As described in An introduction to the event loop in napari, napari, like most GUI applications, runs in an event loop that is continually receiving and responding to events like button presses and mouse events. This works fine until one of the events takes a very long time to process. A long-running function (such as training a machine learning model or running a complicated analysis routine) may “block” the event loop in the main thread, leading to a completely unresponsive viewer. The example used there was:

import napari
import numpy as np

viewer = napari.Viewer()
# everything is fine so far... but if we trigger a long computation
image = np.random.rand(1024, 512, 512).mean(0)
# the entire interface freezes!


In order to avoid freezing the viewer during a long-running blocking function, you must run your function in another thread or process.

## Processes, threads, and asyncio¶

There are multiple ways to achieve “concurrency” (multiple things happening at the same time) in python, each with their own advantages and disadvantages. It’s a rich, complicated topic, and a full treatment is well beyond the scope of this document, but strategies generally fall into one of three camps:

2. Multprocessing

For a good high level overview on concurrency in python, see this post. See the trio docs for a good introduction to Python’s new async/await syntax. And of course, see the python docs on threading, multiprocessing, concurrent.futures, and asyncio.

If you already have experience with any of these methods, you should be able to immediately leverage them in napari. napari also provides a few convenience functions that allow you to easily run your long-running methods in another thread.

## Threading in napari with @thread_worker¶

The simplest way to run a function in another thread in napari is to decorate your function with the @thread_worker decorator. Continuing with the example above:

import napari
import numpy as np

def average_large_image():
return np.random.rand(1024, 512, 512).mean(0)

viewer = napari.Viewer()
worker = average_large_image()  # create "worker" object
napari.run()


The @thread_worker decorator converts your function into one that returns a WorkerBase instance. The worker manages the work being done by your function in another thread. It also exposes a few “signals” that let you respond to events happening in the other thread. Here, we connect the worker.returned signal to the viewer.add_image function, which has the effect of adding the result to the viewer when it is ready. Lastly, we start the worker with start() because workers do not start themselves by default.

The @thread_worker decorator also accepts keyword arguments like connect, and start_thread, which may enable more concise syntax. The example below is equivalent to lines 7-15 in the above example:

viewer = napari.Viewer()

def average_large_image():
return np.random.rand(1024, 512, 512).mean(0)

average_large_image()
napari.run()


Note

When the connect argument to @thread_worker is not None, the thread will start by default when the decorated function is called. Otherwise the thread must be manually started by calling worker.start().

## Responding to feedback from threads¶

As shown above, the worker object returned by a function decorated with @thread_worker has a number of signals that are emitted in response to certain events. The base signals provided by the worker are:

• started - emitted when the work is started

• finished - emitted when the work is finished

• returned [value] - emitted with return value when the function returns

• errored [exception] - emitted with an Exception object if an exception is raised in the thread.

### Example: Custom exception handler¶

Because debugging issues in multithreaded applications can be tricky, the default behavior of a @thread_worker - decorated function is to re-raise any exceptions in the main thread. But just as we connected the worker.returned event above to the viewer.add_image method, you can also connect your own custom handler to the worker.errored event:

def my_handler(exc):
if isinstance(exc, ValueError):
print(f"We had a minor problem {exc}")
else:
raise exc

def error_prone_function():
...


## Generators for the win!¶

quick reminder

A generator function is a special kind of function that returns a lazy iterator. To make a generator, you “yield” results rather than (or in addition to) “returning” them:

def my_generator():
for i in range(10):
yield i


Use a generator! By writing our decorated function as a generator that yields results instead of a function that returns a single result at the end, we gain a number of valuable features, and a few extra signals and methods on the worker.

