def slope_error_hist(thickness_cropped,
fitted,
pixelSize,
delta=1,
sourcedistance=1.0,
saveFigFlag=True,
str4title=''):
errorThickness = thickness_cropped - fitted
plt.figure(figsize=(15, 8))
plt.subplot(121)
slope_error_h = np.diff(errorThickness, axis=0) / pixelSize[0] * delta
argNotNAN = np.isfinite(slope_error_h)
factor_seh, unit_seh = wpu.choose_unit(slope_error_h[argNotNAN])
sigma_seh = np.std(slope_error_h[argNotNAN].flatten())
plt.hist(slope_error_h[argNotNAN].flatten() * factor_seh,
100,
histtype='stepfilled')
plt.xlabel(r'Slope Error [$ ' + unit_seh + ' rad$ ]')
plt.title('Horizontal, SDV = ' + '{:.2f}'.format(sigma_seh * factor_seh) +
' $' + unit_seh + ' rad$')
plt.subplot(122)
slope_error_v = np.diff(errorThickness, axis=1) / pixelSize[1] * delta
argNotNAN = np.isfinite(slope_error_v)
factor_sev, unit_sev = wpu.choose_unit(slope_error_v[argNotNAN])
sigma_sev = np.std(slope_error_v[argNotNAN].flatten())
plt.hist(slope_error_v[argNotNAN].flatten() * factor_sev,
100,
histtype='stepfilled')
plt.xlabel(r'Slope Error [$ ' + unit_sev + ' rad$ ]')
plt.title('Vertical, SDV = ' + '{:.2f}'.format(sigma_sev * factor_sev) +
' $' + unit_sev + ' rad$')
if delta != 1:
str4title += ' WF slope error'
else:
str4title += ' Thickness slope error'
plt.suptitle(str4title, fontsize=18, weight='bold')
if saveFigFlag:
wpu.save_figs_with_idx(fname2save, extension='png')
wpu.log_this('Slope Error Hor SDV = ' +
'{:.3f}'.format(sigma_seh * factor_seh) + unit_seh + ' rad')
wpu.log_this('Slope Error Ver SDV = ' +
'{:.3f}'.format(sigma_sev * factor_sev) + unit_sev + ' rad')
plt.show(block=True)
return sigma_seh, sigma_sev
(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))
# %% initial pars
(fname, str4title, nominalRadius, diameter4fit_list,
lensGeometry) = _load_experimental_pars(sys.argv)
fname2save = fname.split('.')[0].split('/')[-1] + '_fit'
data_dir = fname.rsplit('/', 1)[0]
print(fname)
print(data_dir)
os.chdir(data_dir)
os.makedirs('residuals', exist_ok=True)
os.chdir('residuals')
wpu.log_this(preffname=fname.split('.')[0].split('/')[-1] + '_fit',
inifname=inifname)
# %% Load Input File
if fname.split('.')[-1] == 'sdf':
thickness, pixelSize, headerdic = wpu.load_sdf_file(fname)
xx, yy = wpu.realcoordmatrix(thickness.shape[1], pixelSize[1],
thickness.shape[0], pixelSize[0])
elif fname.split('.')[-1] == 'pickle':
thickness, xx, yy = load_pickle_surf(fname, False)
thickness *= 1e-6
# thickness *= -1.0 # minus1 here
xx *= 1e-6
def dpc_profile_analysis(fnameH,
fnameV,
phenergy,
grazing_angle=0.0,
projectionFromDiv=1.0,
nprofiles=1,
remove2ndOrder=False,
filter_width=0):
wavelength = wpu.hc / phenergy
if fnameH is not None:
diffPhaseH, virtual_pixelsize, _ = wpu.load_sdf_file(fnameH)
if fnameV is not None:
