Speckle Analyses¶
The analyses of the speckle tracking data is split in three steps:
- Pre-processing:
- Consist of loading and cropping the raw image(s) and doing basic operations like normalization, filtering and subtraction of background.
- Data analyses per se:
- Here the physics of the method is used to convert the pre processed data to some meaninfull physical information. It will likely require experimental information like pixel size, photon energy and distance sample to detector. It should not require any information about the sample.
- Post Processing:
- Perform additional steps to the physical data, like integration, mask, filtering and unwrap. At this point some information about the sample may be required. It must produce graphics and results to be presented to others.
The two files below present examples of speckle tracking data analyses. The first is speckleAnalyses.py
and it perfrom basic pre processing (interactive crop), data analyses, and save the result in
hdf5 format.
Then speckleAnalyses_postProcessing.py
loads the results obtained with speckleAnalyses.py
, and perform operations like undersampling, mask, integration and extra calculations to obtain the final desired result, in this case thisckness of the sample. It also plot the results in a meaninful manner.
Code¶
Speckle Tracking Analyses¶
This section contains the speckleAnalyses script.
Download file: speckleAnalyses.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
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"""
Example of speckle tracking data analyses
"""
import numpy as np
from scipy import constants
import dxchange
import h5py as h5
import wavepy.utils as wpu
import wavepy.speckletracking as wps
__authors__ = "Walan Grizolli"
__copyright__ = "Copyright (c) 2016, Affiliation"
__version__ = "0.1.0"
# =============================================================================
# %% preamble. Load parameters from ini file
# =============================================================================
inifname = '.speckleAnalyses.ini'
config, ini_pars, ini_file_list = wpu.load_ini_file_terminal_dialog(inifname)
fname = ini_file_list.get('image_filename')
image = dxchange.read_tiff(fname)
image_ref = dxchange.read_tiff(ini_file_list.get('ref_filename'))
idx = list(map(int, ini_pars.get('crop').split(',')))
pixelsize = float(ini_pars.get('pixel size'))
phenergy = float(ini_pars.get('photon energy'))
distDet2sample = float(ini_pars.get('distance detector to sample'))
halfsubwidth = int(ini_pars.get('halfsubwidth'))
halfTemplateSize = int(ini_pars.get('halfTemplateSize'))
subpixelResolution = int(ini_pars.get('subpixelResolution'))
npointsmax = int(ini_pars.get('npointsmax'))
ncores = float(ini_pars.get('ncores')) / float(ini_pars.get('ncores of machine'))
saveH5 = ini_pars.get('save hdf5 files')
if subpixelResolution < 1: subpixelResolution = None
if halfTemplateSize < 1: halfTemplateSize = None
# %%
#
#dummy = wpu.dummy_images('Shapes', shape=(110, 110), noise = 1)
#fname = 'dummy.tif'
#image = dummy[5:-5,5:-5]
#image_ref = dummy[7:-3,4:-6]
# =============================================================================
# %% parameters
# =============================================================================
rad2deg = np.rad2deg(1)
deg2rad = np.deg2rad(1)
NAN = float('Nan') # not a number alias
hc = constants.value('inverse meter-electron volt relationship') # hc
wavelength = hc/phenergy
kwave = 2*np.pi/wavelength
# =============================================================================
# %% Crop
# =============================================================================
#image = np.rot90(image) # rotate images, good for sanity checks
#image_ref = np.rot90(image_ref)
kb_input = input('\nGraphic Crop? [N/y] : ')
if kb_input.lower() == 'y':
# Graphical Crop
idx = wpu.graphical_roi_idx(image, verbose=True)
