In [1]:

    

import zmq
import numpy


    

In [2]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

pylab.rcParams['figure.figsize'] = 14,5


    

In [4]:

    

def recv_array(socket, flags=0, copy=False, track=False):
    """recv a numpy array"""
    md = socket.recv_json(flags=flags)
    msg = socket.recv(flags=flags, copy=copy, track=track)
    buf = buffer(msg)
    A = numpy.frombuffer(buf, dtype=md['dtype'])
    return A.reshape(md['shape'])


    

In [5]:

    

REQUEST_TIMEOUT = 2500
SERVER_ENDPOINT = "tcp://localhost:5555"

context = zmq.Context()

#  Socket to talk to server
print "Connecting to array server..."
client = context.socket(zmq.REQ)
client.connect(SERVER_ENDPOINT)

poll = zmq.Poller()
poll.register(client, zmq.POLLIN)


    

    




Connecting to array server...

In [25]:

    

shots_requested = 10
request = str(shots_requested)  # ask for one shot of data
print "I: Sending (%s)" % request

client.send(request)


    

    




I: Sending (10)

In [26]:

    

socks = dict(poll.poll(REQUEST_TIMEOUT))
if socks.get(client) == zmq.POLLIN:
    data_array = recv_array(client)
    #reply = client.recv()

    if len(data_array) > 1:  # TODO test for the right size array
        print "I: Server replied OK: " + str(data_array.shape)

    else:
        # print "E: Malformed reply from server: %s" % reply
        print "E: no reply from server"


    

    




I: Server replied OK: (400, 1340, 10)

In [27]:

    

imshow(data_array[:,:,0])


    

    
Out[27]:





<matplotlib.image.AxesImage at 0x625f210>






    

In [28]:

    

plot(data_array[200,:,0])


    

    
Out[28]:





[<matplotlib.lines.Line2D at 0x6959e10>]






    

In [29]:

    

imshow(data_array[:,:,1]-data_array[:,:,2])


    

    
Out[29]:





<matplotlib.image.AxesImage at 0x7440c90>






    

In [30]:

    

imshow(numpy.fft.fftshift(numpy.log(numpy.abs(numpy.fft.fft2(data_array[:,:,0],axes=[1]))),axes=[1]))


    

    
Out[30]:





<matplotlib.image.AxesImage at 0x79f1110>






    

In [31]:

    

plot(data_array[200,:,1] - data_array[200,:,5])


    

    
Out[31]:





[<matplotlib.lines.Line2D at 0x7a1c450>]






    

In [22]:

    

noise = data_array[200,:,1] - data_array[200,:,4]


    

In [23]:

    

import scipy.stats


    

In [64]:

    

noise.std()


    

    
Out[64]:





61.135537480026386

In [53]:

    

hist_data, bin_edges = numpy.histogram(noise,bins=20)


    

In [55]:

    

bar(bin_edges[:-1], hist_data,width=20)


    

    
Out[55]:





<Container object of 20 artists>






    

In [ ]: