def get_delta_factors_(cls, neuron: ASTNeuron,
equations_block: ASTEquationsBlock) -> dict:
r"""
For every occurrence of a convolution of the form `x^(n) = a * convolve(kernel, inport) + ...` where `kernel` is a delta function, add the element `(x^(n), inport) --> a` to the set.
"""
delta_factors = {}
for ode_eq in equations_block.get_ode_equations():
var = ode_eq.get_lhs()
expr = ode_eq.get_rhs()
conv_calls = OdeTransformer.get_convolve_function_calls(expr)
for conv_call in conv_calls:
assert len(
conv_call.args
) == 2, "convolve() function call should have precisely two arguments: kernel and spike input port"
kernel = conv_call.args[0]
if cls.is_delta_kernel(
neuron.get_kernel_by_name(
kernel.get_variable().get_name())):
inport = conv_call.args[1].get_variable()
expr_str = str(expr)
sympy_expr = sympy.parsing.sympy_parser.parse_expr(
expr_str)
sympy_expr = sympy.expand(sympy_expr)
sympy_conv_expr = sympy.parsing.sympy_parser.parse_expr(
str(conv_call))
factor_str = []
for term in sympy.Add.make_args(sympy_expr):
if term.find(sympy_conv_expr):
factor_str.append(
str(term.replace(sympy_conv_expr, 1)))
factor_str = " + ".join(factor_str)
delta_factors[(var, inport)] = factor_str
return delta_factors
def generate_kernel_buffers_(
cls, neuron: ASTNeuron, equations_block: ASTEquationsBlock
) -> Mapping[ASTKernel, ASTInputPort]:
"""
For every occurrence of a convolution of the form `convolve(var, spike_buf)`: add the element `(kernel, spike_buf)` to the set, with `kernel` being the kernel that contains variable `var`.
"""
kernel_buffers = set()
convolve_calls = OdeTransformer.get_convolve_function_calls(
equations_block)
for convolve in convolve_calls:
el = (convolve.get_args()[0], convolve.get_args()[1])
sym = convolve.get_args()[0].get_scope().resolve_to_symbol(
convolve.get_args()[0].get_variable().name,
SymbolKind.VARIABLE)
if sym is None:
raise Exception(
"No initial value(s) defined for kernel with variable \"" +
convolve.get_args()[0].get_variable().get_complete_name() +
"\"")
if sym.block_type == BlockType.INPUT:
# swap the order
el = (el[1], el[0])
# find the corresponding kernel object
var = el[0].get_variable()
assert var is not None
kernel = neuron.get_kernel_by_name(var.get_name())
assert kernel is not None, "In convolution \"convolve(" + str(
var.name) + ", " + str(
el[1]) + ")\": no kernel by name \"" + var.get_name(
) + "\" found in neuron."
el = (kernel, el[1])
kernel_buffers.add(el)
return kernel_buffers
