Basics of Laue Pattern peak search and Unit cell Refinement
This Notebook is a part of Tutorials on LaueTools Suite.
Author: J.-S. Micha
Last Revision: August 2019
tested with python3
Objectives
Load and display Laue pattern images
Perform a Peak Search
Perform the indexation of a Laue spots list
Perform the crystal orientation and unit cell refinement
Setting absolute path to LaueTools Modules if Lauetools has not been installed with pip. It is assumed that this notebook is located in a subfolder (normally Notebooks)
LaueToolsCode_Folder = '..'
import sys,os
abspathLaueTools =os.path.abspath(LaueToolsCode_Folder)
print('abspathLaueTools',abspathLaueTools)
sys.path.append(LaueToolsCode_Folder)
abspathLaueTools /home/micha/LaueToolsPy3/LaueTools
import LaueTools
LaueTools.__file__
'/home/micha/LaueToolsPy3/LaueTools/__init__.py'
#%matplotlib inline
%matplotlib notebook
import time,copy,os
# Third party modules
import matplotlib # graphs and plots
import matplotlib.pyplot as plt
import numpy as np # numerical arrays
# LaueTools modules
import LaueTools.IOLaueTools as IOLT # read and write ASCII file (IO)
import LaueTools.readmccd as RMCCD # read CCD and detector binary file, PeakSearch methods
LaueToolsProjectFolder /home/micha/LaueToolsPy3/LaueTools
/home/micha/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type. from ._conv import register_converters as _register_converters
module Image / PIL is not installed
Cython compiled module 'gaussian2D' for fast computation is not installed!
module Image / PIL is not installed
Considering single image analysis (that belong to the LaueTools distribution)
t0 = time.time()
LaueToolsExamplesFolder = os.path.join(LaueToolsCode_Folder,'Examples')
imageindex = None
imagefolder =os.path.join(LaueToolsCode_Folder,'LaueImages')
imagefilename = 'Ge_blanc_0000.mccd'
#imagefolder =os.path.join(LaueToolsCode_Folder,'LaueImages')
#imagefilename = 'CdTe_I999_03Jul06_0200.mccd'
Considering analysis of one image in dataset
For information: select image file of interest, in case of set of images with index. Then, splitting imagefilename allows to loop over images: prefix+index.extension
%%script false
# just to show (cell not executed)
imagefolder ='/home/micha/LaueProjects/VO2/ToScript/Data_VO2'
prefixfilename= 'CT30_'
imageindex=20
imagefilename = prefixfilename+'%04d.mccd'%imageindex
print("imagefilename :",imagefilename)
# you should see: imagefilename : CT30_0020.mccd
Read image file, get data and display it
Function readCCDimage() returns dataimage as a 2D numpy array
with the proper dimensions and orientation given by framedim and the
geometrical transformations labelled by fliprot
print('Displaying %s\n'%imagefilename)
dataimage, framedim, fliprot = RMCCD.readCCDimage(imagefilename,dirname=imagefolder,CCDLabel='MARCCD165')
fullpathimagefile= os.path.join(imagefolder,imagefilename)
fig, ax = plt.subplots(figsize=(4,4))
ax.imshow(dataimage,vmin=0,vmax=2000)
ax.set_title('%s'%imagefilename)
Displaying Ge_blanc_0000.mccd
nb elements 4194304
framedim (2048, 2048)
framedim nb of elements 4194304
<IPython.core.display.Javascript object>
Text(0.5,1,'Ge_blanc_0000.mccd')
*peaksearch* is performed in two main steps: - 1) blobs or local maxima finder - 2) for blob, refinement starting from blob average center.
For the first step, readCCDimage() is called to obtain raw data if
no different data array is provided with the argument
Data_for_localMaxima (set to None by default). After second
step, Peaksearch results can be purged from peaks already present in a
file as an optional argument Remove_BlackListedPeaks_fromfile.
import os
ti1= time.time()
#blacklistedpeaksfile=os.path.join(folder,'Blacklist.dat')
blacklistedpeaksfile = None
res=RMCCD.PeakSearch(fullpathimagefile,CCDLabel='MARCCD165',
return_histo=0,local_maxima_search_method=0,
IntensityThreshold=200,
boxsize=5,
fit_peaks_gaussian=1,
FitPixelDev=10,
Data_for_localMaxima=None,#newdataimage,
Remove_BlackListedPeaks_fromfile=blacklistedpeaksfile)
tps =time.time()
print("peak search time",tps-ti1)
CCDLabel: MARCCD165
nb of pixels (4194304,)
nb elements 4194304
framedim (2048, 2048)
framedim nb of elements 4194304
image from filename ../LaueImages/Ge_blanc_0000.mccd read!
