# (C) Copyright 2005-2022 Enthought, Inc., Austin, TX # All rights reserved. # # This software is provided without warranty under the terms of the BSD # license included in LICENSE.txt and may be redistributed only under # the conditions described in the aforementioned license. The license # is also available online at http://www.enthought.com/licenses/BSD.txt # # Thanks for using Enthought open source! """ Defines the HasTraits class, along with several useful subclasses and associated metaclasses. """ import abc import copy as copy_module import inspect import os import pickle import re import types import warnings import weakref from types import FunctionType from . import __version__ as TraitsVersion from .adaptation.adaptation_error import AdaptationError from .constants import DefaultValue, TraitKind from .ctrait import CTrait, __newobj__ from .ctraits import CHasTraits from .observation import api as observe_api from .traits import ( ForwardProperty, Property, Trait, generic_trait, ) from .trait_types import Any, Bool, Disallow, Event, Python, Str from .trait_notifiers import ( ExtendedTraitChangeNotifyWrapper, FastUITraitChangeNotifyWrapper, NewTraitChangeNotifyWrapper, StaticAnytraitChangeNotifyWrapper, StaticTraitChangeNotifyWrapper, TraitChangeNotifyWrapper, ui_dispatch, ) from .trait_base import ( SequenceTypes, TraitsCache, Undefined, is_none, not_event, not_false, ) from .trait_errors import TraitError from .util.deprecated import deprecated from .util._traitsui_helpers import check_traitsui_major_version from .trait_converters import check_trait, mapped_trait_for, trait_for # Set CHECK_INTERFACES to one of the following values: # # - 0: Does not check to see if classes implement their declared interfaces. # - 1: Ensures that classes implement the interfaces they say they do, and # logs a warning if they don't. # - 2: Ensures that classes implement the interfaces they say they do, and # raises an InterfaceError if they don't. # # This constant is used by the @provides decorator when deciding whether to # do interface checking. This behaviour is deprecated. In the future, the # provides decorator will no longer perform interface checking, regardless of # the value of this constant. CHECK_INTERFACES = 0 # This ABC is a placeholder for the TraitsUI ViewElement class, which should # inherit from or register as implementing the API. This has to be done here # so that the metaclass machinery has something to check against when # filtering out TraitsUI elements that are declared as part of a HasTraits # class. class AbstractViewElement(abc.ABC): pass # Constants WrapperTypes = ( StaticAnytraitChangeNotifyWrapper, StaticTraitChangeNotifyWrapper, ) # Class dictionary entries used to save trait, listener and view information # and definitions: BaseTraits = "__base_traits__" ClassTraits = "__class_traits__" PrefixTraits = "__prefix_traits__" ListenerTraits = "__listener_traits__" ObserverTraits = "__observer_traits__" ViewTraits = "__view_traits__" InstanceTraits = "__instance_traits__" # The default Traits View name DefaultTraitsView = "traits_view" # Trait types which cannot have default values CantHaveDefaultValue = ("event", "delegate", "constant") # The trait types that should be copied last when doing a 'copy_traits': DeferredCopy = ("delegate", "property") # Quick test for normal vs extended trait name extended_trait_pat = re.compile(r".*[ :\+\-,\.\*\?\[\]]") # Generic 'Any' trait: any_trait = Any().as_ctrait() # Mapping from user-facing strings to the dispatcher callable in observe. _ObserverDispatchers = { "same": observe_api.dispatch_same, "ui": ui_dispatch, } def _clone_trait(clone, metadata=None): """ Creates a clone of a specified trait. """ trait = CTrait(TraitKind.trait) trait.clone(clone) if clone.__dict__ is not None: trait.__dict__ = clone.__dict__.copy() if metadata is not None: trait.__dict__.update(metadata) return trait def _get_method(cls, method): """ Get the definition of a specified method (if any). """ result = getattr(cls, method, None) if (result is not None) and is_unbound_method_type(result): return result return None def _get_def(class_name, class_dict, bases, method): """ Gets the definition of a specified method (if any). """ if method[0:2] == "__": # When name-mangling to handle the __ case (for _private traits), # leading underscores in the class name are stripped out. method = "_%s%s" % (class_name.lstrip('_'), method) result = class_dict.get(method) if ( (result is not None) and is_unbound_method_type(result) and (getattr(result, "on_trait_change", None) is None) and (getattr(result, "_observe_inputs", None) is None) ): return result for base in bases: result = getattr(base, method, None) if ( (result is not None) and is_unbound_method_type(result) and (getattr(result, "on_trait_change", None) is None) and (getattr(result, "_observe_inputs", None) is None) ): return result return None def is_unbound_method_type(method): """ Check for something that looks like an unbound class method. This is used in practice to identify magic-named _name_changed and _name_fired methods. """ # The ismethoddescriptor check catches methods written in C or Cython # extensions. It excludes things that pass an isfunction check, so we have # to explicitly re-include that check. return inspect.isfunction(method) or inspect.ismethoddescriptor(method) def _is_serializable(value): """ Returns whether or not a specified value is serializable. """ if isinstance(value, (list, tuple)): for item in value: if not _is_serializable(item): return False return True if isinstance(value, dict): for name, item in value.items(): if (not _is_serializable(name)) or (not _is_serializable(item)): return False return True return (not isinstance(value, HasTraits)) or value.has_traits_interface( ISerializable ) def _get_instance_handlers(class_dict, bases): """ Returns a dictionary of potential 'Instance' or 'List(Instance)' handlers. """ # Create the results dictionary: instance_traits = {} # Merge all of the base class information into the result: for base in bases: for name, base_arg_lists in base.__dict__.get(InstanceTraits).items(): arg_lists = instance_traits.get(name) if arg_lists is None: instance_traits[name] = base_arg_lists[:] else: for arg_list in base_arg_lists: if arg_list not in arg_lists: arg_lists.append(arg_list) # Merge in the information from the class dictionary: for name, value in class_dict.items(): if (name[:1] == "_") and is_unbound_method_type(value): n = 13 col = name.find("_changed_for_") if col < 2: n = 11 col = name.find("_fired_for_") if col >= 2: key = name[col + n:] if key != "": arg_list = (name, name[1:col]) arg_lists = instance_traits.setdefault(key, []) if arg_list not in arg_lists: arg_lists.append(arg_list) # Return the dictionary of possible arg_lists: return instance_traits def get_delegate_pattern(name, trait): """ Returns the correct 'delegate' listener pattern for a specified name and delegate trait. """ prefix = trait._prefix if prefix == "": prefix = name elif (len(prefix) > 1) and (prefix[-1] == "*"): prefix = prefix[:-1] + name return " %s:%s" % (trait._delegate, prefix) class _SimpleTest: def __init__(self, value): self.value = value def __call__(self, test): return test == self.value def _add_notifiers(notifiers, handlers): """ Adds a list of handlers to a specified notifiers list. """ for handler in handlers: if not isinstance(handler, WrapperTypes): handler = StaticTraitChangeNotifyWrapper(handler) notifiers.append(handler) def _add_event_handlers(trait, cls, handlers): """ Adds any specified event handlers defined for a trait by a class. """ events = trait.event if events is not None: if isinstance(events, str): events = [events] for event in events: handlers.append(_get_method(cls, "_%s_changed" % event)) handlers.append(_get_method(cls, "_%s_fired" % event)) def _property_method(class_dict, name): """ Returns the method associated with a particular class property getter/setter. """ return class_dict.get(name) def _create_property_observe_state(observe, property_name, cached): """ Create the metadata for setting up an observer for Property. Parameters ---------- observe : str or list or Expression As is accepted by HasTraits.observe expression argument This is the value provided in Property(observe=...) property_name : str The name of the property trait. cached : boolean Whether the property is cached or not. Returns ------- state : dict State to be used by _init_traits_observers """ def handler(instance, event): if cached: cache_name = TraitsCache + property_name old = instance.__dict__.pop(cache_name, Undefined) else: old = Undefined instance.trait_property_changed(property_name, old) def handler_getter(instance, name): return types.MethodType(handler, instance) graphs = _compile_expression(observe) return dict( graphs=graphs, dispatch="same", handler_getter=handler_getter, post_init=False, ) def _compile_expression(expression): """ Compile a user-supplied expression or list of expressions. Converts a list of strings or ObserverExpressions to a list of ObserverGraphs representing the observation patterns to be applied. Parameters ---------- expression : str or list or ObserverExpression A description of what traits are being observed. If this is a list, each item must be a string or an ObserverExpression. Returns ------- graphs : list of ObserverGraph List of graphs representing the observation patterns to be applied to the relevant objects and handlers. """ # Handle the overloaded signature. # Support list to be consistent with on_trait_change. if isinstance(expression, list): expressions = expression else: expressions = [expression] graphs = [] for expr in expressions: graphs.extend( observe_api.compile_str(expr) if isinstance(expr, str) else observe_api.compile_expr(expr) ) return graphs # This really should be 'HasTraits', but it's not defined yet: _HasTraits = None class MetaHasTraits(type): """ Controls the creation of HasTraits classes. The heavy work is done by the `update_traits_class_dict` function, which takes the ``class_dict`` dictionary of class members and extracts and processes the trait declarations in it. The trait declarations are then added back to the class dictionary and passed off to the __new__ method of the type superclass, to be added to the class. """ # All registered class creation listeners. # # { Str class_name : Callable listener } _listeners = {} def __new__(cls, class_name, bases, class_dict): update_traits_class_dict(class_name, bases, class_dict) # Finish building the class using the updated class dictionary: klass = type.__new__(cls, class_name, bases, class_dict) # Call all listeners that registered for this specific class: name = "%s.%s" % (klass.__module__, klass.__name__) for listener in MetaHasTraits._listeners.get(name, []): listener(klass) # Call all listeners that registered for ANY class: for listener in MetaHasTraits._listeners.get("", []): listener(klass) return klass @classmethod def add_listener(cls, listener, class_name=""): """ Adds a class creation listener. If the class name is the empty string then the listener will be called when *any* class is created. .. deprecated:: 6.3.0 """ warnings.warn( "add_listener is deprecated", DeprecationWarning, stacklevel=2 ) MetaHasTraits._listeners.setdefault(class_name, []).append(listener) @classmethod def remove_listener(cls, listener, class_name=""): """ Removes a class creation listener. .. deprecated:: 6.3.0 """ warnings.warn( "remove_listener is deprecated", DeprecationWarning, stacklevel=2 ) MetaHasTraits._listeners[class_name].remove(listener) def update_traits_class_dict(class_name, bases, class_dict): """ Processes all of the traits related data in the class dictionary. This is called during the construction of a new HasTraits class. The first three parameters have the same interpretation as the corresponding parameters of ``type.__new__``. This function modifies ``class_dict`` in-place. Parameters ---------- class_name : str The name of the HasTraits class. bases : tuple The base classes for the HasTraits class. class_dict : dict A dictionary of class members. """ # Create the various class dictionaries, lists and objects needed to # hold trait and view information and definitions: base_traits = {} class_traits = {} prefix_traits = {} listeners = {} prefix_list = [] view_elements = {} # Mapping from method/trait names to list(dict) # where each nested dict provides the input arguments for calling # ``HasTraits.observe`` once. See ``_init_trait_observers``.` observers = {} # Create a list of just those base classes that derive from HasTraits: hastraits_bases = [ base for base in bases if base.__dict__.get(ClassTraits) is not None ] # Create a list of all inherited trait dictionaries: inherited_class_traits = [ base.