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
temp_Data[:, profilenumber] = target_original - fitted_target
plt.figure(12)
plt.subplot(121)
plt.plot(temp_Data[:, 0], target_original, '-r', temp_Data[:, 0],
fitted_target, '*k')
plt.legend(['original', 'spherical fitting'])
plt.title('target wavefront spherical fitting')
plt.ylabel('rad')
plt.xlabel('position')
plt.subplot(122)
plt.plot(temp_Data[:, 0], temp_Data[:, profilenumber], '-r')
plt.title('residual')
plt.ylabel('rad')
plt.xlabel('position')
figname = wpu.get_unique_filename(dirName + '/respons_func', 'png')
plt.savefig(figname)
plt.show()
# %%
npoints_interp = 200
xnew = np.linspace(-lim_xnew, lim_xnew, npoints_interp)
# %%
#exit()
# %% to filter the data to make it smooth for the analyze
import wavepy.utils as wpu
import dxchange
# %% Load
img1fname, img2fname = wpu.gui_load_data_dark_filenames()
img1 = dxchange.read_tiff(img1fname)
img2 = dxchange.read_tiff(img2fname)
# %% alignment 1
img1_aligned, img2_aligned, pixel_shift = wpu.gui_align_two_images(img1, img2, option='pad')
# save files
outfname = wpu.get_unique_filename(img1fname.split('.')[0] + '_aligned',
'tiff')
dxchange.write_tiff(img1_aligned, outfname)
wpu.print_blue('MESSAGE: file ' + outfname + ' saved.')
outfname = wpu.get_unique_filename(img2fname.split('.')[0] + '_aligned',
'tiff')
dxchange.write_tiff(img2_aligned, outfname)
wpu.print_blue('MESSAGE: file ' + outfname + ' saved.')
# %%
plt.figure(figsize=(12, 12))
plt.imshow(img1_aligned[::5, ::5], cmap='viridis')
plt.title('img1')
wpu.save_figs_with_idx('aligned')
contrastV = np.asarray([x[0] for x in res])
contrastH = np.asarray([x[1] for x in res])
p0 = np.asarray([x[2] for x in res])
pv = np.asarray([x[3] for x in res])
ph = np.asarray([x[4] for x in res])
pattern_period_Vert_z = pixelSize / (pv[:, 0] - p0[:, 0]) * img.shape[0]
pattern_period_Horz_z = pixelSize / (ph[:, 1] - p0[:, 1]) * img.shape[1]
# =============================================================================
# %% Save csv file
# =============================================================================
outputfname = wpu.get_unique_filename(fname2save, 'csv')
wpu.save_csv_file(np.c_[zvec.T, contrastV.T, contrastH.T,
pattern_period_Vert_z.T, pattern_period_Horz_z.T],
outputfname,
headerList=[
'z [m]', 'Vert Contrast', 'Horz Contrast',
'Vert Period [m]', 'Horz Period [m]'
])
wpu.log_this('\nOutput file: ' + outputfname)
# =============================================================================
# %% Plot
# =============================================================================
label = fname.rsplit('/', 1)[1].split('.')[0]
plt.plot(data[:, 0] * 1e6, data[:, 1], 'o', label=label)
pfit = np.polyfit(data[:, 0], data[:, 1], 1)
plt.plot(data[:, 0] * 1e6,
pfit[0] * data[:, 0] + pfit[1],
'--',
label=label)
plt.ylabel('WF ' + headers[2])
plt.xlabel('[µm]')
plt.legend(loc='best', fontsize='x-small')
figname = wpu.get_unique_filename(dirName + '/respons_func', 'png')
plt.savefig(figname)
print('MESSAGE: Saving ' + figname)
plt.show(block=True)
# %%
plt.figure(figsize=(12, 8))
listOfDPC_removed = []
for data, fname in zip(listOfArrays, listOfFiles):
label = fname.rsplit('/', 1)[1].split('.')[0]
pfit = np.polyfit(data[:, 0], data[:, 1], 1)
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 plot_residual_1d(xvec,
data,
fitted,
str4title='',
saveFigFlag=False,
saveAsciiFlag=False):
# Plot Horizontal profile
errorThickness = -data + fitted
argNotNAN = np.isfinite(errorThickness)
factorx, unitx = wpu.choose_unit(xvec)
factory1, unity1 = wpu.choose_unit(data)
factory2, unity2 = wpu.choose_unit(errorThickness)
ptp = np.ptp(errorThickness[argNotNAN].flatten() * factory2)
wpu.print_red('PV: {0:4.3g} '.format(ptp) + unity2[-1] + 'm')
sigmaError = np.std(errorThickness[argNotNAN].flatten() * factory2)
wpu.print_red('SDV: {0:4.3g} '.format(sigmaError) + unity2[-1] + 'm')
str4title += '\n' + \
r'PV $= {0:.2f}$ '.format(ptp) + '$' + unity2 + ' m$, '\
'SDV $= {0:.2f}$ '.format(sigmaError) + '$' + unity2 + ' m$'
plt.figure(figsize=(10, 7))
ax1 = plt.gca()
