Make RX like graphs about streamz

[CJ-Wright]

import time
from streams import Stream
from operator import add
import matplotlib.pyplot as plt


def star(f):
    """Take tuple and unpack it into args"""
    @ft.wraps(f)
    def wraps(args):
        return f(*args)

    return wraps

a = Stream()
b = Stream()
c = a.zip(b)

# Make the combo data
d = c.map(star(add))

def timer(*args):
    return time.time()

# Get the times
Ls = [s.map(timer).sink_to_list() for s in [a, b, c]]

# Get the labels
lab = [s.sink_to_list() for s in [a, b, d]]


# do the specific time trial(s)
t0 = time.time()
for i in range(5):
    a.emit(str(i))
for j in ['A', 'B', 'C', 'D', 'E']:
    b.emit(j)

for j, (L, M) in enumerate(zip(Ls, lab)):
    for l, m in zip(L, M):
        plt.scatter(l - t0, (3-j) * .01, marker=r"$ {} $".format(m), s=1000)
    plt.axhline((3-j) * .01)
fLs = [item - t0 for sublist in Ls for item in sublist]
plt.xlim(min(fLs) * .9, max(fLs) * 1.1)
plt.show()

This almost works, except on my machine the timings aren't just right. Is there any way I can slow each node down?

[CJ-Wright]

@mrocklin is there a way to have the map function delay asynchronously? It seems that the map (and it's timer sink) are actually executed before the b timer sink.

[jrmlhermitte]

FYI after discussion with @CJ-Wright we may try to follow Rx (the figures are interactive)
http://rxmarbles.com/

combine_latest here