def _check_value(value, schema):
"""
Perform the actual validation.
"""
if value is None:
if schema.get('fits_required'):
name = schema.get("fits_keyword") or schema.get("fits_hdu")
raise jsonschema.ValidationError("%s is a required value"
% name)
else:
validator_context = AsdfFile()
validator_resolver = validator_context.resolver
temp_schema = {
'$schema':
'http://stsci.edu/schemas/asdf-schema/0.1.0/asdf-schema'}
temp_schema.update(schema)
validator = asdf_schema.get_validator(temp_schema,
validator_context,
validator_callbacks,
validator_resolver)
value = yamlutil.custom_tree_to_tagged_tree(value, validator_context)
validator.validate(value, _schema=temp_schema)
validator_context.close()
def _check_value(value, schema):
"""
Perform the actual validation.
"""
if value is None:
if schema.get('fits_required'):
name = schema.get("fits_keyword") or schema.get("fits_hdu")
raise jsonschema.ValidationError("%s is a required value"
% name)
else:
validator_context = AsdfFile()
validator_resolver = validator_context.resolver
temp_schema = {
'$schema':
'http://stsci.edu/schemas/asdf-schema/0.1.0/asdf-schema'}
temp_schema.update(schema)
validator = asdf_schema.get_validator(temp_schema,
validator_context,
validator_callbacks,
validator_resolver)
value = yamlutil.custom_tree_to_tagged_tree(value, validator_context)
validator.validate(value, _schema=temp_schema)
validator_context.close()
def _check_value(value):
"""
Perform the actual validation.
"""
validator_context = AsdfFile()
validator_resolver = validator_context.resolver
temp_schema = {
'$schema': 'http://stsci.edu/schemas/asdf-schema/0.1.0/asdf-schema'
}
validator = asdf_schema.get_validator(temp_schema, validator_context,
validator_callbacks,
validator_resolver)
value = yamlutil.custom_tree_to_tagged_tree(value, validator_context)
validator.validate(value, _schema=temp_schema)
validator_context.close()
class DataModel(properties.ObjectNode, ndmodel.NDModel):
"""
Base class of all of the data models.
"""
schema_url = None
"""
The schema URI to validate the model against. If
None, only basic validation of required metadata
properties (filename, date, model_type) will occur.
"""
def __init__(self,
init=None,
schema=None,
memmap=False,
pass_invalid_values=None,
strict_validation=None,
ignore_missing_extensions=True,
**kwargs):
"""
Parameters
----------
init : str, 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 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
- `~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
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`.
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`.
"""
# Override value of validation parameters if not explicitly set.
if pass_invalid_values is None:
pass_invalid_values = get_envar_as_boolean("PASS_INVALID_VALUES",
False)
self._pass_invalid_values = pass_invalid_values
if strict_validation is None:
strict_validation = get_envar_as_boolean("STRICT_VALIDATION",
False)
self._strict_validation = strict_validation
self._ignore_missing_extensions = ignore_missing_extensions
kwargs.update({'ignore_missing_extensions': ignore_missing_extensions})
# Load the schema files
if schema is None:
if self.schema_url is None:
schema = _DEFAULT_SCHEMA
else:
# Create an AsdfFile so we can use its resolver for loading schemas
schema = asdf_schema.load_schema(self.schema_url,
resolve_references=True)
self._schema = mschema.merge_property_trees(schema)
