def __init__(self, morphology=None, model=None, threshold=None, refractory=False, reset=None, threshold_location=None, dt=None, clock=None, order=0, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm, name='spatialneuron*', dtype=None, namespace=None, method=('linear', 'exponential_euler', 'rk2', 'milstein')): # #### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Insert the threshold mechanism at the specified location if threshold_location is not None: if hasattr(threshold_location, '_indices'): # assuming this is a method threshold_location = threshold_location._indices() # for now, only a single compartment allowed if len(threshold_location) == 1: threshold_location = threshold_location[0] else: raise AttributeError(('Threshold can only be applied on a ' 'single location')) threshold = '(' + threshold + ') and (i == ' + str(threshold_location) + ')' # Check flags (we have point currents) model.check_flags({DIFFERENTIAL_EQUATION: ('point current',), PARAMETER: ('constant', 'shared', 'linked', 'point current'), SUBEXPRESSION: ('shared', 'point current')}) # Add the membrane potential model += Equations(''' v:volt # membrane potential ''') # Extract membrane equation if 'Im' in model: membrane_eq = model['Im'] # the membrane equation else: raise TypeError('The transmembrane current Im must be defined') # Insert point currents in the membrane equation for eq in model.itervalues(): if 'point current' in eq.flags: fail_for_dimension_mismatch(eq.unit, amp, "Point current " + eq.varname + " should be in amp") eq.flags.remove('point current') membrane_eq.expr = Expression( str(membrane_eq.expr.code) + '+' + eq.varname + '/area') ###### Process model equations (Im) to extract total conductance and the remaining current # Check conditional linearity with respect to v # Match to _A*v+_B var = sp.Symbol('v', real=True) wildcard = sp.Wild('_A', exclude=[var]) constant_wildcard = sp.Wild('_B', exclude=[var]) pattern = wildcard * var + constant_wildcard # Expand expressions in the membrane equation membrane_eq.type = DIFFERENTIAL_EQUATION for var, expr in model._get_substituted_expressions(): # this returns substituted expressions for diff eqs if var == 'Im': Im_expr = expr membrane_eq.type = SUBEXPRESSION # Factor out the variable s_expr = sp.collect(Im_expr.sympy_expr.expand(), var) matches = s_expr.match(pattern) if matches is None: raise TypeError, "The membrane current must be linear with respect to v" a, b = (matches[wildcard], matches[constant_wildcard]) # Extracts the total conductance from Im, and the remaining current minusa_str, b_str = sympy_to_str(-a), sympy_to_str(b) # Add correct units if necessary if minusa_str == '0': minusa_str += '*siemens/meter**2' if b_str == '0': b_str += '*amp/meter**2' gtot_str = "gtot__private=" + minusa_str + ": siemens/meter**2" I0_str = "I0__private=" + b_str + ": amp/meter**2" model += Equations(gtot_str + "\n" + I0_str) # Equations for morphology # TODO: check whether Cm and Ri are already in the equations # no: should be shared instead of constant # yes: should be constant (check) eqs_constants = Equations(""" diameter : meter (constant) length : meter (constant) x : meter (constant) y : meter (constant) z : meter (constant) distance : meter (constant) area : meter**2 (constant) Cm : farad/meter**2 (constant) Ri : ohm*meter (constant, shared) space_constant = (diameter/(4*Ri*gtot__private))**.5 : meter # Not so sure about the name ### Parameters and intermediate variables for solving the cable equation ab_star0 : siemens/meter**2 ab_plus0 : siemens/meter**2 ab_minus0 : siemens/meter**2 ab_star1 : siemens/meter**2 ab_plus1 : siemens/meter**2 ab_minus1 : siemens/meter**2 ab_star2 : siemens/meter**2 ab_plus2 : siemens/meter**2 ab_minus2 : siemens/meter**2 b_plus : siemens/meter**2 b_minus : siemens/meter**2 v_star : volt u_plus : 1 u_minus : 1 """) # Possibilities for the name: characteristic_length, electrotonic_length, length_constant, space_constant # Insert morphology self.morphology = morphology # Link morphology variables to neuron's state variables self.morphology_data = MorphologyData(len(morphology)) self.morphology.compress(self.morphology_data) NeuronGroup.__init__(self, len(morphology), model=model + eqs_constants, threshold=threshold, refractory=refractory, reset=reset, method=method, dt=dt, clock=clock, order=order, namespace=namespace, dtype=dtype, name=name) self.Cm = Cm self.Ri = Ri # TODO: View instead of copy for runtime? self.diameter_ = self.morphology_data.diameter self.distance_ = self.morphology_data.distance self.length_ = self.morphology_data.length self.area_ = self.morphology_data.area self.x_ = self.morphology_data.x self.y_ = self.morphology_data.y self.z_ = self.morphology_data.z # Performs numerical integration step self.add_attribute('diffusion_state_updater') self.diffusion_state_updater = SpatialStateUpdater(self, method, clock=self.clock, order=order) # Creation of contained_objects that do the work self.contained_objects.extend([self.diffusion_state_updater])
def __init__(self, source, target=None, model=None, pre=None, post=None, connect=False, delay=None, namespace=None, dtype=None, codeobj_class=None, clock=None, method=None, name='synapses*'): self._N = 0 Group.__init__(self, when=clock, name=name) self.codeobj_class = codeobj_class self.source = weakref.proxy(source) if target is None: self.target = self.source else: self.target = weakref.proxy(target) ##### Prepare and validate equations if model is None: model = '' if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({DIFFERENTIAL_EQUATION: ['event-driven'], STATIC_EQUATION: ['summed'], PARAMETER: ['constant']}) # Separate the equations into event-driven and continuously updated # equations event_driven = [] continuous = [] for single_equation in model.itervalues(): if 'event-driven' in single_equation.flags: event_driven.append(single_equation) else: continuous.append(single_equation) # Add the lastupdate variable, used by event-driven equations continuous.append(SingleEquation(PARAMETER, 'lastupdate', second)) if len(event_driven): self.event_driven = Equations(event_driven) else: self.event_driven = None self.equations = Equations(continuous) # Setup the namespace self._given_namespace = namespace self.namespace = create_namespace(namespace) self._queues = {} self._delays = {} # Setup variables self._create_variables() #: Set of `Variable` objects that should be resized when the #: number of synapses changes self._registered_variables = set() for varname, var in self.variables.iteritems(): if isinstance(var, DynamicArrayVariable): # Register the array with the `SynapticItemMapping` object so # it gets automatically resized self.register_variable(var) #: List of names of all updaters, e.g. ['pre', 'post'] self._synaptic_updaters = [] #: List of all `SynapticPathway` objects self._pathways = [] for prepost, argument in zip(('pre', 'post'), (pre, post)): if not argument: continue if isinstance(argument, basestring): self._add_updater(argument, prepost) elif isinstance(argument, collections.Mapping): for key, value in argument.iteritems(): if not isinstance(key, basestring): err_msg = ('Keys for the "{}" argument' 'have to be strings, got ' '{} instead.').format(prepost, type(key)) raise TypeError(err_msg) self._add_updater(value, prepost, objname=key) # If we have a pathway called "pre" (the most common use case), provide # direct access to its delay via a delay attribute (instead of having # to use pre.delay) if 'pre' in self._synaptic_updaters: self.variables.add_reference('delay', self.pre.variables['delay']) if delay is not None: if isinstance(delay, Quantity): if not 'pre' in self._synaptic_updaters: raise ValueError(('Cannot set delay, no "pre" pathway exists.' 