def save_profile_from_sdf(fname, nprofiles=3, direction='V', savecsv=True):
data, pixelsize, headerdic = wpu.load_sdf_file(fname)
saveFileSuf = fname.replace('.sdf', '')
if 'V' in direction:
xvec = wpu.realcoordvec(data.shape[0], pixelsize[0])[np.newaxis]
data2save = np.c_[xvec.T]
for j in np.linspace(0, np.shape(data)[1] - 1, nprofiles + 2, dtype=int):
data2save = np.c_[data2save, data[:, j]]
else:
xvec = wpu.realcoordvec(data.shape[1], pixelsize[1])[np.newaxis]
data2save = np.c_[xvec.T]
for i in np.linspace(0, np.shape(data)[0] - 1, nprofiles + 2, dtype=int):
data2save = np.c_[data2save, data[i, :]]
if savecsv:
wpu.save_csv_file(data2save,
wpu.get_unique_filename(saveFileSuf +
'_profiles_V', 'csv'),
headerList='bla')
return data2save, headerdic
def _lsq_fit_1D_parabola(yy, pixelsize):
xx = wpu.realcoordvec(yy.shape[0], pixelsize)
if np.all(np.isfinite(yy)): # if there is no nan
f = yy.flatten()
x = xx.flatten()
else:
argNotNAN = np.isfinite(yy)
f = yy[argNotNAN].flatten()
x = xx[argNotNAN].flatten()
X_matrix = np.vstack([x**2, x, x * 0.0 + 1]).T
beta_matrix = np.linalg.lstsq(X_matrix, f)[0]
fit = (beta_matrix[0] * xx**2 + beta_matrix[1] * xx + beta_matrix[2])
if np.all(np.isfinite(yy)):
mask = yy * 0.0 + 1.0
else:
mask = yy * 0.0 + 1.0
mask[~argNotNAN] = np.nan
return fit * mask, beta_matrix
sx, sy, \
error, step = wps.speckleDisplacement(image, image_ref,
halfsubwidth=halfsubwidth,
halfTemplateSize=halfTemplateSize,
subpixelResolution=subpixelResolution,
npointsmax=npointsmax,
ncores=ncores, taskPerCore=15,
verbose=True)
totalS = np.sqrt(sx**2 + sy**2)
xVec2 = wpu.realcoordvec(sx.shape[1], pixelsize*step)
yVec2 = wpu.realcoordvec(sx.shape[0], pixelsize*step)
# %%
# =============================================================================
# Save data in hdf5 format
# =============================================================================
fname_output = fname[:-4] + '_' + wpu.datetime_now_str() + ".h5"
f = h5.File(fname_output, "w")
h5rawdata = f.create_group('raw')
f.create_dataset("raw/image_sample", data=image)
f.create_dataset("raw/image_ref", data=image_ref)
def fit_radius_dpc(dpx,
dpy,
pixelsize,
radius4fit,
kwave,
saveFigFlag=False,
str4title=''):
xVec = wpu.realcoordvec(dpx.shape[1], pixelsize[1])
yVec = wpu.realcoordvec(dpx.shape[0], pixelsize[0])
lim_x = np.argwhere(xVec >= -radius4fit * 1.01)[0, 0]
lim_y = np.argwhere(yVec >= -radius4fit * 1.01)[0, 0]
xmatrix, ymatrix = np.meshgrid(xVec[lim_x:-lim_x + 1],
yVec[lim_y:-lim_y + 1])
fig = plt.figure(figsize=(14, 5))
fig.suptitle(str4title + 'Phase [rad]', fontsize=14)
ax1 = plt.subplot(121)
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
ax1.plot(xVec[lim_x:-lim_x + 1] * 1e6,
dpx[dpx.shape[1] // 4, lim_x:-lim_x + 1],
'-ob',
label='1/4')
ax1.plot(xVec[lim_x:-lim_x + 1] * 1e6,
dpx[dpx.shape[1] // 2, lim_x:-lim_x + 1],
'-or',
label='1/2')
ax1.plot(xVec[lim_x:-lim_x + 1] * 1e6,
dpx[dpx.shape[1] // 4 * 3, lim_x:-lim_x + 1],
'-og',
label='3/4')
lin_fitx = np.polyfit(xVec[lim_x:-lim_x + 1], dpx[dpx.shape[1] // 2,
lim_x:-lim_x + 1], 1)
lin_funcx = np.poly1d(lin_fitx)
ax1.plot(xVec[lim_x:-lim_x + 1] * 1e6,
lin_funcx(xVec[lim_x:-lim_x + 1]),
'--c',
lw=2,
label='Fit 1/2')
curvrad_x = kwave / (lin_fitx[0])
wpu.print_blue('lin_fitx[0] x: {:.3g} m'.format(lin_fitx[0]))
wpu.print_blue('lin_fitx[1] x: {:.3g} m'.format(lin_fitx[1]))
wpu.print_blue('Curvature Radius of WF x: {:.3g} m'.format(curvrad_x))
ax1.ticklabel_format(style='sci', axis='y', scilimits=(0, 1))
ax1.set_xlabel(r'[$\mu m$]')
ax1.set_ylabel('dpx [radians]')
ax1.legend(loc=0, fontsize='small')
ax1.set_title('Curvature Radius of WF {:.3g} m'.format(curvrad_x),
fontsize=16)
ax1.set_adjustable('box-forced')
