Source code for

Module defining the `Network` object, the basis of all simulation runs.


.. document_brian_prefs::


import os
import sys
import time
from collections import defaultdict, Counter, namedtuple
from import Sequence, Mapping
import pickle as pickle

from brian2.synapses.synapses import SummedVariableUpdater
from brian2.utils.logger import get_logger
from brian2.core.names import Nameable
from brian2.core.base import BrianObject, BrianObjectException
from brian2.core.clocks import Clock, defaultclock
from brian2.devices.device import get_device, all_devices, RuntimeDevice
from import Group
from brian2.units.fundamentalunits import check_units, Quantity
from brian2.units.allunits import second, msecond
from brian2.core.preferences import prefs, BrianPreference
from brian2.core.namespace import get_local_namespace
from .base import device_override

__all__ = ['Network', 'profiling_summary', 'scheduling_summary']

logger = get_logger(__name__)

prefs.register_preferences('', 'Network preferences',
                               Default schedule used for networks that
                               don't specify a schedule.

def _format_time(time_in_s):
    Helper function to format time in seconds, minutes, hours, days, depending
    on the magnitude.

    >>> from import _format_time
    >>> _format_time(12345)
    '3h 25m 45s'
    >>> _format_time(123)
    '2m 3s'
    >>> _format_time(12.5)
    >>> _format_time(.5)
    '< 1s'

    divisors = [24*60*60, 60*60, 60, 1]
    letters = ['d', 'h', 'm', 's']
    remaining = time_in_s
    text = ''
    for divisor, letter in zip(divisors, letters):
        time_to_represent = int(remaining / divisor)
        remaining -= time_to_represent * divisor
        if time_to_represent > 0 or len(text):
            if len(text):
                text += ' '
            text += f'{int(time_to_represent)}{letter}'

    # less than one second
    if len(text) == 0:
        text = '< 1s'

    return text

[docs]class TextReport(object): """ Helper object to report simulation progress in ``. Parameters ---------- stream : file The stream to write to, commonly `sys.stdout` or `sys.stderr`. """ def __init__(self, stream): = stream
[docs] def __call__(self, elapsed, completed, start, duration): if completed == 0.0:"Starting simulation at t={start} for a duration of " f"{duration}\n") else: t = str(completed * duration) percent = int(completed * 100.) real_t = _format_time(float(elapsed)) report_msg = (f"{t} ({percent}%) simulated in " f"{real_t}") if completed < 1.0: remaining = int(round((1-completed)/completed*float(elapsed))) remaining_msg = (f", estimated {_format_time(remaining)} " f"remaining.\n") else: remaining_msg = '\n' + remaining_msg) # Flush the stream, this is useful if stream is a file
def _format_table(header, values, cell_formats): # table = [header] + values table_format = len(values)*[cell_formats] col_widths = [max(len(format.format(cell, 0)) for format, cell in zip(col_format, col)) for col_format, col in zip(list(zip(*([len(header)*['{}']] + table_format))), list(zip(*([header] + values))))] line = '-+-'.join('-'*width for width in col_widths) content = [' | '.join(format.format(cell, width) for format, cell, width in zip(row_format, row, col_widths)) for row_format, row in zip(table_format, values)] formatted_header = ' | '.join('{:^{}}'.format(h, width) for h, width in zip(header, col_widths)) return '\n'.join([formatted_header, line] + content)
[docs]class SchedulingSummary(object): """ Object representing the schedule that is used to simulate the objects in a network. Objects of this type are returned by `scheduling_summary`, they should not be created manually by the user. Parameters ---------- objects : list of `BrianObject` The sorted list of objects that are simulated by the network. """ def __init__(self, objects): # Map each dt to a rank (i.e. smallest dt=0, second smallest=1, etc.) self.dts = dict((dt, rank) for rank, dt in enumerate(sorted({float(obj.clock.dt) for obj in objects}))) ScheduleEntry = namedtuple('ScheduleEntry', field_names=['when', 'order', 'dt', 'name', 'type', 'active', 'owner_name', 'owner_type']) entries = [] for obj in objects: if len(obj.contained_objects): continue owner = getattr(obj, 'group', None) if owner is None: owner_name, owner_type = None, None else: owner_name = owner_type = owner.__class__.__name__ entries.append(ScheduleEntry(when=obj.when, order=obj.order, dt=obj.clock.dt,, type=obj.__class__.