Source code for napari._qt.qthreading

import inspect
import warnings
from functools import partial, wraps
from types import FunctionType, GeneratorType
from typing import (
    Callable,
    Dict,
    List,
    Optional,
    Sequence,
    Type,
    TypeVar,
    Union,
)

from superqt.utils import _qthreading
from typing_extensions import ParamSpec

from napari.utils.progress import progress
from napari.utils.translations import trans

wait_for_workers_to_quit = _qthreading.WorkerBase.await_workers


class _NotifyingMixin:
    def __init__(self: _qthreading.WorkerBase, *args, **kwargs) -> None:  # type: ignore
        super().__init__(*args, **kwargs)  # type: ignore
        self.errored.connect(self._relay_error)
        self.warned.connect(self._relay_warning)

    def _relay_error(self, exc: Exception):
        from napari.utils.notifications import notification_manager

        notification_manager.receive_error(type(exc), exc, exc.__traceback__)

    def _relay_warning(self, show_warn_args: tuple):
        from napari.utils.notifications import notification_manager

        notification_manager.receive_warning(*show_warn_args)


_Y = TypeVar("_Y")
_S = TypeVar("_S")
_R = TypeVar("_R")
_P = ParamSpec("_P")


[docs] class FunctionWorker(_qthreading.FunctionWorker[_R], _NotifyingMixin): ...
[docs] class GeneratorWorker( _qthreading.GeneratorWorker[_Y, _S, _R], _NotifyingMixin ): ...
# these are re-implemented from superqt just to provide progress
[docs] def create_worker( func: Union[FunctionType, GeneratorType], *args, _start_thread: Optional[bool] = None, _connect: Optional[Dict[str, Union[Callable, Sequence[Callable]]]] = None, _progress: Optional[Union[bool, Dict[str, Union[int, bool, str]]]] = None, _worker_class: Union[ Type[GeneratorWorker], Type[FunctionWorker], None ] = None, _ignore_errors: bool = False, **kwargs, ) -> Union[FunctionWorker, GeneratorWorker]: """Convenience function to start a function in another thread. By default, uses :class:`Worker`, but a custom ``WorkerBase`` subclass may be provided. If so, it must be a subclass of :class:`Worker`, which defines a standard set of signals and a run method. Parameters ---------- func : Callable The function to call in another thread. _start_thread : bool, optional Whether to immediaetly start the thread. If False, the returned worker must be manually started with ``worker.start()``. by default it will be ``False`` if the ``_connect`` argument is ``None``, otherwise ``True``. _connect : Dict[str, Union[Callable, Sequence]], optional A mapping of ``"signal_name"`` -> ``callable`` or list of ``callable``: callback functions to connect to the various signals offered by the worker class. by default None _progress : Union[bool, Dict[str, Union[int, bool, str]]], optional Can be True, to provide indeterminate progress bar, or dictionary. If dict, requires mapping of 'total' to number of expected yields. If total is not provided, progress bar will be indeterminate. Will connect progress bar update to yields and display this progress in the viewer. Can also take a mapping of 'desc' to the progress bar description. Progress bar will become indeterminate when number of yields exceeds 'total'. By default None. _worker_class : Type[WorkerBase], optional The :class`WorkerBase` to instantiate, by default :class:`FunctionWorker` will be used if ``func`` is a regular function, and :class:`GeneratorWorker` will be used if it is a generator. _ignore_errors : bool, optional If ``False`` (the default), errors raised in the other thread will be reraised in the main thread (makes debugging significantly easier). *args will be passed to ``func`` **kwargs will be passed to ``func`` Returns ------- worker : WorkerBase An instantiated worker. If ``_start_thread`` was ``False``, the worker will have a `.start()` method that can be used to start the thread. Raises ------ TypeError If a worker_class is provided that is not a subclass of WorkerBase. TypeError If _connect is provided and is not a dict of ``{str: callable}`` TypeError If _progress is provided and function is not a generator Examples -------- .. code-block:: python def long_function(duration): import time time.sleep(duration) worker = create_worker(long_function, 10) """ # provide our own classes with the notification mixins if not _worker_class: if inspect.isgeneratorfunction(func): _worker_class = GeneratorWorker else: _worker_class = FunctionWorker worker = _qthreading.create_worker( func, *args, _start_thread=False, _connect=_connect, _worker_class=_worker_class, _ignore_errors=_ignore_errors, **kwargs, ) # either True or a non-empty dictionary if _progress: if isinstance(_progress, bool): _progress = {} desc = _progress.get('desc', None) total = int(_progress.get('total', 0)) if isinstance(worker, FunctionWorker) and total != 0: warnings.warn( trans._( "_progress total != 0 but worker is FunctionWorker and will not yield. Returning indeterminate progress bar...", deferred=True, ), RuntimeWarning, ) total = 0 with progress._all_instances.events.changed.blocker(): pbar = progress(total=total, desc=desc) worker.started.connect( partial( lambda prog: progress._all_instances.events.changed( added={prog}, removed={} ), pbar, ) ) worker.finished.connect(pbar.close) if total != 0 and isinstance(worker, GeneratorWorker): worker.yielded.connect(pbar.increment_with_overflow) worker.pbar = pbar if _start_thread is None: _start_thread = _connect is not None if _start_thread: worker.start() return worker