'Use a dictionary if you want to set the ' 'delay for a pathway with a different name.')) delay = {'pre': delay} if not isinstance(delay, collections.Mapping): raise TypeError('Delay argument has to be a quantity or a ' 'dictionary, is type %s instead.' % type(delay)) for pathway, pathway_delay in delay.iteritems(): if not pathway in self._synaptic_updaters: raise ValueError(('Cannot set the delay for pathway ' '"%s": unknown pathway.') % pathway) if not isinstance(pathway_delay, Quantity): raise TypeError(('Cannot set the delay for pathway "%s": ' 'expected a quantity, got %s instead.') % (pathway, type(pathway_delay))) if pathway_delay.size != 1: raise TypeError(('Cannot set the delay for pathway "%s": ' 'expected a scalar quantity, got a ' 'quantity with shape %s instead.') % str(pathway_delay.shape)) fail_for_dimension_mismatch(pathway_delay, second, ('Delay has to be ' 'specified in units ' 'of seconds')) updater = getattr(self, pathway) # For simplicity, store the delay as a one-element array # so that for example updater._delays[:] works. updater._delays.resize(1) updater._delays.set_value(float(pathway_delay)) updater._delays.scalar = True # Do not resize the scalar delay variable when adding synapses self.unregister_variable(updater._delays) #: Performs numerical integration step self.state_updater = StateUpdater(self, method) self.contained_objects.append(self.state_updater) #: "Summed variable" mechanism -- sum over all synapses of a #: pre-/postsynaptic target self.summed_updaters = {} # We want to raise an error if the same variable is updated twice # using this mechanism. This could happen if the Synapses object # connected a NeuronGroup to itself since then all variables are # accessible as var_pre and var_post. summed_targets = set() for single_equation in self.equations.itervalues(): if 'summed' in single_equation.flags: varname = single_equation.varname if not (varname.endswith('_pre') or varname.endswith('_post')): raise ValueError(('The summed variable "%s" does not end ' 'in "_pre" or "_post".') % varname) if not varname in self.variables: raise ValueError(('The summed variable "%s" does not refer' 'do any known variable in the ' 'target group.') % varname) if varname.endswith('_pre'): summed_target = self.source orig_varname = varname[:-4] else: summed_target = self.target orig_varname = varname[:-5] target_eq = getattr(summed_target, 'equations', {}).get(orig_varname, None) if target_eq is None or target_eq.type != PARAMETER: raise ValueError(('The summed variable "%s" needs a ' 'corresponding parameter "%s" in the ' 'target group.') % (varname, orig_varname)) fail_for_dimension_mismatch(self.variables['_summed_'+varname].unit, self.variables[varname].unit, ('Summed variables need to have ' 'the same units in Synapses ' 'and the target group')) if self.variables[varname] in summed_targets: raise ValueError(('The target variable "%s" is already ' 'updated by another summed ' 'variable') % orig_varname) summed_targets.add(self.variables[varname]) updater = SummedVariableUpdater(single_equation.expr, varname, self, summed_target) self.summed_updaters[varname] = updater self.contained_objects.append(updater) # Do an initial connect, if requested if not isinstance(connect, (bool, basestring)): raise TypeError(('"connect" keyword has to be a boolean value or a ' 'string, is type %s instead.' % type(connect))) self._initial_connect = connect if not connect is False: self.connect(connect, level=1) # Activate name attribute access self._enable_group_attributes()
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, name=name) if dtype is None: dtype = {} if isinstance(dtype, collections.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("NeuronGroup size should be at least 1, was " + str(N)) self.start = 0 self.stop = self._N ##### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # 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("Creating NeuronGroup of size {self._N}, " "equations {self.equations}.".format(self=self)) if namespace is None: namespace = {} #: The group-specific namespace self.namespace = namespace # 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: 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, basestring): raise TypeError(('Keys in the "events" dictionary have to be ' 'strings, not type %s.') % type(event_name)) if not _valid_event_name(event_name): raise TypeError(("The name '%s' cannot be used as an event " "name.") % 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()
def __init__(self, N, model, method=('linear', 'euler', 'heun'), 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, name=name) 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("NeuronGroup size should be at least 1, was " + str(N)) self.start = 0 self.stop = self._N ##### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({DIFFERENTIAL_EQUATION: ('unless refractory',), PARAMETER: ('constant', 'shared', 'linked'), SUBEXPRESSION: ('shared',)}) # add refractoriness if refractory is not False: model = add_refractoriness(model) self.equations = model uses_refractoriness = len(model) and any(['unless refractory' in eq.flags for eq in model.itervalues() if eq.type == DIFFERENTIAL_EQUATION]) self._linked_variables = set() logger.debug("Creating NeuronGroup of size {self._N}, " "equations {self.equations}.".format(self=self)) if namespace is None: namespace = {} #: The group-specific namespace self.namespace = namespace # 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: 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=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.iterkeys(): if not isinstance(event_name, basestring): raise TypeError(('Keys in the "events" dictionary have to be ' 'strings, not type %s.') % type(event_name)) if not _valid_event_name(event_name): raise TypeError(("The name '%s' cannot be used as an event " "name.") % 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') # We try to run a before_run already now. This might fail because of an # incomplete namespace but if the namespace is already complete we # can spot unit errors in the equation already here. try: self.before_run(None) except KeyError: pass #: Performs numerical integration step self.state_updater = StateUpdater(self, method) # Creation of contained_objects that do the work self.contained_objects.append(self.state_updater) if refractory is not False: # Set the refractoriness information self.variables['lastspike'].set_value(-np.inf*second) self.variables['not_refractory'].set_value(True) # Activate name attribute access self._enable_group_attributes()
def __init__(self, N, model, method=None, threshold=None, reset=None, refractory=False, namespace=None, dtype=None, clock=None, name='neurongroup*', codeobj_class=None): Group.__init__(self, when=clock, name=name) 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("NeuronGroup size should be at least 1, was " + str(N)) self.start = 0 self.stop = self._N ##### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({DIFFERENTIAL_EQUATION: ('unless refractory'), PARAMETER: ('constant')}) # add refractoriness if refractory is not False: model = add_refractoriness(model) self.equations = model uses_refractoriness = len(model) and any(['unless refractory' in eq.flags for eq in model.itervalues() if eq.type == DIFFERENTIAL_EQUATION]) logger.debug("Creating NeuronGroup of size {self._N}, " "equations {self.equations}.".format(self=self)) # Setup the namespace self.namespace = create_namespace(namespace) # Setup variables self._create_variables(dtype) # All of the following will be created in before_run #: The threshold condition self.threshold = threshold #: The reset statement(s) self.reset = reset #: 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 #: Performs thresholding step, sets the value of `spikes` self.thresholder = None if self.threshold is not None: self.thresholder = Thresholder(self) #: Resets neurons which have spiked (`spikes`) self.resetter = None if self.reset is not None: self.resetter = Resetter(self) # We try to run a before_run already now. This might fail because of an # incomplete namespace but if the namespace is already complete we # can spot unit errors in the equation already here. try: self.before_run(None) except KeyError: pass #: Performs numerical integration step self.state_updater = StateUpdater(self, method) # Creation of contained_objects that do the work self.contained_objects.append(self.state_updater) if self.thresholder is not None: self.contained_objects.append(self.thresholder) if self.resetter is not None: self.contained_objects.append(self.resetter) if refractory is not False: # Set the refractoriness information self.variables['lastspike'].set_value(-np.inf*second) self.variables['not_refractory'].set_value(True) # Activate name attribute access self._enable_group_attributes()
