import copy
import datetime
import json
import warnings
from collections import OrderedDict
from collections.abc import Mapping
from functools import lru_cache
from numbers import Integral
from operator import methodcaller
import numpy as np
import yaml
from jsonschema import validators as mvalidators
from jsonschema.exceptions import RefResolutionError, ValidationError
from . import constants, extension, generic_io, reference, tagged, treeutil, util, versioning, yamlutil
from .config import get_config
from .exceptions import AsdfDeprecationWarning, AsdfWarning
from .util import patched_urllib_parse
YAML_SCHEMA_METASCHEMA_ID = "http://stsci.edu/schemas/yaml-schema/draft-01"
if getattr(yaml, "__with_libyaml__", None): # pragma: no cover
_yaml_base_loader = yaml.CSafeLoader
else: # pragma: no cover
_yaml_base_loader = yaml.SafeLoader
__all__ = ["validate", "fill_defaults", "remove_defaults", "check_schema"]
def default_ext_resolver(uri):
"""
Resolver that uses tag/url mappings from all installed extensions
"""
# Deprecating this because it doesn't play nicely with the caching on
# load_schema(...).
warnings.warn(
"The 'default_ext_resolver(...)' function is deprecated. Use "
"'asdf.extension.get_default_resolver()(...)' instead.",
AsdfDeprecationWarning,
)
return extension.get_default_resolver()(uri)
PYTHON_TYPE_TO_YAML_TAG = {
None: "null",
str: "str",
bytes: "str",
bool: "bool",
int: "int",
float: "float",
list: "seq",
dict: "map",
set: "set",
OrderedDict: "omap",
}
# Prepend full YAML tag prefix
for k, v in PYTHON_TYPE_TO_YAML_TAG.items():
PYTHON_TYPE_TO_YAML_TAG[k] = constants.YAML_TAG_PREFIX + v
def _type_to_tag(type_):
for base in type_.mro():
if base in PYTHON_TYPE_TO_YAML_TAG:
return PYTHON_TYPE_TO_YAML_TAG[base]
return None
def validate_tag(validator, tag_pattern, instance, schema):
"""
Implements the tag validation directive, which checks the
tag against a pattern that may include wildcards. See
`asdf.util.uri_match` for details on the matching behavior.
"""
if hasattr(instance, "_tag"):
instance_tag = instance._tag
else:
# Try tags for known Python builtins
instance_tag = _type_to_tag(type(instance))
if instance_tag is None:
yield ValidationError(
f"mismatched tags, wanted '{tag_pattern}', got unhandled object type '{util.get_class_name(instance)}'"
)
if not util.uri_match(tag_pattern, instance_tag):
yield ValidationError(f"mismatched tags, wanted '{tag_pattern}', got '{instance_tag}'")
def validate_propertyOrder(validator, order, instance, schema):
"""
Stores a value on the `tagged.TaggedDict` instance so that
properties can be written out in the preferred order. In that
sense this isn't really a "validator", but using the `jsonschema`
library's extensible validation system is the easiest way to get
this property assigned.
"""
if not validator.is_type(instance, "object"):
return
if not order:
# propertyOrder may be an empty list
return
instance.property_order = order
def validate_flowStyle(validator, flow_style, instance, schema):
"""
Sets a flag on the `tagged.TaggedList` or `tagged.TaggedDict`
object so that the YAML generator knows which style to use to
write the element. In that sense this isn't really a "validator",
but using the `jsonschema` library's extensible validation system
is the easiest way to get this property assigned.
"""
if not (validator.is_type(instance, "object") or validator.is_type(instance, "array")):
return
instance.flow_style = flow_style
def validate_style(validator, style, instance, schema):
"""
Sets a flag on the `tagged.TaggedString` object so that the YAML
generator knows which style to use to write the string. In that
sense this isn't really a "validator", but using the `jsonschema`
library's extensible validation system is the easiest way to get
this property assigned.
"""
if not validator.is_type(instance, "string"):
return
instance.style = style
def validate_type(validator, types, instance, schema):
"""
PyYAML returns strings that look like dates as datetime objects.
However, as far as JSON is concerned, this is type==string and
format==date-time. That detects for that case and doesn't raise
an error, otherwise falling back to the default type checker.
