Source code for

# Copyright (c) 2012 - 2024, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
# The full license is in the file LICENSE.txt, distributed with this software.
""" Provide base classes for the Bokeh property system.

.. 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
from copy import copy
from typing import (

# Bokeh imports
from ...util.dependencies import uses_pandas
from ...util.strings import nice_join
from ..has_props import HasProps
from ._sphinx import property_link, register_type_link, type_link
from .descriptor_factory import PropertyDescriptorFactory
from .descriptors import PropertyDescriptor
from .singletons import (

    from typing_extensions import TypeAlias

    from import DocumentPatchedEvent

# Globals and constants

__all__ = (

# General API

# Dev API

T = TypeVar("T")

TypeOrInst: TypeAlias = Union[type[T], T]

Init: TypeAlias = Union[T, UndefinedType, IntrinsicType]

[docs] class Property(PropertyDescriptorFactory[T]): """ Base class for Bokeh property instances, which can be added to Bokeh Models. Args: default (obj, optional) : A default value for attributes created from this property to have. help (str or None, optional) : A documentation string for this property. It will be automatically used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when generating Spinx documentation. (default: None) """ # This class attribute is controlled by external helper API for validation _should_validate: ClassVar[bool] = True _readonly: ClassVar[bool] = False _serialized: ClassVar[bool] = True _self_serialized: bool alternatives: list[tuple[Property[Any], Callable[[Any], T]]] assertions: list[tuple[Callable[[HasProps, T], bool], str | Callable[[HasProps, str, T], None]]]
[docs] def __init__(self, *, default: Init[T] = Intrinsic, help: str | None = None) -> None: default = default if default is not Intrinsic else Undefined if self._serialized: self._self_serialized = not (self.readonly and default is Undefined) else: self._self_serialized = False self._default = default self._help = help self.__doc__ = help self.alternatives = [] self.assertions = []
@property def default(self) -> Init[T]: return self._default @property def help(self) -> str | None: return self._help
[docs] def __str__(self) -> str: return self.__class__.__name__
[docs] def __repr__(self) -> str: return str(self)
[docs] def __call__(self, *, default: Init[T] = Intrinsic, help: str | None = None) -> Property[T]: """ Clone this property and allow to override ``default`` and ``help``. """ default = self._default if default is Intrinsic else default help = self._help if help is None else help prop = self.__class__(default=default, help=help) prop.alternatives = list(self.alternatives) prop.assertions = list(self.assertions) return prop
[docs] def __eq__(self, other: object) -> bool: if isinstance(other, self.__class__): return ( self._default == other._default and self._help == other._help and self.alternatives == other.alternatives and self.assertions == other.assertions ) else: return False
[docs] def make_descriptors(self, name: str) -> list[PropertyDescriptor[T]]: """ Return a list of ``PropertyDescriptor`` instances to install on a class, in order to delegate attribute access to this property. Args: name (str) : the name of the property these descriptors are for Returns: list[PropertyDescriptor] The descriptors returned are collected by the ``MetaHasProps`` metaclass and added to ``HasProps`` subclasses during class creation. """ return [ PropertyDescriptor(name, self) ]
def _may_have_unstable_default(self) -> bool: """ False if we have a default that is immutable, and will be the same every time (some defaults are generated on demand by a function to be called). """ return callable(self._default) @classmethod def _copy_default(cls, default: Callable[[], T] | T, *, no_eval: bool = False) -> T: """ Return a copy of the default, or a new value if the default is specified by a function. """ if not callable(default): return copy(default) else: if no_eval: return default return default() def _raw_default(self, *, no_eval: bool = False) -> T: """ Return the untransformed default value. The raw_default() needs to be validated and transformed by prepare_value() before use, and may also be replaced later by subclass overrides or by themes. """ return self._copy_default(self._default, no_eval=no_eval)
[docs] def themed_default(self, cls: type[HasProps], name: str, theme_overrides: dict[str, Any] | None, *, no_eval: bool = False) -> T: """ The default, transformed by prepare_value() and the theme overrides. """ overrides = theme_overrides if overrides is None or name not in overrides: overrides = cls._overridden_defaults() if name in overrides: default = self._copy_default(overrides[name], no_eval=no_eval) else: default = self._raw_default(no_eval=no_eval) return self.prepare_value(cls, name, default)
