Python saveHDF5 Exemples

Exemple #1

Fichier : listings_4_noisy_reconstruct.py Projet : MiroDur/Multiple-Scattering_Tensorflow

        print('MY loss: @'+str(iterx)+': ' + str(my_loss) + ' - Fidelity: '+str(my_fidelity)+', Neg: '+str(my_negloss)+', Pos: '+str(my_posloss)+', TV: '+str(my_tvloss))        
    else:
        sess.run([tf_lossop], feed_dict={tf_meas:np_meas})
        #plt.imshow(np.abs(my_res[:,50,:]))
        
#%% Display the results
myfwd, mymeas, my_res = sess.run([tf_fwd, tf_meas, muscat.TF_obj], feed_dict={tf_meas:np_meas})
        
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(mymeas))**.2)[:,muscat.mysize[1]//2,:]), plt.colorbar(), plt.show()    
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(mymeas))**.2)[muscat.mysize[0]//2,:,:]), plt.colorbar(), plt.show()   
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(mymeas))**.2)[:,:,muscat.mysize[2]//2]), plt.colorbar(), plt.show()     
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(myfwd))**.2)[:,muscat.mysize[1]//2,:]), plt.colorbar(), plt.show()
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(myfwd))**.2)[muscat.mysize[0]//2,:,:]), plt.colorbar(), plt.show()    
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(myfwd))**.2)[:,:,muscat.mysize[2]//2]), plt.colorbar(), plt.show()    
tf_helper.saveHDF5(np.abs(np.fft.fftshift(np.fft.fftn(myfwd))), 'FFT3D_FWD')

if(is_display): plt.title('XZ'),plt.imshow(np.abs(myfwd)[:,muscat.mysize[1]//2,:]), plt.colorbar(), plt.show()
if(is_display): plt.title('XZ'),plt.imshow(np.abs(myfwd)[:,:,muscat.mysize[2]//2]), plt.colorbar(), plt.show()
if(is_display): plt.title('XY'),plt.imshow(np.abs(myfwd)[muscat.mysize[0]//2,:,:]), plt.colorbar(), plt.show()

if(is_display): plt.title('XZ'),plt.imshow(np.angle(myfwd)[:,muscat.mysize[1]//2,:]), plt.colorbar(), plt.show()
if(is_display): plt.title('XZ'),plt.imshow(np.angle(myfwd)[:,:,muscat.mysize[2]//2]), plt.colorbar(), plt.show()
if(is_display): plt.title('XY'),plt.imshow(np.angle(myfwd)[muscat.mysize[0]//2,:,:]), plt.colorbar(), plt.show()


my_residual = tf_helper.abssqr(myfwd-mymeas)
if(is_display): plt.title('Residual: XZ'),plt.imshow(my_residual [:,muscat.mysize[1]//2,:]), plt.colorbar(), plt.show()
if(is_display): plt.title('Residual: XZ'),plt.imshow(my_residual [:,:,muscat.mysize[2]//2]), plt.colorbar(), plt.show()
if(is_display): plt.title('Residual: XY'),plt.imshow(my_residual [muscat.mysize[0]//2,:,:]), plt.colorbar(), plt.show()

Exemple #2

Fichier : listings_4_genmeas_noisy.py Projet : MiroDur/Multiple-Scattering_Tensorflow

#%% Display results

# add noise
myres_noise = myres + 0.005*np.random.randn(muscat.Nz,muscat.Nx,muscat.Ny)

if(is_display): plt.title('XZ-PLot of the result (magn)'), plt.imshow(np.abs(myres[:,muscat.mysize[1]//2,:])), plt.colorbar(), plt.show()
if(is_display): plt.title('YZ-PLot of the result (angle)'),plt.imshow(np.abs(myres[:,:,muscat.mysize[2]//2])), plt.colorbar(), plt.show()

# dipslay spectrum
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(myres))**.2)[:,muscat.Nx//2,:]), plt.colorbar(), plt.show()
if(is_display): plt.imshow(np.abs(np.fft.fftshift(np.fft.fftn(myres))**.2)[muscat.Nz//2+1,:,:]), plt.colorbar(), plt.show()

if(is_display): plt.title('XZ'),plt.imshow(np.angle(myres_noise)[:,muscat.mysize[1]//2,:]), plt.colorbar(), plt.show()
if(is_display): plt.title('XZ'),plt.imshow(np.angle(myres_noise)[:,:,muscat.mysize[2]//2]), plt.colorbar(), plt.show()
if(is_display): plt.title('XY'),plt.imshow(np.angle(myres_noise)[muscat.mysize[0]//2,:,:]), plt.colorbar(), plt.show()

# save result 
np.save('myres.npy', myres)
np.save('myobj.npy', obj)
np.save('myres_noisy.npy', myres_noise)
'''
TODO: 
- Decaying Learning Rate
- Tensorboard INtegration
- Write Samples to disc
- Load external data in the placeholder
tf_helper.saveHDF5(results, 'Obj_Guess')
muscat.sess.run(tf.assign(muscat.TF_obj, results))

'''

