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This section contains snippets that were automatically translated from C++ to Python and may contain errors.

Concurrent Map and Map-Reduce

Transforming values from a sequence and combining them, all in parallel.

The QtConcurrent::map(), QtConcurrent::mapped() and QtConcurrent::mappedReduced() functions run computations in parallel on the items in a sequence such as a QList . QtConcurrent::map() modifies a sequence in-place, QtConcurrent::mapped() returns a new sequence containing the modified content, and QtConcurrent::mappedReduced() returns a single result.

These functions are part of the Qt Concurrent framework.

Each of the above functions has a blocking variant that returns the final result instead of a QFuture . You use them in the same way as the asynchronous variants.

images = ...
# Each call blocks until the entire operation is finished.
future = QtConcurrent.blockingMapped(images, scaled)
QtConcurrent.blockingMap(images, scale)
collage = QtConcurrent.blockingMappedReduced(images, scaled, addToCollage)

Note that the result types above are not QFuture objects, but real result types (in this case, QList < QImage > and QImage ).

Concurrent Map

QtConcurrent::mapped() takes an input sequence and a map function. This map function is then called for each item in the sequence, and a new sequence containing the return values from the map function is returned.

The map function must be of the form:

function = U(T t)

T and U can be any type (and they can even be the same type), but T must match the type stored in the sequence. The function returns the modified or mapped content.

This example shows how to apply a scale function to all the items in a sequence:

def scaled(image):

    return image.scaled(100, 100)

images = ...
thumbnails = QtConcurrent.mapped(images, scaled)

The results of the map are made available through QFuture . See the QFuture and QFutureWatcher documentation for more information on how to use QFuture in your applications.

If you want to modify a sequence in-place, use QtConcurrent::map(). The map function must then be of the form:

function = U(T t)

Note that the return value and return type of the map function are not used.

Using QtConcurrent::map() is similar to using QtConcurrent::mapped():

def scale(image):

    image = image.scaled(100, 100)

images = ...
future = QtConcurrent.map(images, scale)

Since the sequence is modified in place, QtConcurrent::map() does not return any results via QFuture . However, you can still use QFuture and QFutureWatcher to monitor the status of the map.

Concurrent Map-Reduce

QtConcurrent::mappedReduced() is similar to QtConcurrent::mapped(), but instead of returning a sequence with the new results, the results are combined into a single value using a reduce function.

The reduce function must be of the form:

def function(result,intermediate):

T is the type of the final result, U is the return type of the map function. Note that the return value and return type of the reduce function are not used.

Call QtConcurrent::mappedReduced() like this:

def addToCollage(collage, thumbnail):

    p = QPainter(collage)
    offset = QPoint(0, 0)
    p.drawImage(offset, thumbnail)
    offset += ...

images = ...
collage = QtConcurrent.mappedReduced(images, scaled, addToCollage)

The reduce function will be called once for each result returned by the map function, and should merge the intermediate into the result variable. QtConcurrent::mappedReduced() guarantees that only one thread will call reduce at a time, so using a mutex to lock the result variable is not necessary. The ReduceOptions enum provides a way to control the order in which the reduction is done. If UnorderedReduce is used (the default), the order is undefined, while OrderedReduce ensures that the reduction is done in the order of the original sequence.

Additional API Features

Using Iterators instead of Sequence

Each of the above functions has a variant that takes an iterator range instead of a sequence. You use them in the same way as the sequence variants:

images = ...
thumbnails = QtConcurrent.mapped(images.constBegin(), images.constEnd(), scaled)
# Map in-place only works on non-const iterators.
future = QtConcurrent.map(images.begin(), images.end(), scale)
collage = QtConcurrent.mappedReduced(images.constBegin(), images.constEnd(), scaled, addToCollage)

Blocking Variants

Each of the above functions has a blocking variant that returns the final result instead of a QFuture . You use them in the same way as the asynchronous variants.

images = ...
# Each call blocks until the entire operation is finished.
future = QtConcurrent.blockingMapped(images, scaled)
QtConcurrent.blockingMap(images, scale)
collage = QtConcurrent.blockingMappedReduced(images, scaled, addToCollage)

Note that the result types above are not QFuture objects, but real result types (in this case, QList < QImage > and QImage ).