__dict__.get(ClassTraits) for base in hastraits_bases ] # Move all trait definitions from the class dictionary to the # appropriate trait class dictionaries: for name, value in list(class_dict.items()): value = check_trait(value) rc = isinstance(value, CTrait) if (not rc) and isinstance(value, ForwardProperty): rc = True # Create Property trait from getter, setter, validator getter = _property_method(class_dict, "_get_" + name) setter = _property_method(class_dict, "_set_" + name) if (setter is None) and (getter is not None): if getattr(getter, "settable", False): setter = HasTraits._set_traits_cache elif getattr(getter, "flushable", False): setter = HasTraits._flush_traits_cache validate = _property_method(class_dict, "_validate_" + name) if validate is None: validate = value.validate value = Property( getter, setter, validate, True, value.handler, **value.metadata ) if rc: del class_dict[name] if name[-1:] != "_": base_traits[name] = class_traits[name] = value value_type = value.type if value_type == "trait": handler = value.handler if handler is not None: if handler.has_items: items_trait = _clone_trait( handler.items_event(), value.__dict__ ) if ( items_trait.instance_handler == "_list_changed_handler" ): items_trait.instance_handler = ( "_list_items_changed_handler" ) class_traits[name + "_items"] = items_trait if handler.is_mapped: class_traits[name + "_"] = mapped_trait_for( value, name ) elif value_type == "delegate": # Only add a listener if the trait.listenable metadata # is not False: if value._listenable is not False: listeners[name] = ( "delegate", get_delegate_pattern(name, value), ) elif value_type == "event": on_trait_change = value.on_trait_change if isinstance(on_trait_change, str): listeners[name] = ("event", on_trait_change) else: name = name[:-1] prefix_list.append(name) prefix_traits[name] = value elif is_unbound_method_type(value): pattern = getattr(value, "on_trait_change", None) if pattern is not None: listeners[name] = ("method", pattern) observer_states = getattr(value, "_observe_inputs", None) if observer_states is not None: observers[name] = observer_states elif isinstance(value, property): class_traits[name] = generic_trait # Handle any view elements found in the class: elif isinstance(value, AbstractViewElement): view_elements[name] = value # Remove the view element from the class definition: del class_dict[name] else: for ct in inherited_class_traits: if name in ct: # The subclass is providing a default value for the # trait defined in a superclass. ictrait = ct[name] if ictrait.type in CantHaveDefaultValue: raise TraitError( "Cannot specify a default value " "for the %s trait '%s'. You must override the " "the trait definition instead." % (ictrait.type, name) ) class_traits[name] = ictrait(value) del class_dict[name] break # Process all HasTraits base classes: migrated_properties = {} for base in hastraits_bases: base_dict = base.__dict__ # Merge listener information: for name, value in base_dict.get(ListenerTraits).items(): if (name not in class_traits) and (name not in class_dict): listeners[name] = value # Merge observer information: for name, states in base_dict[ObserverTraits].items(): if (name not in class_traits) and (name not in class_dict): observers[name] = states # Merge base traits: for name, value in base_dict.get(BaseTraits).items(): if name not in base_traits: property_info = value.property_fields if property_info is not None: key = id(value) migrated_properties[key] = value = migrate_property( name, value, property_info, class_dict ) base_traits[name] = value # Merge class traits: for name, value in base_dict.get(ClassTraits).items(): if name not in class_traits: property_info = value.property_fields if property_info is not None: new_value = migrated_properties.get(id(value)) if new_value is not None: value = new_value else: value = migrate_property( name, value, property_info, class_dict ) class_traits[name] = value # Merge prefix traits: base_prefix_traits = base_dict.get(PrefixTraits) for name in base_prefix_traits["*"]: if name not in prefix_list: prefix_list.append(name) prefix_traits[name] = base_prefix_traits[name] # Make sure there is a definition for 'undefined' traits: if prefix_traits.get("") is None: prefix_list.append("") prefix_traits[""] = Python().as_ctrait() # Save a link to the prefix_list: prefix_traits["*"] = prefix_list # Make sure the trait prefixes are sorted longest to shortest # so that we can easily bind dynamic traits to the longest matching # prefix: prefix_list.sort(key=len, reverse=True) # Get the list of all possible 'Instance'/'List(Instance)' handlers: instance_traits = _get_instance_handlers(class_dict, hastraits_bases) # If there is an 'anytrait_changed' event handler, wrap it so that # it can be attached to all traits in the class: anytrait = _get_def(class_name, class_dict, bases, "_anytrait_changed") if anytrait is not None: anytrait = StaticAnytraitChangeNotifyWrapper(anytrait) # Save it in the prefix traits dictionary so that any dynamically # created traits (e.g. 'prefix traits') can re-use it: prefix_traits["@"] = anytrait # Make one final pass over the class traits dictionary, making sure # all static trait notification handlers are attached to a 'cloned' # copy of the original trait: cloned = set() for name in list(class_traits.keys()): trait = class_traits[name] handlers = [ anytrait, _get_def(class_name, class_dict, bases, "_%s_changed" % name), _get_def(class_name, class_dict, bases, "_%s_fired" % name), ] # Check for an 'Instance' or 'List(Instance)' trait with defined # handlers: instance_handler = trait.instance_handler if ( (instance_handler is not None) and (name in instance_traits) or ( (instance_handler == "_list_items_changed_handler") and (name[-6:] == "_items") and (name[:-6] in instance_traits) ) ): handlers.append(getattr(HasTraits, instance_handler)) events = trait.event if events is not None: if isinstance(events, str): events = [events] for event in events: handlers.append( _get_def( class_name, class_dict, bases, "_%s_changed" % event ) ) handlers.append( _get_def( class_name, class_dict, bases, "_%s_fired" % event ) ) handlers = [h for h in handlers if h is not None] default = _get_def(class_name, class_dict, [], "_%s_default" % name) if (len(handlers) > 0) or (default is not None): if name not in cloned: cloned.add(name) class_traits[name] = trait = _clone_trait(trait) if len(handlers) > 0: _add_notifiers(trait._notifiers(True), handlers) if default is not None: trait.set_default_value(DefaultValue.callable, default) # Handle the case of properties whose value depends upon the value # of other traits: if (trait.type == "property") and (trait.depends_on is not None): cached = trait.cached if cached is True: cached = TraitsCache + name depends_on = trait.depends_on if isinstance(depends_on, SequenceTypes): depends_on = ",".join(depends_on) else: # Note: We add the leading blank to force it to be treated # as using the extended trait notation so that it will # automatically add '_items' listeners to lists/dicts: depends_on = " " + depends_on listeners[name] = ("property", cached, depends_on) if trait.type == "property" and trait.observe is not None: observer_state = _create_property_observe_state( observe=trait.observe, property_name=name, cached=trait.cached, ) observers[name] = [observer_state] # Add processed traits back into class_dict. class_dict[BaseTraits] = base_traits class_dict[ClassTraits] = class_traits class_dict[InstanceTraits] = instance_traits class_dict[PrefixTraits] = prefix_traits class_dict[ListenerTraits] = listeners class_dict[ObserverTraits] = observers class_dict[ViewTraits] = view_elements def migrate_property(name, property, property_info, class_dict): """ Migrates an existing property to the class being defined (allowing for method overrides). """ get = _property_method(class_dict, "_get_" + name) set = _property_method(class_dict, "_set_" + name) val = _property_method(class_dict, "_validate_" + name) if (get is not None) or (set is not None) or (val is not None): old_get, old_set, old_val = property_info return Property( get or old_get, set or old_set, val or old_val, True, **property.__dict__ ) return property # 'HasTraits' decorators def observe(expression, *, post_init=False, dispatch="same"): """ Marks the wrapped method as being a handler to be called when the specified traits change. This decorator can be stacked, e.g.:: @observe("attr1") @observe("attr2", post_init=True) def updated(self, event): ... The decorated function must accept one argument which is the event object representing the change. See :mod:`traits.observation.events` for details. Parameters ---------- expression : str or list or ObserverExpression A description of what traits are being observed. If this is a list, each item must be a string or Expression. See :py:func:`HasTraits.observe` for details on the semantics when passing a string. post_init : boolean, optional Whether the change handler should be attached after the state is set when instantiating an object. Default is false, and values provided to the instance constructor will trigger the change handler to fire if the value is different from the default. Set to true to avoid this change event. dispatch : str, optional A string indicating how the handler should be run. Default is to run it on the same thread where the change occurs. Possible values are: =========== ======================================================= value dispatch =========== ======================================================= ``same`` Run notifications on the same thread where the change occurs. The notifications are executed immediately. ``ui`` Run notifications on the UI thread. If the current thread is the UI thread, the notifications are executed immediately; otherwise, they are placed on the UI event queue. =========== ======================================================= See Also -------- HasTraits.observe """ graphs = _compile_expression(expression) def observe_decorator(handler): """ Create input arguments for HasTraits.observe and attach the input to the callable. The metaclass will then collect this information for calling HasTraits.observe with the decorated function. Parameters ---------- handler : callable Method of a subclass of HasTraits, with signature of the form ``my_method(self, event)``. """ # Warn on a dubious handler signature. The handler should accept a call # that passes a single positional argument (conventionally named # "event") in addition to the usual "self". handler_signature = inspect.signature(handler) try: handler_signature.bind("self", "event") except TypeError: warnings.warn( ( "Dubious signature for observe-decorated method. " "The decorated method should be callable with a " "single positional argument in addition to 'self'. " "Did you forget to add an 'event' parameter?" ), UserWarning, stacklevel=2, ) try: observe_inputs = handler._observe_inputs except AttributeError: observe_inputs = [] handler._observe_inputs = observe_inputs observe_input = dict( graphs=graphs, dispatch=dispatch, post_init=post_init, handler_getter=getattr, ) observe_inputs.append(observe_input) return handler return observe_decorator def on_trait_change(name, post_init=False, dispatch="same"): """ Marks the following method definition as being a handler for the extended trait change specified by *name(s)*. Refer to the documentation for the on_trait_change() method of the **HasTraits** class for information on the correct syntax for the *name* argument and the semantics of the *dispatch* keyword argument. A handler defined using this decorator is normally effective immediately. However, if *post_init* is **True**, then the handler only becomes effective after all object constructor arguments have been processed. That is, trait values assigned as part of object construction will not cause the handler to be invoked. See Also -------- observe : A newer API for defining traits notifications. """ def decorator(function): function.on_trait_change = { "pattern": name, "post_init": post_init, "dispatch": dispatch, } return function return decorator def cached_property(function): """ Marks the following method definition as being a "cached property". That is, it is a property getter which, for performance reasons, caches its most recently computed result in an attribute whose name is of the form: *_traits_cache_name*, where *name* is the name of the property. A method marked as being a cached property needs only to compute and return its result. The @cached_property decorator automatically wraps the decorated method in cache management code, eliminating the need to write boilerplate cache management code explicitly. For example:: file_name = File file_contents = Property(observe='file_name') @cached_property def _get_file_contents(self): with open(self.file_name, 'rb') as fh: return fh.read() In this example, accessing the *file_contents* trait calls the _get_file_contents() method only once each time after the **file_name** trait is modified. In all other cases, the cached value **_file_contents**, which maintained by the @cached_property wrapper code, is returned. Note the use, in the example, of the **observe** metadata attribute to specify that the value of **file_contents** depends on **file_name**, so that _get_file_contents() is called only when **file_name** changes. For details, see the traits.traits.Property() function. """ name = TraitsCache + function.__name__[5:] def decorator(self): result = self.__dict__.get(name, Undefined) if result is Undefined: self.