print('New idx:')
print(idx)
ini_pars['crop'] = str('{0}, {1}, {2}, {3}'.format(idx[0], idx[1], idx[2], idx[3]))
with open(inifname, 'w') as configfile: # update values in the ini file
config.write(configfile)
image = wpu.crop_matrix_at_indexes(image, idx)
image_ref = wpu.crop_matrix_at_indexes(image_ref, idx)
# %%
# =============================================================================
# Displacement
# =============================================================================
sx, sy, \
error, step = wps.speckleDisplacement(image, image_ref,
halfsubwidth=halfsubwidth,
halfTemplateSize=halfTemplateSize,
subpixelResolution=subpixelResolution,
npointsmax=npointsmax,
ncores=ncores, taskPerCore=15,
verbose=True)
totalS = np.sqrt(sx**2 + sy**2)
xVec2 = wpu.realcoordvec(sx.shape[1], pixelsize*step)
yVec2 = wpu.realcoordvec(sx.shape[0], pixelsize*step)
# %%
# =============================================================================
# Save data in hdf5 format
# =============================================================================
fname_output = fname[:-4] + '_' + wpu.datetime_now_str() + ".h5"
f = h5.File(fname_output, "w")
h5rawdata = f.create_group('raw')
f.create_dataset("raw/image_sample", data=image)
f.create_dataset("raw/image_ref", data=image_ref)
h5rawdata.attrs['Pixel Size Detector [m]'] = pixelsize
h5rawdata.attrs['Distance Detector to Sample [m]'] = distDet2sample
h5rawdata.attrs['Photon Energy [eV]'] = phenergy
h5displacement = f.create_group('displacement')
f.create_dataset("displacement/displacement_x", data=sx)
f.create_dataset("displacement/displacement_y", data=sy)
f.create_dataset("displacement/error", data=error)
f.create_dataset("displacement/xvec", data=xVec2)
f.create_dataset("displacement/yvec", data=yVec2)
h5displacement.attrs['Comments'] = 'Created by Walan Grizolli at ' + wpu.datetime_now_str()
h5displacement.attrs['Pixel Size Processed images [m]'] = pixelsize*step
h5displacement.attrs['Distance Detector to Sample [m]'] = distDet2sample
h5displacement.attrs['Photon Energy [eV]'] = phenergy
h5displacement.attrs['ini file'] = '\n' + open(inifname, 'r').read()
f.flush()
f.close()
with open(fname_output[:-3] + '.log', 'w') as logfile: # save ini files as log
config.write(logfile)
wpu.print_blue("File saved at:\n{0}".format(fname_output))
|
Speckle Analyses Post Processing¶
This section contains the speckleAnalyses_postProcessing script.
Download file: speckleAnalyses_postProcessing.py
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"""
Created on Sat Aug 13 16:00:19 2016
@author: wcgrizolli
"""
#==============================================================================
# %%
#==============================================================================
import numpy as np
from numpy.fft import fft2, ifft2, fftfreq
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import h5py as h5
import wavepy.utils as wpu
import wavepy.surface_from_grad as wpsg
import itertools
#==============================================================================
# %% preamble
#==============================================================================
# Flags
saveFigFlag = True
# useful constants
rad2deg = np.rad2deg(1)
deg2rad = np.deg2rad(1)
NAN = float('Nan') # not a number alias
from scipy import constants
hc = constants.value('inverse meter-electron volt relationship') # hc
figCount = itertools.count() # itera
next(figCount)
def mpl_settings_4_nice_graphs():
'''
Settings for latex fonts in the graphs
ATTENTION: This will make the program slow because it will compile all
latex text. This means that you also need latex and any latex package
you want to use. I suggest to only use this when you want produce final
graphs to publish or to make public. The latex dependecies are not taken
care by the installation scripts. You are by your own to solve these
dependencies.
