Source code for ANNarchy.core.Simulate

#===============================================================================
#
#     Simulate.py
#
#     This file is part of ANNarchy.
#
#     Copyright (C) 2013-2016  Julien Vitay <julien.vitay@gmail.com>,
#     Helge Uelo Dinkelbach <helge.dinkelbach@gmail.com>
#
#     This program is free software: you can redistribute it and/or modify
#     it under the terms of the GNU General Public License as published by
#     the Free Software Foundation, either version 3 of the License, or
#     (at your option) any later version.
#
#     ANNarchy is distributed in the hope that it will be useful,
#     but WITHOUT ANY WARRANTY; without even the implied warranty of
#     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#     GNU General Public License for more details.
#
#     You should have received a copy of the GNU General Public License
#     along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
#===============================================================================
from .Global import _network
from .Global import get_current_step, dt
from .Global import _error, _print
from math import ceil
import time
import operator

# Callbacks
_callbacks = [[]]
_callbacks_enabled = [True]


[docs]def simulate(duration, measure_time = False, callbacks=True, net_id=0): """ Simulates the network for the given duration in milliseconds. The number of simulation steps is computed relative to the discretization step ``dt`` declared in ``setup()`` (default: 1ms):: simulate(1000.0) *Parameters*: * **duration**: the duration in milliseconds. * **measure_time**: defines whether the simulation time should be printed. Default: False. * **callbacks**: defines if the callback method (decorator ``every`` should be called). Default: True. """ if not _network[net_id]['instance']: _error('simulate(): the network is not compiled yet.') # Compute the number of steps nb_steps = ceil(float(duration) / dt()) if measure_time: tstart = time.time() if callbacks and _callbacks_enabled[net_id] and len(_callbacks[net_id]) > 0: _simulate_with_callbacks(duration, net_id) else: _network[net_id]['instance'].pyx_run(nb_steps) if measure_time: _print('Simulating', duration/1000.0, 'seconds of the network took', time.time() - tstart, 'seconds.')
[docs]def simulate_until(max_duration, population, operator='and', measure_time = False, net_id=0): """ Runs the network for the maximal duration in milliseconds. If the ``stop_condition`` defined in the population becomes true during the simulation, it is stopped. One can specify several populations. If the stop condition is true for any of the populations, the simulation will stop ('or' function). Example:: pop1 = Population( ..., stop_condition = "r > 1.0 : any") compile() simulate_until(max_duration=1000.0. population=pop1) *Parameters*: * **duration**: the maximum duration of the simulation in milliseconds. * **population**: the (list of) population whose ``stop_condition`` should be checked to stop the simulation. * **operator**: operator to be used ('and' or 'or') when multiple populations are provided (default: 'and'). * **measure_time**: defines whether the simulation time should be printed (default=False). *Returns*: * the actual duration of the simulation in milliseconds. """ if not _network[net_id]['instance']: _error('simulate_until(): the network is not compiled yet.') nb_steps = ceil(float(max_duration) / dt()) if not isinstance(population, list): population = [population] if measure_time: tstart = time.time() nb = _network[net_id]['instance'].pyx_run_until(nb_steps, [pop.id for pop in population], True if operator=='and' else False) sim_time = float(nb) / dt() if measure_time: _print('Simulating', nb/dt()/1000.0, 'seconds of the network took', time.time() - tstart, 'seconds.') return sim_time
[docs]def step(net_id=0): """ Performs a single simulation step (duration = ``dt``). """ if not _network[net_id]['instance']: _error('simulate_until(): the network is not compiled yet.') _network[net_id]['instance'].pyx_step()
################################ ## Decorators ################################ def callbacks_enabled(net_id=0): """ Returns True if callbacks are enabled for the network. """ return _callbacks_enabled[net_id]
[docs]def disable_callbacks(net_id=0): """ Disables all callbacks for the network. """ _callbacks_enabled[net_id] = False
[docs]def enable_callbacks(net_id=0): """ Enables all declared callbacks for the network. """ _callbacks_enabled[net_id] = True
[docs]def clear_all_callbacks(net_id=0): """ Clears the list of declared callbacks for the network. Cannot be undone! """ _callbacks[net_id].clear()
[docs]class every(object): """ Decorator to declare a callback method that will be called periodically during the simulation. Example of setting increasing inputs to a population every 100 ms, with an offset of 90 ms (or -10 ms relative to the period):: @every(period=100., offset=-10.) def step_input(n): pop.I = float(n) / 100. simulate(10000.) ``step_input()`` will be called at times 90, 190, ..., 9990 ms during the call to ``simulate()``. The method must accept only ``n`` as parameter (an integer being 0 the first time the method is called, and incremented afterwards) and can not return anything. The times at which the method is called are relative to the time when ``simulate()`` is called (if ``t`` is already 150 before calling ``simulate()``, the first call will then be made at ``t=240`` with the previous example). If multiple callbacks are defined, they will be called in the order of their declaration if they occur at the same time. """ def __init__(self, period, offset=0., wait=0.0, net_id=0): """ *Parameters:* * **period**: interval in ms between two calls to the function. If less than ``dt``, will be called every step. * **offset**: by default, the first call to the method will be made at the start of the simulation. The offset delays the call within the period (default: 0.0). Can be negative, in which case it will be counted from the end of the period. * **wait**: allows to wait for a certain amount of time (in ms) before starting to call the method. ``wait`` can be combined with ``offset``, so if ``period=100.``, ``offset=50.`` and ``wait=500.``, the first call will be made 550 ms after the call to ``simulate()`` """ self.period = max(float(period), dt()) self.offset = min(float(offset), self.period) self.wait = max(float(wait), 0.0) _callbacks[net_id].append(self) def __call__(self, f): """ If there are decorator arguments, __call__() is only called once, as part of the decoration process! You can only give it a single argument, which is the function object. """ self.func = f return f
def _simulate_with_callbacks(duration, net_id=0): """ replaces simulate() when call_backs are defined. """ t_start = get_current_step(net_id) length = int(duration/dt()) # Compute the times times = [] for c in _callbacks[net_id]: period = int(c.period/dt()) offset = int(c.offset/dt()) % period wait = int(c.wait/dt()) moments = range(t_start + wait + offset, t_start + length, period) n = 0 for m in moments: times.append((m, c, n)) n += 1 # Sort the times to be sure they are in the right order. times = sorted(times, key=operator.itemgetter(0)) for time, callback, n in times: # Advance the simulation to the desired time if time != get_current_step(net_id): _network[net_id]['instance'].pyx_run(time-get_current_step(net_id)) # Call the callback callback.func(n) # Go to the end of the duration if get_current_step(net_id) < t_start + length: _network[net_id]['instance'].pyx_run(t_start + length - get_current_step(net_id))