__dict__[name] = result = function(self) return result decorator.cached_property = True return decorator def property_depends_on(dependency, settable=False, flushable=False): """ Marks the following method definition as being a "cached property" that depends on the specified extended trait names. That is, it is a property getter which, for performance reasons, caches its most recently computed result in an attribute whose name is of the form: *_traits_cache_name*, where *name* is the name of the property. A method marked as being a cached property needs only to compute and return its result. The @property_depends_on decorator automatically wraps the decorated method in cache management code that will cache the most recently computed value and flush the cache when any of the specified dependencies are modified, thus eliminating the need to write boilerplate cache management code explicitly. For example:: file_name = File file_contents = Property @property_depends_on( 'file_name' ) def _get_file_contents(self): with open(self.file_name, 'rb') as fh: return fh.read() In this example, accessing the *file_contents* trait calls the _get_file_contents() method only once each time after the **file_name** trait is modified. In all other cases, the cached value **_file_contents**, which is maintained by the @cached_property wrapper code, is returned. """ def decorator(function): name = TraitsCache + function.__name__[5:] def wrapper(self): result = self.__dict__.get(name, Undefined) if result is Undefined: self.__dict__[name] = result = function(self) return result wrapper.cached_property = True wrapper.depends_on = dependency wrapper.settable = settable wrapper.flushable = flushable return wrapper return decorator def weak_arg(arg): """ Create a weak reference to arg and wrap the function so that the dereferenced weakref is passed as the first argument. If arg has been deleted then the function is not called. """ # Create the weak reference weak_arg = weakref.ref(arg) def decorator(function): # We need multiple wrappers to traits can find the number of arguments. # The all just dereference the weak reference and the call the # function if it is not None. def wrapper0(): arg = weak_arg() if arg is not None: return function(arg) def wrapper1(arg1): arg = weak_arg() if arg is not None: return function(arg, arg1) def wrapper2(arg1, arg2): arg = weak_arg() if arg is not None: return function(arg, arg1, arg2) def wrapper3(arg1, arg2, arg3): arg = weak_arg() if arg is not None: return function(arg, arg1, arg2, arg3) def wrapper4(arg1, arg2, arg3, arg4): arg = weak_arg() if arg is not None: return function(arg, arg1, arg2, arg3, arg4) def wrappern(*args): arg = weak_arg() if arg is not None: function(arg, *args) # Return the correct wrapper depending on the arg count args = function.__code__.co_argcount - 1 if args == 0: return wrapper0 elif args == 1: return wrapper1 elif args == 2: return wrapper2 elif args == 3: return wrapper3 elif args == 4: return wrapper4 else: return wrappern return decorator class HasTraits(CHasTraits, metaclass=MetaHasTraits): """ Enables any Python class derived from it to have trait attributes. Most of the methods of HasTraits operated by default only on the trait attributes explicitly defined in the class definition. They do not operate on trait attributes defined by way of wildcards or by calling **add_trait()**. For example:: >>> class Person(HasTraits): ... name = Str ... age = Int ... temp_ = Any >>> bob = Person() >>> bob.temp_lunch = 'sandwich' >>> bob.add_trait('favorite_sport', Str('football')) >>> print(bob.trait_names()) ['trait_added', 'age', 'name'] In this example, the trait_names() method returns only the *age* and *name* attributes defined on the Person class. (The **trait_added** attribute is an explicit trait event defined on the HasTraits class.) The wildcard attribute *temp_lunch* and the dynamically-added trait attribute *favorite_sport* are not listed. Subclass should avoid defining new traits and/or methods with names starting with "trait" or "_trait" to avoid overshadowing existing methods, unless it has been documented as being safe to do so. """ # -- Trait Prefix Rules --------------------------------------------------- #: Make traits 'property cache' values private with no type checking: _traits_cache__ = Any(private=True, transient=True) # -- Class Variables ------------------------------------------------------ #: Mapping from dispatch type to notification wrapper class type wrappers = { "same": TraitChangeNotifyWrapper, "extended": ExtendedTraitChangeNotifyWrapper, "new": NewTraitChangeNotifyWrapper, "fast_ui": FastUITraitChangeNotifyWrapper, "ui": FastUITraitChangeNotifyWrapper, } # -- Trait Definitions ---------------------------------------------------- #: An event fired when a new trait is dynamically added to the object. The #: value is the name of the trait that was added. trait_added = Event(Str()) #: An event that can be fired to indicate that the state of the object has #: been modified. trait_modified = Event() def _trait_added_changed(self, name): """ Handles a 'trait_added' event being fired. """ # fixme: This test should be made more comprehensive by also verifying # that if the trait name does end in '_items', its base trait is also # a list or dictionary (in order to eliminate a false positive on an # unfortunately named trait: trait = self.trait(name) if (trait.type == "delegate") and (name[-6:] != "_items"): self._init_trait_delegate_listener( name, "delegate", get_delegate_pattern(name, trait) ) @classmethod def add_class_trait(cls, name, *trait): """ Adds a named trait attribute to this class. Also adds the same attribute to all subclasses. Parameters ---------- name : str Name of the attribute to add. *trait : A trait or a value that can be converted to a trait using Trait() Trait definition of the attribute. It can be a single value or a list equivalent to an argument list for the Trait() function. """ # Make sure a trait argument was specified: if len(trait) == 0: raise ValueError("No trait definition was specified.") # Make sure only valid traits get added: if len(trait) > 1: trait = Trait(*trait) else: trait = trait_for(trait[0]) # Add the trait to the class: cls._add_class_trait(name, trait, is_subclass=False) # Also add the trait to all subclasses of this class: for subclass in cls.trait_subclasses(True): subclass._add_class_trait(name, trait, is_subclass=True) @classmethod def _add_class_trait(cls, name, trait, is_subclass): """ Add a named trait attribute to this class. Does not affect subclasses. Parameters ---------- name : str Name of the attribute to add. trait : CTrait The trait to be added. is_subclass : bool True if we're adding the trait to a strict subclass of the original class that add_class_trait was called for. This is used to decide how to behave if ``cls`` already has a trait named ``name``: in that circumstance, if ``is_subclass`` is False, an error will be raised, while if ``is_subclass`` is True, no trait will be added. Raises ------ TraitError If a trait with the given name already exists, and is_subclass is ``False``. """ # Get a reference to the class's dictionary and 'prefix' traits: class_dict = cls.__dict__ prefix_traits = class_dict[PrefixTraits] # See if the trait is a 'prefix' trait: if name[-1:] == "_": name = name[:-1] if name in prefix_traits: if is_subclass: return raise TraitError("The '%s_' trait is already defined." % name) prefix_traits[name] = trait # Otherwise, add it to the list of known prefixes: prefix_list = prefix_traits["*"] prefix_list.append(name) # Resort the list from longest to shortest: prefix_list.sort(key=len, reverse=True) return # Check to see if the trait is already defined: class_traits = class_dict[ClassTraits] if class_traits.get(name) is not None: if is_subclass: return raise TraitError("The '%s' trait is already defined." % name) # Check to see if the trait has additional sub-traits that need to be # defined also: handler = trait.handler if handler is not None: if handler.has_items: cls._add_class_trait( name + "_items", handler.items_event(), is_subclass=is_subclass, ) if handler.is_mapped: cls._add_class_trait( name + "_", mapped_trait_for(trait, name), is_subclass=is_subclass, ) # Make the new trait inheritable (if allowed): if trait.is_base is not False: class_dict[BaseTraits][name] = trait # See if there are any static notifiers defined: handlers = [ _get_method(cls, "_%s_changed" % name), _get_method(cls, "_%s_fired" % name), ] # Add any special trait defined event handlers: _add_event_handlers(trait, cls, handlers) # Add the 'anytrait' handler (if any): handlers.append(prefix_traits.get("@")) # Filter out any 'None' values: handlers = [h for h in handlers if h is not None] # If there are and handlers, add them to the trait's notifier's list: if len(handlers) > 0: trait = _clone_trait(trait) _add_notifiers(trait._notifiers(True), handlers) # Finally, add the new trait to the class trait dictionary: class_traits[name] = trait @classmethod def set_trait_dispatch_handler(cls, name, klass, override=False): """ Sets a trait notification dispatch handler. """ try: if issubclass(klass, TraitChangeNotifyWrapper): if (not override) and (name in cls.wrappers): raise TraitError( "A dispatch handler called '%s' has " "already been defined." % name ) cls.wrappers[name] = klass return except TypeError: pass raise TraitError( "%s is not a subclass of TraitChangeNotifyWrapper." % klass ) @classmethod def trait_subclasses(cls, all=False): """ Returns a list of the immediate (or all) subclasses of this class. Parameters ---------- all : bool Indicates whether to return all subclasses of this class. If False, only immediate subclasses are returned. """ if not all: return cls.__subclasses__() return cls._trait_subclasses([]) @classmethod def _trait_subclasses(cls, subclasses): for subclass in cls.__subclasses__(): if subclass not in subclasses: subclasses.append(subclass) subclass._trait_subclasses(subclasses) return subclasses def has_traits_interface(self, *interfaces): """Returns whether the object implements a specified traits interface. Tests whether the object implements one or more of the interfaces specified by *interfaces*. Return **True** if it does, and **False** otherwise. Parameters ---------- *interfaces : One or more traits Interface (sub)classes. """ return isinstance(self, interfaces) def __getstate__(self): """ Returns a dictionary of traits to pickle. In general, avoid overriding __getstate__ in subclasses. Instead, mark traits that should not be pickled with 'transient = True' metadata. In cases where this strategy is not sufficient, override __getstate__ in subclasses using the following pattern to remove items that should not be persisted:: def __getstate__(self): state = super().__getstate__() for key in ['foo', 'bar']: if key in state: del state[key] return state """ # Save all traits which do not have any 'transient' metadata: result = self.trait_get(transient=is_none) # Add all delegate traits that explicitly have 'transient = False' # metadata: dic = self.__dict__ result.update( dict( [ (name, dic[name]) for name in self.trait_names( type="delegate", transient=False ) if name in dic ] ) ) # If this object implements ISerializable, make sure that all # contained HasTraits objects in its persisted state also implement # ISerializable: if self.has_traits_interface(ISerializable): for name, value in result.items(): if not _is_serializable(value): raise TraitError( "The '%s' trait of a '%s' instance " "contains the unserializable value: %s" % (name, self.__class__.__name__, value) ) # Store the traits version in the state dictionary (if possible): result.setdefault("__traits_version__", TraitsVersion) # Return the final state dictionary: return result def __reduce_ex__(self, protocol): return (__newobj__, (self.__class__,), self.__getstate__()) def __setstate__(self, state, trait_change_notify=True): """ Restores the previously pickled state of an object. """ pop = state.pop if pop("__traits_version__", None) is None: # If the state was saved by a version of Traits prior to 3.0, then # use Traits 2.0 compatible code to restore it: values = [ (name, pop(name)) for name in pop("__HasTraits_restore__", []) ] self.__dict__.update(state) self.trait_set( trait_change_notify=trait_change_notify, **dict(values) ) else: # Otherwise, apply the Traits 3.0 restore logic: self._init_trait_listeners() self._init_trait_observers() self.trait_set(trait_change_notify=trait_change_notify, **state) self._post_init_trait_listeners() self._post_init_trait_observers() self.traits_init() self._trait_set_inited() def trait_get(self, *names, **metadata): """ Retrieve trait values for one or more traits. This function can be called in one of three ways. In the first form, the user passes the names of one or more traits to be retrieved:: my_object.trait_get("trait_name1", "trait_name2") In the second form, the user passes a list of zero or more names of traits:: my_object.trait_get(["trait_name1", "trait_name2"]) In the final form, no trait names are passed, and all trait names and trait values are returned, subject to the given metadata filters:: my_object.trait_get(transient=True, frombicated=False) In all cases, a dictionary mapping trait names to trait values is returned. For the first two forms, if any name does not correspond to a defined trait, it is not included in the result. Parameters ---------- *names Names of the traits to look up, or a single positional argument providing a sequence of trait names. **metadata Metadata information used to filter the traits to return. This information is used only when no names are provided. Returns ------- result : dict A dictionary mapping the selected trait names to their corresponding values. """ result = {} n = len(names) if (n == 1) and (type(names[0]) in SequenceTypes): names = names[0] elif n == 0: names = self.trait_names(**metadata) # Sentinel for missing attributes. missing = object() for name in names: value = getattr(self, name, missing) if value is not missing: result[name] = value return result # Defines the deprecated alias for 'trait_get' @deprecated('use "HasTraits.trait_get" instead') def get(self, *names, **metadata): return self.trait_get(*names, **metadata) get.