psygnal #
Psygnal implements the observer pattern for Python.
It emulates the signal/slot pattern from Qt, but it does not require Qt.
Modules:
-
containers–Containers backed by psygnal events.
-
qt–Module that provides Qt-specific functionality for psygnal.
-
testing–Utilities for testing psygnal Signals.
-
utils–These utilities may help when using signals and evented objects.
Classes:
-
EmissionInfo–Tuple containing information about an emission event.
-
EmitLoopError–Error type raised when an exception occurs during a callback.
-
EventedModel–A pydantic BaseModel that emits a signal whenever a field value is changed.
-
PathStep–A single step in a path to a nested signal.
-
Signal–Declares a signal emitter on a class.
-
SignalGroup–A collection of signals that can be connected to as a single unit.
-
SignalGroupDescriptor–Create a
psygnal.SignalGroupon first instance attribute access. -
SignalInstance–A signal instance (optionally) bound to an object.
Functions:
-
debounced–Create a debounced function that delays invoking
func. -
emit_queued–Trigger emissions of all callbacks queued in the current thread.
-
evented–A decorator to add events to a dataclass.
-
get_async_backend–Get the current async backend. Returns None if no backend is set.
-
get_evented_namespace–Return the name of the evented SignalGroup for an object.
-
is_evented–Return
Trueif the object or its class has been decorated with evented. -
set_async_backend–Set the async backend to use. Must be one of: 'asyncio', 'anyio', 'trio'.
-
throttled–Create a throttled function that invokes func at most once per timeout.
EmissionInfo dataclass #
EmissionInfo(
signal: SignalInstance,
args: tuple[Any, ...],
path: tuple[PathStep, ...] = (),
)
Tuple containing information about an emission event.
Attributes:
-
signal(SignalInstance) –The SignalInstance doing the emitting
-
args(tuple) –The args that were emitted
-
path(tuple[PathStep, ...]) –A tuple of PathStep objects that describe the path to the signal that was emitted. This is useful for nested signals, where the path can be used to determine the hierarchy of the signals. For example, if a signal is emitted from a nested object like
bar.foo[3]['user'], the path will contain the steps to get to that object, such as (PathStep(attr='foo'), PathStep(index=3), PathStep(key='user')).
Methods:
-
insert_path–Return a new EmissionInfo with the given path steps inserted at the front.
insert_path #
insert_path(*path: PathStep) -> EmissionInfo
Return a new EmissionInfo with the given path steps inserted at the front.
Source code in src/psygnal/_group.py
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EmitLoopError #
EmitLoopError(
exc: BaseException,
signal: SignalInstance | None = None,
recursion_depth: int = 0,
reemission: str | None = None,
emit_queue: Sequence[tuple] = (),
)
Bases: Exception
Error type raised when an exception occurs during a callback.
Source code in src/psygnal/_exceptions.py
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EventedModel #
EventedModel(_model_self_, **data: Any)
Bases: BaseModel
A pydantic BaseModel that emits a signal whenever a field value is changed.
Important
This class requires pydantic to be installed. You can install directly (pip install pydantic) or by using the psygnal extra: pip install psygnal[pydantic]
In addition to standard pydantic BaseModel properties (see pydantic docs), this class adds the following:
-
Gains an
eventsattribute that is an instance ofpsygnal.SignalGroup. This group will have a signal for each field in the model (excluding private attributes and non-mutable fields). Whenever a field in the model is mutated, the corresponding signal will emit with the new value (see example below). -
Gains support for properties and property.setters (not supported in pydantic's BaseModel). Enable by adding
allow_property_setters = Trueto your modelConfig. -
If you would like properties (i.e. "computed fields") to emit an event when one of the model fields it depends on is mutated you must set one of the following options in the
Config:field_dependenciesmay be aDict[str, List[str]], where the keys are the names of properties, and the values are a list of field names (strings) that the property depends on for its valueguess_property_dependenciesmay be set toTrueto "guess" property dependencies by inspecting the source code of the property getter for.
-
If you would like to allow custom fields to provide their own json_encoders, you can either:
- use the standard pydantic method of adding json_encoders to your model, for each field type you'd like to support: 1. This
EventedModelclass will additionally look for a_json_encodemethod on any field types in the model. If a field type declares a_json_encodemethod, it will be added to thejson_encodersdict in the modelConfig. (Prefer using the standard pydantic method)
- use the standard pydantic method of adding json_encoders to your model, for each field type you'd like to support: 1. This
Examples:
Standard EventedModel example:
class MyModel(EventedModel):
x: int = 1
m = MyModel()
m.events.x.connect(lambda v: print(f"new value is {v}"))
m.x = 3 # prints 'new value is 3'
An example of using property_setters and emitting signals when a field dependency is mutated.
class MyModel(EventedModel):
a: int = 1
b: int = 1
@property
def c(self) -> List[int]:
return [self.a, self.b]
@c.setter
def c(self, val: Sequence[int]) -> None:
self.a, self.b = val
class Config:
allow_property_setters = True
field_dependencies = {"c": ["a", "b"]}
m = MyModel()
assert m.c == [1, 1]
m.events.c.connect(lambda v: print(f"c updated to {v}"))
m.a = 2 # prints 'c updated to [2, 1]'
Methods:
Attributes:
-
events(SignalGroup) –Return the
SignalGroupcontaining all events for this model.
Source code in src/psygnal/_evented_model.py
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reset #
reset() -> None
Reset the state of the model to default values.