def __init__(self, N, model, method=None, threshold=None, reset=None, refractory=False, namespace=None, dtype=None, clock=None, name='neurongroup*', codeobj_class=None): BrianObject.__init__(self, when=clock, name=name) 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("NeuronGroup size should be at least 1, was " + str(N)) ##### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({ DIFFERENTIAL_EQUATION: ('unless-refractory'), PARAMETER: ('constant') }) # add refractoriness model = add_refractoriness(model) self.equations = model uses_refractoriness = len(model) and any([ 'unless-refractory' in eq.flags for eq in model.itervalues() if eq.type == DIFFERENTIAL_EQUATION ]) logger.debug("Creating NeuronGroup of size {self.N}, " "equations {self.equations}.".format(self=self)) ##### Setup the memory self.arrays = self._allocate_memory(dtype=dtype) self._spikespace = np.zeros(N + 1, dtype=np.int32) # Setup the namespace self.namespace = create_namespace(namespace) # Setup variables self.variables = self._create_variables() # All of the following will be created in pre_run #: The threshold condition self.threshold = threshold #: The reset statement(s) self.reset = reset #: 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 #: Performs thresholding step, sets the value of `spikes` self.thresholder = None if self.threshold is not None: self.thresholder = Thresholder(self) #: Resets neurons which have spiked (`spikes`) self.resetter = None if self.reset is not None: self.resetter = Resetter(self) # We try to run a pre_run already now. This might fail because of an # incomplete namespace but if the namespace is already complete we # can spot unit or syntax errors already here, at creation time. try: self.pre_run(None) except KeyError: pass #: Performs numerical integration step self.state_updater = StateUpdater(self, method) # Creation of contained_objects that do the work self.contained_objects.append(self.state_updater) if self.thresholder is not None: self.contained_objects.append(self.thresholder) if self.resetter is not None: self.contained_objects.append(self.resetter) # Activate name attribute access Group.__init__(self) # Set the refractoriness information self.lastspike = -np.inf * second self.not_refractory = True
def __init__(self, morphology=None, model=None, threshold=None, refractory=False, reset=None, events=None, threshold_location=None, dt=None, clock=None, order=0, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm, name='spatialneuron*', dtype=None, namespace=None, method=('linear', 'exponential_euler', 'rk2', 'heun')): # #### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Insert the threshold mechanism at the specified location if threshold_location is not None: if hasattr(threshold_location, '_indices'): # assuming this is a method threshold_location = threshold_location._indices() # for now, only a single compartment allowed if len(threshold_location) == 1: threshold_location = threshold_location[0] else: raise AttributeError(('Threshold can only be applied on a ' 'single location')) threshold = '(' + threshold + ') and (i == ' + str(threshold_location) + ')' # Check flags (we have point currents) model.check_flags({DIFFERENTIAL_EQUATION: ('point current',), PARAMETER: ('constant', 'shared', 'linked', 'point current'), SUBEXPRESSION: ('shared', 'point current')}) # Add the membrane potential model += Equations(''' v:volt # membrane potential ''') # Extract membrane equation if 'Im' in model: membrane_eq = model['Im'] # the membrane equation else: raise TypeError('The transmembrane current Im must be defined') # Insert point currents in the membrane equation for eq in model.itervalues(): if 'point current' in eq.flags: fail_for_dimension_mismatch(eq.unit, amp, "Point current " + eq.varname + " should be in amp") eq.flags.remove('point current') membrane_eq.expr = Expression( str(membrane_eq.expr.code) + '+' + eq.varname + '/area') ###### Process model equations (Im) to extract total conductance and the remaining current # Check conditional linearity with respect to v # Match to _A*v+_B var = sp.Symbol('v', real=True) wildcard = sp.Wild('_A', exclude=[var]) constant_wildcard = sp.Wild('_B', exclude=[var]) pattern = wildcard * var + constant_wildcard # Expand expressions in the membrane equation membrane_eq.type = DIFFERENTIAL_EQUATION for var, expr in model.get_substituted_expressions(): if var == 'Im': Im_expr = expr membrane_eq.type = SUBEXPRESSION # Factor out the variable s_expr = sp.collect(str_to_sympy(Im_expr.code).expand(), var) matches = s_expr.match(pattern) if matches is None: raise TypeError, "The membrane current must be linear with respect to v" a, b = (matches[wildcard], matches[constant_wildcard]) # Extracts the total conductance from Im, and the remaining current minusa_str, b_str = sympy_to_str(-a), sympy_to_str(b) # Add correct units if necessary if minusa_str == '0': minusa_str += '*siemens/meter**2' if b_str == '0': b_str += '*amp/meter**2' gtot_str = "gtot__private=" + minusa_str + ": siemens/meter**2" I0_str = "I0__private=" + b_str + ": amp/meter**2" model += Equations(gtot_str + "\n" + I0_str) # Insert morphology (store a copy) self.morphology = copy.deepcopy(morphology) # Flatten the morphology self.flat_morphology = FlatMorphology(morphology) # Equations for morphology # TODO: check whether Cm and Ri are already in the equations # no: should be shared instead of constant # yes: should be constant (check) eqs_constants = Equations(""" length : meter (constant) distance : meter (constant) area : meter**2 (constant) volume : meter**3 diameter : meter (constant) Cm : farad/meter**2 (constant) Ri : ohm*meter (constant, shared) r_length_1 : meter (constant) r_length_2 : meter (constant) time_constant = Cm/gtot__private : second space_constant = (2/pi)**(1.0/3.0) * (area/(1/r_length_1 + 1/r_length_2))**(1.0/6.0) / (2*(Ri*gtot__private)**(1.0/2.0)) : meter """) if self.flat_morphology.has_coordinates: eqs_constants += Equations(''' x : meter (constant) y : meter (constant) z : meter (constant) ''') NeuronGroup.__init__(self, morphology.total_compartments, model=model + eqs_constants, threshold=threshold, refractory=refractory, reset=reset, events=events, method=method, dt=dt, clock=clock, order=order, namespace=namespace, dtype=dtype, name=name) # Parameters and intermediate variables for solving the cable equations # Note that some of these variables could have meaningful physical # units (e.g. _v_star is in volt, _I0_all is in amp/meter**2 etc.) but # since these variables should never be used in user code, we don't # assign them any units self.variables.add_arrays(['_ab_star0', '_ab_star1', '_ab_star2', '_a_minus0', '_a_minus1', '_a_minus2', '_a_plus0', '_a_plus1', '_a_plus2', '_b_plus', '_b_minus', '_v_star', '_u_plus', '_u_minus', # The following three are for solving the # three tridiag systems in parallel '_c1', '_c2', '_c3', # The following two are only necessary for # C code where we cannot deal with scalars # and arrays interchangeably: '_I0_all', '_gtot_all'], unit=1, size=self.N, read_only=True) self.Cm = Cm self.Ri = Ri # These explict assignments will load the morphology values from disk # in standalone mode self.distance_ = self.flat_morphology.distance self.length_ = self.flat_morphology.length self.area_ = self.flat_morphology.area self.diameter_ = self.flat_morphology.diameter self.r_length_1_ = self.flat_morphology.r_length_1 self.r_length_2_ = self.flat_morphology.r_length_2 if self.flat_morphology.has_coordinates: self.x_ = self.flat_morphology.x self.y_ = self.flat_morphology.y self.z_ = self.flat_morphology.z # Performs numerical integration step self.add_attribute('diffusion_state_updater') self.diffusion_state_updater = SpatialStateUpdater(self, method, clock=self.clock, order=order) # Creation of contained_objects that do the work self.contained_objects.extend([self.diffusion_state_updater])
def __init__(self, morphology=None, model=None, threshold=None, refractory=False, reset=None, threshold_location=None, dt=None, clock=None, order=0, Cm=0.9 * uF / cm**2, Ri=150 * ohm * cm, name='spatialneuron*', dtype=None, namespace=None, method=('linear', 'exponential_euler', 'rk2', 'heun')): # #### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Insert the threshold mechanism at the specified location if threshold_location is not None: if hasattr(threshold_location, '_indices'): # assuming this is a method threshold_location = threshold_location._indices() # for now, only a single compartment allowed if len(threshold_location) == 1: threshold_location = threshold_location[0] else: raise AttributeError(('Threshold can only be applied on a ' 'single location')) threshold = '(' + threshold + ') and (i == ' + str( threshold_location) + ')' # Check flags (we have point currents) model.check_flags({ DIFFERENTIAL_EQUATION: ('point current', ), PARAMETER: ('constant', 'shared', 'linked', 'point current'), SUBEXPRESSION: ('shared', 'point current') }) # Add the membrane potential model += Equations(''' v:volt # membrane potential ''') # Extract membrane equation if 'Im' in model: membrane_eq = model['Im'] # the membrane equation else: raise TypeError('The transmembrane current Im must be defined') # Insert point currents in the membrane equation for eq in model.itervalues(): if 'point current' in eq.flags: fail_for_dimension_mismatch( eq.unit, amp, "Point current " + eq.varname + " should be in amp") eq.flags.remove('point current') membrane_eq.expr = Expression( str(membrane_eq.expr.code) + '+' + eq.varname + '/area') ###### Process model equations (Im) to extract total conductance and the remaining current # Check conditional linearity with respect to v # Match to _A*v+_B var = sp.Symbol('v', real=True) wildcard = sp.Wild('_A', exclude=[var]) constant_wildcard = sp.Wild('_B', exclude=[var]) pattern = wildcard * var + constant_wildcard # Expand expressions in the membrane equation membrane_eq.type = DIFFERENTIAL_EQUATION for var, expr in model._get_substituted_expressions( ): # this returns substituted expressions for diff eqs if var == 'Im': Im_expr = expr membrane_eq.type = SUBEXPRESSION # Factor out the variable s_expr = sp.collect(Im_expr.sympy_expr.expand(), var) matches = s_expr.match(pattern) if matches is None: raise TypeError, "The membrane current must be linear with respect to v" a, b = (matches[wildcard], matches[constant_wildcard]) # Extracts the total conductance from Im, and the remaining current minusa_str, b_str = sympy_to_str(-a), sympy_to_str(b) # Add correct units if necessary if minusa_str == '0': minusa_str += '*siemens/meter**2' if b_str == '0': b_str += '*amp/meter**2' gtot_str = "gtot__private=" + minusa_str + ": siemens/meter**2" I0_str = "I0__private=" + b_str + ": amp/meter**2" model += Equations(gtot_str + "\n" + I0_str) # Equations for morphology # TODO: check whether Cm and Ri are already in the equations # no: should be shared instead of constant # yes: should be constant (check) eqs_constants = Equations(""" diameter : meter (constant) length : meter (constant) x : meter (constant) y : meter (constant) z : meter (constant) distance : meter (constant) area : meter**2 (constant) Cm : farad/meter**2 (constant) Ri : ohm*meter (constant, shared) space_constant = (diameter/(4*Ri*gtot__private))**.5 : meter # Not so sure about the name ### Parameters and intermediate variables for solving the cable equation ab_star0 : siemens/meter**2 ab_plus0 : siemens/meter**2 ab_minus0 : siemens/meter**2 ab_star1 : siemens/meter**2 ab_plus1 : siemens/meter**2 ab_minus1 : siemens/meter**2 ab_star2 : siemens/meter**2 ab_plus2 : siemens/meter**2 ab_minus2 : siemens/meter**2 b_plus : siemens/meter**2 b_minus : siemens/meter**2 v_star : volt u_plus : 1 u_minus : 1 # The following two are only necessary for C code where we cannot deal # with scalars and arrays interchangeably gtot_all : siemens/meter**2 I0_all : amp/meter**2 """) # Possibilities for the name: characteristic_length, electrotonic_length, length_constant, space_constant # Insert morphology self.morphology = morphology # Link morphology variables to neuron's state variables self.morphology_data = MorphologyData(len(morphology)) self.morphology.compress(self.morphology_data) NeuronGroup.__init__(self, len(morphology), model=model + eqs_constants, threshold=threshold, refractory=refractory, reset=reset, method=method, dt=dt, clock=clock, order=order, namespace=namespace, dtype=dtype, name=name) self.Cm = Cm self.Ri = Ri # TODO: View instead of copy for runtime? self.diameter_ = self.morphology_data.diameter self.distance_ = self.morphology_data.distance self.length_ = self.morphology_data.length self.area_ = self.morphology_data.area self.x_ = self.morphology_data.x self.y_ = self.morphology_data.y self.z_ = self.morphology_data.z # Performs numerical integration step self.add_attribute('diffusion_state_updater') self.diffusion_state_updater = SpatialStateUpdater(self, method, clock=self.clock, order=order) # Creation of contained_objects that do the work self.contained_objects.extend([self.diffusion_state_updater])
def __init__(self, source, target=None, model=None, pre=None, post=None, connect=False, delay=None, namespace=None, dtype=None, codeobj_class=None, clock=None, method=None, name='synapses*'): self._N = 0 Group.__init__(self, when=clock, name=name) self.codeobj_class = codeobj_class self.source = weakref.proxy(source) if target is None: self.target = self.source else: self.target = weakref.proxy(target) ##### Prepare and validate equations if model is None: model = '' if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({ DIFFERENTIAL_EQUATION: ['event-driven'], SUBEXPRESSION: ['summed', 'scalar'], PARAMETER: ['constant', 'scalar'] }) # Add the lastupdate variable, needed for event-driven updates if 'lastupdate' in model._equations: raise SyntaxError('lastupdate is a reserved name.') model._equations['lastupdate'] = SingleEquation( PARAMETER, 'lastupdate', second) self._create_variables(model) # Separate the equations into event-driven equations, # continuously updated equations and summed variable updates event_driven = [] continuous = [] summed_updates = [] for single_equation in model.itervalues(): if 'event-driven' in single_equation.flags: event_driven.append(single_equation) elif 'summed' in single_equation.flags: summed_updates.append(single_equation) else: continuous.append(single_equation) if len(event_driven): self.event_driven = Equations(event_driven) else: self.event_driven = None self.equations = Equations(continuous) if namespace is None: namespace = {} #: The group-specific namespace self.namespace = namespace #: Set of `Variable` objects that should be resized when the #: number of synapses changes self._registered_variables = set() for varname, var in self.variables.iteritems(): if isinstance(var, DynamicArrayVariable): # Register the array with the `SynapticItemMapping` object so # it gets automatically resized self.register_variable(var) if delay is None: delay = {} if isinstance(delay, Quantity): delay = {'pre': delay} elif not isinstance(delay, collections.Mapping): raise TypeError('Delay argument has to be a quantity or a ' 'dictionary, is type %s instead.' % type(delay)) #: List of names of all updaters, e.g. ['pre', 'post'] self._synaptic_updaters = [] #: List of all `SynapticPathway` objects self._pathways = [] for prepost, argument in zip(('pre', 'post'), (pre, post)): if not argument: continue if isinstance(argument, basestring): pathway_delay = delay.get(prepost, None) self._add_updater(argument, prepost, delay=pathway_delay) elif isinstance(argument, collections.Mapping): for key, value in argument.iteritems(): if not isinstance(key, basestring): err_msg = ('Keys for the "{}" argument' 'have to be strings, got ' '{} instead.').format(prepost, type(key)) raise TypeError(err_msg) pathway_delay = delay.get(key, None) self._add_updater(value, prepost, objname=key, delay=pathway_delay) # Check whether any delays were specified for pathways that don't exist for pathway in delay: if not pathway in self._synaptic_updaters: raise ValueError(('Cannot set the delay for pathway ' '"%s": unknown pathway.') % pathway) # If we have a pathway called "pre" (the most common use case), provide # direct access to its delay via a delay attribute (instead of having # to use pre.delay) if 'pre' in self._synaptic_updaters: self.variables.add_reference('delay', self.pre.variables['delay']) #: Performs numerical integration step self.state_updater = None # We only need a state update if we have differential equations if len(self.equations.diff_eq_names): self.state_updater = StateUpdater(self, method) self.contained_objects.append(self.state_updater) #: "Summed variable" mechanism -- sum over all synapses of a #: pre-/postsynaptic target self.summed_updaters = {} # We want to raise an error if the same variable is updated twice # using this mechanism. This could happen if the Synapses object # connected a NeuronGroup to itself since then all variables are # accessible as var_pre and var_post. summed_targets = set() for single_equation in summed_updates: varname = single_equation.varname if not (varname.endswith('_pre') or varname.endswith('_post')): raise ValueError(('The summed variable "%s" does not end ' 'in "_pre" or "_post".') % varname) if not varname in self.variables: raise ValueError(('The summed variable "%s" does not refer' 'do any known variable in the ' 'target group.') % varname) if varname.endswith('_pre'): summed_target = self.source orig_varname = varname[:-4] else: summed_target = self.target orig_varname = varname[:-5] target_eq = getattr(summed_target, 'equations', {}).get(orig_varname, None) if target_eq is None or target_eq.type != PARAMETER: raise ValueError(('The summed variable "%s" needs a ' 'corresponding parameter "%s" in the ' 'target group.') % (varname, orig_varname)) fail_for_dimension_mismatch( self.variables['_summed_' + varname].unit, self.variables[varname].unit, ('Summed variables need to have ' 'the same units in Synapses ' 'and the target group')) if self.variables[varname] in summed_targets: raise ValueError(('The target variable "%s" is already ' 'updated by another summed ' 'variable') % orig_varname) summed_targets.add(self.variables[varname]) updater = SummedVariableUpdater(single_equation.expr, varname, self, summed_target) self.summed_updaters[varname] = updater self.contained_objects.append(updater) # Do an initial connect, if requested if not isinstance(connect, (bool, basestring)): raise TypeError( ('"connect" keyword has to be a boolean value or a ' 'string, is type %s instead.' % type(connect))) self._initial_connect = connect if not connect is False: self.connect(connect, level=1) # Activate name attribute access self._enable_group_attributes()