ax1.plot(xvec[argNotNAN] * factorx,
data[argNotNAN] * factory1,
'-ko',
markersize=5,
label='1D data')
ax1.plot(xvec[argNotNAN] * factorx,
fitted[argNotNAN] * factory1,
'-+r',
label='Fit parabolic')
ax2 = ax1.twinx()
# trick to add both axes to legend
ax2.plot(np.nan, '-ko', label='1D data')
ax2.plot(np.nan, '-+r', label='Fit parabolic')
ax2.plot(xvec[argNotNAN] * factorx,
errorThickness[argNotNAN] * factory2,
'-+',
markersize=5,
label='fit residual')
plt.title(str4title)
for tl in ax2.get_yticklabels():
tl.set_color('b')
ax2.legend(loc=1, fontsize='small')
# trick to add both axes to legend
ax1.grid(color='gray')
ax1.set_xlabel(r'[$' + unitx + ' m$]')
ax1.set_ylabel(r'Thickness ' + r'[$' + unity1 + ' m$]')
# ax2.set_ylim([-20, 20])
ax2.set_ylim(-1.1 * np.max(np.abs(errorThickness[argNotNAN]) * factory2),
1.1 * np.max(np.abs(errorThickness[argNotNAN]) * factory2))
ax2.set_ylabel(r'Residual' + r'[$' + unity2 + ' m$]')
ax2.grid(b='off')
plt.xlim(-1.1 * np.max(xvec * factorx), 1.1 * np.max(xvec * factorx))
plt.tight_layout(rect=(0, 0, 1, .98))
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
if saveAsciiFlag:
csv_fname = wpu.get_unique_filename(fname2save, 'csv')
np.savetxt(csv_fname,
np.transpose([xvec, data, fitted, fitted - data]),
delimiter=',\t',
header="xvec, data, fitted, residual, " + str4title,
fmt='%.6g')
plt.show(block=False)
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 _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 integrate_DPC_cumsum(data_DPC,
wavelength,
grazing_angle=0.0,
projectionFromDiv=1.0,
labels=[],
xlabel='x',
ylabel='Height',
titleStr='',
saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=data_DPC.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle // .00001 > 0:
projection = 1 / np.sin(grazing_angle) * projectionFromDiv
else:
projection = projectionFromDiv
xvec = data_DPC[:, 0] * projection
plt.figure(figsize=(12, 12 * 9 / 16))
list_integrated = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, data_DPC.shape[1]):
integrated = (
np.cumsum(data_DPC[:, j_line] - np.mean(data_DPC[:, j_line])) *
(xvec[1] - xvec[0]))
integrated *= -1 / 2 / np.pi * wavelength * np.abs(projection)
# TODO: check here!!
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
factor_y, unit_y = wpu.choose_unit(integrated)
list_integrated.append(integrated)
header.append(labels[j_line - 1])
plt.plot(xvec * factor_x,
integrated * factor_y,
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' [$' + unit_y + ' m$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle // .00001 > 0:
plt.title(titleStr + 'Mirror Height,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle * 1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Integration Cumulative Sum')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_integrated).T
header.append(ylabel + ' [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 + '_integrated_' + xlabel, 'csv'),
headerList=header)
return np.asarray(list_integrated).T
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 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 integrate_DPC_cumsum(data_DPC, wavelength,
grazing_angle=0.0, projectionFromDiv=1.0,
remove2ndOrder=False,
labels=[],
xlabel='x', ylabel='Height',
titleStr='', saveFileSuf=''):
ls_cycle, lc_cycle = wpu.line_style_cycle(['-'], ['o', 's', 'd', '^'],
ncurves=data_DPC.shape[1] - 1,
cmap_str='gist_rainbow_r')
if grazing_angle//.00001 > 0:
projection = 1/np.sin(grazing_angle)*projectionFromDiv
else:
projection = projectionFromDiv
xvec = data_DPC[:, 0]*projection
plt.figure(figsize=(12, 12*9/16))
list_integrated = [xvec]
header = [xlabel + ' [m]']
for j_line in range(1, data_DPC.shape[1]):
integrated = (np.cumsum(data_DPC[:, j_line] - np.mean(data_DPC[:, j_line]))
* (xvec[1]-xvec[0])) # TODO: removed mean 20181020
# integrated = (np.cumsum(data_DPC[:, j_line])) * (xvec[1]-xvec[0])
integrated *= -1/2/np.pi*wavelength*np.abs(projection)
p02 = np.polyfit(xvec, integrated, 2)
fitted_pol2 = p02[0]*xvec**2 + p02[1]*xvec + p02[2]
if remove2ndOrder:
integrated -= fitted_pol2
titleStr += 'Removed 2nd order, '