Source code in src/psygnal/_evented_model.py
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update #
update(
values: Union[EventedModel, dict], recurse: bool = True
) -> None
Update a model in place.
Parameters:
-
(values#Union[dict, EventedModel]) –Values to update the model with. If an EventedModel is passed it is first converted to a dictionary. The keys of this dictionary must be found as attributes on the current model.
-
(recurse#bool, default:True) –If True, recursively update fields that are EventedModels. Otherwise, just update the immediate fields of this EventedModel, which is useful when the declared field type (e.g.
Union) can have different realized types with different fields.
Source code in src/psygnal/_evented_model.py
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PathStep dataclass #
PathStep(
attr: str | None = None,
index: SupportsIndex | None = None,
key: Hashable | None = None,
)
A single step in a path to a nested signal.
This is used to represent a path to a signal that is nested inside an object, such as when a signal is emitted from a nested object or a list. The EmissionInfo.path is a tuple of PathStep objects, where each PathStep represents either:
- an attribute access (e.g.
.foo) - an index access (e.g.
[3]) - a key access (e.g.
['user'])
Note
yes, index and key both just get passed to __getitem__ in the end, but we differentiate them here to make it clearer whether the step is a key in a Mapping or an index in a Sequence.
Signal #
Signal(
*types: type[Any] | Signature,
description: str = "",
name: str | None = None,
check_nargs_on_connect: bool = True,
check_types_on_connect: bool = False,
reemission: ReemissionVal = DEFAULT_REEMISSION,
signal_instance_class: type[SignalInstance]
| None = None,
)
Declares a signal emitter on a class.
This is class implements the descriptor protocol and is designed to be used as a class attribute, with the supported signature types provided in the constructor:
from psygnal import Signal
class MyEmitter:
changed = Signal(int)
def receiver(arg: int):
print("new value:", arg)
emitter = MyEmitter()
emitter.changed.connect(receiver)
emitter.changed.emit(1) # prints 'new value: 1'
Note
in the example above, MyEmitter.changed is an instance of Signal, and emitter.changed is an instance of SignalInstance. See the documentation on SignalInstance for details on how to connect to and/or emit a signal on an instance of an object that has a Signal.
Parameters:
-
(*types#Type[Any] | Signature, default:()) –A sequence of individual types, or a single
inspect.Signatureobject. -
(description#str, default:'') –Optional descriptive text for the signal. (not used internally).
-
(name#str | None, default:None) –Optional name of the signal. If it is not specified then the name of the class attribute that is bound to the signal will be used. default None
-
(check_nargs_on_connect#bool, default:True) –Whether to check the number of positional args against
signaturewhen connecting a new callback. This can also be provided at connection time using.connect(..., check_nargs=True). By default,True. -
(check_types_on_connect#bool, default:False) –Whether to check the callback parameter types against
signaturewhen connecting a new callback. This can also be provided at connection time using.connect(..., check_types=True). By default,False. -
(reemission#Literal['immediate', 'queued', 'latest-only'] | None, default:DEFAULT_REEMISSION) –Determines the order and manner in which connected callbacks are invoked when a callback re-emits a signal. Default is
"immediate".-
"immediate": Signals emitted by callbacks are immediately processed in a deeper emission loop, before returning to process signals emitted at the current level (after all callbacks in the deeper level have been called). -
"queued": Signals emitted by callbacks are enqueued for emission after the current level of emission is complete. This ensures all connected callbacks are called with the first emitted value, before any of them are called with values emitted while calling callbacks. -
"latest-only": Signals emitted by callbacks are immediately processed in a deeper emission loop, and remaining callbacks in the current level are never called with the original value.
-
Methods:
-
current_emitter–Return currently emitting
SignalInstance, if any. -
sender–Return currently emitting object, if any.
Attributes:
Source code in src/psygnal/_signal.py
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current_emitter classmethod #
current_emitter() -> SignalInstance | None
Return currently emitting SignalInstance, if any.
This will typically be used in a callback.
Examples:
from psygnal import Signal
def my_callback():
source = Signal.current_emitter()
Source code in src/psygnal/_signal.py
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sender classmethod #
sender() -> Any
Return currently emitting object, if any.
This will typically be used in a callback.
Source code in src/psygnal/_signal.py
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SignalGroup #
SignalGroup(instance: Any = None)
A collection of signals that can be connected to as a single unit.
This class is not intended to be instantiated directly. Instead, it should be subclassed, and the subclass should define Signal instances as class attributes. The SignalGroup will then automatically collect these signals and provide a SignalRelay instance (at group.all) that can be used to connect to all of the signals in the group.
This class is used in both the EventedModels and the evented dataclass patterns. See also: psygnal.SignalGroupDescriptor, which provides convenient and explicit way to create a SignalGroup on a dataclass-like class.
Parameters:
Attributes:
-
all(SignalRelay) –A special SignalRelay instance that can be used to connect to all signals in this group.
Examples:
from psygnal import Signal, SignalGroup
class MySignals(SignalGroup):
sig1 = Signal()
sig2 = Signal()
group = MySignals()
group.all.connect(print) # connect to all signals in the group
list(group) # ['sig1', 'sig2']
len(group) # 2
group.sig1 is group["sig1"] # True
Methods:
-
is_uniform–Return true if all signals in the group have the same signature.
-
psygnals_uniform–Return true if all signals in the group have the same signature.