def __init__(self, N, model, method=('linear', 'euler', 'milstein'), threshold=None, reset=None, refractory=False, 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, name=name) 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("NeuronGroup size should be at least 1, was " + str(N)) self.start = 0 self.stop = self._N ##### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({ DIFFERENTIAL_EQUATION: ('unless refractory', ), PARAMETER: ('constant', 'shared', 'linked'), SUBEXPRESSION: ('shared', ) }) # add refractoriness if refractory is not False: model = add_refractoriness(model) self.equations = model uses_refractoriness = len(model) and any([ 'unless refractory' in eq.flags for eq in model.itervalues() if eq.type == DIFFERENTIAL_EQUATION ]) self._linked_variables = set() logger.debug("Creating NeuronGroup of size {self._N}, " "equations {self.equations}.".format(self=self)) if namespace is None: namespace = {} #: The group-specific namespace self.namespace = namespace # Setup variables self._create_variables(dtype) # All of the following will be created in before_run #: The threshold condition self.threshold = threshold #: The reset statement(s) self.reset = reset #: 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 #: Performs thresholding step, sets the value of `spikes` self.thresholder = None if self.threshold is not None: self.thresholder = Thresholder(self) #: Resets neurons which have spiked (`spikes`) self.resetter = None if self.reset is not None: self.resetter = Resetter(self) # We try to run a before_run already now. This might fail because of an # incomplete namespace but if the namespace is already complete we # can spot unit errors in the equation already here. try: self.before_run(None) except KeyError: pass #: Performs numerical integration step self.state_updater = StateUpdater(self, method) # Creation of contained_objects that do the work self.contained_objects.append(self.state_updater) if self.thresholder is not None: self.contained_objects.append(self.thresholder) if self.resetter is not None: self.contained_objects.append(self.resetter) if refractory is not False: # Set the refractoriness information self.variables['lastspike'].set_value(-np.inf * second) self.variables['not_refractory'].set_value(True) # Activate name attribute access self._enable_group_attributes()
def __init__(self, morphology=None, model=None, threshold=None, refractory=False, reset=None, events=None, threshold_location=None, dt=None, clock=None, order=0, Cm=0.9 * uF / cm**2, Ri=150 * ohm * cm, name='spatialneuron*', dtype=None, namespace=None, method=('linear', 'exponential_euler', 'rk2', 'heun')): # #### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Insert the threshold mechanism at the specified location if threshold_location is not None: if hasattr(threshold_location, '_indices'): # assuming this is a method threshold_location = threshold_location._indices() # for now, only a single compartment allowed if len(threshold_location) == 1: threshold_location = threshold_location[0] else: raise AttributeError(('Threshold can only be applied on a ' 'single location')) threshold = '(' + threshold + ') and (i == ' + str( threshold_location) + ')' # Check flags (we have point currents) model.check_flags({ DIFFERENTIAL_EQUATION: ('point current', ), PARAMETER: ('constant', 'shared', 'linked', 'point current'), SUBEXPRESSION: ('shared', 'point current', 'constant over dt') }) #: The original equations as specified by the user (i.e. before #: inserting point-currents into the membrane equation, before adding #: all the internally used variables and constants, etc.). self.user_equations = model # Separate subexpressions depending whether they are considered to be # constant over a time step or not (this would also be done by the # NeuronGroup initializer later, but this would give incorrect results # for the linearity check) model, constant_over_dt = extract_constant_subexpressions(model) # Extract membrane equation if 'Im' in model: if len(model['Im'].flags): raise TypeError( 'Cannot specify any flags for the transmembrane ' 'current Im.') membrane_expr = model['Im'].expr # the membrane equation else: raise TypeError('The transmembrane current Im must be defined') model_equations = [] # Insert point currents in the membrane equation for eq in model.itervalues(): if eq.varname == 'Im': continue # ignore -- handled separately if 'point current' in eq.flags: fail_for_dimension_mismatch( eq.dim, amp, "Point current " + eq.varname + " should be in amp") membrane_expr = Expression( str(membrane_expr.code) + '+' + eq.varname + '/area') eq = SingleEquation( eq.type, eq.varname, eq.dim, expr=eq.expr, flags=list(set(eq.flags) - set(['point current']))) model_equations.append(eq) model_equations.append( SingleEquation(SUBEXPRESSION, 'Im', dimensions=(amp / meter**2).dim, expr=membrane_expr)) model_equations.append(SingleEquation(PARAMETER, 'v', volt.dim)) model = Equations(model_equations) ###### Process model equations (Im) to extract total conductance and the remaining current # Expand expressions in the membrane equation for var, expr in model.get_substituted_expressions( include_subexpressions=True): if var == 'Im': Im_expr = expr break else: raise AssertionError('Model equations did not contain Im!') # Differentiate Im with respect to v Im_sympy_exp = str_to_sympy(Im_expr.code) v_sympy = sp.Symbol('v', real=True) diffed = sp.diff(Im_sympy_exp, v_sympy) unevaled_derivatives = diffed.atoms(sp.Derivative) if len(unevaled_derivatives): raise TypeError( 'Cannot take the derivative of "{Im}" with respect ' 'to v.'.format(Im=Im_expr.code)) gtot_str = sympy_to_str(sp.simplify(-diffed)) I0_str = sympy_to_str(sp.simplify(Im_sympy_exp - diffed * v_sympy)) if gtot_str == '0': gtot_str += '*siemens/meter**2' if I0_str == '0': I0_str += '*amp/meter**2' gtot_str = "gtot__private=" + gtot_str + ": siemens/meter**2" I0_str = "I0__private=" + I0_str + ": amp/meter**2" model += Equations(gtot_str + "\n" + I0_str) # Insert morphology (store a copy) self.morphology = copy.deepcopy(morphology) # Flatten the morphology self.flat_morphology = FlatMorphology(morphology) # Equations for morphology # TODO: check whether Cm and Ri are already in the equations # no: should be shared instead of constant # yes: should be constant (check) eqs_constants = Equations(""" length : meter (constant) distance : meter (constant) area : meter**2 (constant) volume : meter**3 Ic : amp/meter**2 diameter : meter (constant) Cm : farad/meter**2 (constant) Ri : ohm*meter (constant, shared) r_length_1 : meter (constant) r_length_2 : meter (constant) time_constant = Cm/gtot__private : second space_constant = (2/pi)**(1.0/3.0) * (area/(1/r_length_1 + 1/r_length_2))**(1.0/6.0) / (2*(Ri*gtot__private)**(1.0/2.0)) : meter """) if self.flat_morphology.has_coordinates: eqs_constants += Equations(''' x : meter (constant) y : meter (constant) z : meter (constant) ''') NeuronGroup.__init__(self, morphology.total_compartments, model=model + eqs_constants, threshold=threshold, refractory=refractory, reset=reset, events=events, method=method, dt=dt, clock=clock, order=order, namespace=namespace, dtype=dtype, name=name) # Parameters and intermediate variables for solving the cable equations # Note that some of these variables could have meaningful physical # units (e.g. _v_star is in volt, _I0_all is in amp/meter**2 etc.) but # since these variables should never be used in user code, we don't # assign them any units self.variables.add_arrays( [ '_ab_star0', '_ab_star1', '_ab_star2', '_a_minus0', '_a_minus1', '_a_minus2', '_a_plus0', '_a_plus1', '_a_plus2', '_b_plus', '_b_minus', '_v_star', '_u_plus', '_u_minus', '_v_previous', # The following three are for solving the # three tridiag systems in parallel '_c1', '_c2', '_c3', # The following two are only necessary for # C code where we cannot deal with scalars # and arrays interchangeably: '_I0_all', '_gtot_all' ], size=self.N, read_only=True) self.Cm = Cm self.Ri = Ri # These explict assignments will load the morphology values from disk # in standalone mode self.distance_ = self.flat_morphology.distance self.length_ = self.flat_morphology.length self.area_ = self.flat_morphology.area self.diameter_ = self.flat_morphology.diameter self.r_length_1_ = self.flat_morphology.r_length_1 self.r_length_2_ = self.flat_morphology.r_length_2 if self.flat_morphology.has_coordinates: self.x_ = self.flat_morphology.x self.y_ = self.flat_morphology.y self.z_ = self.flat_morphology.z # Performs numerical integration step self.add_attribute('diffusion_state_updater') self.diffusion_state_updater = SpatialStateUpdater(self, method, clock=self.clock, order=order) # Update v after the gating variables to obtain consistent Ic and Im self.diffusion_state_updater.order = 1 # Creation of contained_objects that do the work self.contained_objects.extend([self.diffusion_state_updater]) if len(constant_over_dt): self.subexpression_updater = SubexpressionUpdater( self, constant_over_dt) self.contained_objects.append(self.subexpression_updater)
