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stringlengths 0
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44,042 | jsons.deserializers.default_timedelta | default_timedelta_deserializer |
Deserialize a float to a timedelta instance.
:param obj: the float that is to be deserialized.
:param cls: not used.
:param kwargs: not used.
:return: a ``datetime.timedelta`` instance.
| def default_timedelta_deserializer(obj: float,
cls: type = float,
**kwargs) -> timedelta:
"""
Deserialize a float to a timedelta instance.
:param obj: the float that is to be deserialized.
:param cls: not used.
:param kwargs: not used.
:return: a ``datetime.timedelta`` instance.
"""
return timedelta(seconds=obj)
| (obj: float, cls: type = <class 'float'>, **kwargs) -> datetime.timedelta | [
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|
44,043 | jsons.serializers.default_timedelta | default_timedelta_serializer |
Serialize the given timedelta instance to a float holding the total
seconds.
:param obj: the timedelta instance that is to be serialized.
:param kwargs: not used.
:return: ``timedelta`` as a float.
| def default_timedelta_serializer(obj: timedelta, **kwargs) -> float:
"""
Serialize the given timedelta instance to a float holding the total
seconds.
:param obj: the timedelta instance that is to be serialized.
:param kwargs: not used.
:return: ``timedelta`` as a float.
"""
return obj.total_seconds()
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|
44,044 | jsons.deserializers.default_timezone | default_timezone_deserializer |
Deserialize a dict to a timezone instance.
:param obj: the dict that is to be deserialized.
:param cls: not used.
:param kwargs: not used.
:return: a ``datetime.timezone`` instance.
| def default_timezone_deserializer(obj: dict,
cls: type = timezone,
**kwargs) -> timezone:
"""
Deserialize a dict to a timezone instance.
:param obj: the dict that is to be deserialized.
:param cls: not used.
:param kwargs: not used.
:return: a ``datetime.timezone`` instance.
"""
return timezone(load(obj['offset'], timedelta), obj['name'])
| (obj: dict, cls: type = <class 'datetime.timezone'>, **kwargs) -> datetime.timezone | [
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|
44,045 | jsons.serializers.default_timezone | default_timezone_serializer |
Serialize the given timezone instance to a dict holding the total
seconds.
:param obj: the timezone instance that is to be serialized.
:param kwargs: not used.
:return: ``timezone`` as a dict.
| def default_timezone_serializer(obj: timezone, **kwargs) -> dict:
"""
Serialize the given timezone instance to a dict holding the total
seconds.
:param obj: the timezone instance that is to be serialized.
:param kwargs: not used.
:return: ``timezone`` as a dict.
"""
name = obj.tzname(None)
offset = dump(obj.utcoffset(None), **kwargs)
return {
'name': name,
'offset': offset
}
| (obj: datetime.timezone, **kwargs) -> dict | [
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|
44,046 | jsons.deserializers.default_tuple | default_tuple_deserializer |
Deserialize a (JSON) list into a tuple by deserializing all items of that
list.
:param obj: the tuple that needs deserializing.
:param cls: the type optionally with a generic (e.g. Tuple[str, int]).
:param kwargs: any keyword arguments.
:return: a deserialized tuple instance.
| def default_tuple_deserializer(obj: list,
cls: type = None,
*,
key_transformer: Optional[Callable[[str], str]] = None,
**kwargs) -> object:
"""
Deserialize a (JSON) list into a tuple by deserializing all items of that
list.
:param obj: the tuple that needs deserializing.
:param cls: the type optionally with a generic (e.g. Tuple[str, int]).
:param kwargs: any keyword arguments.
:return: a deserialized tuple instance.
"""
if hasattr(cls, '_fields'):
return default_namedtuple_deserializer(obj, cls, key_transformer=key_transformer, **kwargs)
cls_args = get_args(cls)
if cls_args:
tuple_types = getattr(cls, '__tuple_params__', cls_args)
if tuple_with_ellipsis(cls):
tuple_types = [tuple_types[0]] * len(obj)
list_ = [load(value, tuple_types[i], **kwargs)
for i, value in enumerate(obj)]
else:
list_ = [load(value, **kwargs) for i, value in enumerate(obj)]
return tuple(list_)
| (obj: list, cls: Optional[type] = None, *, key_transformer: Optional[Callable[[str], str]] = None, **kwargs) -> object | [
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|
44,047 | jsons.serializers.default_tuple | default_tuple_serializer |
Serialize the given ``obj`` to a list of serialized objects.
:param obj: the tuple that is to be serialized.
:param cls: the type of the ``obj``.
:param kwargs: any keyword arguments that may be given to the serialization
process.
:return: a list of which all elements are serialized.
| def default_tuple_serializer(obj: tuple,
cls: Optional[type] = None,
**kwargs) -> Union[list, dict]:
"""
Serialize the given ``obj`` to a list of serialized objects.
:param obj: the tuple that is to be serialized.
:param cls: the type of the ``obj``.
:param kwargs: any keyword arguments that may be given to the serialization
process.
:return: a list of which all elements are serialized.
"""
if hasattr(obj, '_fields'):
return default_namedtuple_serializer(obj, **kwargs)
cls_ = cls
if cls and tuple_with_ellipsis(cls):
cls_ = Tuple[(get_args(cls)[0],) * len(obj)]
return default_iterable_serializer(obj, cls_, **kwargs)
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|
44,048 | jsons.deserializers.default_union | default_union_deserializer |
Deserialize an object to any matching type of the given union. The first
successful deserialization is returned.
:param obj: The object that needs deserializing.
:param cls: The Union type with a generic (e.g. Union[str, int]).
:param kwargs: Any keyword arguments that are passed through the
deserialization process.
:return: An object of the first type of the Union that could be
deserialized successfully.
| def default_union_deserializer(obj: object, cls: Union, **kwargs) -> object:
"""
Deserialize an object to any matching type of the given union. The first
successful deserialization is returned.
:param obj: The object that needs deserializing.
:param cls: The Union type with a generic (e.g. Union[str, int]).
:param kwargs: Any keyword arguments that are passed through the
deserialization process.
:return: An object of the first type of the Union that could be
deserialized successfully.
"""
for sub_type in get_union_params(cls):
try:
return load(obj, sub_type, **kwargs)
except JsonsError:
pass # Try the next one.
else:
args_msg = ', '.join([get_class_name(cls_)
for cls_ in get_union_params(cls)])
err_msg = ('Could not match the object of type "{}" to any type of '
'the Union: {}'.format(type(obj).__name__, args_msg))
raise DeserializationError(err_msg, obj, cls)
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|
44,049 | jsons.serializers.default_union | default_union_serializer |
Serialize an object to any matching type of the given union. The first
successful serialization is returned.
:param obj: The object that is to be serialized.
:param cls: The Union type with a generic (e.g. Union[str, int]).
:param kwargs: Any keyword arguments that are passed through the
serialization process.
:return: An object of the first type of the Union that could be
serialized successfully.
| def default_union_serializer(obj: object, cls: Union, **kwargs) -> object:
"""
Serialize an object to any matching type of the given union. The first
successful serialization is returned.
:param obj: The object that is to be serialized.
:param cls: The Union type with a generic (e.g. Union[str, int]).
:param kwargs: Any keyword arguments that are passed through the
serialization process.
:return: An object of the first type of the Union that could be
serialized successfully.
"""
sub_types = get_union_params(cls)
# Cater for Optional[...]/Union[None, ...] first to avoid blindly
# string-ifying None in later serializers.
if obj is None and NoneType in sub_types:
return obj
for sub_type in sub_types:
try:
return dump(obj, sub_type, **kwargs)
except JsonsError:
pass # Try the next one.
else:
args_msg = ', '.join([get_class_name(cls_)
for cls_ in get_union_params(cls)])
err_msg = ('Could not match the object of type "{}" to any type of '
'the Union: {}'.format(type(obj), args_msg))
raise SerializationError(err_msg)
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|
44,050 | jsons.deserializers.default_uuid | default_uuid_deserializer |
Deserialize a UUID. Expected format for string is specified in RFC 4122.
e.g. '12345678-1234-1234-1234-123456789abc'
:param obj: the string that is to be deserialized.
:param cls: not used.
:param kwargs: any keyword arguments.
:return: the deserialized obj.
| def default_uuid_deserializer(obj: str,
cls: Optional[type] = None,
**kwargs) -> UUID:
"""
Deserialize a UUID. Expected format for string is specified in RFC 4122.
e.g. '12345678-1234-1234-1234-123456789abc'
:param obj: the string that is to be deserialized.
:param cls: not used.
:param kwargs: any keyword arguments.
:return: the deserialized obj.
"""
return UUID(obj)
| (obj: str, cls: Optional[type] = None, **kwargs) -> uuid.UUID | [
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|
44,051 | jsons.serializers.default_uuid | default_uuid_serializer |
Serialize the given obj. By default, it is serialized as specified in RFC 4122.
e.g. '12345678-1234-1234-1234-123456789abc'
:param obj: an instance of an uuid.UUID.
:param kwargs: any keyword arguments.
:return: ``obj`` serialized as a string.
| def default_uuid_serializer(obj: UUID, **kwargs) -> str:
"""
Serialize the given obj. By default, it is serialized as specified in RFC 4122.
e.g. '12345678-1234-1234-1234-123456789abc'
:param obj: an instance of an uuid.UUID.
:param kwargs: any keyword arguments.
:return: ``obj`` serialized as a string.
"""
return str(obj)
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|
44,052 | jsons.deserializers.default_zone_info | default_zone_info_deserializer |
Deserialize a ZoneInfo.
:param obj: a serialized ZoneInfo object.
:return: an instance of ZoneInfo.
| def default_zone_info_deserializer(obj: ZoneInfo, *_, **__) -> ZoneInfo:
"""
Deserialize a ZoneInfo.
:param obj: a serialized ZoneInfo object.
:return: an instance of ZoneInfo.
"""
return ZoneInfo(obj['key'])
| (obj: zoneinfo.ZoneInfo, *_, **__) -> zoneinfo.ZoneInfo | [
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|
44,053 | jsons.serializers.default_zone_info | default_zone_info_serializer |
Serialize a ZoneInfo object.
:return: a serialized ZoneInfo instance.
| def default_zone_info_serializer(obj: ZoneInfo, *_, **__) -> Dict[str, str]:
"""
Serialize a ZoneInfo object.
:return: a serialized ZoneInfo instance.
"""
return {'key': obj.key}
| (obj: zoneinfo.ZoneInfo, *_, **__) -> Dict[str, str] | [
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|
44,055 | jsons._dump_impl | dump |
Serialize the given ``obj`` to a JSON equivalent type (e.g. dict, list,
int, ...).
The way objects are serialized can be finetuned by setting serializer
functions for the specific type using ``set_serializer``.
You can also provide ``cls`` to specify that ``obj`` needs to be serialized
as if it was of type ``cls`` (meaning to only take into account attributes
from ``cls``). The type ``cls`` must have a ``__slots__`` defined. Any type
will do, but in most cases you may want ``cls`` to be a base class of
``obj``.
:param obj: a Python instance of any sort.
:param cls: if given, ``obj`` will be dumped as if it is of type ``type``.
:param strict: a bool to determine if the serializer should be strict
(i.e. only dumping stuff that is known to ``cls``).
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:param kwargs: the keyword args are passed on to the serializer function.
:return: the serialized obj as a JSON type.
| def dump(obj: object,
cls: Optional[type] = None,
*,
strict: bool = False,
fork_inst: Optional[type] = StateHolder,
**kwargs) -> object:
"""
Serialize the given ``obj`` to a JSON equivalent type (e.g. dict, list,
int, ...).
The way objects are serialized can be finetuned by setting serializer
functions for the specific type using ``set_serializer``.
You can also provide ``cls`` to specify that ``obj`` needs to be serialized
as if it was of type ``cls`` (meaning to only take into account attributes
from ``cls``). The type ``cls`` must have a ``__slots__`` defined. Any type
will do, but in most cases you may want ``cls`` to be a base class of
``obj``.
:param obj: a Python instance of any sort.
:param cls: if given, ``obj`` will be dumped as if it is of type ``type``.
:param strict: a bool to determine if the serializer should be strict
(i.e. only dumping stuff that is known to ``cls``).
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:param kwargs: the keyword args are passed on to the serializer function.
:return: the serialized obj as a JSON type.
"""
cls_ = cls or obj.__class__
serializer = get_serializer(cls_, fork_inst)
# Is this the initial call or a nested?
initial = kwargs.get('_initial', True)
kwargs_ = {
'fork_inst': fork_inst,
'_initial': False,
'strict': strict,
**kwargs
}
announce_class(cls_, fork_inst=fork_inst)
return _do_dump(obj, serializer, cls, initial, kwargs_)
| (obj: object, cls: Optional[type] = None, *, strict: bool = False, fork_inst: Optional[type] = <class 'jsons._common_impl.StateHolder'>, **kwargs) -> object | [
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|
44,056 | jsons._dump_impl | dumpb |
Extend ``json.dumps``, allowing any Python instance to be dumped to bytes.
Any extra (keyword) arguments are passed on to ``json.dumps``.
:param obj: the object that is to be dumped to bytes.
:param encoding: the encoding that is used to transform to bytes.
:param jdkwargs: extra keyword arguments for ``json.dumps`` (not
``jsons.dumps``!)
:param args: extra arguments for ``jsons.dumps``.
:param kwargs: Keyword arguments that are passed on through the
serialization process.
passed on to the serializer function.
:return: ``obj`` as ``bytes``.
| def dumpb(obj: object,
encoding: str = 'utf-8',
jdkwargs: Optional[Dict[str, object]] = None,
*args,
**kwargs) -> bytes:
"""
Extend ``json.dumps``, allowing any Python instance to be dumped to bytes.
Any extra (keyword) arguments are passed on to ``json.dumps``.
:param obj: the object that is to be dumped to bytes.
:param encoding: the encoding that is used to transform to bytes.
:param jdkwargs: extra keyword arguments for ``json.dumps`` (not
``jsons.dumps``!)
:param args: extra arguments for ``jsons.dumps``.
:param kwargs: Keyword arguments that are passed on through the
serialization process.
passed on to the serializer function.
:return: ``obj`` as ``bytes``.
"""
jdkwargs = jdkwargs or {}
dumped_dict = dump(obj, *args, **kwargs)
dumped_str = json.dumps(dumped_dict, **jdkwargs)
return dumped_str.encode(encoding=encoding)
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|
44,057 | jsons._dump_impl | dumps |
Extend ``json.dumps``, allowing any Python instance to be dumped to a
string. Any extra (keyword) arguments are passed on to ``json.dumps``.
:param obj: the object that is to be dumped to a string.
:param jdkwargs: extra keyword arguments for ``json.dumps`` (not
``jsons.dumps``!)
:param args: extra arguments for ``jsons.dumps``.
:param kwargs: Keyword arguments that are passed on through the
serialization process.
passed on to the serializer function.
:return: ``obj`` as a ``str``.
| def dumps(obj: object,
jdkwargs: Optional[Dict[str, object]] = None,
*args,
**kwargs) -> str:
"""
Extend ``json.dumps``, allowing any Python instance to be dumped to a
string. Any extra (keyword) arguments are passed on to ``json.dumps``.
:param obj: the object that is to be dumped to a string.
:param jdkwargs: extra keyword arguments for ``json.dumps`` (not
``jsons.dumps``!)
:param args: extra arguments for ``jsons.dumps``.
:param kwargs: Keyword arguments that are passed on through the
serialization process.
passed on to the serializer function.
:return: ``obj`` as a ``str``.
