Source code for brian2.groups.neurongroup

"""
This model defines the `NeuronGroup`, the core of most simulations.
"""
import numbers
import string
from collections.abc import MutableMapping, Sequence

import numpy as np
import sympy
from pyparsing import Word

from brian2.codegen.translation import analyse_identifiers
from brian2.core.preferences import prefs
from brian2.core.spikesource import SpikeSource
from brian2.core.variables import (
    DynamicArrayVariable,
    LinkedVariable,
    Subexpression,
    Variables,
)
from brian2.equations.equations import (
    DIFFERENTIAL_EQUATION,
    PARAMETER,
    SUBEXPRESSION,
    Equations,
    check_subexpressions,
    extract_constant_subexpressions,
)
from brian2.equations.refractory import add_refractoriness
from brian2.parsing.expressions import (
    is_boolean_expression,
    parse_expression_dimensions,
)
from brian2.stateupdaters.base import StateUpdateMethod
from brian2.units.allunits import second
from brian2.units.fundamentalunits import (
    DIMENSIONLESS,
    DimensionMismatchError,
    Quantity,
    fail_for_dimension_mismatch,
)
from brian2.utils.logger import get_logger
from brian2.utils.stringtools import get_identifiers

from .group import CodeRunner, Group, get_dtype
from .subgroup import Subgroup

__all__ = ["NeuronGroup"]

logger = get_logger(__name__)


IDENTIFIER = Word(
    f"{string.ascii_letters}_", f"{string.ascii_letters + string.digits}_"
).setResultsName("identifier")


def _valid_event_name(event_name):
    """
    Helper function to check whether a name is a valid name for an event.

    Parameters
    ----------
    event_name : str
        The name to check

    Returns
    -------
    is_valid : bool
        Whether the given name is valid
    """
    parse_result = list(IDENTIFIER.scanString(event_name))

    # parse_result[0][0][0] refers to the matched string -- this should be the
    # full identifier, if not it is an illegal identifier like "3foo" which only
    # matched on "foo"
    return len(parse_result) == 1 and parse_result[0][0][0] == event_name


def _guess_membrane_potential(equations):
    """
    Little helper function to guess which variable represents the membrane
    potential. This follows the same logic as in Brian1 but is only used to
    give a suggestion in the error message when a Brian1-style syntax is used
    for threshold or reset.
    """
    if len(equations) == 1:
        return list(equations.keys())[0]
    for name, eq in equations.items():
        if name in ["V", "v", "Vm", "vm"]:
            return name

    # nothing found
    return None


# Note that we do not register this function with
# Equations.register_identifier_check, because we do not want this check to
# apply unconditionally to all equation objects ("x_post = ... : ... (summed)"
# needs to be allowed)
[docs]def check_identifier_pre_post(identifier): "Do not allow names ending in ``_pre`` or ``_post`` to avoid confusion." if identifier.endswith("_pre") or identifier.endswith("_post"): raise ValueError( f"'{identifier}' cannot be used as a variable name, the " "'_pre' and '_post' suffixes are used to refer to pre- and " "post-synaptic variables in synapses." )
[docs]def to_start_stop(item, N): """ Helper function to transform a single number, a slice or an array of contiguous indices to a start and stop value. This is used to allow for some flexibility in the syntax of specifying subgroups in `.NeuronGroup` and `.SpatialNeuron`. Parameters ---------- item : slice, int or sequence The slice, index, or sequence of indices to use. Note that a sequence of indices has to be a sorted ascending sequence of subsequent integers. N : int The total number of elements in the group. Returns ------- start : int The start value of the slice. stop : int The stop value of the slice. Examples -------- >>> from brian2.groups.neurongroup import to_start_stop >>> to_start_stop(slice(3, 6), 10) (3, 6) >>> to_start_stop(slice(3, None), 10) (3, 10) >>> to_start_stop(5, 10) (5, 6) >>> to_start_stop([3, 4, 5], 10) (3, 6) >>> to_start_stop([3, 5, 7], 10) Traceback (most recent call last): ... IndexError: Subgroups can only be constructed using contiguous indices. """ if isinstance(item, slice): start, stop, step = item.indices(N) elif isinstance(item, numbers.Integral): start = item stop = item + 1 step = 1 elif isinstance(item, (Sequence, np.ndarray)) and not isinstance(item, str): if not (len(item) > 0 and np.all(np.diff(item) == 1)): raise IndexError( "Subgroups can only be constructed using contiguous indices." ) if not np.issubdtype(np.asarray(item).dtype, np.integer): raise TypeError("Subgroups can only be constructed using integer values.") start = int(item[0]) stop = int(item[-1]) + 1 step = 1 else: raise TypeError( "Subgroups can only be constructed using slicing " "syntax, a single index, or an array of contiguous " "indices." ) if step != 1: raise IndexError("Subgroups have to be contiguous") if start >= stop: raise IndexError( f"Illegal start/end values for subgroup, {int(start)}>={int(stop)}" ) if start >= N: raise IndexError(f"Illegal start value for subgroup, {int(start)}>={int(N)}") if stop > N: raise IndexError(f"Illegal stop value for subgroup, {int(stop)}>{int(N)}") if start < 0: raise IndexError("Indices have to be positive.") return start, stop
[docs]class StateUpdater(CodeRunner): """ The `CodeRunner` that updates the state variables of a `NeuronGroup` at every timestep. """ def __init__(self, group, method, method_options=None): self.method_choice = method self.method_options = method_options CodeRunner.__init__( self, group, "stateupdate", code="", # will be set in update_abstract_code clock=group.clock, when="groups", order=group.order, name=f"{group.name}_stateupdater", check_units=False, generate_empty_code=False, ) def _get_refractory_code(self, run_namespace): ref = self.group._refractory if ref is False: # No refractoriness abstract_code = "" elif isinstance(ref, Quantity): fail_for_dimension_mismatch( ref, second, "Refractory period has to " "be specified in units " "of seconds but got " "{value}", value=ref, ) if prefs.legacy.refractory_timing: abstract_code = f"not_refractory = (t - lastspike) > {ref:f}\n" else: abstract_code = ( f"not_refractory = timestep(t - lastspike, dt) >= timestep({ref:f}," " dt)\n" ) else: identifiers = get_identifiers(ref) variables = self.group.resolve_all( identifiers, run_namespace, user_identifiers=identifiers ) dims = parse_expression_dimensions(str(ref), variables) if dims is second.dim: if prefs.legacy.refractory_timing: abstract_code = f"(t - lastspike) > {ref}\n" else: abstract_code = ( "not_refractory = timestep(t - lastspike, dt) >=" f" timestep({ref}, dt)\n" ) elif dims is DIMENSIONLESS: if not is_boolean_expression(str(ref), variables): raise TypeError( "Refractory expression is dimensionless " "but not a boolean value. It needs to " "either evaluate to a timespan or to a " "boolean value." ) # boolean condition # we have to be a bit careful here, we can't just use the given # condition as it is, because we only want to *leave* # refractoriness, based on the condition abstract_code = f"not_refractory = not_refractory or not ({ref})\n" else: raise TypeError( "Refractory expression has to evaluate to a " "timespan or a boolean value, expression" f"'{ref}' has units {dims} instead" ) return abstract_code
