def _default_plot_for_pickle(data, pixelsize, patternforpickle='graph',
title='', xlabel=r'$x$', ylabel=r'$y$', ctitle='',
removeSpark=True, cmap='viridis'):
if removeSpark:
vmin = wpu.mean_plus_n_sigma(data, -6)
vmax = wpu.mean_plus_n_sigma(data, 6)
else:
vmin = np.min(data)
vmax = np.max(data)
# vmax = np.max((np.abs(vmin), np.abs(vmax)))
# vmin = -vmax
fig = plt.figure(figsize=(12, 9.5))
plt.imshow(data,
extent=wpu.extent_func(data, pixelsize)*1e6,
cmap=cmap, vmin=vmin, vmax=vmax)
plt.xlabel(xlabel, fontsize=24)
plt.ylabel(ylabel, fontsize=24)
cbar = plt.colorbar(shrink=0.9)
cbar.ax.set_title(ctitle, y=1.01)
plt.title(title, fontsize=24, weight='bold', y=1.01)
wpu.save_figs_with_idx_pickle(fig, patternforpickle)
plt.show(block=True)
def plot_DPC(dpc01,
dpc10,
pixelsize,
titleStr='',
saveFigFlag=False,
saveFileSuf='graph'):
'''
TODO: Write Docstring
Plot differencial phase signal
'''
if titleStr is not '':
titleStr = ', ' + titleStr
factor, unit_xy = wpu.choose_unit(np.sqrt(dpc01.size) * pixelsize[0])
dpc01_plot = dpc01 * pixelsize[1] / np.pi
dpc10_plot = dpc10 * pixelsize[0] / np.pi
vlim01 = np.max((np.abs(wpu.mean_plus_n_sigma(dpc01_plot, -5)),
np.abs(wpu.mean_plus_n_sigma(dpc01_plot, 5))))
vlim10 = np.max((np.abs(wpu.mean_plus_n_sigma(dpc10_plot, -5)),
np.abs(wpu.mean_plus_n_sigma(dpc10_plot, 5))))
plt.figure(figsize=(12, 6))
plt.subplot(121)
plt.imshow(dpc01_plot,
cmap='RdGy_r',
vmin=-vlim01,
vmax=vlim01,
extent=wpu.extent_func(dpc01_plot, pixelsize) * factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('DPC - Horizontal', fontsize=18, weight='bold') # 01
plt.subplot(122)
plt.imshow(dpc10_plot,
cmap='RdGy_r',
vmin=-vlim10,
vmax=vlim10,
extent=wpu.extent_func(dpc10_plot, pixelsize) * factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('DPC - Vertical', fontsize=18, weight='bold')
plt.suptitle('Differential Phase ' + r'[$\pi$ rad]' + titleStr,
fontsize=18,
weight='bold')
plt.tight_layout(rect=[0, 0, 1, 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf)
plt.show(block=False)
def dpc_integration(dpc01,
dpc10,
pixelsize,
idx4crop=[0, -1, 0, -1],
plotErrorIntegration=False,
saveFileSuf=None,
shifthalfpixel=False,
method='FC'):
'''
TODO: Write Docstring
Integration of DPC to obtain phase. Currently only supports
Frankot Chellappa
'''
if idx4crop == '':
vmin = wpu.mean_plus_n_sigma(dpc01**2 + dpc10**2, -3)
vmax = wpu.mean_plus_n_sigma(dpc01**2 + dpc10**2, 3)
_, idx = wpu.crop_graphic_image(dpc01**2 + dpc10**2,
kargs4graph={
'cmap': 'viridis',
'vmin': vmin,
'vmax': vmax
})
else:
idx = idx4crop
dpc01 = wpu.crop_matrix_at_indexes(dpc01, idx)
dpc10 = wpu.crop_matrix_at_indexes(dpc10, idx)
if method == 'FC':
phase = wps.frankotchellappa(dpc01 * pixelsize[1],
dpc10 * pixelsize[0],
reflec_pad=True)
phase = np.real(phase)
else:
wpu.print_red('ERROR: Unknown integration method: ' + method)
if plotErrorIntegration:
wps.error_integration(dpc01 * pixelsize[1],
dpc10 * pixelsize[0],
phase,
pixelsize,
errors=False,
shifthalfpixel=shifthalfpixel,
plot_flag=True)
if saveFileSuf is not None:
wpu.save_figs_with_idx(saveFileSuf)
return phase
def plot_intensities_harms(int00,
int01,
int10,
pixelsize,
titleStr,
saveFigFlag=False,
saveFileSuf='graph'):