# Provide the object as context to other classes and functions
self._ctx = self
# Initialize with an empty AsdfFile instance as this is needed for
# reading in FITS files where validate._check_value() gets called, and
# ctx needs to have an _asdf attribute.
self._asdf = AsdfFile()
# Determine what kind of input we have (init) and execute the
# proper code to intiailize the model
self._files_to_close = []
self._iscopy = False
is_array = False
is_shape = False
shape = None
if init is None:
asdffile = self.open_asdf(init=None, **kwargs)
elif isinstance(init, dict):
asdffile = self.open_asdf(init=init, **kwargs)
elif isinstance(init, np.ndarray):
asdffile = self.open_asdf(init=None, **kwargs)
shape = init.shape
is_array = True
elif isinstance(init, tuple):
for item in init:
if not isinstance(item, int):
raise ValueError("shape must be a tuple of ints")
shape = init
is_shape = True
asdffile = self.open_asdf(init=None, **kwargs)
elif isinstance(init, DataModel):
asdffile = None
self.clone(self, init)
if not isinstance(init, self.__class__):
self.validate()
return
elif isinstance(init, AsdfFile):
asdffile = init
elif isinstance(init, fits.HDUList):
asdffile = fits_support.from_fits(init, self._schema, self._ctx,
**kwargs)
elif isinstance(init, (str, bytes, PurePath)):
if isinstance(init, PurePath):
init = str(init)
if isinstance(init, bytes):
init = init.decode(sys.getfilesystemencoding())
file_type = filetype.check(init)
if file_type == "fits":
if s3_utils.is_s3_uri(init):
init_fitsopen = s3_utils.get_object(init)
memmap = None
else:
init_fitsopen = init
hdulist = fits.open(init_fitsopen, memmap=memmap)
asdffile = fits_support.from_fits(hdulist, self._schema,
self._ctx, **kwargs)
self._files_to_close.append(hdulist)
elif file_type == "asdf":
asdffile = self.open_asdf(init=init, **kwargs)
else:
# TODO handle json files as well
raise IOError(
"File does not appear to be a FITS or ASDF file.")
else:
raise ValueError("Can't initialize datamodel using {0}".format(
str(type(init))))
# Initialize object fields as determined from the code above
self._shape = shape
self._instance = asdffile.tree
self._asdf = asdffile
# Initalize class dependent hidden fields
self._no_asdf_extension = False
# Instantiate the primary array of the image
if is_array:
primary_array_name = self.get_primary_array_name()
if not primary_array_name:
raise TypeError(
"Array passed to DataModel.__init__, but model has "
"no primary array in its schema")
setattr(self, primary_array_name, init)
# If a shape has been given, initialize the primary array.
if is_shape:
primary_array_name = self.get_primary_array_name()
if not primary_array_name:
raise TypeError(
"Shape passed to DataModel.__init__, but model has "
"no primary array in its schema")
# Initialization occurs when the primary array is first
# referenced. Do so now.
getattr(self, primary_array_name)
# if the input is from a file, set the filename attribute
if isinstance(init, str):
self.meta.filename = os.path.basename(init)
elif isinstance(init, fits.HDUList):
info = init.fileinfo(0)
if info is not None:
filename = info.get('filename')
if filename is not None:
self.meta.filename = os.path.basename(filename)
# if the input model doesn't have a date set, use the current date/time
if not self.meta.hasattr('date'):
current_date = Time(datetime.datetime.now())
current_date.format = 'isot'
self.meta.date = current_date.value
# store the data model type, if not already set
klass = self.__class__.__name__
if klass != 'DataModel':
if not self.meta.hasattr('model_type'):
self.meta.model_type = klass
# initialize arrays from keyword arguments when they are present
for attr, value in kwargs.items():
if value is not None:
subschema = properties._get_schema_for_property(
self._schema, attr)
if 'datatype' in subschema:
setattr(self, attr, value)
@property
def _model_type(self):
return self.__class__.__name__
def __repr__(self):
buf = ['<']
buf.append(self._model_type)
if self.shape:
buf.append(str(self.shape))
try:
filename = self.meta.filename
except AttributeError:
filename = None
if filename:
buf.append(" from ")
buf.append(filename)
buf.append('>')
return "".join(buf)
def __del__(self):
"""Ensure closure of resources when deleted."""
self.close()
@property
def override_handle(self):
"""override_handle identifies in-memory models where a filepath
would normally be used.
"""
# Arbitrary choice to look something like crds://
return "override://" + self.__class__.__name__
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def _drop_arrays(self):
def _drop_array(d):
# Walk tree and delete numpy arrays
if isinstance(d, dict):
for val in d.values():
_drop_array(val)
elif isinstance(d, list):
for val in d:
_drop_array(val)
elif isinstance(d, np.ndarray):
del d
else:
pass
_drop_array(self._instance)
def close(self):
if not self._iscopy:
if self._asdf is not None:
self._asdf.close()
self._drop_arrays()
for fd in self._files_to_close:
if fd is not None:
fd.close()
@staticmethod
def clone(target, source, deepcopy=False, memo=None):
if deepcopy:
instance = copy.deepcopy(source._instance, memo=memo)
target._asdf = AsdfFile(instance)
target._instance = instance
target._iscopy = source._iscopy
else:
target._asdf = source._asdf
target._instance = source._instance
target._iscopy = True
target._files_to_close = []
target._shape = source._shape
target._ctx = target
target._no_asdf_extension = source._no_asdf_extension
def copy(self, memo=None):
"""
Returns a deep copy of this model.