ax2.plot(yVec[lim_y:-lim_y + 1] * 1e6,
dpy[lim_y:-lim_y + 1, dpy.shape[0] // 4],
'-ob',
label='1/4')
ax2.plot(yVec[lim_y:-lim_y + 1] * 1e6,
dpy[lim_y:-lim_y + 1, dpy.shape[0] // 2],
'-or',
label='1/2')
ax2.plot(yVec[lim_y:-lim_y + 1] * 1e6,
dpy[lim_y:-lim_y + 1, dpy.shape[0] // 4 * 3],
'-og',
label='3/4')
lin_fity = np.polyfit(yVec[lim_y:-lim_y + 1], dpy[lim_y:-lim_y + 1,
dpy.shape[0] // 2], 1)
lin_funcy = np.poly1d(lin_fity)
ax2.plot(yVec[lim_y:-lim_y + 1] * 1e6,
lin_funcy(yVec[lim_y:-lim_y + 1]),
'--c',
lw=2,
label='Fit 1/2')
curvrad_y = kwave / (lin_fity[0])
wpu.print_blue('Curvature Radius of WF y: {:.3g} m'.format(curvrad_y))
ax2.ticklabel_format(style='sci', axis='y', scilimits=(0, 1))
ax2.set_xlabel(r'[$\mu m$]')
ax2.set_ylabel('dpy [radians]')
ax2.legend(loc=0, fontsize='small')
ax2.set_title('Curvature Radius of WF {:.3g} m'.format(curvrad_y),
fontsize=16)
ax2.set_adjustable('box-forced')
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
plt.show(block=False)
error_raw = np.array(f['displacement/error'])[::stride, ::stride] xVec_raw = np.array(f['displacement/xvec'])[::stride] yVec_raw = np.array(f['displacement/yvec'])[::stride] #============================================================================== # %% Crop #============================================================================== idx4crop = wpu.graphical_roi_idx(np.sqrt(sx_raw**2 + sy_raw**2), verbose=True) sx = wpu.crop_matrix_at_indexes(sx_raw, idx4crop) sy = wpu.crop_matrix_at_indexes(sy_raw, idx4crop) error = wpu.crop_matrix_at_indexes(error_raw, idx4crop) xVec = wpu.realcoordvec(sx.shape[1], pixelsizeImg) yVec = wpu.realcoordvec(sx.shape[0], pixelsizeImg) xmatrix, ymatrix = np.meshgrid(xVec, yVec) #============================================================================== # %% Calculations of physical quantities #============================================================================== totalS = np.sqrt(sx**2 + sy**2) # Differenctial Phase dpx = kwave * np.arctan2(sx * pixelsizeDetector, distDet2sample) dpy = kwave * np.arctan2(sy * pixelsizeDetector, distDet2sample) # Differenctial Thickness
def curv_from_height(height,
virtual_pixelsize,
grazing_angle=0.0,
projectionFromDiv=1.0,
labels=[],
xlabel='x',
ylabel='Curvature',
titleStr='',
saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=height.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle // .00001 > 0:
projection = 1 / np.sin(grazing_angle) * projectionFromDiv
else:
projection = projectionFromDiv
projected_pixel = virtual_pixelsize * projection
xvec = wpu.realcoordvec(height.shape[0] - 2, projected_pixel)
print('projected_pixel')
print(projected_pixel)
plt.figure(figsize=(12, 12 * 9 / 16))
list_curv = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, height.shape[1]):
curv = np.diff(np.diff(height[:, j_line])) / projected_pixel**2
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
#factor_y, unit_y = wpu.choose_unit(curv)
list_curv.append(curv)
header.append(labels[j_line - 1])
plt.plot(xvec * factor_x,
curv,
next(ls_cycle),
c=next(lc_cycle),
label=labels[j_line - 1])
marginx = 0.1 * np.ptp(xvec * factor_x)
plt.xlim(
[np.min(xvec * factor_x) - marginx,
np.max(xvec * factor_x) + marginx])
plt.xlabel(xlabel + r' [$' + unit_x + ' m$]')
plt.ylabel(ylabel + r'[$m^{-1}$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle // .00001 > 0:
plt.title(titleStr + 'Mirror Curvature,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle * 1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Curvature')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_curv).T
header.append(ylabel + ' [1/m]')
if grazing_angle // .00001 > 0:
header.append(', grazing_angle = {:.4g}'.format(grazing_angle))