__doc__ = trait_get.__doc__ def trait_set(self, trait_change_notify=True, **traits): """ Shortcut for setting object trait attributes. Treats each keyword argument to the method as the name of a trait attribute and sets the corresponding trait attribute to the value specified. This is a useful shorthand when a number of trait attributes need to be set on an object, or a trait attribute value needs to be set in a lambda function. For example, you can write:: person.trait_set(name='Bill', age=27) instead of:: person.name = 'Bill' person.age = 27 Parameters ---------- trait_change_notify : bool If **True** (the default), then each value assigned may generate a trait change notification. If **False**, then no trait change notifications will be generated. (see also: trait_setq) **traits : Key/value pairs, the trait attributes and their values to be set Returns ------- self : The method returns this object, after setting attributes. """ if not trait_change_notify: self._trait_change_notify(False) try: for name, value in traits.items(): setattr(self, name, value) finally: self._trait_change_notify(True) else: for name, value in traits.items(): setattr(self, name, value) return self # Defines the deprecated alias for 'trait_set' @deprecated('use "HasTraits.trait_set" instead') def set(self, trait_change_notify=True, **traits): return self.trait_set( trait_change_notify=trait_change_notify, **traits ) set.__doc__ = trait_set.__doc__ def trait_setq(self, **traits): """ Shortcut for setting object trait attributes. Treats each keyword argument to the method as the name of a trait attribute and sets the corresponding trait attribute to the value specified. This is a useful shorthand when a number of trait attributes need to be set on an object, or a trait attribute value needs to be set in a lambda function. For example, you can write:: person.trait_setq(name='Bill', age=27) instead of:: person.name = 'Bill' person.age = 27 Parameters ---------- **traits : Key/value pairs, the trait attributes and their values to be set. No trait change notifications will be generated for any values assigned (see also: trait_set). Returns ------- self : The method returns this object, after setting attributes. """ return self.trait_set(trait_change_notify=False, **traits) def reset_traits(self, traits=None, **metadata): """ Resets some or all of an object's trait attributes to their default values. Resets each of the traits whose names are specified in the *traits* list to their default values. If *traits* is None or omitted, the method resets all explicitly-defined object trait attributes to their default values. Note that this does not affect wildcard trait attributes or trait attributes added via add_trait(), unless they are explicitly named in *traits*. Parameters ---------- traits : list of strings Names of trait attributes to reset. Returns ------- unresetable : list of strings A list of attributes that the method was unable to reset, which is empty if all the attributes were successfully reset. """ unresetable = [] if traits is None: traits = self.trait_names(**metadata) for name in traits: try: delattr(self, name) except (AttributeError, TraitError): unresetable.append(name) return unresetable def copyable_trait_names(self, **metadata): """ Returns the list of trait names to copy or clone by default. """ metadata.setdefault("transient", lambda t: t is not True) return self.trait_names(**metadata) def all_trait_names(self): """ Returns the list of all trait names, including implicitly defined traits. """ return list(self.__class_traits__.keys()) def __dir__(self): """ Returns the list of trait names when calling the dir() builtin on the object. This enables tab-completion in IPython. """ return super().__dir__() + self.trait_names() def copy_traits( self, other, traits=None, memo=None, copy=None, **metadata ): """ Copies another object's trait attributes into this one. Parameters ---------- other : object The object whose trait attribute values should be copied. traits : list of strings A list of names of trait attributes to copy. If None or unspecified, the set of names returned by trait_names() is used. If 'all' or an empty list, the set of names returned by all_trait_names() is used. memo : dict A dictionary of objects that have already been copied. copy : None | 'deep' | 'shallow' The type of copy to perform on any trait that does not have explicit 'copy' metadata. A value of None means 'copy reference'. Returns ------- unassignable : list of strings A list of attributes that the method was unable to copy, which is empty if all the attributes were successfully copied. """ if traits is None: traits = self.copyable_trait_names(**metadata) elif (traits == "all") or (len(traits) == 0): traits = self.all_trait_names() if memo is not None: memo["traits_to_copy"] = "all" unassignable = [] deferred = [] deep_copy = copy == "deep" shallow_copy = copy == "shallow" for name in traits: try: trait = self.trait(name) if trait.type in DeferredCopy: deferred.append(name) continue base_trait = other.base_trait(name) if base_trait.type == "event": continue value = getattr(other, name) copy_type = base_trait.copy if copy_type == "shallow": value = copy_module.copy(value) elif copy_type == "ref": pass elif (copy_type == "deep") or deep_copy: if memo is None: value = copy_module.deepcopy(value) else: value = copy_module.deepcopy(value, memo) elif shallow_copy: value = copy_module.copy(value) setattr(self, name, value) except: unassignable.append(name) for name in deferred: try: value = getattr(other, name) copy_type = other.base_trait(name).copy if copy_type == "shallow": value = copy_module.copy(value) elif copy_type == "ref": pass elif (copy_type == "deep") or deep_copy: if memo is None: value = copy_module.deepcopy(value) else: value = copy_module.deepcopy(value, memo) elif shallow_copy: value = copy_module.copy(value) setattr(self, name, value) except: unassignable.append(name) return unassignable def clone_traits(self, traits=None, memo=None, copy=None, **metadata): """ Clones a new object from this one, optionally copying only a specified set of traits. Creates a new object that is a clone of the current object. If *traits* is None (the default), then all explicit trait attributes defined for this object are cloned. If *traits* is 'all' or an empty list, the list of traits returned by all_trait_names() is used; otherwise, *traits* must be a list of the names of the trait attributes to be cloned. Parameters ---------- traits : list of strings The list of names of the trait attributes to copy. memo : dict A dictionary of objects that have already been copied. copy : str The type of copy ``deep`` or ``shallow`` to perform on any trait that does not have explicit 'copy' metadata. A value of None means 'copy reference'. Returns ------- new : The newly cloned object. """ if memo is None: memo = {} if traits is None: traits = self.copyable_trait_names(**metadata) elif (traits == "all") or (len(traits) == 0): traits = self.all_trait_names() memo["traits_to_copy"] = "all" memo["traits_copy_mode"] = copy new = self.__new__(self.__class__) memo[id(self)] = new new._init_trait_listeners() new._init_trait_observers() new.copy_traits(self, traits, memo, copy, **metadata) new._post_init_trait_listeners() new._post_init_trait_observers() new.traits_init() new._trait_set_inited() return new def __deepcopy__(self, memo): """ Creates a deep copy of the object. """ return self.clone_traits( memo=memo, traits=memo.get("traits_to_copy"), copy=memo.get("traits_copy_mode"), ) def edit_traits( self, view=None, parent=None, kind=None, context=None, handler=None, id="", scrollable=None, **args ): """ Displays a user interface window for editing trait attribute values. Parameters ---------- view : View or string A View object (or its name) that defines a user interface for editing trait attribute values of the current object. If the view is defined as an attribute on this class, use the name of the attribute. Otherwise, use a reference to the view object. If this attribute is not specified, the View object returned by trait_view() is used. parent : toolkit control The reference to a user interface component to use as the parent window for the object's UI window. kind : str The type of user interface window to create. See the **traitsui.view.kind_trait** trait for values and their meanings. If *kind* is unspecified or None, the **kind** attribute of the View object is used. context : object or dictionary A single object or a dictionary of string/object pairs, whose trait attributes are to be edited. If not specified, the current object is used. handler : Handler A handler object used for event handling in the dialog box. If None, the default handler for Traits UI is used. id : str A unique ID for persisting preferences about this user interface, such as size and position. If not specified, no user preferences are saved. scrollable : bool Indicates whether the dialog box should be scrollable. When set to True, scroll bars appear on the dialog box if it is not large enough to display all of the items in the view at one time. Returns ------- A UI object. """ if context is None: context = self view = self.trait_view(view) return view.ui( context, parent, kind, self.trait_view_elements(), handler, id, scrollable, args, ) def trait_context(self): """ Returns the default context to use for editing or configuring traits. """ return {"object": self} def trait_view(self, name=None, view_element=None): """ Gets or sets a ViewElement associated with an object's class. If both *name* and *view_element* are specified, the view element is associated with *name* for the current object's class. (That is, *view_element* is added to the ViewElements object associated with the current object's class, indexed by *name*.) If only *name* is specified, the function returns the view element object associated with *name*, or None if *name* has no associated view element. View elements retrieved by this function are those that are bound to a class attribute in the class definition, or that are associated with a name by a previous call to this method. If neither *name* nor *view_element* is specified, the method returns a View object, based on the following order of preference: 1. If there is a View object named ``traits_view`` associated with the current object, it is returned. 2. If there is exactly one View object associated the current object, it is returned. 3. Otherwise, it returns a View object containing items for all the non-event trait attributes on the current object. Parameters ---------- name : str Name of a view element view_element : ViewElement View element to associate Returns ------- A view element. """ return self.__class__._trait_view( name, view_element, self.default_traits_view, self.trait_view_elements, self.visible_traits, self, ) @classmethod def class_trait_view(cls, name=None, view_element=None): return cls._trait_view( name, view_element, cls.class_default_traits_view, cls.class_trait_view_elements, cls.class_visible_traits, None, ) @classmethod def _trait_view( cls, name, view_element, default_name, view_elements, trait_selector_f, handler, ): """ Gets or sets a ViewElement associated with an object's class. """ # If a view element was passed instead of a name or None, return it: if isinstance(name, AbstractViewElement): return name # Get the ViewElements object associated with the class: view_elements = view_elements() # The following test should only succeed for objects created before # traits has been fully initialized (such as the default Handler): if view_elements is None: return None # Provide a nicer failure mode when upgrading to Traits using # TraitsUI 6.x check_traitsui_major_version(7) if name: if view_element is None: # If only a name was specified, return the ViewElement it # matches, if any: result = view_elements.find(name) if (result is None) and (handler is not None): method = getattr(handler, name, None) if callable(method): result = method() return result # Otherwise, save the specified ViewElement under the name # specified: view_elements.content[name] = view_element return None # Get the default view/view name: name = default_name() # If the default is a View, return it: if isinstance(name, AbstractViewElement): return name # Otherwise, get all View objects associated with the object's class: names = view_elements.filter_by() # If the specified default name is in the list, return its View: if name in names: return view_elements.find(name) if handler is not None: method = getattr(handler, name, None) if callable(method): result = method() if isinstance(result, AbstractViewElement): return result # If there is only one View, return it: if len(names) == 1: return view_elements.find(names[0]) # Otherwise, create and return a View based on the set of editable # traits defined for the object: from traitsui.api import View return View(trait_selector_f(), buttons=["OK", "Cancel"]) def default_traits_view(self): """ Returns the name of the default traits view for the object's class. """ return self.