diffPhaseV, virtual_pixelsize, _ = wpu.load_sdf_file(fnameV)
if fnameH is None:
diffPhaseH = diffPhaseV * np.nan
if fnameV is None:
diffPhaseV = diffPhaseH * np.nan
saveFileSuf = fnameH.rsplit('/', 1)[0] + '/profiles/' +\
fnameH.rsplit('/', 1)[1]
saveFileSuf = saveFileSuf.rsplit('_X')[0] + '_profiles'
else:
saveFileSuf = fnameV.rsplit('/', 1)[0] + '/profiles/' +\
fnameV.rsplit('/', 1)[1]
saveFileSuf = saveFileSuf.rsplit('_Y')[0] + '_profiles'
if not os.path.exists(saveFileSuf.rsplit('/', 1)[0]):
os.makedirs(saveFileSuf.rsplit('/', 1)[0])
(dataH, dataV, labels_H, labels_V,
fit_coefs) = _n_profiles_H_V(diffPhaseH,
diffPhaseV,
virtual_pixelsize,
'DPC [rad/m]',
titleH='WF DPC Horz',
titleV='WF DPC Vert',
saveFileSuf=saveFileSuf,
nprofiles=nprofiles,
remove2ndOrder=remove2ndOrder,
filter_width=filter_width)
fit_coefsH = np.array(fit_coefs[0])
fit_coefsV = np.array(fit_coefs[1])
print(fit_coefsH)
print(fit_coefsV)
if __name__ == '__main__':
wpu.log_this(preffname=saveFileSuf, inifname=inifname)
if fnameH is not None:
radii_fit_H = (2 * np.pi / wavelength / fit_coefsH[:][0])
wpu.print_blue('MESSAGE: Radius H from fit profiles: ')
print(radii_fit_H)
wpu.log_this('radius fit Hor = ' + str(radii_fit_H))
integratedH = integrate_DPC_cumsum(
dataH,
wavelength,
#grazing_angle=grazing_angle,
xlabel='x',
labels=labels_H,
titleStr='Horizontal, ',
saveFileSuf=saveFileSuf + '_X')
curv_H = curv_from_height(
integratedH,
virtual_pixelsize[0],
#grazing_angle=grazing_angle,
#projectionFromDiv=projectionFromDiv,
xlabel='x',
labels=labels_H,
titleStr='Horizontal, ',
saveFileSuf=saveFileSuf + '_X')
if fnameV is not None:
radii_fit_V = (2 * np.pi / wavelength / fit_coefsV[:][0])
wpu.print_blue('MESSAGE: Radius V from fit profiles: ')
print(radii_fit_V)
wpu.log_this('radius fit Vert = ' + str(radii_fit_V))
integratedV = integrate_DPC_cumsum(dataV,
wavelength,
grazing_angle=grazing_angle,
projectionFromDiv=projectionFromDiv,
xlabel='y',
labels=labels_V,
titleStr='Vertical, ',
saveFileSuf=saveFileSuf + '_Y')
curv_V = curv_from_height(integratedV,
virtual_pixelsize[1],
grazing_angle=grazing_angle,
projectionFromDiv=projectionFromDiv,
xlabel='y',
labels=labels_V,
titleStr='Vertical, ',
saveFileSuf=saveFileSuf + '_Y')
def dpc_profile_analysis(fnameH, fnameV,
phenergy,
grazing_angle=0.0, projectionFromDiv=1.0,
nprofiles=1,
remove1stOrderDPC=False,
remove2ndOrder=False,
filter_width=0):
wavelength = wpu.hc/phenergy
if fnameH is not None:
diffPhaseH, virtual_pixelsize, _ = wpu.load_sdf_file(fnameH)
if fnameV is not None:
diffPhaseV, virtual_pixelsize, _ = wpu.load_sdf_file(fnameV)
if fnameH is None:
diffPhaseH = diffPhaseV*np.nan
if fnameV is None:
diffPhaseV = diffPhaseH*np.nan
saveFileSuf = fnameH.rsplit('/', 1)[0] + '/profiles/' +\
fnameH.rsplit('/', 1)[1]
