def example_of_no_value(lst):
v = sum(lst)
if not v%2:
# v is even.
# The output stream should have
# two elements, v/2 and v, for
# this single value of v.
return _multivalue([v/2, v])
else:
# v is odd
# The output stream should not have
# any value for this v.
return _no_value
def generate_numbers():
print 'in generate numbers'
return _multivalue(range(5))
def lossy_integration(data, initial_value, FACTOR):
data[0] -= initial_value * FACTOR
for i in range(1, np.shape(data)[0]):
data[i] -= data[i-1]*FACTOR
return (_multivalue(data), data[-1])
def interpolate(lst):
if len(lst) < 2:
return lst
increment = (lst[1] - lst[0]) / float(n)
return_list = [lst[0] + k * increment for k in range(n)]
return _multivalue(return_list)
def demean_across_multiple_windows(window, state):
state = np.roll(state, -1, axis=1)
state[:, -1] = [window.sum(), len(window)]
output = _multivalue(
window - state[0,:].sum()/float(state[1,:].sum()))
return (output, state)
def generate_numbers():
return_value = _multivalue(range(5))
print 'in generate_numbers. return value is', return_value
return return_value
def generate_numbers():
return_value = _multivalue(range(5))
print 'in generate_numbers. return value is', return_value
return return_value
def f(v):
if not v%2:
return _multivalue([v/2, v])
else:
return v
def generate_numbers(trigger):
# Expect trigger values to be 1, 2, 3,...
# return [0, 1, 2] then [3, 4, 5], then [6, 7, 8],..
result = range(trigger*3, trigger*3+3, 1)
print 'generating numbers', result
return _multivalue(result)
def generate_numbers():
print "in generate numbers"
return _multivalue(range(5))
def generate_numbers(trigger):
# Expect trigger values to be 1, 2, 3,...
# return [0, 1, 2] then [3, 4, 5], then [6, 7, 8],..
result = range(trigger * 3, trigger * 3 + 3, 1)
print 'generating numbers', result
return _multivalue(result)
def func(window, drop_start, b, a):
y = filter_type(b, a, window)[drop_start: drop_start+len(window)]
return _multivalue(y)