__name__,, owner_name=owner_name, owner_type=owner_type)) self.entries = entries self.all_dts = sorted({float(entry.dt) for entry in self.entries}) # How many steps compared to the fastest clock? self.steps = {float(dt): int(dt / self.all_dts[0]) for dt in self.all_dts} def __repr__(self): return _format_table(['object', 'part of', 'Clock dt', 'when', 'order', 'active'], [[f'{} ({entry.type})', f'{entry.owner_name} ({entry.owner_type})' if entry.owner_name is not None else '--', '{} (every {})'.format(str(entry.dt), 'step' if self.steps[float(entry.dt)] == 1 else '{} steps'.format(self.steps[float(entry.dt)])), entry.when, entry.order, 'yes' if else 'no'] for entry in self.entries], ['{:<{}}', '{:<{}}', '{:<{}}', '{:<{}}', '{:{}d}', '{:^{}}']) def _repr_html_(self): rows = ["""\ <tr> <td style="text-align: left;">{}</td> <td style="text-align: left;">{}</td> <td style="text-align: left;">{}</td> <td style="text-align: left;">{}</td> <td style="text-align: right;">{}</td> <td style="text-align: center;">{}</td> </tr> """.format('<b>{}</b> (<em>{}</em>)'.format(, entry.type), '{} (<em>{}</em>)'.format(entry.owner_name, entry.owner_type) if entry.owner_name is not None else '&ndash;', '{} (every {})'.format(str(entry.dt), 'step' if self.steps[float(entry.dt)] == 1 else f'{self.steps[float(entry.dt)]} steps'), entry.when, entry.order, 'yes' if else 'no') for entry in self.entries] html_code = """ <table> <thead> <tr> <th style="text-align: center;">object</th> <th style="text-align: center;">part of</th> <th style="text-align: center;">Clock dt</th> <th style="text-align: center;">when</th> <th style="text-align: center;">order</th> <th style="text-align: center;">active</th> </tr> </thead> <tbody> {rows} </tbody> </table> """.format(rows='\n'.join(rows)) return html_code
def _check_multiple_summed_updaters(objects): """ Helper function that checks whether multiple `SummedVariableUpdater` target the same target variable. Raises a `NotImplementedError` if this is the case (and problematic, i.e. not when using non-overlapping subgroups). Parameters ---------- objects : list of `BrianObject` The list of objects in the network. """ summed_targets = {} for obj in objects: if isinstance(obj, SummedVariableUpdater): if obj.target_var in summed_targets: other_target = summed_targets[obj.target_var] if == other_target: # We raise an error, even though this could be ok in # principle (e.g. two Synapses could target different # subsets of the target groups, without using subgroups) msg = (f"Multiple 'summed variables' target the " f"variable '{}' in group " f"'{}'. Use multiple variables in " f"the target group instead.") raise NotImplementedError(msg) elif ( < other_target.stop and other_target.start < # Overlapping subgroups msg = (f"Multiple 'summed variables' target the " f"variable '{}' in overlapping " f"groups '{}' and '{}'. " f"Use separate variables in the target groups instead.") raise NotImplementedError(msg) summed_targets[obj.target_var] = def _get_all_objects(objs): """ Helper function to get all objects of a 'Network' along with their corresponding ``contained_objects``. Parameters ---------- objs : Iterable List or set of objects Returns ------- all_objects : set A set of all Network's objects and respective child objects. """ all_objects = set() for obj in objs: all_objects.add(obj) all_objects |= _get_all_objects(obj.contained_objects) return all_objects
[docs]class Network(Nameable): """ Network(*objs, name='network*') The main simulation controller in Brian `Network` handles the running of a simulation. It contains a set of Brian objects that are added with `~Network.add`. The `` method actually runs the simulation. The main run loop, determining which objects get called in what order is described in detail in the notes below. The objects in the `Network` are accesible via their names, e.g. `net['neurongroup']` would return the `NeuronGroup` with this name. Parameters ---------- objs : (`BrianObject`, container), optional A list of objects to be added to the `Network` immediately, see `~Network.add`. name : str, optional An explicit name, if not specified gives an automatically generated name Notes ----- The main run loop performs the following steps: 1. Prepare the objects if necessary, see `~Network.prepare`. 2. Determine the end time of the simulation as `~Network.t`+``duration``. 3. Determine which set of clocks to update. This will be the clock with the smallest value of `~Clock.t`. If there are several with the same value, then all objects with these clocks will be updated simultaneously. Set `~Network.t` to the clock time. 4. If the `~Clock.t` value of these clocks is past the end time of the simulation, stop running. If the `Network.stop` method or the `stop` function have been called, stop running. Set `~Network.t` to the end time of the simulation. 5. For each object whose `~BrianObject.clock` is set to one of the clocks from the previous steps, call the `~BrianObject.update` method. This method will not be called if the `` flag is set to ``False``. The order in which the objects are called is described below. 