[docs] def update_abstract_code(self, run_namespace): # Update the not_refractory variable for the refractory period mechanism self.abstract_code = self._get_refractory_code(run_namespace=run_namespace) # Get the names used in the refractory code _, used_known, unknown = analyse_identifiers( self.abstract_code, self.group.variables, recursive=True ) # Get all names used in the equations (and always get "dt") names = self.group.equations.names external_names = self.group.equations.identifiers | {"dt"} variables = self.group.resolve_all( used_known | unknown | names | external_names, run_namespace, # we don't need to raise any warnings # for the user here, warnings will # be raised in create_runner_codeobj user_identifiers=set(), ) if len(self.group.equations.diff_eq_names) > 0: stateupdate_output = StateUpdateMethod.apply_stateupdater( self.group.equations, variables, self.method_choice, method_options=self.method_options, group_name=self.group.name, ) if isinstance(stateupdate_output, str): self.abstract_code += stateupdate_output else: # Note that the reason to send self along with this method is so the StateUpdater # can be modified! i.e. in GSL StateUpdateMethod a custom CodeObject gets added # to the StateUpdater together with some auxiliary information self.abstract_code += stateupdate_output(self) user_code = "\n".join( [ f"{var} = {expr}" for var, expr in self.group.equations.get_substituted_expressions( variables ) ] ) self.user_code = user_code
[docs]class SubexpressionUpdater(CodeRunner): """ The `CodeRunner` that updates the state variables storing the values of subexpressions that have been marked as "constant over dt". """ def __init__(self, group, subexpressions, when="before_start"): code_lines = [] for subexpr in subexpressions.ordered: code_lines.append(f"{subexpr.varname} = {subexpr.expr}") code = "\n".join(code_lines) CodeRunner.__init__( self, group, "stateupdate", code=code, # will be set in update_abstract_code clock=group.clock, when=when, order=group.order, name=f"{group.name}_subexpression_update*", )
[docs]class Thresholder(CodeRunner): """ The `CodeRunner` that applies the threshold condition to the state variables of a `NeuronGroup` at every timestep and sets its ``spikes`` and ``refractory_until`` attributes. """ def __init__(self, group, when="thresholds", event="spike"): self.event = event if group._refractory is False or event != "spike": template_kwds = {"_uses_refractory": False} needed_variables = [] else: template_kwds = {"_uses_refractory": True} needed_variables = ["t", "not_refractory", "lastspike"] # Since this now works for general events not only spikes, we have to # pass the information about which variable to use to the template, # it can not longer simply refer to "_spikespace" eventspace_name = f"_{event}space" template_kwds["eventspace_variable"] = group.variables[eventspace_name] needed_variables.append(eventspace_name) self.variables = Variables(self) self.variables.add_auxiliary_variable("_cond", dtype=bool) CodeRunner.__init__( self, group, "threshold", code="", # will be set in update_abstract_code clock=group.clock, when=when, order=group.order, name=f"{group.name}_{event}_thresholder", needed_variables=needed_variables, template_kwds=template_kwds, )
[docs] def update_abstract_code(self, run_namespace): code = self.group.events[self.event] # Raise a useful error message when the user used a Brian1 syntax if not isinstance(code, str): if isinstance(code, Quantity): t = "a quantity" else: t = f"{type(code)}" error_msg = f"Threshold condition has to be a string, not {t}." if self.event == "spike": try: vm_var = _guess_membrane_potential(self.group.equations) except AttributeError: # not a group with equations... vm_var = None if vm_var is not None: error_msg += f" Probably you intended to use '{vm_var} > ...'?" raise TypeError(error_msg) self.user_code = f"_cond = {code}" identifiers = get_identifiers(code) variables = self.group.resolve_all( identifiers, run_namespace, user_identifiers=identifiers ) if not is_boolean_expression(code, variables): raise TypeError(f"Threshold condition '{code}' is not a boolean expression") if self.group._refractory is False or self.event != "spike": self.abstract_code = f"_cond = {code}" else: self.abstract_code = f"_cond = ({code}) and not_refractory"