Read Image. Execution time : 0.006 seconds
Data.shape for local maxima (2048, 2048)
Using simple intensity thresholding to detect local maxima (method 1/3)
len(peaklist) 82
Local maxima search. Execution time : 0.336 seconds
Keep 82 from 82 initial peaks (ready for peak positions and shape fitting)
*************
82 local maxima found
Fitting of each local maxima
addImax False
nb elements 4194304
framedim (2048, 2048)
framedim nb of elements 4194304
framedim in readoneimage_manycrops (2048, 2048)
fitting time for 82 peaks is : 0.2039
nb of results: 82
After fitting, 0/82 peaks have been rejected
due to (final - initial position)> FitPixelDev = 10
0 spots have been rejected
due to negative baseline
0 spots have been rejected
due to much intensity
0 spots have been rejected
due to weak intensity
0 spots have been rejected
due to small peak size
0 spots have been rejected
due to large peak size
ToTake {0, 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}
len(ToTake) 82
82 fitted peak(s)
Removing duplicates from fit
82 peaks found after removing duplicates (minimum intermaxima distance = 5)
peak search time 0.5514366626739502
Spots properties:
peak_X, peak_Y, peak_I, peak_fwaxmaj, peak_fwaxmin, peak_inclination, Xdev, Ydev, peak_bkg, Ipixmax,
Spots are sorted by intensity (according to the 2D gaussian fit)
peaklist=res[0]
print('Digital Spots properties for the 5 most intense spots')
print(peaklist[:6])
Digital Spots properties for the 5 most intense spots
[[ 6.231334887002143e+02 1.657728161614024e+03 2.979937967247360e+04
8.515577956136006e-01 7.511212178165599e-01 1.820409415704489e+01
1.334887002143432e-01 -2.718383859764799e-01 2.966046444454810e+02
2.000000000000000e+02]
[ 1.244326205473488e+03 1.662150473603958e+03 2.242563070589073e+04
6.977180389301006e-01 6.390759549880105e-01 1.293529253564775e+02
3.262054734875619e-01 1.504736039580621e-01 1.998717405717028e+02
2.000000000000000e+02]
[ 9.330365915823824e+02 1.215440948340315e+03 2.219753607623998e+04
7.846021988134166e-01 7.862341648303387e-01 3.246533552343026e+02
3.659158238235705e-02 4.409483403153445e-01 2.110241433113940e+02
2.000000000000000e+02]
[ 5.852254505694141e+02 5.887990375606668e+02 9.528825561251553e+03
7.791458064142315e-01 7.399755695034808e-01 1.068330480796285e+02
2.254505694140789e-01 -2.009624393332388e-01 1.501265419627265e+02
2.000000000000000e+02]
[ 1.276607259246803e+03 6.002998208781320e+02 9.153971543092421e+03
8.153699693677648e-01 8.035243472697837e-01 8.044575533084571e+01
-3.927407531973586e-01 2.998208781319818e-01 1.324821469609317e+02
2.000000000000000e+02]
[ 9.326643784727646e+02 7.500763813513167e+02 6.940078500922347e+03
7.065049204848781e-01 7.482895847319173e-01 8.063512928783894e+00
-3.356215272353893e-01 7.638135131674062e-02 1.317606341369902e+02
2.000000000000000e+02]]
print('X, Y pixel refinement positions for the first 5 spots')
peaklist[:5,:2]
X, Y pixel refinement positions for the first 5 spots
array([[ 623.1334887002143, 1657.7281616140235],
[1244.3262054734876, 1662.150473603958 ],
[ 933.0365915823824, 1215.4409483403153],
[ 585.2254505694141, 588.7990375606668],
[1276.6072592468026, 600.299820878132 ]])
add markers to image
if len(peaklist)<=1: raise ValueError
#datatoplot=newdataimage
datatoplot = dataimage
fig, ax = plt.subplots()
ax.imshow(datatoplot,vmin=0,vmax=1000,cmap='hot')
from matplotlib.patches import Circle
F=plt.gcf()
axes=F.gca()
F.get_dpi()
defaultSize=F.get_size_inches()
F.set_size_inches(defaultSize*1.5)
# delete previous patches:
axes.patches = []
