def __init__(self, source, target = None, model = None, pre = None, post = None,
max_delay = 0*ms,
level = 0,
clock = None,code_namespace=None,
unit_checking = True, method = None, freeze = False, implicit = False, order = 1): # model (state updater) related
target=target or source # default is target=source
# Check clocks. For the moment we enforce the same clocks for all objects
clock = clock or source.clock
if source.clock!=target.clock:
raise ValueError,"Source and target groups must have the same clock"
if pre is None:
pre_list=[]
elif isSequenceType(pre) and not isinstance(pre,str): # a list of pre codes
pre_list=pre
else:
pre_list=[pre]
pre_list=[flattened_docstring(pre) for pre in pre_list]
if post is not None:
post=flattened_docstring(post)
# Pre and postsynaptic indexes (synapse -> pre/post)
self.presynaptic=DynamicArray1D(0,dtype=smallest_inttype(len(source))) # this should depend on number of neurons
self.postsynaptic=DynamicArray1D(0,dtype=smallest_inttype(len(target))) # this should depend on number of neurons
if not isinstance(model,SynapticEquations):
model=SynapticEquations(model,level=level+1)
# Insert the lastupdate variable if necessary (if it is mentioned in pre/post, or if there is event-driven code)
expr=re.compile(r'\blastupdate\b')
if (len(model._eventdriven)>0) or \
any([expr.search(pre) for pre in pre_list]) or \
(post is not None and expr.search(post) is not None):
model+='\nlastupdate : second\n'
pre_list=[pre+'\nlastupdate=t\n' for pre in pre_list]
if post is not None:
post=post+'\nlastupdate=t\n'
# Identify pre and post variables in the model string
# They are identified by _pre and _post suffixes
# or no suffix for postsynaptic variables
ids=set()
for RHS in model._string.itervalues():
ids.update(get_identifiers(RHS))
pre_ids = [id[:-4] for id in ids if id[-4:]=='_pre']
post_ids = [id[:-5] for id in ids if id[-5:]=='_post']
post_vars = [var for var in source.var_index if isinstance(var,str)] # postsynaptic variables
post_ids2 = list(ids.intersection(set(post_vars))) # post variables without the _post suffix
# remember whether our equations refer to any variables in the pre- or
# postsynaptic group. This is important for the state-updater, e.g. the
# equations can no longer be solved as linear equations.
model.refers_others = (len(pre_ids) + len(post_ids) + len(post_ids2) > 0)
# Insert static equations for pre and post variables
S=self
for name in pre_ids:
model.add_eq(name+'_pre', 'S.source.'+name+'[S.presynaptic[:]]', source.unit(name),
global_namespace={'S':S})
for name in post_ids:
model.add_eq(name+'_post', 'S.target.'+name+'[S.postsynaptic[:]]', target.unit(name),
global_namespace={'S':S})
for name in post_ids2: # we have to change the name of the variable to avoid problems with equation processing
if name not in model._string: # check that it is not already defined
model.add_eq(name, 'S.target.state_(__'+name+')[S.postsynaptic[:]]', target.unit(name),
global_namespace={'S':S,'__'+name:name})
self.source=source
self.target=target
NeuronGroup.__init__(self, 0,model=model,clock=clock,level=level+1,unit_checking=unit_checking,method=method,freeze=freeze,implicit=implicit,order=order)
'''
At this point we have:
* a state matrix _S with all variables
* units, state dictionary with each value being a row of _S + the static equations
* subgroups of synapses
* link_var (i.e. we can link two synapses objects)
* __len__
* __setattr__: we can write S.w=array of values
* var_index is a dictionary from names to row index in _S
* num_states()
Things we have that we don't want:
* LS structure (but it will not be filled since the object does not spike)
* (from Group) __getattr_ needs to be rewritten
* a complete state updater, but we need to extract parameters and event-driven parts
* The state matrix is not dynamic
Things we may need to add:
* _pre and _post suffixes
'''
self._iscompressed=False # True if compress() has already been called
# Look for event-driven code in the differential equations
if use_sympy:
eqs=self._eqs # an Equations object
#vars=eqs._diffeq_names_nonzero # Dynamic variables
vars=eqs._eventdriven.keys()
var_set=set(vars)
for var,RHS in eqs._eventdriven.iteritems():
ids=get_identifiers(RHS)
if len(set(list(ids)+[var]).intersection(var_set))==1:
# no external dynamic variable
# Now we test if it is a linear equation
_namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero)
# plus units problems? (maybe not since these are identifiers too)
# another option is to use random numbers, but that doesn't solve all problems
_namespace[var]=AffineFunction()
try:
eval(RHS,eqs._namespace[var],_namespace)
except: # not linear
raise TypeError,"Cannot turn equation for "+var+" into event-driven code"
z=symbolic_eval(RHS)
symbol_var=sympy.Symbol(var)
symbol_t=sympy.Symbol('t')-sympy.Symbol('lastupdate')
b=z.subs(symbol_var,0)
a=sympy.simplify(z.subs(symbol_var,1)-b)
if a==0:
expr=symbol_var+b*symbol_t
else:
expr=-b/a+sympy.exp(a*symbol_t)*(symbol_var+b/a)
expr=var+'='+str(expr)