def __init__(self, morphology=None, model=None, threshold=None, refractory=False, reset=None, events=None, threshold_location=None, dt=None, clock=None, order=0, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm, name='spatialneuron*', dtype=None, namespace=None, method=('exact', 'exponential_euler', 'rk2', 'heun'), method_options=None): # #### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Insert the threshold mechanism at the specified location if threshold_location is not None: if hasattr(threshold_location, '_indices'): # assuming this is a method threshold_location = threshold_location._indices() # for now, only a single compartment allowed if len(threshold_location) == 1: threshold_location = threshold_location[0] else: raise AttributeError(('Threshold can only be applied on a ' 'single location')) threshold = '(' + threshold + ') and (i == ' + str(threshold_location) + ')' # Check flags (we have point currents) model.check_flags({DIFFERENTIAL_EQUATION: ('point current',), PARAMETER: ('constant', 'shared', 'linked', 'point current'), SUBEXPRESSION: ('shared', 'point current', 'constant over dt')}) #: The original equations as specified by the user (i.e. before #: inserting point-currents into the membrane equation, before adding #: all the internally used variables and constants, etc.). self.user_equations = model # Separate subexpressions depending whether they are considered to be # constant over a time step or not (this would also be done by the # NeuronGroup initializer later, but this would give incorrect results # for the linearity check) model, constant_over_dt = extract_constant_subexpressions(model) # Extract membrane equation if 'Im' in model: if len(model['Im'].flags): raise TypeError('Cannot specify any flags for the transmembrane ' 'current Im.') membrane_expr = model['Im'].expr # the membrane equation else: raise TypeError('The transmembrane current Im must be defined') model_equations = [] # Insert point currents in the membrane equation for eq in model.itervalues(): if eq.varname == 'Im': continue # ignore -- handled separately if 'point current' in eq.flags: fail_for_dimension_mismatch(eq.dim, amp, "Point current " + eq.varname + " should be in amp") membrane_expr = Expression( str(membrane_expr.code) + '+' + eq.varname + '/area') eq = SingleEquation(eq.type, eq.varname, eq.dim, expr=eq.expr, flags=list(set(eq.flags)-set(['point current']))) model_equations.append(eq) model_equations.append(SingleEquation(SUBEXPRESSION, 'Im', dimensions=(amp/meter**2).dim, expr=membrane_expr)) model_equations.append(SingleEquation(PARAMETER, 'v', volt.dim)) model = Equations(model_equations) ###### Process model equations (Im) to extract total conductance and the remaining current # Expand expressions in the membrane equation for var, expr in model.get_substituted_expressions(include_subexpressions=True): if var == 'Im': Im_expr = expr break else: raise AssertionError('Model equations did not contain Im!') # Differentiate Im with respect to v Im_sympy_exp = str_to_sympy(Im_expr.code) v_sympy = sp.Symbol('v', real=True) diffed = sp.diff(Im_sympy_exp, v_sympy) unevaled_derivatives = diffed.atoms(sp.Derivative) if len(unevaled_derivatives): raise TypeError('Cannot take the derivative of "{Im}" with respect ' 'to v.'.format(Im=Im_expr.code)) gtot_str = sympy_to_str(sp.simplify(-diffed)) I0_str = sympy_to_str(sp.simplify(Im_sympy_exp - diffed*v_sympy)) if gtot_str == '0': gtot_str += '*siemens/meter**2' if I0_str == '0': I0_str += '*amp/meter**2' gtot_str = "gtot__private=" + gtot_str + ": siemens/meter**2" I0_str = "I0__private=" + I0_str + ": amp/meter**2" model += Equations(gtot_str + "\n" + I0_str) # Insert morphology (store a copy) self.morphology = copy.deepcopy(morphology) # Flatten the morphology self.flat_morphology = FlatMorphology(morphology) # Equations for morphology # TODO: check whether Cm and Ri are already in the equations # no: should be shared instead of constant # yes: should be constant (check) eqs_constants = Equations(""" length : meter (constant) distance : meter (constant) area : meter**2 (constant) volume : meter**3 Ic : amp/meter**2 diameter : meter (constant) Cm : farad/meter**2 (constant) Ri : ohm*meter (constant, shared) r_length_1 : meter (constant) r_length_2 : meter (constant) time_constant = Cm/gtot__private : second space_constant = (2/pi)**(1.0/3.0) * (area/(1/r_length_1 + 1/r_length_2))**(1.0/6.0) / (2*(Ri*gtot__private)**(1.0/2.0)) : meter """) if self.flat_morphology.has_coordinates: eqs_constants += Equations(''' x : meter (constant) y : meter (constant) z : meter (constant) ''') NeuronGroup.__init__(self, morphology.total_compartments, model=model + eqs_constants, method_options=method_options, threshold=threshold, refractory=refractory, reset=reset, events=events, method=method, dt=dt, clock=clock, order=order, namespace=namespace, dtype=dtype, name=name) # Parameters and intermediate variables for solving the cable equations # Note that some of these variables could have meaningful physical # units (e.g. _v_star is in volt, _I0_all is in amp/meter**2 etc.) but # since these variables should never be used in user code, we don't # assign them any units self.variables.add_arrays(['_ab_star0', '_ab_star1', '_ab_star2', '_b_plus', '_b_minus', '_v_star', '_u_plus', '_u_minus', '_v_previous', '_c', # The following two are only necessary for # C code where we cannot deal with scalars # and arrays interchangeably: '_I0_all', '_gtot_all'], size=self.N, read_only=True) self.Cm = Cm self.Ri = Ri # These explict assignments will load the morphology values from disk # in standalone mode self.distance_ = self.flat_morphology.distance self.length_ = self.flat_morphology.length self.area_ = self.flat_morphology.area self.diameter_ = self.flat_morphology.diameter self.r_length_1_ = self.flat_morphology.r_length_1 self.r_length_2_ = self.flat_morphology.r_length_2 if self.flat_morphology.has_coordinates: self.x_ = self.flat_morphology.x self.y_ = self.flat_morphology.y self.z_ = self.flat_morphology.z # Performs numerical integration step self.add_attribute('diffusion_state_updater') self.diffusion_state_updater = SpatialStateUpdater(self, method, clock=self.clock, order=order) # Update v after the gating variables to obtain consistent Ic and Im self.diffusion_state_updater.order = 1 # Creation of contained_objects that do the work self.contained_objects.extend([self.diffusion_state_updater]) if len(constant_over_dt): self.subexpression_updater = SubexpressionUpdater(self, constant_over_dt) self.contained_objects.append(self.subexpression_updater)
def __init__(self, source, target=None, model=None, pre=None, post=None, connect=False, delay=None, namespace=None, dtype=None, codeobj_class=None, clock=None, method=None, name='synapses*'): BrianObject.__init__(self, when=clock, name=name) self.codeobj_class = codeobj_class self.source = weakref.proxy(source) if target is None: self.target = self.source else: self.target = weakref.proxy(target) ##### Prepare and validate equations if model is None: model = '' if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({DIFFERENTIAL_EQUATION: ['event-driven', 'lumped'], STATIC_EQUATION: ['lumped'], PARAMETER: ['constant', 'lumped']}) # Separate the equations into event-driven and continuously updated # equations event_driven = [] continuous = [] for single_equation in model.itervalues(): if 'event-driven' in single_equation.flags: if 'lumped' in single_equation.flags: raise ValueError(('Event-driven variable %s cannot be ' 'a lumped variable.') % single_equation.varname) event_driven.append(single_equation) else: continuous.append(single_equation) # Add the lastupdate variable, used by event-driven equations continuous.append(SingleEquation(PARAMETER, 'lastupdate', second)) if len(event_driven): self.event_driven = Equations(event_driven) else: self.event_driven = None self.equations = Equations(continuous) ##### Setup the memory self.arrays = self._allocate_memory(dtype=dtype) # Setup the namespace self._given_namespace = namespace self.namespace = create_namespace(namespace) self._queues = {} self._delays = {} self.item_mapping = SynapticItemMapping(self) self.indices = {'_idx': self.item_mapping, '_presynaptic_idx': self.item_mapping.synaptic_pre, '_postsynaptic_idx': self.item_mapping.synaptic_post} # Allow S.i instead of S.indices.i, etc. self.i = self.item_mapping.i self.j = self.item_mapping.j self.k = self.item_mapping.k # Setup variables self.variables = self._create_variables() #: List of names of all updaters, e.g. ['pre', 'post'] self._updaters = [] for prepost, argument in zip(('pre', 'post'), (pre, post)): if not argument: continue if isinstance(argument, basestring): self._add_updater(argument, prepost) elif isinstance(argument, collections.Mapping): for key, value in argument.iteritems(): if not isinstance(key, basestring): err_msg = ('Keys for the "{}" argument' 'have to be strings, got ' '{} instead.').format(prepost, type(key)) raise TypeError(err_msg) self._add_updater(value, prepost, objname=key) # If we have a pathway called "pre" (the most common use case), provide # direct access to its delay via a delay attribute (instead of having # to use pre.delay) if 'pre' in self._updaters: self.variables['delay'] = self.pre.variables['delay'] if delay is not None: if isinstance(delay, Quantity): if not 'pre' in self._updaters: raise ValueError(('Cannot set delay, no "pre" pathway exists.' 