# rebuild circular markers
largehollowcircles = []
smallredcircles = []
# correction only to fit peak position to the display
offset_convention = np.array([1, 1])
XYlist = peaklist[:, :2] - offset_convention
for po in XYlist:
large_circle = Circle(po, 7, fill=False, color='b')
center_circle = Circle(po, .5 , fill=True, color='r')
axes.add_patch(large_circle)
axes.add_patch(center_circle)
largehollowcircles.append(large_circle)
smallredcircles.append(center_circle)
<IPython.core.display.Javascript object>
List of peaks props is written in a file with extension .dat, here the variable is ``datfilename``
if imageindex is not None:
peaklistprefix=prefixfilename+'cor_%04d'%imageindex
else:
peaklistprefix=imagefilename.split('.')[0]+'Notebook'
print('peaklist.shape',peaklist.shape)
print("fullpathimagefile",fullpathimagefile)
print('imagefolder',imagefolder)
RMCCD.writepeaklist(peaklist,peaklistprefix,outputfolder=imagefolder,initialfilename=fullpathimagefile)
datfilename = peaklistprefix+'.dat'
peaklist.shape (82, 10)
fullpathimagefile ../LaueImages/Ge_blanc_0000.mccd
imagefolder ../LaueImages
table of 82 peak(s) with 10 columns has been written in
/home/micha/LaueToolsPy3/LaueTools/LaueImages/Ge_blanc_0000Notebook.dat
Now indexing
geometry calibration parameters
Either you fill manually the dict of parameters or you read a file .det
# detector geometry and parameters as read from Geblanc0000.det
calibration_parameters = [70.775, 941.74, 1082.57, 0.631, -0.681]
CCDCalibdict = {}
CCDCalibdict['CCDCalibParameters'] = calibration_parameters
CCDCalibdict['framedim'] = (2048, 2048)
CCDCalibdict['detectordiameter'] = 165.
CCDCalibdict['kf_direction'] = 'Z>0'
CCDCalibdict['xpixelsize'] = 0.07914
# CCDCalibdict can also be simply build by reading the proper .det file
print("reading geometry calibration file")
CCDCalibdict=IOLT.readCalib_det_file(os.path.join(imagefolder,'Geblanc0000.det'))
CCDCalibdict['kf_direction'] = 'Z>0'
reading geometry calibration file
calib = [ 7.07760e+01 9.41760e+02 1.08244e+03 6.29000e-01 -6.85000e-01
7.91400e-02 2.04800e+03 2.04800e+03]
matrix = [ 0.995829 -0.071471 -0.056709 0.012247 0.720654 -0.693187 0.09041
0.689602 0.718523]
creation of a .cor file containing accurate scattering angles thanks to detector geometry parameters
Only list of spots with scattering angles can be indexed. In LaueTools .dat file contains only X, Y pixel positions, .cor file contains in addition 2theta and chi scattering angles, and .fit file in addition indexed results properties (such as h, k, l, energy, grain index …)
import LaueTools.LaueGeometry as LTGeo
LTGeo.convert2corfile(datfilename,
calibration_parameters,
dirname_in=imagefolder,
dirname_out=imagefolder,
CCDCalibdict=CCDCalibdict)
corfilename = datfilename.split('.')[0] + '.cor'
fullpathcorfile = os.path.join(imagefolder,corfilename)
Entering CrystalParameters **---*********************** nb of spots and columns in .dat file (82, 3) file :../LaueImages/Ge_blanc_0000Notebook.dat containing 82 peaks (2theta chi X Y I) written in ../LaueImages/Ge_blanc_0000Notebook.cor
create instance of an objet spotsset class
import LaueTools.indexingSpotsSet as ISS
DataSet = ISS.spotsset()
DataSet.importdatafromfile(fullpathcorfile)
Cython compiled module for fast computation of Laue spots is not installed!
Cython compiled 'angulardist' module for fast computation of angular distance is not installed!
Using default module
Cython compiled module for fast computation of angular distance is not installed!
