#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
''' Provide a base class for objects that can have declarative, typed,
serializable properties.
.. note::
These classes form part of the very low-level machinery that implements
the Bokeh model and property system. It is unlikely that any of these
classes or their methods will be applicable to any standard usage or to
anyone who is not directly developing on Bokeh's own infrastructure.
'''
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import annotations
import logging # isort:skip
log = logging.getLogger(__name__)
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Standard library imports
import difflib
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Iterable,
Literal,
NoReturn,
Type,
TypedDict,
TypeVar,
Union,
overload,
)
from warnings import warn
if TYPE_CHECKING:
F = TypeVar("F", bound=Callable[..., Any])
def lru_cache(arg: int | None) -> Callable[[F], F]: ...
else:
from functools import lru_cache
# External imports
from typing_extensions import TypeAlias
# Bokeh imports
from ..util.strings import append_docstring, nice_join
from .property.descriptor_factory import PropertyDescriptorFactory
from .property.descriptors import PropertyDescriptor, UnsetValueError
from .property.override import Override
from .property.singletons import Intrinsic, Undefined
from .property.wrappers import PropertyValueContainer
from .serialization import (
ObjectRep,
Ref,
Serializable,
Serializer,
)
from .types import ID
if TYPE_CHECKING:
from ..client.session import ClientSession
from ..server.session import ServerSession
from .property.bases import Property
from .property.dataspec import DataSpec
#-----------------------------------------------------------------------------
# Globals and constants
#-----------------------------------------------------------------------------
__all__ = (
'abstract',
'HasProps',
'MetaHasProps',
'NonQualified',
'Qualified',
)
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
if TYPE_CHECKING:
Setter: TypeAlias = Union[ClientSession, ServerSession]
C = TypeVar("C", bound=Type["HasProps"])
[docs]def abstract(cls: C) -> C:
''' A decorator to mark abstract base classes derived from |HasProps|.
'''
if not issubclass(cls, HasProps):
raise TypeError(f"{cls.__name__} is not a subclass of HasProps")
cls.__doc__ = append_docstring(cls.__doc__, _ABSTRACT_ADMONITION)
return cls
def is_DataModel(cls: Type[HasProps]) -> bool:
from ..model import DataModel
return issubclass(cls, HasProps) and getattr(cls, "__data_model__", False) and cls != DataModel
def _overridden_defaults(class_dict: dict[str, Any]) -> dict[str, Any]:
overridden_defaults: dict[str, Any] = {}
for name, prop in tuple(class_dict.items()):
if isinstance(prop, Override):
del class_dict[name]
if prop.default_overridden:
overridden_defaults[name] = prop.default
return overridden_defaults
def _generators(class_dict: dict[str, Any]):
generators: dict[str, PropertyDescriptorFactory[Any]] = {}
for name, generator in tuple(class_dict.items()):
if isinstance(generator, PropertyDescriptorFactory):
del class_dict[name]
generators[name] = generator
return generators
class _ModelResolver:
""" """
_known_models: dict[str, Type[HasProps]]
def __init__(self) -> None:
self._known_models = {}
def add(self, cls: Type[HasProps]) -> None:
if not (issubclass(cls, Local) or cls.__name__.startswith("_")):
# update the mapping of view model names to classes, checking for any duplicates
previous = self._known_models.get(cls.__qualified_model__, None)
if previous is not None and not hasattr(cls, "__implementation__"):
raise Warning(f"Duplicate qualified model declaration of '{cls.__qualified_model__}'. Previous definition: {previous}")
self._known_models[cls.__qualified_model__] = cls
def remove(self, cls: Type[HasProps]) -> None:
del self._known_models[cls.__qualified_model__]
@property
def known_models(self) -> dict[str, Type[HasProps]]:
return dict(self._known_models)
def clear_extensions(self) -> None:
def is_extension(obj: Type[HasProps]) -> bool:
return getattr(obj, "__implementation__", None) is not None or \
getattr(obj, "__javascript__", None) is not None or \
getattr(obj, "__css__", None) is not None
self._known_models = {key: val for key, val in self._known_models.items() if not is_extension(val)}
_default_resolver = _ModelResolver()
class Local:
"""Don't register this class in model registry. """
[docs]class Qualified:
"""Resolve this class by a fully qualified name. """
[docs]class NonQualified:
"""Resolve this class by a non-qualified name. """
[docs]class HasProps(Serializable, metaclass=MetaHasProps):
''' Base class for all class types that have Bokeh properties.
