def example_1():
print "example_1"
# This examples uses the function total()
# from list and state to a list and state.
# The state is cumulative --- the cumulative
# sum up to this point in the stream.
# The result_list is the list of cumulative
# values of the input list.
def total(lst, cumulative):
result_list = []
for v in lst:
cumulative += v
result_list.append(cumulative)
return (result_list, cumulative)
x = Stream('x')
# The initial state is 0.
state = 0
y = stream_operator_stateful(total, x, state)
# Since call_streams is not specified, values will be
# appended to y when values are appended to x.
print "initial state = ", state
print 'function on stream is total()'
print 'This function computes the cumulative sum of the stream'
print 'x is the input stream and y is the output stream.'
print 'The state is the cumulative so far.'
y.set_name('y')
x.extend([3, 7])
# Now the value of x is [3, 7], and so the
# value of y is [3, 10], and the state is 10
print_streams_recent([x, y])
print ""
x.extend([0, 11, 5])
# Now the value of x is [3, 7, 0, 11, 5]
# so the value of y is [3, 10, 10, 21, 26] and
# the value of state is 26.
print_streams_recent([x, y])
def example_2():
print ""
print "example_2"
# This example uses the function:
# avg_since_last_drop()
# from a list and state to a list and state.
# The output list is the list of averages of
# the input list from the point that the value
# in the input list drops by more than
# drop_threshold.
# The state is a tuple:
# (time_since_last_drop, sum_since_last_drop,
# last_value, drop_threshold)
# where time_since_last_drop is the number of
# values since the last drop over the threshold,
# sum_since_last_drop is the sum of the values
# since the last drop over the threshold,
# last_value is the most recent value read from
# the input stream, and
# drop_threshold is the specified threshold.
def avg_since_last_drop(lst, state):
time_since_last_drop, sum_since_last_drop,\
last_value, drop_threshold = state
result_list = []
if lst == []:
return (result_list, state)
if last_value is None:
last_value = lst[0]
for v in lst:
if last_value - v < drop_threshold:
time_since_last_drop += 1
sum_since_last_drop += v
else:
time_since_last_drop = 1
sum_since_last_drop = v
last_value = float(v)
avg = sum_since_last_drop/float(time_since_last_drop)
result_list.append(avg)
return (result_list, state)
x = Stream('x')
time_since_last_drop = 0
sum_since_last_drop = 0
last_value = None
drop_threshold = 100
print "INITIAL VALUES"
print 'time_since_last_drop = ', time_since_last_drop
print 'sum_since_last_drop = ', sum_since_last_drop
print 'last_value = ', last_value
print 'drop_threshold = ', drop_threshold
print ''
state = (time_since_last_drop, sum_since_last_drop, last_value, drop_threshold)
y = stream_operator_stateful(avg_since_last_drop, x, state)
y.set_name('y')
print 'first step'
x.extend([16500, 16750, 16550, 16600, 16581])
# The first drop of over drop_threshold is from 16750 to 16550
# So, the output before that drop is the average for the window:
# 16500.0, followed by average of (16500, 16750) = 16625.0
# The output then starts computing averages afresh after the drop
# starting with the value after the drop: 16550.0, 16575.0, 16577.0
print_streams_recent([x, y])
print ""
print 'second step'
x.extend([16400, 16500, 17002])
# Another drop over the threshold of 100 occurs from 16581 to 16400
# So, compute averages afresh from this point to get:
# 16400.0, 16450.0, 16634.0
print_streams_recent([x, y])
def cumulative_stream(x_stream): return stream_operator_stateful(total, x_stream, state=0)
x_stream = Stream('x_stream')