"""
jdkwargs = jdkwargs or {}
dumped = dump(obj, *args, **kwargs)
return json.dumps(dumped, **jdkwargs)
| (obj: object, jdkwargs: Optional[Dict[str, object]] = None, *args, **kwargs) -> str | [
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|
44,059 | jsons._fork_impl | fork |
Fork from the given ``StateHolder`` to create a separate "branch" of
serializers and deserializers.
:param fork_inst: The ``StateHolder`` on which the new fork is based.
:param name: The ``__name__`` of the new ``type``.
:return: A "fork inst" that can be used to separately store
(de)serializers from the regular ``StateHolder``.
| def fork(
fork_inst: Type[T] = StateHolder,
name: Optional[str] = None) -> Type[T]:
"""
Fork from the given ``StateHolder`` to create a separate "branch" of
serializers and deserializers.
:param fork_inst: The ``StateHolder`` on which the new fork is based.
:param name: The ``__name__`` of the new ``type``.
:return: A "fork inst" that can be used to separately store
(de)serializers from the regular ``StateHolder``.
"""
fork_inst._fork_counter += 1
if name:
class_name = name
else:
class_name = '{}_fork{}'.format(
get_class_name(fork_inst),
fork_inst._fork_counter
)
result = type(class_name, (fork_inst,), {})
result._classes_serializers = fork_inst._classes_serializers.copy()
result._classes_deserializers = fork_inst._classes_deserializers.copy()
result._serializers = fork_inst._serializers.copy()
result._deserializers = fork_inst._deserializers.copy()
result._fork_counter = 0
result._suppress_warnings = fork_inst._suppress_warnings
result._suppressed_warnings = fork_inst._suppressed_warnings.copy()
return result
| (fork_inst: Type[~T] = <class 'jsons._common_impl.StateHolder'>, name: Optional[str] = None) -> Type[~T] | [
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|
44,061 | jsons._load_impl | load |
Deserialize the given ``json_obj`` to an object of type ``cls``. If the
contents of ``json_obj`` do not match the interface of ``cls``, a
DeserializationError is raised.
If ``json_obj`` contains a value that belongs to a custom class, there must
be a type hint present for that value in ``cls`` to let this function know
what type it should deserialize that value to.
**Example**:
>>> from typing import List
>>> import jsons
>>> class Person:
... # No type hint required for name
... def __init__(self, name):
... self.name = name
>>> class Family:
... # Person is a custom class, use a type hint
... def __init__(self, persons: List[Person]):
... self.persons = persons
>>> loaded = jsons.load({'persons': [{'name': 'John'}]}, Family)
>>> loaded.persons[0].name
'John'
If no ``cls`` is given, a dict is simply returned, but contained values
(e.g. serialized ``datetime`` values) are still deserialized.
If `strict` mode is off and the type of `json_obj` exactly matches `cls`
then `json_obj` is simply returned.
:param json_obj: the dict that is to be deserialized.
:param cls: a matching class of which an instance should be returned.
:param strict: a bool to determine if the deserializer should be strict
(i.e. fail on a partially deserialized `json_obj` or on `None`).
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:param attr_getters: a ``dict`` that may hold callables that return values
for certain attributes.
:param kwargs: the keyword args are passed on to the deserializer function.
:return: an instance of ``cls`` if given, a dict otherwise.
| def load(
json_obj: object,
cls: Optional[Type[T]] = None,
*,
strict: bool = False,
fork_inst: Optional[type] = StateHolder,
attr_getters: Optional[Dict[str, Callable[[], object]]] = None,
**kwargs) -> T:
"""
Deserialize the given ``json_obj`` to an object of type ``cls``. If the
contents of ``json_obj`` do not match the interface of ``cls``, a
DeserializationError is raised.
If ``json_obj`` contains a value that belongs to a custom class, there must
be a type hint present for that value in ``cls`` to let this function know
what type it should deserialize that value to.
**Example**:
>>> from typing import List
>>> import jsons
>>> class Person:
... # No type hint required for name
... def __init__(self, name):
... self.name = name
>>> class Family:
... # Person is a custom class, use a type hint
... def __init__(self, persons: List[Person]):
... self.persons = persons
>>> loaded = jsons.load({'persons': [{'name': 'John'}]}, Family)
>>> loaded.persons[0].name
'John'
If no ``cls`` is given, a dict is simply returned, but contained values
(e.g. serialized ``datetime`` values) are still deserialized.
If `strict` mode is off and the type of `json_obj` exactly matches `cls`
then `json_obj` is simply returned.
:param json_obj: the dict that is to be deserialized.
:param cls: a matching class of which an instance should be returned.
:param strict: a bool to determine if the deserializer should be strict
(i.e. fail on a partially deserialized `json_obj` or on `None`).
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:param attr_getters: a ``dict`` that may hold callables that return values
for certain attributes.
:param kwargs: the keyword args are passed on to the deserializer function.
:return: an instance of ``cls`` if given, a dict otherwise.
"""
_check_for_none(json_obj, cls)
if _should_skip(json_obj, cls, strict):
validate(json_obj, cls, fork_inst)
return json_obj
if isinstance(cls, str):
cls = get_cls_from_str(cls, json_obj, fork_inst)
original_cls = cls
cls, meta_hints = _check_and_get_cls_and_meta_hints(
json_obj, cls, fork_inst, kwargs.get('_inferred_cls', False))
deserializer = get_deserializer(cls, fork_inst)
# Is this the initial call or a nested?
initial = kwargs.get('_initial', True)
kwargs_ = {
'meta_hints': meta_hints, # Overridable by kwargs.
**kwargs,
'strict': strict,
'fork_inst': fork_inst,
'attr_getters': attr_getters,
'_initial': False,
'_inferred_cls': cls is not original_cls,
}
return _do_load(json_obj, deserializer, cls, initial, **kwargs_)
| (json_obj: object, cls: Optional[Type[~T]] = None, *, strict: bool = False, fork_inst: Optional[type] = <class 'jsons._common_impl.StateHolder'>, attr_getters: Optional[Dict[str, Callable[[], object]]] = None, **kwargs) -> ~T | [
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|
44,062 | jsons._load_impl | loadb |
Extend ``json.loads``, allowing bytes to be loaded into a dict or a Python
instance of type ``cls``. Any extra (keyword) arguments are passed on to
``json.loads``.
:param bytes_: the bytes that are to be loaded.
:param cls: a matching class of which an instance should be returned.
:param encoding: the encoding that is used to transform from bytes.
:param jdkwargs: extra keyword arguments for ``json.loads`` (not
``jsons.loads``!)
:param args: extra arguments for ``jsons.loads``.
:param kwargs: extra keyword arguments for ``jsons.loads``.
:return: a JSON-type object (dict, str, list, etc.) or an instance of type
``cls`` if given.
| def loadb(
bytes_: bytes,
cls: Optional[Type[T]] = None,
encoding: str = 'utf-8',
jdkwargs: Optional[Dict[str, object]] = None,
*args,
**kwargs) -> T:
"""
Extend ``json.loads``, allowing bytes to be loaded into a dict or a Python
instance of type ``cls``. Any extra (keyword) arguments are passed on to
``json.loads``.
:param bytes_: the bytes that are to be loaded.
:param cls: a matching class of which an instance should be returned.
:param encoding: the encoding that is used to transform from bytes.
:param jdkwargs: extra keyword arguments for ``json.loads`` (not
``jsons.loads``!)
:param args: extra arguments for ``jsons.loads``.
:param kwargs: extra keyword arguments for ``jsons.loads``.
:return: a JSON-type object (dict, str, list, etc.) or an instance of type
``cls`` if given.
"""
if not isinstance(bytes_, bytes):
raise DeserializationError('loadb accepts bytes only, "{}" was given'
.format(type(bytes_)), bytes_, cls)
jdkwargs = jdkwargs or {}
str_ = bytes_.decode(encoding=encoding)
return loads(str_, cls, jdkwargs=jdkwargs, *args, **kwargs)
| (bytes_: bytes, cls: Optional[Type[~T]] = None, encoding: str = 'utf-8', jdkwargs: Optional[Dict[str, object]] = None, *args, **kwargs) -> ~T | [
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|
44,063 | jsons._load_impl | loads |
Extend ``json.loads``, allowing a string to be loaded into a dict or a
Python instance of type ``cls``. Any extra (keyword) arguments are passed
on to ``json.loads``.
:param str_: the string that is to be loaded.
:param cls: a matching class of which an instance should be returned.
:param jdkwargs: extra keyword arguments for ``json.loads`` (not
``jsons.loads``!)
:param args: extra arguments for ``jsons.loads``.
:param kwargs: extra keyword arguments for ``jsons.loads``.
:return: a JSON-type object (dict, str, list, etc.) or an instance of type
``cls`` if given.
| def loads(
str_: str,
cls: Optional[Type[T]] = None,
jdkwargs: Optional[Dict[str, object]] = None,
*args,
**kwargs) -> T:
"""
Extend ``json.loads``, allowing a string to be loaded into a dict or a
Python instance of type ``cls``. Any extra (keyword) arguments are passed
on to ``json.loads``.
:param str_: the string that is to be loaded.
:param cls: a matching class of which an instance should be returned.
:param jdkwargs: extra keyword arguments for ``json.loads`` (not
``jsons.loads``!)
:param args: extra arguments for ``jsons.loads``.
:param kwargs: extra keyword arguments for ``jsons.loads``.
:return: a JSON-type object (dict, str, list, etc.) or an instance of type
``cls`` if given.
"""
jdkwargs = jdkwargs or {}
try:
obj = json.loads(str_, **jdkwargs)
except JSONDecodeError as err:
raise DecodeError('Could not load a dict; the given string is not '
'valid JSON.', str_, cls, err) from err
else:
return load(obj, cls, *args, **kwargs)
| (str_: str, cls: Optional[Type[~T]] = None, jdkwargs: Optional[Dict[str, object]] = None, *args, **kwargs) -> ~T | [
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|
44,066 | jsons._lizers_impl | set_deserializer |
Set a deserializer function for the given type. You may override the
default behavior of ``jsons.dump`` by setting a custom deserializer.
The ``func`` argument must take two arguments (i.e. the dict containing the
serialized values and the type that the values should be deserialized into)
and also a ``kwargs`` parameter. For example:
>>> def func(dict_, cls, **kwargs):
... return cls()
You may ask additional arguments between ``cls`` and ``kwargs``.
:param func: the deserializer function.
:param cls: the type or sequence of types this serializer can handle.
:param high_prio: determines the order in which is looked for the callable.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
| def set_deserializer(
func: callable,
cls: Union[type, Sequence[type]],
high_prio: bool = True,
fork_inst: type = StateHolder) -> None:
"""
Set a deserializer function for the given type. You may override the
default behavior of ``jsons.dump`` by setting a custom deserializer.
The ``func`` argument must take two arguments (i.e. the dict containing the
serialized values and the type that the values should be deserialized into)
and also a ``kwargs`` parameter. For example:
>>> def func(dict_, cls, **kwargs):
... return cls()
You may ask additional arguments between ``cls`` and ``kwargs``.
:param func: the deserializer function.
:param cls: the type or sequence of types this serializer can handle.
:param high_prio: determines the order in which is looked for the callable.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
"""
if isinstance(cls, Sequence):
for cls_ in cls:
set_deserializer(func, cls_, high_prio, fork_inst)
elif cls:
index = 0 if high_prio else len(fork_inst._classes_deserializers)
fork_inst._classes_deserializers.insert(index, cls)
cls_name = get_class_name(cls, fully_qualified=True)
fork_inst._deserializers[cls_name.lower()] = func
else:
fork_inst._deserializers['nonetype'] = func
| (func: <built-in function callable>, cls: Union[type, Sequence[type]], high_prio: bool = True, fork_inst: type = <class 'jsons._common_impl.StateHolder'>) -> NoneType | [
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|
44,067 | jsons._lizers_impl | set_serializer |
Set a serializer function for the given type. You may override the default
behavior of ``jsons.load`` by setting a custom serializer.
The ``func`` argument must take one argument (i.e. the object that is to be
serialized) and also a ``kwargs`` parameter. For example:
>>> def func(obj, **kwargs):
... return dict()
You may ask additional arguments between ``cls`` and ``kwargs``.
:param func: the serializer function.
:param cls: the type or sequence of types this serializer can handle.
:param high_prio: determines the order in which is looked for the callable.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
| def set_serializer(
func: callable,
cls: Union[type, Sequence[type]],
high_prio: bool = True,
fork_inst: type = StateHolder) -> None:
"""
Set a serializer function for the given type. You may override the default
behavior of ``jsons.load`` by setting a custom serializer.
The ``func`` argument must take one argument (i.e. the object that is to be
serialized) and also a ``kwargs`` parameter. For example:
>>> def func(obj, **kwargs):
... return dict()
You may ask additional arguments between ``cls`` and ``kwargs``.
:param func: the serializer function.
:param cls: the type or sequence of types this serializer can handle.
:param high_prio: determines the order in which is looked for the callable.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
"""
if isinstance(cls, Sequence):
for cls_ in cls:
set_serializer(func, cls_, high_prio, fork_inst)
elif cls:
index = 0 if high_prio else len(fork_inst._classes_serializers)
fork_inst._classes_serializers.insert(index, cls)
cls_name = get_class_name(cls, fully_qualified=True)
fork_inst._serializers[cls_name.lower()] = func
else:
fork_inst._serializers['nonetype'] = func
| (func: <built-in function callable>, cls: Union[type, Sequence[type]], high_prio: bool = True, fork_inst: type = <class 'jsons._common_impl.StateHolder'>) -> NoneType | [
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|
44,068 | jsons._validation | set_validator |
Set a validator function for the given ``cls``. The function should accept
an instance of the type it should validate and must return ``False`` or
raise any exception in case of a validation failure.
:param func: the function that takes an instance of type ``cls`` and
returns a bool (``True`` if the validation was successful).
:param cls: the type or types that ``func`` is able to validate.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
| def set_validator(
func: Callable[[object], bool],
cls: Union[type, Sequence[type]],
*,
fork_inst: type = StateHolder) -> None:
"""
Set a validator function for the given ``cls``. The function should accept
an instance of the type it should validate and must return ``False`` or
raise any exception in case of a validation failure.
:param func: the function that takes an instance of type ``cls`` and
returns a bool (``True`` if the validation was successful).
:param cls: the type or types that ``func`` is able to validate.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
"""
if isinstance(cls, Sequence):
for cls_ in cls:
set_validator(func, cls=cls_, fork_inst=fork_inst)
else:
cls_name = get_class_name(cls, fully_qualified=True)
fork_inst._validators[cls_name.lower()] = func
fork_inst._classes_validators.append(cls)
| (func: Callable[[object], bool], cls: Union[type, Sequence[type]], *, fork_inst: type = <class 'jsons._common_impl.StateHolder'>) -> NoneType | [
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|
44,070 | jsons._extra_impl | suppress_warning |
Suppress a specific warning that corresponds to the given code (see the
warning).
:param code: the code of the warning that is to be suppressed.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
| def suppress_warning(
code: str,
fork_inst: Optional[type] = StateHolder):
"""
Suppress a specific warning that corresponds to the given code (see the
warning).
:param code: the code of the warning that is to be suppressed.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
"""
fork_inst._suppressed_warnings |= {code}
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|
44,071 | jsons._extra_impl | suppress_warnings |
Suppress (or stop suppressing) warnings altogether.
:param do_suppress: if ``True``, warnings will be suppressed from now on.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
| def suppress_warnings(
do_suppress: Optional[bool] = True,
fork_inst: Optional[type] = StateHolder):
"""
Suppress (or stop suppressing) warnings altogether.