.. autoclasstoc::
'''
_initialized: bool = False
_property_values: dict[str, Any]
_unstable_default_values: dict[str, Any]
_unstable_themed_values: dict[str, Any]
__view_model__: ClassVar[str]
__view_module__: ClassVar[str]
__qualified_model__: ClassVar[str]
__implementation__: ClassVar[Any] # TODO: specific type
__data_model__: ClassVar[bool]
@classmethod
def __init_subclass__(cls):
super().__init_subclass__()
# use an explicitly provided view model name if there is one
if "__view_model__" not in cls.__dict__:
cls.__view_model__ = cls.__qualname__.replace("<locals>.", "")
if "__view_module__" not in cls.__dict__:
cls.__view_module__ = cls.__module__
if "__qualified_model__" not in cls.__dict__:
def qualified():
module = cls.__view_module__
model = cls.__view_model__
if issubclass(cls, NonQualified):
return model
if not issubclass(cls, Qualified):
head = module.split(".")[0]
if head == "bokeh" or head == "__main__" or "__implementation__" in cls.__dict__:
return model
return f"{module}.{model}"
cls.__qualified_model__ = qualified()
_default_resolver.add(cls)
[docs] def __init__(self, **properties: Any) -> None:
'''
'''
super().__init__()
self._property_values = {}
self._unstable_default_values = {}
self._unstable_themed_values = {}
for name, value in properties.items():
# TODO: this would be better to handle in descriptors
if value is Undefined or value is Intrinsic:
continue
setattr(self, name, value)
for name in self.properties() - set(properties.keys()):
desc = self.lookup(name)
if desc.has_unstable_default(self):
desc._get(self) # this fills-in `_unstable_*_values`
self._initialized = True
def __setattr__(self, name: str, value: Any) -> None:
''' Intercept attribute setting on HasProps in order to special case
a few situations:
* short circuit all property machinery for ``_private`` attributes
* suggest similar attribute names on attribute errors
Args:
name (str) : the name of the attribute to set on this object
value (obj) : the value to set
Returns:
None
'''
if name.startswith("_"):
return super().__setattr__(name, value)
properties = self.properties(_with_props=True)
if name in properties:
return super().__setattr__(name, value)
descriptor = getattr(self.__class__, name, None)
if isinstance(descriptor, property): # Python property
return super().__setattr__(name, value)
self._raise_attribute_error_with_matches(name, properties)
def __getattr__(self, name: str) -> Any:
''' Intercept attribute setting on HasProps in order to special case
a few situations:
* short circuit all property machinery for ``_private`` attributes
* suggest similar attribute names on attribute errors
Args:
name (str) : the name of the attribute to set on this object
Returns:
Any
'''
if name.startswith("_"):
return super().__getattribute__(name)
properties = self.properties(_with_props=True)
if name in properties:
return super().__getattribute__(name)
descriptor = getattr(self.__class__, name, None)
if isinstance(descriptor, property): # Python property
return super().__getattribute__(name)
self._raise_attribute_error_with_matches(name, properties)
def _raise_attribute_error_with_matches(self, name: str, properties: Iterable[str]) -> NoReturn:
matches, text = difflib.get_close_matches(name.lower(), properties), "similar"
if not matches:
matches, text = sorted(properties), "possible"
raise AttributeError(f"unexpected attribute {name!r} to {self.__class__.__name__}, {text} attributes are {nice_join(matches)}")
def __str__(self) -> str:
name = self.__class__.__name__
return f"{name}(...)"
__repr__ = __str__
# Unfortunately we cannot implement __eq__. We rely on the default __hash__
# based on object identity, in order to put HasProps instances in sets.
# Implementing __eq__ as structural equality would necessitate a __hash__
# that returns the same value different HasProps instances that compare
# equal [1], and this would break many things.
#
# [1] https://docs.python.org/3/reference/datamodel.html#object.__hash__
#
[docs] def equals(self, other: HasProps) -> bool:
''' Structural equality of models.
