Running a simulation
====================
.. sidebar:: For Brian 1 users

    See the document :doc:`../introduction/brian1_to_2/networks_and_clocks` for
    details how to convert Brian 1 code.

.. contents::
    :local:
    :depth: 1


To run a simulation, one either constructs a new `Network` object and calls its
`Network.run` method, or uses the "magic" system and a plain `run` call,
collecting all the objects in the current namespace.

Note that Brian has several different ways of running the actual computations,
and choosing the right one can make orders of magnitude of difference in
terms of simplicity and efficiency. See :doc:`computation` for more details.

Networks
--------
In most straightforward simulations, you do not have to explicitly create a
`Network` object but instead can simply call `run` to run a simulation. This is
what is called the "magic" system, because Brian figures out automatically what
you want to do.

When calling `run`, Brian runs the `collect` function to gather all the objects
in the current context. It will include all the objects that are "visible", i.e.
that you could refer to with an explicit name::

  G = NeuronGroup(10, 'dv/dt = -v / tau : volt')
  S = Synapses(G, G, model='w:1', on_pre='v+=w')
  S.connect('i!=j')
  mon = SpikeMonitor(G)

  run(10*ms)  # will include G, S, mon

Note that it will not automatically include objects that are "hidden" in
containers, e.g. if you store several monitors in a list. Use an explicit
`Network` object in this case. It might be convenient to use the `collect`
function when creating the `Network` object in that case::

    G = NeuronGroup(10, 'dv/dt = -v / tau : volt')
    S = Synapses(G, G, model='w:1', on_pre='v+=w')
    S.connect('i!=j')
    monitors = [SpikeMonitor(G), StateMonitor(G, 'v', record=True)]

    # a simple run would not include the monitors
    net = Network(collect())  # automatically include G and S
    net.add(monitors)  # manually add the monitors

    net.run(10*ms)

.. _time_steps:

Setting the simulation time step
--------------------------------

To set the simulation time step for every simulated object, set the ``dt`` attribute of the `defaultclock` which is used
by all objects that do not explicitly specify a ``clock`` or ``dt`` value during construction::

    defaultclock.dt = 0.05*ms

If some objects should use a different clock (e.g. to record values with a `StateMonitor` not at every time step in a
long running simulation), you can provide a ``dt`` argument to the respective object::

    s_mon = StateMonitor(group, 'v', record=True, dt=1*ms)

To sum up:

* Set ``defaultclock.dt`` to the time step that should be used by most (or all) of your objects.
* Set ``dt`` explicitly when creating objects that should use a different time step.

Behind the scenes, a new `Clock` object will be created for each object that defines its own ``dt`` value.

.. _progress_reporting:

Progress reporting
------------------
Especially for long simulations it is useful to get some feedback about the
progress of the simulation. Brian offers a few built-in options and an
extensible system to report the progress of the simulation. In the `Network.run`
or `run` call, two arguments determine the output: ``report`` and
``report_period``. When ``report`` is set to ``'text'`` or ``'stdout'``, the
progress will be printed to the standard output, when it is set to ``'stderr'``,
it will be printed to "standard error". There will be output at the start and
the end of the run, and during the run in ``report_period`` intervals. It is
also possible to do :ref:`custom progress reporting <custom_progress_reporting>`.

.. _continue_repeat:

Continuing/repeating simulations
--------------------------------

To store the current state of the simulation, call
`store` (use the `Network.store` method for a `Network`). You
can store more than one snapshot of a system by providing a name for the
snapshot; if `store` is called without a specified name,
``'default'`` is used as the name. To restore the state, use
`restore`.

The following simple example shows how this system can be used to run several
trials of an experiment::

    # set up the network
    G = NeuronGroup(...)
    ...
    spike_monitor = SpikeMonitor(G)

    # Snapshot the state
    store()

    # Run the trials
    spike_counts = []
    for trial in range(3):
        restore()  # Restore the initial state
        run(...)
        # store the results
        spike_counts.append(spike_monitor.count)

The following schematic shows how multiple snapshots can be used to run a
network with a separate "train" and "test" phase. After training, the test is
run several times based on the trained network. The whole process of training
and testing is repeated several times as well::

    # set up the network
    G = NeuronGroup(..., '''...
                         test_input : amp
                         ...''')
    S = Synapses(..., '''...
                         plastic : boolean (shared)
                         ...''')
    G.v = ...
    S.connect(...)
    S.w = ...