"""
result = self.__class__(init=None,
pass_invalid_values=self._pass_invalid_values,
strict_validation=self._strict_validation)
self.clone(result, self, deepcopy=True, memo=memo)
return result
__copy__ = __deepcopy__ = copy
def validate(self):
"""
Re-validate the model instance againsst its schema
"""
validate.value_change(str(self), self._instance, self._schema, self)
def validate_required_fields(self):
"""
Walk the schema and make sure all required fields are
in the model
"""
def callback(schema, path, combiner, ctx, recurse):
if 'fits_required' not in schema:
return
# Get the value pointed at by the path to the node,
# or None in case there is no entry for the node
node = ctx
for attr in path:
node = getattr(node, attr)
if node is None:
break
validate.value_change(path, node, schema, self)
mschema.walk_schema(self._schema, callback, ctx=self)
def info(self, *args, **kwargs):
return self._asdf.info(**kwargs)
def search(self, *args, **kwargs):
return self._asdf.search(*args, **kwargs)
try:
info.__doc__ = AsdfFile.info.__doc__
search.__doc__ = AsdfFile.search.__doc__
except AttributeError:
pass
def get_primary_array_name(self):
"""
Returns the name "primary" array for this model, which
controls the size of other arrays that are implicitly created.
This is intended to be overridden in the subclasses if the
primary array's name is not "data".
"""
if properties._find_property(self._schema, 'data'):
primary_array_name = 'data'
else:
primary_array_name = ''
return primary_array_name
def on_save(self, path=None):
"""
This is a hook that is called just before saving the file.
It can be used, for example, to update values in the metadata
that are based on the content of the data.
Override it in the subclass to make it do something, but don't
forget to "chain up" to the base class, since it does things
there, too.
Parameters
----------
path : str
The path to the file that we're about to save to.
"""
if isinstance(path, str):
self.meta.filename = os.path.basename(path)
current_date = Time(datetime.datetime.now())
current_date.format = 'isot'
self.meta.date = current_date.value
# Enforce model_type to be the actual type of model being saved.
self.meta.model_type = self._model_type
def save(self, path, dir_path=None, *args, **kwargs):
"""
Save to either a FITS or ASDF file, depending on the path.
Parameters
----------
path : string 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_path: string
Directory to save to. If not None, this will override
any directory information in the `path`
Returns
-------
output_path: str
The file path the model was saved in.
"""
if callable(path):
path_head, path_tail = os.path.split(path(self.meta.filename))
else:
path_head, path_tail = os.path.split(path)
base, ext = os.path.splitext(path_tail)
if isinstance(ext, bytes):
ext = ext.decode(sys.getfilesystemencoding())
if dir_path:
path_head = dir_path
output_path = os.path.join(path_head, path_tail)
# TODO: Support gzip-compressed fits
if ext == '.fits':
# TODO: remove 'clobber' check once depreciated fully in astropy
if 'clobber' not in kwargs:
kwargs.setdefault('overwrite', True)
self.to_fits(output_path, *args, **kwargs)
elif ext == '.asdf':
self.to_asdf(output_path, *args, **kwargs)
else:
raise ValueError("unknown filetype {0}".format(ext))
return output_path
@staticmethod
def open_asdf(init=None,
ignore_version_mismatch=True,
ignore_unrecognized_tag=False,
**kwargs):
"""
Open an asdf object from a filename or create a new asdf object
"""
if isinstance(init, str):
if s3_utils.is_s3_uri(init):
init = s3_utils.get_object(init)
asdffile = asdf.open(
init,
ignore_version_mismatch=ignore_version_mismatch,
ignore_unrecognized_tag=ignore_unrecognized_tag)
else:
asdffile = AsdfFile(
init,
ignore_version_mismatch=ignore_version_mismatch,
ignore_unrecognized_tag=ignore_unrecognized_tag)
return asdffile
@classmethod
def from_asdf(cls, init, schema=None, **kwargs):
"""
Load a data model from an ASDF file.
Parameters
----------
init : str, 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`.
schema :
Same as for `__init__`
kwargs : dict
Aadditional arguments passed to lower level functions
Returns
-------
model : `~jwst.datamodels.DataModel` instance
A data model.