Args:
other (HasProps) : the other instance to compare to
Returns:
True, if properties are structurally equal, otherwise False
'''
if not isinstance(other, self.__class__):
return False
else:
return self.properties_with_values() == other.properties_with_values()
[docs] def to_serializable(self, serializer: Serializer) -> ObjectRep:
rep = ObjectRep(
type="object",
name=self.__qualified_model__,
)
properties = self.properties_with_values(include_defaults=False)
attributes = {key: serializer.encode(val) for key, val in properties.items()}
if attributes:
rep["attributes"] = attributes
return rep
# FQ type name required to suppress Sphinx error "more than one target found for cross-reference 'JSON'"
[docs] def set_from_json(self, name: str, value: Any, *, setter: Setter | None = None) -> None:
''' Set a property value on this object from JSON.
Args:
name: (str) : name of the attribute to set
json: (JSON-value) : value to set to the attribute to
models (dict or None, optional) :
Mapping of model ids to models (default: None)
This is needed in cases where the attributes to update also
have values that have references.
setter(ClientSession or ServerSession or None, optional) :
This is used to prevent "boomerang" updates to Bokeh apps.
In the context of a Bokeh server application, incoming updates
to properties will be annotated with the session that is
doing the updating. This value is propagated through any
subsequent change notifications that the update triggers.
The session can compare the event setter to itself, and
suppress any updates that originate from itself.
Returns:
None
'''
if name in self.properties(_with_props=True):
log.trace(f"Patching attribute {name!r} of {self!r} with {value!r}") # type: ignore # TODO: log.trace()
descriptor = self.lookup(name)
descriptor.set_from_json(self, value, setter=setter)
else:
log.warning("JSON had attr %r on obj %r, which is a client-only or invalid attribute that shouldn't have been sent", name, self)
[docs] def update(self, **kwargs: Any) -> None:
''' Updates the object's properties from the given keyword arguments.
Returns:
None
Examples:
The following are equivalent:
.. code-block:: python
from bokeh.models import Range1d
r = Range1d
# set properties individually:
r.start = 10
r.end = 20
# update properties together:
r.update(start=10, end=20)
'''
for k, v in kwargs.items():
setattr(self, k, v)
@overload
@classmethod
def lookup(cls, name: str, *, raises: Literal[True] = True) -> PropertyDescriptor[Any]: ...
@overload
@classmethod
def lookup(cls, name: str, *, raises: Literal[False] = False) -> PropertyDescriptor[Any] | None: ...
[docs] @classmethod
def lookup(cls, name: str, *, raises: bool = True) -> PropertyDescriptor[Any] | None:
''' Find the ``PropertyDescriptor`` for a Bokeh property on a class,
given the property name.
Args:
name (str) : name of the property to search for
raises (bool) : whether to raise or return None if missing
Returns:
PropertyDescriptor : descriptor for property named ``name``
'''
attr = getattr(cls, name, None)
if attr is not None or (attr is None and not raises):
return attr
raise AttributeError(f"{cls.__name__}.{name} property descriptor does not exist")
@overload
@classmethod
@lru_cache(None)
def properties(cls, *, _with_props: Literal[False] = False) -> set[str]: ...
@overload
@classmethod
@lru_cache(None)
def properties(cls, *, _with_props: Literal[True] = True) -> dict[str, Property[Any]]: ...
[docs] @classmethod
@lru_cache(None)
def properties(cls, *, _with_props: bool = False) -> set[str] | dict[str, Property[Any]]:
''' Collect the names of properties on this class.
.. warning::
In a future version of Bokeh, this method will return a dictionary
mapping property names to property objects. To future-proof this
current usage of this method, wrap the return value in ``list``.