# Replace pre and post code
# N.B.: the differential equations are kept, we will probably want to remove them!
pre_list=[expr+'\n'+pre for pre in pre_list]
if post is not None:
post=expr+'\n'+post
else:
raise TypeError,"Cannot turn equation for "+var+" into event-driven code"
elif len(self._eqs._eventdriven)>0:
raise TypeError,"The Sympy package must be installed to produce event-driven code"
if len(self._eqs._diffeq_names_nonzero)==0:
self._state_updater=None
# Set last spike to -infinity
if 'lastupdate' in self.var_index:
self.lastupdate=-1e6
# _S is turned to a dynamic array - OK this is probably not good! we may lose references at this point
S=self._S
self._S=DynamicArray(S.shape)
self._S[:]=S
# Pre and postsynaptic delays (synapse -> delay_pre/delay_post)
self._delay_pre=[DynamicArray1D(len(self),dtype=np.int16) for _ in pre_list] # max 32767 delays
self._delay_post=DynamicArray1D(len(self),dtype=np.int16) # Actually only useful if there is a post code!
# Pre and postsynaptic synapses (i->synapse indexes)
max_synapses=2147483647 # it could be explicitly reduced by a keyword
# We use a loop instead of *, otherwise only 1 dynamic array is created
self.synapses_pre=[DynamicArray1D(0,dtype=smallest_inttype(max_synapses)) for _ in range(len(self.source))]
self.synapses_post=[DynamicArray1D(0,dtype=smallest_inttype(max_synapses)) for _ in range(len(self.target))]
# Code generation
self._binomial = lambda n,p:np.random.binomial(np.array(n,dtype=int),p)
self.contained_objects = []
self.codes=[]
self.namespaces=[]
self.queues=[]
for i,pre in enumerate(pre_list):
code,_namespace=self.generate_code(pre,level+1,code_namespace=code_namespace)
self.codes.append(code)
self.namespaces.append(_namespace)
self.queues.append(SpikeQueue(self.source, self.synapses_pre, self._delay_pre[i], max_delay = max_delay))
if post is not None:
code,_namespace=self.generate_code(post,level+1,direct=True,code_namespace=code_namespace)
self.codes.append(code)
self.namespaces.append(_namespace)
self.queues.append(SpikeQueue(self.target, self.synapses_post, self._delay_post, max_delay = max_delay))
self.queues_namespaces_codes = zip(self.queues,
self.namespaces,
self.codes)
self.contained_objects+=self.queues
def __init__(self, expr):
self.expr = expr
self.sympy_expr = symbolic_eval(self.expr)
self.dependencies = set(Read(x) for x in get_identifiers(expr))
self.resolved = set()
def __init__(self, expr):
self.expr = expr
self.sympy_expr = symbolic_eval(self.expr)
def __init__(self, expr):
self.expr = expr
self.sympy_expr = symbolic_eval(self.expr)
self.dependencies = set(Read(x) for x in get_identifiers(expr))
self.resolved = set()
def __init__(self, expr):
self.expr = expr
self.sympy_expr = symbolic_eval(self.expr)