Source code in src/psygnal/_group.py
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all property #
all: SignalRelay
SignalInstance that can be used to connect to all signals in this group.
Examples:
from psygnal import Signal, SignalGroup
class MySignals(SignalGroup):
sig1 = Signal()
sig2 = Signal()
group = MySignals()
group.sig2.connect(...) # connect to a single signal by name
group.all.connect(...) # connect to all signals in the group
signals property #
signals: Mapping[str, SignalInstance]
A mapping of signal names to SignalInstance instances.
is_uniform classmethod #
is_uniform() -> bool
Return true if all signals in the group have the same signature.
Source code in src/psygnal/_group.py
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psygnals_uniform classmethod #
psygnals_uniform() -> bool
Return true if all signals in the group have the same signature.
Source code in src/psygnal/_group.py
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SignalGroupDescriptor #
SignalGroupDescriptor(
*,
equality_operators: dict[str, EqOperator] | None = None,
warn_on_no_fields: bool = True,
cache_on_instance: bool = True,
patch_setattr: bool = True,
signal_group_class: type[SignalGroup] | None = None,
collect_fields: bool = True,
connect_child_events: bool = True,
signal_aliases: Mapping[str, str | None]
| FieldAliasFunc
| None = None,
)
Create a psygnal.SignalGroup on first instance attribute access.
This descriptor is designed to be used as a class attribute on a dataclass-like class (e.g. a dataclass, a pydantic.BaseModel, an attrs class, a msgspec.Struct) On first access of the descriptor on an instance, it will create a SignalGroup bound to the instance, with a SignalInstance for each field in the dataclass.
Important
Using this descriptor will patch the class's __setattr__ method to emit events when fields change. (That patching occurs on first access of the descriptor name on an instance). To prevent this patching, you can set patch_setattr=False when creating the descriptor, but then you will need to manually call emit on the appropriate SignalInstance when you want to emit an event. Or you can use evented_setattr yourself
from psygnal._group_descriptor import evented_setattr
from psygnal import SignalGroupDescriptor
from dataclasses import dataclass
from typing import ClassVar
@dataclass
class Foo:
x: int
_events: ClassVar = SignalGroupDescriptor(patch_setattr=False)
@evented_setattr("_events") # pass the name of your SignalGroup
def __setattr__(self, name: str, value: Any) -> None:
super().__setattr__(name, value)
This currently requires a private import, please open an issue if you would like to depend on this functionality.
Parameters:
-
(equality_operators#dict[str, Callable[[Any, Any], bool]], default:None) –A dictionary mapping field names to custom equality operators, where an equality operator is a callable that accepts two arguments and returns True if the two objects are equal. This will be used when comparing the old and new values of a field to determine whether to emit an event. If not provided, the default equality operator is
operator.eq, except for numpy arrays, wherenp.array_equalis used. -
(warn_on_no_fields#bool, default:True) –If
True(the default), a warning will be emitted if no mutable dataclass-like fields are found on the object. -
(cache_on_instance#bool, default:True) –If
True(the default), a newly-created SignalGroup instance will be cached on the instance itself, so that subsequent accesses to the descriptor will return the same SignalGroup instance. This makes for slightly faster subsequent access, but means that the owner instance will no longer be pickleable. IfFalse, the SignalGroup instance will still be cached, but not on the instance itself. -
(patch_setattr#bool, default:True) –If
True(the default), a new__setattr__method will be created that emits events when fields change. IfFalse, no__setattr__method will be created. (This will prevent signal emission, and assumes you are using a different mechanism to emit signals when fields change.) -
(signal_group_class#type[SignalGroup] | None, default:None) –A custom SignalGroup class to use, SignalGroup if None, by default None
-
(collect_fields#bool, default:True) –Create a signal for each field in the dataclass. If True, the
SignalGroupinstance will be a subclass ofsignal_group_class(SignalGroup if it is None). If False, a deepcopy ofsignal_group_classwill be used. Default to True -
(connect_child_events#bool, default:True) –If
True, will connect events from all fields on the dataclass whose type is also "evented" to the group on the parent object. An object is considered "evented" if theis_eventedfunction returnsTruefor it (i.e. it has been decorated with@evented, or if it has a SignalGroupDescriptor). This is useful for nested evented dataclasses, where you want to monitor events emitted from arbitrarily deep children on the parent object. By default True. -
(signal_aliases#Mapping[str, str | None] | FieldAliasFunc | None, default:None) –If defined, a mapping between field name and signal name. Field names that are not
signal_aliaseskeys are not aliased (the signal name is the field name). If the dict value is None, do not create a signal associated with this field. If a callable, the signal name is the output of the function applied to the field name. If the output is None, no signal is created for this field. If None, defaults to an empty dict, no aliases. Default to None
Examples:
from typing import ClassVar
from dataclasses import dataclass
from psygnal import SignalGroupDescriptor
@dataclass
class Person:
name: str
age: int = 0
events: ClassVar[SignalGroupDescriptor] = SignalGroupDescriptor()
john = Person("John", 40)
john.events.age.connect(print)
john.age += 1 # prints 41
Source code in src/psygnal/_group_descriptor.py
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SignalInstance #
SignalInstance(
signature: Signature | tuple = _empty_signature,
*,
instance: Any = None,
name: str | None = None,
description: str = "",
check_nargs_on_connect: bool = True,
check_types_on_connect: bool = False,
reemission: ReemissionVal = DEFAULT_REEMISSION,
)
A signal instance (optionally) bound to an object.