# Plot Real image (intensity)
if titleStr is not '':
titleStr = ', ' + titleStr
factor, unit_xy = wpu.choose_unit(np.sqrt(int00.size) * pixelsize[0])
plt.figure(figsize=(14, 6))
plt.subplot(131)
plt.imshow(int00,
cmap='viridis',
vmax=wpu.mean_plus_n_sigma(int00, 4),
extent=wpu.extent_func(int00, pixelsize) * factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('00', fontsize=18, weight='bold')
plt.subplot(132)
plt.imshow(int01,
cmap='viridis',
vmax=wpu.mean_plus_n_sigma(int01, 4),
extent=wpu.extent_func(int01, pixelsize) * factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('01', fontsize=18, weight='bold')
plt.subplot(133)
plt.imshow(int10,
cmap='viridis',
vmax=wpu.mean_plus_n_sigma(int10, 4),
extent=wpu.extent_func(int10, pixelsize) * factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('10', fontsize=18, weight='bold')
plt.suptitle('Absorption obtained from the Harmonics' + titleStr,
fontsize=18,
weight='bold')
plt.tight_layout(rect=[0, 0, 1, 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf)
plt.show(block=False)
def plot_DPC(dpc01, dpc10,
pixelsize, titleStr='',
saveFigFlag=False, saveFileSuf='graph'):
'''
TODO: Write Docstring
Plot differencial phase signal
'''
if titleStr is not '':
titleStr = ', ' + titleStr
factor, unit_xy = wpu.choose_unit(np.sqrt(dpc01.size)*pixelsize[0])
dpc01_plot = dpc01*pixelsize[1]/np.pi
dpc10_plot = dpc10*pixelsize[0]/np.pi
vlim01 = np.max((np.abs(wpu.mean_plus_n_sigma(dpc01_plot, -5)),
np.abs(wpu.mean_plus_n_sigma(dpc01_plot, 5))))
vlim10 = np.max((np.abs(wpu.mean_plus_n_sigma(dpc10_plot, -5)),
np.abs(wpu.mean_plus_n_sigma(dpc10_plot, 5))))
plt.figure(figsize=(12, 6))
plt.subplot(121)
plt.imshow(dpc01_plot, cmap='RdGy_r',
vmin=-vlim01, vmax=vlim01,
extent=wpu.extent_func(dpc01_plot, pixelsize)*factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('DPC - Horizontal', fontsize=18, weight='bold') # 01
plt.subplot(122)
plt.imshow(dpc10_plot, cmap='RdGy_r',
vmin=-vlim10, vmax=vlim10,
extent=wpu.extent_func(dpc10_plot, pixelsize)*factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('DPC - Vertical', fontsize=18,
weight='bold')
plt.suptitle('Differential Phase ' + r'[$\pi$ rad]' + titleStr,
fontsize=18, weight='bold')
plt.tight_layout(rect=[0, 0, 1, 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf)
plt.show(block=False)
def plot_dark_field(darkField01,
darkField10,
pixelsize,
titleStr='',
saveFigFlag=False,
saveFileSuf='graph'):
'''
TODO: Write Docstring
Plot Dark field
'''
if titleStr is not '':
titleStr = ', ' + titleStr
factor, unit_xy = wpu.choose_unit(np.sqrt(darkField01.size) * pixelsize[0])
plt.figure(figsize=(14, 6))
plt.subplot(121)
plt.imshow(darkField01,
cmap='viridis',
vmax=wpu.mean_plus_n_sigma(darkField01, 4),
extent=wpu.extent_func(darkField01, pixelsize) * factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('Horizontal', fontsize=18, weight='bold') # 01
plt.subplot(122)
plt.imshow(darkField10,
cmap='viridis',
vmax=wpu.mean_plus_n_sigma(darkField01, 4),
extent=wpu.extent_func(darkField10, pixelsize) * factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('Vertical', fontsize=18, weight='bold') # 10
plt.suptitle('Dark Field', fontsize=18, weight='bold')