• yielded [value]- emitted with a value when a value is yielded

• 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

Additionally, generator workers will also have a few additional methods:

• send - send a value into the thread (see below)

• pause - send a request to pause a running worker

• resume - send a request to resume a paused worker

• toggle_pause - send a request to toggle the running state of the worker

• quit - send a request to abort the worker

### Retrieving intermediate results¶

The most obvious benefit of using a generator is that you can monitor intermediate results back in the main thread. Continuing with our example of taking the mean projection of a large stack, if we yield the cumulative average as it is generated (rather than taking the average of the fully generated stack) we can watch the mean projection as it builds:

import napari
import numpy as np

viewer = napari.Viewer()

def update_layer(new_image):
try:
# if the layer exists, update the data
viewer.layers['result'].data = new_image
except KeyError:
# otherwise add it to the viewer
new_image, contrast_limits=(0.45, 0.55), name='result'
)

def large_random_images():
cumsum = np.zeros((512, 512))
for i in range(1024):
cumsum += np.random.rand(512, 512)
if i % 16 == 0:
yield cumsum / (i + 1)

large_random_images()  # call the function!
napari.run()


Note how we periodically (every 16 iterations) yield the image result in the large_random_images function. We also connected the yielded event in the @thread_worker decorator to the previously-defined update_layer function. The result is that the image in the viewer is updated every time a new image is yielded.

Any time you can break up a long-running function into a stream of shorter-running yield statements like this, you not only benefit from the increased responsiveness in the viewer, you can often save on precious memory resources.

#### Flow control and escape hatches¶

A perhaps even more useful aspect of yielding periodically in our long running function is that we provide a “hook” for the main thread to control the flow of our long running function. When you use the @thread_worker decorator on a generator function, the ability to stop, start, and quit a thread comes for free. In the example below we decorate what would normally be an infinitely yielding generator, but add a button that aborts the worker when clicked:

import time
import napari
from qtpy.QtWidgets import QPushButton

viewer = napari.Viewer()

def update_layer(new_image):
try:
viewer.layers['result'].data = new_image
except KeyError:
new_image, name='result', contrast_limits=(-0.8, 0.8)
)

def yield_random_images_forever():
i = 0
while True:  # infinite loop!
yield np.random.rand(512, 512) * np.cos(i * 0.2)
i += 1
time.sleep(0.05)

worker = yield_random_images_forever()
worker.yielded.connect(update_layer)

# add a button to the viewer that, when clicked, stops the worker
button = QPushButton("STOP!")
button.clicked.connect(worker.quit)
worker.finished.connect(button.clicked.disconnect)

worker.start()
napari.run()


#### Graceful exit¶

A side-effect of this added flow control is that napari can gracefully shutdown any still-running workers when you try to quit the program. Try the example above, but quit the program without pressing the “STOP” button. No problem! napari asks the thread to stop itself the next time it yields, and then closes without leaving any orphaned threads.

Now go back to the first example with the pure (non-generator) function, and try quitting before the function has returned (i.e. before the image appears). You’ll notice that it takes a while to quit: it has to wait for the background thread to finish because there is no good way to communicate the request that it quit! If you had a very long function, you’d be left with no choice but to force quit your program.

So whenever possible, sprinkle your long-running functions with yield.

## Full two-way communication¶

So far we’ve mostly been receiving results from the threaded function, but we can send values into a generator-based thread as well using worker.send() This works exactly like a standard python generator.send pattern. This next example ties together a number of concepts and demonstrates two-thread communication with conditional flow control. It’s a simple cumulative multiplier that runs in another thread, and exits if the product hits “0”:

import napari
import time

from qtpy.QtWidgets import QLineEdit, QLabel, QWidget, QVBoxLayout
from qtpy.QtGui import QDoubleValidator

def multiplier():
total = 1
while True:
time.sleep(0.1)
new = yield total
total *= new if new is not None else 1
if total == 0:
return "Game Over!"

viewer = napari.Viewer()

# make a widget to control the worker
# (not the main point of this example...)
widget = QWidget()
layout = QVBoxLayout()
widget.setLayout(layout)
result_label = QLabel()
line_edit = QLineEdit()
line_edit.setValidator(QDoubleValidator())

# create the worker
worker = multiplier()

# define some callbacks
def on_yielded(value):
worker.pause()
result_label.setText(str(value))
line_edit.setText('1')

def on_return(value):
line_edit.setText('')
line_edit.setEnabled(False)
result_label.setText(value)

def send_next_value():
worker.send(float(line_edit.text()))
worker.resume()

worker.yielded.connect(on_yielded)
worker.returned.connect(on_return)
line_edit.returnPressed.connect(send_next_value)

worker.start()
napari.run()


Let’s break it down:

1. As usual, we decorate our generator function with @thread_worker and instantiate it to create a worker.

2. The most interesting line in this example is where we both yield the current total to the main thread (yield total), and receive a new value from the main thread (with new = yield).