6. Increase `Clock.t` by `Clock.dt` for each of the clocks and return to step 2. The order in which the objects are updated in step 4 is determined by the `Network.schedule` and the objects `~BrianObject.when` and `~BrianObject.order` attributes. The `~Network.schedule` is a list of string names. Each `~BrianObject.when` attribute should be one of these strings, and the objects will be updated in the order determined by the schedule. The default schedule is ``['start', 'groups', 'thresholds', 'synapses', 'resets', 'end']``. In addition to the names provided in the schedule, automatic names starting with ``before_`` and ``after_`` can be used. That means that all objects with ``when=='before_start'`` will be updated first, then those with ``when=='start'``, ``when=='after_start'``, ``when=='before_groups'``, ``when=='groups'`` and so forth. If several objects have the same `~BrianObject.when` attribute, then the order is determined by the `~BrianObject.order` attribute (lower first). See Also -------- MagicNetwork, run, stop """ def __init__(self, *objs, **kwds): #: The set of objects in the Network, should not normally be modified #: directly. #: Note that in a `MagicNetwork`, this attribute only contains the #: objects during a run: it is filled in `before_run` and emptied in #: `after_run` self.objects = set() name = kwds.pop('name', 'network*') if kwds: raise TypeError("Only keyword argument to Network is 'name'.") Nameable.__init__(self, name=name) #: Current time as a float self.t_ = 0.0 for obj in objs: self.add(obj) #: Stored state of objects (store/restore) self._stored_state = {} # Stored profiling information (if activated via the keyword option) self._profiling_info = None self._schedule = None t = property(fget=lambda self: Quantity(self.t_, dim=second.dim, copy=False), doc=""" Current simulation time in seconds (`Quantity`) """)
[docs] @device_override('network_get_profiling_info') def get_profiling_info(self): """ The only reason this is not directly implemented in `profiling_info` is to allow devices (e.g. `CPPStandaloneDevice`) to overwrite this. """ if self._profiling_info is None: raise ValueError("No profiling info collected (did you run with " "'profile=True?')") return sorted(self._profiling_info, key=lambda item: item[1], reverse=True)
@property def profiling_info(self): """ The time spent in executing the various `CodeObject` s. A list of ``(name, time)`` tuples, containing the name of the `CodeObject` and the total execution time for simulations of this object (as a `Quantity` with unit `second`). The list is sorted descending with execution time. Profiling has to be activated using the ``profile`` keyword in `run` or ``. """ return self.get_profiling_info() _globally_stopped = False def __getitem__(self, item): if not isinstance(item, str): raise TypeError(f"Need a name to access objects in a Network, " f"got {type(item)} instead") all_objects = _get_all_objects(self.objects) for obj in all_objects: if == item: return obj raise KeyError(f'No object with name "{item}" found') def __delitem__(self, key): if not isinstance(key, str): raise TypeError("Need a name to access objects in a Network, " "got {type(key)} instead") for obj in self.objects: if == key: self.remove(obj) return raise KeyError(f"No object with name '{key}' found") def __contains__(self, item): all_objects = _get_all_objects(self.objects) for obj in all_objects: if == item: return True return False def __len__(self): all_objects = _get_all_objects(self.objects) return len(all_objects) def __iter__(self): all_objects = _get_all_objects(self.objects) return iter(all_objects)
[docs] def add(self, *objs): """ Add objects to the `Network` Parameters ---------- objs : (`BrianObject`, container) The `BrianObject` or container of Brian objects to be added. Specify multiple objects, or lists (or other containers) of objects. Containers will be added recursively. If the container is a `dict` then it will add the values from the dictionary but not the keys. If you want to add the keys, do ``add(objs.keys())``. """ for obj in objs: if isinstance(obj, BrianObject): if obj._network is not None: raise RuntimeError(f"{} has already been simulated, cannot " f"add it to the network. If you were " f"trying to remove and add an object to " f"temporarily stop it from being run, " f"set its active flag to False instead.") self.objects.add(obj) else: # allow adding values from dictionaries if isinstance(obj, Mapping): self.add(*list(obj.values())) else: try: for o in obj: # The following "if" looks silly but avoids an infinite # recursion if a string is provided as an argument # (which might occur during testing) if o is obj: raise TypeError() self.add(o) except TypeError: raise TypeError("Can only add objects of type BrianObject, " "or containers of such objects to Network")