# TODO: check here!!
if j_line == 1:
factor_x, unit_x = wpu.choose_unit(xvec)
factor_y, unit_y = wpu.choose_unit(integrated)
list_integrated.append(integrated)
header.append(labels[j_line - 1])
lc = next(lc_cycle)
plt.plot(xvec*factor_x,
integrated*factor_y,
next(ls_cycle), c=lc,
label=labels[j_line - 1])
if not remove2ndOrder:
plt.plot(xvec*1e6, (fitted_pol2)*factor_y,
'--', color=lc, lw=3)
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' [$' + unit_y + ' m$]')
plt.legend(loc=0, fontsize=12)
if grazing_angle//.00001 > 0:
plt.title(titleStr + 'Mirror Height,\n' +
'grazing angle {:.2f} mrad,\n'.format(grazing_angle*1e3) +
'projection due divergence = ' +
r'$ \times $ {:.2f}'.format(projectionFromDiv))
else:
plt.title(titleStr + 'Integration Cumulative Sum')
plt.tight_layout()
wpu.save_figs_with_idx(saveFileSuf)
plt.show()
data2saveV = np.asarray(list_integrated).T
header.append(ylabel + ' [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 +
'_integrated_' + xlabel, 'csv'),
headerList=header)
return np.asarray(list_integrated).T
def main_terminal(data_dir,
zvec_from,
startDist,
step_z_scan,
image_per_point,
strideFile,
pixelSize=0.65e-6,
gratingPeriod=4.8e-6,
pattern='Diagonal',
sourceDistanceV=-1,
sourceDistanceH=32,
unFilterSize=1,
searchRegion=20,
idx4crop=[0, -1, 0, -1],
darkRegionSelctionFlag=True):
'''
*** all unit in [m]
data_dir: data folder path
zvec_from: distance type:
'Calculated'
'Tabled'
startDist: started distance postion
step_z_scan: step size
image_per_point: images number for every distance
strideFile: Stride (Use only every XX files)
pixelSize: Pixel Size
gratingPeriod: CB Grating Period
pattern: grating pattern
'Diagonal' or 'Edge']
sourceDistanceV: Distance to Source
in the VERTICAL [m]
sourceDistanceH: Distance to Source
in the Horizontal [m]
unFilterSize: Size for Uniform Filter [Pixels]
default_value 1
searchRegion: Size of Region for Searching
the Peak [in Pixels]
default_value=20
idx4crop: crop area
[low_y, high_y, low_x, high_x ]
darkRegionSelctionFlag: use dark region [0, 20, 0, 20]?
'''
wpu._mpl_settings_4_nice_graphs()
# =============================================================================
# %% Load Image
# =============================================================================
originalDir = os.getcwd()
# samplefileName = easyqt.get_file_names("Choose one of the scan files")[0]
# data_dir = samplefileName.rsplit('/', 1)[0]
os.chdir(data_dir)
try:
os.mkdir(data_dir + '/output/')
except:
pass
fname2save = data_dir + '/output/' + 'zscan'
# wpu.print_blue('MESSAGE: Loading files ' +
# samplefileName.rsplit('_', 1)[0] + '*.tif')
wpu.print_blue('MESSAGE: Loading files ' + data_dir + '/*.tif')
# listOfDataFiles = glob.glob(samplefileName.rsplit('_', 2)[0] + '*.tif')
listOfDataFiles = glob.glob(os.path.join(data_dir, '*.tif'))
listOfDataFiles.sort()
nfiles = len(listOfDataFiles)
# zvec_from = easyqt.get_choice(message='z distances is calculated or from table?',
# title='Title',
# choices=['Calculated', 'Tabled'])
# %%
if zvec_from == 'Calculated':
# startDist = easyqt.get_float('Starting distance scan [mm]',
# title='Title',
# default_value=20)*1e-3
# step_z_scan = easyqt.get_float('Step size scan [mm]',
# title='Title',
# default_value=5)*1e-3
# image_per_point = easyqt.get_int('Number of images by step',
# title='Title',
# default_value=1)
zvec = np.linspace(
startDist,
startDist + step_z_scan * (nfiles / image_per_point - 1),
int(nfiles / image_per_point))
zvec = zvec.repeat(image_per_point)
# strideFile = easyqt.get_int('Stride (Use only every XX files)',
# title='Title',
# default_value=1)
listOfDataFiles = listOfDataFiles[0::strideFile]
zvec = zvec[0::strideFile]
print(zvec)
elif zvec_from == 'Tabled':
zvec = np.loadtxt(
easyqt.get_file_names("Table with the z distance values in mm")
[0]) * 1e-3
step_z_scan = np.mean(np.diff(zvec))
