IPython Events

Extension code can register callbacks functions which will be called on specific events within the IPython code. You can see the current list of available callbacks, and the parameters that will be passed with each, in the callback prototype functions defined in IPython.core.callbacks.

To register callbacks, use IPython.core.events.EventManager.register(). For example:

class VarWatcher(object):
    def __init__(self, ip):
        self.shell = ip
        self.last_x = None

    def pre_execute(self):
        self.last_x = self.shell.user_ns.get('x', None)

    def post_execute(self):
        if self.shell.user_ns.get('x', None) != self.last_x:
            print("x changed!")

def load_ipython_extension(ip):
    vw = VarWatcher(ip)
    ip.events.register('pre_execute', vw.pre_execute)
    ip.events.register('post_execute', vw.post_execute)


These are the events IPython will emit. Callbacks will be passed no arguments, unless otherwise specified.


def shell_initialized(ipython):

This event is triggered only once, at the end of setting up IPython. Extensions registered to load by default as part of configuration can use this to execute code to finalize setup. Callbacks will be passed the InteractiveShell instance.


pre_run_cell fires prior to interactive execution (e.g. a cell in a notebook). It can be used to note the state prior to execution, and keep track of changes.


pre_execute is like pre_run_cell, but is triggered prior to any execution. Sometimes code can be executed by libraries, etc. which skipping the history/display mechanisms, in which cases pre_run_cell will not fire.


post_run_cell runs after interactive execution (e.g. a cell in a notebook). It can be used to cleanup or notify or perform operations on any side effects produced during execution. For instance, the inline matplotlib backend uses this event to display any figures created but not explicitly displayed during the course of the cell.


The same as pre_execute, post_execute is like post_run_cell, but fires for all executions, not just interactive ones.

See also

Module IPython.core.hooks
The older ‘hooks’ system allows end users to customise some parts of IPython’s behaviour.
Custom input transformation
By registering input transformers that don’t change code, you can monitor what is being executed.