plt.tight_layout(rect=[0, 0, 1, 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf)
plt.show(block=False)
def plot_intensities_harms(int00, int01, int10,
pixelsize, titleStr,
saveFigFlag=False, saveFileSuf='graph'):
# Plot Real image (intensity)
if titleStr is not '':
titleStr = ', ' + titleStr
factor, unit_xy = wpu.choose_unit(np.sqrt(int00.size)*pixelsize[0])
plt.figure(figsize=(14, 6))
plt.subplot(131)
plt.imshow(int00, cmap='viridis',
vmax=wpu.mean_plus_n_sigma(int00, 4),
extent=wpu.extent_func(int00, pixelsize)*factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('00', fontsize=18, weight='bold')
plt.subplot(132)
plt.imshow(int01, cmap='viridis',
vmax=wpu.mean_plus_n_sigma(int01, 4),
extent=wpu.extent_func(int01, pixelsize)*factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('01', fontsize=18, weight='bold')
plt.subplot(133)
plt.imshow(int10, cmap='viridis',
vmax=wpu.mean_plus_n_sigma(int10, 4),
extent=wpu.extent_func(int10, pixelsize)*factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('10', fontsize=18, weight='bold')
plt.suptitle('Absorption obtained from the Harmonics' + titleStr,
fontsize=18, weight='bold')
plt.tight_layout(rect=[0, 0, 1, 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf)
plt.show(block=False)
def dpc_integration(dpc01, dpc10, pixelsize, idx4crop=[0, -1, 0, -1],
plotErrorIntegration=False,
saveFileSuf=None,
shifthalfpixel=False, method='FC'):
'''
TODO: Write Docstring
Integration of DPC to obtain phase. Currently only supports
Frankot Chellappa
'''
if idx4crop == '':
vmin = wpu.mean_plus_n_sigma(dpc01**2+dpc10**2, -3)
vmax = wpu.mean_plus_n_sigma(dpc01**2+dpc10**2, 3)
_, idx = wpu.crop_graphic_image(dpc01**2+dpc10**2,
kargs4graph={'cmap': 'viridis',
'vmin': vmin,
'vmax': vmax})
else:
idx = idx4crop
dpc01 = wpu.crop_matrix_at_indexes(dpc01, idx)
dpc10 = wpu.crop_matrix_at_indexes(dpc10, idx)
if method == 'FC':
phase = wps.frankotchellappa(dpc01*pixelsize[1],
dpc10*pixelsize[0],
reflec_pad=True)
phase = np.real(phase)
else:
wpu.print_red('ERROR: Unknown integration method: ' + method)
if plotErrorIntegration:
wps.error_integration(dpc01*pixelsize[1],
dpc10*pixelsize[0],
phase, pixelsize, errors=False,
shifthalfpixel=shifthalfpixel, plot_flag=True)
if saveFileSuf is not None:
wpu.save_figs_with_idx(saveFileSuf)
return phase
def plot_dark_field(darkField01, darkField10,
pixelsize, titleStr='',
saveFigFlag=False, saveFileSuf='graph'):
'''
TODO: Write Docstring
Plot Dark field
'''
if titleStr is not '':
titleStr = ', ' + titleStr
factor, unit_xy = wpu.choose_unit(np.sqrt(darkField01.size)*pixelsize[0])
plt.figure(figsize=(14, 6))
plt.subplot(121)
plt.imshow(darkField01, cmap='viridis',
vmax=wpu.mean_plus_n_sigma(darkField01, 4),
extent=wpu.extent_func(darkField01, pixelsize)*factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('Horizontal', fontsize=18, weight='bold') # 01
plt.subplot(122)
plt.imshow(darkField10, cmap='viridis',
vmax=wpu.mean_plus_n_sigma(darkField01, 4),
extent=wpu.extent_func(darkField10, pixelsize)*factor)
plt.xlabel(r'$[{0} m]$'.format(unit_xy))
plt.ylabel(r'$[{0} m]$'.format(unit_xy))
plt.colorbar(shrink=0.5)
plt.title('Vertical', fontsize=18, weight='bold') # 10
plt.suptitle('Dark Field', fontsize=18, weight='bold')