:param do_suppress: if ``True``, warnings will be suppressed from now on.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
"""
fork_inst._suppress_warnings = do_suppress
| (do_suppress: Optional[bool] = True, fork_inst: Optional[type] = <class 'jsons._common_impl.StateHolder'>) | [
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|
44,075 | jsons._transform_impl | transform |
Transform the given ``obj`` to an instance of ``cls``.
:param obj: the object that is to be transformed into a type of ``cls``.
:param cls: the type that ``obj`` is to be transformed into.
:param mapper: a callable that takes the dumped dict and returns a mapped
dict right before it is loaded into ``cls``.
:param dump_cls: the ``cls`` parameter that is given to ``dump``.
:param dump_args: the ``args`` parameter that is given to ``dump``.
:param dump_kwargs: the ``kwargs`` parameter that is given to ``dump``.
:param kwargs: any keyword arguments that are given to ``load``.
:return: an instance of ``cls``.
| def transform(
obj: object,
cls: Type[T],
*,
mapper: Callable[[Dict[str, Any]], Dict[str, Any]] = None,
dump_cls: type = None,
dump_args: List[Any] = None,
dump_kwargs: List[Dict[str, Any]] = None,
**kwargs) -> T:
"""
Transform the given ``obj`` to an instance of ``cls``.
:param obj: the object that is to be transformed into a type of ``cls``.
:param cls: the type that ``obj`` is to be transformed into.
:param mapper: a callable that takes the dumped dict and returns a mapped
dict right before it is loaded into ``cls``.
:param dump_cls: the ``cls`` parameter that is given to ``dump``.
:param dump_args: the ``args`` parameter that is given to ``dump``.
:param dump_kwargs: the ``kwargs`` parameter that is given to ``dump``.
:param kwargs: any keyword arguments that are given to ``load``.
:return: an instance of ``cls``.
"""
dump_args_ = dump_args or []
dump_kwargs_ = dump_kwargs or {}
dumped = dump(obj, dump_cls, *dump_args_, **dump_kwargs_)
mapper_ = mapper or (lambda x: x)
dumped_mapped = mapper_(dumped)
return load(dumped_mapped, cls, **kwargs)
| (obj: object, cls: Type[~T], *, mapper: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, dump_cls: Optional[type] = None, dump_args: Optional[List[Any]] = None, dump_kwargs: Optional[List[Dict[str, Any]]] = None, **kwargs) -> ~T | [
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|
44,076 | jsons._validation | validate |
Validate the given ``obj`` with the validator that was registered for
``cls``. Raises a ``ValidationError`` if the validation failed.
:param obj: the object that is to be validated.
:param cls: the type of which the validator function was registered.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
| def validate(
obj: object,
cls: type,
fork_inst: type = StateHolder) -> None:
"""
Validate the given ``obj`` with the validator that was registered for
``cls``. Raises a ``ValidationError`` if the validation failed.
:param obj: the object that is to be validated.
:param cls: the type of which the validator function was registered.
:param fork_inst: if given, it uses this fork of ``JsonSerializable``.
:return: None.
"""
validator = get_validator(cls, fork_inst)
result = True
msg = 'Validation failed.'
if validator:
try:
result = validator(obj)
except Exception as err:
if err.args:
msg = err.args[0]
result = False
if not result:
raise ValidationError(msg)
| (obj: object, cls: type, fork_inst: type = <class 'jsons._common_impl.StateHolder'>) -> NoneType | [
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]
|
44,077 | dataclass_array.array_dataclass | DataclassArray | Dataclass which behaves like an array.
Usage:
```python
class Square(DataclassArray):
pos: f32['*shape 2']
scale: f32['*shape']
name: str
# Create 3 squares batched
p = Square(
pos=[[x0, y0], [x1, y1], [x2, y2]],
scale=[scale0, scale1, scale2],
name='my_square',
)
p.shape == (3,)
p.pos.shape == (3, 2)
p[0] == Square(pos=[x0, y0], scale=scale0)
p = p.reshape((3, 1)) # Reshape the inner-shape
p.shape == (3, 1)
p.pos.shape == (3, 1, 2)
p.name == 'my_square'
```
`DataclassArray` has 2 types of fields:
* Array fields: Fields batched like numpy arrays, with reshape, slicing,...
(`pos` and `scale` in the above example).
* Static fields: Other non-numpy field. Are not modified by reshaping,... (
`name` in the above example).
Static fields are also ignored in `jax.tree_map`.
`DataclassArray` detect array fields if either:
* The typing annotation is a `etils.array_types` annotation (in which
case shape/dtype are automatically infered from the typing annotation)
Example: `x: f32[..., 3]`
* The typing annotation is another `dca.DataclassArray` (in which case
`my_dataclass.field.shape == my_dataclass.shape`)
Example: `x: MyDataclass`
* The field is explicitly defined in `dca.array_field`, in which case
the typing annotation is ignored.
Example: `x: Any = dca.field(shape=(), dtype=np.int64)`
Field which do not satisfy any of the above conditions are static (including
field annotated with `field: np.ndarray` or similar).
| class DataclassArray(metaclass=MetaDataclassArray):
"""Dataclass which behaves like an array.
Usage:
```python
class Square(DataclassArray):
pos: f32['*shape 2']
scale: f32['*shape']
name: str
# Create 3 squares batched
p = Square(
pos=[[x0, y0], [x1, y1], [x2, y2]],
scale=[scale0, scale1, scale2],
name='my_square',
)
p.shape == (3,)
p.pos.shape == (3, 2)
p[0] == Square(pos=[x0, y0], scale=scale0)
p = p.reshape((3, 1)) # Reshape the inner-shape
p.shape == (3, 1)
p.pos.shape == (3, 1, 2)
p.name == 'my_square'
```
`DataclassArray` has 2 types of fields:
* Array fields: Fields batched like numpy arrays, with reshape, slicing,...
(`pos` and `scale` in the above example).
* Static fields: Other non-numpy field. Are not modified by reshaping,... (
`name` in the above example).
Static fields are also ignored in `jax.tree_map`.
`DataclassArray` detect array fields if either:
* The typing annotation is a `etils.array_types` annotation (in which
case shape/dtype are automatically infered from the typing annotation)
Example: `x: f32[..., 3]`
* The typing annotation is another `dca.DataclassArray` (in which case
`my_dataclass.field.shape == my_dataclass.shape`)
Example: `x: MyDataclass`
* The field is explicitly defined in `dca.array_field`, in which case
the typing annotation is ignored.
Example: `x: Any = dca.field(shape=(), dtype=np.int64)`
Field which do not satisfy any of the above conditions are static (including
field annotated with `field: np.ndarray` or similar).
"""
# Child class inherit the default params by default, but can also
# overwrite them.
__dca_params__: ClassVar[DataclassParams] = DataclassParams()
# TODO(epot): Could be removed with py3.10 and using `kw_only=True`
# Fields defined here will be forwarded with `.replace`
# TODO(py39): Replace Set -> set
__dca_non_init_fields__: ClassVar[Set[str]] = set()
_shape: Shape
_xnp: enp.NpModule
def __init_subclass__(
cls,
frozen=True,
**kwargs,
):
super().__init_subclass__(**kwargs)
if not frozen:
raise ValueError(f'{cls} cannot be `frozen=False`.')
# Apply dataclass (in-place)
if not typing.TYPE_CHECKING:
# TODO(b/227290126): Create pytype issues
dataclasses.dataclass(frozen=True)(cls)
# TODO(epot): Could have smart __repr__ which display types if array have
# too many values (maybe directly in `edc.field(repr=...)`).
edc.dataclass(kw_only=True, repr=True, auto_cast=False)(cls)
cls._dca_jax_tree_registered = False
cls._dca_torch_tree_registered = False
# Typing annotations have to be lazily evaluated (to support
# `from __future__ import annotations` and forward reference)
# To avoid costly `typing.get_type_hints` which perform `eval` and `str`
# convertions, we cache the type annotations here.
cls._dca_fields_metadata: Optional[dict[str, _ArrayFieldMetadata]] = None
# Normalize the `cls.__dca_non_init_fields__`
# TODO(epot): Support inheritance if the parents also define
# `__dca_non_init_fields__` (fields should be merged from `.mro()`)
cls.__dca_non_init_fields__ = set(cls.__dca_non_init_fields__)
if typing.TYPE_CHECKING:
# TODO(b/242839979): pytype do not support PEP 681 -- Data Class Transforms
def __init__(self, **kwargs):
pass
def __post_init__(self) -> None:
"""Validate and normalize inputs."""
cls = type(self)
# First time, we perform additional check & updates
if cls._dca_fields_metadata is None: # pylint: disable=protected-access
_init_cls(self)
# Register the tree_map here instead of `__init_subclass__` as `jax` may
# not have been imported yet during import.
if enp.lazy.has_jax and not cls._dca_jax_tree_registered: # pylint: disable=protected-access
enp.lazy.jax.tree_util.register_pytree_node_class(cls)
cls._dca_jax_tree_registered = True # pylint: disable=protected-access
if enp.lazy.has_torch and not cls._dca_torch_tree_registered: # pylint: disable=protected-access
# Note: Torch is updating it's tree API to make it public and use `optree`
# as backend: https://github.com/pytorch/pytorch/issues/65761
enp.lazy.torch.utils._pytree._register_pytree_node( # pylint: disable=protected-access
cls,
flatten_fn=lambda a: a.tree_flatten(),
unflatten_fn=lambda vals, ctx: cls.tree_unflatten(ctx, vals),
)
cls._dca_torch_tree_registered = True # pylint: disable=protected-access
# Validate and normalize array fields
# * Maybe cast (list, np) -> xnp
# * Maybe cast dtype
# * Maybe broadcast shapes
# Because this is only done inside `__init__`, it is ok to mutate self.
# Cast and validate the array xnp are consistent
xnp = self._cast_xnp_dtype_inplace()
# Validate the batch shape is consistent
# However, we need to be careful that `_ArrayField` never uses
# `@epy.cached_property`
shape = self._broadcast_shape_inplace()
# TODO(epot): When to validate (`field.validate()`)
if xnp is None: # No values
# Inside `jax.tree_utils`, tree-def can be created with `None` values.
# Inside `jax.vmap`, tree can be created with `object()` sentinel values.
assert shape is None
xnp = None
# Cache results
# Should the state be stored in a separate object to avoid collisions ?
assert shape is None or isinstance(shape, tuple), shape
self._setattr('_shape', shape)
self._setattr('_xnp', xnp)
# ====== Array functions ======
@property
def shape(self) -> Shape:
"""Returns the batch shape common to all fields."""
return self._shape
@property
def size(self) -> int:
"""Returns the number of elements."""
return np_utils.size_of(self._shape)
@property
def ndim(self) -> int:
"""Returns the number of dimensions."""
return len(self._shape)
def reshape(self: _DcT, shape: Union[Shape, str], **axes_length: int) -> _DcT:
"""Reshape the batch shape according to the pattern.
Supports both tuple and einops mode:
```python
rays.reshape('b h w -> b (h w)')
rays.reshape((128, -1))
```
Args:
shape: Target shape. Can be string for `einops` support.
**axes_length: Any additional specifications for dimensions for einops
support.
Returns:
The dataclass array with the new shape
"""
if isinstance(shape, str): # Einops support
return self._map_field( # pylint: disable=protected-access
array_fn=lambda f: einops.rearrange( # pylint: disable=g-long-lambda
f.value,
np_utils.to_absolute_einops(shape, nlastdim=len(f.inner_shape)),
**axes_length,
),
dc_fn=lambda f: f.value.reshape( # pylint: disable=g-long-lambda
np_utils.to_absolute_einops(shape, nlastdim=len(f.inner_shape)),
**axes_length,
),
)
else: # Numpy support
assert isinstance(shape, tuple) # For pytest
def _reshape(f: _ArrayField):
return f.value.reshape(shape + f.inner_shape)
return self._map_field(array_fn=_reshape, dc_fn=_reshape) # pylint: disable=protected-access
def flatten(self: _DcT) -> _DcT:
"""Flatten the batch shape."""
return self.reshape((-1,))
def broadcast_to(self: _DcT, shape: Shape) -> _DcT:
"""Broadcast the batch shape."""
return self._map_field( # pylint: disable=protected-access
array_fn=lambda f: f.broadcast_to(shape),
dc_fn=lambda f: f.broadcast_to(shape),
)
def __getitem__(self: _DcT, indices: _IndicesArg) -> _DcT:
"""Slice indexing."""
indices = np.index_exp[indices] # Normalize indices
# Replace `...` by explicit shape
indices = _to_absolute_indices(indices, shape=self.shape)
return self._map_field(
array_fn=lambda f: f.value[indices],
dc_fn=lambda f: f.value[indices],
)
# _DcT[n *d] -> Iterator[_DcT[*d]]
def __iter__(self: _DcT) -> Iterator[_DcT]:
"""Iterate over the outermost dimension."""
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|
44,078 | dataclass_array.array_dataclass | __bool__ | `dca.DataclassArray` always evaluate to `True`.
Like all python objects (including dataclasses), `dca.DataclassArray` always
evaluate to `True`. So:
`Ray(pos=None)`, `Ray(pos=0)` all evaluate to `True`.
This allow construct like:
```python
def fn(ray: Optional[dca.Ray] = None):
if ray:
...
```
Or:
```python
def fn(ray: Optional[dca.Ray] = None):
ray = ray or default_ray
```
Only in the very rare case of empty-tensor (`shape=(0, ...)`)
```python
assert ray is not None
assert len(ray) == 0
bool(ray) # TypeError: Truth value is ambigous
```
Returns:
True
Raises:
ValueError: If `len(self) == 0` to avoid ambiguity.
| def __bool__(self) -> Literal[True]:
"""`dca.DataclassArray` always evaluate to `True`.
Like all python objects (including dataclasses), `dca.DataclassArray` always
evaluate to `True`. So:
`Ray(pos=None)`, `Ray(pos=0)` all evaluate to `True`.
This allow construct like:
```python
def fn(ray: Optional[dca.Ray] = None):
if ray:
...
```
Or:
```python
def fn(ray: Optional[dca.Ray] = None):
ray = ray or default_ray
```
Only in the very rare case of empty-tensor (`shape=(0, ...)`)
```python
assert ray is not None
assert len(ray) == 0
bool(ray) # TypeError: Truth value is ambigous
```
Returns:
True
Raises:
ValueError: If `len(self) == 0` to avoid ambiguity.
"""
if self.shape and not len(self): # pylint: disable=g-explicit-length-test
raise ValueError(
f'The truth value of {self.__class__.__name__} when `len(x) == 0` '
'is ambigous. Use `len(x)` or `x is not None`.'