"""
return cls(init, schema=schema, **kwargs)
def to_asdf(self, init, *args, **kwargs):
"""
Write a data model to an ASDF file.
Parameters
----------
init : file path or file object
args : tuple, list
Additional positional arguments passed to `~asdf.AsdfFile.write_to`.
kwargs : dict
Any additional keyword arguments are passed along to
`~asdf.AsdfFile.write_to`.
"""
self.on_save(init)
asdffile = self.open_asdf(self._instance, **kwargs)
asdffile.write_to(init, *args, **kwargs)
@classmethod
def from_fits(cls, init, schema=None, **kwargs):
"""
Load a model from a FITS file.
Parameters
----------
init : file 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`.
schema : dict, str
Same as for `__init__`
kwargs : dict
Aadditional arguments passed to lower level functions.
Returns
-------
model : `~jwst.datamodels.DataModel`
A data model.
"""
return cls(init, schema=schema, **kwargs)
def to_fits(self, init, *args, **kwargs):
"""
Write a data model to a FITS file.
Parameters
----------
init : file path or file object
args, kwargs
Any additional arguments are passed along to
`astropy.io.fits.writeto`.
"""
self.on_save(init)
with fits_support.to_fits(self._instance, self._schema) as ff:
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='Card is too long')
if self._no_asdf_extension:
ff._hdulist.writeto(init, *args, **kwargs)
else:
ff.write_to(init, *args, **kwargs)
@property
def shape(self):
if self._shape is None:
primary_array_name = self.get_primary_array_name()
if primary_array_name and self.hasattr(primary_array_name):
primary_array = getattr(self, primary_array_name)
self._shape = primary_array.shape
return self._shape
def my_attribute(self, attr):
properties = frozenset(("shape", "history", "_extra_fits", "schema"))
return attr in properties
def __setattr__(self, attr, value):
if self.my_attribute(attr):
object.__setattr__(self, attr, value)
elif ndmodel.NDModel.my_attribute(self, attr):
ndmodel.NDModel.__setattr__(self, attr, value)
else:
properties.ObjectNode.__setattr__(self, attr, value)
def extend_schema(self, new_schema):
"""
Extend the model's schema using the given schema, by combining
it in an "allOf" array.
Parameters
----------
new_schema : dict
Schema tree.
"""
schema = {'allOf': [self._schema, new_schema]}
self._schema = mschema.merge_property_trees(schema)
self.validate()
return self
def add_schema_entry(self, position, new_schema):
"""
Extend the model's schema by placing the given new_schema at
the given dot-separated position in the tree.
Parameters
----------
position : str
Dot separated string indicating the position, e.g. ``meta.instrument.name``.
new_schema : dict
Schema tree.
"""
parts = position.split('.')
schema = new_schema
for part in parts[::-1]:
schema = {'type': 'object', 'properties': {part: schema}}
return self.extend_schema(schema)
# return_result retained for backward compatibility
def find_fits_keyword(self, keyword, return_result=True):
"""
Utility function to 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
----------
keyword : str
A FITS keyword name.
Returns
-------
locations : list 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.
"""
from . import schema
return schema.find_fits_keyword(self.schema, keyword)
def search_schema(self, substring):
"""
Utility function to 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
----------
substring : str
The substring to search for.
Returns
-------
locations : list of tuples
"""
from . import schema
return schema.search_schema(self.schema, substring)
def __getitem__(self, key):
"""
Get a metadata value using a dotted name.
"""
assert isinstance(key, str)
meta = self
for part in key.split('.'):
try:
meta = getattr(meta, part)
except AttributeError:
raise KeyError(repr(key))
return meta
def get_item_as_json_value(self, key):
"""
Equivalent to __getitem__, except returns the value as a JSON
basic type, rather than an arbitrary Python type.
"""
assert isinstance(key, str)
meta = self
parts = key.split('.')
for part in parts:
try:
meta = getattr(meta, part)
except AttributeError:
raise KeyError(repr(key))
return yamlutil.custom_tree_to_tagged_tree(meta, self._instance)
def __setitem__(self, key, value):
"""
Set a metadata value using a dotted name.