__class__.class_default_traits_view() @classmethod def class_default_traits_view(cls): """ Returns the name of the default traits view for the class. """ return DefaultTraitsView def trait_views(self, klass=None): """ Returns a list of the names of all view elements associated with the current object's class. If *klass* is specified, the list of names is filtered such that only objects that are instances of the specified class are returned. Parameters ---------- klass : class A class, such that all returned names must correspond to instances of this class. Possible values include: * Group * Item * View * ViewElement * ViewSubElement """ view_elements = self.__class__.__dict__[ViewTraits] if isinstance(view_elements, dict): view_elements = self._init_trait_view_elements() return view_elements.filter_by(klass) def trait_view_elements(self): """ Returns the ViewElements object associated with the object's class. The returned object can be used to access all the view elements associated with the class. """ return self.__class__.class_trait_view_elements() @classmethod def class_trait_view_elements(cls): """ Returns the ViewElements object associated with the class. The returned object can be used to access all the view elements associated with the class. """ view_elements = cls.__dict__[ViewTraits] if isinstance(view_elements, dict): view_elements = cls._init_trait_view_elements() return view_elements @classmethod def _init_trait_view_elements(cls): """ Lazily Initialize the ViewElements object from a dictionary. """ from traitsui.view_elements import ViewElements hastraits_bases = [ base for base in cls.__bases__ if ClassTraits in base.__dict__ ] view_elements = ViewElements() elements_dict = cls.__dict__[ViewTraits] for name, element in elements_dict.items(): # Add the view element to the class's 'ViewElements' if it is # not already defined (duplicate definitions are errors): if name in view_elements.content: raise TraitError( "Duplicate definition for view element '%s'" % name ) view_elements.content[name] = element # Replace all substitutable view sub elements with 'Include' # objects, and add the substituted items to the # 'ViewElements': element.replace_include(view_elements) for base in hastraits_bases: # If the base class has a 'ViewElements' object defined, add it to # the 'parents' list of this class's 'ViewElements': parent_view_elements = base.class_trait_view_elements() if parent_view_elements is not None: view_elements.parents.append(parent_view_elements) setattr(cls, ViewTraits, view_elements) return view_elements def configure_traits( self, filename=None, view=None, kind=None, edit=None, context=None, handler=None, id="", scrollable=None, **args ): ### JMS: Is it correct to assume that non-modal options for 'kind' ### behave modally when called from this method? """Creates and displays a dialog box for editing values of trait attributes, as if it were a complete, self-contained GUI application. This method is intended for use in applications that do not normally have a GUI. Control does not resume in the calling application until the user closes the dialog box. The method attempts to open and unpickle the contents of *filename* before displaying the dialog box. When editing is complete, the method attempts to pickle the updated contents of the object back to *filename*. If the file referenced by *filename* does not exist, the object is not modified before displaying the dialog box. If *filename* is unspecified or None, no pickling or unpickling occurs. If *edit* is True (the default), a dialog box for editing the current object is displayed. If *edit* is False or None, no dialog box is displayed. You can use ``edit=False`` if you want the object to be restored from the contents of *filename*, without being modified by the user. Parameters ---------- filename : str The name (including path) of a file that contains a pickled representation of the current object. When this parameter is specified, the method reads the corresponding file (if it exists) to restore the saved values of the object's traits before displaying them. If the user confirms the dialog box (by clicking **OK**), the new values are written to the file. If this parameter is not specified, the values are loaded from the in-memory object, and are not persisted when the dialog box is closed. .. deprecated:: 6.0.0 view : View or str A View object (or its name) that defines a user interface for editing trait attribute values of the current object. If the view is defined as an attribute on this class, use the name of the attribute. Otherwise, use a reference to the view object. If this attribute is not specified, the View object returned by trait_view() is used. kind : str The type of user interface window to create. See the **traitsui.view.kind_trait** trait for values and their meanings. If *kind* is unspecified or None, the **kind** attribute of the View object is used. edit : bool Indicates whether to display a user interface. If *filename* specifies an existing file, setting *edit* to False loads the saved values from that file into the object without requiring user interaction. .. deprecated:: 6.2.0 context : object or dictionary A single object or a dictionary of string/object pairs, whose trait attributes are to be edited. If not specified, the current object is used handler : Handler A handler object used for event handling in the dialog box. If None, the default handler for Traits UI is used. id : str A unique ID for persisting preferences about this user interface, such as size and position. If not specified, no user preferences are saved. scrollable : bool Indicates whether the dialog box should be scrollable. When set to True, scroll bars appear on the dialog box if it is not large enough to display all of the items in the view at one time. Returns ------- True on success. """ if filename is not None: message = ('Restoring from pickle will not be supported starting ' 'with traits 7.0.0') warnings.warn(message, DeprecationWarning) if os.path.exists(filename): with open(filename, "rb") as fd: self.copy_traits(pickle.Unpickler(fd).load()) if edit is None: edit = True else: message = ( 'The edit argument to configure_traits is ' 'deprecated, and will be removed in Traits 7.0.0' ) warnings.warn(message, DeprecationWarning) if edit: from traitsui.api import toolkit if context is None: context = self rc = toolkit().view_application( context, self.trait_view(view), kind, handler, id, scrollable, args, ) if rc and (filename is not None): with open(filename, "wb") as fd: pickle.Pickler(fd, protocol=3).dump(self) return rc return True def editable_traits(self): """Returns an alphabetically sorted list of the names of non-event trait attributes associated with the current object. """ names = self.trait_names(type=not_event, editable=not_false) names.sort() return names @classmethod def class_editable_traits(cls): """Returns an alphabetically sorted list of the names of non-event trait attributes associated with the current class. """ names = cls.class_trait_names(type=not_event, editable=not_false) names.sort() return names def visible_traits(self): """Returns an alphabetically sorted list of the names of non-event trait attributes associated with the current object, that should be GUI visible """ return self.trait_names(type=not_event, visible=not_false) @classmethod def class_visible_traits(cls): """Returns an alphabetically sorted list of the names of non-event trait attributes associated with the current class, that should be GUI visible """ return cls.class_trait_names(type=not_event, visible=not_false) def print_traits(self, show_help=False, **metadata): """Prints the values of all explicitly-defined, non-event trait attributes on the current object, in an easily readable format. Parameters ---------- show_help : bool Indicates whether to display additional descriptive information. """ if len(metadata) > 0: names = self.trait_names(**metadata) else: names = self.trait_names(type=not_event) if len(names) == 0: print("") return result = [] pad = max([len(x) for x in names]) + 1 maxval = 78 - pad names.sort() for name in names: try: value = repr(getattr(self, name)).replace("\n", "\\n") if len(value) > maxval: value = "%s...%s" % ( value[:(maxval - 2) // 2], value[-((maxval - 3) // 2):], ) except: value = "" lname = (name + ":").ljust(pad) if show_help: result.append( "%s %s\n The value must be %s." % (lname, value, self.base_trait(name).setter.info()) ) else: result.append("%s %s" % (lname, value)) print("\n".join(result)) def _on_trait_change( self, handler, name=None, remove=False, dispatch="same", priority=False, target=None, ): """Causes the object to invoke a handler whenever a trait attribute is modified, or removes the association. Multiple handlers can be defined for the same object, or even for the same trait attribute on the same object. If *name* is not specified or is None, *handler* is invoked when any trait attribute on the object is changed. Parameters ---------- handler : function A trait notification function for the attribute specified by *name*. name : str Specifies the trait attribute whose value changes trigger the notification. remove : bool If True, removes the previously-set association between *handler* and *name*; if False (the default), creates the association. """ if type(name) is list: for name_i in name: self._on_trait_change( handler, name_i, remove, dispatch, priority, target ) return name = name or "anytrait" if remove: if name == "anytrait": notifiers = self._notifiers(False) else: trait = self._trait(name, 1) if trait is None: return notifiers = trait._notifiers(False) if notifiers is not None: for i, notifier in enumerate(notifiers): if notifier.equals(handler): del notifiers[i] notifier.dispose() break return if name == "anytrait": notifiers = self._notifiers(True) else: notifiers = self._trait(name, 2)._notifiers(True) for notifier in notifiers: if notifier.equals(handler): break else: wrapper = self.wrappers[dispatch](handler, notifiers, target) if priority: notifiers.insert(0, wrapper) else: notifiers.append(wrapper) def observe(self, handler, expression, *, remove=False, dispatch="same"): """ Causes the object to invoke a handler whenever a trait attribute matching a specified pattern is modified, or removes the association. The *expression* parameter can be a single string or an Expression object. A list of expressions is also accepted. When *expression* is a string, its content should follow Traits Mini Language semantics: ============================== ====================================== Expression Meaning ============================== ====================================== ``item1.item2`` Observes trait *item2* on the object under trait *item1*. Changes to either *item1* or *item2* cause a notification to be fired. ``item1:item2`` Similar to the above, except changes to *item1* will not fire events (the ':' indicates no notifications). ``[item1, item2, ..., itemN]`` A list which matches any of the specified items. Each item can itself be an expression. ``items`` Special keyword for observing a trait named *items* or items inside a list / dict / set. ``+metadata_name`` Matches any trait on the object having *metadata_name* metadata. ============================== ====================================== All spaces will be ignored. The :py:class:`ObserverExpression` object supports the above features and more. Parameters ---------- handler : callable(event) A callable that will be invoked when the observed trait changes. It must accept one argument, which is an event object providing information about the change. See :py:mod:`traits.observation.events` for details. expression : str or list or ObserverExpression A description of what traits are being observed. If this is a list, each item must be a string or an Expression. remove : boolean, optional Whether to remove the event handler. Default is to add the event handler. dispatch : str, optional A string indicating how the handler should be run. Default is to run on the same thread where the change occurs. Possible values are: =========== ======================================================= value dispatch =========== ======================================================= ``same`` Run notifications on the same thread where the change occurs. The notifications are executed immediately. ``ui`` Run notifications on the UI thread. If the current thread is the UI thread, the notifications are executed immediately; otherwise, they are placed on the UI event queue. =========== ======================================================= Raises ------ NotifierNotFound When attempting to remove a handler that doesn't exist. """ graphs = _compile_expression(expression) observe_api.apply_observers( object=self, graphs=graphs, handler=handler, dispatcher=_ObserverDispatchers[dispatch], remove=remove, ) def on_trait_change( self, handler, name=None, remove=False, dispatch="same", priority=False, deferred=False, target=None, ): """Causes the object to invoke a handler whenever a trait attribute matching a specified pattern is modified, or removes the association. Multiple handlers can be defined for the same object, or even for the same trait attribute on the same object. If *name* is not specified or is None, *handler* is invoked when any trait attribute on the object is changed. The *name* parameter is a single *xname* or a list of *xname* names, where an *xname* is an extended name of the form:: xname2[('.'