Exemple #3

Fichier : listings_5_reconstruct_droplets_wzernikes.py Projet : MiroDur/Multiple-Scattering_Tensorflow

if (is_display):
    plt.imshow(
        np.abs(np.fft.fftshift(
            np.fft.fftn(myfwd))**.2)[:, muscat.mysize[1] //
                                     2, :]), plt.colorbar(), plt.show()
if (is_display):
    plt.imshow(
        np.abs(np.fft.fftshift(
            np.fft.fftn(myfwd))**.2)[muscat.mysize[0] //
                                     2, :, :]), plt.colorbar(), plt.show()
if (is_display):
    plt.imshow(
        np.abs(np.fft.fftshift(
            np.fft.fftn(myfwd))**.2)[:, :, muscat.mysize[2] //
                                     2]), plt.colorbar(), plt.show()
tf_helper.saveHDF5(np.abs(np.fft.fftshift(np.fft.fftn(myfwd))), 'FFT3D_FWD')

if (is_display):
    plt.title('XZ'), plt.imshow(
        np.abs(myfwd)[:,
                      muscat.mysize[1] // 2, :]), plt.colorbar(), plt.show()
if (is_display):
    plt.title('XZ'), plt.imshow(np.abs(myfwd)[:, :, muscat.mysize[2] //
                                              2]), plt.colorbar(), plt.show()
if (is_display):
    plt.title('XY'), plt.imshow(
        np.abs(myfwd)[muscat.mysize[0] //
                      2, :, :]), plt.colorbar(), plt.show()

if (is_display):
    plt.title('XZ'), plt.imshow(

Exemple #4

        np.abs(np_meas)[muscat.mysize[2] //
                        2, :, :]), plt.colorbar(), plt.show()

# this is the initial guess of the reconstruction
init_guess = np.angle(np_meas) - np.min(np.angle(np_meas))
init_guess = init_guess / np.max(init_guess) * muscat.dn
''' Evaluate the model '''
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(
    tf.assign(muscat.TF_obj,
              init_guess))  # assign abs of measurement as initial guess of

#my_res = sess.run(tf_my_res, feed_dict={muscat.TF_obj:obj})
#plt.imshow(np.abs(my_res[:,50,:]))
'''
TODO: 
- Decaying Learning Rate
- Tensorboard INtegration
- Write Samples to disc
- Load external data in the placeholder
tf_helper.saveHDF5(results, 'Obj_Guess')
muscat.sess.run(tf.assign(muscat.TF_obj, results))
- Why there is this asymmetry? 
'''

#%%
''' Optimize the model '''
print('Start optimizing')
for iterx in range(1, 1000):
    # try to optimize

Exemple #5

Fichier : listings_5_reconstruct_droplets.py Projet : MiroDur/Multiple-Scattering_Tensorflow

if (is_display):
    plt.imshow(
        np.abs(np.fft.fftshift(
            np.fft.fftn(myfwd))**.2)[:, muscat.mysize[1] //
                                     2, :]), plt.colorbar(), plt.show()
if (is_display):
    plt.imshow(
        np.abs(np.fft.fftshift(
            np.fft.fftn(myfwd))**.2)[muscat.mysize[0] //
                                     2, :, :]), plt.colorbar(), plt.show()
if (is_display):
    plt.imshow(
        np.abs(np.fft.fftshift(
            np.fft.fftn(myfwd))**.2)[:, :, muscat.mysize[2] //
                                     2]), plt.colorbar(), plt.show()
tf_helper.saveHDF5(np.abs(np.fft.fftshift(np.fft.fftn(myfwd))), 'FFT3D_FWD')

if (is_display):
    plt.title('XZ'), plt.imshow(
        np.abs(myfwd)[:,
                      muscat.mysize[1] // 2, :]), plt.colorbar(), plt.show()
if (is_display):
    plt.title('XZ'), plt.imshow(np.abs(myfwd)[:, :, muscat.mysize[2] //
                                              2]), plt.colorbar(), plt.show()
if (is_display):
    plt.title('XY'), plt.imshow(
        np.abs(myfwd)[muscat.mysize[0] //
                      2, :, :]), plt.colorbar(), plt.show()

if (is_display):
    plt.title('XZ'), plt.imshow(

Exemple #6

Fichier : listings_2_generatedata.py Projet : MiroDur/Multiple-Scattering_Tensorflow

''' write the results to disk'''
mm.saveResults()

if(mm.if_optimize):
    # feed tensor externally
    if(1):
        results = mm.allSumAmp_mes 
        results = np.angle(results)
        results = results - np.min(results)
        results = results/np.max(results)*(mm.dn-0.01)
#        results = np.flip(results, 0)
#        plt.imshow(results[:,mm.Nx/2,:]), plt.colorbar(), plt.show()
        

        tf_helper.saveHDF5(results, 'Obj_Guess')
        
    mm.sess.run(tf.assign(mm.TF_obj, results))
    
else:
    import sys; 
    print('sample was generated successfully')
    sys.exit(0)
    
    
print('Start optimizing')
tv_lambda = 1e-1
obj_reg_lambda = 1e6
my_learningrate =1e-4
gr_lambda = 1e-2