"""
assert isinstance(key, str)
meta = self
parts = key.split('.')
for part in parts[:-1]:
try:
part = int(part)
except ValueError:
try:
meta = getattr(meta, part)
except AttributeError:
raise KeyError(repr(key))
else:
meta = meta[part]
part = parts[-1]
try:
part = int(part)
except ValueError:
setattr(meta, part, value)
else:
meta[part] = value
def iteritems(self):
"""
Iterates over all of the schema items 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")
"""
def recurse(tree, path=[]):
if isinstance(tree, dict):
for key, val in tree.items():
for x in recurse(val, path + [key]):
yield x
elif isinstance(tree, (list, tuple)):
for i, val in enumerate(tree):
for x in recurse(val, path + [i]):
yield x
elif tree is not None:
yield ('.'.join(str(x) for x in path), tree)
for x in recurse(self._instance):
yield x
# We are just going to define the items to return the iteritems
items = iteritems
def iterkeys(self):
"""
Iterates over all of the schema keys in a flat way.
Each result of the iterator is a `key`. 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"`.
"""
for key, val in self.iteritems():
yield key
keys = iterkeys
def itervalues(self):
"""
Iterates over all of the schema values in a flat way.
"""
for key, val in self.iteritems():
yield val
values = itervalues
def update(self, d, only=None, extra_fits=False):
"""
Updates this model with the metadata elements from another model.
Note: The ``update`` method skips a WCS object, if present.
Parameters
----------
d : `~jwst.datamodels.DataModel` or dictionary-like object
The model to copy the metadata elements from. Can also be a
dictionary or dictionary of dictionaries or lists.
only: str, None
Update only the named hdu, e.g. ``only='PRIMARY'``. Can either be
a string or list of hdu names. Default is to update all the hdus.
extra_fits : boolean
Update from ``extra_fits``. Default is False.
"""
def hdu_keywords_from_data(d, path, hdu_keywords):
# Walk tree and add paths to keywords to hdu keywords
if isinstance(d, dict):
for key, val in d.items():
if len(path) > 0 or key != 'extra_fits':
hdu_keywords_from_data(val, path + [key], hdu_keywords)
elif isinstance(d, list):
for key, val in enumerate(d):
hdu_keywords_from_data(val, path + [key], hdu_keywords)
elif isinstance(d, np.ndarray):
# skip data arrays
pass
else:
hdu_keywords.append(path)
def hdu_keywords_from_schema(subschema, path, combiner, ctx, recurse):
# Add path to keyword to hdu_keywords if in list of hdu names
if 'fits_keyword' in subschema:
fits_hdu = subschema.get('fits_hdu', 'PRIMARY')
if fits_hdu in hdu_names:
ctx.append(path)
def hdu_names_from_schema(subschema, path, combiner, ctx, recurse):
# Build a set of hdu names from the schema
hdu_name = subschema.get('fits_hdu')
if hdu_name:
hdu_names.add(hdu_name)
def included(cursor, part):
# Test if part is in the cursor
if isinstance(part, int):
return part >= 0 and part < len(cursor)
else:
return part in cursor
def set_hdu_keyword(this_cursor, that_cursor, path):
# Copy an element pointed to by path from that to this
part = path.pop(0)
if not included(that_cursor, part):
return
if len(path) == 0:
this_cursor[part] = copy.deepcopy(that_cursor[part])
else:
that_cursor = that_cursor[part]
if not included(this_cursor, part):
if isinstance(path[0], int):
if isinstance(part, int):
this_cursor.append([])
else:
this_cursor[part] = []
else:
if isinstance(part, int):
this_cursor.append({})
elif isinstance(that_cursor, list):
this_cursor[part] = []
else:
this_cursor[part] = {}
this_cursor = this_cursor[part]
set_hdu_keyword(this_cursor, that_cursor, path)
def protected_keyword(path):
# Some keywords are protected and
# should not be copied frpm the other image
if len(path) == 2:
if path[0] == 'meta':
if path[1] in ('date', 'model_type'):
return True
return False
# Get the list of hdu names from the model so that updates
# are limited to those hdus
if only is not None:
if isinstance(only, str):
hdu_names = set([only])
else:
hdu_names = set(list(only))
else:
hdu_names = set(['PRIMARY'])
mschema.walk_schema(self._schema, hdu_names_from_schema, hdu_names)
# Get the paths to all the keywords that will be updated from
hdu_keywords = []
if isinstance(d, DataModel):
schema = d._schema
d = d._instance
mschema.walk_schema(schema, hdu_keywords_from_schema, hdu_keywords)
else:
path = []
hdu_keywords_from_data(d, path, hdu_keywords)
# Perform the updates to the keywords mentioned in the schema
for path in hdu_keywords:
if not protected_keyword(path):
set_hdu_keyword(self._instance, d, path)
# Update from extra_fits as well, if indicated
if extra_fits:
for hdu_name in hdu_names:
path = ['extra_fits', hdu_name, 'header']
set_hdu_keyword(self._instance, d, path)
self.validate()
def to_flat_dict(self, include_arrays=True):
"""
Returns a dictionary of all of the schema items 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" }
"""
def convert_val(val):
if isinstance(val, datetime.datetime):
return val.isoformat()
elif isinstance(val, Time):
return str(val)
return val
if include_arrays:
return dict(
(key, convert_val(val)) for (key, val) in self.iteritems())
else:
return dict((key, convert_val(val))
for (key, val) in self.iteritems()
if not isinstance(val, np.ndarray))
@property
def schema(self):
return self._schema
def get_fileext(self):
return 'fits'
# TODO: This is just here for backward compatibility
@property
def _extra_fits(self):
return self.extra_fits
# TODO: For backward compatibility
def get_section(self, name):
return getattr(self, name)
@property
def history(self):
"""
Get the history as a list of entries
"""
return HistoryList(self._asdf)
@history.setter
def history(self, values):
"""
Set a history entry.