[docs] def remove(self, *objs): """ Remove an object or sequence of objects from a `Network`. Parameters ---------- objs : (`BrianObject`, container) The `BrianObject` or container of Brian objects to be removed. Specify multiple objects, or lists (or other containers) of objects. Containers will be removed recursively. """ for obj in objs: if isinstance(obj, BrianObject): self.objects.remove(obj) else: try: for o in obj: self.remove(o) except TypeError: raise TypeError("Can only remove objects of type " "BrianObject, or containers of such " "objects from Network")
def _full_state(self): all_objects = _get_all_objects(self.objects) state = {} for obj in all_objects: if hasattr(obj, '_full_state'): state[] = obj._full_state() clocks = {obj.clock for obj in all_objects} for clock in clocks: state[] = clock._full_state() # Store the time as "0_t" -- this name is guaranteed not to clash with # the name of an object as names are not allowed to start with a digit state['0_t'] = self.t_ return state
[docs] @device_override('network_store') def store(self, name='default', filename=None): """ store(name='default', filename=None) Store the state of the network and all included objects. Parameters ---------- name : str, optional A name for the snapshot, if not specified uses ``'default'``. filename : str, optional A filename where the state should be stored. If not specified, the state will be stored in memory. Notes ----- The state stored to disk can be restored with the `Network.restore` function. Note that it will only restore the *internal state* of all the objects (including undelivered spikes) -- the objects have to exist already and they need to have the same name as when they were stored. Equations, thresholds, etc. are *not* stored -- this is therefore not a general mechanism for object serialization. Also, the format of the file is not guaranteed to work across platforms or versions. If you are interested in storing the state of a network for documentation or analysis purposes use `Network.get_states` instead. """ clocks = {obj.clock for obj in _get_all_objects(self.objects)} # Make sure that all clocks are up to date for clock in clocks: clock._set_t_update_dt(target_t=self.t) state = self._full_state() # Store the state of the random number generator dev = get_device() state['_random_generator_state'] = dev.get_random_state() if filename is None: self._stored_state[name] = state else: # A single file can contain several states, so we'll read in the # existing file first if it exists if os.path.exists(filename): with open(filename, 'rb') as f: store_state = pickle.load(f) else: store_state = {} store_state[name] = state with open(filename, 'wb') as f: pickle.dump(store_state, f, protocol=pickle.HIGHEST_PROTOCOL)
[docs] @device_override('network_restore') def restore(self, name='default', filename=None, restore_random_state=False): """ restore(name='default', filename=None, restore_random_state=False) Retore the state of the network and all included objects. Parameters ---------- name : str, optional The name of the snapshot to restore, if not specified uses ``'default'``. filename : str, optional The name of the file from where the state should be restored. If not specified, it is expected that the state exist in memory (i.e. `` was previously called without the ``filename`` argument). restore_random_state : bool, optional Whether to restore the state of the random number generator. If set to ``True``, going back to an earlier state of the simulation will continue exactly where it left off, even if the simulation is stochastic. If set to ``False`` (the default), random numbers are independent between runs (except for explicitly set random seeds), regardless of whether `store`/`restore` has been used or not. Note that this also restores numpy's random number generator (since it is used internally by Brian), but it does *not* restore Python's builtin random number generator in the ``random`` module. """ all_objects = _get_all_objects(self.objects) if filename is None: state = self._stored_state[name] else: with open(filename, 'rb') as f: state = pickle.load(f)[name] self.t_ = state['0_t'] if restore_random_state: dev = get_device() dev.set_random_state(state['_random_generator_state']) clocks = {obj.clock for obj in all_objects} restored_objects = set() for obj in all_objects: if in state: obj._restore_from_full_state(state[]) restored_objects.add( elif hasattr(obj, '_restore_from_full_state'): raise KeyError(f"Stored state does not have a stored state for " f"'{}'. Note that the names of all objects have " f"to be identical to the names when they were " f"stored.") for clock in clocks: clock._restore_from_full_state(state[]) clock_names = { for c in clocks} unnused = (set(state.keys()) - restored_objects - clock_names - {'0_t', '_random_generator_state'}) if len(unnused): raise KeyError(f"The stored state contains the state of the " f"following objects which were not present in the " f"network: {', '.join(unnused)}. Note that the names of all " f"objects have to be identical to the names when they " f"were stored.")