[docs]class Resetter(CodeRunner): """ The `CodeRunner` that applies the reset statement(s) to the state variables of neurons that have spiked in this timestep. """ def __init__(self, group, when="resets", order=None, event="spike"): self.event = event # Since this now works for general events not only spikes, we have to # pass the information about which variable to use to the template, # it can not longer simply refer to "_spikespace" eventspace_name = f"_{event}space" template_kwds = {"eventspace_variable": group.variables[eventspace_name]} needed_variables = [eventspace_name] order = order if order is not None else group.order CodeRunner.__init__( self, group, "reset", code="", # will be set in update_abstract_code clock=group.clock, when=when, order=order, name=f"{group.name}_{event}_resetter", override_conditional_write=["not_refractory"], needed_variables=needed_variables, template_kwds=template_kwds, )
[docs] def update_abstract_code(self, run_namespace): code = self.group.event_codes[self.event] # Raise a useful error message when the user used a Brian1 syntax if not isinstance(code, str): if isinstance(code, Quantity): t = "a quantity" else: t = f"{type(code)}" error_msg = f"Reset statement has to be a string, not {t}." if self.event == "spike": vm_var = _guess_membrane_potential(self.group.equations) if vm_var is not None: error_msg += f" Probably you intended to use '{vm_var} = ...'?" raise TypeError(error_msg) self.abstract_code = code
[docs]class NeuronGroup(Group, SpikeSource): """ A group of neurons. Parameters ---------- N : int Number of neurons in the group. model : str, `Equations` The differential equations defining the group method : (str, function), optional The numerical integration method. Either a string with the name of a registered method (e.g. "euler") or a function that receives an `Equations` object and returns the corresponding abstract code. If no method is specified, a suitable method will be chosen automatically. threshold : str, optional The condition which produces spikes. Should be a single line boolean expression. reset : str, optional The (possibly multi-line) string with the code to execute on reset. refractory : {str, `Quantity`}, optional Either the length of the refractory period (e.g. ``2*ms``), a string expression that evaluates to the length of the refractory period after each spike (e.g. ``'(1 + rand())*ms'``), or a string expression evaluating to a boolean value, given the condition under which the neuron stays refractory after a spike (e.g. ``'v > -20*mV'``) events : dict, optional User-defined events in addition to the "spike" event defined by the ``threshold``. Has to be a mapping of strings (the event name) to strings (the condition) that will be checked. namespace: dict, optional A dictionary mapping identifier names to objects. If not given, the namespace will be filled in at the time of the call of `Network.run`, with either the values from the ``namespace`` argument of the `Network.run` method or from the local context, if no such argument is given. dtype : (`dtype`, `dict`), optional The `numpy.dtype` that will be used to store the values, or a dictionary specifying the type for variable names. If a value is not provided for a variable (or no value is provided at all), the preference setting `core.default_float_dtype` is used. codeobj_class : class, optional The `CodeObject` class to run code with. dt : `Quantity`, optional The time step to be used for the simulation. Cannot be combined with the `clock` argument. clock : `Clock`, optional The update clock to be used. If neither a clock, nor the `dt` argument is specified, the `defaultclock` will be used. order : int, optional The priority of of this group for operations occurring at the same time step and in the same scheduling slot. Defaults to 0. name : str, optional A unique name for the group, otherwise use ``neurongroup_0``, etc. Notes ----- `NeuronGroup` contains a `StateUpdater`, `Thresholder` and `Resetter`, and these are run at the 'groups', 'thresholds' and 'resets' slots (i.e. the values of their `when` attribute take these values). The `order` attribute will be passed down to the contained objects but can be set individually by setting the `order` attribute of the `state_updater`, `thresholder` and `resetter` attributes, respectively. """ add_to_magic_network = True def __init__( self, N, model, method=("exact", "euler", "heun"), method_options=None, threshold=None, reset=None, refractory=False, events=None, namespace=None, dtype=None, dt=None, clock=None, order=0, name="neurongroup*", codeobj_class=None, ): Group.