# Copyright (c) 2015-2026 Vector 35 Inc

#

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"""

.. py:module:: mainthread

This module provides two ways to execute "jobs":

1. On the Binary Ninja main thread (the UI event thread when running in the GUI application):

* :py:func:`.execute_on_main_thread`

* :py:func:`.execute_on_main_thread_and_wait`

2. On a worker thread

Any manipulation of the GUI should be performed on the main thread, but any

non-GUI work is generally better to be performed using a worker. This is

especially true for any longer-running work, as the user interface will

be unable to update itself while a job is executing on the main thread.

There are three worker queues, in order of decreasing priority:

1. The Interactive Queue (:py:func:`.worker_interactive_enqueue`)

2. The Priority Queue (:py:func:`.worker_priority_enqueue`)

3. The Worker Queue (:py:func:`.worker_enqueue`)

All of these queues are serviced by the same pool of worker threads. The

difference between the queues is basically one of priority: one queue must

be empty of jobs before a worker thread will execute a job from a lower

priority queue.

The default maximum number of concurrent worker threads is controlled by the

`analysis.limits.workerThreadCount` setting but can be adjusted at runtime via

:py:func:`.set_worker_thread_count`.

The worker threads are native threads, managed by the Binary Ninja core. If

more control over the thread is required, consider using the

:py:class:`~binaryninja.plugin.BackgroundTaskThread` class.

"""

# Binary Ninja components

from . import _binaryninjacore as core

from . import scriptingprovider

from . import plugin

def execute_on_main_thread(func):

"""

The ``execute_on_main_thread`` function takes a single parameter which is a function that will be executed

on the main Binary Ninja thread.

.. warning:: May be required for some GUI operations, but should be used sparingly as it can block the UI.

:Example:

>>> # Execute a GUI operation on the main thread

>>> import binaryninjaui

>>> from binaryninja import execute_on_main_thread

>>> execute_on_main_thread(lambda: binaryninjaui.UIContext.activeContext().openFileContext(context))

"""

action = scriptingprovider._ThreadActionContext(func)

obj = core.BNExecuteOnMainThread(0, action.callback)

if obj:

return plugin.MainThreadAction(obj)

return None

def execute_on_main_thread_and_wait(func):

"""

The ``execute_on_main_thread`` function takes a single parameter which is a function that will be

executed on the main Binary Ninja thread and will block execution of further python until the function returns.

.. warning:: May be required for some GUI operations, but should be used sparingly as it can block the UI.

:Example:

>>> # Execute a GUI operation and wait for it to complete

>>> import binaryninjaui

>>> from binaryninja import execute_on_main_thread_and_wait

>>> execute_on_main_thread_and_wait(lambda: binaryninjaui.UIContext.activeContext().rebaseCurrentView(0x800000))

"""

action = scriptingprovider._ThreadActionContext(func)

core.BNExecuteOnMainThreadAndWait(0, action.callback)

def worker_enqueue(func, name=""):

action = scriptingprovider._ThreadActionContext(func)

core.BNWorkerEnqueueNamed(0, action.callback, "Python " + name)

def worker_priority_enqueue(func, name=""):

action = scriptingprovider._ThreadActionContext(func)

core.BNWorkerPriorityEnqueueNamed(0, action.callback, "Python " + name)

def worker_interactive_enqueue(func, name=""):

action = scriptingprovider._ThreadActionContext(func)

core.BNWorkerInteractiveEnqueueNamed(0, action.callback, "Python " + name)

def get_worker_thread_count():

"""

The ``get_worker_thread_count`` function returns the number of worker threads that are currently running.

By default, this is the number of cores on the system minus one, however this can be changed with

``set_worker_thread_count``.

"""

return core.BNGetWorkerThreadCount()

def set_worker_thread_count(count):

"""

The ``set_worker_thread_count`` function sets the number of worker threads that are currently running.

By default, this is the number of cores on the system minus one.

"""

core.BNSetWorkerThreadCount(count)

def is_main_thread():

return core.BNIsMainThread()