'''
plt.style.use('default')
#Direct input
plt.rcParams['text.latex.preamble']=[r"\usepackage[utopia]{mathdesign}"]
##Options
params = {'text.usetex' : True,
'font.size' : 16,
'font.family' : 'utopia',
'text.latex.unicode': True,
'figure.facecolor' : 'white'
}
plt.rcParams.update(params)
# mpl_settings_4_nice_graphs()
#==============================================================================
# %% Some definitions
#==============================================================================
def mySimplePlot(array, title=''):
plt.figure()
plt.imshow(array, cmap='spectral', interpolation='none')
plt.title(title)
plt.colorbar()
if saveFigFlag: wpu.save_figs_with_idx(foutname)
plt.show(block=True)
#==============================================================================
# %% Load files
#==============================================================================
fname = wpu.select_file('**/*.h5')
foutname = fname.rsplit('.')[0]
f = h5.File(fname,'r')
#print(wpu.h5ListOfGroups(f))
#==============================================================================
# %% parameters
#==============================================================================
delta = 5.3265E-06
# real part refractive index Be at 8KeV from http://henke.lbl.gov/
pixelsizeDetector = f['raw'].attrs['Pixel Size Detector [m]']
pixelsizeImg = f['displacement'].attrs['Pixel Size Processed images [m]']
distDet2sample = f['displacement'].attrs['Distance Detector to Sample [m]']
phenergy = f['displacement'].attrs['Photon Energy [eV]']
wavelength = hc/phenergy
kwave = 2*np.pi/wavelength
print('MESSAGE: Comments from hdf5 files')
print('MESSAGE: '+ f['displacement'].attrs['Comments'])
# %%
sx_raw = np.array(f['displacement/displacement_x'])
sy_raw = np.array(f['displacement/displacement_y'])
error_raw = np.array(f['displacement/error'])
xVec_raw = np.array(f['displacement/xvec'])
yVec_raw = np.array(f['displacement/yvec'])
#==============================================================================
# %% Crop
#==============================================================================
idx4crop = wpu.graphical_roi_idx(np.sqrt(sx_raw**2 + sy_raw**2), verbose=True)
sx = wpu.crop_matrix_at_indexes(sx_raw, idx4crop)
sy = wpu.crop_matrix_at_indexes(sy_raw, idx4crop)
error = wpu.crop_matrix_at_indexes(error_raw, idx4crop)
xVec = wpu.realcoordvec(sx.shape[1], pixelsizeImg)
yVec = wpu.realcoordvec(sx.shape[0], pixelsizeImg)
xmatrix, ymatrix = np.meshgrid(xVec, yVec)
#==============================================================================
# %% Calculations of physical quantities
#==============================================================================
totalS = np.sqrt(sx**2 + sy**2)
# Differenctial Phase
dpx = kwave*np.arctan2(sx*pixelsizeDetector, distDet2sample)
dpy = kwave*np.arctan2(sy*pixelsizeDetector, distDet2sample)
# Differenctial Thickness
dTx = 1.0/delta*np.arctan2(sx*pixelsizeDetector, distDet2sample)
dTy = 1.0/delta*np.arctan2(sy*pixelsizeDetector, distDet2sample)
#==============================================================================
# %% quiver
#==============================================================================
#
#
#
#from skimage.io import imread, imsave
#
#tempImage = imread('/home/grizolli/workspace/pythonWorkspace/data/Be1x50um/set1/very_small_sample.jpg')
#
#
#fig = plt.figure(figsize=(10, 7.5))
#stride = 1
#plt.contourf(xmatrix*1e6,
# ymatrix*1e6,
# wpu.crop_matrix_at_indexes(tempImage, idx4crop),
# 1001, cmap='Greys_r')
#
#plt.xlabel('[um]')
#plt.ylabel('[um]')
#
#plt.show()
#wpu.save_figs_with_idx(foutname)
#
#
#
##
#
#fig = plt.figure(figsize=(10, 7.5))
#stride = 1
#plt.contourf(xmatrix*1e6,
# ymatrix*1e6,
# wpu.crop_matrix_at_indexes(tempImage, idx4crop),
# 1001, cmap='Greys_r')
#
#stride = 12
#plt.quiver(xmatrix[::stride, ::stride]*1e6,
# ymatrix[::stride, ::stride]*1e6,
# sx[::stride, ::stride], sy[::stride, ::stride],
# angles='xy', minlength=2.2, color='c', lw=.5, width=0.005)
#
#plt.xlabel('[um]')