'Use a dictionary if you want to set the ' 'delay for a pathway with a different name.')) delay = {'pre': delay} if not isinstance(delay, collections.Mapping): raise TypeError('Delay argument has to be a quantity or a ' 'dictionary, is type %s instead.' % type(delay)) for pathway, pathway_delay in delay.iteritems(): if not pathway in self._updaters: raise ValueError(('Cannot set the delay for pathway ' '"%s": unknown pathway.') % pathway) if not isinstance(pathway_delay, Quantity): raise TypeError(('Cannot set the delay for pathway "%s": ' 'expected a quantity, got %s instead.') % (pathway, type(pathway_delay))) if pathway_delay.size != 1: raise TypeError(('Cannot set the delay for pathway "%s": ' 'expected a scalar quantity, got a ' 'quantity with shape %s instead.') % str(pathway_delay.shape)) fail_for_dimension_mismatch(pathway_delay, second, ('Delay has to be ' 'specified in units ' 'of seconds')) updater = getattr(self, pathway) self.item_mapping.unregister_variable(updater._delays) del updater._delays # For simplicity, store the delay as a one-element array # so that for example updater._delays[:] works. updater._delays = np.array([float(pathway_delay)]) variable = ArrayVariable('delay', second, updater._delays, group_name=self.name, scalar=True) updater.variables['delay'] = variable if pathway == 'pre': self.variables['delay'] = variable #: Performs numerical integration step self.state_updater = StateUpdater(self, method) self.contained_objects.append(self.state_updater) #: "Lumped variable" mechanism -- sum over all synapses of a #: postsynaptic target self.lumped_updaters = {} for single_equation in self.equations.itervalues(): if 'lumped' in single_equation.flags: varname = single_equation.varname # For a lumped variable, we need an equivalent parameter in the # target group if not varname in self.target.variables: raise ValueError(('The lumped variable %s needs a variable ' 'of the same name in the target ' 'group ') % single_equation.varname) fail_for_dimension_mismatch(self.variables[varname].unit, self.target.variables[varname], ('Lumped variables need to have ' 'the same units in Synapses ' 'and the target group')) # TODO: Add some more stringent check about the type of # variable in the target group updater = LumpedUpdater(varname, self, self.target) self.lumped_updaters[varname] = updater self.contained_objects.append(updater) # Do an initial connect, if requested if not isinstance(connect, (bool, basestring)): raise TypeError(('"connect" keyword has to be a boolean value or a ' 'string, is type %s instead.' % type(connect))) self._initial_connect = connect if not connect is False: self.connect(connect, level=1) # Activate name attribute access Group.__init__(self)
def __init__(self, source, target=None, model=None, pre=None, post=None, connect=False, delay=None, namespace=None, dtype=None, codeobj_class=None, clock=None, method=None, name='synapses*'): BrianObject.__init__(self, when=clock, name=name) self.codeobj_class = codeobj_class self.source = weakref.proxy(source) if target is None: self.target = self.source else: self.target = weakref.proxy(target) ##### Prepare and validate equations if model is None: model = '' if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Check flags model.check_flags({ DIFFERENTIAL_EQUATION: ['event-driven', 'lumped'], STATIC_EQUATION: ['lumped'], PARAMETER: ['constant', 'lumped'] }) # Separate the equations into event-driven and continuously updated # equations event_driven = [] continuous = [] for single_equation in model.itervalues(): if 'event-driven' in single_equation.flags: if 'lumped' in single_equation.flags: raise ValueError( ('Event-driven variable %s cannot be ' 'a lumped variable.') % single_equation.varname) event_driven.append(single_equation) else: continuous.append(single_equation) # Add the lastupdate variable, used by event-driven equations continuous.append(SingleEquation(PARAMETER, 'lastupdate', second)) if len(event_driven): self.event_driven = Equations(event_driven) else: self.event_driven = None self.equations = Equations(continuous) ##### Setup the memory self.arrays = self._allocate_memory(dtype=dtype) # Setup the namespace self._given_namespace = namespace self.namespace = create_namespace(namespace) self._queues = {} self._delays = {} self.item_mapping = SynapticItemMapping(self) self.indices = { '_idx': self.item_mapping, '_presynaptic_idx': self.item_mapping.synaptic_pre, '_postsynaptic_idx': self.item_mapping.synaptic_post } # Allow S.i instead of S.indices.i, etc. self.i = self.item_mapping.i self.j = self.item_mapping.j self.k = self.item_mapping.k # Setup variables self.variables = self._create_variables() #: List of names of all updaters, e.g. ['pre', 'post'] self._updaters = [] for prepost, argument in zip(('pre', 'post'), (pre, post)): if not argument: continue if isinstance(argument, basestring): self._add_updater(argument, prepost) elif isinstance(argument, collections.Mapping): for key, value in argument.iteritems(): if not isinstance(key, basestring): err_msg = ('Keys for the "{}" argument' 'have to be strings, got ' '{} instead.').format(prepost, type(key)) raise TypeError(err_msg) self._add_updater(value, prepost, objname=key) # If we have a pathway called "pre" (the most common use case), provide # direct access to its delay via a delay attribute (instead of having # to use pre.delay) if 'pre' in self._updaters: self.variables['delay'] = self.pre.variables['delay'] if delay is not None: if isinstance(delay, Quantity): if not 'pre' in self._updaters: raise ValueError( ('Cannot set delay, no "pre" pathway exists.' 'Use a dictionary if you want to set the ' 'delay for a pathway with a different name.')) delay = {'pre': delay} if not isinstance(delay, collections.Mapping): raise TypeError('Delay argument has to be a quantity or a ' 'dictionary, is type %s instead.' % type(delay)) for pathway, pathway_delay in delay.iteritems(): if not pathway in self._updaters: raise ValueError(('Cannot set the delay for pathway ' '"%s": unknown pathway.') % pathway) if not isinstance(pathway_delay, Quantity): raise TypeError(('Cannot set the delay for pathway "%s": ' 'expected a quantity, got %s instead.') % (pathway, type(pathway_delay))) if pathway_delay.size != 1: raise TypeError(('Cannot set the delay for pathway "%s": ' 'expected a scalar quantity, got a ' 'quantity with shape %s instead.') % str(pathway_delay.shape)) fail_for_dimension_mismatch(pathway_delay, second, ('Delay has to be ' 'specified in units ' 'of seconds')) updater = getattr(self, pathway) self.item_mapping.unregister_variable(updater._delays) del updater._delays # For simplicity, store the delay as a one-element array # so that for example updater._delays[:] works. updater._delays = np.array([float(pathway_delay)]) variable = ArrayVariable('delay', second, updater._delays, group_name=self.name, scalar=True) updater.variables['delay'] = variable if pathway == 'pre': self.variables['delay'] = variable #: Performs numerical integration step self.state_updater = StateUpdater(self, method) self.contained_objects.append(self.state_updater) #: "Lumped variable" mechanism -- sum over all synapses of a #: postsynaptic target self.lumped_updaters = {} for single_equation in self.equations.itervalues(): if 'lumped' in single_equation.flags: varname = single_equation.varname # For a lumped variable, we need an equivalent parameter in the # target group if not varname in self.target.variables: raise ValueError( ('The lumped variable %s needs a variable ' 'of the same name in the target ' 'group ') % single_equation.varname) fail_for_dimension_mismatch(self.variables[varname].unit, self.target.variables[varname], ('Lumped variables need to have ' 'the same units in Synapses ' 'and the target group')) # TODO: Add some more stringent check about the type of # variable in the target group updater = LumpedUpdater(varname, self, self.target) self.lumped_updaters[varname] = updater self.contained_objects.append(updater) # Do an initial connect, if requested if not isinstance(connect, (bool, basestring)): raise TypeError( ('"connect" keyword has to be a boolean value or a ' 'string, is type %s instead.' % type(connect))) self._initial_connect = connect if not connect is False: self.connect(connect, level=1) # Activate name attribute access Group.__init__(self)
