def fourier_integration2DShift(del_f_del_x, delx, del_f_del_y, dely):
'''
This function is the direct use of the CONTINOUS formulation of
Frankot-Chellappa, eq 21 in the article:
T. Frankot and R. Chellappa
A Method for Enforcing Integrability in Shape from Shading Algorithms,
IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol 10,
No 4, Jul 1988
In addition, it uses the CONTINOUS shift property to avoid singularities
at zero frequencies
'''
fx, fy = np.meshgrid(np.fft.fftfreq(del_f_del_x.shape[1], delx),
np.fft.fftfreq(del_f_del_x.shape[0], dely))
xx, yy = wpu.realcoordmatrix(del_f_del_x.shape[1], delx,
del_f_del_x.shape[0], dely)
fo_x = -np.abs(fx[0, 1] / 15) # shift fx value
fo_y = -np.abs(fy[1, 0] / 15) # shift fy value
phaseShift = np.exp(2 * np.pi * 1j *
(fo_x * xx + fo_y * yy)) # exp factor for shift
mult_factor = 1 / (2 * np.pi * 1j) / (fx - fo_x - 1j * fy + 1j * fo_y)
bigGprime = fft2((del_f_del_x - 1j * del_f_del_y) * phaseShift)
bigG = bigGprime * mult_factor
func_g = ifft2(bigG) / phaseShift
func_g -= np.min(
np.real(func_g)) # since the integral have and undefined constant,
# here it is applied an arbritary offset
return func_g
dpc_x = np.array(fh5['dpcHorizontal']) dpc_y = np.array(fh5['dpcVertical']) phase = np.array(fh5['phase']) # ============================================================================= # %% parameters # ============================================================================= size = np.shape(dpc_x) pixelsize = .650*1e-6 xx, yy = wpu.realcoordmatrix(size[1], pixelsize, size[0], pixelsize) #============================================================================== # %% Integration #============================================================================== from wavepy.surface_from_grad import frankotchellappa, error_integration result = frankotchellappa(dpc_x, dpc_y, reflec_pad=True) result = np.real(result) result *= -1
title='Dark Field',
xlabel=r'x [$\mu m$]',
ylabel=r'y [$\mu m$]',
extent=wpu.extent_func(dpc_1d, pixelSize) * 1e6)
# %% DPC
dpc_1d = unwrap_phase(dpc_1d)
wpu.plot_slide_colorbar(dpc_1d / np.pi / 2.0,
title=r'DPC [$\pi rad$]',
xlabel=r'x [$\mu m$]',
ylabel=r'y [$\mu m$]',
extent=wpu.extent_func(dpc_1d, pixelSize) * 1e6)
# %%
xx, yy = wpu.realcoordmatrix(dpc_1d.shape[1], pixelSize, dpc_1d.shape[0],
pixelSize)
wpu.plot_profile(xx * 1e3,
yy * 1e3,
dpc_1d / np.pi / 2.0,
xlabel='[mm]',
ylabel='[mm]')
# %% chi2
plt.figure()
hist = plt.hist(chi2[np.where(chi2 < 10 * np.std(chi2))], 100, log=False)
plt.title(r'$\chi^2$', fontsize=14, weight='bold')
plt.show(block=False)
chi2_copy = np.copy(chi2)
data_dir = fname.rsplit('/', 1)[0]
print(fname)
print(data_dir)
os.chdir(data_dir)
os.makedirs('residuals', exist_ok=True)
os.chdir('residuals')
wpu.log_this(preffname=fname.split('.')[0].split('/')[-1] + '_fit',
inifname=inifname)
# %% Load Input File
if fname.split('.')[-1] == 'sdf':
thickness, pixelSize, headerdic = wpu.load_sdf_file(fname)
xx, yy = wpu.realcoordmatrix(thickness.shape[1], pixelSize[1],
thickness.shape[0], pixelSize[0])
elif fname.split('.')[-1] == 'pickle':
thickness, xx, yy = load_pickle_surf(fname, False)
thickness *= 1e-6
# thickness *= -1.0 # minus1 here
xx *= 1e-6
yy *= 1e-6
pixelSize = [np.mean(np.diff(xx[0, :])), np.mean(np.diff(yy[:, 0]))]
else:
wpu.print_red('ERROR: Wrong file type!')