__init__( self, dt=dt, clock=clock, when="start", order=order, namespace=namespace, name=name, ) if dtype is None: dtype = {} if isinstance(dtype, MutableMapping): dtype["lastspike"] = self._clock.variables["t"].dtype self.codeobj_class = codeobj_class try: self._N = N = int(N) except ValueError: if isinstance(N, str): raise TypeError( "First NeuronGroup argument should be size, not equations." ) raise if N < 1: raise ValueError(f"NeuronGroup size should be at least 1, was {str(N)}") self.start = 0 self.stop = self._N ##### Prepare and validate equations if isinstance(model, str): model = Equations(model) if not isinstance(model, Equations): raise TypeError( "model has to be a string or an Equations " f"object, is '{type(model)}' instead." ) # Check flags model.check_flags( { DIFFERENTIAL_EQUATION: ("unless refractory",), PARAMETER: ("constant", "shared", "linked"), SUBEXPRESSION: ("shared", "constant over dt"), } ) # add refractoriness #: The original equations as specified by the user (i.e. without #: the multiplied `int(not_refractory)` term for equations marked as #: `(unless refractory)`) self.user_equations = model if refractory is not False: model = add_refractoriness(model) uses_refractoriness = len(model) and any( [ "unless refractory" in eq.flags for eq in model.values() if eq.type == DIFFERENTIAL_EQUATION ] ) # Separate subexpressions depending whether they are considered to be # constant over a time step or not model, constant_over_dt = extract_constant_subexpressions(model) self.equations = model self._linked_variables = set() logger.diagnostic( f"Creating NeuronGroup of size {self._N}, equations {self.equations}." ) # All of the following will be created in before_run #: The refractory condition or timespan self._refractory = refractory if uses_refractoriness and refractory is False: logger.warn( 'Model equations use the "unless refractory" flag but ' "no refractory keyword was given.", "no_refractory", ) #: The state update method selected by the user self.method_choice = method if events is None: events = {} if threshold is not None and (reset is None and refractory is False): if not ("rand(" in threshold or "randn(" in threshold): logger.warn( f"The NeuronGroup '{self.name}' sets a threshold but " "neither a reset condition nor a refractory " "condition has been set. Did you forget either of " "those? If this was intended, set the reset " "argument to an empty string in order to avoid " "this warning.", name_suffix="only_threshold", ) if threshold is not None: if "spike" in events: raise ValueError( "The NeuronGroup defines both a threshold and a 'spike' event" ) events["spike"] = threshold # Setup variables # Since we have to create _spikespace and possibly other "eventspace" # variables, we pass the supported events self._create_variables(dtype, events=list(events.keys())) #: Events supported by this group self.events = events #: Code that is triggered on events (e.g. reset) self.event_codes = {} #: Checks the spike threshold (or abitrary user-defined events) self.thresholder = {} #: Reset neurons which have spiked (or perform arbitrary actions for #: user-defined events) self.resetter = {} for event_name in events.keys(): if not isinstance(event_name, str): raise TypeError( "Keys in the 'events' dictionary have to be " f"strings, not type {event_name}." ) if not _valid_event_name(event_name): raise TypeError( f"The name '{event_name}' cannot be used as an event name." ) # By default, user-defined events are checked after the threshold when = "thresholds" if event_name == "spike" else "after_thresholds" # creating a Thresholder will take care of checking the validity # of the condition thresholder = Thresholder(self, event=event_name, when=when) self.thresholder[event_name] = thresholder self.contained_objects.append(thresholder) if reset is not None: self.run_on_event("spike", reset, when="resets") #: Performs numerical integration step self.state_updater = StateUpdater(self, method, method_options) self.contained_objects.append(self.state_updater) #: Update the "constant over a time step" subexpressions self.subexpression_updater = None if len(constant_over_dt): self.subexpression_updater = SubexpressionUpdater(self, constant_over_dt) self.contained_objects.append(self.subexpression_updater) if refractory is not False: # Set the refractoriness information self.variables["lastspike"].set_value(-1e4 * second) self.variables["not_refractory"].set_value(True) # Activate name attribute access self._enable_group_attributes() @property def spikes(self): """ The spikes returned by the most recent thresholding operation. """ # Note that we have to directly access the ArrayVariable object here # instead of using the Group mechanism by accessing self._spikespace # Using the latter would cut _spikespace to the length of the group spikespace = self.variables["_spikespace"].get_value() return spikespace[: spikespace[-1]]
[docs] def state(self, name, use_units=True, level=0): try: return Group.state(self, name, use_units=use_units, level=level + 1) except KeyError as ex: if name in self._linked_variables: raise TypeError(f"Link target for variable {name} has not been set.") else: raise ex