if projectionFromDiv // 1 != 1:
header.append('projection due divergence = ' +
'{:.2f}x'.format(projectionFromDiv))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(saveFileSuf + '_curv_' + xlabel,
'csv'),
headerList=header)
return np.asarray(list_curv).T
def curv_from_height(height, virtual_pixelsize,
grazing_angle=0.0, projectionFromDiv=1.0,
labels=[],
xlabel='x', ylabel='Curvature',
titleStr='', saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=height.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle//.00001 > 0:
projection = 1/np.sin(grazing_angle)*projectionFromDiv
else:
projection = projectionFromDiv
projected_pixel = virtual_pixelsize*projection
xvec = wpu.realcoordvec(height.shape[0]-2, projected_pixel)
print('projected_pixel')
print(projected_pixel)
plt.figure(figsize=(12, 12*9/16))
list_curv = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, height.shape[1]):
curv = np.diff(np.diff(height[:, j_line]))/projected_pixel**2
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
#factor_y, unit_y = wpu.choose_unit(curv)
list_curv.append(curv)
header.append(labels[j_line - 1])
plt.plot(xvec*factor_x, curv,
next(ls_cycle), c=next(lc_cycle),
label=labels[j_line - 1])
marginx = 0.1*np.ptp(xvec*factor_x)
plt.xlim([np.min(xvec*factor_x)-marginx,
np.max(xvec*factor_x)+marginx])
plt.xlabel(xlabel + r' [$' + unit_x + ' m$]')
plt.ylabel(ylabel + r'[$m^{-1}$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle//.00001 > 0:
plt.title(titleStr + 'Mirror Curvature,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle*1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Curvature')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_curv).T
header.append(ylabel + ' [1/m]')
if grazing_angle//.00001 > 0:
header.append(', grazing_angle = {:.4g}'.format(grazing_angle))
if projectionFromDiv//1 != 1:
header.append('projection due divergence = ' +
'{:.2f}x'.format(projectionFromDiv))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(saveFileSuf +
'_curv_' + xlabel, 'csv'),
headerList=header)
return np.asarray(list_curv).T
yVec_raw = np.array(f['displacement/yvec']) #============================================================================== # %% Crop #============================================================================== idx4crop = wpu.graphical_roi_idx(np.sqrt(sx_raw**2 + sy_raw**2), verbose=True) sx = wpu.crop_matrix_at_indexes(sx_raw, idx4crop) sy = wpu.crop_matrix_at_indexes(sy_raw, idx4crop) error = wpu.crop_matrix_at_indexes(error_raw, idx4crop) xVec = wpu.realcoordvec(sx.shape[1], pixelsizeImg) yVec = wpu.realcoordvec(sx.shape[0], pixelsizeImg) xmatrix, ymatrix = np.meshgrid(xVec, yVec) #============================================================================== # %% Calculations of physical quantities #============================================================================== totalS = np.sqrt(sx**2 + sy**2)
sx, sy, \
error, step = wps.speckleDisplacement(image, image_ref,
halfsubwidth=halfsubwidth,
halfTemplateSize=halfTemplateSize,
subpixelResolution=subpixelResolution,
npointsmax=npointsmax,
ncores=ncores, taskPerCore=15,
verbose=True)
totalS = np.sqrt(sx**2 + sy**2)
xVec2 = wpu.realcoordvec(sx.shape[1], pixelsize*step)
yVec2 = wpu.realcoordvec(sx.shape[0], pixelsize*step)
# %%
# =============================================================================
# Save data in hdf5 format
# =============================================================================
fname_output = fname[:-4] + '_' + wpu.datetime_now_str() + ".h5"
f = h5.File(fname_output, "w")
h5rawdata = f.create_group('raw')
f.create_dataset("raw/image_sample", data=image)
f.create_dataset("raw/image_ref", data=image_ref)