3. In the main thread, we have connected that worker.yielded event to a callback that pauses the worker and updates the result_label widget.

4. The thread will then wait indefinitely for the resume() command, which we have connected to the line_edit.returnPressed signal.

5. However, before that resume() command gets sent, we use worker.send() to send the current value of the line_edit widget into the thread for multiplication by the existing total.

6. Lastly, if the thread total ever goes to “0”, we stop the thread by returning the string "Game Over". In the main thread, the worker.returned event is connected to a callback that disables the line_edit widget and shows the string returned from the thread.

This example is a bit contrived, since there’s little need to put such a basic computation in another thread. But it demonstrates some of the power and features provided when decorating a generator function with the @thread_worker decorator.

## Syntactic sugar¶

The @thread_worker decorator is just syntactic sugar for calling create_worker() on your function. In turn, create_worker() is just a convenient “factory function” that creates the right subtype of Worker depending on your function type. The following three examples are equivalent:

Using the @thread_worker decorator:

from napari.qt.threading import thread_worker

def my_function(arg1, arg2=None):
...

worker = my_function('hello', arg2=42)


Using the create_worker function:

from napari.qt.threading import create_worker

def my_function(arg1, arg2=None):
...

worker = create_worker(my_function, 'hello', arg2=42)


Using a Worker class:

from napari.qt.threading import FunctionWorker

def my_function(arg1, arg2=None):
...

worker = FunctionWorker(my_function, 'hello', arg2=42)


(the main difference between using create_worker and directly instantiating the FunctionWorker class is that create_worker will automatically dispatch the appropriate type of Worker class depending on whether the function is a generator or not).

## Using a custom worker class¶

If you need even more control over the worker – such as the ability to define custom methods or signals that the worker can emit, then you can subclass the napari WorkerBase class. When doing so, please keep in mind the following guidelines:

1. The subclass must either implement the work() method (preferred), or in extreme cases, may directly reimplement the run() method. (When a worker “start” is started with start(), the call order is always worker.start()worker.run()worker.work().

2. When implementing the work() method, it is important that you periodically check self.abort_requested in your thread loop, and exit the thread accordingly, otherwise napari will not be able to gracefully exit a long-running thread.

def work(self):
i = 0
while True:
if self.abort_requested:
self.aborted.emit()
break
time.sleep(0.5)

3. It is also important to be mindful of the fact that the worker.start() method adds the worker to a global Pool, such that it can request shutdown when exiting napari. So if you re-implement start, please be sure to call super().start() to keep track of the worker.

4. When reimplementing the run() method, it is your responsibility to emit the started, returned, finished, and errored signals at the appropriate moments.

For examples of subclassing WorkerBase, have a look at the two main concrete subclasses in napari: FunctionWorker and GeneratorWorker. You may also wish to simply subclass one of those two classes.

In order to emit signals, an object must inherit from QObject. However, due to challenges with multiple inheritance in Qt, the signals for WorkerBase objects actually live in the WorkerBase._signals attribute (though they are accessible directly in the worker namespace). To add custom signals to a WorkerBase subclass you must first create a new QObject with signals as class attributes:

from qtpy.QtCore import QObject, Signal

class MyWorkerSignals(QObject):
signal_name = Signal()

# or subclass one of the existing signals objects to "add"

# WorkerBaseSignals already has started, finished, errored...
class MyWorkerSignals(WorkerBaseSignals):
signal_name = Signal()


and then either directly override the self._signals attribute on the WorkerBase class with an instance of your signals class:

class MyWorker(WorkerBase):

def __init__(self):
super().__init__()
self._signals = MyWorkerSignals()


… or pass the signals class as the SignalsClass argument when initializing the superclass in your __init__ method:

class MyWorker(WorkerBase):

def __init__(self):
super().__init__(SignalsClass=MyWorkerSignals)