)
return True
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|
44,079 | dataclass_array.array_dataclass | __getitem__ | Slice indexing. | def __getitem__(self: _DcT, indices: _IndicesArg) -> _DcT:
"""Slice indexing."""
indices = np.index_exp[indices] # Normalize indices
# Replace `...` by explicit shape
indices = _to_absolute_indices(indices, shape=self.shape)
return self._map_field(
array_fn=lambda f: f.value[indices],
dc_fn=lambda f: f.value[indices],
)
| (self: ~_DcT, indices: Union[ellipsis, NoneType, int, slice, Any, Tuple[Union[ellipsis, NoneType, int, slice, Any]]]) -> ~_DcT | [
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|
44,080 | dataclass_array.array_dataclass | __iter__ | Iterate over the outermost dimension. | def __iter__(self: _DcT) -> Iterator[_DcT]:
"""Iterate over the outermost dimension."""
if not self.shape:
raise TypeError(f'iteration over 0-d array: {self!r}')
# Similar to `etree.unzip(self)` (but work with any backend)
field_names = [f.name for f in self._array_fields] # pylint: disable=not-an-iterable
field_values = [f.value for f in self._array_fields] # pylint: disable=not-an-iterable
for vals in zip(*field_values):
yield self.replace(**dict(zip(field_names, vals)))
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|
44,081 | dataclass_array.array_dataclass | __len__ | Length of the first array dimension. | def __len__(self) -> int:
"""Length of the first array dimension."""
if not self.shape:
raise TypeError(
f'len() of unsized {self.__class__.__name__} (shape={self.shape})'
)
return self.shape[0]
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|
44,082 | dataclass_array.array_dataclass | __post_init__ | Validate and normalize inputs. | def __post_init__(self) -> None:
"""Validate and normalize inputs."""
cls = type(self)
# First time, we perform additional check & updates
if cls._dca_fields_metadata is None: # pylint: disable=protected-access
_init_cls(self)
# Register the tree_map here instead of `__init_subclass__` as `jax` may
# not have been imported yet during import.
if enp.lazy.has_jax and not cls._dca_jax_tree_registered: # pylint: disable=protected-access
enp.lazy.jax.tree_util.register_pytree_node_class(cls)
cls._dca_jax_tree_registered = True # pylint: disable=protected-access
if enp.lazy.has_torch and not cls._dca_torch_tree_registered: # pylint: disable=protected-access
# Note: Torch is updating it's tree API to make it public and use `optree`
# as backend: https://github.com/pytorch/pytorch/issues/65761
enp.lazy.torch.utils._pytree._register_pytree_node( # pylint: disable=protected-access
cls,
flatten_fn=lambda a: a.tree_flatten(),
unflatten_fn=lambda vals, ctx: cls.tree_unflatten(ctx, vals),
)
cls._dca_torch_tree_registered = True # pylint: disable=protected-access
# Validate and normalize array fields
# * Maybe cast (list, np) -> xnp
# * Maybe cast dtype
# * Maybe broadcast shapes
# Because this is only done inside `__init__`, it is ok to mutate self.
# Cast and validate the array xnp are consistent
xnp = self._cast_xnp_dtype_inplace()
# Validate the batch shape is consistent
# However, we need to be careful that `_ArrayField` never uses
# `@epy.cached_property`
shape = self._broadcast_shape_inplace()
# TODO(epot): When to validate (`field.validate()`)
if xnp is None: # No values
# Inside `jax.tree_utils`, tree-def can be created with `None` values.
# Inside `jax.vmap`, tree can be created with `object()` sentinel values.
assert shape is None
xnp = None
# Cache results
# Should the state be stored in a separate object to avoid collisions ?
assert shape is None or isinstance(shape, tuple), shape
self._setattr('_shape', shape)
self._setattr('_xnp', xnp)
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|
44,083 | dataclass_array.array_dataclass | __tf_flatten__ | null | def __tf_flatten__(self) -> tuple[_TreeMetadata, tuple[DcOrArray, ...]]:
components, metadata = self.tree_flatten()
return metadata, components
| (self) -> 'tuple[_TreeMetadata, tuple[DcOrArray, ...]]' | [
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|
44,084 | dataclass_array.array_dataclass | _broadcast_shape_inplace | Validate the shapes are consistent and broadcast values in-place. | def _broadcast_shape_inplace(self) -> Optional[Shape]:
"""Validate the shapes are consistent and broadcast values in-place."""
if self._all_fields_empty: # pylint: disable=using-constant-test
return None
# First collect all shapes and compute the final shape.
shape_to_names = epy.groupby(
self._array_fields,
key=lambda f: f.host_shape,
value=lambda f: f.name,
)
shape_lengths = {len(s) for s in shape_to_names.keys()}
# Broadcast all shape together
try:
final_shape = np.broadcast_shapes(*shape_to_names.keys())
except ValueError:
final_shape = None # Bad broadcast
# Currently, we restrict broadcasting to either scalar or fixed length.
# This is to avoid confusion broadcasting vs vectorization rules.
# This restriction could be lifted if we encounter a use-case.
if (
final_shape is None
or len(shape_lengths) > 2
or (len(shape_lengths) == 2 and 0 not in shape_lengths)
):
raise ValueError(
f'Conflicting batch shapes: {shape_to_names}. '
f'Currently {type(self).__qualname__}.__init__ broadcasting is '
'restricted to scalar or dim=1 . '
'Please open an issue if you need more fine-grained broadcasting.'
)
def _broadcast_field(f: _ArrayField) -> None:
if f.host_shape == final_shape: # Already broadcasted
return
elif not self.__dca_params__.broadcast: # Broadcasing disabled
raise ValueError(
f'{type(self).__qualname__} has `broadcast=False`. '
f'Cannot broadcast {f.name} from {f.full_shape} to {final_shape}. '
'To enable broadcast, use `@dca.dataclass_array(broadcast=True)`.'
)
self._setattr(f.name, f.broadcast_to(final_shape))
self._map_field(
array_fn=_broadcast_field,
dc_fn=_broadcast_field, # pytype: disable=wrong-arg-types
_inplace=True,
)
return final_shape
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|
44,085 | dataclass_array.array_dataclass | _cast_xnp_dtype_inplace | Validate `xnp` are consistent and cast `np` -> `xnp` in-place. | def _cast_xnp_dtype_inplace(self) -> Optional[enp.NpModule]:
"""Validate `xnp` are consistent and cast `np` -> `xnp` in-place."""
if self._all_fields_empty: # pylint: disable=using-constant-test
return None
# Validate the dtype
def _get_xnp(f: _ArrayField) -> enp.NpModule:
try:
return np_utils.get_xnp(
f.value,
strict=not self.__dca_params__.cast_list,
)
except Exception as e: # pylint: disable=broad-except
epy.reraise(e, prefix=f'Invalid {f.qualname}: ')
xnps = epy.groupby(
self._array_fields,
key=_get_xnp,
value=lambda f: f.name,
)
if not xnps:
return None
xnp = _infer_xnp(xnps)
def _cast_field(f: _ArrayField) -> None:
try:
# Supports for TensorSpec (e.g. in `tf.function` signature)
if enp.lazy.is_tf_xnp(xnp) and isinstance(
f.value, enp.lazy.tf.TensorSpec
):
# TODO(epot): Actually check the dtype
new_value = f.value
else:
new_value = np_utils.asarray(
f.value,
xnp=xnp,
dtype=f.dtype,
cast_dtype=self.__dca_params__.cast_dtype,
)
self._setattr(f.name, new_value)
# After the field has been set, we validate the shape
f.assert_shape()
except Exception as e: # pylint: disable=broad-except
epy.reraise(e, prefix=f'Invalid {f.qualname}: ')
self._map_field(
array_fn=_cast_field,
dc_fn=_cast_field, # pytype: disable=wrong-arg-types
_inplace=True,
)
return xnp
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|
44,086 | dataclass_array.array_dataclass | _map_field | Apply a transformation on all array fields structure.
Args:
array_fn: Function applied on the `xnp.ndarray` fields
dc_fn: Function applied on the `dca.DataclassArray` fields (to recurse)
_inplace: If True, assume the function mutate the object in-place. Should
only be used inside `__init__` for performances.
Returns:
The transformed dataclass array.
| def _map_field(
self: _DcT,
*,
array_fn: Callable[[_ArrayField[Array['*din']]], Array['*dout']],
dc_fn: Optional[Callable[[_ArrayField[_DcT]], _DcT]],
_inplace: bool = False,
) -> _DcT:
"""Apply a transformation on all array fields structure.
Args:
array_fn: Function applied on the `xnp.ndarray` fields
dc_fn: Function applied on the `dca.DataclassArray` fields (to recurse)
_inplace: If True, assume the function mutate the object in-place. Should
only be used inside `__init__` for performances.
Returns:
The transformed dataclass array.
"""
def _apply_field_dn(f: _ArrayField):
if f.is_dataclass: # Recurse on dataclasses
return dc_fn(f) # pylint: disable=protected-access
else:
return array_fn(f)
new_values = {f.name: _apply_field_dn(f) for f in self._array_fields} # pylint: disable=not-an-iterable,protected-access
# For performance, do not call replace to save the constructor call
if not _inplace:
return self.replace(**new_values)
else:
return self
| (self: ~_DcT, *, array_fn: Callable[[dataclass_array.array_dataclass._ArrayField[etils.enp.array_types.typing.Array]], etils.enp.array_types.typing.Array], dc_fn: Optional[Callable[[dataclass_array.array_dataclass._ArrayField[~_DcT]], ~_DcT]], _inplace: bool = False) -> ~_DcT | [
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|
44,087 | dataclass_array.array_dataclass | _setattr | Like setattr, but support `frozen` dataclasses. | def _setattr(self, name: str, value: Any) -> None:
"""Like setattr, but support `frozen` dataclasses."""
object.__setattr__(self, name, value)
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|
44,088 | dataclass_array.array_dataclass | _to_absolute_axis | Normalize the axis to absolute value. | def _to_absolute_axis(self, axis: Axes) -> Axes:
"""Normalize the axis to absolute value."""
try:
return np_utils.to_absolute_axis(axis, ndim=self.ndim)
except Exception as e: # pylint: disable=broad-except
epy.reraise(
e,
prefix=f'For {self.__class__.__qualname__} with shape={self.shape}: ',
)
| (self, axis: Union[NoneType, Tuple[int, ...], int]) -> Union[NoneType, Tuple[int, ...], int] | [
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|
44,089 | dataclass_array.array_dataclass | as_jax | Returns the instance as containing `jnp.ndarray`. | def as_jax(self: _DcT) -> _DcT:
"""Returns the instance as containing `jnp.ndarray`."""
return self.as_xnp(enp.lazy.jnp)
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|
44,090 | dataclass_array.array_dataclass | as_np | Returns the instance as containing `np.ndarray`. | def as_np(self: _DcT) -> _DcT:
"""Returns the instance as containing `np.ndarray`."""
return self.as_xnp(enp.lazy.np)
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|
44,091 | dataclass_array.array_dataclass | as_tf | Returns the instance as containing `tf.Tensor`. | def as_tf(self: _DcT) -> _DcT:
"""Returns the instance as containing `tf.Tensor`."""
return self.as_xnp(enp.lazy.tnp)
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|
44,092 | dataclass_array.array_dataclass | as_torch | Returns the instance as containing `torch.Tensor`. | def as_torch(self: _DcT) -> _DcT:
"""Returns the instance as containing `torch.Tensor`."""
return self.as_xnp(enp.lazy.torch)
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|
44,093 | dataclass_array.array_dataclass | as_xnp | Returns the instance as containing `xnp.ndarray`. | def as_xnp(self: _DcT, xnp: enp.NpModule) -> _DcT:
"""Returns the instance as containing `xnp.ndarray`."""
if xnp is self.xnp: # No-op
return self
# Direct `torch` <> `tf`/`jax` conversion not supported, so convert to
# `numpy`
if enp.lazy.is_torch_xnp(xnp) or enp.lazy.is_torch_xnp(self.xnp):
def _as_torch(f):
arr = np.asarray(f.value)
# Torch fail for scalar arrays:
# https://github.com/pytorch/pytorch/issues/97021
if enp.lazy.is_torch_xnp(xnp) and not arr.shape: # Destination is torch
return xnp.asarray(arr.item(), dtype=lazy.as_torch_dtype(arr.dtype))
return xnp.asarray(arr)
array_fn = _as_torch
else:
array_fn = lambda f: xnp.asarray(f.value)
# Update all childs
new_self = self._map_field( # pylint: disable=protected-access
array_fn=array_fn,
dc_fn=lambda f: f.value.as_xnp(xnp),
)
return new_self
| (self: ~_DcT, xnp: Any) -> ~_DcT | [
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|
44,094 | dataclass_array.array_dataclass | assert_same_xnp | Assert the given array is of the same type as the current object. | def assert_same_xnp(self, x: Union[Array[...], DataclassArray]) -> None:
"""Assert the given array is of the same type as the current object."""
xnp = np_utils.get_xnp(x)
if xnp is not self.xnp:
raise ValueError(
f'{self.__class__.__name__} is {self.xnp.__name__} but got input '
f'{xnp.__name__}. Please cast input first.'
)
| (self, x: Union[etils.enp.array_types.typing.Array, dataclass_array.array_dataclass.DataclassArray]) -> NoneType | [
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|
44,095 | dataclass_array.array_dataclass | broadcast_to | Broadcast the batch shape. | def broadcast_to(self: _DcT, shape: Shape) -> _DcT:
"""Broadcast the batch shape."""
return self._map_field( # pylint: disable=protected-access
array_fn=lambda f: f.broadcast_to(shape),
dc_fn=lambda f: f.broadcast_to(shape),
)
| (self: ~_DcT, shape: Tuple[int, ...]) -> ~_DcT | [
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|
44,096 | dataclass_array.array_dataclass | cpu | Move the dataclass array to the CPU device. | def cpu(self: _DcT, *args, **kwargs) -> _DcT:
"""Move the dataclass array to the CPU device."""
if not lazy.is_torch_xnp(self.xnp):
raise ValueError('`.cpu` can only be called when `xnp == torch`')
return self.map_field(lambda f: f.cpu(*args, **kwargs))
| (self: ~_DcT, *args, **kwargs) -> ~_DcT | [
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|
44,097 | dataclass_array.array_dataclass | cuda | Move the dataclass array to the CUDA device. | def cuda(self: _DcT, *args, **kwargs) -> _DcT:
"""Move the dataclass array to the CUDA device."""
if not lazy.is_torch_xnp(self.xnp):
raise ValueError('`.cuda` can only be called when `xnp == torch`')
return self.map_field(lambda f: f.cuda(*args, **kwargs))
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|
44,098 | dataclass_array.array_dataclass | flatten | Flatten the batch shape. | def flatten(self: _DcT) -> _DcT:
"""Flatten the batch shape."""
return self.reshape((-1,))
| (self: ~_DcT) -> ~_DcT | [
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|
44,099 | dataclass_array.array_dataclass | map_field | Apply a transformation on all arrays from the fields. | def map_field(
self: _DcT,
fn: Callable[[Array['*din']], Array['*dout']],
) -> _DcT:
"""Apply a transformation on all arrays from the fields."""
return self._map_field( # pylint: disable=protected-access
array_fn=lambda f: fn(f.value),
dc_fn=lambda f: f.value.map_field(fn),
)
| (self: ~_DcT, fn: Callable[[etils.enp.array_types.typing.Array], etils.enp.array_types.typing.Array]) -> ~_DcT | [
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]
|
44,100 | dataclass_array.array_dataclass | replace | Alias for `dataclasses.replace`. | def replace(self: _DcT, **kwargs: Any) -> _DcT:
"""Alias for `dataclasses.replace`."""
init_kwargs = {
k: v for k, v in kwargs.items() if k not in self.__dca_non_init_fields__
}
non_init_kwargs = {
k: v for k, v in kwargs.items() if k in self.__dca_non_init_fields__
}
# Create the new object
new_self = dataclasses.replace(self, **init_kwargs) # pytype: disable=wrong-arg-types # re-none
# TODO(epot): Could try to unify logic bellow with `tree_unflatten`
# Additionally forward the non-init kwargs
# `dataclasses.field(init=False) kwargs are required because `init=True`
# creates conflicts:
# * Inheritance fails with non-default argument 'K' follows default argument
# * Pytype complains too
# TODO(py310): Cleanup using `dataclass(kw_only)`
assert new_self is not self
for k in self.__dca_non_init_fields__:
if k in non_init_kwargs:
v = non_init_kwargs[k]
else:
v = getattr(self, k)
new_self._setattr(k, v) # pylint: disable=protected-access
return new_self
| (self: ~_DcT, **kwargs: Any) -> ~_DcT | [
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|
44,101 | dataclass_array.array_dataclass | reshape | Reshape the batch shape according to the pattern.
