# Experimental parameters
# ==========================================================================
(list_sample_files, list_ref_files, list_dark_files, pixelSize,
stepSize) = intial_setup()
# ==========================================================================
# % % Load one image and crop
# ==========================================================================
img = dxchange.read_tiff(list_sample_files[0])
[colorlimit, cmap
] = wpu.plot_slide_colorbar(img,
title='Raw Image',
xlabel=r'x [$\mu m$ ]',
ylabel=r'y [$\mu m$ ]',
extent=wpu.extent_func(img, pixelSize) * 1e6)
img_croped, idx4crop = wpu.crop_graphic(zmatrix=img,
verbose=True,
kargs4graph={
'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]
})
# ==========================================================================
# %% Load tiff files to numpy array
# ==========================================================================
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)
img = wpu.crop_matrix_at_indexes(img, idx4crop)
# ==========================================================================
# Experimental parameters
# ==========================================================================
(list_sample_files, list_ref_files, list_dark_files,
pixelSize, stepSize) = intial_setup()
# ==========================================================================
# % % Load one image and crop
# ==========================================================================
img = dxchange.read_tiff(list_sample_files[0])
[colorlimit,
cmap] = wpu.plot_slide_colorbar(img, title='Raw Image',
xlabel=r'x [$\mu m$ ]',
ylabel=r'y [$\mu m$ ]',
extent=wpu.extent_func(img, pixelSize)*1e6)
img_croped, idx4crop = wpu.crop_graphic(zmatrix=img, verbose=True,
kargs4graph={'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]})
# ==========================================================================
# %% Load tiff files to numpy array
# ==========================================================================
img_stack, ref_stack = files_to_array(list_sample_files,
list_ref_files,
list_dark_files,
if (figx.axes[0].images != []):
data = figx.axes[0].images[0].get_array().data
[xi, xf, yi, yf] = figx.axes[0].images[0].get_extent()
ax = figx.axes[0].images[0].get_axes()
title = figx.axes[0].images[0].axes.properties()['title']
xlabel = figx.axes[0].images[0].axes.properties()['xlabel']
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')
def main_single_gr_Talbot(img, imgRef,
phenergy, pixelsize, distDet2sample,
period_harm, saveFileSuf,
unwrapFlag=True,
plotFlag=True,
saveFigFlag=False):
global inifname # name of .ini file
[period_harm_Vert, period_harm_Hor] = period_harm
# img, imgRef = wpu.align_two_images(img, imgRef)
# Crop
img_size_o = np.shape(img)
# take index from ini file
idx4crop = list(map(int, (wpu.get_from_ini_file(inifname, 'Parameters',
'Crop').split(','))))
# Plot Real Image wiht default crop
tmpImage = wpu.crop_matrix_at_indexes(img, idx4crop)
plt.figure()
plt.imshow(tmpImage,
cmap='viridis',
extent=wpu.extent_func(tmpImage, pixelsize)*1e6)
plt.xlabel(r'$[\mu m]$')
plt.ylabel(r'$[\mu m]$')
plt.colorbar()
plt.title('Raw Image with initial Crop', fontsize=18, weight='bold')
plt.pause(.1)
# ask if the crop need to be changed
newCrop = easyqt.get_yes_or_no('New Crop?')
if saveFigFlag and not newCrop:
wpu.save_figs_with_idx(saveFileSuf + '_Talbot_image')
plt.close(plt.gcf())
if newCrop:
[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.set_at_ini_file(inifname, 'Parameters', 'Crop',
'{}, {}, {}, {}'.format(idx4crop[0], idx4crop[1],
idx4crop[2], idx4crop[3]))
img = wpu.crop_matrix_at_indexes(img, idx4crop)
# Plot Real Image AFTER crop
plt.imshow(img, cmap='viridis',
extent=wpu.extent_func(img, pixelsize)*1e6)
plt.xlabel(r'$[\mu m]$')
plt.ylabel(r'$[\mu m]$')
plt.colorbar()
plt.title('Raw Image with New Crop', fontsize=18, weight='bold')
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Talbot_image')
plt.show(block=True)
else:
img = tmpImage
imgRef = wpu.crop_matrix_at_indexes(imgRef, idx4crop)
# calculate harmonic position after crop
period_harm_Vert = int(period_harm_Vert*(idx4crop[1] - idx4crop[0]) /
img_size_o[0])
period_harm_Hor = int(period_harm_Hor*(idx4crop[3] - idx4crop[2]) /
img_size_o[1])
# Obtain harmonic periods from images
(period_harm_Vert,
_) = wgi.exp_harm_period(img, [period_harm_Vert,
period_harm_Hor],
harmonic_ij=['1', '0'],
searchRegion=20,
isFFT=False, verbose=True)
(_,
period_harm_Horz) = wgi.exp_harm_period(img, [period_harm_Vert,
period_harm_Hor],
harmonic_ij=['0', '1'],
searchRegion=20,
isFFT=False, verbose=True)
# Calculate everything
harmPeriod = [period_harm_Vert, period_harm_Hor]
[int00, int01, int10,
darkField01, darkField10,
phaseFFT_01,
phaseFFT_10] = wgi.single_2Dgrating_analyses(img, imgRef,
harmonicPeriod=harmPeriod,
plotFlag=plotFlag,
unwrapFlag=unwrapFlag,
verbose=True)
virtual_pixelsize = [0, 0]
virtual_pixelsize[0] = pixelsize[0]*img.shape[0]/int00.shape[0]
virtual_pixelsize[1] = pixelsize[1]*img.shape[1]/int00.shape[1]
diffPhase01 = phaseFFT_01*virtual_pixelsize[1]/distDet2sample/hc*phenergy
diffPhase10 = phaseFFT_10*virtual_pixelsize[0]/distDet2sample/hc*phenergy
return [int00, int01, int10,
darkField01, darkField10,
diffPhase01, diffPhase10,
virtual_pixelsize]
totalShift_j = easyqt.get_int('Total Horizontal Shift',
title='Title',
default_value=0)
_, allShifts = align_many_imgs_linearshifts(
samplefileName, totalShift=[-totalShift_i, -totalShift_j])
else:
img_ref = dxchange.read_tiff(samplefileName)
if easyqt.get_yes_or_no('New Crop?'):
[colorlimit, cmap] = wpu.plot_slide_colorbar(
img_ref,
title='SELECT COLOR SCALE,\n' + 'Raw Image, No Crop',
xlabel=r'x [$\mu m$ ]',
ylabel=r'y [$\mu m$ ]')
idxROI = wpu.graphical_roi_idx(img_ref,
kargs4graph={
'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]
})
wpu.set_at_ini_file(
inifname, 'Parameters', 'Crop',
'{}, {}, {}, {}'.format(idxROI[0], idxROI[1], idxROI[2],
idxROI[3]))
else:
def main():
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/' + samplefileName.rsplit(
'_', 1)[0].rsplit('/', 1)[1]
wpu.print_blue('MESSAGE: Loading files ' +
samplefileName.rsplit('_', 1)[0] + '*.tif')
listOfDataFiles = glob.glob(samplefileName.rsplit('_', 2)[0] + '*.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]')
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 files: ' + samplefileName.rsplit('_', 1)[0] + '*.tif',
preffname=fname2save)
wpu.log_this('\nNumber of files : ' + str(nfiles))
wpu.log_this('Stride : ' + str(strideFile))
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 - 5)
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)