[docs] def get_states(self, units=True, format='dict', subexpressions=False, read_only_variables=True, level=0): """ Return a copy of the current state variable values of objects in the network.. The returned arrays are copies of the actual arrays that store the state variable values, therefore changing the values in the returned dictionary will not affect the state variables. Parameters ---------- vars : list of str, optional The names of the variables to extract. If not specified, extract all state variables (except for internal variables, i.e. names that start with ``'_'``). If the ``subexpressions`` argument is ``True``, the current values of all subexpressions are returned as well. units : bool, optional Whether to include the physical units in the return value. Defaults to ``True``. format : str, optional The output format. Defaults to ``'dict'``. subexpressions: bool, optional Whether to return subexpressions when no list of variable names is given. Defaults to ``False``. This argument is ignored if an explicit list of variable names is given in ``vars``. read_only_variables : bool, optional Whether to return read-only variables (e.g. the number of neurons, the time, etc.). Setting it to ``False`` will assure that the returned state can later be used with `set_states`. Defaults to ``True``. level : int, optional How much higher to go up the stack to resolve external variables. Only relevant if extracting subexpressions that refer to external variables. Returns ------- values : dict A dictionary mapping object names to the state variables of that object, in the specified ``format``. See Also -------- VariableOwner.get_states """ states = dict() for obj in self.sorted_objects: if hasattr(obj, 'get_states'): states[] = obj.get_states(vars=None, units=units, format=format, subexpressions=subexpressions, read_only_variables=read_only_variables, level=level+1) return states
[docs] def set_states(self, values, units=True, format='dict', level=0): """ Set the state variables of objects in the network. Parameters ---------- values : dict A dictionary mapping object names to objects of ``format``, setting the states of this object. units : bool, optional Whether the ``values`` include physical units. Defaults to ``True``. format : str, optional The format of ``values``. Defaults to ``'dict'`` level : int, optional How much higher to go up the stack to _resolve external variables. Only relevant when using string expressions to set values. See Also -------- Group.set_states """ # For the moment, 'dict' is the only supported format -- later this will # be made into an extensible system, see github issue #306 for obj_name, obj_values in values.items(): if obj_name not in self: raise KeyError(("Network does not include a network with " "name '%s'.") % obj_name) self[obj_name].set_states(obj_values, units=units, format=format, level=level+1)
def _get_schedule(self): if self._schedule is None: return list( else: return list(self._schedule) def _set_schedule(self, schedule): if schedule is None: self._schedule = None logger.debug('Resetting network {} schedule to ' 'default schedule') else: if (not isinstance(schedule, Sequence) or not all(isinstance(slot, str) for slot in schedule)): raise TypeError("Schedule has to be None or a sequence of " "scheduling slots") if any(slot.startswith('before_') or slot.startswith('after_') for slot in schedule): raise ValueError("Slot names are not allowed to start with " "'before_' or 'after_' -- such slot names " "are created automatically based on the " "existing slot names.") self._schedule = list(schedule) logger.debug(f"Setting network '{}' schedule to {self._schedule}", "_set_schedule") schedule = property(fget=_get_schedule, fset=_set_schedule, doc=""" List of ``when`` slots in the order they will be updated, can be modified. See notes on scheduling in `Network`. Note that additional ``when`` slots can be added, but the schedule should contain at least all of the names in the default schedule: ``['start', 'groups', 'thresholds', 'synapses', 'resets', 'end']``. The schedule can also be set to ``None``, resetting it to the default schedule set by the `` preference. """) @property def sorted_objects(self): """ The sorted objects of this network in the order defined by the schedule. Objects are sorted first by their ``when`` attribute, and secondly by the ``order`` attribute. The order of the ``when`` attribute is defined by the ``schedule``. In addition to the slot names defined in the schedule, automatic slot names starting with ``before_`` and ``after_`` can be used (e.g. the slots ``['groups', 'thresholds']`` allow to use ``['before_groups', 'groups', 'after_groups', 'before_thresholds', 'thresholds', 'after_thresholds']``). Final ties are resolved using the objects' names, leading to an arbitrary but deterministic sorting. """ # Provided slot names are assigned positions 1, 4, 7, ... # before_... names are assigned positions 0, 3, 6, ... # after_... names are assigned positions 2, 5, 8, ... all_objects = _get_all_objects(self.objects) when_to_int = dict((when, 1+i*3) for i, when in enumerate(self.schedule)) when_to_int.update((f"before_{when}", i*3) for i, when in enumerate(self.schedule)) when_to_int.update((f"after_{when}", 2+i*3) for i, when in enumerate(self.schedule)) return sorted(all_objects, key=lambda obj: (when_to_int[obj.when], obj.order,
[docs] def scheduling_summary(self): """ Return a `SchedulingSummary` object, representing the scheduling information for all objects included in the network. Returns ------- summary : `SchedulingSummary` Object representing the scheduling information. """ return SchedulingSummary(self.sorted_objects)
[docs] def check_dependencies(self): all_objects = _get_all_objects(self.objects) all_ids = [ for obj in all_objects] for obj in all_objects: for dependency in obj._dependencies: if not dependency in all_ids: raise ValueError(f"'{}' has been included in the network " f"but not the object on which it " f"depends.")