Returns:
property names
'''
props: dict[str, Property[Any]] = {}
for c in reversed(cls.__mro__):
props.update(getattr(c, "__properties__", {}))
if not _with_props:
return set(props)
return props
[docs] @classmethod
@lru_cache(None)
def descriptors(cls) -> list[PropertyDescriptor[Any]]:
""" List of property descriptors in the order of definition. """
return [ cls.lookup(name) for name, _ in cls.properties(_with_props=True).items() ]
[docs] @classmethod
@lru_cache(None)
def properties_with_refs(cls) -> dict[str, Property[Any]]:
''' Collect the names of all properties on this class that also have
references.
This method *always* traverses the class hierarchy and includes
properties defined on any parent classes.
Returns:
set[str] : names of properties that have references
'''
return {k: v for k, v in cls.properties(_with_props=True).items() if v.has_ref}
[docs] @classmethod
@lru_cache(None)
def dataspecs(cls) -> dict[str, DataSpec]:
''' Collect the names of all ``DataSpec`` properties on this class.
This method *always* traverses the class hierarchy and includes
properties defined on any parent classes.
Returns:
set[str] : names of ``DataSpec`` properties
'''
from .property.dataspec import DataSpec # avoid circular import
return {k: v for k, v in cls.properties(_with_props=True).items() if isinstance(v, DataSpec)}
[docs] def properties_with_values(self, *, include_defaults: bool = True, include_undefined: bool = False) -> dict[str, Any]:
''' Collect a dict mapping property names to their values.
This method *always* traverses the class hierarchy and includes
properties defined on any parent classes.
Non-serializable properties are skipped and property values are in
"serialized" format which may be slightly different from the values
you would normally read from the properties; the intent of this method
is to return the information needed to losslessly reconstitute the
object instance.
Args:
include_defaults (bool, optional) :
Whether to include properties that haven't been explicitly set
since the object was created. (default: True)
Returns:
dict : mapping from property names to their values
'''
return self.query_properties_with_values(lambda prop: prop.serialized,
include_defaults=include_defaults, include_undefined=include_undefined)
@classmethod
def _overridden_defaults(cls) -> dict[str, Any]:
''' Returns a dictionary of defaults that have been overridden.
.. note::
This is an implementation detail of ``Property``.
'''
defaults: dict[str, Any] = {}
for c in reversed(cls.__mro__):
defaults.update(getattr(c, "__overridden_defaults__", {}))
return defaults
[docs] def query_properties_with_values(self, query: Callable[[PropertyDescriptor[Any]], bool], *,
include_defaults: bool = True, include_undefined: bool = False) -> dict[str, Any]:
''' Query the properties values of |HasProps| instances with a
predicate.
Args:
query (callable) :
A callable that accepts property descriptors and returns True
or False
include_defaults (bool, optional) :
Whether to include properties that have not been explicitly
set by a user (default: True)
Returns:
dict : mapping of property names and values for matching properties
'''
themed_keys: set[str] = set()
result: dict[str, Any] = {}
keys = self.properties(_with_props=True)
if include_defaults:
selected_keys = set(keys)
else:
# TODO (bev) For now, include unstable default values. Things rely on Instances
# always getting serialized, even defaults, and adding unstable defaults here
# accomplishes that. Unmodified defaults for property value containers will be
# weeded out below.
selected_keys = set(self._property_values.keys()) | set(self._unstable_default_values.keys())
themed_values = self.themed_values()
if themed_values is not None:
themed_keys = set(themed_values.keys())
selected_keys |= themed_keys
for key in keys:
if key not in selected_keys:
continue
descriptor = self.lookup(key)
if not query(descriptor):
continue
try:
value = descriptor.get_value(self)
except UnsetValueError:
if include_undefined:
value = Undefined
else:
continue
else:
if not include_defaults and key not in themed_keys:
if isinstance(value, PropertyValueContainer) and key in self._unstable_default_values:
continue
result[key] = value
return result
[docs] def themed_values(self) -> dict[str, Any] | None:
''' Get any theme-provided overrides.
Results are returned as a dict from property name to value, or
``None`` if no theme overrides any values for this instance.
Returns:
dict or None
'''
return getattr(self, '__themed_values__', None)
[docs] def apply_theme(self, property_values: dict[str, Any]) -> None:
''' Apply a set of theme values which will be used rather than
defaults, but will not override application-set values.