exit(-1)
norm_abs_fftimg = abs_fftimg / np.nanmax(abs_fftimg)
img2 = np.log(norm_abs_fftimg + 1e-10)
# %% Rotation in the Reciprocal space
img_rotated, angle = wpu.rotate_img_graphical(img2, mode='edge')
norm_abs_fftimg = skimage.transform.rotate(norm_abs_fftimg,
-angle,
mode='edge')
# %% x and y axes
qx, qy = wpu.realcoordmatrix(img_rotated.shape[1],
pixelsize / dist2detector * kwave,
img_rotated.shape[0],
pixelsize / dist2detector * kwave)
rho_x, rho_y = wpu.reciprocalcoordmatrix(qx.shape[1], qy[1, 0] - qy[0, 0],
qy.shape[0], qx[0, 1] - qx[0, 0])
rho_x *= 2 * np.pi
rho_y *= 2 * np.pi
# %%
#plotThis = norm_abs_fftimg # *wpu.nan_mask_threshold(norm_abs_fftimg,1e-2*1j)
#plotThis = scipy.ndimage.uniform_filter(norm_abs_fftimg, size=(1,1))
plotThis = scipy.ndimage.uniform_filter(norm_abs_fftimg, size=(1, 1))
plotThis[plotThis >= .01] = 0.0
def error_integration(delx_f, delx_y, func,
pixelsize, errors=False,
shifthalfpixel=False, plot_flag=True):
func = np.real(func)
if shifthalfpixel:
func = wpu.shift_subpixel_2d(func, 2)
xx, yy = wpu.realcoordmatrix(func.shape[1], pixelsize[1],
func.shape[0], pixelsize[0])
midleX = xx.shape[0] // 2
midleY = xx.shape[1] // 2
grad_x, grad_y = _grad(func)
grad_x -= np.mean(grad_x)
grad_y -= np.mean(grad_y)
delx_f -= np.mean(delx_f)
delx_y -= np.mean(delx_y)
amp_x = np.max(delx_f) - np.min(delx_f)
amp_y = np.max(delx_y) - np.min(delx_y)
error_x = np.abs(grad_x - delx_f)/amp_x*100
error_y = np.abs(grad_y - delx_y)/amp_y*100
if plot_flag:
plt.figure(figsize=(14, 10))
ax1 = plt.subplot(221)
ax1.ticklabel_format(style='sci', axis='both', scilimits=(0, 1))
ax1.plot(xx[midleX, :], delx_f[midleX, :], '-kx',
markersize=10, label='dx data')
ax1.plot(xx[midleX, :], grad_x[midleX, :], '-r+',
markersize=10, label='dx reconstructed')
ax1.legend(loc=0)
ax2 = plt.subplot(223, sharex=ax1)
ax2.plot(xx[midleX, :],
error_x[midleX, :], '-g.', label='error x')
plt.title(r'$\mu$ = {:.2g}'.format(np.mean(error_x[midleX, :])))
ax2.legend(loc=0)
ax3 = plt.subplot(222, sharex=ax1, sharey=ax1)
ax3.plot(yy[:, midleY], delx_y[:, midleY], '-kx',
markersize=10, label='dy data')
ax3.plot(yy[:, midleY], grad_y[:, midleY], '-r+',
markersize=10, label='dy reconstructed')
ax3.legend(loc=0)
ax4 = plt.subplot(224, sharex=ax1, sharey=ax2)
ax4.plot(yy[:, midleY],
error_y[:, midleY], '-g.', label='error y')
plt.title(r'$\mu$ = {:.2g}'.format(np.mean(error_y[:, midleY])))
ax4.legend(loc=0)
plt.suptitle('Error integration', fontsize=22)
plt.show(block=False)
if errors:
return error_x, error_y
import numpy as np
import matplotlib.pyplot as plt
import wavepy.utils as wpu
# =============================================================================
# %% parameters
# =============================================================================
size = (501,491)
pixelsize = .0093085684600
xx, yy = wpu.realcoordmatrix(size[1], pixelsize, size[0], pixelsize)
#===============================================================================
# %% Functions
#===============================================================================
parab_waved = -xx**2/.2 -yy**2/.3 + 0.5*np.cos(100*np.sqrt(xx**2+yy**2))
fermidirac = 1.0/(1 + np.exp((np.sqrt(xx**2+yy**2/.5)-1.1)/.1))
xo = -2
yo = -3.54356
trigfunc = 5*np.sin((xx-xo)+(yy-yo)) + 2*np.sin((xx-xo)*6*(yy-yo)) + \
np.sin((xx-xo)*5) + .5*np.sin((xx-xo)**2*5)
# %%
pixelsize = .1e-6
dist2detector = 450e-3
phenergy = 778.00
wavelength = hc / phenergy
kwave = 2 * np.pi / wavelength
# %% Make the mask
positions_x = np.array([-22.5, -7.5, -1.5, 0, 3, 12, 0, 0, 0, 0, 0]) * 1e-6
positions_y = np.array([0, 0, 0, 0, 0, 0, -22.5, -7.5, -1.5, 3, 12]) * 1e-6
xx, yy = wpu.realcoordmatrix(2001, pixelsize, 2001, pixelsize)
# abs mask
mask_NRA = xx * 0.0
for i in range(positions_x.size):
xo, yo = positions_x[i], positions_y[i]
mask_NRA[(np.sqrt((xx - xo)**2 + (yy - yo)**2) < .50000000e-6)] += 1.0
# phase mask
#mask_NRA = xx*0.0*1j + 1.0
#
#for i in range(positions_x.size):
#
# xo, yo = positions_x[i], positions_y[i]
wpu.plot_slide_colorbar(dk_field, title='Dark Field',
xlabel=r'x [$\mu m$]',
ylabel=r'y [$\mu m$]',
extent=wpu.extent_func(dpc_1d, pixelSize)*1e6)
# %% DPC
dpc_1d = unwrap_phase(dpc_1d)
wpu.plot_slide_colorbar(dpc_1d/np.pi/2.0,
title=r'DPC [$\pi rad$]',
xlabel=r'x [$\mu m$]',
ylabel=r'y [$\mu m$]',
extent=wpu.extent_func(dpc_1d, pixelSize)*1e6)
# %%
xx, yy = wpu.realcoordmatrix(dpc_1d.shape[1], pixelSize,
dpc_1d.shape[0], pixelSize)
wpu.plot_profile(xx*1e3, yy*1e3, dpc_1d/np.pi/2.0,
xlabel='[mm]', ylabel='[mm]')
# %% chi2
plt.figure()
hist = plt.hist(chi2[np.where(chi2 < 10*np.std(chi2))], 100, log=False)
plt.title(r'$\chi^2$', fontsize=14, weight='bold')
plt.show(block=False)
chi2_copy = np.copy(chi2)
wpu.plot_slide_colorbar(chi2_copy, title=r'$\chi^2$ sample',
xlabel=r'x [$\mu m$ ]',
ylabel=r'y [$\mu m$ ]',
ylabel = figx.axes[0].images[0].axes.properties()['ylabel']
cmap = figx.axes[0].images[0].properties()['cmap'].name
[[vmin, vmax], cmap] = wpu.plot_slide_colorbar(data,
title=title,
xlabel=xlabel,
ylabel=ylabel,
extent=[xi, xf, yi, yf])
# plot surface
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
pixelsize = [(xf - xi) / data.shape[1], (yf - yi) / data.shape[0]]
wpu.realcoordmatrix()
xxGrid, yyGrid = np.meshgrid(np.linspace(xi, xf, data.shape[1]),
np.linspace(yi, yf, data.shape[0]),
indexing='xy')
if np.all(np.isfinite(data)):
surf = ax.plot_surface(xxGrid,
yyGrid,
data,
vmin=vmin,
vmax=vmax,
rstride=data.shape[0] // 101 + 1,
cstride=data.shape[1] // 101 + 1,
cmap=cmap,
linewidth=.3,
#==============================================================================
# %% Crop ROI
#==============================================================================
#idx4crop = [635, 1993, 841, 1792]
#img = wpu.crop_matrix_at_indexes(spe_file.data[0], idx4crop)
img, idx4crop = wpu.crop_graphic_image(spe_file.data[0], verbose=False)
print(idx4crop)
img = wpu.pad_to_make_square(img, mode='edge')
# %% Plot Detector coordinates
xx, yy = wpu.realcoordmatrix(img.shape[1], pixelsize, img.shape[0], pixelsize)
plt.figure(figsize=plt.figaspect(.6))
plt.contourf(xx * 1e3,
yy * 1e3,
img / np.nanmax(img),
201,
cmap='plasma',
vmin=0.1)
plt.xlabel(r'$x$ [mm]')
plt.ylabel(r'$y$ [mm]')
plt.title(r'Data ')
plt.colorbar()
plt.show(block=True)
#xVec = wpu.realcoordvec(img.shape[1], pixelsize)