Supports both tuple and einops mode:
```python
rays.reshape('b h w -> b (h w)')
rays.reshape((128, -1))
```
Args:
shape: Target shape. Can be string for `einops` support.
**axes_length: Any additional specifications for dimensions for einops
support.
Returns:
The dataclass array with the new shape
| def reshape(self: _DcT, shape: Union[Shape, str], **axes_length: int) -> _DcT:
"""Reshape the batch shape according to the pattern.
Supports both tuple and einops mode:
```python
rays.reshape('b h w -> b (h w)')
rays.reshape((128, -1))
```
Args:
shape: Target shape. Can be string for `einops` support.
**axes_length: Any additional specifications for dimensions for einops
support.
Returns:
The dataclass array with the new shape
"""
if isinstance(shape, str): # Einops support
return self._map_field( # pylint: disable=protected-access
array_fn=lambda f: einops.rearrange( # pylint: disable=g-long-lambda
f.value,
np_utils.to_absolute_einops(shape, nlastdim=len(f.inner_shape)),
**axes_length,
),
dc_fn=lambda f: f.value.reshape( # pylint: disable=g-long-lambda
np_utils.to_absolute_einops(shape, nlastdim=len(f.inner_shape)),
**axes_length,
),
)
else: # Numpy support
assert isinstance(shape, tuple) # For pytest
def _reshape(f: _ArrayField):
return f.value.reshape(shape + f.inner_shape)
return self._map_field(array_fn=_reshape, dc_fn=_reshape) # pylint: disable=protected-access
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|
44,102 | dataclass_array.array_dataclass | to | Move the dataclass array to the device. | def to(self: _DcT, device, **kwargs) -> _DcT:
"""Move the dataclass array to the device."""
if not lazy.is_torch_xnp(self.xnp):
raise ValueError('`.to` can only be called when `xnp == torch`')
return self.map_field(lambda f: f.to(device, **kwargs))
| (self: ~_DcT, device, **kwargs) -> ~_DcT | [
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|
44,103 | dataclass_array.array_dataclass | tree_flatten | `jax.tree_utils` support. | def tree_flatten(self) -> tuple[tuple[DcOrArray, ...], _TreeMetadata]:
"""`jax.tree_utils` support."""
# We flatten all values (and not just the non-None ones)
array_field_values = tuple(f.value for f in self._all_array_fields.values())
metadata = _TreeMetadata(
array_field_names=list(self._all_array_fields.keys()),
non_array_field_kwargs={
f.name: getattr(self, f.name)
for f in dataclasses.fields(self) # pytype: disable=wrong-arg-types # re-none
if f.name not in self._all_array_fields # pylint: disable=unsupported-membership-test
},
)
return (array_field_values, metadata)
| (self) -> 'tuple[tuple[DcOrArray, ...], _TreeMetadata]' | [
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]
|
44,105 | dataclass_array.ops | concat | Concatenate dataclasses together. | def concat(arrays: Iterable[DcT], *, axis: int = 0) -> DcT:
"""Concatenate dataclasses together."""
return _ops_base(
arrays,
axis=axis,
array_fn=lambda xnp, axis, f: xnp.concatenate( # pylint: disable=g-long-lambda
[getattr(arr, f.name) for arr in arrays], axis=axis
),
dc_fn=lambda xnp, axis, f: concat( # pylint: disable=g-long-lambda
[getattr(arr, f.name) for arr in arrays],
axis=axis,
),
)
| (arrays: Iterable[~DcT], *, axis: int = 0) -> ~DcT | [
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|
44,106 | dataclass_array.array_dataclass | dataclass_array | Optional decorator to customize `dca.DataclassArray` params.
Usage:
```python
@dca.dataclass_array()
class MyDataclass(dca.DataclassArray):
...
```
This decorator has to be added in addition of inheriting from
`dca.DataclassArray`.
Args:
broadcast: If `True`, enable input broadcasting
cast_dtype: If `True`, auto-cast inputs `dtype`
cast_list: If `True`, auto-cast lists to `xnp.ndarray`
Returns:
decorator: The decorator which will apply the options to the dataclass
| def dataclass_array(
*,
# If modifying this, make sure to modify `DataclassParams` too!
broadcast: bool = False,
cast_dtype: bool = False,
cast_list: bool = True,
) -> Callable[[type[_DcT]], type[_DcT]]:
"""Optional decorator to customize `dca.DataclassArray` params.
Usage:
```python
@dca.dataclass_array()
class MyDataclass(dca.DataclassArray):
...
```
This decorator has to be added in addition of inheriting from
`dca.DataclassArray`.
Args:
broadcast: If `True`, enable input broadcasting
cast_dtype: If `True`, auto-cast inputs `dtype`
cast_list: If `True`, auto-cast lists to `xnp.ndarray`
Returns:
decorator: The decorator which will apply the options to the dataclass
"""
def decorator(cls):
if not issubclass(cls, DataclassArray):
raise TypeError(
'`@dca.dataclass_array` can only be applied on `dca.DataclassArray`. '
f'Got: {cls}'
)
cls.__dca_params__ = DataclassParams(
broadcast=broadcast,
cast_dtype=cast_dtype,
cast_list=cast_list,
)
return cls
return decorator
| (*, broadcast: bool = False, cast_dtype: bool = False, cast_list: bool = True) -> Callable[[type[~_DcT]], type[~_DcT]] | [
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|
44,107 | dataclass_array.array_dataclass | array_field | Dataclass array field.
See `dca.DataclassArray` for example.
Args:
shape: Inner shape of the field
dtype: Type of the field
**field_kwargs: Args forwarded to `dataclasses.field`
Returns:
The dataclass field.
| def array_field(
shape: Shape,
dtype: DTypeArg = float,
**field_kwargs,
) -> dataclasses.Field[DcOrArray]:
"""Dataclass array field.
See `dca.DataclassArray` for example.
Args:
shape: Inner shape of the field
dtype: Type of the field
**field_kwargs: Args forwarded to `dataclasses.field`
Returns:
The dataclass field.
"""
# TODO(epot): Validate shape, dtype
dca_field = _ArrayFieldMetadata(
inner_shape_non_static=shape,
dtype=dtype,
)
return dataclasses.field(**field_kwargs, metadata={_METADATA_KEY: dca_field})
| (shape: 'Shape', dtype: 'DTypeArg' = <class 'float'>, **field_kwargs) -> 'dataclasses.Field[DcOrArray]' | [
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]
|
44,111 | dataclass_array.ops | stack | Stack dataclasses together. | def stack(
arrays: Iterable[DcT], # list[_DcT['*shape']]
*,
axis: int = 0,
) -> DcT: # _DcT['len(arrays) *shape']:
"""Stack dataclasses together."""
return _ops_base(
arrays,
axis=axis,
array_fn=lambda xnp, axis, f: xnp.stack( # pylint: disable=g-long-lambda
[getattr(arr, f.name) for arr in arrays], axis=axis
),
dc_fn=lambda xnp, axis, f: stack( # pylint: disable=g-long-lambda
[getattr(arr, f.name) for arr in arrays],
axis=axis,
),
)
| (arrays: Iterable[~DcT], *, axis: int = 0) -> ~DcT | [
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|
44,117 | dataclass_array.vectorization | vectorize_method | Vectorize a `dca.DataclassArray` method.
Allow to implement method in `dca.DataclassArray` assuming `shape == ()`.
This is similar to `jax.vmap` but:
* Only work on `dca.DataclassArray` methods
* Instead of vectorizing a single axis, `@dca.vectorize_method` will vectorize
over `*self.shape` (not just `self.shape[0]`). This is like if `vmap`
was applied to `self.flatten()`
* Axis with dimension `1` are brodcasted.
For example, with `__matmul__(self, x: T) -> T`:
```python
() @ (*x,) -> (*x,)
(b,) @ (b, *x) -> (b, *x)
(b,) @ (1, *x) -> (b, *x)
(1,) @ (b, *x) -> (b, *x)
(b, h, w) @ (b, h, w, *x) -> (b, h, w, *x)
(1, h, w) @ (b, 1, 1, *x) -> (b, h, w, *x)
```
Example:
```
class Point3d(dca.DataclassArray):
p: f32['*shape 3']
@dca.vectorize_method
def first_value(self):
return self.p[0]
point = Point3d(p=[ # 4 points batched together
[10, 11, 12],
[20, 21, 22],
[30, 31, 32],
[40, 41, 42],
])
point.first_value() == [10, 20, 30, 40] # First value of each points
```
Args:
fn: DataclassArray method to decorate
static_args: If given, should be a set of the static argument names
Returns:
fn: Decorated function with vectorization applied to self.
| def vectorize_method(
fn=None,
*,
static_args=None,
):
"""Vectorize a `dca.DataclassArray` method.
Allow to implement method in `dca.DataclassArray` assuming `shape == ()`.
This is similar to `jax.vmap` but:
* Only work on `dca.DataclassArray` methods
* Instead of vectorizing a single axis, `@dca.vectorize_method` will vectorize
over `*self.shape` (not just `self.shape[0]`). This is like if `vmap`
was applied to `self.flatten()`
* Axis with dimension `1` are brodcasted.
For example, with `__matmul__(self, x: T) -> T`:
```python
() @ (*x,) -> (*x,)
(b,) @ (b, *x) -> (b, *x)
(b,) @ (1, *x) -> (b, *x)
(1,) @ (b, *x) -> (b, *x)
(b, h, w) @ (b, h, w, *x) -> (b, h, w, *x)
(1, h, w) @ (b, 1, 1, *x) -> (b, h, w, *x)
```
Example:
```
class Point3d(dca.DataclassArray):
p: f32['*shape 3']
@dca.vectorize_method
def first_value(self):
return self.p[0]
point = Point3d(p=[ # 4 points batched together
[10, 11, 12],
[20, 21, 22],
[30, 31, 32],
[40, 41, 42],
])
point.first_value() == [10, 20, 30, 40] # First value of each points
```
Args:
fn: DataclassArray method to decorate
static_args: If given, should be a set of the static argument names
Returns:
fn: Decorated function with vectorization applied to self.
"""
# Called as decorator with options (`@dca.vectorize_method(**options)`)
if fn is None:
return functools.partial(vectorize_method, static_args=static_args) # pytype: disable=bad-return-type
# Signature util also make sure explicit error message are raised (e.g.
# `Error in <fn> for arg <arg-name>` )
sig = inspect_utils.Signature(fn)
if sig.has_var:
raise NotImplementedError(
'`@dca.vectorize_method` does not support function with variable args '
f'(`*args` or `**kwargs`). For {sig.fn_name}. Please open an issue.'
)
if static_args is not None:
if not isinstance(static_args, set):
raise TypeError(
f'Unexpected `static_args={static_args!r}`. Expected `set`.'
)
map_non_static = functools.partial(
_map_non_static,
static_args=static_args,
)
@functools.wraps(fn)
@epy.maybe_reraise(prefix=lambda: f'Error in {fn.__qualname__}: ')
def decorated(
self: array_dataclass.DataclassArray,
*args: Any,
**kwargs: Any,
) -> _Out:
if not isinstance(self, array_dataclass.DataclassArray):
raise TypeError(
'dca.vectorize_method should be applied on DataclassArray method. '
f'Not: {type(self)}'
)
if not self.shape: # No batch shape, no-need to vectorize
return fn(self, *args, **kwargs)
original_args = sig.bind(self, *args, **kwargs)
# TODO(epot): Tree support (with masking inside args)
# Validation
# TODO(epot): Normalize `np`, `list` -> `xnp`
assert_is_array = functools.partial(_assert_is_array, xnp=self.xnp)
map_non_static(assert_is_array, original_args)
# Broadcast and flatten args. Exemple:
# Broadcast the batch shape when dim == 1:
# (h, w), (h, w, c) -> (h, w), (h, w, c)
# (h, w), (1, 1, c) -> (h, w), (h, w, c)
# (1, 1), (h, w, c) -> (h, w), (h, w, c)
# Flatten:
# (h, w), (h, w, c) -> (b*h*w,), (b*h*w, c)
flat_args, batch_shape = _broadcast_and_flatten_args(
original_args,
map_non_static=map_non_static,
)
# Call the vectorized function
out = _vmap_method(
flat_args,
map_non_static=map_non_static,
xnp=self.xnp,
)
# Unflatten the output
unflatten = functools.partial(_unflatten, batch_shape=batch_shape)
out = tree_utils.tree_map(unflatten, out)
return out
return decorated
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|
44,118 | gwcs.coordinate_frames | CelestialFrame |
Celestial Frame Representation
Parameters
----------
axes_order : tuple of int
A dimension in the input data that corresponds to this axis.
reference_frame : astropy.coordinates.builtin_frames
A reference frame.
unit : str or units.Unit instance or iterable of those
Units on axes.
axes_names : list
Names of the axes in this frame.
name : str
Name of this frame.
| class CelestialFrame(CoordinateFrame):
"""
Celestial Frame Representation
Parameters
----------
axes_order : tuple of int
A dimension in the input data that corresponds to this axis.
reference_frame : astropy.coordinates.builtin_frames
A reference frame.
unit : str or units.Unit instance or iterable of those
Units on axes.
axes_names : list
Names of the axes in this frame.
name : str
Name of this frame.
"""
def __init__(self, axes_order=None, reference_frame=None,
unit=None, axes_names=None,
name=None, axis_physical_types=None):
naxes = 2
if reference_frame is not None:
if not isinstance(reference_frame, str):
if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES:
_axes_names = list(reference_frame.representation_component_names.values())
if 'distance' in _axes_names:
_axes_names.remove('distance')
if axes_names is None:
axes_names = _axes_names
naxes = len(_axes_names)
_unit = list(reference_frame.representation_component_units.values())
if unit is None and _unit:
unit = _unit
if axes_order is None:
axes_order = tuple(range(naxes))
if unit is None:
unit = tuple([u.degree] * naxes)
axes_type = ['SPATIAL'] * naxes
super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type,
axes_order=axes_order,
reference_frame=reference_frame,
unit=unit,
axes_names=axes_names,
name=name, axis_physical_types=axis_physical_types)
@property
def _default_axis_physical_types(self):
if isinstance(self.reference_frame, coord.Galactic):
return "pos.galactic.lon", "pos.galactic.lat"
elif isinstance(self.reference_frame, (coord.GeocentricTrueEcliptic,
coord.GCRS,
coord.PrecessedGeocentric)):
return "pos.bodyrc.lon", "pos.bodyrc.lat"
elif isinstance(self.reference_frame, coord.builtin_frames.BaseRADecFrame):
return "pos.eq.ra", "pos.eq.dec"
elif isinstance(self.reference_frame, coord.builtin_frames.BaseEclipticFrame):
return "pos.ecliptic.lon", "pos.ecliptic.lat"
else:
return tuple("custom:{}".format(t) for t in self.axes_names)
@property
def _world_axis_object_classes(self):
return {'celestial': (
coord.SkyCoord,
(),
{'frame': self.reference_frame,
'unit': self.unit})}
@property
def _world_axis_object_components(self):
return [('celestial', 0, 'spherical.lon'),
('celestial', 1, 'spherical.lat')]
def coordinates(self, *args):
"""
Create a SkyCoord object.