[docs] @device_override('network_before_run') def before_run(self, run_namespace): """ before_run(namespace) Prepares the `Network` for a run. Objects in the `Network` are sorted into the correct running order, and their `BrianObject.before_run` methods are called. Parameters ---------- run_namespace : dict-like, optional A namespace in which objects which do not define their own namespace will be run. """ all_objects = self.sorted_objects prefs.check_all_validated() # Check names in the network for uniqueness names = [ for obj in all_objects] non_unique_names = [name for name, count in Counter(names).items() if count > 1] if len(non_unique_names): formatted_names = ', '.join(f"'{name}'" for name in non_unique_names) raise ValueError(f"All objects in a network need to have unique " f"names, the following name(s) were used more than " f"once: {formatted_names}") # Check that there are no SummedVariableUpdaters targeting the same # target variable _check_multiple_summed_updaters(all_objects) self._stopped = False Network._globally_stopped = False device = get_device() if device.network_schedule is not None: # The device defines a fixed network schedule if device.network_schedule != self.schedule: # TODO: The human-readable name of a device should be easier to get device_name = list(all_devices.keys())[list(all_devices.values()).index(device)] logger.warn((f"The selected device '{device_name}' only " f"supports a fixed schedule, but this schedule is " f"not consistent with the network's schedule. The " f"simulation will use the device's schedule.\n" f"Device schedule: {device.network_schedule}\n" f"Network schedule: {self.schedule}\n" f"Set the network schedule explicitly or set the " f" preference to " f"avoid this warning."), name_suffix='schedule_conflict', once=True) objnames = ', '.join( for obj in all_objects) logger.debug(f"Preparing network '{}' with {len(all_objects)} " f"objects: {objnames}", "before_run") self.check_dependencies() for obj in all_objects: if try: obj.before_run(run_namespace) except Exception as ex: raise BrianObjectException("An error occurred when preparing an object.", obj) from ex # Check that no object has been run as part of another network before for obj in all_objects: if obj._network is None: obj._network = elif obj._network != raise RuntimeError(f"'{}' has already been run in the " f"context of another network. Use " f"add/remove to change the objects " f"in a simulated network instead of " f"creating a new one.") clocknames = ', '.join(f"{} (dt={obj.dt})" for obj in self._clocks) logger.debug(f"Network '{}' uses {len(self._clocks)} " f"clocks: {clocknames}", "before_run")
[docs] @device_override('network_after_run') def after_run(self): """ after_run() """ for obj in self.sorted_objects: if obj.after_run()
def _nextclocks(self): clocks_times_dt = [(c, self._clock_variables[c][1][0], self._clock_variables[c][2][0]) for c in self._clocks] minclock, min_time, minclock_dt = min(clocks_times_dt, key=lambda k: k[1]) curclocks = {clock for clock, time, dt in clocks_times_dt if (time == min_time or abs(time - min_time)/min(minclock_dt, dt) < Clock.epsilon_dt)} return minclock, curclocks @device_override('network_run') @check_units(duration=second, report_period=second) def run(self, duration, report=None, report_period=10*second, namespace=None, profile=None, level=0): """ run(duration, report=None, report_period=60*second, namespace=None, level=0) Runs the simulation for the given duration. Parameters ---------- duration : `Quantity` The amount of simulation time to run for. report : {None, 'text', 'stdout', 'stderr', function}, optional How to report the progress of the simulation. If ``None``, do not report progress. If ``'text'`` or ``'stdout'`` is specified, print the progress to stdout. If ``'stderr'`` is specified, print the progress to stderr. Alternatively, you can specify a callback ``callable(elapsed, completed, start, duration)`` which will be passed the amount of time elapsed as a `Quantity`, the fraction ``completed`` from 0.0 to 1.0, the ``start`` time of the simulation as a `Quantity` and the total duration of the simulation (in biological time) as a `Quantity`. The function will always be called at the beginning and the end (i.e. for fractions 0.0 and 1.0), regardless of the ``report_period``. report_period : `Quantity` How frequently (in real time) to report progress. namespace : dict-like, optional A namespace that will be used in addition to the group-specific namespaces (if defined). If not specified, the locals and globals around the run function will be used. profile : bool, optional Whether to record profiling information (see `Network.profiling_info`). Defaults to ``None`` (which will use the value set by ``set_device``, if any). level : int, optional How deep to go up the stack frame to look for the locals/global (see `namespace` argument). Only used by run functions that call