saveFileSuf = saveFileSuf.rsplit('_X')[0] + '_profiles'
else:
saveFileSuf = fnameV.rsplit('/', 1)[0] + '/profiles/' +\
fnameV.rsplit('/', 1)[1]
saveFileSuf = saveFileSuf.rsplit('_Y')[0] + '_profiles'
if not os.path.exists(saveFileSuf.rsplit('/', 1)[0]):
os.makedirs(saveFileSuf.rsplit('/', 1)[0])
(dataH, dataV,
labels_H, labels_V,
fit_coefs) = _n_profiles_H_V(diffPhaseH,
diffPhaseV,
virtual_pixelsize,
'DPC [rad/m]',
titleH='WF DPC Horz',
titleV='WF DPC Vert',
saveFileSuf=saveFileSuf,
nprofiles=nprofiles,
remove1stOrderDPC=remove1stOrderDPC,
filter_width=filter_width)
fit_coefsH = np.array(fit_coefs[0])
fit_coefsV = np.array(fit_coefs[1])
print(fit_coefsH)
print(fit_coefsV)
if __name__ == '__main__':
wpu.log_this(preffname=saveFileSuf, inifname=inifname)
if fnameH is not None:
radii_fit_H = (2*np.pi/wavelength/fit_coefsH[:][0])
wpu.print_blue('MESSAGE: Radius H from fit profiles: ')
print(radii_fit_H)
wpu.log_this('radius fit Hor = ' + str(radii_fit_H))
integratedH = integrate_DPC_cumsum(dataH, wavelength,
#grazing_angle=grazing_angle,
remove2ndOrder=remove2ndOrder,
xlabel='x',
labels=labels_H,
titleStr='Horizontal, ',
saveFileSuf=saveFileSuf + '_X')
curv_H = curv_from_height(integratedH, virtual_pixelsize[0],
#grazing_angle=grazing_angle,
#projectionFromDiv=projectionFromDiv,
xlabel='x',
labels=labels_H,
titleStr='Horizontal, ',
saveFileSuf=saveFileSuf + '_X')
if fnameV is not None:
radii_fit_V = (2*np.pi/wavelength/fit_coefsV[:][0])
wpu.print_blue('MESSAGE: Radius V from fit profiles: ')
print(radii_fit_V)
wpu.log_this('radius fit Vert = ' + str(radii_fit_V))
integratedV = integrate_DPC_cumsum(dataV, wavelength,
grazing_angle=grazing_angle,
projectionFromDiv=projectionFromDiv,
remove2ndOrder=remove2ndOrder,
xlabel='y',
labels=labels_V,
titleStr='Vertical, ',
saveFileSuf=saveFileSuf + '_Y')
curv_V = curv_from_height(integratedV, virtual_pixelsize[1],
grazing_angle=grazing_angle,
projectionFromDiv=projectionFromDiv,
xlabel='y',
labels=labels_V,
titleStr='Vertical, ',
saveFileSuf=saveFileSuf + '_Y')
wgi.plot_DPC(diffPhase01, diffPhase10,
virtual_pixelsize, saveFigFlag=True,
saveFileSuf=saveFileSuf)
plt.pause(.5)
# %% remove linear component of DPC
removeLinearFromDPC = easyqt.get_yes_or_no('Remove Linear ' +
'component from DPC?')
plt.close(plt.gcf())
if removeLinearFromDPC:
wpu.log_this('%%% COMMENT: Removed Linear Component from DPC',
saveFileSuf)
def _fit_lin_surface(zz, pixelsize):
from numpy.polynomial import polynomial
xx, yy = wpu.grid_coord(zz, pixelsize)
f = zz.flatten()
deg = np.array([1, 1])
vander = polynomial.polyvander2d(xx.flatten(), yy.flatten(), deg)
vander = vander.reshape((-1, vander.shape[-1]))
f = f.reshape((vander.shape[0],))
c = np.linalg.lstsq(vander, f)[0]
print(c)
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)