@property def serialized(self) -> bool: """ Whether the property should be serialized when serializing an object. This would be False for a "virtual" or "convenience" property that duplicates information already available in other properties, for example. """ return self._self_serialized @property def readonly(self) -> bool: """ Whether this property is read-only. Read-only properties may only be modified by the client (i.e., by BokehJS in the browser). """ return self._readonly
[docs] def matches(self, new: T, old: T) -> bool: """ Whether two parameters match values. If either ``new`` or ``old`` is a NumPy array or Pandas Series or Index, then the result of ``np.array_equal`` will determine if the values match. Otherwise, the result of standard Python equality will be returned. Returns: True, if new and old match, False otherwise """ import numpy as np if isinstance(new, np.ndarray) or isinstance(old, np.ndarray): return np.array_equal(new, old) if uses_pandas(new) or uses_pandas(old): import pandas as pd if isinstance(new, pd.Series) or isinstance(old, pd.Series): return np.array_equal(new, old) if isinstance(new, pd.Index) or isinstance(old, pd.Index): return np.array_equal(new, old) try: # this handles the special but common case where there is a dict with array # or series as values (e.g. the .data property of a ColumnDataSource) if isinstance(new, dict) and isinstance(old, dict): if set(new.keys()) != set(old.keys()): return False return all(self.matches(new[k], old[k]) for k in new) # FYI Numpy can erroneously raise a warning about elementwise # comparison here when a timedelta is compared to another scalar. # return new == old # if the comparison fails for some reason, just punt and return no-match except ValueError: return False
[docs] def transform(self, value: Any) -> T: """ Change the value into the canonical format for this property. Args: value (obj) : the value to apply transformation to. Returns: obj: transformed value """ return value
[docs] def validate(self, value: Any, detail: bool = True) -> None: """ Determine whether we can set this property from this value. Validation happens before transform() Args: value (obj) : the value to validate against this property type detail (bool, options) : whether to construct detailed exceptions Generating detailed type validation error messages can be expensive. When doing type checks internally that will not escape exceptions to users, these messages can be skipped by setting this value to False (default: True) Returns: None Raises: ValueError if the value is not valid for this property type """ pass
[docs] def is_valid(self, value: Any) -> bool: """ Whether the value passes validation Args: value (obj) : the value to validate against this property type Returns: True if valid, False otherwise """ try: if validation_on(): self.validate(value, False) except ValueError: return False else: return True
[docs] def wrap(self, value: T) -> T: """ Some property types need to wrap their values in special containers, etc. """ return value
def _hinted_value(self, value: Any, hint: DocumentPatchedEvent | None) -> Any: return value def prepare_value(self, owner: HasProps | type[HasProps], name: str, value: Any, *, hint: DocumentPatchedEvent | None = None) -> T: if value is Intrinsic: value = self._raw_default() if value is Undefined: return value error = None try: if validation_on(): hinted_value = self._hinted_value(value, hint) self.validate(hinted_value) except ValueError as e: for tp, converter in self.alternatives: if tp.is_valid(value): value = converter(value) break else: error = e if error is None: value = self.transform(value) else: obj_repr = owner if isinstance(owner, HasProps) else owner.__name__ raise ValueError(f"failed to validate {obj_repr}.{name}: {error}") if isinstance(owner, HasProps): obj = owner for fn, msg_or_fn in self.assertions: if isinstance(fn, bool): result = fn else: result = fn(obj, value) assert isinstance(result, bool) if not result: if isinstance(msg_or_fn, str): raise ValueError(msg_or_fn) else: msg_or_fn(obj, name, value) return self.wrap(value) @property def has_ref(self) -> bool: return False
[docs] def accepts(self, tp: TypeOrInst[Property[Any]], converter: Callable[[Any], T]) -> Property[T]: """ Declare that other types may be converted to this property type. Args: tp (Property) : A type that may be converted automatically to this property type. converter (callable) : A function accepting ``value`` to perform conversion of the value to this property type. Returns: self """ tp = ParameterizedProperty._validate_type_param(tp) self.alternatives.append((tp, converter)) return self
[docs] def asserts(self, fn: Callable[[HasProps, T], bool], msg_or_fn: str | Callable[[HasProps, str, T], None]) -> Property[T]: """ Assert that prepared values satisfy given conditions. Assertions are intended in enforce conditions beyond simple value type validation. For instance, this method can be use to assert that the columns of a ``ColumnDataSource`` all collectively have the same length at all times. Args: fn (callable) : A function accepting ``(obj, value)`` that returns True if the value passes the assertion, or False otherwise. msg_or_fn (str or callable) : A message to print in case the assertion fails, or a function accepting ``(obj, name, value)`` to call in in case the assertion fails. Returns: self """ self.assertions.append((fn, msg_or_fn)) return self
