napari’s application model

Important

This is currently not a part of the public napari interface but we are considering ways to expose execution of actions registered with app. This documentation will be updated accordingly.

Warning

Work in progress! The napari application model is currently being developed. This document is here to give guidance but things may change as we develop. It may link to current pull requests and issues which have more up to date information on specific areas.

App-model

app-model is a Python package that provides a declarative schema for a GUI-based application. It is an abstraction developed by napari developers, with the needs of napari in mind, but it is agnostic to napari itself (i.e. it should be reusable by any Python GUI application).

The NapariApplication (app) is the top level object that stores information about the commands, keybindings and menus that make up the application. It is a subclass of app_model.Application and is a global application singleton. It can be retrieved with napari._app_model.get_app().

Currently, the primary purpose of the app is to compose the following app_model.registries into a single name-spaced object:

• CommandsRegistry: maintains all of the commands (the actual callable objects) that have been registered with the application. Accessible via app.commands.

• MenusRegistry: maintains all of the menus and submenus that have been registered with the application. Accessible via app.menus.

• KeyBindingsRegistry: maintains the association between a KeyBinding and a command ID in the CommandsRegistry. Accessible via app.keybindings.

The app-model ‘Getting started’ page provides a good general introduction to app-model. This documentation will focus on napari specific aspects.

Actions

The app_model.types.Action class is designed to easily create a high level ‘Action’ object that is the “complete” representation of a command: a pointer to a callable object and optionally placement in menus, keybindings, and additional metadata like title, icons, tooltips etc. It subclasses app_model.types.CommandRule and takes app_model.types.MenuRule and app_model.types.KeyBindingRule arguments.

The following code demonstrates the definition of a app_model.types.Action comprised of a “Split RGB” command, which is to be added to a specific section (group) of the “layerlist context” menu, with a Cmd+Alt+T keybinding.

Note that while strings could be used for id, title, menus.id and keybindings.primary, the usage of enums and constants makes refactoring and maintenance much easier (and provides autocompletion in an IDE!). However, there is currently intention to use plain strings for command id and title to simplify the Action definitions.

from app_model.types import Action, KeyMod, KeyCode
from napari._app_model.constants import CommandId, MenuId, MenuGroup
from napari._app_model.context import LayerListContextKeys as LLCK


# `layers` will be injected later when this action is invoked
def split_rgb_layer(layers: 'LayerList'):
    ...


action = Action(
    id=CommandId.LAYER_SPLIT_RGB,
    title=CommandId.LAYER_SPLIT_RGB.title,
    callback=split_rgb_layer,
    menus = [
        {
            'id': MenuId.LAYERLIST_CONTEXT,
            'group': MenuGroup.LAYERLIST_CONTEXT.SPLIT_MERGE,
            'when': LLCK.active_layer_is_rgb,
        }
    ],
    keybindings=[{'primary': KeyMod.CtrlCmd | KeyMod.Alt | KeyCode.KeyT }]
)

Actions can be registered via app_model.Application.register_action(). This is essentially a shortcut for registering objects with the following registries; CommandsRegistry, MenusRegistry and KeyBindingsRegistry. Note that a command ID may currently only be registered once though this MAY change in the future if a need arises.

The code below shows how to register the action defined above with the napari singleton app:

from napari._app_model import get_app


get_app().register_action(action)

Note

If you’re following along in the console, you may see the following error when executing the above code:

ValueError: Command 'napari:layer:split_rgb' already registered

This is because command id’s may currently only be registered once, and associated with a single callback (and napari’s internal app already used the CommandId.LAYER_SPLIT_RGB id).

Actions in napari

In napari, non-Qt Actions are defined in napari/_app_model/actions while Qt Actions are defined in napari/_qt/_qapp_model/qactions. Non-Qt Actions get registered with app during initialization of the napari app, in NapariApplication’s __init__(). Qt Actions get registered in init_qactions(), which gets called during initialization of _QtMainWindow. Note this is relevant when considering the differences between app-model and action manager implementation, see app-model vs action manager implementation differences for more.

Commands

Commands represent callable objects that “do something” in napari, and usually have a corresponding representation somewhere in the GUI (such as a button, a menu item, or a keybinding).

Important

Inline (lambda) and nested functions should be avoided for command callbacks to prevent memory leakage.