if step_z_scan > 0:
pass
else:
listOfDataFiles = listOfDataFiles[::-1]
zvec = zvec[::-1]
img = dxchange.read_tiff(listOfDataFiles[0])
# =============================================================================
# %% Experimental parameters
# =============================================================================
# pixelSize = easyqt.get_float("Enter Pixel Size [um]",
# title='Experimental Values',
# default_value=.6500, decimals=5)*1e-6
# gratingPeriod = easyqt.get_float("Enter CB Grating Period [um]",
# title='Experimental Values',
# default_value=4.8)*1e-6
# pattern = easyqt.get_choice(message='Select CB Grating Pattern',
# title='Title',
# choices=['Diagonal', 'Edge'])
# # choices=['Edge', 'Diagonal'])
# sourceDistanceV = easyqt.get_float("Enter Distance to Source\n in the VERTICAL [m]",
# title='Experimental Values',
# default_value=-0.73)
# sourceDistanceH = easyqt.get_float("Enter Distance to Source\n in the Horizontal [m]",
# title='Experimental Values',
# default_value=34.0)
# unFilterSize = easyqt.get_int("Enter Size for Uniform Filter [Pixels]\n" +
# " (Enter 1 to NOT use the filter)",
# title='Experimental Values',
# default_value=1)
# searchRegion = easyqt.get_int("Enter Size of Region for Searching\n the Peak [in Pixels]",
# title='Experimental Values',
# default_value=20)
os.chdir(originalDir)
# =============================================================================
# %% Crop
# =============================================================================
idx4crop = [0, -1, 0, -1]
# [colorlimit,
# cmap] = wpu.plot_slide_colorbar(img,
# title='SELECT COLOR SCALE,\n' +
# 'Raw Image, No Crop',
# xlabel=r'x [$\mu m$ ]',
# ylabel=r'y [$\mu m$ ]',
# extent=wpu.extent_func(img,
# pixelSize)*1e6)
# idx4crop = wpu.graphical_roi_idx(img, verbose=True,
# kargs4graph={'cmap': cmap,
# 'vmin': colorlimit[0],
# 'vmax': colorlimit[1]})
wpu.print_blue("MESSAGE: idx for cropping")
wpu.print_blue(idx4crop)
# =============================================================================
# %% Dark indexes
# =============================================================================
# darkRegionSelctionFlag = easyqt.get_yes_or_no('Do you want to select ' +
# 'region for dark calculation?\n' +
# 'Press ESC to use [0, 20, 0, 20]')
print(darkRegionSelctionFlag)
if darkRegionSelctionFlag:
idx4cropDark = wpu.graphical_roi_idx(img,
verbose=True,
kargs4graph={
'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]
})
else:
idx4cropDark = [0, 20, 0, 20]
# dark_im = dxchange.read_tiff(listOfDataFiles[0])*0.0 + avgDark
img = wpu.crop_matrix_at_indexes(img, idx4crop)
# ==============================================================================
# %% Harmonic Periods
# ==============================================================================
if pattern == 'Diagonal':
period_harm_Vert = np.int(
np.sqrt(2) * pixelSize / gratingPeriod * img.shape[0])
period_harm_Horz = np.int(
np.sqrt(2) * pixelSize / gratingPeriod * img.shape[1])
elif pattern == 'Edge':
period_harm_Vert = np.int(2 * pixelSize / gratingPeriod * img.shape[0])
period_harm_Horz = np.int(2 * pixelSize / gratingPeriod * img.shape[1])
# Obtain harmonic periods from images
(period_harm_Vert,
_) = wgi.exp_harm_period(img, [period_harm_Vert, period_harm_Horz],
harmonic_ij=['1', '0'],
searchRegion=40,
isFFT=False,
verbose=True)
(_, period_harm_Horz) = wgi.exp_harm_period(
img, [period_harm_Vert, period_harm_Horz],
harmonic_ij=['0', '1'],
searchRegion=40,
isFFT=False,
verbose=True)
wpu.log_this('Input folder: ' + data_dir, preffname=fname2save)
wpu.log_this('\nNumber of files : ' + str(nfiles))
wpu.log_this('Stride : ' + str(strideFile))
print(zvec_from)
wpu.log_this('Z distances is ' + zvec_from)
if zvec_from == 'Calculated':
wpu.log_this('Step zscan [mm] : {:.4g}'.format(step_z_scan * 1e3))
wpu.log_this('Start point zscan [mm] : {:.4g}'.format(startDist * 1e3))
