Source code for pyNN.common.control
# encoding: utf-8
"""
Common implementation of functions for simulation set-up and control
This module contains:
* partial implementations of API functions which can be reused by
backend-specific implementations (in some cases only the docstring
is intended to be reused)
* function factories for generating backend-specific API functions.
:copyright: Copyright 2006-2020 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
"""
DEFAULT_MAX_DELAY = 'auto'
DEFAULT_TIMESTEP = 0.1
DEFAULT_MIN_DELAY = 'auto'
assert 'simulator' not in locals()
class BaseState(object):
"""Base class for simulator _State classes."""
def __init__(self):
"""Initialize the simulator."""
self.running = False
self.t_start = 0
self.write_on_end = [] # a list of (population, variable, filename) combinations that should be written to file on end()
self.recorders = set([])
def setup(timestep=DEFAULT_TIMESTEP, min_delay=DEFAULT_MIN_DELAY,
**extra_params):
"""
Initialises/reinitialises the simulator. Any existing network structure is
destroyed.
`timestep`, `min_delay` and `max_delay` should all be in milliseconds.
`extra_params` contains any keyword arguments that are required by a given
simulator but not by others.
"""
max_delay = extra_params.get('max_delay', DEFAULT_MAX_DELAY)
invalid_extra_params = ('mindelay', 'maxdelay', 'dt', 'time_step')
for param in invalid_extra_params:
if param in extra_params:
raise Exception("%s is not a valid argument for setup()" % param)
if min_delay != 'auto':
if max_delay != 'auto' and min_delay > max_delay:
raise Exception("min_delay has to be less than or equal to max_delay.")
if min_delay < timestep:
raise Exception("min_delay (%g) must be greater than timestep (%g)" % (min_delay, timestep))
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
raise NotImplementedError
def build_run(simulator):
def run_until(time_point, callbacks=None):
"""
Advance the simulation until `time_point` (in ms).
`callbacks` is an optional list of callables, each of which should
accept the current time as an argument, and return the next time it
wishes to be called.
``run_until()`` and ``run()`` may be combined freely. See the
documentation of the ``run()`` function for further information.
"""
now = simulator.state.t
if time_point - now < -simulator.state.dt / 2.0: # allow for floating point error
raise ValueError("Time %g is in the past (current time %g)" % (time_point, now))
if callbacks:
callback_events = [(callback(simulator.state.t), callback)
for callback in callbacks]
while simulator.state.t + 1e-9 < time_point:
callback_events.sort(key=lambda cbe: cbe[0], reverse=True)
next, callback = callback_events.pop()
# collapse multiple events that happen within the same timestep
active_callbacks = [callback]
while len(callback_events) > 0 and\
abs(next - callback_events[-1][0]) < simulator.state.dt:
active_callbacks.append(callback_events.pop()[1])
next = min(next, time_point)
simulator.state.run_until(next)
callback_events.extend((callback(simulator.state.t), callback)
for callback in active_callbacks)
else:
simulator.state.run_until(time_point)
return simulator.state.t
def run(simtime, callbacks=None):
"""
Advance the simulation by `simtime` ms.
`callbacks` is an optional list of callables, each of which should
accept the current time as an argument, and return the next time it
wishes to be called.
``run()`` may be called multiple times during a simulation.
In between calls to ``run()`` it is possible to retrieve data
and modify neuron/synapse parameters. Some backends allow modification of
the network structure. ``run(x + y)`` is equivalent to ``run(x)``
followed by ``run(y)``. If you wish to reset the simulation state to
the initial conditions (time ``t = 0``), use the ``reset()`` function.
"""
return run_until(simulator.state.t + simtime, callbacks)
return run, run_until
def build_reset(simulator):
def reset(annotations={}):
"""
Reset the time to zero, neuron membrane potentials and synaptic weights to
their initial values, and begin a new Segment for recorded data.
The network structure is not changed, nor are neuron/synapse parameters,
nor the specification of which neurons to record from.
"""
for recorder in simulator.state.recorders:
recorder.store_to_cache(annotations)
simulator.state.reset()
return reset
def build_state_queries(simulator):
def get_current_time():
"""Return the current time in the simulation (in milliseconds)."""
return simulator.state.t
def get_time_step():
"""Return the integration time step (in milliseconds)."""
return simulator.state.dt
def get_min_delay():
"""Return the minimum allowed synaptic delay (in milliseconds)."""
return simulator.state.min_delay
def get_max_delay():
"""Return the maximum allowed synaptic delay (in milliseconds)."""
return simulator.state.max_delay
def num_processes():
"""Return the number of MPI processes."""
return simulator.state.num_processes
def rank():
"""Return the MPI rank of the current node."""
return simulator.state.mpi_rank
return get_current_time, get_time_step, get_min_delay, get_max_delay, num_processes, rank