QuTiP development notebook for testing functions qutip.ipynbtools

In [28]:

    

%pylab inline


    

In [29]:

    

from qutip import *
import qutip.ipynbtools


    

In [30]:

    

import time


    

In [31]:

    

qutip.ipynbtools.version_table()


    

    
Out[31]:




Software
Version
Cython
0.19-dev
SciPy
0.13.0.dev-38ad5d2
QuTiP
2.3.0.dev-71d6e0c
Python
2.7.3 (default, Sep 26 2012, 21:51:14) 
[GCC 4.7.2]
IPython
0.13
OS
posix [linux2]
Numpy
1.8.0.dev-bd7104c
matplotlib
1.3.x
Tue Apr 23 16:06:27 2013 JST

In [32]:

    

delay_times = numpy.random.rand(10)

delay_times


    

    
Out[32]:





array([ 0.85397305,  0.6850936 ,  0.99509335,  0.85106784,  0.65212566,
        0.47303261,  0.18808245,  0.13273333,  0.72033813,  0.53014309])

In [33]:

    

def task(delay):
    import os, time

    t0 = time.time()
    pid = os.getpid()
    time.sleep(delay)
    t1 = time.time()
    
    return (t1-t0)  # return the actual delay


    

In [34]:

    

result = map(task, delay_times)

result


    

    
Out[34]:





[0.8548479080200195,
 0.6857919692993164,
 0.9961011409759521,
 0.8519401550292969,
 0.6527969837188721,
 0.4735140800476074,
 0.1882779598236084,
 0.1328730583190918,
 0.7210910320281982,
 0.530709981918335]

In [35]:

    

result = parfor(task, delay_times)

result


    

    
Out[35]:





[0.8548691272735596,
 0.685809850692749,
 0.996117115020752,
 0.8517839908599854,
 0.6528029441833496,
 0.47353196144104004,
 0.18828606605529785,
 0.13289308547973633,
 0.7210750579833984,
 0.5306930541992188]

In [36]:

    

result = qutip.ipynbtools.parfor(task, delay_times, show_scheduling=True, show_progressbar=True)


    

In [37]:

    

result


    

    
Out[37]:





[0.8548541069030762,
 0.6857941150665283,
 0.9953198432922363,
 0.8512799739837646,
 0.6527988910675049,
 0.4735231399536133,
 0.18829107284545898,
 0.13288402557373047,
 0.7210788726806641,
 0.5306930541992188]

In [38]:

    

def visualize_results(g_vec, n_vec):
    
    fig, ax = subplots()
    ax.plot(g_vec, n_vec, lw=2)
    ax.set_xlabel('Coupling strength (g)')
    ax.set_ylabel('Photon Number')
    ax.set_title('# of photons in the steady state')


    

In [39]:

    

def compute_task(g, args):
    wc, wa, N, kappa, n_th = args['wc'], args['wa'], args['N'], args['kappa'], args['n_th']
    
    a = tensor(destroy(N), qeye(2))
    sm = tensor(qeye(N), destroy(2))
    nc = a.dag() * a
    na = sm.dag() * sm

    c_ops = [sqrt(kappa * (1 + n_th)) * a, sqrt(kappa * n_th) * a.dag()]

    H0 = wc * nc + wa * na
    H1 = (a.dag() + a) * (sm + sm.dag())
    H = H0 + g * H1
    
    rho_ss = steadystate(H, c_ops)
    return expect(nc, rho_ss)


    

In [40]:

    

# problem parameters
args = {'wc': 1.0 * 2 * pi,  # cavity frequency
        'wa': 1.0 * 2 * pi,  # atom frequency
        'N':  25,            # number of cavity fock states
        'kappa': 0.05,
        'n_th':  0.5,
        }

g_vec = linspace(0, 2.5, 50) * 2 * pi


    

In [41]:

    

# serial calculation
t0 = time.time()
n_vec = array([compute_task(g, args) for g in g_vec])
t1 = time.time()

print "elapsed =", (t1-t0)


    

    




elapsed = 4.68055295944

In [42]:

    

visualize_results(g_vec / (2 * pi), n_vec)


    

In [43]:

    

# parallel calculation using qutip parfor
t0 = time.time()
n_vec = parfor(compute_task, g_vec, args=args)
t1 = time.time()

print "elapsed =", (t1-t0)


    

    




elapsed = 1.57517004013

In [44]:

    

visualize_results(g_vec / (2 * pi), n_vec)


    

In [45]:

    

# parallel calculation using qutip IPython.parallel parfor
t0 = time.time()
n_vec = qutip.ipynbtools.parfor(compute_task, g_vec, args=args, show_scheduling=True, show_progressbar=True)
t1 = time.time()

print "elapsed =", (t1-t0)


    

    






  
 









    















    















    















    















    















    




elapsed = 1.96851205826






    

In [46]:

    

visualize_results(g_vec / (2 * pi), n_vec)


    

In [47]:

    

def compute_task(g, args):
    H0, H1, c_ops, nc = args['H0'], args['H1'], args['c_ops'], args['nc']
    H = H0 + g * H1
    rho_ss = steadystate(H, c_ops)
    return expect(nc, rho_ss)


    

In [48]:

    

# problem parameters
wc = 1.0 * 2 * pi
wa = 1.0 * 2 * pi
N = 75
kappa = 0.05
n_th = 0.5

a = tensor(destroy(N), qeye(2))
sm = tensor(qeye(N), destroy(2))
nc = a.dag() * a
na = sm.dag() * sm

c_ops = [sqrt(kappa * (1 + n_th)) * a, sqrt(kappa * n_th) * a.dag()]

H0 = wc * nc + wa * na
H1 = (a.dag() + a) * (sm + sm.dag())

args = {'H0': H0,
        'H1': H1,
        'c_ops': c_ops,
        'nc': nc
        }

g_vec = linspace(0, 2.5, 50) * 2 * pi


    

In [49]:

    

# serial calculation
t0 = time.time()
n_vec = array([compute_task(g, args) for g in g_vec])
t1 = time.time()

print "elapsed =", (t1-t0)


    

    




elapsed = 30.7636711597

In [50]:

    

visualize_results(g_vec / (2 * pi), n_vec)


    

In [51]:

    

# parallel calculation using qutip parfor
t0 = time.time()
n_vec = parfor(compute_task, g_vec, args=args)
t1 = time.time()

print "elapsed =", (t1-t0)


    

    




elapsed = 13.2212700844

In [52]:

    

visualize_results(g_vec / (2 * pi), n_vec)


    

In [53]:

    

# parallel calculation
t0 = time.time()
n_vec = qutip.ipynbtools.parfor(compute_task, g_vec, args=args, show_scheduling=True, show_progressbar=True)
t1 = time.time()

print "elapsed =", (t1-t0)


    

    






  
 









    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    















    




elapsed = 13.6379978657






    

In [54]:

    

visualize_results(g_vec / (2 * pi), n_vec)