this run function, e.g. `` to adjust for the additional nesting. Notes ----- The simulation can be stopped by calling `Network.stop` or the global `stop` function. """ device = get_device() # Do not use the ProxyDevice -- slightly faster if profile is None: profile = device.build_options.get('profile', False) all_objects = self.sorted_objects self._clocks = {obj.clock for obj in all_objects} single_clock = len(self._clocks) == 1 t_start = self.t t_end = self.t + duration if single_clock: clock = list(self._clocks)[0] clock.set_interval(self.t, t_end) else: # We get direct references to the underlying variables for all clocks # to avoid expensive access during the run loop self._clock_variables = {c : (c.variables['timestep'].get_value(), c.variables['t'].get_value(), c.variables['dt'].get_value()) for c in self._clocks} for clock in self._clocks: clock.set_interval(self.t, t_end) # Get the local namespace if namespace is None: namespace = get_local_namespace(level=level+3) self.before_run(namespace) if len(all_objects) == 0: return # TODO: raise an error? warning? start_time = time.time() logger.debug(f"Simulating network '{}' from time {t_start} to {t_end}.", 'run') if report is not None: report_period = float(report_period) next_report_time = start_time + report_period if report == 'text' or report == 'stdout': report_callback = TextReport(sys.stdout) elif report == 'stderr': report_callback = TextReport(sys.stderr) elif isinstance(report, str): raise ValueError(f'Do not know how to handle report argument "{report}".') elif callable(report): report_callback = report else: raise TypeError(f"Do not know how to handle report argument, " f"it has to be one of 'text', 'stdout', " f"'stderr', or a callable function/object, " f"but it is of type {type(report)}") report_callback(0*second, 0.0, t_start, duration) profiling_info = defaultdict(float) if single_clock: timestep, t, dt = (clock.variables['timestep'].get_value(), clock.variables['t'].get_value(), clock.variables['dt'].get_value()) else: # Find the first clock to be updated (see note below) clock, curclocks = self._nextclocks() timestep, _, _ = self._clock_variables[clock] running = timestep[0] < clock._i_end active_objects = [obj for obj in all_objects if] while running and not self._stopped and not Network._globally_stopped: if not single_clock: timestep, t, dt = self._clock_variables[clock] # update the network time to this clock's time self.t_ = t[0] if report is not None: current = time.time() if current > next_report_time: report_callback((current-start_time)*second, (self.t_ - float(t_start))/float(t_end - t_start), t_start, duration) next_report_time = current + report_period # update the objects and tick forward the clock(s) if single_clock: if profile: for obj in active_objects: obj_time = time.time() profiling_info[] += (time.time() - obj_time) else: for obj in active_objects: timestep[0] += 1 t[0] = timestep[0] * dt[0] else: if profile: for obj in active_objects: if obj._clock in curclocks: obj_time = time.time() profiling_info[] += (time.time() - obj_time) else: for obj in active_objects: if obj._clock in curclocks: for c in curclocks: timestep, t, dt = self._clock_variables[c] timestep[0] += 1 t[0] = timestep[0] * dt[0] # find the next clocks to be updated. The < operator for Clock # determines that the first clock to be updated should be the one # with the smallest t value, unless there are several with the # same t value in which case we update all of them clock, curclocks = self._nextclocks() timestep, _, _ = self._clock_variables[clock] if device._maximum_run_time is not None and time.time()-start_time>float(device._maximum_run_time): self._stopped = True else: running = timestep[0] < clock._i_end end_time = time.time() if self._stopped or Network._globally_stopped: self.t_ = clock.t_ else: self.t_ = float(t_end) device._last_run_time = end_time-start_time if duration>0: device._last_run_completed_fraction = (self.t-t_start)/duration else: device._last_run_completed_fraction = 1.0 # check for nans for obj in all_objects: if isinstance(obj, Group): obj._check_for_invalid_states() if report is not None: report_callback((end_time-start_time)*second, 1.0, t_start, duration) self.after_run() logger.debug(f"Finished simulating network '{}' " f"(took {end_time-start_time:.2f}s)", 'run') # Store profiling info (or erase old info to avoid confusion) if profile: self._profiling_info = [(name, t*second) for name, t in profiling_info.items()] # Dump a profiling summary to the log logger.debug(f"\n{str(profiling_summary(self))}") else: self._profiling_info = None
[docs] @device_override('network_stop') def stop(self): """ stop() Stops the network from running, this is reset the next time `` is called. """ self._stopped = True
def __repr__(self): objects = ', '.join((obj.__repr__() for obj in _get_all_objects(self.objects))) return (f"<{self.__class__.__name__} at time t={self.t!s}, containing " f"objects: {objects}>")
