from IPython import parallel
rc = parallel.Client()
dv = rc[:]
rc.ids

%px a=5

%px print(a)

%px a

with dv.sync_imports():
    import sys

%px from __future__ import print_function
%px print("ERROR", file=sys.stderr)

%pxconfig --noblock

%px import time
%px time.sleep(5)
%px time.time()

%pxresult

%pxconfig --block
%px %matplotlib inline

%%px
import numpy as np
import matplotlib.pyplot as plt

dv.scatter('id', dv.targets, flatten=True)
dv['stride'] = len(dv)

%%px --noblock
x = np.linspace(0,np.pi,1000)
for n in range(id,12, stride):
    print(n)
    plt.plot(x,np.sin(n*x))
plt.title("Plot %i" % id)

%pxresult

%%px --group-outputs=engine
x = np.linspace(0,np.pi,1000)
for n in range(id+1,12, stride):
    print(n)
    plt.figure()
    plt.plot(x,np.sin(n*x))
    plt.title("Plot %i" % n)

%pxresult --group-outputs=order

from __future__ import print_function

def generate_output():
    """function for testing output
    
    publishes two outputs of each type, and returns something
    """
    
    import sys,os
    from IPython.display import display, HTML, Math
    
    print("stdout")
    print("stderr", file=sys.stderr)
    
    display(HTML("<b>HTML</b>"))
    
    print("stdout2")
    print("stderr2", file=sys.stderr)
    
    display(Math(r"\alpha=\beta"))
    
    return os.getpid()

dv['generate_output'] = generate_output

e0 = rc[-1]
e0.block = True
e0.activate('0')

%px0 generate_output()

%px generate_output()

%pxresult --group-outputs order

%pxresult --group-outputs engine

%%px
from numpy.random import random
A = random((100,100,'invalid shape'))

%%px
%%timeit
from numpy.random import random
from numpy.linalg import norm
A = random((100,100))
norm(A, 2)

%%px --local
import os
thispid = os.getpid()
print(thispid)