"""
if isinstance(instance, datetime.datetime) and schema.get("format") == "date-time" and "string" in types:
return
return mvalidators.Draft4Validator.VALIDATORS["type"](validator, types, instance, schema)
def validate_enum(validator, enums, instance, schema):
"""
`asdf.tagged.Tagged` objects will fail in the default enum validator
"""
if isinstance(instance, tagged.Tagged):
instance = instance.base
yield from mvalidators.Draft4Validator.VALIDATORS["enum"](validator, enums, instance, schema)
YAML_VALIDATORS = util.HashableDict(mvalidators.Draft4Validator.VALIDATORS.copy())
YAML_VALIDATORS.update(
{
"tag": validate_tag,
"propertyOrder": validate_propertyOrder,
"flowStyle": validate_flowStyle,
"style": validate_style,
"type": validate_type,
"enum": validate_enum,
}
)
def validate_fill_default(validator, properties, instance, schema):
if not validator.is_type(instance, "object"):
return
for property, subschema in properties.items():
if "default" in subschema:
instance.setdefault(property, subschema["default"])
yield from mvalidators.Draft4Validator.VALIDATORS["properties"](validator, properties, instance, schema)
FILL_DEFAULTS = util.HashableDict()
for key in ("allOf", "items"):
FILL_DEFAULTS[key] = mvalidators.Draft4Validator.VALIDATORS[key]
FILL_DEFAULTS["properties"] = validate_fill_default
def validate_remove_default(validator, properties, instance, schema):
if not validator.is_type(instance, "object"):
return
for property, subschema in properties.items():
if subschema.get("default", None) is not None:
if instance.get(property, None) == subschema["default"]:
del instance[property]
yield from mvalidators.Draft4Validator.VALIDATORS["properties"](validator, properties, instance, schema)
REMOVE_DEFAULTS = util.HashableDict()
for key in ("allOf", "items"):
REMOVE_DEFAULTS[key] = mvalidators.Draft4Validator.VALIDATORS[key]
REMOVE_DEFAULTS["properties"] = validate_remove_default
class _ValidationContext:
"""
Context that tracks (tree node, schema fragment) pairs that have
already been validated.
Instances of this class are context managers that track
how many times they have been entered, and only reset themselves
when exiting the outermost context.
"""
def __init__(self):
self._depth = 0
self._seen = set()
def add(self, instance, schema):
"""
Inform the context that an instance has
been validated against a schema fragment.
"""
self._seen.add(self._make_seen_key(instance, schema))
def seen(self, instance, schema):
"""
Return True if an instance has already been
validated against a schema fragment.
"""
return self._make_seen_key(instance, schema) in self._seen
def __enter__(self):
self._depth += 1
return self
def __exit__(self, exc_type, exc_value, traceback):
self._depth -= 1
if self._depth == 0:
self._seen = set()
def _make_seen_key(self, instance, schema):
return (id(instance), id(schema))
@lru_cache
def _create_validator(validators=YAML_VALIDATORS, visit_repeat_nodes=False):
meta_schema = _load_schema_cached(YAML_SCHEMA_METASCHEMA_ID, extension.get_default_resolver(), False, False)
type_checker = mvalidators.Draft4Validator.TYPE_CHECKER.redefine_many(
{
"array": lambda checker, instance: isinstance(instance, list) or isinstance(instance, tuple),
"integer": lambda checker, instance: not isinstance(instance, bool) and isinstance(instance, Integral),
"string": lambda checker, instance: isinstance(instance, (str, np.str_)),
}
)
id_of = mvalidators.Draft4Validator.ID_OF
ASDFValidator = mvalidators.create(
meta_schema=meta_schema, validators=validators, type_checker=type_checker, id_of=id_of
)
def _patch_init(cls):
original_init = cls.__init__
def __init__(self, *args, **kwargs):
self.ctx = kwargs.pop("ctx", None)
self.serialization_context = kwargs.pop("serialization_context", None)
original_init(self, *args, **kwargs)
cls.__init__ = __init__
def _patch_iter_errors(cls):
original_iter_errors = cls.iter_errors
cls._context = _ValidationContext()
def iter_errors(self, instance, *args, **kwargs):
# We can't validate anything that looks like an external reference,
# since we don't have the actual content, so we just have to defer
# it for now. If the user cares about complete validation, they
# can call `AsdfFile.resolve_references`.
with self._context:
if self._context.seen(instance, self.schema):
