#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2021, 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 #----------------------------------------------------------------------------- import logging # isort:skip log = logging.getLogger(__name__) #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Standard library imports import difflib from typing import Any, Dict, Optional from warnings import warn # Bokeh imports from ..util.string import nice_join from .property.alias import Alias from .property.descriptor_factory import PropertyDescriptorFactory from .property.descriptors import PropertyDescriptor, UnsetValueError from .property.override import Override from .property.singletons import Undefined from .property.wrappers import PropertyValueContainer #----------------------------------------------------------------------------- # Globals and constants #----------------------------------------------------------------------------- __all__ = ( 'abstract', 'accumulate_dict_from_superclasses', 'accumulate_from_superclasses', 'HasProps', 'MetaHasProps', ) #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- [docs]def abstract(cls): ''' A decorator to mark abstract base classes derived from |HasProps|. ''' if not issubclass(cls, HasProps): raise TypeError("%s is not a subclass of HasProps" % cls.__name__) # running python with -OO will discard docstrings -> __doc__ is None if cls.__doc__ is not None: cls.__doc__ += _ABSTRACT_ADMONITION return cls def is_DataModel(cls): return issubclass(cls, HasProps) and getattr(cls, "__data_model__", False) [docs]class MetaHasProps(type): ''' Specialize the construction of |HasProps| classes. This class is a `metaclass`_ for |HasProps| that is responsible for creating and adding the |PropertyDescriptor| instances that delegate validation and serialization to |Property| attributes. .. _metaclass: https://docs.python.org/3/reference/datamodel.html#metaclasses ''' def __new__(meta_cls, class_name, bases, class_dict): ''' ''' names_with_refs = set() container_names = set() # Now handle all the Override overridden_defaults = {} aliased_properties = {} for name, prop in class_dict.items(): if isinstance(prop, Alias): aliased_properties[name] = prop elif isinstance(prop, Override): if prop.default_overridden: overridden_defaults[name] = prop.default for name, default in overridden_defaults.items(): del class_dict[name] def make_property(target_name, help): fget = lambda self: getattr(self, target_name) fset = lambda self, value: setattr(self, target_name, value) return property(fget, fset, None, help) property_aliases = {} for name, alias in aliased_properties.items(): property_aliases[name] = alias.name class_dict[name] = make_property(alias.name, alias.help) generators = dict() for name, generator in class_dict.items(): if isinstance(generator, PropertyDescriptorFactory): generators[name] = generator elif isinstance(generator, type) and issubclass(generator, PropertyDescriptorFactory): # Support the user adding a property without using parens, # i.e. using just the Property subclass instead of an # instance of the subclass generators[name] = generator.autocreate() dataspecs = {} new_class_attrs = {} for name, generator in generators.items(): prop_descriptors = generator.make_descriptors(name) replaced_self = False for prop_descriptor in prop_descriptors: if prop_descriptor.name in generators: if generators[prop_descriptor.name] is generator: # a generator can replace itself, this is the # standard case like `foo = Int()` replaced_self = True prop_descriptor.add_prop_descriptor_to_class(class_name, new_class_attrs, names_with_refs, container_names, dataspecs) else: # if a generator tries to overwrite another # generator that's been explicitly provided, # use the prop that was manually provided # and ignore this one. pass else: prop_descriptor.add_prop_descriptor_to_class(class_name, new_class_attrs, names_with_refs, container_names, dataspecs) # if we won't overwrite ourselves anyway, delete the generator if not replaced_self: del class_dict[name] class_dict.update(new_class_attrs) class_dict["__properties__"] = set(new_class_attrs) class_dict["__properties_with_refs__"] = names_with_refs class_dict["__container_props__"] = container_names class_dict["__property_aliases__"] = property_aliases if len(overridden_defaults) > 0: class_dict["__overridden_defaults__"] = overridden_defaults if dataspecs: class_dict["__dataspecs__"] = dataspecs if "__example__" in class_dict: path = class_dict["__example__"] # running python with -OO will discard docstrings -> __doc__ is None if "__doc__" in class_dict and class_dict["__doc__"] is not None: class_dict["__doc__"] += _EXAMPLE_TEMPLATE % dict(path=path) return super().__new__(meta_cls, class_name, bases, class_dict) def __init__(cls, class_name, bases, nmspc): if class_name == 'HasProps': return # Check for improperly overriding a Property attribute. # Overriding makes no sense except through the Override # class which can be used to tweak the default. # Historically code also tried changing the Property's # type or changing from Property to non-Property: these # overrides are bad conceptually because the type of a # read-write property is invariant. cls_attrs = cls.