In most cases, users will not create a SignalInstance directly -- instead creating a Signal class attribute. This object will be instantiated by the Signal.__get__ method (i.e. the descriptor protocol), when a Signal instance is accessed from an instance of a class with Signal attribute.
However, it is the SignalInstance that you will most often be interacting with when you access the name of a Signal on an instance -- so understanding the SignalInstance API is key to using psygnal.
class Emitter:
signal = Signal()
e = Emitter()
# when accessed on an *instance* of Emitter,
# the signal attribute will be a SignalInstance
e.signal
# This is what you will use to connect your callbacks
e.signal.connect(some_callback)
Parameters:
-
(signature#Signature | None, default:_empty_signature) –The signature that this signal accepts and will emit, by default
Signature(). -
(instance#Any, default:None) –An object to which this signal is bound. Normally this will be provided by the
Signal.__get__method (see above). However, an unboundSignalInstancemay also be created directly. by defaultNone. -
(name#str | None, default:None) –An optional name for this signal. Normally this will be provided by the
Signal.__get__method. by defaultNone -
(check_nargs_on_connect#bool, default:True) –Whether to check the number of positional args against
signaturewhen connecting a new callback. This can also be provided at connection time using.connect(..., check_nargs=True). By default,True. -
(check_types_on_connect#bool, default:False) –Whether to check the callback parameter types against
signaturewhen connecting a new callback. This can also be provided at connection time using.connect(..., check_types=True). By default,False. -
(reemission#Literal['immediate', 'queued', 'latest-only'] | None, default:DEFAULT_REEMISSION) –See docstring for
Signalfor details. By default,"immediate". -
(description#str, default:'') –Optional descriptive text for the signal. (not used internally).
Attributes:
-
signature(Signature) –Signature supported by this
SignalInstance. -
instance(Any) –Object that emits this
SignalInstance. -
name(str) –Name of this
SignalInstance. -
description(str) –Description of this
SignalInstance.
Raises:
-
TypeError–If
signatureis neither an instance ofinspect.Signature, or atupleof types.
Methods:
-
block–Block this signal from emitting.
-
blocked–Context manager to temporarily block this signal.
-
connect–Connect a callback (
slot) to this signal. -
connect_setattr–Bind an object attribute to the emitted value of this signal.
-
connect_setitem–Bind a container item (such as a dict key) to emitted value of this signal.
-
disconnect–Disconnect slot from signal.
-
disconnect_setattr–Disconnect a previously connected attribute setter.
-
disconnect_setitem–Disconnect a previously connected item setter.
-
emit–Emit this signal with arguments
args. -
emit_fast–Fast emit without any checks.
-
pause–Pause all emission and collect *args tuples from emit().
-
paused–Context manager to temporarily pause this signal.
-
resume–Resume (unpause) this signal, emitting everything in the queue.
-
unblock–Unblock this signal, allowing it to emit.
Source code in src/psygnal/_signal.py
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block #
block(
exclude: Container[str | SignalInstance] = (),
) -> None
Block this signal from emitting.
NOTE: the exclude argument is only for SignalGroup subclass, but we have to include it here to make mypyc happy.
Source code in src/psygnal/_signal.py
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blocked #
blocked() -> AbstractContextManager[None]
Context manager to temporarily block this signal.
Useful if you need to temporarily block all emission of a given signal, (for example, to avoid a recursive signal loop)
Examples:
class MyEmitter:
changed = Signal()
def make_a_change(self):
self.changed.emit()
obj = MyEmitter()
with obj.changed.blocked()
obj.make_a_change() # will NOT emit a changed signal.
Source code in src/psygnal/_signal.py
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connect #
connect(
*,
thread: Thread
| Literal["main", "current"]
| None = ...,
check_nargs: bool | None = ...,
check_types: bool | None = ...,
unique: bool | Literal["raise"] = ...,
max_args: int | None = None,
on_ref_error: RefErrorChoice = ...,
priority: int = ...,
emit_on_evented_child_events: bool = ...,
) -> Callable[[F], F]
connect(
slot: F,
*,
thread: Thread
| Literal["main", "current"]
| None = ...,
check_nargs: bool | None = ...,
check_types: bool | None = ...,
unique: bool | Literal["raise"] = ...,
max_args: int | None = None,
on_ref_error: RefErrorChoice = ...,
priority: int = ...,
emit_on_evented_child_events: bool = ...,
) -> F
connect(
slot: F | None = None,
*,
thread: Thread
| Literal["main", "current"]
| None = None,
check_nargs: bool | None = None,
check_types: bool | None = None,
unique: bool | Literal["raise"] = False,
max_args: int | None = None,
on_ref_error: RefErrorChoice = "warn",
priority: int = 0,
emit_on_evented_child_events: bool = False,
) -> Callable[[F], F] | F
Connect a callback (slot) to this signal.
slot is compatible if:
- it requires no more than the number of positional arguments emitted by this
SignalInstance. (It may require less) - it has no required keyword arguments (keyword only arguments that have no default).
- if
check_typesisTrue, the parameter types in the callback signature must match the signature of thisSignalInstance.
This method may be used as a decorator.
@signal.connect
def my_function(): ...
Important
If a signal is connected with thread != None, then it is up to the user to ensure that psygnal.emit_queued is called, or that one of the backend convenience functions is used (e.g. psygnal.qt.start_emitting_from_queue). Otherwise, callbacks that are connected to signals that are emitted from another thread will never be called.