plt.tight_layout(rect=[0, 0, 1, 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf)
plt.show(block=False)
def plot_residual_parabolic_lens_2d(thickness,
pixelsize,
fitted,
fitParameters,
saveFigFlag=False,
savePickle=False,
str4title='',
saveSdfData=False,
vlimErrSigma=1,
plotProfileFlag=True,
plot3dFlag=True,
makeAnimation=False):
xmatrix, ymatrix = wpu.grid_coord(thickness, pixelsize)
errorThickness = thickness - fitted
argNotNAN = np.isfinite(errorThickness)
factorx, unitx = wpu.choose_unit(xmatrix)
factory, unity = wpu.choose_unit(ymatrix)
factorz, unitz = wpu.choose_unit(errorThickness[argNotNAN])
ptp = np.ptp(errorThickness[argNotNAN].flatten() * factorz)
wpu.print_red('PV: {0:4.3g} '.format(ptp) + unitz[-1] + 'm')
sigmaError = np.std(errorThickness[argNotNAN].flatten() * factorz)
wpu.print_red('SDV: {0:4.3g} '.format(sigmaError) + unitz[-1] + 'm')
str4title += r'Residual, ' + \
r'R $= {:.4g} \mu m$,'.format(fitParameters[0]*1e6) + '\n' + \
r'PV $= {0:.2f}$ '.format(ptp) + '$' + unitz + ' m$, '\
'SDV $= {0:.2f}$ '.format(sigmaError) + '$' + unitz + ' m$'
# Plot Histogram
plt.figure(figsize=(7, 8))
plt.hist(errorThickness[argNotNAN] * factorz,
100,
color='r',
histtype='step')
plt.xlabel(r'Residual [$' + unitz + ' m$ ]')
plt.title(str4title)
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
plt.show(block=False)
# Plot Profiles
vlimErr = wpu.mean_plus_n_sigma(errorThickness[argNotNAN] * factorz,
vlimErrSigma / 2)
cmap4graph = plt.cm.Spectral_r
cmap4graph.set_over('m')
cmap4graph.set_under('c')
if plotProfileFlag:
wpu.plot_profile(xmatrix * factorx,
ymatrix * factory,
errorThickness * factorz,
title=str4title,
xlabel=r'[$' + unitx + ' m$ ]',
ylabel=r'[$' + unity + ' m$ ]',
zlabel=r'[$' + unitz + ' m$ ]',
arg4main={
'cmap': 'Spectral_r',
'vmin': -vlimErr,
'vmax': vlimErr,
'extend': 'both'
})
if savePickle or saveFigFlag:
fig = plt.figure(figsize=(10, 7))
cf = plt.contourf(xmatrix * factorx,
ymatrix * factory,
errorThickness * factorz,
256,
cmap=cmap4graph,
extend='both')
plt.clim(-vlimErr, vlimErr)
plt.contour(cf, levels=cf.levels[::32], colors='gray')
plt.xlabel(r'[$' + unitx + ' m$ ]', fontsize=22)
plt.ylabel(r'[$' + unity + ' m$ ]', fontsize=22)
plt.title(str4title, fontsize=22)
cbar = plt.colorbar(cf, shrink=.8, aspect=20)
# cbar.set_clim(-vlimErr, vlimErr)
cbar.ax.set_title(r'[$' + unitz + ' m$ ]', y=1.01)
plt.gca().set_aspect('equal', adjustable='box')
plt.grid(color='grey')
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
if savePickle:
wpu.save_figs_with_idx_pickle(fig, fname2save)
plt.show(block=True)
# Plot 3D
if plot3dFlag:
wpu.print_red('MESSAGE: Ploting 3d in the background')
fig = plt.figure(figsize=(10, 7), facecolor="white")
ax = fig.gca(projection='3d')
plt.tight_layout(pad=2.5)
surf = ax.plot_trisurf(xmatrix[argNotNAN].flatten() * factorx,
ymatrix[argNotNAN].flatten() * factory,
errorThickness[argNotNAN].flatten() * factorz,
vmin=-vlimErr,
vmax=vlimErr,
cmap=cmap4graph,
linewidth=0.1,
shade=False)
ax.view_init(azim=-120, elev=40)
plt.xlabel(r'$x$ [$' + unitx + ' m$ ]')
plt.ylabel(r'$y$ [$' + unity + ' m$ ]')
plt.title(str4title)
cbar = plt.colorbar(surf, shrink=.8, aspect=20, extend='both')
cbar.ax.set_title(r'[$' + unitz + ' m$ ]', y=1.01)