#plt.ylabel('[um]')
#
#plt.show()
#wpu.save_figs_with_idx(foutname)
#
#==============================================================================
# %% integration frankotchellappa
#==============================================================================
#integration_res = frankotchellappa(dTx,dTy)
integration_res = wpsg.frankotchellappa(dTx*pixelsizeImg, dTy*pixelsizeImg)
thickness = np.real(integration_res)
thickness = thickness - np.min(thickness)
# %%
wpsg.error_integration(dTx*pixelsizeImg, dTy*pixelsizeImg, thickness,
[pixelsizeImg, pixelsizeImg], shifthalfpixel=True, plot_flag=True)
#==============================================================================
# %% Thickness
#==============================================================================
stride = 1
wpu.plot_profile(xmatrix[::stride, ::stride]*1e6,
ymatrix[::stride, ::stride]*1e6,
thickness[::stride, ::stride]*1e6,
title='Thickness', xlabel='[um]', ylabel='[um]',
arg4main={'cmap':'spectral'}) #, xo=0.0, yo=0.0)_1Dparabol_4_fit
plt.show(block=True)
#==============================================================================
# %% Plot
#==============================================================================
def plotsidebyside(array1, array2, title1='', title2='', maintitle=''):
fig = plt.figure(figsize=(14, 5))
fig.suptitle(maintitle, fontsize=14)
vmax = np.max([array1, array2])
vmin = np.min([array1, array2])
ax1 = plt.subplot(121)
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
im1 = ax1.imshow(array1, cmap='RdGy',
interpolation='none',
vmin=vmin, vmax=vmax)
ax1.set_title(title1, fontsize=22)
ax1.set_adjustable('box-forced')
fig.colorbar(im1, ax=ax1, shrink=.8, aspect=20)
im2 = ax2.imshow(array2, cmap='RdGy',
interpolation='none',
vmin=vmin, vmax=vmax)
ax2.set_title(title2, fontsize=22)
ax2.set_adjustable('box-forced')
fig.colorbar(im2, ax=ax2, shrink=.8, aspect=20)
if saveFigFlag: wpu.save_figs_with_idx(foutname)
plt.show(block=True)
#==============================================================================
# %% Plot dpx and dpy and fit Curvature Radius of WF
#==============================================================================
fig = plt.figure(figsize=(14, 5))
fig.suptitle('Phase [rad]', fontsize=14)
ax1 = plt.subplot(121)
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
ax1.plot(xVec*1e6, dpx[dpx.shape[1]//4,:],'-ob')
ax1.plot(xVec*1e6, dpx[dpx.shape[1]//2,:],'-or')
ax1.plot(xVec*1e6, dpx[dpx.shape[1]//4*3,:],'-og')
ax1.ticklabel_format(style='sci', axis='y', scilimits=(0, 1))
ax1.set_xlabel('[um]')
ax1.set_ylabel('dpx [radians]')
lin_fitx = np.polyfit(xVec, dpx[dpx.shape[1]//2,:], 1)
lin_funcx = np.poly1d(lin_fitx)
ax1.plot(xVec*1e6, lin_funcx(xVec),'--c',lw=2)
curvrad_x = kwave/(lin_fitx[0])
ax1.set_title('Curvature Radius of WF {:.3g} m'.format(curvrad_x), fontsize=18)
ax1.set_adjustable('box-forced')
ax2.plot(yVec*1e6, dpy[:,dpy.shape[0]//4],'-ob')
ax2.plot(yVec*1e6, dpy[:,dpy.shape[0]//2],'-or')
ax2.plot(yVec*1e6, dpy[:,dpy.shape[0]//4*3],'-og')
ax2.ticklabel_format(style='sci', axis='y', scilimits=(0, 1))
ax2.set_xlabel('[um]')
ax2.set_ylabel('dpy [radians]')
lin_fity = np.polyfit(yVec, dpy[:,dpy.shape[0]//2], 1)
lin_funcy = np.poly1d(lin_fity)
ax2.plot(yVec*1e6, lin_funcy(yVec),'--c',lw=2)
curvrad_y = kwave/(lin_fity[0])
ax2.set_title('Curvature Radius of WF {:.3g} m'.format(curvrad_y), fontsize=18)
ax2.set_adjustable('box-forced')
if saveFigFlag: wpu.save_figs_with_idx(foutname)
plt.show(block=True)
# %%
plotsidebyside(sx, sy, r'Displacement $S_x$ [pixels]',
r'Displacement $S_y$ [pixels]')
# %%
mySimplePlot(totalS, title=r'Displacement Module $|\vec{S}|$ [pixels]')
# %%
fig = plt.figure(figsize=(14, 5))
ax1 = plt.subplot(121)
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
ax1.plot(sx.flatten(),error.flatten(),'.')
ax1.set_xlabel('Sy [pixel]')
ax1.set_title('Error vs Sx', fontsize=22)
ax1.set_adjustable('box-forced')
ax2.plot(sy.flatten(),error.flatten(),'.')