# We've already validated this instance against this schema,
# no need to do it again.
return
if not visit_repeat_nodes:
self._context.add(instance, self.schema)
if (isinstance(instance, dict) and "$ref" in instance) or isinstance(instance, reference.Reference):
return
if not self.schema:
tag = getattr(instance, "_tag", None)
if tag is not None:
if self.serialization_context.extension_manager.handles_tag_definition(tag):
tag_def = self.serialization_context.extension_manager.get_tag_definition(tag)
schema_uris = tag_def.schema_uris
else:
schema_uris = [self.ctx.tag_mapping(tag)]
if schema_uris[0] == tag:
schema_uris = []
# Must validate against all schema_uris
for schema_uri in schema_uris:
try:
with self.resolver.resolving(schema_uri) as resolved:
yield from self.descend(instance, resolved)
except RefResolutionError:
warnings.warn(f"Unable to locate schema file for '{tag}': '{schema_uri}'", AsdfWarning)
if isinstance(instance, dict):
for val in instance.values():
yield from self.iter_errors(val)
elif isinstance(instance, list):
for val in instance:
yield from self.iter_errors(val)
else:
yield from original_iter_errors(self, instance)
cls.iter_errors = iter_errors
_patch_init(ASDFValidator)
_patch_iter_errors(ASDFValidator)
return ASDFValidator
@lru_cache
def _load_schema(url):
if url.startswith("http://") or url.startswith("https://") or url.startswith("asdf://"):
raise FileNotFoundError(f"Unable to fetch schema from non-file URL: {url}")
with generic_io.get_file(url) as fd:
if isinstance(url, str) and url.endswith("json"):
json_data = fd.read().decode("utf-8")
result = json.loads(json_data, object_pairs_hook=OrderedDict)
else:
# The following call to yaml.load is safe because we're
# using a loader that inherits from pyyaml's SafeLoader.
result = yaml.load(fd, Loader=yamlutil.AsdfLoader) # nosec
return result, fd.uri
def _make_schema_loader(resolver):
def load_schema(url):
# Check if this is a URI provided by the new
# Mapping API:
resource_manager = get_config().resource_manager
if url not in resource_manager:
# Allow the resolvers to do their thing, in case they know
# how to turn this string into a URI that the resource manager
# recognizes.
url = resolver(str(url))
if url in resource_manager:
content = resource_manager[url]
# The jsonschema metaschemas are JSON, but pyyaml
# doesn't mind.
# The following call to yaml.load is safe because we're
# using a loader that inherits from pyyaml's SafeLoader.
result = yaml.load(content, Loader=yamlutil.AsdfLoader) # nosec
return result, url
# If not, this must be a URL (or missing). Fall back to fetching
# the schema the old way:
return _load_schema(url)
return load_schema
def _make_resolver(url_mapping):
handlers = {}
schema_loader = _make_schema_loader(url_mapping)
def get_schema(url):
return schema_loader(url)[0]
for x in ["http", "https", "file", "tag", "asdf"]:
handlers[x] = get_schema
# Supplying our own implementation of urljoin_cache
# allows asdf:// URIs to be resolved correctly.
urljoin_cache = lru_cache(1024)(patched_urllib_parse.urljoin)
# We set cache_remote=False here because we do the caching of
# remote schemas here in `load_schema`, so we don't need
# jsonschema to do it on our behalf. Setting it to `True`
# counterintuitively makes things slower.
return mvalidators.RefResolver(
"",
{},
cache_remote=False,
handlers=handlers,
urljoin_cache=urljoin_cache,
)
@lru_cache
def load_custom_schema(url):
warnings.warn(
"The 'load_custom_schema(...)' function is deprecated. Use 'load_schema' instead.", AsdfDeprecationWarning
)
return load_schema(url, resolve_references=True)
def load_schema(url, resolver=None, resolve_references=False, resolve_local_refs=False):
"""
Load a schema from the given URL.
Parameters
----------
url : str
The path to the schema
resolver : callable, optional
A callback function used to map URIs to other URIs. The
callable must take a string and return a string or `None`.
This is useful, for example, when a remote resource has a
mirror on the local filesystem that you wish to use.
resolve_references : bool, optional
If `True`, resolve all `$ref` references.
resolve_local_refs : bool, optional
If `True`, resolve all `$ref` references that refer to other objects
within the same schema. This will automatically be handled when passing
`resolve_references=True`, but it may be desirable in some cases to
control local reference resolution separately.
This parameter is deprecated.
"""
if resolve_local_refs is True:
warnings.warn("The 'resolve_local_refs' parameter is deprecated.", AsdfDeprecationWarning)
if resolver is None:
# We can't just set this as the default in load_schema's definition
# because invoking get_default_resolver at import time leads to a circular import.
resolver = extension.get_default_resolver()
# We want to cache the work that went into constructing the schema, but returning
# the same object is treacherous, because users who mutate the result will not
# expect that they're changing the schema everywhere.
return copy.deepcopy(_load_schema_cached(url, resolver, resolve_references, resolve_local_refs))
def _safe_resolve(resolver, json_id, uri):
"""
This function handles the tricky task of resolving a schema URI
in the presence of both new and legacy extensions.