[docs] def run_on_event(self, event, code, when="after_resets", order=None): """ Run code triggered by a custom-defined event (see `NeuronGroup` documentation for the specification of events).The created `Resetter` object will be automatically added to the group, it therefore does not need to be added to the network manually. However, a reference to the object will be returned, which can be used to later remove it from the group or to set it to inactive. Parameters ---------- event : str The name of the event that should trigger the code code : str The code that should be executed when : str, optional The scheduling slot that should be used to execute the code. Defaults to `'after_resets'`. See :ref:`scheduling` for possible values. order : int, optional The order for operations in the same scheduling slot. Defaults to the order of the `NeuronGroup`. Returns ------- obj : `Resetter` A reference to the object that will be run. """ if event not in self.events: error_message = f"Unknown event '{event}'." if event == "spike": error_message += " Did you forget to define a threshold?" raise ValueError(error_message) if event in self.resetter: raise ValueError( "Cannot add code for event '%s', code for this " "event has already been added." % event ) self.event_codes[event] = code resetter = Resetter(self, when=when, order=order, event=event) self.resetter[event] = resetter self.contained_objects.append(resetter) return resetter
[docs] def set_event_schedule(self, event, when="after_thresholds", order=None): """ Change the scheduling slot for checking the condition of an event. Parameters ---------- event : str The name of the event for which the scheduling should be changed when : str, optional The scheduling slot that should be used to check the condition. Defaults to `'after_thresholds'`. See :ref:`scheduling` for possible values. order : int, optional The order for operations in the same scheduling slot. Defaults to the order of the `NeuronGroup`. """ if event not in self.thresholder: raise ValueError(f"Unknown event '{event}'.") order = order if order is not None else self.order self.thresholder[event].when = when self.thresholder[event].order = order
def __setattr__(self, key, value): # attribute access is switched off until this attribute is created by # _enable_group_attributes if not hasattr(self, "_group_attribute_access_active") or key in self.__dict__: object.__setattr__(self, key, value) elif key in self._linked_variables: if not isinstance(value, LinkedVariable): raise ValueError( "Cannot set a linked variable directly, link " "it to another variable using 'linked_var'." ) linked_var = value.variable if isinstance(linked_var, DynamicArrayVariable): raise NotImplementedError( f"Linking to variable {linked_var.name} is " "not supported, can only link to " "state variables of fixed size." ) eq = self.equations[key] if eq.dim is not linked_var.dim: raise DimensionMismatchError( f"Unit of variable '{key}' does not " "match its link target " f"'{linked_var.name}'" ) if not isinstance(linked_var, Subexpression): var_length = len(linked_var) else: var_length = len(linked_var.owner) if value.index is not None: try: index_array = np.asarray(value.index) if not np.issubsctype(index_array.dtype, int): raise TypeError() except TypeError: raise TypeError( "The index for a linked variable has to be an integer array" ) size = len(index_array) source_index = value.group.variables.indices[value.name] if source_index not in ("_idx", "0"): # we are indexing into an already indexed variable, # calculate the indexing into the target variable index_array = value.group.variables[source_index].get_value()[ index_array ] if not index_array.ndim == 1 or size != len(self): raise TypeError( f"Index array for linked variable '{key}' " "has to be a one-dimensional array of " f"length {len(self)}, but has shape " f"{index_array.shape!s}" ) if min(index_array) < 0 or max(index_array) >= var_length: raise ValueError( f"Index array for linked variable {key} " "contains values outside of the valid " f"range [0, {var_length}[" ) self.variables.add_array( f"_{key}_indices", size=size, dtype=index_array.dtype, constant=True, read_only=True, values=index_array, ) index = f"_{key}_indices" else: if linked_var.scalar or (var_length == 1 and self._N != 1): index = "0" else: index = value.group.variables.indices[value.name] if index == "_idx": target_length = var_length else: target_length = len(value.group.variables[index]) # we need a name for the index that does not clash with # other names and a reference to the index new_index = f"_{value.name}_index_{index}" self.variables.add_reference(new_index, value.group, index) index = new_index if len(self) != target_length: raise ValueError( f"Cannot link variable '{key}' to " f"'{linked_var.name}', the size of the " "target group does not match " f"({len(self)} != {target_length}). You can " "provide an indexing scheme with the " "'index' keyword to link groups with " "different sizes" ) self.variables.add_reference(key, value.group, value.name, index=index) source = (value.variable.owner.name,) sourcevar = value.variable.name log_msg = f"Setting {self.name}.