Parameters
----------
args : float
inputs to wcs.input_frame
"""
if isinstance(args[0], coord.SkyCoord):
return args[0].transform_to(self.reference_frame)
return coord.SkyCoord(*args, unit=self.unit, frame=self.reference_frame)
def coordinate_to_quantity(self, *coords):
""" Convert a ``SkyCoord`` object to quantities."""
if len(coords) == 2:
arg = coords
elif len(coords) == 1:
arg = coords[0]
else:
raise ValueError("Unexpected number of coordinates in "
"input to frame {} : "
"expected 2, got {}".format(self.name, len(coords)))
if isinstance(arg, coord.SkyCoord):
arg = arg.transform_to(self._reference_frame)
try:
lon = arg.data.lon
lat = arg.data.lat
except AttributeError:
lon = arg.spherical.lon
lat = arg.spherical.lat
return lon, lat
elif all(isinstance(a, u.Quantity) for a in arg):
return tuple(arg)
else:
raise ValueError("Could not convert input {} to lon and lat quantities.".format(arg))
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|
44,119 | gwcs.coordinate_frames | __init__ | null | def __init__(self, axes_order=None, reference_frame=None,
unit=None, axes_names=None,
name=None, axis_physical_types=None):
naxes = 2
if reference_frame is not None:
if not isinstance(reference_frame, str):
if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES:
_axes_names = list(reference_frame.representation_component_names.values())
if 'distance' in _axes_names:
_axes_names.remove('distance')
if axes_names is None:
axes_names = _axes_names
naxes = len(_axes_names)
_unit = list(reference_frame.representation_component_units.values())
if unit is None and _unit:
unit = _unit
if axes_order is None:
axes_order = tuple(range(naxes))
if unit is None:
unit = tuple([u.degree] * naxes)
axes_type = ['SPATIAL'] * naxes
super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type,
axes_order=axes_order,
reference_frame=reference_frame,
unit=unit,
axes_names=axes_names,
name=name, axis_physical_types=axis_physical_types)
| (self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None, axis_physical_types=None) | [
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|
44,120 | gwcs.coordinate_frames | __repr__ | null | def __repr__(self):
fmt = '<{0}(name="{1}", unit={2}, axes_names={3}, axes_order={4}'.format(
self.__class__.__name__, self.name,
self.unit, self.axes_names, self.axes_order)
if self.reference_position is not None:
fmt += ', reference_position="{0}"'.format(self.reference_position)
if self.reference_frame is not None:
fmt += ", reference_frame={0}".format(self.reference_frame)
fmt += ")>"
return fmt
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|
44,121 | gwcs.coordinate_frames | __str__ | null | def __str__(self):
if self._name is not None:
return self._name
return self.__class__.__name__
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|
44,122 | gwcs.coordinate_frames | _set_axis_physical_types |
Set the physical type of the coordinate axes using VO UCD1+ v1.23 definitions.
| def _set_axis_physical_types(self, pht):
"""
Set the physical type of the coordinate axes using VO UCD1+ v1.23 definitions.
"""
if pht is not None:
if isinstance(pht, str):
pht = (pht,)
elif not isiterable(pht):
raise TypeError("axis_physical_types must be of type string or iterable of strings")
if len(pht) != self.naxes:
raise ValueError('"axis_physical_types" must be of length {}'.format(self.naxes))
ph_type = []
for axt in pht:
if axt not in VALID_UCDS and not axt.startswith("custom:"):
ph_type.append("custom:{}".format(axt))
else:
ph_type.append(axt)
validate_physical_types(ph_type)
return tuple(ph_type)
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|
44,123 | gwcs.coordinate_frames | coordinate_to_quantity | Convert a ``SkyCoord`` object to quantities. | def coordinate_to_quantity(self, *coords):
""" Convert a ``SkyCoord`` object to quantities."""
if len(coords) == 2:
arg = coords
elif len(coords) == 1:
arg = coords[0]
else:
raise ValueError("Unexpected number of coordinates in "
"input to frame {} : "
"expected 2, got {}".format(self.name, len(coords)))
if isinstance(arg, coord.SkyCoord):
arg = arg.transform_to(self._reference_frame)
try:
lon = arg.data.lon
lat = arg.data.lat
except AttributeError:
lon = arg.spherical.lon
lat = arg.spherical.lat
return lon, lat
elif all(isinstance(a, u.Quantity) for a in arg):
return tuple(arg)
else:
raise ValueError("Could not convert input {} to lon and lat quantities.".format(arg))
| (self, *coords) | [
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|
44,124 | gwcs.coordinate_frames | coordinates |
Create a SkyCoord object.
Parameters
----------
args : float
inputs to wcs.input_frame
| def coordinates(self, *args):
"""
Create a SkyCoord object.
Parameters
----------
args : float
inputs to wcs.input_frame
"""
if isinstance(args[0], coord.SkyCoord):
return args[0].transform_to(self.reference_frame)
return coord.SkyCoord(*args, unit=self.unit, frame=self.reference_frame)
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|
44,125 | gwcs.coordinate_frames | CompositeFrame |
Represents one or more frames.
Parameters
----------
frames : list
List of frames (TemporalFrame, CelestialFrame, SpectralFrame, CoordinateFrame).
name : str
Name for this frame.
| class CompositeFrame(CoordinateFrame):
"""
Represents one or more frames.
Parameters
----------
frames : list
List of frames (TemporalFrame, CelestialFrame, SpectralFrame, CoordinateFrame).
name : str
Name for this frame.
"""
def __init__(self, frames, name=None):
self._frames = frames[:]
naxes = sum([frame._naxes for frame in self._frames])
axes_type = list(range(naxes))
unit = list(range(naxes))
axes_names = list(range(naxes))
axes_order = []
ph_type = list(range(naxes))
for frame in frames:
axes_order.extend(frame.axes_order)
for frame in frames:
for ind, axtype, un, n, pht in zip(frame.axes_order, frame.axes_type,
frame.unit, frame.axes_names, frame.axis_physical_types):
axes_type[ind] = axtype
axes_names[ind] = n
unit[ind] = un
ph_type[ind] = pht
if len(np.unique(axes_order)) != len(axes_order):
raise ValueError("Incorrect numbering of axes, "
"axes_order should contain unique numbers, "
"got {}.".format(axes_order))
super(CompositeFrame, self).__init__(naxes, axes_type=axes_type,
axes_order=axes_order,
unit=unit, axes_names=axes_names,
name=name)
self._axis_physical_types = tuple(ph_type)
@property
def frames(self):
return self._frames
def __repr__(self):
return repr(self.frames)
def coordinates(self, *args):
coo = []
if len(args) == len(self.frames):
for frame, arg in zip(self.frames, args):
coo.append(frame.coordinates(arg))
else:
for frame in self.frames:
fargs = [args[i] for i in frame.axes_order]
coo.append(frame.coordinates(*fargs))
return coo
def coordinate_to_quantity(self, *coords):
if len(coords) == len(self.frames):
args = coords
elif len(coords) == self.naxes:
args = []
for _frame in self.frames:
if _frame.naxes > 1:
# Collect the arguments for this frame based on axes_order
args.append([coords[i] for i in _frame.axes_order])
else:
args.append(coords[_frame.axes_order[0]])
else:
raise ValueError("Incorrect number of arguments")
qs = []
for _frame, arg in zip(self.frames, args):
ret = _frame.coordinate_to_quantity(arg)
if isinstance(ret, tuple):
qs += list(ret)
else:
qs.append(ret)
return qs
@property
def _wao_classes_rename_map(self):
mapper = defaultdict(dict)
seen_names = []
for frame in self.frames:
# ensure the frame is in the mapper
mapper[frame]
for key in frame._world_axis_object_classes.keys():
if key in seen_names:
new_key = f"{key}{seen_names.count(key)}"
mapper[frame][key] = new_key
seen_names.append(key)
return mapper
@property
def _wao_renamed_components_iter(self):
mapper = self._wao_classes_rename_map
for frame in self.frames:
renamed_components = []
for comp in frame._world_axis_object_components:
comp = list(comp)
rename = mapper[frame].get(comp[0])
if rename:
comp[0] = rename
renamed_components.append(tuple(comp))
yield frame, renamed_components
@property
def _wao_renamed_classes_iter(self):
mapper = self._wao_classes_rename_map
for frame in self.frames:
for key, value in frame._world_axis_object_classes.items():
rename = mapper[frame].get(key)
if rename:
key = rename
yield key, value
@property
def _world_axis_object_components(self):
"""
We need to generate the components respecting the axes_order.
"""
out = [None] * self.naxes
for frame, components in self._wao_renamed_components_iter:
for i, ao in enumerate(frame.axes_order):
out[ao] = components[i]
if any([o is None for o in out]):
raise ValueError("axes_order leads to incomplete world_axis_object_components")
return out
@property
def _world_axis_object_classes(self):
return dict(self._wao_renamed_classes_iter)
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|
44,126 | gwcs.coordinate_frames | __init__ | null | def __init__(self, frames, name=None):
self._frames = frames[:]
naxes = sum([frame._naxes for frame in self._frames])
axes_type = list(range(naxes))
unit = list(range(naxes))
axes_names = list(range(naxes))
axes_order = []
ph_type = list(range(naxes))
for frame in frames:
axes_order.extend(frame.axes_order)
for frame in frames:
for ind, axtype, un, n, pht in zip(frame.axes_order, frame.axes_type,
frame.unit, frame.axes_names, frame.axis_physical_types):
axes_type[ind] = axtype
axes_names[ind] = n
unit[ind] = un
ph_type[ind] = pht
if len(np.unique(axes_order)) != len(axes_order):
raise ValueError("Incorrect numbering of axes, "
"axes_order should contain unique numbers, "
"got {}.".format(axes_order))
super(CompositeFrame, self).__init__(naxes, axes_type=axes_type,
axes_order=axes_order,
unit=unit, axes_names=axes_names,
name=name)
self._axis_physical_types = tuple(ph_type)
| (self, frames, name=None) | [
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|
44,127 | gwcs.coordinate_frames | __repr__ | null | def __repr__(self):
return repr(self.frames)
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|
44,130 | gwcs.coordinate_frames | coordinate_to_quantity | null | def coordinate_to_quantity(self, *coords):
if len(coords) == len(self.frames):
args = coords
elif len(coords) == self.naxes:
args = []
for _frame in self.frames:
if _frame.naxes > 1:
# Collect the arguments for this frame based on axes_order
args.append([coords[i] for i in _frame.axes_order])
else:
args.append(coords[_frame.axes_order[0]])
else:
raise ValueError("Incorrect number of arguments")
qs = []
for _frame, arg in zip(self.frames, args):
ret = _frame.coordinate_to_quantity(arg)
if isinstance(ret, tuple):
qs += list(ret)
else:
qs.append(ret)
return qs
| (self, *coords) | [
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|
44,131 | gwcs.coordinate_frames | coordinates | null | def coordinates(self, *args):
coo = []
if len(args) == len(self.frames):
for frame, arg in zip(self.frames, args):
coo.append(frame.coordinates(arg))
else:
for frame in self.frames:
fargs = [args[i] for i in frame.axes_order]
coo.append(frame.coordinates(*fargs))
return coo
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|
44,132 | gwcs.coordinate_frames | CoordinateFrame |
Base class for Coordinate Frames.
Parameters
----------
naxes : int
Number of axes.
axes_type : str
One of ["SPATIAL", "SPECTRAL", "TIME"]
axes_order : tuple of int
A dimension in the input data that corresponds to this axis.
reference_frame : astropy.coordinates.builtin_frames
Reference frame (usually used with output_frame to convert to world coordinate objects).
reference_position : str
Reference position - one of ``STANDARD_REFERENCE_POSITION``
unit : list of astropy.units.Unit
Unit for each axis.
axes_names : list
Names of the axes in this frame.
name : str
Name of this frame.
| class CoordinateFrame:
"""
Base class for Coordinate Frames.
Parameters
----------
naxes : int
Number of axes.
axes_type : str
One of ["SPATIAL", "SPECTRAL", "TIME"]
axes_order : tuple of int
A dimension in the input data that corresponds to this axis.
reference_frame : astropy.coordinates.builtin_frames
Reference frame (usually used with output_frame to convert to world coordinate objects).
reference_position : str
Reference position - one of ``STANDARD_REFERENCE_POSITION``
unit : list of astropy.units.Unit
Unit for each axis.
axes_names : list
Names of the axes in this frame.
name : str
Name of this frame.
"""
def __init__(self, naxes, axes_type, axes_order, reference_frame=None,
reference_position=None, unit=None, axes_names=None,
name=None, axis_physical_types=None):
self._naxes = naxes
self._axes_order = tuple(axes_order)
if isinstance(axes_type, str):
self._axes_type = (axes_type,)
else:
self._axes_type = tuple(axes_type)
self._reference_frame = reference_frame
if unit is not None:
if astutil.isiterable(unit):
unit = tuple(unit)
else:
unit = (unit,)
if len(unit) != naxes:
raise ValueError("Number of units does not match number of axes.")
else:
self._unit = tuple([u.Unit(au) for au in unit])
else:
self._unit = tuple(u.Unit("") for na in range(naxes))
if axes_names is not None:
if isinstance(axes_names, str):
axes_names = (axes_names,)
else:
axes_names = tuple(axes_names)
if len(axes_names) != naxes:
raise ValueError("Number of axes names does not match number of axes.")
else:
axes_names = tuple([""] * naxes)
self._axes_names = axes_names
if name is None:
self._name = self.__class__.__name__
else:
self._name = name
self._reference_position = reference_position
if len(self._axes_type) != naxes:
raise ValueError("Length of axes_type does not match number of axes.")
if len(self._axes_order) != naxes:
raise ValueError("Length of axes_order does not match number of axes.")
super(CoordinateFrame, self).__init__()
# _axis_physical_types holds any user supplied physical types
self._axis_physical_types = self._set_axis_physical_types(axis_physical_types)
def _set_axis_physical_types(self, pht):
"""
Set the physical type of the coordinate axes using VO UCD1+ v1.23 definitions.
"""
if pht is not None:
if isinstance(pht, str):
pht = (pht,)
elif not isiterable(pht):
raise TypeError("axis_physical_types must be of type string or iterable of strings")
if len(pht) != self.naxes:
raise ValueError('"axis_physical_types" must be of length {}'.format(self.naxes))
ph_type = []
for axt in pht:
if axt not in VALID_UCDS and not axt.startswith("custom:"):
ph_type.append("custom:{}".format(axt))
else:
ph_type.append(axt)
validate_physical_types(ph_type)
return tuple(ph_type)
def __repr__(self):
fmt = '<{0}(name="{1}", unit={2}, axes_names={3}, axes_order={4}'.format(
self.__class__.__name__, self.name,
self.unit, self.axes_names, self.axes_order)
if self.reference_position is not None:
fmt += ', reference_position="{0}"'.format(self.reference_position)
if self.reference_frame is not None:
fmt += ", reference_frame={0}".format(self.reference_frame)
fmt += ")>"
return fmt
def __str__(self):
if self._name is not None:
return self._name
return self.__class__.__name__
@property
def name(self):
""" A custom name of this frame."""
return self._name
@name.setter
def name(self, val):
""" A custom name of this frame."""
self._name = val
@property
def naxes(self):
""" The number of axes in this frame."""
return self._naxes
@property
def unit(self):
"""The unit of this frame."""
return self._unit
@property
def axes_names(self):
""" Names of axes in the frame."""
return self._axes_names
@property
def axes_order(self):
""" A tuple of indices which map inputs to axes."""
return self._axes_order
@property
def reference_frame(self):
""" Reference frame, used to convert to world coordinate objects. """
return self._reference_frame
@property
def reference_position(self):
""" Reference Position. """
return getattr(self, "_reference_position", None)
@property
def axes_type(self):
""" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. """
return self._axes_type
def coordinates(self, *args):
""" Create world coordinates object"""
coo = tuple([arg * un if not hasattr(arg, "to") else arg.to(un) for arg, un in zip(args, self.unit)])
if self.naxes == 1:
return coo[0]
return coo
def coordinate_to_quantity(self, *coords):
"""
Given a rich coordinate object return an astropy quantity object.
