#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2019, 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 absolute_import, division, print_function, unicode_literals
import logging
log = logging.getLogger(__name__)
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Standard library imports
import difflib
import inspect
from warnings import warn
# External imports
# Bokeh imports
from ..util.future import with_metaclass
from ..util.string import nice_join
from .property.descriptor_factory import PropertyDescriptorFactory
from .property.override import Override
from .property.wrappers import PropertyValueContainer
#-----------------------------------------------------------------------------
# Globals and constants
#-----------------------------------------------------------------------------
__all__ = (
'abstract',
'accumulate_dict_from_superclasses',
'accumulate_from_superclasses',
'HasProps',
'MetaHasProps',
)
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
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
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 = {}
for name, prop in class_dict.items():
if not isinstance(prop, Override):
continue
if prop.default_overridden:
overridden_defaults[name] = prop.default
for name, default in overridden_defaults.items():
del class_dict[name]
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
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(MetaHasProps, meta_cls).__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)
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 inspect.getmro(cls):
if issubclass(c, HasProps) and hasattr(c, propname):
base = getattr(c, propname)
s.update(base)
setattr(cls, cachename, s)
return cls.__dict__[cachename]
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 inspect.getmro(cls):
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]
class HasProps(with_metaclass(MetaHasProps, object)):
''' Base class for all class types that have Bokeh properties.
'''
def __init__(self, **properties):
'''
'''
super(HasProps, self).__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)
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(HasProps, self).__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(HasProps, self).__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):
return "%s(...)" % self.__class__.__name__
__repr__ = __str__
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()
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)
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)
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)
@classmethod
def lookup(cls, name):
''' Find the ``PropertyDescriptor`` for a Bokeh property on a class,
given the property name.
Args:
name (str) : name of the property to search for
Returns:
PropertyDescriptor : descriptor for property named ``name``
'''
return getattr(cls, name)
@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__")
@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__")
@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__)
@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())
@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__")
def properties_with_values(self, include_defaults=True):
''' 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)
@classmethod
def _overridden_defaults(cls):
''' Returns a dictionary of defaults that have been overridden.
This is an implementation detail of Property.
'''
return accumulate_dict_from_superclasses(cls, "__overridden_defaults__")
def query_properties_with_values(self, query, include_defaults=True):
''' 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
value = descriptor.serializable_value(self)
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
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)
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:
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)
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
#-----------------------------------------------------------------------------