|':') xname2]* An *xname2* is of the form:: ( xname3 | '['xname3[','xname3]*']' ) ['*'] An *xname3* is of the form:: xname | ['+'|'-'][name] | name['?' | ('+'|'-')[name]] A *name* is any valid Python attribute name. The semantic meaning of this notation is as follows: ================================ ====================================== expression meaning ================================ ====================================== ``item1.item2`` means *item1* is a trait containing an object (or objects if *item1* is a list or dict) with a trait called *item2*. Changes to either *item1* or *item2* cause a notification to be generated. ``item1:item2`` means *item1* is a trait containing an object (or objects if *item1* is a list or dict) with a trait called *item2*. Changes to *item2* cause a notification to be generated, while changes to *item1* do not (i.e., the ':' indicates that changes to the *link* object should not be reported). ``[ item1, item2, ..., itemN ]`` A list which matches any of the specified items. Note that at the topmost level, the surrounding square brackets are optional. ``name?`` If the current object does not have an attribute called *name*, the reference can be ignored. If the '?' character is omitted, the current object must have a trait called *name*, otherwise an exception will be raised. ``prefix+`` Matches any trait on the object whose name begins with *prefix*. ``+metadata_name`` Matches any trait on the object having *metadata_name* metadata. ``-metadata_name`` Matches any trait on the object which does not have *metadata_name* metadata. ``prefix+metadata_name`` Matches any trait on the object whose name begins with *prefix* and which has *metadata_name* metadata. ``prefix-metadata_name`` Matches any trait on the object whose name begins with *prefix* and which does not have *metadata_name* metadata. ``+`` Matches all traits on the object. ``pattern*`` Matches object graphs where *pattern* occurs one or more times (useful for setting up listeners on recursive data structures like trees or linked lists). ================================ ====================================== Some examples of valid names and their meaning are as follows: ======================= =============================================== example meaning ======================= =============================================== ``foo,bar,baz`` Listen for trait changes to *object.foo*, *object.bar*, and *object.baz*. ``['foo','bar','baz']`` Equivalent to 'foo,bar,baz', but may be more useful in cases where the individual items are computed. ``foo.bar.baz`` Listen for trait changes to *object.foo.bar.baz* and report changes to *object.foo*, *object.foo.bar* or *object.foo.bar.baz*. ``foo:bar:baz`` Listen for changes to *object.foo.bar.baz*, and only report changes to *object.foo.bar.baz*. ``foo.[bar,baz]`` Listen for trait changes to *object.foo.bar* and *object.foo.baz*. ``[left,right]*.name`` Listen for trait changes to the *name* trait of each node of a tree having *left* and *right* links to other tree nodes, and where *object* the method is applied to the root node of the tree. ``+dirty`` Listen for trait changes on any trait in the *object* which has the 'dirty' metadata set. ``foo.+dirty`` Listen for trait changes on any trait in *object.foo* which has the 'dirty' metadata set. ``foo.[bar,-dirty]`` Listen for trait changes on *object.foo.bar* or any trait on *object.foo* which does not have 'dirty' metadata set. ======================= =============================================== Note that any of the intermediate (i.e., non-final) links in a pattern can be traits of type Instance, List or Dict. In the case of List and Dict traits, the subsequent portion of the pattern is applied to each item in the list, or value in the dictionary. For example, if the self.children is a list, 'children.name' listens for trait changes to the *name* trait for each item in the self.children list. Note that items added to or removed from a list or dictionary in the pattern will cause the *handler* routine to be invoked as well, since this is treated as an *implied* change to the item's trait being monitored. The signature of the *handler* supplied also has an effect on how changes to intermediate traits are processed. The five valid handler signatures are: 1. handler() 2. handler(new) 3. handler(name,new) 4. handler(object,name,new) 5. handler(object,name,old,new) For signatures 1, 4 and 5, any change to any element of a path being listened to invokes the handler with information about the particular element that was modified (e.g., if the item being monitored is 'foo.bar.baz', a change to 'bar' will call *handler* with the following information: - object: object.foo - name: bar - old: old value for object.foo.bar - new: new value for object.foo.bar If one of the intermediate links is a List or Dict, the call to *handler* may report an *_items* changed event. If in the previous example, *bar* is a List, and a new item is added to *bar*, then the information passed to *handler* would be: - object: object.foo - name: bar_items - old: Undefined - new: TraitListEvent whose *added* trait contains the new item added to *bar*. For signatures 2 and 3, the *handler* does not receive enough information to discern between a change to the final trait being listened to and a change to an intermediate link. In this case, the event dispatcher will attempt to map a change to an intermediate link to its effective change on the final trait. This only works if all of the intermediate links are single values (such as an Instance or Any trait) and not Lists or Dicts. If the modified intermediate trait or any subsequent intermediate trait preceding the final trait is a List or Dict, then a TraitError is raised, since the effective value for the final trait cannot in general be resolved unambiguously. To prevent TraitErrors in this case, use the ':' separator to suppress notifications for changes to any of the intermediate links. Handler signature 1 also has the special characteristic that if a final trait is a List or Dict, it will automatically handle '_items' changed events for the final trait as well. This can be useful in cases where the *handler* only needs to know that some aspect of the final trait has been changed. For all other *handler* signatures, you must explicitly specify the 'xxx_items' trait if you want to be notified of changes to any of the items of the 'xxx' trait. Parameters ---------- handler : function A trait notification function for the *name* trait attribute, with one of the signatures described below. name : str The name of the trait attribute whose value changes trigger the notification. The *name* can specify complex patterns of trait changes using an extended *name* syntax, which is described below. remove : bool If True, removes the previously-set association between *handler* and *name*; if False (the default), creates the association. dispatch : str A string indicating the thread on which notifications must be run. Possible values are: =========== ======================================================= value dispatch =========== ======================================================= ``same`` Run notifications on the same thread as this one. ``ui`` Run notifications on the UI thread. If the current thread is the UI thread, the notifications are executed immediately; otherwise, they are placed on the UI event queue. ``fast_ui`` Alias for ``ui``. ``new`` Run notifications in a new thread. =========== ======================================================= See Also -------- HasTraits.observe : A newer API for defining traits notifications. """ # Check to see if we can do a quick exit to the basic trait change # handler: if ( isinstance(name, str) and (extended_trait_pat.match(name) is None) ) or (name is None): self._on_trait_change( handler, name, remove, dispatch, priority, target ) return from .traits_listener import ( TraitsListener, ListenerParser, ListenerHandler, ListenerNotifyWrapper, ) if isinstance(name, list): for name_i in name: self.on_trait_change( handler, name=name_i, remove=remove, dispatch=dispatch, priority=priority, deferred=deferred, target=target, ) return # Make sure we have a name string: name = (name or "anytrait").strip() if remove: dict = self.__dict__.get(TraitsListener) if dict is not None: listeners = dict.get(name) if listeners is not None: for i, wrapper in enumerate(listeners): if wrapper.equals(handler): del listeners[i] if len(listeners) == 0: del dict[name] if len(dict) == 0: del self.__dict__[TraitsListener] wrapper.listener.unregister(self) wrapper.dispose() break else: dict = self.__dict__.setdefault(TraitsListener, {}) listeners = dict.setdefault(name, []) for wrapper in listeners: if wrapper.equals(handler): break else: # The listener notify wrapper needs a reference to the # listener, and the listener needs a (weak) reference to the # wrapper. We first construct the wrapper with a listener of # `None`, then construct the listener with its reference to the # wrapper, then we replace the `None` listener with the correct # one. lnw = ListenerNotifyWrapper(handler, self, name, None, target) listener = ListenerParser( name, handler=ListenerHandler(handler), wrapped_handler_ref=weakref.ref(lnw), dispatch=dispatch, priority=priority, deferred=deferred, handler_type=lnw.type, ).listener lnw.listener = listener listener.register(self) listeners.append(lnw) # A synonym for 'on_trait_change' on_trait_event = on_trait_change def sync_trait( self, trait_name, object, alias=None, mutual=True, remove=False ): """Synchronizes the value of a trait attribute on this object with a trait attribute on another object. In mutual synchronization, any change to the value of the specified trait attribute of either object results in the same value being assigned to the corresponding trait attribute of the other object. In one-way synchronization, any change to the value of the attribute on this object causes the corresponding trait attribute of *object* to be updated, but not vice versa. For ``List`` traits, the list's items are also synchronized, so that mutations to this trait's list will be reflected in the synchronized list (and vice versa in the case of mutual synchronization). For ``Dict`` and ``Set`` traits, items are not synchronized. Parameters ---------- name : str Name of the trait attribute on this object. object : object The object with which to synchronize. alias : str Name of the trait attribute on *other*; if None or omitted, same as *name*. mutual : bool or int Indicates whether synchronization is mutual (True or non-zero) or one-way (False or zero) remove : bool or int Indicates whether synchronization is being added (False or zero) or removed (True or non-zero) """ if alias is None: alias = trait_name is_list = self._is_list_trait(trait_name) and object._is_list_trait( alias ) if remove: info = self._get_sync_trait_info() dic = info.get(trait_name) if dic is not None: key = (id(object), alias) if key in dic: del dic[key] if len(dic) == 0: del info[trait_name] self._on_trait_change( self._sync_trait_modified, trait_name, remove=True ) if is_list: self._on_trait_change( self._sync_trait_items_modified, trait_name + "_items", remove=True, ) if mutual: object.sync_trait(alias, self, trait_name, False, True) return # Callback to use when the synced object goes out of scope. In order # to avoid reference cycles, this must not be a member function. See # Github issue #69 for more detail. def _sync_trait_listener_deleted(ref, info): for key, dic in list(info.items()): if key != "": for name, value in list(dic.items()): if ref is value[0]: del dic[name] if len(dic) == 0: del info[key] info = self._get_sync_trait_info() dic = info.setdefault(trait_name, {}) key = (id(object), alias) callback = lambda ref: _sync_trait_listener_deleted(ref, info) value = (weakref.ref(object, callback), alias) if key not in dic: if len(dic) == 0: self._on_trait_change(self._sync_trait_modified, trait_name) if is_list: self._on_trait_change( self._sync_trait_items_modified, trait_name + "_items" ) dic[key] = value setattr(object, alias, getattr(self, trait_name)) if mutual: object.sync_trait(alias, self, trait_name, False) def _get_sync_trait_info(self): info = getattr(self, "__sync_trait__", None) if info is None: self.