#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2020, 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 #----------------------------------------------------------------------------- import logging # isort:skip log = logging.getLogger(__name__) #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Standard library imports import types from copy import copy # External imports import numpy as np # Bokeh imports from ...util.dependencies import import_optional from ...util.string import nice_join from ..has_props import HasProps from .descriptor_factory import PropertyDescriptorFactory from .descriptors import BasicPropertyDescriptor #----------------------------------------------------------------------------- # Globals and constants #----------------------------------------------------------------------------- pd = import_optional('pandas') #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- __all__ = ( 'ContainerProperty', 'DeserializationError', 'PrimitiveProperty', 'Property', 'validation_on', ) #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- [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) ''' # This class attribute is controlled by external helper API for validation _should_validate = True [docs] def __init__(self, default=None, help=None, serialized=None, readonly=False): # This is how the descriptor is created in the class declaration. if serialized is None: self._serialized = False if readonly else True else: self._serialized = serialized self._readonly = readonly self._default = default self.__doc__ = help self.alternatives = [] self.assertions = [] [docs] def __str__(self): return self.__class__.__name__ @classmethod def _sphinx_prop_link(cls): ''' Generate a sphinx :class: link to this property. ''' # extra space at the end is unfortunately necessary to appease Sphinx 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) # FYI Numpy can erroneously raise a warning about elementwise # comparison here when a timedelta is compared to another scalar. # https://github.com/numpy/numpy/issues/10095 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 transform(self, value): ''' 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, detail=True): ''' 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): ''' 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] @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: if validation_on(): 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) 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, str): 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 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 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 Property as 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, detail=True): super().validate(value, detail) if not (value is None or isinstance(value, self._underlying_type)): msg = "" if not detail else "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__ ) raise ValueError(msg) [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 of type %s" % (self, expected, json, type(json).__name__)) 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 [docs]def validation_on(): ''' Check if property validation is currently active Returns: bool ''' return Property._should_validate #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------