    # First snapshot at t=0
    store('initialized')

    # Run 3 complete trials
    for trial in range(3):
        # Simulate training phase
        restore('initialized')
        S.plastic = True
        run(...)

        # Snapshot after learning
        store('after_learning')

        # Run 5 tests after the training
        for test_number in range(5):
            restore('after_learning')
            S.plastic = False  # switch plasticity off
            G.test_input = test_inputs[test_number]
            # monitor the activity now
            spike_mon = SpikeMonitor(G)
            run(...)
            # Do something with the result
            # ...

.. admonition:: The following topics are not essential for beginners.

    |

Multiple magic runs
-------------------

When you use more than a single `run` statement, the magic system tries to
detect which of the following two situations applies:

1. You want to continue a previous simulation
2. You want to start a new simulation

For this, it uses the following heuristic: if a simulation consists only of
objects that have not been run, it will start a new simulation starting at
time 0 (corresponding to the creation of a new `Network` object). If a
simulation only consists of objects that have been simulated in the previous
`run` call, it will continue that simulation at the previous time.

If neither of these two situations apply, i.e., the network consists of a mix
of previously run objects and new objects, an error will be raised. If this is
not a mistake but intended (e.g. when a new input source and synapses should be
added to a network at a later stage), use an explicit `Network` object.

In these checks, "non-invalidating" objects (i.e. objects that have
`BrianObject.invalidates_magic_network` set to ``False``) are ignored, e.g.
creating new monitors is always possible.

Changing the simulation time step
---------------------------------
You can change the simulation time step after objects have been created or even after a simulation has been run::

    defaultclock.dt = 0.1*ms
    # Set the network
    # ...
    run(initial_time)
    defaultclock.dt = 0.01*ms
    run(full_time - initial_time)

To change the time step between runs for objects that do not use the `defaultclock`, you cannot directly change their
``dt`` attribute (which is read-only) but instead you have to change the ``dt`` of the ``clock`` attribute. If you want
to change the ``dt`` value of several objects at the same time (but not for all of them, i.e. when you cannot use
``defaultclock.dt``) then you might consider creating a `Clock` object explicitly and then passing this clock to each
object with the ``clock`` keyword argument (instead of ``dt``). This way, you can later change the ``dt`` for several
objects at once by assigning a new value to `Clock.dt`.

Note that a change of ``dt`` has to be compatible with the internal representation of
clocks as an integer value (the number of elapsed time steps). For example, you
can simulate an object for 100ms with a time step of 0.1ms (i.e. for 1000 steps)
and then switch to a ``dt`` of 0.5ms, the time will then be internally
represented as 200 steps. You cannot, however, switch to a dt of 0.3ms, because
100ms are not an integer multiple of 0.3ms.

.. _profiling:

Profiling
---------

To get an idea which parts of a simulation take the most time, Brian offers a
basic profiling mechanism. If a simulation is run with the ``profile=True``
keyword argument, it will collect information about the total simulation time
for each `CodeObject`. This information can then be retrieved from
`Network.profiling_info`, which contains a list of ``(name, time)`` tuples or
a string summary can be obtained by calling `profiling_summary`. The
following example shows profiling output after running the CUBA example (where
the neuronal state updates take up the most time)::

    >>> profiling_summary(show=5)  # show the 5 objects that took the longest
    Profiling summary
    =================
    neurongroup_stateupdater    5.54 s    61.32 %
    synapses_pre                1.39 s    15.39 %
    synapses_1_pre              1.03 s    11.37 %
    spikemonitor                0.59 s     6.55 %
    neurongroup_thresholder     0.33 s     3.66 %


.. _scheduling:

Scheduling
----------

Every simulated object in Brian has three attributes that can be specified at
object creation time: ``dt``, ``when``, and ``order``. The time step of the
simulation is determined by ``dt``, if it is specified, or otherwise by
``defaultclock.dt``. Changing this will therefore change the ``dt`` of
all objects that don't specify one. Alternatively, a ``clock`` object
can be specified directly, this can be useful if a clock should be shared
between several objects -- under most circumstances, however, a user should not
have to deal with the creation of `Clock` objects and just define ``dt``.