Parameters
----------
values : list
For FITS files this should be a list of strings.
For ASDF files use a list of ``HistoryEntry`` object. It can be created
with `~jwst.datamodels.util.create_history_entry`.
"""
entries = self.history
entries.clear()
entries.extend(values)
def get_fits_wcs(self, 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`.
Parameters
----------
hdu_name : str, 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'``.
key : str, 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.
hdu_ver: int, optional
The extension version. Used when there is more than one
extension with the same name. The default value, 1,
is the first.
Returns
-------
wcs : `astropy.wcs.WCS` or `pywcs.WCS` object
The type will depend on what libraries are installed on
this system.
"""
ff = fits_support.to_fits(self._instance, self._schema)
hdu = fits_support.get_hdu(ff._hdulist, hdu_name, index=hdu_ver - 1)
header = hdu.header
return WCS(header, key=key, relax=True, fix=True)
def set_fits_wcs(self, wcs, hdu_name='SCI'):
"""
Sets 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.
Parameters
----------
wcs : `astropy.wcs.WCS` or `pywcs.WCS` object
The object containing FITS WCS information
hdu_name : str, 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'``.
"""
header = wcs.to_header()
if hdu_name == 'PRIMARY':
hdu = fits.PrimaryHDU(header=header)
else:
hdu = fits.ImageHDU(name=hdu_name, header=header)
hdulist = fits.HDUList([hdu])
ff = fits_support.from_fits(
hdulist,
self._schema,
self._ctx,
ignore_missing_extensions=self._ignore_missing_extensions)
self._instance = properties.merge_tree(self._instance, ff.tree)
# --------------------------------------------------------
# These two method aliases are here for astropy.registry
# compatibility and should not be called directly
# --------------------------------------------------------
read = __init__
def write(self, path, *args, **kwargs):
self.save(path, *args, **kwargs)
def getarray_noinit(self, attribute):
"""Retrieve array but without initilization
Arrays initialize when directly referenced if they had
not previously been initialized. This circumvents the
initialization and instead raises `AttributeError`.
Parameters
----------
attribute : str
The attribute to retrieve.
Returns
-------
value : object
The value of the attribute.
Raises
------
AttributeError
If the attribute does not exist.
"""
if attribute in self.instance:
return getattr(self, attribute)
raise AttributeError(f'{self} has no attribute "{attribute}"')
tree = {"wcs": wcsobj}
#fa = fits_embed.AsdfInFits(hdul, tree)
#fa = fits_embed.AsdfInFits(fits.HDUList(), tree)
#fa.write_to('test.fits')
af = AsdfFile(tree)
fd = io.BytesIO()
#af._write_tree(tree, fd, pad_blocks=False)
af.write_to(fd)
fd.seek(0)
wcsbytes = fd.read()
fd.close()
# af.write_to('testfull.asdf')
af.close()
fmt = 'A{0}'.format(len(wcsbytes))
col = fits.Column(name='gWCS',
format=fmt,
array=np.array([wcsbytes]),
ascii=True)
#print(col.array)
hdu = fits.TableHDU.from_columns([col], name='WCS')
hdul.append(hdu)
hdul.flush()
# Read back in:
