def save_parameters_config(holo, center, prop_dist, phase, roi, folder, image_numbers, bs_diam, propagate=False):
'''
old function to save parameters in a config file and reconstruction as numpy array. Replaced by saving everything in a hdf file with save_parameters()
reconstruct the shifted and masked hologram (propagation and phase shift are performed here.)
save all parameters in numpy-files (holo and beamstop) and a config file (rest)
if the folder you put in does not exist, it will be created.
'''
image_numbers = np.array(image_numbers)
if not(os.path.exists(folder)):
print("Creating folder " + folder)
os.mkdir(folder)
if propagate:
recon = fth.reconstruct(fth.propagate(holo, prop_dist*1e-6)*np.exp(1j*phase))
else:
recon = fth.reconstruct(holo*np.exp(1j*phase))
print('Shifted phase by %f.'%phase)
if image_numbers.size == 1:
im = image_numbers
elif np.isnan(image_numbers[0]):
im = image_numbers[1]
else:
im = image_numbers[0]
np.save(folder + '%i_recon'%im, recon[roi[2]:roi[3], roi[0]:roi[1]])
fth.save_config(image_numbers, center, bs_diam, prop_dist, phase, roi, folder + '%i_config.ini'%im)
return
def autophase_similar2(reco, reco_original, o_mask, guess=0):
'''find phase such that reco and reco_original are as similar as possible. Searches around guess +/- 0.5'''
#f = lambda phase : phaserot(reco,phase).imag.std()/phaserot(reco,phase).real.std()
f = lambda phase: np.sum(
np.real((fth.reconstruct(phaserot(reco, phase)) - fth.reconstruct(
reco_original)) * o_mask)**2)
phase_guess = np.linspace(guess - 3.14, guess + 3.14, 40)
error = np.array([f(phase) for phase in phase_guess])
try:
phase = least_squares(f, phase_guess[error.argmin()])
return phase.x[0]
except:
return phase_guess[error.argmin()]
def set_roi(holo, scale = (1,99)):
"""
Select a ROI somewhat interactively, not used anymore!
Input the shfited and masked hologram as returned from recon_allNew.
Returns the four input fields. When you are finished, you can save the positions of the fields.
-------
author: KG 2019
"""
recon=fth.reconstruct(holo)
mi, ma = np.percentile(np.real(recon), scale)
fig, ax = plt.subplots()
ax = plt.imshow(np.real(recon), cmap='gray', vmin=mi, vmax=ma)
plt.colorbar()
style = {'description_width': 'initial'}
ROIx1 = widgets.IntText(value=None, description='ROI x1 coordinate:', disabled=False, style=style)
ROIx2 = widgets.IntText(value=None, description='ROI x2 coordinate:', disabled=False, style=style)
ROIy1 = widgets.IntText(value=None, description='ROI y1 coordinate:', disabled=False, style=style)
ROIy2 = widgets.IntText(value=None, description='ROI y2 coordinate:', disabled=False, style=style)
button = widgets.Button(description='Finished')
display(ROIx1, ROIx2, ROIy1, ROIy2, button)
return (ROIx1, ROIx2, ROIy1, ROIy2, button)
def p(x, y):
image = fth.reconstruct(
fth.propagate(holo,
x * 1e-6,
ccd_dist=ccd_dist,
energy=energy,
px_size=px_size) * np.exp(1j * y))
mir, mar = np.percentile(np.real(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]),
scale)
mii, mai = np.percentile(np.imag(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]),
scale)
ax1 = axs[0].imshow(np.real(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]),
cmap='gray',
vmin=mir,
vmax=mar)
#fig.colorbar(ax1, ax=axs[0], fraction=0.046, pad=0.04)
axs[0].set_title("Real Part")
ax2 = axs[1].imshow(np.imag(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]),
cmap='gray',
vmin=mii,
vmax=mai)
#fig.colorbar(ax2, ax=axs[1], fraction=0.046, pad=0.04)
axs[1].set_title("Imaginary Part")
fig.tight_layout()
print('REAL: max=%i, min=%i' %
(np.max(np.real(image)), np.min(np.real(image))))
print('IMAG: max=%i, min=%i' %
(np.max(np.imag(image)), np.min(np.imag(image))))
return
def p(x, y, fx, fy):
image = fth.reconstruct(fth.propagate(holo, x*1e-6, experimental_setup)*np.exp(1j*y))
simage = fth.sub_pixel_centering(image, fx, fy)
ax1 = axs[0].imshow(np.real(simage[roi[2]:roi[3], roi[0]:roi[1]]), cmap='gray')
axs[0].set_title("Real Part")
ax2 = axs[1].imshow(np.imag(simage[roi[2]:roi[3], roi[0]:roi[1]]), cmap='gray')
axs[1].set_title("Imaginary Part")
fig.tight_layout()
print('REAL: max=%i, min=%i'%(np.max(np.real(simage)), np.min(np.real(simage))))
print('IMAG: max=%i, min=%i'%(np.max(np.imag(simage)), np.min(np.imag(simage))))
return
def p(x):
image = fth.reconstruct(holo*np.exp(1j*x))
ax1 = axs[0].imshow(np.real(image[roi[2]:roi[3], roi[0]:roi[1]]), cmap='gray')
#fig.colorbar(ax1, ax=axs[0], fraction=0.046, pad=0.04)
axs[0].set_title("Real Part")
ax2 = axs[1].imshow(np.imag(image[roi[2]:roi[3], roi[0]:roi[1]]), cmap='gray')
#fig.colorbar(ax2, ax=axs[1], fraction=0.046, pad=0.04)
axs[1].set_title("Imaginary Part")
fig.tight_layout()
print('REAL: max=%i, min=%i'%(np.max(np.real(image)), np.min(np.real(image))))
print('IMAG: max=%i, min=%i'%(np.max(np.imag(image)), np.min(np.imag(image))))
return
def p(x,y,phi):
#create bessel function
J=x*2*spec.j1(R*y*Q)/(R*y*Q)
J[(npx//2),npy//2]=x
#Wiener filtering
J_w=(J**2+phi)/J
image = fth.reconstruct(holo/J_w)
mir, mar = np.percentile(np.real(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]), scale)
mii, mai = np.percentile(np.imag(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]), scale)
ax1 = axs[0].imshow(np.real(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]), cmap='gray', vmin = mir, vmax = mar)
#fig.colorbar(ax1, ax=axs[0], fraction=0.046, pad=0.04)
axs[0].set_title("Real Part")
ax2 = axs[1].imshow(np.imag(image[ROI[2]:ROI[3], ROI[0]:ROI[1]]), cmap='gray', vmin = mii, vmax = mai)
#fig.colorbar(ax2, ax=axs[1], fraction=0.046, pad=0.04)
axs[1].set_title("Imaginary Part")
fig.tight_layout()
print('REAL: max=%i, min=%i'%(np.max(np.real(image)), np.min(np.real(image))))
print('IMAG: max=%i, min=%i'%(np.max(np.imag(image)), np.min(np.imag(image))))
return