[docs]class ProfilingSummary(object): """ Class to nicely display the results of profiling. Objects of this class are returned by `profiling_summary`. Parameters ---------- net : `Network` The `Network` object to profile. show : int, optional The number of results to show (the longest results will be shown). If not specified, all results will be shown. See Also -------- Network.profiling_info """ def __init__(self, net, show=None): prof = net.profiling_info if len(prof): names, times = list(zip(*prof)) else: # Can happen if a network has been run for 0ms # Use a dummy entry to prevent problems with empty lists later names = ['no code objects have been run'] times = [0*second] self.total_time = sum(times) self.time_unit = msecond if self.total_time>1*second: self.time_unit = second if show is not None: names = names[:show] times = times[:show] if self.total_time>0*second: self.percentages = [100.0*time/self.total_time for time in times] else: self.percentages = [0. for _ in times] self.names_maxlen = max(len(name) for name in names) self.names = [name+' '*(self.names_maxlen-len(name)) for name in names] self.times = times def __repr__(self): times = [f'{time / self.time_unit:.2f} {self.time_unit}' for time in self.times] times_maxlen = max(len(time) for time in times) times = [' '*(times_maxlen-len(time))+time for time in times] percentages = [f'{percentage:.2f} %' for percentage in self.percentages] percentages_maxlen = max(len(percentage) for percentage in percentages) percentages = [(' '*(percentages_maxlen-len(percentage)))+percentage for percentage in percentages] s = 'Profiling summary' s += f"\n{'=' * len(s)}\n" for name, time, percentage in zip(self.names, times, percentages): s += f'{name} {time} {percentage}\n' return s def _repr_html_(self): times = [f'{time / self.time_unit:.2f} {self.time_unit}' for time in self.times] percentages = [f'{percentage:.2f} %' for percentage in self.percentages] s = '<h2 class="brian_prof_summary_header">Profiling summary</h2>\n' s += '<table class="brian_prof_summary_table">\n' for name, time, percentage in zip(self.names, times, percentages): s += '<tr>' s += f'<td>{name}</td>' s += f'<td style="text-align: right">{time}</td>' s += f'<td style="text-align: right">{percentage}</td>' s += '</tr>\n' s += '</table>' return s
[docs]def profiling_summary(net=None, show=None): """ Returns a `ProfilingSummary` of the profiling info for a run. This object can be transformed to a string explicitly but on an interactive console simply calling `profiling_summary` is enough since it will automatically convert the `ProfilingSummary` object. Parameters ---------- net : {`Network`, None} optional The `Network` object to profile, or `magic_network` if not specified. show : int The number of results to show (the longest results will be shown). If not specified, all results will be shown. """ if net is None: from .magic import magic_network net = magic_network return ProfilingSummary(net, show)
[docs]def scheduling_summary(net=None): """ Returns a `SchedulingSummary` object, representing the scheduling information for all objects included in the given `Network` (or the "magic" network, if none is specified). The returned objects can be printed or converted to a string to give an ASCII table representation of the schedule. In a Jupyter notebook, the output can be displayed as a HTML table. Parameters ---------- net : `Network`, optional The network for which the scheduling information should be displayed. Defaults to the "magic" network. Returns ------- summary : `SchedulingSummary` An object that represents the scheduling information. """ if net is None: from .magic import magic_network magic_network._update_magic_objects(level=1) net = magic_network return net.scheduling_summary()
[docs]def schedule_propagation_offset(net=None): """ Returns the minimal time difference for a post-synaptic effect after a spike. With the default schedule, this time difference is 0, since the ``thresholds`` slot precedes the ``synapses`` slot. For the GeNN device, however, a post-synaptic effect will occur in the following time step, this function therefore returns one ``dt``. Parameters ---------- net : `Network` The network to check (uses the magic network if not specified). Returns ------- offset : `Quantity` The minimum spike propagation delay: ``0*ms`` for the standard schedule but ``dt`` for schedules where ``synapses`` precedes ``thresholds``. Notes ----- This function always returns ``0*ms`` or ``defaultclock.dt`` -- no attempt is made to deal with other clocks. """ from brian2.core.magic import magic_network device = get_device() if device.network_schedule is not None: schedule = device.network_schedule else: if net is None: net = magic_network schedule = net.schedule if schedule.index('thresholds') < schedule.index('synapses'): return 0*second else: return defaultclock.dt