__dict__.keys() # we do NOT want inherited attrs here for attr in cls_attrs: for base in bases: if issubclass(base, HasProps) and attr in base.properties(): warn(('Property "%s" in class %s was overridden by a class attribute ' + \ '"%s" in class %s; it never makes sense to do this. ' + \ 'Either %s.%s or %s.%s should be removed, or %s.%s should not ' + \ 'be a Property, or use Override(), depending on the intended effect.') % (attr, base.__name__, attr, class_name, base.__name__, attr, class_name, attr, base.__name__, attr), RuntimeWarning, stacklevel=2) if "__overridden_defaults__" in cls.__dict__: our_props = cls.properties() for key in cls.__dict__["__overridden_defaults__"].keys(): if key not in our_props: warn(('Override() of %s in class %s does not override anything.') % (key, class_name), RuntimeWarning, stacklevel=2) [docs]def accumulate_from_superclasses(cls, propname): ''' Traverse the class hierarchy and accumulate the special sets of names ``MetaHasProps`` stores on classes: Args: name (str) : name of the special attribute to collect. Typically meaningful values are: ``__container_props__``, ``__properties__``, ``__properties_with_refs__`` ''' cachename = "__cached_all" + propname # we MUST use cls.__dict__ NOT hasattr(). hasattr() would also look at base # classes, and the cache must be separate for each class if cachename not in cls.__dict__: s = set() for c in cls.__mro__: if issubclass(c, HasProps) and hasattr(c, propname): base = getattr(c, propname) s.update(base) setattr(cls, cachename, s) return cls.__dict__[cachename] [docs]def accumulate_dict_from_superclasses(cls, propname): ''' Traverse the class hierarchy and accumulate the special dicts ``MetaHasProps`` stores on classes: Args: name (str) : name of the special attribute to collect. Typically meaningful values are: ``__dataspecs__``, ``__overridden_defaults__`` ''' cachename = "__cached_all" + propname # we MUST use cls.__dict__ NOT hasattr(). hasattr() would also look at base # classes, and the cache must be separate for each class if cachename not in cls.__dict__: d = dict() for c in cls.__mro__: if issubclass(c, HasProps) and hasattr(c, propname): base = getattr(c, propname) for k,v in base.items(): if k not in d: d[k] = v setattr(cls, cachename, d) return cls.__dict__[cachename] [docs]class HasProps(metaclass=MetaHasProps): ''' Base class for all class types that have Bokeh properties. ''' _initialized: bool = False [docs] def __init__(self, **properties): ''' ''' super().__init__() self._property_values = dict() self._unstable_default_values = dict() self._unstable_themed_values = dict() for name, value in properties.items(): setattr(self, name, value) self._initialized = True def __setattr__(self, name, value): ''' 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 ''' # self.properties() below can be expensive so avoid it # if we're just setting a private underscore field if name.startswith("_"): super().__setattr__(name, value) return props = sorted(self.properties()) descriptor = getattr(self.__class__, name, None) if name in props or (descriptor is not None and descriptor.fset is not None): super().__setattr__(name, value) else: matches, text = difflib.get_close_matches(name.lower(), props), "similar" if not matches: matches, text = props, "possible" raise AttributeError("unexpected attribute '%s' to %s, %s attributes are %s" % (name, self.__class__.__name__, text, nice_join(matches))) def __str__(self) -> str: name = self.__class__.__name__ return f"{name}(...)" __repr__ = __str__ [docs] def equals(self, other): ''' Structural equality of models. Args: other (HasProps) : the other instance to compare to Returns: True, if properties are structurally equal, otherwise False ''' # NOTE: don't try to use this to implement __eq__. Because then # you will be tempted to implement __hash__, which would interfere # with mutability of models. However, not implementing __hash__ # will make bokeh unusable in Python 3, where proper implementation # of __hash__ is required when implementing __eq__. if not isinstance(other, self.__class__): return False else: return self.properties_with_values() == other.properties_with_values() # TODO: this assumes that HasProps/Model are defined as in bokehjs, which # isn't the case here. HasProps must be serializable through refs only. [docs] @classmethod def static_to_serializable(cls, serializer): # TODO: resolving already visited objects should be serializer's duty modelref = serializer.get_ref(cls) if modelref is not None: return modelref bases = [ basecls for basecls in cls.__bases__ if is_DataModel(basecls) ] if len(bases) == 0: extends = None elif len(bases) == 1: extends = bases[0].static_to_serializable(serializer) else: raise RuntimeError("multiple bases are not supported") name = cls.__view_model__ module = cls.__view_module__ # TODO: remove this if module == "__main__" or module.split(".")[0] == "bokeh": module = None properties = [] overrides = [] # TODO: don't use unordered sets for prop_name in list(cls.