All commands have a string id (e.g. ‘napari:layer:duplicate’). These are currently CommandID enums but we are considering changing to using plain strings to simplify decalaration of Actions.

app_model.types.CommandRule class (of which app_model.types.Action is a subclass) includes many fields to detail various aspects of commands. See app_model.types.CommandRule for details on every field. Notable CommandRule fields are enablement and toggled, which both can take an app_model.expressions.Expr, an expression that can be evaluated, with context if required (see Contexts and Expressions in napari for more details). enablement determines whether the command is enabled in the UI. It does not prevent the command from being executed via other means, e.g., via app_model.registries.CommandsRegistry.execute_command(). toggled determines whether the command appears checked/toggled in GUI representations (e.g., menu or button). It can also take a app_model.types.ToggleRule, which allows you to use a callable (instead of an expression) to determine toggle state.

Commands should not be confused with the public napari API

While it is conceivable that plugins might need/want to refer to one of these napari commands by its string id, it is not currently a goal that napari end-users would execute any of these commands by their ID. There should always be a “pure python” way to import a napari object and call it. For example, sample data is added to the File -> Open Sample menu but you can also use napari.Viewer.open_sample() to open a sample data in headless mode.

Commands mostly serve as a way to reference some functionality that needs to be exposed in the GUI.

Note

Some of these command ID strings MAY be exposed externally in the future. For example, a plugin may wish to refer to a napari command.

Commands in napari

Commands are usually defined with their Action definition or with its class, if it is a class method. See Actions in napari for details on where Actions are defined in napari.

Menus

App-model menus are not limited to the visible menus in the application menu bar, they can appear anywhere, such as the layerlist context menu that appears when a user right-clicks on the layer list. They may also be used to create a toolbar (a set of clickable buttons that execute a command). For example, the mode buttons in each of the layer controls could be represented as menus with a set of commands.

One of the benefits of the abstraction provided by app-model is that actual Qt Menu objects become simple to construct:

>>> from app_model.backends.qt import QModelMenu
>>> from napari._app_model.constants import MenuId

# create a QMenu with all of the commands registered in the
# layerlist context menu
>>> menu = QModelMenu(menu_id=MenuId.LAYERLIST_CONTEXT, app='napari')

>>> print([i.text() for i in menu.actions()])
[
    'Toggle visibility',
    '',
    'Convert to Labels',
    'Convert to Image',
    'Convert data type',
    '',
    'Duplicate Layer',
    'Split Stack',
    'Split RGB',
    'Merge to Stack',
    'Projections',
    '',
    'Link Layers',
    'Unlink Layers',
    'Select Linked Layers'
]

To specify which menu(s) to add an Action to, use the menus field in Action. menus takes a list of MenuRule. The id field allows you to specify which menu or submenu to add the Action to. All napari menus and submenus will also have a string id (e.g. 'napari/file', or 'napari/layers/context'), which should be declared as a member of the MenuId enum. Internally, an instance of this enum should be used instead of the string literal when referring to a menu.

The group and order fields let you control how menu items are grouped into sections, and how they are ordered within each section. See MenuItemBase for details on each field.

The when field lets you control when the menu item is visible. It can take a Expr, an expression that can be evaluated, with context if required (see Contexts and Expressions in napari for more details).

To add a submenu append it to the app’s MenusRegistry, e.g., via app.menus.append_menu_items. append_menu_items() takes a tuple of the format (MenuId, [MenuItem or SubmenuItem]).

Menus in napari

In napari MenuId and MenuGroup enums are defined in napari/_app_model/constants/_menus.py. Menu bar menus are created during Window initialization via build_qmodel_menu(). This creates a QModelMenu (a subclass of QMenu) instance. It is the creation of the QModelMenu that creates QAction for the Action associated with that menu.

SubmenuItems are defined in napari/_app_model/_submenus.py. These are registered during initialization of the napari app, in NapariApplication’s __init__().

Keybindings

App-model has an extensive internal representation of Key codes, and combinations of key press events (including chorded key press sequences such as Cmd+K followed by Cmd+M).

They will provide independence from vispy’s key codes, and have a nice IntEnum API that allows for declaration of keybindings in a namespaced way that avoids usage of strings:

>>> from app_model.types import KeyCode, KeyMod

>>> ctrl_m = KeyMod.CtrlCmd | KeyCode.KeyM

>>> ctrl_m
<KeyCombo.CtrlCmd|KeyM: 2078>

A keybinding can be specified using the keybindings field in Action. This takes a app_model.types.KeyBindingRule, which will let you provide keybindings for all three operating systems.