def replace(self, old: type[Property[Any]], new: Property[Any]) -> Property[Any]: if self.__class__ == old: return new else: return self
class ParameterizedProperty(Property[T]): """ A base class for Properties that have type parameters, e.g. ``List(String)``. """ _type_params: list[Property[Any]] def __init__(self, *type_params: TypeOrInst[Property[T]], default: Init[T] = Intrinsic, help: str | None = None) -> None: _type_params = [ self._validate_type_param(param) for param in type_params ] default = default if default is not Intrinsic else _type_params[0]._raw_default() self._type_params = _type_params super().__init__(default=default, help=help) def __str__(self) -> str: class_name = self.__class__.__name__ item_types = ", ".join(str(x) for x in self.type_params) return f"{class_name}({item_types})" def __call__(self, *, default: Init[T] = Intrinsic, help: str | None = None) -> ParameterizedProperty[T]: """ Clone this property and allow to override ``default`` and ``help``. """ default = self._default if default is Intrinsic else default help = self._help if help is None else help prop = self.__class__(*self.type_params, default=default, help=help) prop.alternatives = list(self.alternatives) prop.assertions = list(self.assertions) return prop def __eq__(self, other: object) -> bool: if isinstance(other, self.__class__): return super().__eq__(other) and self.type_params == other.type_params else: return False @staticmethod def _validate_type_param(type_param: TypeOrInst[Property[Any]], *, help_allowed: bool = False) -> Property[Any]: if isinstance(type_param, type): if issubclass(type_param, Property): return type_param() else: type_param = type_param.__name__ elif isinstance(type_param, Property): if type_param._help is not None and not help_allowed: raise ValueError("setting 'help' on type parameters doesn't make sense") return type_param raise ValueError(f"expected a Property as type parameter, got {type_param}") @property def type_params(self) -> list[Property[Any]]: return self._type_params @property def has_ref(self) -> bool: return any(type_param.has_ref for type_param in self.type_params) def _may_have_unstable_default(self) -> bool: return super()._may_have_unstable_default() or \ any(type_param._may_have_unstable_default() for type_param in self.type_params) def replace(self, old: type[Property[Any]], new: Property[Any]) -> Property[Any]: if self.__class__ == old: return new else: params = [ type_param.replace(old, new) for type_param in self.type_params ] return self.__class__(*params) class SingleParameterizedProperty(ParameterizedProperty[T]): """ A parameterized property with a single type parameter. """ @property def type_param(self) -> Property[T]: return self._type_params[0] def __init__(self, type_param: TypeOrInst[Property[Any]], *, default: Init[T] = Intrinsic, help: str | None = None): super().__init__(type_param, default=default, help=help) def validate(self, value: Any, detail: bool = True) -> None: super().validate(value, detail=detail) self.type_param.validate(value, detail=detail) def transform(self, value: T) -> T: return self.type_param.transform(value) def wrap(self, value: T) -> T: return self.type_param.wrap(value)
[docs] class PrimitiveProperty(Property[T]): """ A base class for simple property types. Subclasses should define a class attribute ``_underlying_type`` that is a tuple of acceptable type values for the property. Example: A trivial version of a ``Float`` property might look like: .. code-block:: python class Float(PrimitiveProperty): _underlying_type = (numbers.Real,) """ _underlying_type: ClassVar[tuple[type[Any], ...]]
[docs] def validate(self, value: Any, detail: bool = True) -> None: super().validate(value, detail) if isinstance(value, self._underlying_type): return if not detail: raise ValueError("") expected_type = nice_join([ cls.__name__ for cls in self._underlying_type ]) msg = f"expected a value of type {expected_type}, got {value} of type {type(value).__name__}" raise ValueError(msg)
[docs] class ContainerProperty(ParameterizedProperty[T]): """ A base class for Container-like type properties. """ def _may_have_unstable_default(self) -> bool: # all containers are mutable, so the default can be modified return self._default is not Undefined
[docs] def validation_on() -> bool: """ Check if property validation is currently active Returns: bool """ return Property._should_validate
#----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #----------------------------------------------------------------------------- @register_type_link(SingleParameterizedProperty) def _sphinx_type(obj: SingleParameterizedProperty[Any]): return f"{property_link(obj)}({type_link(obj.type_param)})"