Parameters:
-
(slot#Callable, default:None) –A callable to connect to this signal. If the callable accepts less arguments than the signature of this slot, then they will be discarded when calling the slot.
-
(check_nargs#Optional[bool], default:None) –If
Trueand the providedslotrequires more positional arguments than the signature of this Signal, raiseTypeError. by defaultTrue. -
(thread#Thread | Literal['main', 'current'] | None, default:None) –If
None(the default), this slot will be invoked immediately when a signal is emitted, from whatever thread emitted the signal. If a thread object is provided, then the callback will only be immediately invoked if the signal is emitted from that thread. Otherwise, the callback will be added to a queue. Note!, when using thethreadparameter, the user is responsible for callingpsygnal.emit_queued()in the corresponding thread, otherwise the slot will never be invoked. (See note above). (The strings"main"and"current"are also accepted, and will be interpreted as thethreading.main_thread()andthreading.current_thread(), respectively). -
(check_types#Optional[bool], default:None) –If
True, An additional check will be performed to make sure that types declared in the slot signature are compatible with the signature declared by this signal, by defaultFalse. -
(unique#Union[bool, str, None], default:False) –If
True, returns without connecting if the slot has already been connected. If the literal string "raise" is passed tounique, then aValueErrorwill be raised if the slot is already connected. By defaultFalse. -
(max_args#Optional[int], default:None) –If provided,
slotwill be called with no more more thanmax_argswhen this SignalInstance is emitted. (regardless of how many arguments are emitted). -
(on_ref_error#('raise', 'warn', 'ignore'), default:'raise') –What to do if a weak reference cannot be created. If 'raise', a ReferenceError will be raised. If 'warn' (default), a warning will be issued and a strong-reference will be used. If 'ignore' a strong-reference will be used (silently).
-
(priority#int, default:0) –The priority of the callback. This is used to determine the order in which callbacks are called when multiple are connected to the same signal. Higher priority callbacks are called first. Negative values are allowed. The default is 0.
-
(emit_on_evented_child_events#bool, default:False) –If
True, and if this is a SignalInstance associated with a specific field on an evented dataclass, and if that field itself is an evented dataclass, then the slot will be called both when the field is set directly, and when a child member of that field is set. For example, ifTeamis an evented-dataclass with a fieldleader: Personwhich is itself an evented-dataclass, thenteam.events.leader.connect(callback, emit_on_evented_child_events=True)will invoke callback even whenteam.leader.ageis mutated (in addition to whenteam.leaderis set directly).
Raises:
-
TypeError–If a non-callable object is provided.
-
ValueError–If the provided slot fails validation, either due to mismatched positional argument requirements, or failed type checking.
-
ValueError–If
uniqueis'raise'andslothas already been connected.
Source code in src/psygnal/_signal.py
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connect_setattr #
connect_setattr(
obj: object,
attr: str,
maxargs: int | None | object = _NULL,
*,
on_ref_error: RefErrorChoice = "warn",
priority: int = 0,
) -> WeakCallback[None]
Bind an object attribute to the emitted value of this signal.
Equivalent to calling self.connect(functools.partial(setattr, obj, attr)), but with additional weakref safety (i.e. a strong reference to obj will not be retained). The return object can be used to disconnect(), (or you can use disconnect_setattr()).
Parameters:
-
(obj#object) –An object.
-
(attr#str) –The name of an attribute on
objthat should be set to the value of this signal when emitted. -
(maxargs#Optional[int], default:_NULL) –max number of positional args to accept
-
(on_ref_error#RefErrorChoice, default:'warn') –What to do if a weak reference cannot be created. If 'raise', a ReferenceError will be raised. If 'warn' (default), a warning will be issued and a strong-reference will be used. If 'ignore' a strong-reference will be used (silently).
-
(priority#int, default:0) –The priority of the callback. This is used to determine the order in which callbacks are called when multiple are connected to the same signal. Higher priority callbacks are called first. Negative values are allowed. The default is 0.
Returns:
-
Tuple–(weakref.ref, name, callable). Reference to the object, name of the attribute, and setattr closure. Can be used to disconnect the slot.
Raises:
-
ValueError–If this is not a single-value signal
-
AttributeError–If
objhas no attributeattr.
Examples:
>>> class T:
... sig = Signal(int)
>>> class SomeObj:
... x = 1
>>> t = T()
>>> my_obj = SomeObj()
>>> t.sig.connect_setattr(my_obj, "x")
>>> t.sig.emit(5)
>>> assert my_obj.x == 5
Source code in src/psygnal/_signal.py
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connect_setitem #
connect_setitem(
obj: object,
key: str,
maxargs: int | None | object = _NULL,
*,
on_ref_error: RefErrorChoice = "warn",
priority: int = 0,
) -> WeakCallback[None]
Bind a container item (such as a dict key) to emitted value of this signal.
Equivalent to calling self.connect(functools.partial(obj.__setitem__, attr)), but with additional weakref safety (i.e. a strong reference to obj will not be retained). The return object can be used to disconnect(), (or you can use disconnect_setitem()).
Parameters:
-
(obj#object) –An object.