plt.tight_layout()
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
ax.view_init(azim=690, elev=40)
wpu.save_figs_with_idx(fname2save, extension='png')
if makeAnimation:
# plt.show(block=False)
plt.pause(1.0)
wpu.rocking_3d_figure(ax,
wpu.get_unique_filename(fname2save, 'gif'),
elevOffset=45,
azimOffset=60,
elevAmp=0,
azimAmpl=-1,
dpi=80,
npoints=5)
plt.pause(1.0)
plt.close('all')
# plt.show(block=True)
if saveSdfData:
mask_for_sdf = errorThickness * 0.0
mask_for_sdf[~argNotNAN] = 1.0
errorThickness[~argNotNAN] = 00000000
wpu.save_sdf_file(
errorThickness, pixelsize,
wpu.get_unique_filename(fname2save + '_residual', 'sdf'))
wpu.save_sdf_file(
mask_for_sdf, pixelsize,
wpu.get_unique_filename(fname2save + '_residual_mask', 'sdf'))
return sigmaError / factorz, ptp / factorz
def _n_profiles_H_V(arrayH,
arrayV,
virtual_pixelsize,
zlabel=r'z',
titleH='Horiz',
titleV='Vert',
nprofiles=5,
filter_width=0,
remove2ndOrder=False,
saveFileSuf='',
saveFigFlag=True):
xxGrid, yyGrid = wpu.grid_coord(arrayH, virtual_pixelsize)
fit_coefs = [[], []]
data2saveH = None
data2saveV = None
labels_H = None
labels_V = None
plt.rcParams['lines.markersize'] = 4
plt.rcParams['lines.linewidth'] = 2
# Horizontal
if np.all(np.isfinite(arrayH)):
plt.figure(figsize=(12, 12 * 9 / 16))
xvec = xxGrid[0, :]
data2saveH = np.c_[xvec]
header = ['x [m]']
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_H = []
for i, row in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayV)[0] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
if filter_width != 0:
yvec = arrayH[row - filter_width:row + filter_width, :]
yvec = np.sum(yvec, 0) / filter_width / 2
else:
yvec = arrayH[row, :]
lc.append(next(lc_jet))
p01 = np.polyfit(xvec, yvec, 1)
fit_coefs[0].append(p01)
if remove2ndOrder:
yvec -= p01[0] * xvec + p01[1]
plt.plot(xvec * 1e6,
yvec,
next(ls_cycle),
color=lc[i - 1],
label=str(row))
if not remove2ndOrder:
plt.plot(xvec * 1e6,
p01[0] * xvec + p01[1],
'--',
color=lc[i - 1],
lw=3)
data2saveH = np.c_[data2saveH, yvec]
header.append(str(row))
labels_H.append(str(row))
if remove2ndOrder:
titleH = titleH + ', 2nd order removed'
plt.legend(title='Pixel Y', loc=0, fontsize=12)
plt.xlabel(r'x [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleH + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=False)
header.append(zlabel +
', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveH,
wpu.get_unique_filename(
saveFileSuf + '_WF_profiles_H', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12 * 9 / 16))
plt.imshow(arrayH,
cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayH, -3),
vmax=wpu.mean_plus_n_sigma(arrayH, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleH + ', Profiles Position')
currentAxis = plt.gca()
_, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
for i, row in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayV)[0] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(
Rectangle((-.5, row - filter_width // 2 - .5),
np.shape(arrayH)[1],
filter_width,
facecolor=lc[i - 1],
alpha=.5))
plt.axhline(row, color=lc[i - 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=True)
# Vertical
if np.all(np.isfinite(arrayV)):
plt.figure(figsize=(12, 12 * 9 / 16))
