angfact = np.zeros(shape_unwrp)
atany = np.zeros(shape_unwrp)
atanx = np.zeros(shape_unwrp)
org_phase = np.zeros(shape_unwrp)
org_phase_plot = np.zeros(shape_unwrp)
org_unwr = np.zeros(shape_unwrp)
#phase extraction
angfact = delta_i[..., Un_sol[1]] - delta_i[..., Un_sol[0]]
atany = Id_hat[..., Un_sol[0]]
atanx = (Id_hat[..., Un_sol[1]] - np.cos(angfact) * Id_hat[..., Un_sol[0]]) / np.sin(angfact) ## sin needs to be added here for arctan2 to know the correct sign of y and x
for k in range(len(Un_sol[0])):
org_phase[..., k] = np.arctan2(atany[..., k], atanx[..., k])
org_phase[..., k] = pw.filter_wrapped_phase(org_phase[..., k], k_phi[jjj])
org_unwr[...,k] = pw.unwrap_phase_dct(org_phase[..., k], xx_alg, yy_alg, ii, jj, N, N)
org_unwr[...,k] -= delta_i[..., Un_sol[0][k]]
mask = [np.sqrt((xx_alg) ** 2 + (yy_alg) ** 2) >= 1]
mask_tile = np.tile(mask, (org_phase.shape[-1],1,1)).T
org_mask = np.ma.array(org_unwr, mask = mask_tile)
mean_unwr = org_mask.mean(axis=(0,1))
org_unwr -= mean_unwr
org_med = np.median(org_unwr, axis = 2)
org_med_flat = org_med[xy_inside]
a_inter = np.linalg.lstsq(Zernike_2d, org_med_flat)[0]
piston_mat[kkk, jjj], rms_mat[kkk, jjj] = opt.fmin(rms_piston, 0, args = (j_max, a_inter, N, Z_mat, orig, mask, flipint), full_output = True)[:2]
#mins[kkk] = opt.fmin(rms_piston, 0, args = (j_max, a_inter, N, Z_mat, orig, mask, flipint))
k_phi = 17 ## filtering window size
f0 = 15
n = 2
butter_phase = np.zeros(org_phase.shape)
butter_unwr = np.zeros(butter_phase.shape)
[ny, nx] = butter_phase.shape[:2]
res = [2**kji for kji in range(15)]
nx_pad = np.where(res > np.tile(nx, len(res)))
nx_pad = res[nx_pad[0][0]]
dif_x = (nx_pad - int(nx)) / 2
for k in range(org_phase.shape[-1]):
org_pad = np.lib.pad(org_phase[..., k], dif_x, 'reflect')
butter_pad = pw.butter_filter(org_pad, n, f0)
butter_phase[..., k] = butter_pad[dif_x:nx_pad - dif_x,
dif_x:nx_pad - dif_x]
butter_unwr[..., k] = pw.unwrap_phase_dct(butter_phase[..., k], xx_alg,
yy_alg, ii, jj, N_int, N_int)
butter_unwr[..., k] -= delta_i[..., Un_sol[0][k]]
np.save(folder_name + "filtered_phase.npy", butter_unwr)
np.save(folder_name + "delta_i.npy", delta_i)
butter_mask = np.ma.array(butter_unwr, mask=mask_tile)
mean_butt = butter_mask.mean(axis=(0, 1))
butter_unwr -= mean_butt
## smoothing interferogram due to median
but_med = np.median(butter_unwr, axis=2)
but_med *= -1.0
if flip_bool:
but_med = np.fliplr(but_med)
##angfact = np.zeros(shape_unwrp)
##atany = np.zeros(shape_unwrp)
##atanx = np.zeros(shape_unwrp)
##org_phase = np.zeros(shape_unwrp)
##org_phase_plot = np.zeros(shape_unwrp)
org_unwr = np.zeros(shape_unwrp)
##
###phase extraction
##angfact = delta_i[..., Un_sol[1]] - delta_i[..., Un_sol[0]]
##atany = Id_hat[..., Un_sol[0]]
##atanx = (Id_hat[..., Un_sol[1]] - np.cos(angfact) * Id_hat[..., Un_sol[0]]) / np.sin(angfact) ## sin needs to be added here for arctan2 to know the correct sign of y and x
f, axarr = plt.subplots(1, org_phase.shape[-1], figsize=int_im_size)
for k in range(org_phase.shape[-1]):
##org_phase[..., k] = np.arctan2(atany[..., k], atanx[..., k])
org_unwr[..., k] = pw.unwrap_phase_dct(org_phase[..., k], xx_alg, yy_alg,
ii, jj, N, N)
org_unwr[..., k] -= delta_i[..., Un_sol[0][k]]
## remove piston
mean_unwr = np.mean(org_unwr, axis=(0, 1))
org_unwr -= mean_unwr
## plot everything
for k in range(org_phase.shape[-1]):
im = axarr[k].imshow(np.ma.array(org_unwr[..., k], mask=mask),
vmin=-10,
vmax=15)
set_subplot_interferograms(axarr[k], i=k)
make_colorbar(f, im)
if choise == 'y':
f.savefig(