-
(key#str) –Name of the key in
objthat should be set to the value of this signal when emitted -
(maxargs#Optional[int], default:_NULL) –max number of positional args to accept
-
(on_ref_error#RefErrorChoice, default:'warn') –What to do if a weak reference cannot be created. If 'raise', a ReferenceError will be raised. If 'warn' (default), a warning will be issued and a strong-reference will be used. If 'ignore' a strong-reference will be used (silently).
-
(priority#int, default:0) –The priority of the callback. This is used to determine the order in which callbacks are called when multiple are connected to the same signal. Higher priority callbacks are called first. Negative values are allowed. The default is 0.
Returns:
-
Tuple–(weakref.ref, name, callable). Reference to the object, name of the attribute, and setitem closure. Can be used to disconnect the slot.
Raises:
-
ValueError–If this is not a single-value signal
-
TypeError–If
objdoes not support setitem.
Examples:
>>> class T:
... sig = Signal(int)
>>> t = T()
>>> my_obj = dict()
>>> t.sig.connect_setitem(my_obj, "x")
>>> t.sig.emit(5)
>>> assert my_obj == {"x": 5}
Source code in src/psygnal/_signal.py
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disconnect #
disconnect(
slot: Callable | None = None, missing_ok: bool = True
) -> None
Disconnect slot from signal.
Parameters:
-
(slot#callable, default:None) –The specific slot to disconnect. If
None, all slots will be disconnected, by defaultNone -
(missing_ok#Optional[bool], default:True) –If
Falseand the providedslotis not connected, raisesValueError. by defaultTrue`
Raises:
-
ValueError–If
slotis not connected andmissing_okis False.
Source code in src/psygnal/_signal.py
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disconnect_setattr #
disconnect_setattr(
obj: object, attr: str, missing_ok: bool = True
) -> None
Disconnect a previously connected attribute setter.
Parameters:
-
(obj#object) –An object.
-
(attr#str) –The name of an attribute on
objthat was previously used forconnect_setattr. -
(missing_ok#bool, default:True) –If
Falseand the providedslotis not connected, raisesValueError. by defaultTrue
Raises:
-
ValueError–If
missing_okisTrueand no attribute setter is connected.
Source code in src/psygnal/_signal.py
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disconnect_setitem #
disconnect_setitem(
obj: object, key: str, missing_ok: bool = True
) -> None
Disconnect a previously connected item setter.
Parameters:
-
(obj#object) –An object.
-
(key#str) –The name of a key in
objthat was previously used forconnect_setitem. -
(missing_ok#bool, default:True) –If
Falseand the providedslotis not connected, raisesValueError. by defaultTrue
Raises:
-
ValueError–If
missing_okisTrueand no item setter is connected.
Source code in src/psygnal/_signal.py
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emit #
emit(
*args: Any,
check_nargs: bool = False,
check_types: bool = False,
) -> None
Emit this signal with arguments args.
Note
check_args and check_types both add overhead when calling emit.
Parameters:
-
(*args#Any, default:()) –These arguments will be passed when calling each slot (unless the slot accepts fewer arguments, in which case extra args will be discarded.)
-
(check_nargs#bool, default:False) –If
Falseand the provided arguments cannot be successfully bound to the signature of this Signal, raiseTypeError. Incurs some overhead. by default False. -
(check_types#bool, default:False) –If
Falseand the provided arguments do not match the types declared by the signature of this Signal, raiseTypeError. Incurs some overhead. by default False.
Raises:
-
TypeError–If
check_nargsand/orcheck_typesareTrue, and the corresponding checks fail.
Source code in src/psygnal/_signal.py
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emit_fast #
emit_fast(*args: Any) -> None
Fast emit without any checks.
This method can be up to 10x faster than emit(), but it lacks most of the features and safety checks of emit(). Use with caution. Specifically:
- It does not support
check_nargsorcheck_types - It does not use any thread safety locks.
- It is not possible to query the emitter with
Signal.current_emitter() - It is not possible to query the sender with
Signal.sender() - It does not support "queued" or "latest-only" reemission modes for nested emits. It will always use "immediate" mode, wherein signals emitted by callbacks are immediately processed in a deeper emission loop.
It DOES, however, support paused() and blocked()
Parameters:
-
(*args#Any, default:()) –These arguments will be passed when calling each slot (unless the slot accepts fewer arguments, in which case extra args will be discarded.)
Source code in src/psygnal/_signal.py
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pause #
pause() -> None
Pause all emission and collect *args tuples from emit().
args passed to emit will be collected and re-emitted when resume() is called. For a context manager version, see paused().
Source code in src/psygnal/_signal.py
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paused #
paused(
reducer: ReducerFunc | None = None, initial: Any = _NULL
) -> AbstractContextManager[None]
Context manager to temporarily pause this signal.
Parameters:
-
(reducer#Callable | None, default:None) –A optional function to reduce the args collected while paused into a single emitted group of args. If not provided, all emissions will be re-emitted as they were collected when the signal is resumed. May be:
- a function that takes two args tuples and returns a single args tuple. This will be passed to
functools.reduceand is expected to reduce all collected/emitted args into a single tuple. For example, threeemit(1)events would be reduced and re-emitted as follows:self.emit(*functools.reduce(reducer, [(1,), (1,), (1,)])) - a function that takes a single argument (an iterable of args tuples) and returns a tuple (the reduced args). This will be not be passed to
functools.reduce. Ifreduceris a function that takes a single argument,initialwill be ignored.