xvec = yyGrid[:, 0]
data2saveV = np.c_[xvec]
header = ['y [m]']
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_V = []
for i, col in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayH)[1] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
if filter_width != 0:
yvec = arrayV[:, col - filter_width:col + filter_width]
yvec = np.sum(yvec, 1) / filter_width / 2
else:
yvec = arrayV[:, col]
lc.append(next(lc_jet))
p10 = np.polyfit(xvec, yvec, 1)
fit_coefs[1].append(p10)
if remove2ndOrder:
yvec -= p10[0] * xvec + p10[1]
plt.plot(xvec * 1e6,
yvec,
next(ls_cycle),
color=lc[i - 1],
label=str(col))
if not remove2ndOrder:
plt.plot(xvec * 1e6,
p10[0] * xvec + p10[1],
'--',
color=lc[i - 1],
lw=3)
data2saveV = np.c_[data2saveV, yvec]
header.append(str(col))
labels_V.append(str(col))
if remove2ndOrder:
titleV = titleV + ', 2nd order removed'
plt.legend(title='Pixel X', loc=0, fontsize=12)
plt.xlabel(r'y [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleV + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=False)
header.append(zlabel +
', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(
saveFileSuf + '_WF_profiles_V', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12 * 9 / 16))
plt.imshow(arrayV,
cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayV, -3),
vmax=wpu.mean_plus_n_sigma(arrayV, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleV + ', Profiles Position')
currentAxis = plt.gca()
for i, col in enumerate(
np.linspace(filter_width // 2,
np.shape(arrayH)[1] - filter_width // 2 - 1,
nprofiles + 2,
dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(
Rectangle((col - filter_width // 2 - .5, -.5),
filter_width,
np.shape(arrayV)[0],
facecolor=lc[i - 1],
alpha=.5))
plt.axvline(col, color=lc[i - 1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=True)
return data2saveH, data2saveV, labels_H, labels_V, fit_coefs
def _n_profiles_H_V(arrayH, arrayV, virtual_pixelsize,
zlabel=r'z',
titleH='Horiz', titleV='Vert',
nprofiles=5, filter_width=0,
remove1stOrderDPC=False,
saveFileSuf='',
saveFigFlag=True):
xxGrid, yyGrid = wpu.grid_coord(arrayH, virtual_pixelsize)
fit_coefs = [[], []]
data2saveH = None
data2saveV = None
labels_H = None
labels_V = None
plt.rcParams['lines.markersize'] = 4
plt.rcParams['lines.linewidth'] = 2
# Horizontal
if np.all(np.isfinite(arrayH)):
plt.figure(figsize=(12, 12*9/16))
xvec = xxGrid[0, :]
data2saveH = np.c_[xvec]
header = ['x [m]']
if filter_width != 0:
arrayH_filtered = uniform_filter1d(arrayH, filter_width, 0)
else:
arrayH_filtered = arrayH
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_H = []
for i, row in enumerate(np.linspace(filter_width//2,
np.shape(arrayV)[0]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
yvec = arrayH_filtered[row, :]
lc.append(next(lc_jet))
p01 = np.polyfit(xvec, yvec, 1)
fit_coefs[0].append(p01)
if remove1stOrderDPC:
yvec -= p01[0]*xvec + p01[1]
plt.plot(xvec*1e6, yvec, next(ls_cycle), color=lc[i-1],
label=str(row))
if not remove1stOrderDPC:
plt.plot(xvec*1e6, p01[0]*xvec + p01[1], '--',
color=lc[i-1], lw=3)
data2saveH = np.c_[data2saveH, yvec]
header.append(str(row))
labels_H.append(str(row))
if remove1stOrderDPC:
titleH = titleH + ', 2nd order removed'
