a flat dictionary.

update(d, only=None, extra_fits=False, cal_logs=True)

Update this model with the metadata elements from another model.

Note: The update method skips a WCS object, if present.

Parameters:

d~stdatamodels.jwst.datamodels.JwstDataModel or dictionary-like object: The model to copy the metadata elements from. Can also be a dictionary or dictionary of dictionaries or lists.
onlystr, list of str, or None: Update only the named hdu, e.g. only='PRIMARY'. Can either be a string or list of hdu names. If None, all hdus will be updated.
extra_fitsbool: Update from extra_fits as well as meta.
cal_logsbool: Update from cal_logs as well as meta.

validate(): Validate the model instance against its schema.

values()

Iterate over all of the datamodel contents in a flat way.

Yields:

valueobject: The value of the schema element.