- a function that takes two args tuples and returns a single args tuple. This will be passed to
-
(initial#Any, default:_NULL) –initial value to pass to
functools.reduce
Examples:
>>> with obj.signal.paused(lambda a, b: (a[0].union(set(b)),), (set(),)):
... t.sig.emit(1)
... t.sig.emit(2)
... t.sig.emit(3)
>>> # results in obj.signal.emit({1, 2, 3})
Source code in src/psygnal/_signal.py
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resume #
resume(
reducer: ReducerFunc | None = None, initial: Any = _NULL
) -> None
Resume (unpause) this signal, emitting everything in the queue.
Parameters:
-
(reducer#Callable | None, default:None) –A optional function to reduce the args collected while paused into a single emitted group of args. If not provided, all emissions will be re-emitted as they were collected when the signal is resumed. May be:
- a function that takes two args tuples and returns a single args tuple. This will be passed to
functools.reduceand is expected to reduce all collected/emitted args into a single tuple. For example, threeemit(1)events would be reduced and re-emitted as follows:self.emit(*functools.reduce(reducer, [(1,), (1,), (1,)])) - a function that takes a single argument (an iterable of args tuples) and returns a tuple (the reduced args). This will be not be passed to
functools.reduce. Ifreduceris a function that takes a single argument,initialwill be ignored.
- a function that takes two args tuples and returns a single args tuple. This will be passed to
-
(initial#Any, default:_NULL) –initial value to pass to
functools.reduce
Examples:
>>> class T:
... sig = Signal(int)
>>> t = T()
>>> t.sig.pause()
>>> t.sig.emit(1)
>>> t.sig.emit(2)
>>> t.sig.emit(3)
>>> t.sig.resume(lambda a, b: (a[0].union(set(b)),), (set(),))
>>> # results in t.sig.emit({1, 2, 3})
Source code in src/psygnal/_signal.py
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unblock #
unblock() -> None
Unblock this signal, allowing it to emit.
Source code in src/psygnal/_signal.py
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debounced #
debounced(
func: Callable[P, Any],
timeout: int = 100,
leading: bool = False,
) -> Debouncer[P]
debounced(
func: Literal[None] | None = None,
timeout: int = 100,
leading: bool = False,
) -> Callable[[Callable[P, Any]], Debouncer[P]]
debounced(
func: Callable[P, Any] | None = None,
timeout: int = 100,
leading: bool = False,
) -> (
Debouncer[P]
| Callable[[Callable[P, Any]], Debouncer[P]]
)
Create a debounced function that delays invoking func.
func will not be invoked until timeout ms have elapsed since the last time the debounced function was invoked.
The debounced function comes with a cancel method to cancel delayed func invocations and a flush method to immediately invoke them. Options indicate whether func should be invoked on the leading and/or trailing edge of the wait timeout. The func is invoked with the last arguments provided to the debounced function. Subsequent calls to the debounced function return the result of the last func invocation.
This decorator may be used with or without parameters.
Parameters:
-
(func#Callable, default:None) –A function to throttle
-
(timeout#int, default:100) –Timeout in milliseconds to wait before allowing another call, by default 100
-
(leading#bool, default:False) –Whether to invoke the function on the leading edge of the wait timer, by default False
Examples:
from psygnal import Signal, debounced
class MyEmitter:
changed = Signal(int)
def on_change(val: int)
# do something possibly expensive
...
emitter = MyEmitter()
# connect the `on_change` whenever `emitter.changed` is emitted
# ONLY once at least 50 milliseconds have passed since the last signal emission.
emitter.changed.connect(debounced(on_change, timeout=50))
Source code in src/psygnal/_throttler.py
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emit_queued #
emit_queued(thread: Thread | None = None) -> None
Trigger emissions of all callbacks queued in the current thread.
Parameters:
-
(thread#Thread, default:None) –The thread on which to invoke the callback. If not provided, the main thread will be used.
Raises:
-
EmitLoopError–If an exception is raised while invoking a queued callback. This exception can be caught and optionally suppressed or handled by the caller, allowing the emission of other queued callbacks to continue even if one of them raises an exception.
Source code in src/psygnal/_queue.py
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evented #
evented(
cls: T,
*,
events_namespace: str = "events",
equality_operators: dict[str, EqOperator] | None = None,
warn_on_no_fields: bool = ...,
cache_on_instance: bool = ...,
connect_child_events: bool = ...,
signal_aliases: Mapping[str, str | None]
| FieldAliasFunc
| None = ...,
) -> T
evented(
cls: Literal[None] | None = None,
*,
events_namespace: str = "events",
equality_operators: dict[str, EqOperator] | None = None,
warn_on_no_fields: bool = ...,
cache_on_instance: bool = ...,
connect_child_events: bool = ...,
signal_aliases: Mapping[str, str | None]
| FieldAliasFunc
| None = ...,
) -> Callable[[T], T]
evented(
cls: T | None = None,
*,
events_namespace: str = "events",
equality_operators: dict[str, EqOperator] | None = None,
warn_on_no_fields: bool = True,
cache_on_instance: bool = True,
connect_child_events: bool = True,
signal_aliases: Mapping[str, str | None]
| FieldAliasFunc
| None = None,
) -> Callable[[T], T] | T
A decorator to add events to a dataclass.
See also the documentation for SignalGroupDescriptor. This decorator is equivalent setting a class variable named events to a new SignalGroupDescriptor instance.
Note that this decorator will modify cls in place, as well as return it.