"""
# NoOp leaves it to the model to handle
# If coords is a 1-tuple of quantity then return the element of the tuple
# This aligns the behavior with the other implementations
if not hasattr(coords, 'unit') and len(coords) == 1:
return coords[0]
return coords
@property
def _default_axis_physical_types(self):
"""
The default physical types to use for this frame if none are specified
by the user.
"""
return tuple("custom:{}".format(t) for t in self.axes_type)
@property
def axis_physical_types(self):
"""
The axis physical types for this frame.
These physical types are the types in frame order, not transform order.
"""
return self._axis_physical_types or self._default_axis_physical_types
@property
def _world_axis_object_classes(self):
return {f"{at}{i}" if i != 0 else at: (u.Quantity,
(),
{'unit': unit})
for i, (at, unit) in enumerate(zip(self._axes_type, self.unit))}
@property
def _world_axis_object_components(self):
return [(f"{at}{i}" if i != 0 else at, 0, 'value') for i, at in enumerate(self._axes_type)]
| (naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None, axis_physical_types=None) | [
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|
44,133 | gwcs.coordinate_frames | __init__ | null | def __init__(self, naxes, axes_type, axes_order, reference_frame=None,
reference_position=None, unit=None, axes_names=None,
name=None, axis_physical_types=None):
self._naxes = naxes
self._axes_order = tuple(axes_order)
if isinstance(axes_type, str):
self._axes_type = (axes_type,)
else:
self._axes_type = tuple(axes_type)
self._reference_frame = reference_frame
if unit is not None:
if astutil.isiterable(unit):
unit = tuple(unit)
else:
unit = (unit,)
if len(unit) != naxes:
raise ValueError("Number of units does not match number of axes.")
else:
self._unit = tuple([u.Unit(au) for au in unit])
else:
self._unit = tuple(u.Unit("") for na in range(naxes))
if axes_names is not None:
if isinstance(axes_names, str):
axes_names = (axes_names,)
else:
axes_names = tuple(axes_names)
if len(axes_names) != naxes:
raise ValueError("Number of axes names does not match number of axes.")
else:
axes_names = tuple([""] * naxes)
self._axes_names = axes_names
if name is None:
self._name = self.__class__.__name__
else:
self._name = name
self._reference_position = reference_position
if len(self._axes_type) != naxes:
raise ValueError("Length of axes_type does not match number of axes.")
if len(self._axes_order) != naxes:
raise ValueError("Length of axes_order does not match number of axes.")
super(CoordinateFrame, self).__init__()
# _axis_physical_types holds any user supplied physical types
self._axis_physical_types = self._set_axis_physical_types(axis_physical_types)
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|
44,137 | gwcs.coordinate_frames | coordinate_to_quantity |
Given a rich coordinate object return an astropy quantity object.
| def coordinate_to_quantity(self, *coords):
"""
Given a rich coordinate object return an astropy quantity object.
"""
# NoOp leaves it to the model to handle
# If coords is a 1-tuple of quantity then return the element of the tuple
# This aligns the behavior with the other implementations
if not hasattr(coords, 'unit') and len(coords) == 1:
return coords[0]
return coords
| (self, *coords) | [
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|
44,138 | gwcs.coordinate_frames | coordinates | Create world coordinates object | def coordinates(self, *args):
""" Create world coordinates object"""
coo = tuple([arg * un if not hasattr(arg, "to") else arg.to(un) for arg, un in zip(args, self.unit)])
if self.naxes == 1:
return coo[0]
return coo
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|
44,139 | gwcs.coordinate_frames | Frame2D |
A 2D coordinate frame.
Parameters
----------
axes_order : tuple of int
A dimension in the input data that corresponds to this axis.
unit : list of astropy.units.Unit
Unit for each axis.
axes_names : list
Names of the axes in this frame.
name : str
Name of this frame.
| class Frame2D(CoordinateFrame):
"""
A 2D coordinate frame.
Parameters
----------
axes_order : tuple of int
A dimension in the input data that corresponds to this axis.
unit : list of astropy.units.Unit
Unit for each axis.
axes_names : list
Names of the axes in this frame.
name : str
Name of this frame.
"""
def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'),
name=None, axis_physical_types=None):
super(Frame2D, self).__init__(naxes=2, axes_type=["SPATIAL", "SPATIAL"],
axes_order=axes_order, name=name,
axes_names=axes_names, unit=unit,
axis_physical_types=axis_physical_types)
@property
def _default_axis_physical_types(self):
if all(self.axes_names):
ph_type = self.axes_names
else:
ph_type = self.axes_type
return tuple("custom:{}".format(t) for t in ph_type)
def coordinates(self, *args):
args = [args[i] for i in self.axes_order]
coo = tuple([arg * un for arg, un in zip(args, self.unit)])
return coo
def coordinate_to_quantity(self, *coords):
# list or tuple
if len(coords) == 1 and astutil.isiterable(coords[0]):
coords = list(coords[0])
elif len(coords) == 2:
coords = list(coords)
else:
raise ValueError("Unexpected number of coordinates in "
"input to frame {} : "
"expected 2, got {}".format(self.name, len(coords)))
for i in range(2):
if not hasattr(coords[i], 'unit'):
coords[i] = coords[i] * self.unit[i]
return tuple(coords)
| (axes_order=(0, 1), unit=(Unit("pix"), Unit("pix")), axes_names=('x', 'y'), name=None, axis_physical_types=None) | [
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|
44,140 | gwcs.coordinate_frames | __init__ | null | def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'),
name=None, axis_physical_types=None):
super(Frame2D, self).__init__(naxes=2, axes_type=["SPATIAL", "SPATIAL"],
axes_order=axes_order, name=name,
axes_names=axes_names, unit=unit,
axis_physical_types=axis_physical_types)
| (self, axes_order=(0, 1), unit=(Unit("pix"), Unit("pix")), axes_names=('x', 'y'), name=None, axis_physical_types=None) | [
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|
44,144 | gwcs.coordinate_frames | coordinate_to_quantity | null | def coordinate_to_quantity(self, *coords):
# list or tuple
if len(coords) == 1 and astutil.isiterable(coords[0]):
coords = list(coords[0])
elif len(coords) == 2:
coords = list(coords)
else:
raise ValueError("Unexpected number of coordinates in "
"input to frame {} : "
"expected 2, got {}".format(self.name, len(coords)))
for i in range(2):
if not hasattr(coords[i], 'unit'):
coords[i] = coords[i] * self.unit[i]
return tuple(coords)
| (self, *coords) | [
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|
44,145 | gwcs.coordinate_frames | coordinates | null | def coordinates(self, *args):
args = [args[i] for i in self.axes_order]
coo = tuple([arg * un for arg, un in zip(args, self.unit)])
return coo
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|
44,146 | gwcs.selector | LabelMapper |
Maps inputs to regions. Returns the region labels corresponding to the inputs.
Labels are strings or numbers which uniquely identify a location.
For example, labels may represent slices of an IFU or names of spherical polygons.
Parameters
----------
mapper : `~astropy.modeling.Model`
A function which returns a region.
no_label : str or int
"" or 0
A return value for a location which has no corresponding label.
inputs_mapping : `~astropy.modeling.mappings.Mapping` or tuple
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order.
If tuple, a `~astropy.modeling.mappings.Mapping` model will be created from it.
name : str
The name of this transform.
| class LabelMapper(_LabelMapper):
"""
Maps inputs to regions. Returns the region labels corresponding to the inputs.
Labels are strings or numbers which uniquely identify a location.
For example, labels may represent slices of an IFU or names of spherical polygons.
Parameters
----------
mapper : `~astropy.modeling.Model`
A function which returns a region.
no_label : str or int
"" or 0
A return value for a location which has no corresponding label.
inputs_mapping : `~astropy.modeling.mappings.Mapping` or tuple
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order.
If tuple, a `~astropy.modeling.mappings.Mapping` model will be created from it.
name : str
The name of this transform.
"""
n_outputs = 1
def __init__(self, inputs, mapper, no_label=np.nan, inputs_mapping=None, name=None, **kwargs):
self._no_label = no_label
self._inputs = inputs
self._n_inputs = len(inputs)
self._outputs = tuple(['x{0}'.format(ind) for ind in list(range(mapper.n_outputs))])
if isinstance(inputs_mapping, tuple):
inputs_mapping = astmodels.Mapping(inputs_mapping)
elif inputs_mapping is not None and not isinstance(inputs_mapping, astmodels.Mapping):
raise TypeError("inputs_mapping must be an instance of astropy.modeling.Mapping.")
self._inputs_mapping = inputs_mapping
self._mapper = mapper
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(_LabelMapper, self).__init__(name=name, **kwargs)
self.outputs = ('label',)
@property
def inputs(self):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
return self._inputs
@inputs.setter
def inputs(self, val):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
self._inputs = val
@property
def n_inputs(self):
return self._n_inputs
@property
def mapper(self):
return self._mapper
@property
def inputs_mapping(self):
return self._inputs_mapping
@property
def no_label(self):
return self._no_label
def evaluate(self, *args):
if self.inputs_mapping is not None:
args = self.inputs_mapping(*args)
if self.n_outputs == 1:
args = [args]
res = self.mapper(*args)
if np.isscalar(res):
res = np.array([res])
return np.array(res)
| (inputs, mapper, no_label=nan, inputs_mapping=None, name=None, **kwargs) | [
0.062494415789842606,
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|
44,147 | astropy.modeling.core | <lambda> | null | def _model_oper(oper, **kwargs):
"""
Returns a function that evaluates a given Python arithmetic operator
between two models. The operator should be given as a string, like ``'+'``
or ``'**'``.
"""
return lambda left, right: CompoundModel(oper, left, right, **kwargs)
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|
44,149 | astropy.modeling.core | __call__ |
Evaluate this model using the given input(s) and the parameter values
that were specified when the model was instantiated.
| def __call__(self, *args, **kwargs):
"""
Evaluate this model using the given input(s) and the parameter values
that were specified when the model was instantiated.
"""
# Turn any keyword arguments into positional arguments.
args, kwargs = self._get_renamed_inputs_as_positional(*args, **kwargs)
# Read model evaluation related parameters
with_bbox = kwargs.pop("with_bounding_box", False)
fill_value = kwargs.pop("fill_value", np.nan)
# prepare for model evaluation (overridden in CompoundModel)
evaluate, inputs, broadcasted_shapes, kwargs = self._pre_evaluate(
*args, **kwargs
)
outputs = self._generic_evaluate(evaluate, inputs, fill_value, with_bbox)
# post-process evaluation results (overridden in CompoundModel)
return self._post_evaluate(
inputs, outputs, broadcasted_shapes, with_bbox, **kwargs
)
| (self, *args, **kwargs) | [
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|
44,150 | gwcs.selector | __init__ | null | def __init__(self, inputs, mapper, no_label=np.nan, inputs_mapping=None, name=None, **kwargs):
self._no_label = no_label
self._inputs = inputs
self._n_inputs = len(inputs)
self._outputs = tuple(['x{0}'.format(ind) for ind in list(range(mapper.n_outputs))])
if isinstance(inputs_mapping, tuple):
inputs_mapping = astmodels.Mapping(inputs_mapping)
elif inputs_mapping is not None and not isinstance(inputs_mapping, astmodels.Mapping):
raise TypeError("inputs_mapping must be an instance of astropy.modeling.Mapping.")
self._inputs_mapping = inputs_mapping
self._mapper = mapper
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(_LabelMapper, self).__init__(name=name, **kwargs)
self.outputs = ('label',)
| (self, inputs, mapper, no_label=nan, inputs_mapping=None, name=None, **kwargs) | [
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|
44,151 | astropy.modeling.core | __len__ | null | def __len__(self):
return self._n_models
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|
44,155 | astropy.modeling.core | __repr__ | null | def __repr__(self):
return self._format_repr()
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|
44,156 | astropy.modeling.core | __setattr__ | null | def __setattr__(self, attr, value):
if isinstance(self, CompoundModel):
param_names = self._param_names
param_names = self.param_names
if param_names is not None and attr in self.param_names:
param = self.__dict__[attr]
value = _tofloat(value)
if param._validator is not None:
param._validator(self, value)
# check consistency with previous shape and size
eshape = self._param_metrics[attr]["shape"]
if eshape == ():
eshape = (1,)
vshape = np.array(value).shape
if vshape == ():
vshape = (1,)
esize = self._param_metrics[attr]["size"]
if np.size(value) != esize or self._strip_ones(vshape) != self._strip_ones(
eshape
):
raise InputParameterError(
f"Value for parameter {attr} does not match shape or size\nexpected"
f" by model ({vshape}, {np.size(value)}) vs ({eshape}, {esize})"
)
if param.unit is None:
if isinstance(value, Quantity):
param._unit = value.unit
param.value = value.value
else:
param.value = value
else:
if not isinstance(value, Quantity):
raise UnitsError(
f"The '{param.name}' parameter should be given as a"
" Quantity because it was originally "
"initialized as a Quantity"
)
param._unit = value.unit
param.value = value.value
else:
if attr in ["fittable", "linear"]:
self.__dict__[attr] = value
else:
super().__setattr__(attr, value)
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|
44,157 | astropy.modeling.core | __str__ | null | def __str__(self):
return self._format_str()
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|
44,160 | astropy.modeling.core | _array_to_parameters | null | def _array_to_parameters(self):
param_metrics = self._param_metrics
for name in self.param_names:
param = getattr(self, name)
value = self._parameters[param_metrics[name]["slice"]]
value.shape = param_metrics[name]["shape"]
param.value = value
| (self) | [
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|
44,161 | astropy.modeling.core | _calculate_separability_matrix |
This is a hook which customises the behavior of modeling.separable.
This allows complex subclasses to customise the separability matrix.
If it returns `NotImplemented` the default behavior is used.
| def _calculate_separability_matrix(self):
"""
This is a hook which customises the behavior of modeling.separable.
This allows complex subclasses to customise the separability matrix.
If it returns `NotImplemented` the default behavior is used.
"""
return NotImplemented
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|
44,162 | astropy.modeling.core | _check_param_broadcast |
This subroutine checks that all parameter arrays can be broadcast
against each other, and determines the shapes parameters must have in
order to broadcast correctly.
If model_set_axis is None this merely checks that the parameters
broadcast and returns an empty dict if so. This mode is only used for
single model sets.
| def _check_param_broadcast(self, max_ndim):
"""
This subroutine checks that all parameter arrays can be broadcast
against each other, and determines the shapes parameters must have in
order to broadcast correctly.
If model_set_axis is None this merely checks that the parameters
broadcast and returns an empty dict if so. This mode is only used for
single model sets.