{key} as a link to {source}.{sourcevar}" if index is not None: log_msg += f'(using "{index}" as index variable)' logger.diagnostic(log_msg) else: if isinstance(value, LinkedVariable): raise TypeError( f"Cannot link variable '{key}', it has to be marked " "as a linked variable with '(linked)' in the model " "equations." ) else: Group.__setattr__(self, key, value, level=1) def __getitem__(self, item): start, stop = to_start_stop(item, self._N) return Subgroup(self, start, stop) def _create_variables(self, user_dtype, events): """ Create the variables dictionary for this `NeuronGroup`, containing entries for the equation variables and some standard entries. """ self.variables = Variables(self) self.variables.add_constant("N", self._N) # Standard variables always present for event in events: self.variables.add_array( f"_{event}space", size=self._N + 1, dtype=np.int32, constant=False ) # Add the special variable "i" which can be used to refer to the neuron index self.variables.add_arange("i", size=self._N, constant=True, read_only=True) # Add the clock variables self.variables.create_clock_variables(self._clock) for eq in self.equations.values(): dtype = get_dtype(eq, user_dtype) check_identifier_pre_post(eq.varname) if eq.type in (DIFFERENTIAL_EQUATION, PARAMETER): if "linked" in eq.flags: # 'linked' cannot be combined with other flags if not len(eq.flags) == 1: raise SyntaxError( "The 'linked' flag cannot be combined with other flags" ) self._linked_variables.add(eq.varname) else: constant = "constant" in eq.flags shared = "shared" in eq.flags size = 1 if shared else self._N self.variables.add_array( eq.varname, size=size, dimensions=eq.dim, dtype=dtype, constant=constant, scalar=shared, ) elif eq.type == SUBEXPRESSION: self.variables.add_subexpression( eq.varname, dimensions=eq.dim, expr=str(eq.expr), dtype=dtype, scalar="shared" in eq.flags, ) else: raise AssertionError(f"Unknown type of equation: {eq.eq_type}") # Add the conditional-write attribute for variables with the # "unless refractory" flag if self._refractory is not False: for eq in self.equations.values(): if eq.type == DIFFERENTIAL_EQUATION and "unless refractory" in eq.flags: not_refractory_var = self.variables["not_refractory"] var = self.variables[eq.varname] var.set_conditional_write(not_refractory_var) # Stochastic variables for xi in self.equations.stochastic_variables: self.variables.add_auxiliary_variable(xi, dimensions=(second**-0.5).dim) # Check scalar subexpressions for eq in self.equations.values(): if eq.type == SUBEXPRESSION and "shared" in eq.flags: var = self.variables[eq.varname] for identifier in var.identifiers: if identifier in self.variables: if not self.variables[identifier].scalar: raise SyntaxError( f"Shared subexpression '{eq.varname}' " "refers to non-shared variable " f"'{identifier}'." )
[docs] def before_run(self, run_namespace=None): # Check units self.equations.check_units(self, run_namespace=run_namespace) # Check that subexpressions that refer to stateful functions are labeled # as "constant over dt" check_subexpressions(self, self.equations, run_namespace) super(NeuronGroup, self).before_run(run_namespace=run_namespace)
def _repr_html_(self): text = [rf"NeuronGroup '{self.name}' with {self._N} neurons.<br>"] text.append(r"<b>Model:</b><nr>") text.append(sympy.latex(self.equations)) def add_event_to_text(event): if event == "spike": event_header = "Spiking behaviour" event_condition = "Threshold condition" event_code = "Reset statement(s)" else: event_header = f'Event "{event}"' event_condition = "Event condition" event_code = "Executed statement(s)" condition = self.events[event] text.append( rf'<b>{event_header}:</b><ul style="list-style-type: none; margin-top:' r' 0px;">' ) text.append(rf"<li><i>{event_condition}: </i>") text.append(f"<code>{str(condition)}</code></li>") statements = self.event_codes.get(event, None) if statements is not None: text.append(rf"<li><i>{event_code}:</i>") if "\n" in str(statements): text.append("</br>") text.append(rf"<code>{str(statements)}</code></li>") text.append("</ul>") if "spike" in self.events: add_event_to_text("spike") for event in self.events: if event != "spike": add_event_to_text(event) return "\n".join(text)