There are two senses of "resolve" here: one is to resolve the URI
to a file:// URL using the legacy extension resolver object. The other
is to resolve relative URIs against the id of the current schema document,
which is what generic_io.resolve_uri does.
For URIs associated with new-style extensions, we want to resolve with
generic_io.resolve_uri, but not with the resolver object, otherwise we risk
mangling URIs that share a prefix with a resolver mapping.
"""
# We can't use urllib.parse here because tag: URIs don't
# parse correctly.
parts = uri.split("#")
base = parts[0]
if len(parts) > 1:
fragment = parts[1]
else:
fragment = ""
# The generic_io.resolve_uri method cannot operate on tag: URIs.
# New-style extensions don't support $ref with a tag URI target anyway,
# so it's safe to feed this through the resolver right away.
if base.startswith("tag:"):
base = resolver(base)
# Resolve relative URIs (e.g., #foo/bar, ../foo/bar) against
# the current schema id.
base = generic_io.resolve_uri(json_id, base)
# Use the resolver object only if the URI does not belong to one
# of the new-style extensions.
if base not in get_config().resource_manager:
base = resolver(base)
return base, fragment
@lru_cache
def _load_schema_cached(url, resolver, resolve_references, resolve_local_refs):
loader = _make_schema_loader(resolver)
schema, url = loader(url)
if resolve_references or resolve_local_refs:
def resolve_refs(node, json_id):
if json_id is None:
json_id = url
if isinstance(node, dict) and "$ref" in node:
suburl_base, suburl_fragment = _safe_resolve(resolver, json_id, node["$ref"])
if suburl_base == url or suburl_base == schema.get("id"):
# This is a local ref, which we'll resolve in both cases.
subschema = schema
elif resolve_references:
# Only resolve non-local refs when the flag is set.
subschema = load_schema(suburl_base, resolver, True)
else:
# Otherwise return the $ref unmodified.
return node
return reference.resolve_fragment(subschema, suburl_fragment)
else:
return node
schema = treeutil.walk_and_modify(schema, resolve_refs)
return schema
def get_validator(
schema={},
ctx=None,
validators=None,
url_mapping=None,
*args,
_visit_repeat_nodes=False,
_serialization_context=None,
**kwargs,
):
"""
Get a JSON schema validator object for the given schema.
The additional *args and **kwargs are passed along to
`~jsonschema.Validator.validate`.
Parameters
----------
schema : schema, optional
Explicit schema to use. If not provided, the schema to use
is determined by the tag on instance (or subinstance).
ctx : AsdfFile context
Used to resolve tags and urls
validators : dict, optional
A dictionary mapping properties to validators to use (instead
of the built-in ones and ones provided by extension types).
url_mapping : resolver.Resolver, optional
A resolver to convert remote URLs into local ones.
_visit_repeat_nodes : bool, optional
Force the validator to visit nodes that it has already
seen. This flag is a temporary hack to support a specific
project that uses a custom validator to update a .fits file.
Setting `True` is discouraged and will lead to RecursionError
in trees containing reference cycles.
Returns
-------
validator : jsonschema.Validator
"""
if ctx is None:
from .asdf import AsdfFile
ctx = AsdfFile()
if _serialization_context is None:
_serialization_context = ctx._create_serialization_context()
if validators is None:
validators = util.HashableDict(YAML_VALIDATORS.copy())
validators.update(ctx.extension_list.validators)
kwargs["resolver"] = _make_resolver(url_mapping)
# We don't just call validators.validate() directly here, because
# that validates the schema itself, wasting a lot of time (at the
# time of this writing, it was half of the runtime of the unit
# test suite!!!). Instead, we assume that the schemas are valid
# through the running of the unit tests, not at run time.
cls = _create_validator(validators=validators, visit_repeat_nodes=_visit_repeat_nodes)
return cls(schema, *args, ctx=ctx, serialization_context=_serialization_context, **kwargs)
def _validate_large_literals(instance, reading):
"""
Validate that the tree has no large numeric literals.