During a single time step, objects are updated in an order according first
to their ``when``
argument's position in the schedule.  This schedule is determined by
`Network.schedule` which is a list of strings, determining "execution slots" and
their order. It defaults to: ``['start', 'groups', 'thresholds', 'synapses',
'resets', 'end']``. In addition to the names provided in the schedule, names
such as ``before_thresholds`` or ``after_synapses`` can be used that are
understood as slots in the respective positions. The default
for the ``when`` attribute is a sensible value for most objects (resets will
happen in the ``reset`` slot, etc.) but sometimes it make sense to change it,
e.g. if one would like a `StateMonitor`, which by default records in the
``end`` slot, to record the membrane potential before a reset is applied
(otherwise no threshold crossings will be observed in the membrane potential
traces).

Finally, if during a time step two objects fall in the same execution
slot, they will be updated in ascending order according to their
``order`` attribute, an integer number defaulting to 0. If two objects have
the same ``when`` and ``order`` attribute then they will be updated in an
arbitrary but reproducible order (based on the lexicographical order of their
names).

Note that objects that don't do any computation by themselves but only
act as a container for other objects (e.g. a `NeuronGroup` which contains a
`StateUpdater`, a `Resetter` and a `Thresholder`), don't have any value for
``when``, but pass on the given values for ``dt`` and ``order`` to their
containing objects.

To see how the objects in a network are scheduled, you can use the
`scheduling_summary` function::

    >>> group = NeuronGroup(10, 'dv/dt = -v/(10*ms) : 1', threshold='v > 1',
    ...                     reset='v = 0')
    >>> mon = StateMonitor(group, 'v', record=True, dt=1*ms)
    >>> scheduling_summary()
                    object                  |           part of           |        Clock dt        |    when    | order | active
    ----------------------------------------+-----------------------------+------------------------+------------+-------+-------
    statemonitor (StateMonitor)             | statemonitor (StateMonitor) | 1. ms (every 10 steps) | start      |     0 |  yes
    neurongroup_stateupdater (StateUpdater) | neurongroup (NeuronGroup)   | 100. us (every step)   | groups     |     0 |  yes
    neurongroup_thresholder (Thresholder)   | neurongroup (NeuronGroup)   | 100. us (every step)   | thresholds |     0 |  yes
    neurongroup_resetter (Resetter)         | neurongroup (NeuronGroup)   | 100. us (every step)   | resets     |     0 |  yes


As you can see in the output above, the `StateMonitor` will only record the
membrane potential every 10 time steps, but when it does, it will do it at the
start of the time step, before the numerical integration, the thresholding, and
the reset operation takes place.

Every new `Network` starts a simulation at time 0; `Network.t` is a read-only
attribute, to go back to a previous moment in time (e.g. to do another trial
of a simulation with a new noise instantiation) use the mechanism described
below.

Store/restore
-------------

Note that `Network.run`, `Network.store` and `Network.restore` (or `run`,
`store`, `restore`) are the only way of affecting the time of the clocks. In
contrast to Brian1, it is no longer necessary (nor possible) to directly set
the time of the clocks or call a ``reinit`` function.

The state of a network can also be stored on disk with the optional ``filename``
argument of `Network.store`/`store`. This way, you can run the initial part of
a simulation once, store it to disk, and then continue from this state later.
Note that the `store`/`restore` mechanism does not re-create the network as
such, you still need to construct all the `NeuronGroup`, `Synapses`,
`StateMonitor`, ... objects, restoring will only restore all the state variable
values (membrane potential, conductances, synaptic connections/weights/delays,
...). This restoration does however restore the internal state of the objects
as well, e.g. spikes that have not been delivered yet because of synaptic
delays will be delivered correctly.
