logger.info('Time coverage: {0:g} days'.format(num_samples/fs/(3600.*24.))) # 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]')