Using Member Functions

QtConcurrent::map(), QtConcurrent::mapped(), and QtConcurrent::mappedReduced() accept pointers to member functions. The member function class type must match the type stored in the sequence:

# Squeeze all strings in a QStringList.
strings = ...
QFuture<void> squeezedStrings = QtConcurrent::map(strings.squeeze)
# Swap the rgb values of all pixels on a list of images.
images = ...
bgrImages = QtConcurrent.mapped(images,()
    QImage (QImage.*)()(QImage.rgbSwapped))
# Create a set of the lengths of all strings in a list.
strings = ...
QFuture<QSet<int>> wordLengths = QtConcurrent::mappedReduced(strings.length,
                                                             qOverload<int>(QSet<int>::insert))

Note the use of qOverload . It is needed to resolve the ambiguity for the methods, that have multiple overloads.

Also note that when using QtConcurrent::mappedReduced(), you can mix the use of normal and member functions freely:

# Can mix normal functions and member functions with QtConcurrent::mappedReduced().
# Compute the average length of a list of strings.
void = extern(int average, int length)
strings = ...
QFuture<int> averageWordLength = QtConcurrent::mappedReduced(strings.length, computeAverage)
# Create a set of the color distribution of all images in a list.
int = extern(QImage string)
images = ...
totalColorDistribution = QtConcurrent.mappedReduced(images, colorDistribution,()
                                                                        qOverload<int>(QSet<int>::insert))

Using Function Objects

QtConcurrent::map(), QtConcurrent::mapped(), and QtConcurrent::mappedReduced() accept function objects for the map function. These function objects can be used to add state to a function call:

class Scaled():

    Scaled(int size)
    self.m_size = size
    QImage = typedef()
    def operator(image):

        return image.scaled(m_size, m_size)

    m_size = int()

images = ...
thumbnails = QtConcurrent.mapped(images, Scaled(100))

Function objects are also supported for the reduce function:

class ImageTransform():

    def operator(result, value):

thumbNails =
        QtConcurrent.mappedReduced(images, Scaled(100), ImageTransform(),
                                    QtConcurrent.SequentialReduce)

Using Lambda Expressions

QtConcurrent::map(), QtConcurrent::mapped(), and QtConcurrent::mappedReduced() accept lambda expressions for the map and reduce function:

vector = { 1, 2, 3, 4 }
QtConcurrent.blockingMap(vector, [](int x) { x *= 2; })
size = 100
images = ...
thumbnails = QtConcurrent.mapped(images,()
        [size](QImage image) {
            return image.scaled(size, size)

    ).results()

When using QtConcurrent::mappedReduced() or QtConcurrent::blockingMappedReduced(), you can mix the use of normal functions, member functions and lambda expressions freely.

collage = QtConcurrent.mappedReduced(images,()
        [size](QImage image) {
            return image.scaled(size, size)
        },
        addToCollage
   ).results()

You can also pass a lambda as a reduce object:

collage = QtConcurrent.mappedReduced(images,()
        [size](QImage image) {
            return image.scaled(size, size)
        },
        [](QImage result, QImage value) {
            # do some transformation

   ).results()

Wrapping Functions that Take Multiple Arguments

If you want to use a map function that takes more than one argument you can use a lambda function or std::bind() to transform it onto a function that takes one argument.

As an example, we’ll use scaledToWidth() :

QImage.scaledToWidth = QImage(int width, Qt.TransformationMode)

scaledToWidth takes three arguments (including the “this” pointer) and can’t be used with QtConcurrent::mapped() directly, because QtConcurrent::mapped() expects a function that takes one argument. To use scaledToWidth() with QtConcurrent::mapped() we have to provide a value for the width and the transformation mode:

images = ...
std.function<QImage(QImage )> scale = [](QImage img) {
    return img.scaledToWidth(100, Qt.SmoothTransformation)

thumbnails = QtConcurrent.mapped(images, scale)