def concat(self, other):
"""Create new reception parameters by combining this set of reception
parameters with another.
"""
# Combine the filters
if self.filter is None:
new_filter = other.filter
elif other.filter is None:
new_filter = self.filter
elif (isinstance(self.filter, LinearFilter)
and isinstance(other.filter, LinearFilter)):
# Combine linear filters by multiplying their numerators and
# denominators.
new_filter = LinearFilter(
np.polymul(self.filter.num, other.filter.num),
np.polymul(self.filter.den, other.filter.den))
else:
raise NotImplementedError(
"Cannot combine filters of type {} and {}".format(
type(self.filter), type(other.filter)))
# Combine the learning rules
if self.learning_rule is not None and other.learning_rule is not None:
raise NotImplementedError(
"Cannot combine learning rules {} and {}".format(
self.learning_rule, other.learning_rule))
new_learning_rule = self.learning_rule or other.learning_rule
# Create the new reception parameters
return ReceptionParameters(new_filter, other.width, new_learning_rule)
def test_synapses():
check_init_args(LinearFilter, ["num", "den", "analog", "method"])
check_repr(LinearFilter([1, 2], [3, 4]))
check_repr(LinearFilter([1, 2], [3, 4], analog=False))
assert (repr(LinearFilter(
[1], [0.03, 1
])) == "LinearFilter(num=array([1.]), den=array([0.03, 1. ]))")
check_init_args(Lowpass, ["tau"])
check_repr(Lowpass(0.3))
assert repr(Lowpass(0.01)) == "Lowpass(tau=0.01)"
check_init_args(Alpha, ["tau"])
check_repr(Alpha(0.3))
assert repr(Alpha(0.02)) == "Alpha(tau=0.02)"
check_init_args(Triangle, ["t"])
check_repr(Triangle(0.3))
assert repr(Triangle(0.03)) == "Triangle(t=0.03)"