"""
def _validate(value):
if value <= constants.MAX_NUMBER and value >= constants.MIN_NUMBER:
return
if reading:
warnings.warn(
f"Invalid integer literal value {value} detected while reading file. "
"The value has been read safely, but the file should be "
"fixed.",
AsdfWarning,
)
else:
raise ValidationError(f"Integer value {value} is too large to safely represent as a literal in ASDF")
if isinstance(instance, Integral):
_validate(instance)
elif isinstance(instance, Mapping):
for key in instance:
if isinstance(key, Integral):
_validate(key)
def _validate_mapping_keys(instance, reading):
"""
Validate that mappings do not contain illegal key types
(as of ASDF Standard 1.6.0, only str, int, and bool are
permitted).
"""
if not isinstance(instance, Mapping):
return
for key in instance:
if isinstance(key, tagged.Tagged) or not isinstance(key, (str, int, bool)):
if reading:
warnings.warn(
f"Invalid mapping key {key} detected while reading file. "
"The value has been read safely, but the file should be "
"fixed.",
AsdfWarning,
)
else:
raise ValidationError(f"Mapping key {key} is not permitted. Valid types: str, int, bool.")
[docs]def validate(instance, ctx=None, schema={}, validators=None, reading=False, *args, **kwargs):
"""
Validate the given instance (which must be a tagged tree) against
the appropriate schema. The schema itself is located using the
tag on the instance.
The additional ``*args`` and ``**kwargs`` are passed along to
`~jsonschema.Validator.validate`.
Parameters
----------
instance : tagged tree
ctx : AsdfFile context
Used to resolve tags and urls
schema : schema, optional
Explicit schema to use. If not provided, the schema to use
is determined by the tag on instance (or subinstance).
validators : dict, optional
A dictionary mapping properties to validators to use (instead
of the built-in ones and ones provided by extension types).
reading: bool, optional
Indicates whether validation is being performed when the file is being
read. This is useful to allow for different validation behavior when
reading vs writing files.
"""
if ctx is None:
from .asdf import AsdfFile
ctx = AsdfFile()
validator = get_validator(schema, ctx, validators, ctx.resolver, *args, **kwargs)
validator.validate(instance)
additional_validators = [_validate_large_literals]
if ctx.version >= versioning.RESTRICTED_KEYS_MIN_VERSION:
additional_validators.append(_validate_mapping_keys)
def _callback(instance):
for validator in additional_validators:
validator(instance, reading)
treeutil.walk(instance, _callback)
[docs]def fill_defaults(instance, ctx, reading=False):
"""
For any default values in the schema, add them to the tree if they
don't exist.
Parameters
----------
instance : tagged tree
ctx : AsdfFile context
Used to resolve tags and urls
reading: bool, optional
Indicates whether the ASDF file is being read (in contrast to being
written).
"""
validate(instance, ctx, validators=FILL_DEFAULTS, reading=reading)
[docs]def remove_defaults(instance, ctx):
"""
For any values in the tree that are the same as the default values
specified in the schema, remove them from the tree.
Parameters
----------
instance : tagged tree
ctx : AsdfFile context
Used to resolve tags and urls
"""
validate(instance, ctx, validators=REMOVE_DEFAULTS)
[docs]def check_schema(schema, validate_default=True):
"""
Check a given schema to make sure it is valid YAML schema.
Parameters
----------
schema : dict
The schema object, as returned by ``load_schema``.
validate_default : bool, optional
Set to `True` to validate the content of the default
field against the schema.
"""
validators = util.HashableDict(mvalidators.Draft4Validator.VALIDATORS.copy())
if validate_default:
# The jsonschema library doesn't validate defaults
# on its own.
instance_validator = get_validator(schema)
instance_scope = schema.get("id", "")
def _validate_default(validator, default, instance, schema):
if not validator.is_type(instance, "object"):
return
if "default" in instance:
instance_validator.resolver.push_scope(instance_scope)
try:
yield from instance_validator.descend(instance["default"], instance)
finally:
instance_validator.resolver.pop_scope()
validators.update({"default": _validate_default})
def applicable_validators(schema):
items = list(schema.items())
items.append(("default", ""))
return items
else:
applicable_validators = methodcaller("items")
meta_schema_id = schema.get("$schema", YAML_SCHEMA_METASCHEMA_ID)
meta_schema = _load_schema_cached(meta_schema_id, extension.get_default_resolver(), False, False)
resolver = _make_resolver(extension.get_default_resolver())
cls = mvalidators.create(
meta_schema=meta_schema,
validators=validators,
type_checker=mvalidators.Draft4Validator.TYPE_CHECKER,
id_of=mvalidators.Draft4Validator.ID_OF,
applicable_validators=applicable_validators,
)
validator = cls(meta_schema, resolver=resolver)
validator.validate(schema)