__properties__): descriptor = cls.lookup(prop_name) kind = None # TODO: serialize kinds default = descriptor.property._default # TODO: private member properties.append(dict(name=prop_name, kind=kind, default=default)) for prop_name, default in getattr(cls, "__overridden_defaults__", {}).items(): overrides.append(dict(name=prop_name, default=default)) modeldef = dict(name=name, module=module, extends=extends, properties=properties, overrides=overrides) modelref = dict(name=name, module=module) serializer.add_ref(cls, modelref, modeldef) return modelref [docs] def to_serializable(self, serializer): pass # TODO: new serializer, hopefully in near future [docs] def set_from_json(self, name, json, models=None, setter=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(): log.trace("Patching attribute %r of %r with %r", name, self, json) descriptor = self.lookup(name) descriptor.set_from_json(self, json, models, 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): ''' 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) [docs] def update_from_json(self, json_attributes, models=None, setter=None): ''' Updates the object's properties from a JSON attributes dictionary. Args: json_attributes: (JSON-dict) : attributes and values to update 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 ''' for k, v in json_attributes.items(): self.set_from_json(k, v, models, setter) [docs] @classmethod def lookup(cls, name: str, *, raises: bool = True) -> Optional[PropertyDescriptor]: ''' 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`` ''' resolved_name = cls._property_aliases().get(name, name) attr = getattr(cls, resolved_name, None) if attr is not None: return attr elif not raises: return None else: raise AttributeError(f"{cls.__name__}.{name} property descriptor does not exist") [docs] @classmethod def properties_with_refs(cls): ''' 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 accumulate_from_superclasses(cls, "__properties_with_refs__") [docs] @classmethod def properties_containers(cls): ''' Collect the names of all container properties on this class. This method *always* traverses the class hierarchy and includes properties defined on any parent classes. Returns: set[str] : names of container properties ''' return accumulate_from_superclasses(cls, "__container_props__") [docs] @classmethod def properties(cls, with_bases=True): ''' Collect the names of properties on this class. This method *optionally* traverses the class hierarchy and includes properties defined on any parent classes. Args: with_bases (bool, optional) : Whether to include properties defined on parent classes in the results. (default: True) Returns: set[str] : property names ''' if with_bases: return accumulate_from_superclasses(cls, "__properties__") else: return set(cls.__properties__) [docs] @classmethod def dataspecs(cls): ''' 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 ''' return set(cls.dataspecs_with_props().keys()) [docs] @classmethod def dataspecs_with_props(cls): ''' Collect a dict mapping the names of all ``DataSpec`` properties on this class to the associated properties. This method *always* traverses the class hierarchy and includes properties defined on any parent classes. Returns: dict[str, DataSpec] : mapping of names and ``DataSpec`` properties ''' return accumulate_dict_from_superclasses(cls, "__dataspecs__") [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): ''' Returns a dictionary of defaults that have been overridden. .. note:: This is an implementation detail of ``Property``. ''' return accumulate_dict_from_superclasses(cls, "__overridden_defaults__") @classmethod def _property_aliases(cls) -> Dict[str, str]: ''' Returns a dictionary of aliased properties. .. note:: This is an implementation detail of ``Property``. ''' return accumulate_dict_from_superclasses(cls, "__property_aliases__") [docs] def query_properties_with_values(self, query, *, include_defaults: bool = True, include_undefined: bool = False): ''' 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() result = dict() if include_defaults: keys = self.properties() 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. keys = set(self._property_values.keys()) | set(self._unstable_default_values.keys()) if self.themed_values(): themed_keys = set(self.themed_values().keys()) keys |= themed_keys for key in keys: descriptor = self.lookup(key) if not query(descriptor): continue try: value = descriptor.serializable_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): ''' 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): ''' 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() # 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() 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) descriptor.trigger_if_changed(self, v) [docs] def unapply_theme(self): ''' Remove any themed values and restore defaults. Returns: None ''' self.apply_theme(property_values=dict()) def _clone(self): ''' Duplicate a HasProps object. Values that are containers are shallow-copied. ''' return self.__class__(**self._property_values) #----------------------------------------------------------------------------- # 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.** ''' # The "../../" is needed for bokeh-plot to construct the correct path to examples _EXAMPLE_TEMPLATE = ''' Example ------- .. bokeh-plot:: ../../%(path)s :source-position: below ''' #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------