[docs] def thread_worker( function: Optional[Callable] = None, start_thread: Optional[bool] = None, connect: Optional[Dict[str, Union[Callable, Sequence[Callable]]]] = None, progress: Optional[Union[bool, Dict[str, Union[int, bool, str]]]] = None, worker_class: Union[ Type[FunctionWorker], Type[GeneratorWorker], None ] = None, ignore_errors: bool = False, ): """Decorator that runs a function in a separate thread when called. When called, the decorated function returns a :class:`WorkerBase`. See :func:`create_worker` for additional keyword arguments that can be used when calling the function. The returned worker will have these signals: - *started*: emitted when the work is started - *finished*: emitted when the work is finished - *returned*: emitted with return value - *errored*: emitted with error object on Exception It will also have a ``worker.start()`` method that can be used to start execution of the function in another thread. (useful if you need to connect callbacks to signals prior to execution) If the decorated function is a generator, the returned worker will also provide these signals: - *yielded*: emitted with yielded values - *paused*: emitted when a running job has successfully paused - *resumed*: emitted when a paused job has successfully resumed - *aborted*: emitted when a running job is successfully aborted And these methods: - *quit*: ask the thread to quit - *toggle_paused*: toggle the running state of the thread. - *send*: send a value into the generator. (This requires that your decorator function uses the ``value = yield`` syntax) Parameters ---------- function : callable Function to call in another thread. For communication between threads may be a generator function. start_thread : bool, optional Whether to immediaetly start the thread. If False, the returned worker must be manually started with ``worker.start()``. by default it will be ``False`` if the ``_connect`` argument is ``None``, otherwise ``True``. connect : Dict[str, Union[Callable, Sequence]], optional A mapping of ``"signal_name"`` -> ``callable`` or list of ``callable``: callback functions to connect to the various signals offered by the worker class. by default None progress : Union[bool, Dict[str, Union[int, bool, str]]], optional Can be True, to provide indeterminate progress bar, or dictionary. If dict, requires mapping of 'total' to number of expected yields. If total is not provided, progress bar will be indeterminate. Will connect progress bar update to yields and display this progress in the viewer. Can also take a mapping of 'desc' to the progress bar description. Progress bar will become indeterminate when number of yields exceeds 'total'. By default None. Must be used in conjunction with a generator function. worker_class : Type[WorkerBase], optional The :class`WorkerBase` to instantiate, by default :class:`FunctionWorker` will be used if ``func`` is a regular function, and :class:`GeneratorWorker` will be used if it is a generator. ignore_errors : bool, optional If ``False`` (the default), errors raised in the other thread will be reraised in the main thread (makes debugging significantly easier). Returns ------- callable function that creates a worker, puts it in a new thread and returns the worker instance. Examples -------- .. code-block:: python @thread_worker def long_function(start, end): # do work, periodically yielding i = start while i <= end: time.sleep(0.1) yield i # do teardown return 'anything' # call the function to start running in another thread. worker = long_function() # connect signals here if desired... or they may be added using the # `connect` argument in the `@thread_worker` decorator... in which # case the worker will start immediately when long_function() is called worker.start() """ def _inner(func): @wraps(func) def worker_function(*args, **kwargs): # decorator kwargs can be overridden at call time by using the # underscore-prefixed version of the kwarg. kwargs['_start_thread'] = kwargs.get('_start_thread', start_thread) kwargs['_connect'] = kwargs.get('_connect', connect) kwargs['_progress'] = kwargs.get('_progress', progress) kwargs['_worker_class'] = kwargs.get('_worker_class', worker_class) kwargs['_ignore_errors'] = kwargs.get( '_ignore_errors', ignore_errors ) return create_worker( func, *args, **kwargs, ) return worker_function return _inner if function is None else _inner(function)
_new_worker_qthread = _qthreading.new_worker_qthread def _add_worker_data(worker: FunctionWorker, return_type, source=None): from napari._app_model.injection import _processors cb = _processors._add_layer_data_to_viewer worker.signals.returned.connect( partial(cb, return_type=return_type, source=source) ) def _add_worker_data_from_tuple( worker: FunctionWorker, return_type, source=None ): from napari._app_model.injection import _processors cb = _processors._add_layer_data_tuples_to_viewer worker.signals.returned.connect( partial(cb, return_type=return_type, source=source) ) def register_threadworker_processors(): from functools import partial import magicgui from napari import layers, types from napari._app_model import get_app from napari.types import LayerDataTuple from napari.utils import _magicgui as _mgui app = get_app() for _type in (LayerDataTuple, List[LayerDataTuple]): t = FunctionWorker[_type] magicgui.register_type(t, return_callback=_mgui.add_worker_data) app.injection_store.register( processors={t: _add_worker_data_from_tuple} ) for layer_name in layers.NAMES: _type = getattr(types, f'{layer_name.title()}Data') t = FunctionWorker[_type] magicgui.register_type( t, return_callback=partial(_mgui.add_worker_data, _from_tuple=False), ) app.injection_store.register(processors={t: _add_worker_data})