"""
all_shapes = []
model_set_axis = self._model_set_axis
for name in self.param_names:
param = getattr(self, name)
value = param.value
param_shape = np.shape(value)
param_ndim = len(param_shape)
if max_ndim is not None and param_ndim < max_ndim:
# All arrays have the same number of dimensions up to the
# model_set_axis dimension, but after that they may have a
# different number of trailing axes. The number of trailing
# axes must be extended for mutual compatibility. For example
# if max_ndim = 3 and model_set_axis = 0, an array with the
# shape (2, 2) must be extended to (2, 1, 2). However, an
# array with shape (2,) is extended to (2, 1).
new_axes = (1,) * (max_ndim - param_ndim)
if model_set_axis < 0:
# Just need to prepend axes to make up the difference
broadcast_shape = new_axes + param_shape
else:
broadcast_shape = (
param_shape[: model_set_axis + 1]
+ new_axes
+ param_shape[model_set_axis + 1 :]
)
self._param_metrics[name]["broadcast_shape"] = broadcast_shape
all_shapes.append(broadcast_shape)
else:
all_shapes.append(param_shape)
# Now check mutual broadcastability of all shapes
try:
check_broadcast(*all_shapes)
except IncompatibleShapeError as exc:
shape_a, shape_a_idx, shape_b, shape_b_idx = exc.args
param_a = self.param_names[shape_a_idx]
param_b = self.param_names[shape_b_idx]
raise InputParameterError(
f"Parameter {param_a!r} of shape {shape_a!r} cannot be broadcast with "
f"parameter {param_b!r} of shape {shape_b!r}. All parameter arrays "
"must have shapes that are mutually compatible according "
"to the broadcasting rules."
)
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|
44,163 | astropy.modeling.core | _default_inputs_outputs | null | def _default_inputs_outputs(self):
if self.n_inputs == 1 and self.n_outputs == 1:
self._inputs = ("x",)
self._outputs = ("y",)
elif self.n_inputs == 2 and self.n_outputs == 1:
self._inputs = ("x", "y")
self._outputs = ("z",)
else:
try:
self._inputs = tuple("x" + str(idx) for idx in range(self.n_inputs))
self._outputs = tuple("x" + str(idx) for idx in range(self.n_outputs))
except TypeError:
# self.n_inputs and self.n_outputs are properties
# This is the case when subclasses of Model do not define
# ``n_inputs``, ``n_outputs``, ``inputs`` or ``outputs``.
self._inputs = ()
self._outputs = ()
| (self) | [
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|
44,165 | astropy.modeling.core | _format_repr |
Internal implementation of ``__repr__``.
This is separated out for ease of use by subclasses that wish to
override the default ``__repr__`` while keeping the same basic
formatting.
| def _format_repr(self, args=[], kwargs={}, defaults={}):
"""
Internal implementation of ``__repr__``.
This is separated out for ease of use by subclasses that wish to
override the default ``__repr__`` while keeping the same basic
formatting.
"""
parts = [repr(a) for a in args]
parts.extend(
f"{name}={param_repr_oneline(getattr(self, name))}"
for name in self.param_names
)
if self.name is not None:
parts.append(f"name={self.name!r}")
for kwarg, value in kwargs.items():
if kwarg in defaults and defaults[kwarg] == value:
continue
parts.append(f"{kwarg}={value!r}")
if len(self) > 1:
parts.append(f"n_models={len(self)}")
return f"<{self.__class__.__name__}({', '.join(parts)})>"
| (self, args=[], kwargs={}, defaults={}) | [
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|
44,166 | astropy.modeling.core | _format_str |
Internal implementation of ``__str__``.
This is separated out for ease of use by subclasses that wish to
override the default ``__str__`` while keeping the same basic
formatting.
| def _format_str(self, keywords=[], defaults={}):
"""
Internal implementation of ``__str__``.
This is separated out for ease of use by subclasses that wish to
override the default ``__str__`` while keeping the same basic
formatting.
"""
default_keywords = [
("Model", self.__class__.__name__),
("Name", self.name),
("Inputs", self.inputs),
("Outputs", self.outputs),
("Model set size", len(self)),
]
parts = [
f"{keyword}: {value}"
for keyword, value in default_keywords
if value is not None
]
for keyword, value in keywords:
if keyword.lower() in defaults and defaults[keyword.lower()] == value:
continue
parts.append(f"{keyword}: {value}")
parts.append("Parameters:")
if len(self) == 1:
columns = [[getattr(self, name).value] for name in self.param_names]
else:
columns = [getattr(self, name).value for name in self.param_names]
if columns:
param_table = Table(columns, names=self.param_names)
# Set units on the columns
for name in self.param_names:
param_table[name].unit = getattr(self, name).unit
parts.append(indent(str(param_table), 4 * " "))
return "\n".join(parts)
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|
44,167 | astropy.modeling.core | _generic_evaluate | Generic model evaluation routine.
Selects and evaluates model with or without bounding_box enforcement.
| def _generic_evaluate(self, evaluate, _inputs, fill_value, with_bbox):
"""Generic model evaluation routine.
Selects and evaluates model with or without bounding_box enforcement.
"""
# Evaluate the model using the prepared evaluation method either
# enforcing the bounding_box or not.
bbox = self.get_bounding_box(with_bbox)
if (not isinstance(with_bbox, bool) or with_bbox) and bbox is not None:
outputs = bbox.evaluate(evaluate, _inputs, fill_value)
else:
outputs = evaluate(_inputs)
return outputs
| (self, evaluate, _inputs, fill_value, with_bbox) | [
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|
44,168 | astropy.modeling.core | _get_renamed_inputs_as_positional | null | def _get_renamed_inputs_as_positional(self, *args, **kwargs):
def _keyword2positional(kwargs):
# Inputs were passed as keyword (not positional) arguments.
# Because the signature of the ``__call__`` is defined at
# the class level, the name of the inputs cannot be changed at
# the instance level and the old names are always present in the
# signature of the method. In order to use the new names of the
# inputs, the old names are taken out of ``kwargs``, the input
# values are sorted in the order of self.inputs and passed as
# positional arguments to ``__call__``.
# These are the keys that are always present as keyword arguments.
keys = [
"model_set_axis",
"with_bounding_box",
"fill_value",
"equivalencies",
"inputs_map",
]
new_inputs = {}
# kwargs contain the names of the new inputs + ``keys``
allkeys = list(kwargs.keys())
# Remove the names of the new inputs from kwargs and save them
# to a dict ``new_inputs``.
for key in allkeys:
if key not in keys:
new_inputs[key] = kwargs[key]
del kwargs[key]
return new_inputs, kwargs
n_args = len(args)
new_inputs, kwargs = _keyword2positional(kwargs)
n_all_args = n_args + len(new_inputs)
if n_all_args < self.n_inputs:
raise ValueError(
f"Missing input arguments - expected {self.n_inputs}, got {n_all_args}"
)
elif n_all_args > self.n_inputs:
raise ValueError(
f"Too many input arguments - expected {self.n_inputs}, got {n_all_args}"
)
if n_args == 0:
# Create positional arguments from the keyword arguments in ``new_inputs``.
new_args = []
for k in self.inputs:
new_args.append(new_inputs[k])
elif n_args != self.n_inputs:
# Some inputs are passed as positional, others as keyword arguments.
args = list(args)
# Create positional arguments from the keyword arguments in ``new_inputs``.
new_args = []
for k in self.inputs:
if k in new_inputs:
new_args.append(new_inputs[k])
else:
new_args.append(args[0])
del args[0]
else:
new_args = args
return new_args, kwargs
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|
44,169 | astropy.modeling.core | _initialize_constraints |
Pop parameter constraint values off the keyword arguments passed to
`Model.__init__` and store them in private instance attributes.
| def _initialize_constraints(self, kwargs):
"""
Pop parameter constraint values off the keyword arguments passed to
`Model.__init__` and store them in private instance attributes.
"""
# Pop any constraints off the keyword arguments
for constraint in self.parameter_constraints:
values = kwargs.pop(constraint, {})
for ckey, cvalue in values.items():
param = getattr(self, ckey)
setattr(param, constraint, cvalue)
self._mconstraints = {}
for constraint in self.model_constraints:
values = kwargs.pop(constraint, [])
self._mconstraints[constraint] = values
| (self, kwargs) | [
0.019666366279125214,
0.022935064509510994,
-0.003801441751420498,
0.01385133434087038,
-0.06739658117294312,
-0.019846957176923752,
0.017815308645367622,
-0.027522077783942223,
0.03944108635187149,
-0.02338654175400734,
-0.005580263677984476,
0.046520255506038666,
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|
44,170 | astropy.modeling.core | _initialize_parameter_value | Mostly deals with consistency checks and determining unit issues. | def _initialize_parameter_value(self, param_name, value):
"""Mostly deals with consistency checks and determining unit issues."""
if isinstance(value, Parameter):
self.__dict__[param_name] = value
return
param = getattr(self, param_name)
# Use default if value is not provided
if value is None:
default = param.default
if default is None:
# No value was supplied for the parameter and the
# parameter does not have a default, therefore the model
# is underspecified
raise TypeError(
f"{self.__class__.__name__}.__init__() requires a value for "
f"parameter {param_name!r}"
)
value = default
unit = param.unit
else:
if isinstance(value, Quantity):
unit = value.unit
value = value.value
else:
unit = None
if unit is None and param.unit is not None:
raise InputParameterError(
f"{self.__class__.__name__}.__init__() requires a Quantity for"
f" parameter {param_name!r}"
)
param._unit = unit
param._set_unit(unit, force=True)
param.internal_unit = None
if param._setter is not None:
if unit is not None:
_val = param._setter(value * unit)
else:
_val = param._setter(value)
if isinstance(_val, Quantity):
param.internal_unit = _val.unit
param._internal_value = np.array(_val.value)
else:
param.internal_unit = None
param._internal_value = np.array(_val)
else:
param._value = np.array(value)
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|
44,171 | astropy.modeling.core | _initialize_parameters |
Initialize the _parameters array that stores raw parameter values for
all parameter sets for use with vectorized fitting algorithms; on
FittableModels the _param_name attributes actually just reference
slices of this array.
| def _initialize_parameters(self, args, kwargs):
"""
Initialize the _parameters array that stores raw parameter values for
all parameter sets for use with vectorized fitting algorithms; on
FittableModels the _param_name attributes actually just reference
slices of this array.
"""
n_models = kwargs.pop("n_models", None)
if not (
n_models is None
or (isinstance(n_models, (int, np.integer)) and n_models >= 1)
):
raise ValueError(
"n_models must be either None (in which case it is "
"determined from the model_set_axis of the parameter initial "
"values) or it must be a positive integer "
f"(got {n_models!r})"
)
model_set_axis = kwargs.pop("model_set_axis", None)
if model_set_axis is None:
if n_models is not None and n_models > 1:
# Default to zero
model_set_axis = 0
else:
# Otherwise disable
model_set_axis = False
else:
if not (
model_set_axis is False
or np.issubdtype(type(model_set_axis), np.integer)
):
raise ValueError(
"model_set_axis must be either False or an integer "
"specifying the parameter array axis to map to each "
f"model in a set of models (got {model_set_axis!r})."
)
# Process positional arguments by matching them up with the
# corresponding parameters in self.param_names--if any also appear as
# keyword arguments this presents a conflict
params = set()
if len(args) > len(self.param_names):
raise TypeError(
f"{self.__class__.__name__}.__init__() takes at most "
f"{len(self.param_names)} positional arguments ({len(args)} given)"
)
self._model_set_axis = model_set_axis
self._param_metrics = defaultdict(dict)
for idx, arg in enumerate(args):
if arg is None:
# A value of None implies using the default value, if exists
continue
# We use quantity_asanyarray here instead of np.asanyarray because
# if any of the arguments are quantities, we need to return a
# Quantity object not a plain Numpy array.
param_name = self.param_names[idx]
params.add(param_name)
if not isinstance(arg, Parameter):
value = quantity_asanyarray(arg, dtype=float)
else:
value = arg
self._initialize_parameter_value(param_name, value)
# At this point the only remaining keyword arguments should be
# parameter names; any others are in error.
for param_name in self.param_names:
if param_name in kwargs:
if param_name in params:
raise TypeError(
f"{self.__class__.__name__}.__init__() got multiple values for"
f" parameter {param_name!r}"
)
value = kwargs.pop(param_name)
if value is None:
continue
# We use quantity_asanyarray here instead of np.asanyarray
# because if any of the arguments are quantities, we need
# to return a Quantity object not a plain Numpy array.
value = quantity_asanyarray(value, dtype=float)
params.add(param_name)
self._initialize_parameter_value(param_name, value)
# Now deal with case where param_name is not supplied by args or kwargs
for param_name in self.param_names:
if param_name not in params:
self._initialize_parameter_value(param_name, None)
if kwargs:
# If any keyword arguments were left over at this point they are
# invalid--the base class should only be passed the parameter
# values, constraints, and param_dim
for kwarg in kwargs:
# Just raise an error on the first unrecognized argument
raise TypeError(
f"{self.__class__.__name__}.__init__() got an unrecognized"
f" parameter {kwarg!r}"
)
# Determine the number of model sets: If the model_set_axis is
# None then there is just one parameter set; otherwise it is determined
# by the size of that axis on the first parameter--if the other
# parameters don't have the right number of axes or the sizes of their
# model_set_axis don't match an error is raised
if model_set_axis is not False and n_models != 1 and params:
max_ndim = 0
if model_set_axis < 0:
min_ndim = abs(model_set_axis)
else:
min_ndim = model_set_axis + 1
for name in self.param_names:
value = getattr(self, name)
param_ndim = np.ndim(value)
if param_ndim < min_ndim:
raise InputParameterError(
"All parameter values must be arrays of dimension at least"
f" {min_ndim} for model_set_axis={model_set_axis} (the value"
f" given for {name!r} is only {param_ndim}-dimensional)"
)
max_ndim = max(max_ndim, param_ndim)
if n_models is None:
# Use the dimensions of the first parameter to determine
# the number of model sets
n_models = value.shape[model_set_axis]
elif value.shape[model_set_axis] != n_models:
raise InputParameterError(
f"Inconsistent dimensions for parameter {name!r} for"
f" {n_models} model sets. The length of axis"
f" {model_set_axis} must be the same for all input parameter"
" values"
)
self._check_param_broadcast(max_ndim)
else:
if n_models is None:
n_models = 1
self._check_param_broadcast(None)
self._n_models = n_models
# now validate parameters
for name in params:
param = getattr(self, name)
if param._validator is not None:
param._validator(self, param.value)
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|
44,172 | astropy.modeling.core | _initialize_setters |
This exists to inject defaults for settable properties for models
originating from `custom_model`.
| def _initialize_setters(self, kwargs):
"""
This exists to inject defaults for settable properties for models
originating from `custom_model`.
"""
if hasattr(self, "_settable_properties"):
setters = {
name: kwargs.pop(name, default)
for name, default in self._settable_properties.items()
}
for name, value in setters.items():
setattr(self, name, value)
return kwargs
| (self, kwargs) | [
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|
44,173 | astropy.modeling.core | _initialize_slices | null | def _initialize_slices(self):
param_metrics = self._param_metrics
total_size = 0
for name in self.param_names:
param = getattr(self, name)
value = param.value
param_size = np.size(value)
param_shape = np.shape(value)
param_slice = slice(total_size, total_size + param_size)
param_metrics[name]["slice"] = param_slice
param_metrics[name]["shape"] = param_shape
param_metrics[name]["size"] = param_size
total_size += param_size
self._parameters = np.empty(total_size, dtype=np.float64)
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|
44,174 | astropy.modeling.core | _initialize_unit_support |
Convert self._input_units_strict and
self.input_units_allow_dimensionless to dictionaries
mapping input name to a boolean value.
| def _initialize_unit_support(self):
"""
Convert self._input_units_strict and
self.input_units_allow_dimensionless to dictionaries
mapping input name to a boolean value.
"""
if isinstance(self._input_units_strict, bool):
self._input_units_strict = {
key: self._input_units_strict for key in self.inputs
}
if isinstance(self._input_units_allow_dimensionless, bool):
self._input_units_allow_dimensionless = {
key: self._input_units_allow_dimensionless for key in self.inputs
}
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|
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