__dict__["__sync_trait__"] = info = {} info[""] = {} return info def _sync_trait_modified(self, object, name, old, new): info = self.__sync_trait__ if name not in info: return locked = info[""] locked[name] = None for object, object_name in info[name].values(): object = object() if object_name not in object._get_sync_trait_info()[""]: try: setattr(object, object_name, new) except: pass del locked[name] def _sync_trait_items_modified(self, object, name, old, event): n0 = event.index n1 = n0 + len(event.removed) name = name[:-6] info = self.__sync_trait__ locked = info[""] locked[name] = None for object, object_name in info[name].values(): object = object() if object_name not in object._get_sync_trait_info()[""]: try: getattr(object, object_name)[n0:n1] = event.added except: pass del locked[name] def _is_list_trait(self, trait_name): handler = self.base_trait(trait_name).handler return (handler is not None) and ( handler.default_value_type == DefaultValue.trait_list_object ) def add_trait(self, name, *trait): """Adds a trait attribute to this object. Parameters ---------- name : str Name of the attribute to add. *trait : Trait or a value that can be converted to a trait by Trait(). Trait definition for *name*. If more than one value is specified, it is equivalent to passing the entire list of values to Trait(). """ # Make sure a trait argument was specified: if len(trait) == 0: raise ValueError("No trait definition was specified.") # Make sure only valid traits get added: if len(trait) > 1: trait = Trait(*trait) else: trait = trait_for(trait[0]) # Check to see if the trait has additional sub-traits that need to be # defined also: handler = trait.handler if handler is not None: if handler.has_items: self.add_trait(name + "_items", handler.items_event()) if handler.is_mapped: self.add_trait(name + "_", mapped_trait_for(trait, name)) # See if there already is a class or instance trait with the same name: old_trait = self._trait(name, 0) # Get the object's instance trait dictionary and add a clone of the new # trait to it: itrait_dict = self._instance_traits() itrait_dict[name] = trait = _clone_trait(trait) # If there already was a trait with the same name: if old_trait is not None: # Copy the old traits notifiers into the new trait: old_notifiers = old_trait._notifiers(False) if old_notifiers is not None: trait._notifiers(True).extend(old_notifiers) else: # Otherwise, see if there are any static notifiers that should be # applied to the trait: cls = self.__class__ handlers = [ _get_method(cls, "_%s_changed" % name), _get_method(cls, "_%s_fired" % name), ] # Add any special trait defined event handlers: _add_event_handlers(trait, cls, handlers) # Add the 'anytrait' handler (if any): handlers.append(self.__prefix_traits__.get("@")) # Filter out any 'None' values: handlers = [h for h in handlers if h is not None] # If there are any static notifiers, attach them to the trait: if len(handlers) > 0: _add_notifiers(trait._notifiers(True), handlers) # If this was a new trait, fire the 'trait_added' event: if old_trait is None: self.trait_added = name def remove_trait(self, name): """Removes a trait attribute from this object. Parameters ---------- name : str Name of the attribute to remove. Returns ------- result : bool True if the trait was successfully removed. """ # Get the trait definition: trait = self._trait(name, 0) if trait is not None: # Check to see if the trait has additional sub-traits that need to # be removed also: handler = trait.handler if handler is not None: if handler.has_items: self.remove_trait(name + "_items") if handler.is_mapped: self.remove_trait(name + "_") # Remove the trait value from the object dictionary as well: if name in self.__dict__: del self.__dict__[name] # Get the object's instance trait dictionary and remove the trait # from it: itrait_dict = self._instance_traits() if name in itrait_dict: del itrait_dict[name] return True return False def trait(self, name, force=False, copy=False): """Returns the trait definition for the *name* trait attribute. If *force* is False (the default) and *name* is the name of an implicitly defined trait attribute that has never been referenced explicitly (i.e., has not yet been defined), the result is None. In all other cases, the result is the trait definition object associated with *name*. If *copy* is True, and a valid trait definition is found for *name*, a copy of the trait found is returned. In all other cases, the trait definition found is returned unmodified (the default). Parameters ---------- name : str Name of the attribute whose trait definition is to be returned. force : bool Indicates whether to return a trait definition if *name* is not explicitly defined. copy : bool Indicates whether to return the original trait definition or a copy. """ mode = 0 if force: mode = -1 result = self._trait(name, mode) if (not copy) or (result is None): return result return _clone_trait(result) def base_trait(self, name): """Returns the base trait definition for a trait attribute. This method is similar to the trait() method, and returns a different result only in the case where the trait attribute defined by *name* is a delegate. In this case, the base_trait() method follows the delegation chain until a non-delegated trait attribute is reached, and returns the definition of that attribute's trait as the result. Parameters ---------- name : str Name of the attribute whose trait definition is returned. """ return self._trait(name, -2) def validate_trait(self, name, value): """ Validates whether a value is legal for a trait. Returns the validated value if it is valid. """ return self.base_trait(name).validate(self, name, value) def traits(self, **metadata): """Returns a dictionary containing the definitions of all of the trait attributes of this object that match the set of *metadata* criteria. Note that any traits with a name containing the suffix "_items" are always excluded. The keys of the returned dictionary are the trait attribute names, and the values are their corresponding trait definition objects. If no *metadata* information is specified, then all explicitly defined trait attributes defined for the object are returned. Otherwise, the *metadata* keyword dictionary is assumed to define a set of search criteria for selecting trait attributes of interest. The *metadata* dictionary keys correspond to the names of trait metadata attributes to examine, and the values correspond to the values the metadata attribute must have in order to be included in the search results. The *metadata* values either may be simple Python values like strings or integers, or may be lambda expressions or functions that return True if the trait attribute is to be included in the result. A lambda expression or function must receive a single argument, which is the value of the trait metadata attribute being tested. If more than one metadata keyword is specified, a trait attribute must match the metadata values of all keywords to be included in the result. Parameters ---------- **metadata : Criteria for selecting trait attributes. """ traits = self.__base_traits__.copy() # Update with instance-defined traits. for name, trt in self._instance_traits().items(): if name[-6:] != "_items": traits[name] = trt for name in self.__dict__.keys(): if name not in traits: trait = self.trait(name) if trait is not None: traits[name] = trait if len(metadata) == 0: return traits for meta_name, meta_eval in list(metadata.items()): if type(meta_eval) is not FunctionType: metadata[meta_name] = _SimpleTest(meta_eval) result = {} for name, trait in traits.items(): for meta_name, meta_eval in metadata.items(): if not meta_eval(getattr(trait, meta_name, None)): break else: result[name] = trait return result @classmethod def class_traits(cls, **metadata): """Returns a dictionary containing the definitions of all of the trait attributes of the class that match the set of *metadata* criteria. The keys of the returned dictionary are the trait attribute names, and the values are their corresponding trait definition objects. If no *metadata* information is specified, then all explicitly defined trait attributes defined for the class are returned. Otherwise, the *metadata* keyword dictionary is assumed to define a set of search criteria for selecting trait attributes of interest. The *metadata* dictionary keys correspond to the names of trait metadata attributes to examine, and the values correspond to the values the metadata attribute must have in order to be included in the search results. The *metadata* values either may be simple Python values like strings or integers, or may be lambda expressions or functions that return **True** if the trait attribute is to be included in the result. A lambda expression or function must receive a single argument, which is the value of the trait metadata attribute being tested. If more than one metadata keyword is specified, a trait attribute must match the metadata values of all keywords to be included in the result. Parameters ---------- **metadata : Criteria for selecting trait attributes. """ if len(metadata) == 0: return cls.__base_traits__.copy() result = {} for meta_name, meta_eval in list(metadata.items()): if type(meta_eval) is not FunctionType: metadata[meta_name] = _SimpleTest(meta_eval) for name, trait in cls.__base_traits__.items(): for meta_name, meta_eval in metadata.items(): if not meta_eval(getattr(trait, meta_name, None)): break else: result[name] = trait return result def trait_names(self, **metadata): """Returns a list of the names of all trait attributes whose definitions match the set of *metadata* criteria specified. This method is similar to the traits() method, but returns only the names of the matching trait attributes, not the trait definitions. Parameters ---------- **metadata : Criteria for selecting trait attributes. """ return list(self.traits(**metadata).keys()) @classmethod def class_trait_names(cls, **metadata): """Returns a list of the names of all trait attributes whose definitions match the set of *metadata* criteria specified. This method is similar to the traits() method, but returns only the names of the matching trait attributes, not the trait definitions. Parameters ---------- **metadata : Criteria for selecting trait attributes. """ return list(cls.class_traits(**metadata).keys()) def _set_traits_cache(self, name, value): """ Explicitly sets the value of a cached property. """ cached = TraitsCache + name old_value = self.__dict__.get(cached, Undefined) self.__dict__[cached] = value if old_value != value: self.trait_property_changed(name, old_value, value) def _flush_traits_cache(self, name, value): """ Explicitly flushes the value of a cached property. """ self.trait_property_changed( name, self.__dict__.pop(TraitsCache + name, Undefined) ) def __prefix_trait__(self, name, is_set): """ Return the trait definition for a specified name when there is no explicit definition in the class. """ # Check to see if the name is of the form '__xxx__': if (name[:2] == "__") and (name[-2:] == "__"): if name == "__class__": return generic_trait # If this is for purposes of performing a 'setattr', always map the # name to an 'Any' trait: if is_set: return any_trait # Otherwise, it is a 'getattr' request, so indicate that no such # attribute exists: raise AttributeError( "'%s' object has no attribute '%s'" % (self.__class__.__name__, name) ) # Handle the special case of 'delegated' traits: if name[-1:] == "_": trait = self._trait(name[:-1], 0) if (trait is not None) and (trait.type == "delegate"): return _clone_trait(trait) prefix_traits = self.__prefix_traits__ for prefix in prefix_traits["*"]: if prefix == name[: len(prefix)]: # If we found a match, use its trait as a template for a new # trait: trait = prefix_traits[prefix] # Get any change notifiers that apply to the trait: cls = self.__class__ handlers = [ _get_method(cls, "_%s_changed" % name), _get_method(cls, "_%s_fired" % name), ] # Add any special trait defined event handlers: _add_event_handlers(trait, cls, handlers) # Add the 'anytrait' handler (if any): handlers.append(prefix_traits.get("@")) # Filter out any 'None' values: handlers = [h for h in handlers if h is not None] # If there are any handlers, add them to the trait's notifier's # list: if len(handlers) > 0: trait = _clone_trait(trait) _add_notifiers(trait._notifiers(True), handlers) return trait # There should ALWAYS be a prefix match in the trait classes, since '' # is at the end of the list, so we should never get here: raise SystemError( "Trait class look-up failed for attribute '%s' " "for an object of type '%s'" % (name, self.__class__.__name__) ) def add_trait_listener(self, object, prefix=""): """ Add (Java-style) event listener to an object. """ self._trait_listener(object, prefix, False) def remove_trait_listener(self, object, prefix=""): """ Remove (Java-style) event listener to an object. """ self._trait_listener(object, prefix, True) def _trait_listener(self, object, prefix, remove): if prefix[-1:] != "_": prefix += "_" n = len(prefix) traits = self.