The passed-in dictionary may be kept around as-is and shared with
other instances to save memory (so neither the caller nor the
|HasProps| instance should modify it).
Args:
property_values (dict) : theme values to use in place of defaults
Returns:
None
'''
old_dict = self.themed_values()
# if the same theme is set again, it should reuse the same dict
if old_dict is property_values: # lgtm [py/comparison-using-is]
return
removed: set[str] = set()
# we're doing a little song-and-dance to avoid storing __themed_values__ or
# an empty dict, if there's no theme that applies to this HasProps instance.
if old_dict is not None:
removed.update(set(old_dict.keys()))
added = set(property_values.keys())
old_values: dict[str, Any] = {}
for k in added.union(removed):
old_values[k] = getattr(self, k)
if len(property_values) > 0:
setattr(self, '__themed_values__', property_values)
elif hasattr(self, '__themed_values__'):
delattr(self, '__themed_values__')
# Property container values might be cached even if unmodified. Invalidate
# any cached values that are not modified at this point.
for k, v in old_values.items():
if k in self._unstable_themed_values:
del self._unstable_themed_values[k]
# Emit any change notifications that result
for k, v in old_values.items():
descriptor = self.lookup(k)
if isinstance(descriptor, PropertyDescriptor):
descriptor.trigger_if_changed(self, v)
[docs] def unapply_theme(self) -> None:
''' Remove any themed values and restore defaults.
Returns:
None
'''
self.apply_theme(property_values={})
def _clone(self) -> HasProps:
''' Duplicate a HasProps object.
Values that are containers are shallow-copied.
'''
attrs = self.properties_with_values(include_defaults=False, include_undefined=True)
return self.__class__(**{key: val for key, val in attrs.items() if val is not Undefined})
KindRef = Any # TODO
class _PropertyDef(TypedDict):
name: str
kind: KindRef
class PropertyDef(_PropertyDef, total=False):
default: Any
class OverrideDef(TypedDict):
name: str
default: Any
class _ModelDef(TypedDict):
type: Literal["model"]
name: str
class ModelDef(_ModelDef, total=False):
extends: Ref | None
properties: list[PropertyDef]
overrides: list[OverrideDef]
def _HasProps_to_serializable(cls: Type[HasProps], serializer: Serializer) -> Ref | ModelDef:
from ..model import DataModel, Model
ref = Ref(id=ID(cls.__qualified_model__))
serializer.add_ref(cls, ref)
if not is_DataModel(cls):
return ref
# TODO: consider supporting mixin models
bases: list[Type[HasProps]] = [ base for base in cls.__bases__ if issubclass(base, Model) and base != DataModel ]
if len(bases) == 0:
extends = None
elif len(bases) == 1:
[base] = bases
extends = serializer.encode(base)
else:
serializer.error("multiple bases are not supported")
properties: list[PropertyDef] = []
overrides: list[OverrideDef] = []
# TODO: don't use unordered sets
for prop_name in cls.__properties__:
descriptor = cls.lookup(prop_name)
kind = "Any" # TODO: serialize kinds
default = descriptor.property._default
if default is Undefined:
prop_def = PropertyDef(name=prop_name, kind=kind)
else:
if descriptor.is_unstable(default):
default = default()
prop_def = PropertyDef(name=prop_name, kind=kind, default=serializer.encode(default))
properties.append(prop_def)
for prop_name, default in getattr(cls, "__overridden_defaults__", {}).items():
overrides.append(OverrideDef(name=prop_name, default=serializer.encode(default)))
modeldef = ModelDef(
type="model",
name=cls.__qualified_model__,
)
if extends is not None:
modeldef["extends"] = extends
if properties:
modeldef["properties"] = properties
if overrides:
modeldef["overrides"] = overrides
return modeldef
Serializer.register(MetaHasProps, _HasProps_to_serializable)
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
_ABSTRACT_ADMONITION = '''
.. note::
This is an abstract base class used to help organize the hierarchy of Bokeh
model types. **It is not useful to instantiate on its own.**
'''
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------