Tip
It is recommended to use the SignalGroupDescriptor descriptor rather than the decorator, as it it is more explicit and provides for easier static type inference.
Parameters:
-
(cls#type, default:None) –The class to decorate.
-
(events_namespace#str, default:'events') –The name of the namespace to add the events to, by default
"events" -
(equality_operators#dict[str, Callable] | None, default:None) –A dictionary mapping field names to equality operators (a function that takes two values and returns
Trueif they are equal). These will be used to determine if a field has changed when setting a new value. By default, this will use the__eq__method of the field type, or np.array_equal, for numpy arrays. But you can provide your own if you want to customize how equality is checked. Alternatively, if the class has an__eq_operators__class attribute, it will be used. -
(warn_on_no_fields#bool, default:True) –If
True(the default), a warning will be emitted if no mutable dataclass-like fields are found on the object. -
(cache_on_instance#bool, default:True) –If
True(the default), a newly-created SignalGroup instance will be cached on the instance itself, so that subsequent accesses to the descriptor will return the same SignalGroup instance. This makes for slightly faster subsequent access, but means that the owner instance will no longer be pickleable. IfFalse, the SignalGroup instance will still be cached, but not on the instance itself. -
(connect_child_events#bool, default:True) –If
True, will connect events from all fields on the dataclass whose type is also "evented" (as determined by thepsygnal.is_eventedfunction, which returns True if the class has been decorated with@evented, or if it has a SignalGroupDescriptor) to the group on the parent object. By default True. This is useful for nested evented dataclasses, where you want to monitor events emitted from arbitrarily deep children on the parent object. -
(signal_aliases#Mapping[str, str | None] | FieldAliasFunc | None, default:None) –If defined, a mapping between field name and signal name. Field names that are not
signal_aliaseskeys are not aliased (the signal name is the field name). If the dict value is None, do not create a signal associated with this field. If a callable, the signal name is the output of the function applied to the field name. If the output is None, no signal is created for this field. If None, defaults to an empty dict, no aliases. Default to None
Returns:
-
type–The decorated class, which gains a new SignalGroup instance at the
events_namespaceattribute (by default,events).
Raises:
-
TypeError–If the class is frozen or is not a class.
Examples:
from psygnal import evented
from dataclasses import dataclass
@evented
@dataclass
class Person:
name: str
age: int = 0
Source code in src/psygnal/_evented_decorator.py
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get_async_backend #
get_async_backend() -> _AsyncBackend | None
Get the current async backend. Returns None if no backend is set.
Source code in src/psygnal/_async.py
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get_evented_namespace #
get_evented_namespace(obj: object) -> str | None
Return the name of the evented SignalGroup for an object.
Note: if you get the returned name as an attribute of the object, it will be a SignalGroup instance only if obj is an instance of an evented class. If obj is the evented class itself, it will be a _SignalGroupDescriptor.
Examples:
from psygnal import evented, get_evented_namespace, is_evented
@evented(events_namespace="my_events")
class Foo: ...
assert get_evented_namespace(Foo) == "my_events"
assert is_evented(Foo)
Source code in src/psygnal/_group_descriptor.py
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is_evented #
is_evented(obj: object) -> bool
Return True if the object or its class has been decorated with evented.
This also works for a setattr method that has been patched by psygnal.
Source code in src/psygnal/_group_descriptor.py
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set_async_backend #
set_async_backend(
backend: Literal["asyncio"],
) -> AsyncioBackend
set_async_backend(
backend: Literal["anyio"],
) -> AnyioBackend
set_async_backend(backend: Literal['trio']) -> TrioBackend
set_async_backend(
backend: SupportedBackend = "asyncio",
) -> _AsyncBackend
Set the async backend to use. Must be one of: 'asyncio', 'anyio', 'trio'.
This should be done as early as possible, and must be called before calling SignalInstance.connect with a coroutine function.
Source code in src/psygnal/_async.py
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throttled #
throttled(
func: Callable[P, Any],
timeout: int = 100,
leading: bool = True,
) -> Throttler[P]
throttled(
func: Literal[None] | None = None,
timeout: int = 100,
leading: bool = True,
) -> Callable[[Callable[P, Any]], Throttler[P]]
throttled(
func: Callable[P, Any] | None = None,
timeout: int = 100,
leading: bool = True,
) -> (
Throttler[P]
| Callable[[Callable[P, Any]], Throttler[P]]
)
Create a throttled function that invokes func at most once per timeout.
The throttled function comes with a cancel method to cancel delayed func invocations and a flush method to immediately invoke them. Options to indicate whether func should be invoked on the leading and/or trailing edge of the wait timeout. The func is invoked with the last arguments provided to the throttled function. Subsequent calls to the throttled function return the result of the last func invocation.
This decorator may be used with or without parameters.
Parameters:
-
(func#Callable, default:None) –A function to throttle
-
(timeout#int, default:100) –Timeout in milliseconds to wait before allowing another call, by default 100
-
(leading#bool, default:True) –Whether to invoke the function on the leading edge of the wait timer, by default True
Examples:
from psygnal import Signal, throttled
class MyEmitter:
changed = Signal(int)
def on_change(val: int)
# do something possibly expensive
...
emitter = MyEmitter()
# connect the `on_change` whenever `emitter.changed` is emitted
# BUT, no more than once every 50 milliseconds
emitter.changed.connect(throttled(on_change, timeout=50))
Source code in src/psygnal/_throttler.py
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