ax2.set_xlabel('Sy [pixel]')
ax2.set_title('Error vs Sy', fontsize=22)
ax2.set_adjustable('box-forced')
if saveFigFlag: wpu.save_figs_with_idx(foutname)
plt.show(block=True)
#==============================================================================
# %% Histograms to evaluate data quality
#==============================================================================
fig = plt.figure(figsize=(14, 5))
fig.suptitle('Histograms to evaluate data quality', fontsize=16)
ax1 = plt.subplot(121)
ax1 = plt.hist(sx.flatten(), 51)
ax1 = plt.title(r'$S_x$ [pixels]', fontsize=16)
ax1 = plt.subplot(122)
ax2 = plt.hist(sy.flatten(), 51)
ax2 = plt.title(r'$S_y$ [pixels]', fontsize=16)
if saveFigFlag: wpu.save_figs_with_idx(foutname)
plt.show(block=True)
##==============================================================================
## %% Total displacement
##==============================================================================
#
#plt.figure()
#plt.hist(totalS.flatten(), 51)[0]
#plt.title(r'Total displacement $|\vec{S}|$ [pixels]', fontsize=16)
#if saveFigFlag: wpu.save_figs_with_idx(foutname)
#plt.show(block=True)
#==============================================================================
# %% Integration Real and Imgainary part
#==============================================================================
fig = plt.figure(figsize=(14, 5))
fig.suptitle('Histograms to evaluate data quality', fontsize=16)
ax1 = plt.subplot(121)
ax1 = plt.hist(np.real(integration_res).flatten()*1e6, 51)
ax1 = plt.title(r'Integration Real part', fontsize=16)
ax1 = plt.subplot(122)
ax2 = plt.hist(np.imag(integration_res).flatten()*1e6, 51)
ax2 = plt.title(r'Integration Imag part', fontsize=16)
if saveFigFlag: wpu.save_figs_with_idx(foutname)
plt.show(block=True)
# %% Crop Result and plot surface
(xVec_croped1, yVec_croped1,
thickness_croped, _) = wpu.crop_graphic(xVec, yVec,
thickness*1e6, verbose=True)
thickness_croped *= 1e-6
thickness_croped -= np.max(thickness_croped)
xmatrix_croped1, ymatrix_croped1 = wpu.realcoordmatrix_fromvec(xVec_croped1,
yVec_croped1)
# %% center fig
def center_max_2darray(array):
'''
crop the array in order to have the max at the center of the array
'''
center_i, center_j = np.unravel_index(array.argmax(), array.shape)
if 2*center_i > array.shape[0]:
array = array[2*center_i-array.shape[0]:-1,:]
else:
array = array[0:2*center_i,:]
if 2*center_j > array.shape[1]:
array = array[:, 2*center_j-array.shape[1]:-1]
else:
array = array[:,0:2*center_j]
return array
# %%
thickness_croped = center_max_2darray(thickness_croped)
xVec_croped1 = wpu.realcoordvec(thickness_croped.shape[1], pixelsizeImg)
yVec_croped1 = wpu.realcoordvec(thickness_croped.shape[0], pixelsizeImg)
xmatrix_croped1, ymatrix_croped1 = np.meshgrid(xVec_croped1, yVec_croped1)
# %%
lim = 1
wpu.plot_profile(xmatrix_croped1[lim:-lim,lim:-lim]*1e6,
ymatrix_croped1[lim:-lim,lim:-lim]*1e6,
thickness_croped[lim:-lim,lim:-lim]*1e6,
title='Thickness centered [um]', xlabel='[um]', ylabel='[um]',
arg4main={'cmap':'spectral'}) #, xo=0.0, yo=0.0)
plt.show(block=True)
# %%
#
fig = plt.figure(figsize=(10, 7))
ax = fig.add_subplot(111, projection='3d')
stride = thickness_croped.shape[0] // 100
if stride == 0: stride = 1
surf = ax.plot_surface(xmatrix_croped1*1e6,
ymatrix_croped1*1e6,
thickness_croped*1e6,
rstride=stride, cstride=stride,
#vmin=-120, vmax=0,
cmap='spectral', linewidth=0.1)
plt.xlabel('[um]')
plt.ylabel('[um]')
plt.title('Thickness [um]', fontsize=18, weight='bold')
plt.colorbar(surf, shrink=.8, aspect=20)
plt.tight_layout()
if saveFigFlag: wpu.save_figs_with_idx(foutname)
plt.show(block=False)
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