view = c.load_balanced_view()
# Submit tasks:
print("Submitting tasks...")
t1 = time.time()
async_results = []
for i in range(400):
ar = view.apply_async(exposure)
async_results.append(ar)
print("Submitted tasks: ", len(async_results))
# Block until all tasks are completed.
c.wait(async_results)
t2 = time.time()
t = t2-t1
print("Parallel calculation completed, time = %s s" % t)
# Get the results using the `get` method:
results = [ar.get() for ar in async_results]
# Plot the value of the European call in (volatility, strike) space.
qfuncoutput = qf.qfuncimage(np.real(results),np.imag(results),30)
show()
amp = 380
darkcts = 0.0 # no idea what is reasonable here, just tinkering
# the ccd specifies 2-3 electrons per pixel per hour
# seems best way to model (classical) detector is with partial loss (2%)
# and added noise (dark counts)
values = []
for i in range(5000): #this is the loop to parallelize
print i
total = bopt.plane_wave_beam(x,y,0,amp,k1) +
exp(1j*1)*bopt.plane_wave_beam(x,y,0,0.001*amp,k2)
intensity = total * total.conjugate() # +
#darkcts*(random.random([max(shape(x)),max(shape(y))]) +
#1j*random.random([max(shape(x)),max(shape(y))]))
# add dark noise and QE
K = fftshift(fft(intensity[:,200])) # complex intensity after FFT2
values.append(K[643]/1e5)
# pixel of interest in FFT is 643 (this is 131 pixels away from the
# center @ 512) 131 pixels corresponds to the off-axis component of
# the wave:
# ∆K is 2π / (20e-6 * 1024) = 306.796 rad/m
# 306.796 * 131 is 40190 rad/m which is k * sin(0.005)
# where k = 2π/780e-9 = 8055365 rad/m TADA!
qfuncoutput = qf.qfuncimage(real(values),imag(values),30)
show()
def makeQfig(self, mode=None): # makes a Qfig for a specified mode
if mode is None:
mode = self.peak_mode
mode_array = self.dataOut[mode, :, :].flatten()
# takes only the desired mode
self.qfig = Qfunc.qfuncimage(mode_array, 30, 0)
def plotMode(modearray, mode, binsize = 10): qfig = Qfunc.qfuncimage(modearray[2*mode:2*mode+2], binsize) return qfig
