''' 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.
'''
from __future__ import absolute_import
import logging
logger = logging.getLogger(__name__)
from copy import copy
import types
from six import string_types
import numpy as np
from ...util.dependencies import import_optional
from ...util.string import nice_join
from .descriptor_factory import PropertyDescriptorFactory
from .descriptors import BasicPropertyDescriptor
pd = import_optional('pandas')
[docs]class DeserializationError(Exception):
pass
[docs]class Property(PropertyDescriptorFactory):
''' Base class for Bokeh property instances, which can be added to Bokeh
Models.
Args:
default (obj or None, optional) :
A default value for attributes created from this property to
have (default: None)
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)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
'''
[docs] def __init__(self, default=None, help=None, serialized=True, readonly=False):
# This is how the descriptor is created in the class declaration.
self._serialized = False if readonly else serialized
self._readonly = readonly
self._default = default
self.__doc__ = help
self.alternatives = []
self.assertions = []
# "fail early" when a default is invalid
self.validate(self._raw_default())
[docs] def __str__(self):
return self.__class__.__name__
@classmethod
def _sphinx_prop_link(cls):
''' Generate a sphinx :class: link to this property.
'''
return ":class:`~bokeh.core.properties.%s` " % cls.__name__
@staticmethod
def _sphinx_model_link(name):
''' Generate a sphinx :class: link to given named model.
'''
return ":class:`~%s` " % name
def _sphinx_type(self):
''' Generate a Sphinx-style reference to this type for documentation
automation purposes.
'''
return self._sphinx_prop_link()
[docs] def make_descriptors(self, base_name):
''' Return a list of ``BasicPropertyDescriptor`` 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[BasicPropertyDescriptor]
The descriptors returned are collected by the ``MetaHasProps``
metaclass and added to ``HasProps`` subclasses during class creation.
'''
return [ BasicPropertyDescriptor(base_name, self) ]
def _may_have_unstable_default(self):
''' 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 isinstance(self._default, types.FunctionType)
@classmethod
def _copy_default(cls, default):
''' Return a copy of the default, or a new value if the default
is specified by a function.
'''
if not isinstance(default, types.FunctionType):
return copy(default)
else:
return default()
def _raw_default(self):
''' 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)
[docs] def themed_default(self, cls, name, theme_overrides):
''' 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])
else:
default = self._raw_default()
return self.prepare_value(cls, name, default)
@property
def serialized(self):
''' 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._serialized
@property
def readonly(self):
''' 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, old):
''' 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
'''
if isinstance(new, np.ndarray) or isinstance(old, np.ndarray):
return np.array_equal(new, old)
if 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)
return new == old
# if the comparison fails for some reason, just punt and return no-match
except ValueError:
return False
[docs] def from_json(self, json, models=None):
''' Convert from JSON-compatible values into a value for this property.
JSON-compatible values are: list, dict, number, string, bool, None
'''
return json
[docs] def serialize_value(self, value):
''' Change the value into a JSON serializable format.
'''
return value
[docs] def validate(self, value):
''' 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
Returns:
None
Raises:
ValueError if the value is not valid for this property type
'''
pass
[docs] def is_valid(self, value):
''' Whether the value passes validation
Args:
value (obj) : the value to validate against this property type
Returns:
True if valid, False otherwise
'''
try:
self.validate(value)
except ValueError:
return False
else:
return True
[docs] @classmethod
def wrap(cls, value):
''' Some property types need to wrap their values in special containers, etc.
'''
return value
def prepare_value(self, obj_or_cls, name, value):
try:
self.validate(value)
except ValueError as e:
for tp, converter in self.alternatives:
if tp.is_valid(value):
value = converter(value)
break
else:
raise e
else:
value = self.transform(value)
from ..has_props import HasProps
if isinstance(obj_or_cls, HasProps):
obj = obj_or_cls
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, string_types):
raise ValueError(msg_or_fn)
else:
msg_or_fn(obj, name, value)
return self.wrap(value)
@property
def has_ref(self):
return False
[docs] def accepts(self, tp, converter):
''' 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, msg_or_fn):
''' 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 othwise
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
[docs]class ParameterizedProperty(Property):
''' A base class for Properties that have type parameters, e.g.
``List(String)``.
'''
@staticmethod
def _validate_type_param(type_param):
if isinstance(type_param, type):
if issubclass(type_param, Property):
return type_param()
else:
type_param = type_param.__name__
elif isinstance(type_param, Property):
return type_param
raise ValueError("expected a Propertyas type parameter, got %s" % type_param)
@property
def type_params(self):
raise NotImplementedError("abstract method")
@property
def has_ref(self):
return any(type_param.has_ref for type_param in self.type_params)
[docs]class PrimitiveProperty(Property):
''' 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 = None
[docs] def validate(self, value):
super(PrimitiveProperty, self).validate(value)
if not (value is None or isinstance(value, self._underlying_type)):
raise ValueError("expected a value of type %s, got %s of type %s" %
(nice_join([ cls.__name__ for cls in self._underlying_type ]), value, type(value).__name__))
[docs] def from_json(self, json, models=None):
if json is None or isinstance(json, self._underlying_type):
return json
else:
expected = nice_join([ cls.__name__ for cls in self._underlying_type ])
raise DeserializationError("%s expected %s, got %s" % (self, expected, json))
def _sphinx_type(self):
return self._sphinx_prop_link()
[docs]class ContainerProperty(ParameterizedProperty):
''' A base class for Container-like type properties.
'''
def _may_have_unstable_default(self):
# all containers are mutable, so the default can be modified
return True