__base_traits__ for name in self._each_trait_method(object): if name[:n] == prefix: if name[-8:] == "_changed": short_name = name[n:-8] if short_name in traits: self._on_trait_change( getattr(object, name), short_name, remove=remove ) elif short_name == "anytrait": self._on_trait_change( getattr(object, name), remove=remove ) elif name[:-6] == "_fired": short_name = name[n:-6] if short_name in traits: self._on_trait_change( getattr(object, name), short_name, remove=remove ) elif short_name == "anytrait": self._on_trait_change( getattr(object, name), remove=remove ) def _each_trait_method(self, object): """ Generates each (name, method) pair for a specified object. """ dic = {} for klass in object.__class__.__mro__: for name, method in klass.__dict__.items(): if (is_unbound_method_type(method) and name not in dic): dic[name] = True yield name def _instance_changed_handler(self, name, old, new): """ Handles adding/removing listeners for a generic 'Instance' trait. """ arg_lists = self._get_instance_handlers(name) if old is not None: for args in arg_lists: old.on_trait_change(remove=True, *args) if new is not None: for args in arg_lists: new.on_trait_change(*args) def _list_changed_handler(self, name, old, new): """ Handles adding/removing listeners for a generic 'List( Instance )' trait. """ arg_lists = self._get_instance_handlers(name) for item in old: for args in arg_lists: item.on_trait_change(remove=True, *args) for item in new: for args in arg_lists: item.on_trait_change(*args) def _list_items_changed_handler(self, name, not_used, event): """ Handles adding/removing listeners for a generic 'List( Instance )' trait. """ arg_lists = self._get_instance_handlers(name[:-6]) for item in event.removed: for args in arg_lists: item.on_trait_change(remove=True, *args) for item in event.added: for args in arg_lists: item.on_trait_change(*args) def _get_instance_handlers(self, name): """ Returns a list of ( name, method ) pairs for a specified 'Instance' or 'List( Instance )' trait name: """ return [ (getattr(self, method_name), item_name) for method_name, item_name in self.__class__.__instance_traits__[ name ] ] def _post_init_trait_listeners(self): """ Initializes the object's statically parsed, but dynamically registered, traits listeners (called at object creation and unpickling times). """ for name, data in self.__class__.__listener_traits__.items(): if data[0] == "method": config = data[1] if config["post_init"]: self.on_trait_change( getattr(self, name), config["pattern"], deferred=True, dispatch=config["dispatch"], ) def _init_trait_listeners(self): """ Initializes the object's statically parsed, but dynamically registered, traits listeners (called at object creation and unpickling times). """ for name, data in self.__class__.__listener_traits__.items(): getattr(self, "_init_trait_%s_listener" % data[0])(name, *data) def _init_trait_method_listener(self, name, kind, config): """ Sets up the listener for a method with the @on_trait_change decorator. """ if not config["post_init"]: self.on_trait_change( getattr(self, name), config["pattern"], deferred=True, dispatch=config["dispatch"], ) def _init_trait_event_listener(self, name, kind, pattern): """ Sets up the listener for an event with on_trait_change metadata. """ @weak_arg(self) def notify(self): setattr(self, name, True) self.on_trait_change(notify, pattern, target=self) def _init_trait_property_listener(self, name, kind, cached, pattern): """ Sets up the listener for a property with 'depends_on' metadata. """ if cached is None: @weak_arg(self) def notify(self): self.trait_property_changed(name, None) else: cached_old = cached + ":old" @weak_arg(self) def pre_notify(self): dict = self.__dict__ old = dict.get(cached_old, Undefined) if old is Undefined: dict[cached_old] = dict.pop(cached, None) self.on_trait_change( pre_notify, pattern, priority=True, target=self ) @weak_arg(self) def notify(self): old = self.__dict__.pop(cached_old, Undefined) if old is not Undefined: self.trait_property_changed(name, old) self.on_trait_change(notify, pattern, target=self) def _init_trait_delegate_listener(self, name, kind, pattern): """ Sets up the listener for a delegate trait. """ name_pattern = self._trait_delegate_name(name, pattern) target_name_len = len(name_pattern.split(":")[-1]) @weak_arg(self) def notify(self, object, notify_name, old, new): self.trait_property_changed( name + notify_name[target_name_len:], old, new ) self.on_trait_change(notify, name_pattern, target=self) self.__dict__.setdefault(ListenerTraits, {})[name] = notify def _remove_trait_delegate_listener(self, name, remove): """ Removes a delegate listener when the local delegate value is set. """ dict = self.__dict__.setdefault(ListenerTraits, {}) if remove: # Although the name should be in the dict, it may not be if a value # was assigned to a delegate in a constructor or setstate: if name in dict: # Remove the delegate listener: self.on_trait_change( dict[name], self._trait_delegate_name( name, self.__class__.__listener_traits__[name][1] ), remove=True, ) del dict[name] if len(dict) == 0: del self.__dict__[ListenerTraits] return # Otherwise the local copy of the delegate value was deleted, restore # the delegate listener (unless it's already there): if name not in dict: self._init_trait_delegate_listener( name, 0, self.__class__.__listener_traits__[name][1] ) def _init_trait_observers(self): """ Initialize observers prior to setting object state. """ for name, states in self.__class__.__observer_traits__.items(): for state in states: if not state["post_init"]: observe_api.apply_observers( object=self, handler=state["handler_getter"](self, name), graphs=state["graphs"], dispatcher=_ObserverDispatchers[state["dispatch"]], ) def _post_init_trait_observers(self): """ Initialize observers after setting object state. """ for name, states in self.__class__.__observer_traits__.items(): for state in states: if state["post_init"]: observe_api.apply_observers( object=self, handler=state["handler_getter"](self, name), graphs=state["graphs"], dispatcher=_ObserverDispatchers[state["dispatch"]], ) def _trait_delegate_name(self, name, pattern): """ Returns the fully-formed 'on_trait_change' name for a specified delegate. """ if pattern[-1] == "*": pattern = "%s%s%s" % ( pattern[:-1], self.__class__.__prefix__, name, ) return pattern # Patch the definition of _HasTraits to be the real 'HasTraits': _HasTraits = HasTraits class HasStrictTraits(HasTraits): """ This class guarantees that any object attribute that does not have an explicit or wildcard trait definition results in an exception. This feature can be useful in cases where a more rigorous software engineering approach is being used than is typical for Python programs. It also helps prevent typos and spelling mistakes in attribute names from going unnoticed; a misspelled attribute name typically causes an exception. """ _ = Disallow # Disallow access to any traits not explicitly defined class HasRequiredTraits(HasStrictTraits): """ This class builds on the functionality of HasStrictTraits and ensures that any object attribute with `required=True` in its metadata must be passed as an argument on object initialization. This can be useful in cases where an object has traits which are required for it to function correctly. Raises ------ TraitError If a required trait is not passed as an argument. Examples -------- A class with required traits: >>> class RequiredTest(HasRequiredTraits): ... required_trait = Any(required=True) ... non_required_trait = Any() Creating an instance of a HasRequiredTraits subclass: >>> test_instance = RequiredTest(required_trait=13, non_required_trait=11) >>> test_instance2 = RequiredTest(required_trait=13) Forgetting to specify a required trait: >>> test_instance = RequiredTest(non_required_trait=11) traits.trait_errors.TraitError: The following required traits were not provided: required_trait. """ def __init__(self, **traits): missing_required_traits = [ name for name in self.trait_names(required=True) if name not in traits ] if missing_required_traits: raise TraitError( "The following required traits were not provided: " "{}.".format(", ".join(sorted(missing_required_traits))) ) super().__init__(**traits) class HasPrivateTraits(HasTraits): """ This class ensures that any public object attribute that does not have an explicit or wildcard trait definition results in an exception, but "private" attributes (whose names start with '_') have an initial value of **None**, and are not type-checked. This feature is useful in cases where a class needs private attributes to keep track of its internal object state, which are not part of the class's public API. Such attributes do not need to be type-checked, because they are manipulated only by the (presumably correct) methods of the class itself. """ # Make 'private' traits (leading '_') have no type checking: __ = Any(private=True, transient=True) # Disallow access to all other traits not explicitly defined: _ = Disallow # ABC classes with traits class ABCMetaHasTraits(abc.ABCMeta, MetaHasTraits): """ A MetaHasTraits subclass which also inherits from abc.ABCMeta. .. note:: The ABCMeta class is cooperative and behaves nicely with MetaHasTraits, provided it is inherited first. """ pass class ABCHasTraits(HasTraits, metaclass=ABCMetaHasTraits): """ A HasTraits subclass which enables the features of Abstract Base Classes (ABC). See the 'abc' module in the standard library for more information. """ class ABCHasStrictTraits(ABCHasTraits): """ A HasTraits subclass which behaves like HasStrictTraits but also enables the features of Abstract Base Classes (ABC). See the 'abc' module in the standard library for more information. """ _ = Disallow # Singleton classes with traits: # # This code is based on a recipe taken from: # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/66531 # Specifically, the implementation of Oren Tirosh is used. class SingletonHasTraits(HasTraits): """ Singleton class that support trait attributes. """ @deprecated("SingletonHasTraits has been deprecated and will be removed " "in the future. Avoid using it") def __new__(cls, *args, **traits): if "_the_instance" not in cls.__dict__: cls._the_instance = HasTraits.__new__(cls, *args, **traits) return cls._the_instance class SingletonHasStrictTraits(HasStrictTraits): """ Singleton class that supports strict trait attributes. Non-trait attributes generate an exception. """ @deprecated("SingletonHasStrictTraits has been deprecated and will be " "removed in the future. Avoid using it") def __new__(cls, *args, **traits): return SingletonHasTraits.__new__(cls, *args, **traits) class SingletonHasPrivateTraits(HasPrivateTraits): """ Singleton class that supports trait attributes, with private attributes being unchecked. """ @deprecated("SingletonHasPrivateTraits has been deprecated and will be " "removed in the future. Avoid using it") def __new__(cls, *args, **traits): return SingletonHasTraits.__new__(cls, *args, **traits) class Vetoable(HasStrictTraits): """ Defines a 'vetoable' request object and an associated event. """ # Should the request be vetoed? (Can only be set to 'True') veto = Bool(False) def _veto_changed(self, state): self._trait_veto_notify(state) VetoableEvent = Event(Vetoable) class MetaInterface(ABCMetaHasTraits): """ Meta class for interfaces. Interfaces are simple ABCs with the following features:- 1) They cannot be instantiated (they are interfaces, not implementations!). 2) Calling them is equivalent to calling 'adapt'. """ @deprecated('use "adapt(adaptee, protocol)" instead.') def __call__(self, adaptee, default=AdaptationError): """ Attempt to adapt the adaptee to this interface. Note that this means that (intentionally ;^) that interfaces cannot be instantiated! """ from traits.adaptation.api import adapt return adapt(adaptee, self, default=default) class Interface(HasTraits, metaclass=MetaInterface): """ The base class for all interfaces. """ def provides(*protocols): """ Class decorator to declare the protocols that a class provides. Parameters ---------- *protocols : A list of protocols (Interface classes or Python ABCs) that the decorated class provides. """ from abc import ABCMeta # Exit immediately if there is nothing to do. if len(protocols) == 0: return lambda klass: klass # Verify that each argument is a valid protocol. for protocol in protocols: if not issubclass(type(protocol), ABCMeta): raise TraitError( "All arguments to 'provides' must be " "subclasses of Interface or be a Python ABC." ) def wrapped_class(klass): for protocol in protocols: # We use 'type(protocol)' in case the 'protocol' implements # its own 'register' method that overrides the ABC method. type(protocol).register(protocol, klass) # Make sure the class does provide the protocols it claims to. if CHECK_INTERFACES: from .interface_checker import check_implements warnings.warn( ( "In the future, the @provides decorator will not perform " "interface checks. Set has_traits.CHECK_INTERFACES to 0 " "to suppress this warning." ), DeprecationWarning, stacklevel=2, ) check_implements(klass, protocols, CHECK_INTERFACES) return klass return wrapped_class def isinterface(klass): """ Return True if the class is an Interface. """ return isinstance(klass, MetaInterface) class ISerializable(Interface): """ A class that implemented ISerializable requires that all HasTraits objects saved as part of its state also implement ISerializable. """