wpu.log_this('Pixel Size [um] : {:.4g}'.format(pixelSize * 1e6))
wpu.log_this('Grating Period [um] : {:.4g}'.format(gratingPeriod * 1e6))
wpu.log_this('Grating Pattern : ' + pattern)
wpu.log_this('Crop idxs : ' + str(idx4crop))
wpu.log_this('Dark idxs : ' + str(idx4cropDark))
wpu.log_this('Vertical Source Distance: ' + str(sourceDistanceV))
wpu.log_this('Horizontal Source Distance: ' + str(sourceDistanceH))
wpu.log_this('Uniform Filter Size : {:d}'.format(unFilterSize))
wpu.log_this('Search Region : {:d}'.format(searchRegion))
# =============================================================================
# %% Calculate everything
# =============================================================================
# =============================================================================
# %% multiprocessing
# =============================================================================
ncpus = cpu_count()
wpu.print_blue("MESSAGE: %d cpu's available" % ncpus)
tzero = time.time()
p = Pool(ncpus - 2)
indexes = range(len(listOfDataFiles))
parameters = []
for i in indexes:
parameters.append([
i, listOfDataFiles, zvec, idx4cropDark, idx4crop, period_harm_Vert,
sourceDistanceV, period_harm_Horz, sourceDistanceH, searchRegion,
unFilterSize
])
res = p.map(_func, parameters)
p.close()
wpu.print_blue('MESSAGE: Time spent: {0:.3f} s'.format(time.time() -
tzero))
'''
res = []
for i in range(len(listOfDataFiles)):
res.append(_func(i))
print(res)
'''
# =============================================================================
# %% Sorting the data
# =============================================================================
contrastV = np.asarray([x[0] for x in res])
contrastH = np.asarray([x[1] for x in res])
p0 = np.asarray([x[2] for x in res])
pv = np.asarray([x[3] for x in res])
ph = np.asarray([x[4] for x in res])
pattern_period_Vert_z = pixelSize / (pv[:, 0] - p0[:, 0]) * img.shape[0]
pattern_period_Horz_z = pixelSize / (ph[:, 1] - p0[:, 1]) * img.shape[1]
# =============================================================================
# %% Save csv file
# =============================================================================
outputfname = wpu.get_unique_filename(fname2save, 'csv')
wpu.save_csv_file(np.c_[zvec.T, contrastV.T, contrastH.T,
pattern_period_Vert_z.T, pattern_period_Horz_z.T],
outputfname,
headerList=[
'z [m]', 'Vert Contrast', 'Horz Contrast',
'Vert Period [m]', 'Horz Period [m]'
])
wpu.log_this('\nOutput file: ' + outputfname)
# =============================================================================
# %% Plot
# =============================================================================
# contrast vs z
fig = plt.figure(figsize=(10, 7))
plt.plot(zvec * 1e3, contrastV * 100, '-ko', label='Vert')
plt.plot(zvec * 1e3, contrastH * 100, '-ro', label='Hor')
plt.xlabel(r'Distance $z$ [mm]', fontsize=14)
plt.ylabel(r'Visibility $\times$ 100 [%]', fontsize=14)
plt.title('Visibility vs detector distance', fontsize=14, weight='bold')
plt.legend(fontsize=14, loc=0)
wpu.save_figs_with_idx(fname2save)
plt.show(block=False)
# =============================================================================
# %% Plot Harmonic position and calculate source distance
# =============================================================================
from wavepytools.diag.coherence.fit_singleGratingCoherence_z_scan import fit_period_vs_z
#xshi 20190719
#from fit_singleGratingCoherence_z_scan import fit_period_vs_z
(sourceDistance_from_fit_V,
patternPeriodFromData_V) = fit_period_vs_z(zvec,
pattern_period_Vert_z,
contrastV,
direction='Vertical',
threshold=.002,
fname4graphs=fname2save)
(sourceDistance_from_fit_H,
patternPeriodFromData_H) = fit_period_vs_z(zvec,
pattern_period_Horz_z,
contrastH,
direction='Horizontal',
threshold=0.0005,
fname4graphs=fname2save)