b = Brane(m,dx,wavelength=1.3e-3,nphi=(2**12,2**12),screen_res=6,r_inner=50)
# points along the uv axes where to record the visibility
u = np.linspace(0.,10e9,num=50)
# generate a bunch of scattering instances
tic = time.time()
u_vis = []
v_vis = []
num_sims = 100
avg = np.zeros_like(b.isrc)
for i in range(num_sims):
# create a new instance of the random phases
b.generatePhases()
# calculate the scattered image
b.scatter()
# keep track of the average
avg += b.iss
# calculate the visibility function along the u and v axis.
u_vis.append(np.array([utilities.FTElementFast(b.iss,b.dx,[u_,0]) for u_ in u]))
v_vis.append(np.array([utilities.FTElementFast(b.iss,b.dx,[0,u_]) for u_ in u]))
avg /= num_sims
logger.info('took {0:g}s'.format(time.time()-tic))
# make figures
write_figs = False
fig_file = '../_static/ensemble_average/'
extent=b.dx*b.nx//2*np.array([1,-1,-1,1])
ensemble = utilities.ensembleSmooth(b.isrc,b.dx,b)
u_kernel = utilities.getUVKernel(u,np.zeros_like(u),b)
v_kernel = utilities.getUVKernel(np.zeros_like(u),u,b)
# generate the screen (this takes a while)
logger.info('generating screen...')
tic = time.time()
b.generatePhases()
logger.info('took {0:g}s'.format(time.time()-tic))
# generate time series (this takes a while)
logger.info('generating time series...')
fluxes = []
frames = []
tic = time.time()
for i in range(num_samples):
# update source image to include a sinusoidal flux modulation
b.setModel(I*(1. - 0.4*np.sin(2*np.pi*i/(2*num_samples))), dx) # comment out to speedup
b.scatter(move_pix=i*b.ips)
fluxes.append(b.iss.sum())
frames.append(b.iss)
logger.info('took {0:g}s'.format(time.time()-tic))
# 1962.92s
# make figures
fig_file = '../_static/time_variability/'
extent=b.dx*b.nx//2*np.array([1,-1,-1,1])
plt.figure()
plt.subplot(121)
isrc_smooth = utilities.smoothImage(b.isrc,b.dx,2.*b.dx)
plt.imshow(isrc_smooth,extent=extent,cmap=cmap)
plt.xlabel('$\Delta\\alpha$ [$\mu$as]'); plt.ylabel('$\Delta\delta$ [$\mu$as]')
plt.subplot(122)