Keybindings in napari

App-model keybindings are currently only being used for menu bar items that have migrated to using app-model Actions. Pull request 6204 migrates layer and viewer actions to app-model as well as implements NAP-7, which addresses how keybindings are dispatched according to their priority, enablement, and potential conflicts. It also enables keybindings to be editable by the user at runtime (see issue 6600 for more on this). Note that it is likely that this pull request will be split into smaller pull requests for ease of review and better git history.

Dependency injection and result processing

Dependency injection allows to write functions using parameter type annotations, and then inject dependencies (arguments) into those functions at call time. Very often in a GUI application, you may wish to infer some command arguments from the current state of the application. For example, if you have menu item linked to a “close window” action, you likely want to close the current window. Dependency injection enables this by providing arguments to commands at runtime, based on the type annotations in the command function definition. A ‘provider’, a function that can be called to return an instance of a given type, is used to obtain the dependency to be injected.

Result processing allows you to process the return value of the command function at execution time, based on command return type annotations. For example, you may wish to automatically add layer data output from a command to the viewer. It is performed by processors, functions that accept an instance of a given type and do something with it. Note that any value returned by a processor will be ignored, it is the ‘processor’ function side effects that perform the desired action (e.g., adding layer data to the viewer).

Dependency injection and result processing are provided by the package in-n-out (which spun out of an internal napari module). App-model uses in-n-out to inject dependencies into all commands in the CommandsRegistry. See the in-n-out documentation for details on how providers and processors work.

In napari, providers and processors can be registered in the app’s injection_store attribute, which is an instance of in_n_out.Store. This Store is just a collection of providers and processors. Providers and processors in the Store will automatically be used when executing CommandsRegistry commands.

Note

Injection is implemented in app-model, via the run_injected property of _RegisteredCommand. All commands in the CommandsRegistry are represented using the _RegisteredCommand type.

Below is an example of provider use in napari, which demonstrates implementation details.

A user/plugin provides a function, using the Points annotation:

# some user provided function declares a need
# for Points by using type annotations.
def process_points(points: 'Points'):
    # do something with points
    print(points.name)

Tip

If a default value is provided to a function parameter, no dependency injection will occur. This can be used if you want to bind an object at Action definition time instead of command execution time.

Internally, napari registers a provider that returns the first Points layer of the current viewer, if one present (returning None if not). It is registered in the app.injection_store via app.injection_store.register_provider. Processors can be registered in the same way.

from napari._app_model import get_app

# return annotation indicates what this provider provides
def provide_points() -> Optional['Points']:
    import napari.viewer
    from napari.layers import Points

    viewer = napari.viewer.current_viewer()
    if viewer is not None:
        return next(
            (i for i in viewer.layers if isinstance(i, Points)),
            None
        )

get_app().injection_store.register_provider(provide_points)

This allows both internal and external functions to be injected with these provided objects, and therefore called without parameters in certain cases. This is particularly important in a GUI context, where a user can’t always be providing arguments:

>>> injected_func = get_app().injection_store.inject(process_points)

Note: injection doesn’t inherently mean that it’s always safe to call an injected function without parameters. In this case, we have no viewer and no points:

>>> injected_func()

TypeError: After injecting dependencies for NO arguments,
process_points() missing 1 required positional argument: 'points'

Our provider was context dependent. Only when we have an active viewer with a points layer, can it be provided:

>>> viewer = napari.view_points(name='Some Points')

>>> injected_func()
Some Points

The fact that injected_func may now be called without parameters allows it to be used easily as a command in a menu, or bound to a keybinding. It is up to napari to determine what providers it will make available, and what type hints plugins/users may use to request dependencies.

Annotation and namespaces

For parameter annotations to be resolved by in-n-out, the object must be in the “global” or “local” namespace. The “global” namespace consists of the items in the callback function’s function.__globals__ attribute. In in-n-out the “local” namespace consists of objects in the typing and types modules’ namespace, and objects in the napari injection_store’s namespace attribute. In the napari app’s Store, some basic napari objects are added to the namespace attribute:

• all public types from napari.components, napari.layers and napari.viewer

• Window

• QtViewer

This means that when annotating a callback function, you will not need to import any of the above classes for the annotation to be resolved. For other classes you will need to import them at the module level. Importing in any of the following ways will not work:

• import within typing.TYPE_CHECKING

• import within an outer function, where the callback function is an inner function

Providers and processors in napari

Non-Qt providers and processors are defined in napari/_app_model/injection. Qt providers and processors are defined in napari/_qt/_qapp_model/injection.