plt.legend(title='Pixel Y', loc=0, fontsize=12)
plt.xlabel(r'x [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleH + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=False)
header.append(zlabel + ', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveH,
wpu.get_unique_filename(saveFileSuf +
'_WF_profiles_H', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12*9/16))
plt.imshow(arrayH, cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayH, -3),
vmax=wpu.mean_plus_n_sigma(arrayH, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleH + ', Profiles Position')
currentAxis = plt.gca()
_, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
for i, row in enumerate(np.linspace(filter_width//2,
np.shape(arrayV)[0]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(Rectangle((-.5, row - filter_width//2 - .5),
np.shape(arrayH)[1], filter_width,
facecolor=lc[i-1], alpha=.5))
plt.axhline(row, color=lc[i-1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_H')
plt.show(block=True)
# Vertical
if np.all(np.isfinite(arrayV)):
plt.figure(figsize=(12, 12*9/16))
xvec = yyGrid[:, 0]
data2saveV = np.c_[xvec]
header = ['y [m]']
if filter_width != 0:
arrayV_filtered = uniform_filter1d(arrayV, filter_width, 1)
else:
arrayV_filtered = arrayV
ls_cycle, lc_jet = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=nprofiles,
cmap_str='gist_rainbow_r')
lc = []
labels_V = []
for i, col in enumerate(np.linspace(filter_width//2,
np.shape(arrayH)[1]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
yvec = arrayV_filtered[:, col]
lc.append(next(lc_jet))
p10 = np.polyfit(xvec, yvec, 1)
fit_coefs[1].append(p10)
if remove1stOrderDPC:
yvec -= p10[0]*xvec + p10[1]
plt.plot(xvec*1e6, yvec, next(ls_cycle), color=lc[i-1],
label=str(col))
if not remove1stOrderDPC:
plt.plot(xvec*1e6, p10[0]*xvec + p10[1], '--',
color=lc[i-1], lw=3)
data2saveV = np.c_[data2saveV, yvec]
header.append(str(col))
labels_V.append(str(col))
if remove1stOrderDPC:
titleV = titleV + ', 2nd order removed'
plt.legend(title='Pixel X', loc=0, fontsize=12)
plt.xlabel(r'y [$\mu m$]', fontsize=18)
plt.ylabel(zlabel, fontsize=18)
plt.title(titleV + ', Filter Width = {:d} pixels'.format(filter_width),
fontsize=20)
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=False)
header.append(zlabel + ', Filter Width = {:d} pixels'.format(filter_width))
wpu.save_csv_file(data2saveV,
wpu.get_unique_filename(saveFileSuf +
'_WF_profiles_V', 'csv'),
headerList=header)
plt.figure(figsize=(12, 12*9/16))
plt.imshow(arrayV, cmap='RdGy',
vmin=wpu.mean_plus_n_sigma(arrayV, -3),
vmax=wpu.mean_plus_n_sigma(arrayV, 3))
plt.xlabel('Pixel')
plt.ylabel('Pixel')
plt.title(titleV + ', Profiles Position')
currentAxis = plt.gca()
for i, col in enumerate(np.linspace(filter_width//2,
np.shape(arrayH)[1]-filter_width//2-1,
nprofiles + 2, dtype=int)):
if i == 0 or i == nprofiles + 1:
continue
currentAxis.add_patch(Rectangle((col - filter_width//2 - .5, -.5),
filter_width, np.shape(arrayV)[0],
facecolor=lc[i-1], alpha=.5))
plt.axvline(col, color=lc[i-1])
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Y')
plt.show(block=True)
return data2saveH, data2saveV, labels_H, labels_V, fit_coefs
def correct_zero_DPC(dpc01, dpc10,
pixelsize, distDet2sample, phenergy, saveFileSuf):
