import inspect
import warnings
from collections.abc import Sequence
from functools import partial, wraps
from types import FunctionType, GeneratorType
from typing import (
Callable,
Optional,
TypeVar,
Union,
)
from superqt.utils import _qthreading
from napari.utils.progress import progress
from napari.utils.translations import trans
__all__ = [
'FunctionWorker',
'GeneratorWorker',
'create_worker',
'thread_worker',
'register_threadworker_processors',
]
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')
[docs]
class FunctionWorker(_qthreading.FunctionWorker[_R], _NotifyingMixin): ...
_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._qt._qapp_model.injection._qprocessors import (
_add_layer_data_to_viewer,
)
cb = _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._qt._qapp_model.injection._qprocessors import (
_add_layer_data_tuples_to_viewer,
)
cb = _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_model
from napari.types import LayerDataTuple
from napari.utils import _magicgui as _mgui
app = get_app_model()
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})