Non-Qt providers and processors are registered in the app Store during initialization of the napari app, in NapariApplication’s __init__(). Qt providers and processors are registered in init_qactions(), which gets called during initialization of _QtMainWindow. This is the same as registration of Actions.

app-model testing

This section provides a guide to testing app-model aspects of napari. For general information on napari testing see Testing.

Mock app

The NapariApplication, app, is a global application singleton. This is not ideal because changes made in a previous test will persist and can cause conflicts for subsequent tests, when running a suite of tests. Segmentation faults can also occur as a singleton app may keep a reference to an object, e.g., a Window, that has since been cleaned up at the end of a previous test. Thus, we mock the app in a _mock_app fixture, and explicitly use it in make_napari_viewer as well as in all tests that use the get_app function. This way, a new instance of app is returned every time get_app() is used inside a test. This ‘test’ app is available for use throughout the test’s duration and will get cleaned up at the end.

Note

Since the _mock_app fixture is used in make_napari_viewer, plugins and other external users that write tests using make_napari_viewer will also have the benefit of a new app for each test.

The mock app registers non-Qt Actions, providers and processors. This is because it was thought that these would be required for the majority of tests. If a plain app is required, it can be created by making a new NapariApplication instance. Qt items are not registered because we did not think it would be best practice to have Qt objects registered for every test. If Qt Actions, providers or processors are required, they can be registered by using the make_napari_viewer fixture, which will run init_qactions(), _initialize_plugins() as well as create a Viewer. Alternatively, manually run the required function, ensuring you run cache_clear first, to bypass the functools.lru_cache() on these functions.

The app gives you access to its CommandsRegistry, MenusRegistry and KeyBindingsRegistry. Some useful methods:

• app_model.registries.CommandsRegistry.execute_command() allows you to manually execute registered commands. Note that commands can always be executed this way regardless of its enablement state.

• app_model.registries.MenusRegistry.get_menu() allows you to obtain any registered app_model.types.MenuItem or app_model.types.SubmenuItem.

Menus

The QModelMenu of menu bar items are saved as Window attributes. These are useful as the QActions of the commands in these menus can be found via the QModelMenu’s findAction() method.

Contexts

A context is essentially a mapping between variable names and their values. For details on contexts and expressions see Contexts and Expressions in napari.

There are currently two ContextNamespace classes in napari; LayerListContextKeys and LayerListSelectionContextKeys. These map variables to various values obtained using LayerList and selection LayerList (e.g., “Number of selected points layers”). They are defined in napari/_app_model/context. An instance of each class is created and saved as an attribute of LayerList. The update() methods of each are then connected to events that change the LayerList or selected LayerList.

Variables in these classes can be used in expressions in the Action enablement field and menus MenuRule’s when field (see Menus for details).

Currently, the app_model.Application class does not have a context registry but it may in future (more details can be found here). napari therefore needs to manually update the context via update_from_context(). For menu bar items, we connect update_from_context() to the aboutToShow event of each menu bar QModelMenu instance, in napari.window.Window._add_menus().

Migration from action manager

Motivation & Future Vision

While it’s certainly possible that there will be cases where this abstraction in app-model proves to be a bit more annoying than the previous “procedural” approach, there are a number of motivations for adopting this abstraction.

1. It gives us an abstraction layer on top of Qt that will make it much easier to explore different application backends (such as a web-based app, etc..)

2. It’s easier to test: app-model can take care of making sure that commands, menus, keybindings, and actions are rendered, updated, and triggered correctly, and napari can focus on testing the napari-specific logic.

3. It’s becomes much easier to add & remove contributions from plugins if our internal representation of a command, menu, keybinding is similar to the schema that plugins use. The previous procedural approach made this marriage much more cumbersome.

4. The Dream: The unification of napari commands and plugin commands into a registry that can execute commands in response to user input provides an excellent base for “recording” a user workflow. If all GUI user interactions go through dependency-injected commands, then it becomes much easier to export a script that reproduces a set of interactions.

app-model vs action manager implementation differences

App-model and action manager differ in when actions are registered. In app-model, actions are registered (on NapariApplication and Window initialization). This is much earlier than with action manager, where actions are registered when building the menus.

For example, with action manager, a PluginsMenu class instance is created when building the menu bar. Plugins menu actions are thus registered at a time when we know we have access to Qt. In app-model, plugins menu actions are registered during _initialize_plugins(), which is run on initialization of Viewer. This is much earlier in napari startup and is before we know if we have access to Qt. Thus, we register with _safe_register_qt_actions(), which first checks if Qt is available.