def __init__(self, N, equations, method=euler, threshold=None, reset=None, dtype=None, language=None, clock=None, name=None, level=0): BrianObject.__init__(self, when=clock, name=name) ##### VALIDATE ARGUMENTS AND STORE ATTRIBUTES self.method = method self.level = level = int(level) 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("NeuronGroup size should be at least 1, was "+str(N)) # Validate equations if isinstance(equations, basestring): equations = Equations(equations, level=level+1) if not isinstance(equations, Equations): raise ValueError(('equations has to be a string or an Equations ' 'object, is "%s" instead.') % type(equations)) # add refractoriness equations = add_refractoriness(equations) self.equations = equations logger.debug("Creating NeuronGroup of size {self.N}, " "equations {self.equations}.".format(self=self)) # Check flags equations.check_flags({DIFFERENTIAL_EQUATION: ('active'), PARAMETER: ('constant')}) # Set dtypes and units self.prepare_dtypes(dtype=dtype) self.units = dict((var, equations.units[var]) for var in equations.equations.keys()) # Allocate memory (TODO: this should be refactored somewhere at some point) self.allocate_memory() #: The array of spikes from the most recent threshold operation self.spikes = array([], dtype=int) # Set these for documentation purposes #: Performs numerical integration step self.state_updater = None #: Performs thresholding step, sets the value of `spikes` self.thresholder = None #: Resets neurons which have spiked (`spikes`) self.resetter = None # Code generation (TODO: this should be refactored and modularised) # Temporary, set default language to Python if language is None: language = PythonLanguage() self.language = language self.create_state_updater() self.create_thresholder(threshold, level=level+1) self.create_resetter(reset, level=level+1) # Creation of contained_objects that do the work self.contained_objects.append(self.state_updater) if self.thresholder is not None: self.contained_objects.append(self.thresholder) if self.resetter is not None: self.contained_objects.append(self.resetter) # Activate name attribute access Group.__init__(self)
def __init__(self, morphology=None, model=None, threshold=None, refractory=False, reset=None, events=None, threshold_location=None, dt=None, clock=None, order=0, Cm=0.9 * uF / cm**2, Ri=150 * ohm * cm, name='spatialneuron*', dtype=None, namespace=None, method=('linear', 'exponential_euler', 'rk2', 'heun')): # #### Prepare and validate equations if isinstance(model, basestring): model = Equations(model) if not isinstance(model, Equations): raise TypeError(('model has to be a string or an Equations ' 'object, is "%s" instead.') % type(model)) # Insert the threshold mechanism at the specified location if threshold_location is not None: if hasattr(threshold_location, '_indices'): # assuming this is a method threshold_location = threshold_location._indices() # for now, only a single compartment allowed if len(threshold_location) == 1: threshold_location = threshold_location[0] else: raise AttributeError(('Threshold can only be applied on a ' 'single location')) threshold = '(' + threshold + ') and (i == ' + str( threshold_location) + ')' # Check flags (we have point currents) model.check_flags({ DIFFERENTIAL_EQUATION: ('point current', ), PARAMETER: ('constant', 'shared', 'linked', 'point current'), SUBEXPRESSION: ('shared', 'point current') }) # Add the membrane potential model += Equations(''' v:volt # membrane potential ''') # Extract membrane equation if 'Im' in model: membrane_eq = model['Im'] # the membrane equation else: raise TypeError('The transmembrane current Im must be defined') # Insert point currents in the membrane equation for eq in model.itervalues(): if 'point current' in eq.flags: fail_for_dimension_mismatch( eq.unit, amp, "Point current " + eq.varname + " should be in amp") eq.flags.remove('point current') membrane_eq.expr = Expression( str(membrane_eq.expr.code) + '+' + eq.varname + '/area') ###### Process model equations (Im) to extract total conductance and the remaining current # Check conditional linearity with respect to v # Match to _A*v+_B var = sp.Symbol('v', real=True) wildcard = sp.Wild('_A', exclude=[var]) constant_wildcard = sp.Wild('_B', exclude=[var]) pattern = wildcard * var + constant_wildcard # Expand expressions in the membrane equation membrane_eq.type = DIFFERENTIAL_EQUATION for var, expr in model.get_substituted_expressions(): if var == 'Im': Im_expr = expr membrane_eq.type = SUBEXPRESSION # Factor out the variable s_expr = sp.collect(str_to_sympy(Im_expr.code).expand(), var) matches = s_expr.match(pattern) if matches is None: raise TypeError, "The membrane current must be linear with respect to v" a, b = (matches[wildcard], matches[constant_wildcard]) # Extracts the total conductance from Im, and the remaining current minusa_str, b_str = sympy_to_str(-a), sympy_to_str(b) # Add correct units if necessary if minusa_str == '0': minusa_str += '*siemens/meter**2' if b_str == '0': b_str += '*amp/meter**2' gtot_str = "gtot__private=" + minusa_str + ": siemens/meter**2" I0_str = "I0__private=" + b_str + ": amp/meter**2" model += Equations(gtot_str + "\n" + I0_str) # Insert morphology (store a copy) self.morphology = copy.deepcopy(morphology) # Flatten the morphology self.flat_morphology = FlatMorphology(morphology) # Equations for morphology # TODO: check whether Cm and Ri are already in the equations # no: should be shared instead of constant # yes: should be constant (check) eqs_constants = Equations(""" length : meter (constant) distance : meter (constant) area : meter**2 (constant) volume : meter**3 diameter : meter (constant) Cm : farad/meter**2 (constant) Ri : ohm*meter (constant, shared) r_length_1 : meter (constant) r_length_2 : meter (constant) time_constant = Cm/gtot__private : second space_constant = (2/pi)**(1.0/3.0) * (area/(1/r_length_1 + 1/r_length_2))**(1.0/6.0) / (2*(Ri*gtot__private)**(1.0/2.0)) : meter """) if self.flat_morphology.has_coordinates: eqs_constants += Equations(''' x : meter (constant) y : meter (constant) z : meter (constant) ''') NeuronGroup.__init__(self, morphology.total_compartments, model=model + eqs_constants, threshold=threshold, refractory=refractory, reset=reset, events=events, method=method, dt=dt, clock=clock, order=order, namespace=namespace, dtype=dtype, name=name) # Parameters and intermediate variables for solving the cable equations # Note that some of these variables could have meaningful physical # units (e.g. _v_star is in volt, _I0_all is in amp/meter**2 etc.) but # since these variables should never be used in user code, we don't # assign them any units self.variables.add_arrays( [ '_ab_star0', '_ab_star1', '_ab_star2', '_a_minus0', '_a_minus1', '_a_minus2', '_a_plus0', '_a_plus1', '_a_plus2', '_b_plus', '_b_minus', '_v_star', '_u_plus', '_u_minus', # The following three are for solving the # three tridiag systems in parallel '_c1', '_c2', '_c3', # The following two are only necessary for # C code where we cannot deal with scalars # and arrays interchangeably: '_I0_all', '_gtot_all' ], unit=1, size=self.N, read_only=True) self.Cm = Cm self.Ri = Ri # These explict assignments will load the morphology values from disk # in standalone mode self.distance_ = self.flat_morphology.distance self.length_ = self.flat_morphology.length self.area_ = self.flat_morphology.area self.diameter_ = self.flat_morphology.diameter self.r_length_1_ = self.flat_morphology.r_length_1 self.r_length_2_ = self.flat_morphology.r_length_2 if self.flat_morphology.has_coordinates: self.x_ = self.flat_morphology.x self.y_ = self.flat_morphology.y self.z_ = self.flat_morphology.z # Performs numerical integration step self.add_attribute('diffusion_state_updater') self.diffusion_state_updater = SpatialStateUpdater(self, method, clock=self.clock, order=order) # Creation of contained_objects that do the work self.contained_objects.extend([self.diffusion_state_updater])