title = ['Angle displacement of fringes 01',
'Angle displacement of fringes 10']
factor = distDet2sample*hc/phenergy
angle = [dpc01/pixelsize[1]*factor, dpc10/pixelsize[0]*factor]
dpc = [dpc01, dpc10]
pi_jump = [0, 0]
iamhappy = False
while not iamhappy:
pi_jump[0] = int(np.round(np.mean(angle[0])/np.pi))
pi_jump[1] = int(np.round(np.mean(angle[1])/np.pi))
plt.figure()
plt.hist(angle[0].flatten()/np.pi, 201,
histtype='step')
plt.hist(angle[1].flatten()/np.pi, 201,
histtype='step')
plt.xlabel(r'Angle [$\pi$rad]')
plt.title('Correct DPC\n' +
r'Angle displacement of fringes $[\pi$ rad]' +
'\n' + r'Calculated jumps $x$ and $y$ : ' +
'{:d}, {:d} $\pi$'.format(pi_jump[0], pi_jump[1]))
plt.legend(('DPC x', 'DPC y'))
plt.show(block=False)
plt.pause(.5)
if pi_jump == [0, 0]:
break
if easyqt.get_yes_or_no('Subtract pi jump of DPC?'):
plt.close('all')
angle[0] -= pi_jump[0]*np.pi
angle[1] -= pi_jump[1]*np.pi
dpc01 = angle[0]*pixelsize[0]/factor
dpc10 = angle[1]*pixelsize[1]/factor
dpc = [dpc01, dpc10]
wgi.plot_DPC(dpc01, dpc10,
virtual_pixelsize, saveFigFlag=True,
saveFileSuf=saveFileSuf)
else:
plt.close('all')
iamhappy = True
wpu.print_blue('MESSAGE: mean angle/pi ' +
'0: {:} pi'.format(np.mean(angle[0]/np.pi)))
wpu.print_blue('MESSAGE: mean angle/pi ' +
'1: {:} pi'.format(np.mean(angle[1]/np.pi)))
if easyqt.get_yes_or_no('Subtract mean of DPC?'):
plt.close('all')
angle[0] -= np.mean(angle[0])
angle[1] -= np.mean(angle[1])
dpc01 = angle[0]*pixelsize[0]/factor
dpc10 = angle[1]*pixelsize[1]/factor
dpc = [dpc01, dpc10]
wgi.plot_DPC(dpc01, dpc10,
virtual_pixelsize, saveFigFlag=True,
saveFileSuf=saveFileSuf)
plt.pause(.1)
else:
pass
if easyqt.get_yes_or_no('Correct DPC center?'):
plt.close('all')
for i in [0, 1]:
iamhappy = False
while not iamhappy:
angle[i], pi_jump[i] = correct_zero_from_unwrap(angle[i])
wpu.print_blue('MESSAGE: pi jump ' +
'{:}: {:} pi'.format(i, pi_jump[i]))
wpu.print_blue('MESSAGE: mean angle/pi ' +
'{:}: {:} pi'.format(i, np.mean(angle[i]/np.pi)))
plt.figure()
plt.hist(angle[i].flatten() / np.pi, 101)
plt.title(r'Angle displacement of fringes $[\pi$ rad]')
plt.show()
plt.figure()
vlim = np.max((np.abs(wpu.mean_plus_n_sigma(angle[i]/np.pi,
-5)),
np.abs(wpu.mean_plus_n_sigma(angle[i]/np.pi,
5))))
plt.imshow(angle[i] / np.pi,
cmap='RdGy',
vmin=-vlim, vmax=vlim)
plt.colorbar()
plt.title(title[i] + r' [$\pi$ rad],')
plt.xlabel('Pixels')
plt.ylabel('Pixels')
plt.pause(.1)
iamhappy = easyqt.get_yes_or_no('Happy?')
plt.close('all')
dpc[i] = angle[i]*pixelsize[i]/factor
plt.close('all')
return dpc
img_0 = dxchange.read_tiff(imgfname)
all_img = np.zeros((len(listOfDataFiles),
img_0.shape[0], img_0.shape[1]), dtype=int)
all_img[i,:,:] = dxchange.read_tiff(imgfname)
wpu.print_blue('MESSAGE: Loading ' + imgfname)
for i, imgfname in enumerate(listOfDataFiles):
plt.figure()
plt.figure(figsize=(12, 12*9/16))
plt.imshow(all_img[i, :, :],
cmap='jet',
vmin=0, vmax=wpu.mean_plus_n_sigma(all_img, 6)//1)
outfname = 'png_figs/' + \
imgfname.split('.')[0].rsplit('/', 1)[-1] + '.png'
plt.title(imgfname.split('/')[-1])
plt.savefig(outfname)
print(outfname + ' saved!')
plt.close()
# if i > 0: break
# %%
# wpu.print_red('MESSAGE: Done!')
# plt.show(block=True)