module Image / PIL is not installed
CCDcalib in readfile_cor {'dd': 70.776, 'xcen': 941.76, 'ycen': 1082.44, 'xbet': 0.629, 'xgam': -0.685, 'xpixelsize': 0.07914, 'ypixelsize': 0.07914, 'CCDLabel': 'MARCCD165', 'framedim': [2048.0, 2048.0], 'detectordiameter': 162.07872, 'kf_direction': 'Z>0', 'pixelsize': 0.07914}
CCD Detector parameters read from .cor file
CCDcalibdict {'dd': 70.776, 'xcen': 941.76, 'ycen': 1082.44, 'xbet': 0.629, 'xgam': -0.685, 'xpixelsize': 0.07914, 'ypixelsize': 0.07914, 'CCDLabel': 'MARCCD165', 'framedim': [2048.0, 2048.0], 'detectordiameter': 162.07872, 'kf_direction': 'Z>0', 'pixelsize': 0.07914}
True
DataSet.getUnIndexedSpotsallData()[:3]
array([[ 0.0000000e+00, 5.8426915e+01, 2.0130035e+01, 6.2313000e+02,
1.6577300e+03, 2.9799380e+04],
[ 1.0000000e+00, 5.7634672e+01, -1.8415523e+01, 1.2443300e+03,
1.6621500e+03, 2.2425630e+04],
[ 2.0000000e+00, 8.0919846e+01, 6.6158100e-01, 9.3304000e+02,
1.2154400e+03, 2.2197540e+04]])
*Set parameters for indexation: Ge, maximum energy*
All materials are listed in dict_LaueTools.py in dict_Materials. You can edit/modify the module (then a restart of the kernel is necessary)
emin=5
# emax can be lowered for large unit cell indexation (but greater than BM32 highest energy is meaningless)
emax=22
# key of materials
key_material='Ge'
dict_indexrefine = {# recognition angle parameters from two sets A and B
'AngleTolLUT': 0.5,
'nlutmax':3,
'central spots indices': [0,1,2,3,4], # spots set A
'NBMAXPROBED': 10, # spots set B
'MATCHINGRATE_ANGLE_TOL': 0.2,
# refinement parameters (loop over narrower matching angles)
'list matching tol angles':[0.5,0.2,0.1],
# minor parameters
'MATCHINGRATE_THRESHOLD_IAL': 100,
'UseIntensityWeights': False,
'nbSpotsToIndex':10000,
'MinimumNumberMatches': 3,
'MinimumMatchingRate':3
}
#
grainindex=0
DataSet = ISS.spotsset()
DataSet.pixelsize = CCDCalibdict['xpixelsize']
DataSet.dim = CCDCalibdict['framedim']
DataSet.detectordiameter = CCDCalibdict['detectordiameter']
DataSet.kf_direction = CCDCalibdict['kf_direction']
DataSet.key_material = key_material
DataSet.emin = emin
DataSet.emax = emax
Before launching the indexation procedure you may want to check a solution found elsewhere or sometimes ago. Then fill ``previousResults`` as shown below
#CheckFirstThisMatrix=np.array([[-0.44486058225058 , 0.098996190230096 ,-0.897868909077371],[-0.883970521873963,0.1130536332378 , 0.462465547362675],
# [ 0.143878606007886, 0.993706753289519 , 0.035064809225047]])
# nb of matrices, list of matrices to check, dummy parameter, dummy parameter
#previousResults = 1,[CheckFirstThisMatrix],50,50
previousResults = None
Then launch indexation by specifying some arguments of the method ``IndexSpotsSet``:
- nbGrainstoFind: nb of grains of this material you want to find
- set_central_spots_hkl: imposed miller indices [h,k,l] of central spots (set A of spots) else : None
...
t0 =time.time()
DataSet.IndexSpotsSet(fullpathcorfile, key_material, emin, emax, dict_indexrefine, None,
use_file=1, # read .cor file and reset also spots properties dictionary
IMM=False,LUT=None,n_LUT=dict_indexrefine['nlutmax'],angletol_list=dict_indexrefine['list matching tol angles'],
nbGrainstoFind=1, # nb of grains of the same material in this case
set_central_spots_hkl=[0,1,1], # set hkl of spots of set A
MatchingRate_List=[10, 10, 10,10,10,10,10,10], # minimum matching rate figure to keep on looping for refinement
verbose=0,
previousResults=previousResults, # check before the orientation if not None
corfilename=corfilename)
# write unindexed spots list in a .cor file
DataSet.writecorFile_unindexedSpots(corfilename=corfilename,
dirname=imagefolder,
filename_nbdigits=4)
# write .fit file of indexed spots belonging to grain #0
DataSet.writeFitFile(0,corfilename=corfilename,dirname=imagefolder)
tf = time.time()-t0
CCDcalib in readfile_cor {'dd': 70.776, 'xcen': 941.76, 'ycen': 1082.44, 'xbet': 0.629, 'xgam': -0.685, 'xpixelsize': 0.07914, 'ypixelsize': 0.07914, 'CCDLabel': 'MARCCD165', 'framedim': [2048.0, 2048.0], 'detectordiameter': 162.07872, 'kf_direction': 'Z>0', 'pixelsize': 0.07914}
CCD Detector parameters read from .cor file
CCDcalibdict {'dd': 70.776, 'xcen': 941.76, 'ycen': 1082.44, 'xbet': 0.629, 'xgam': -0.685, 'xpixelsize': 0.07914, 'ypixelsize': 0.07914, 'CCDLabel': 'MARCCD165', 'framedim': [2048.0, 2048.0], 'detectordiameter': 162.07872, 'kf_direction': 'Z>0', 'pixelsize': 0.07914}
self.pixelsize in IndexSpotsSet 0.07914
ResolutionAngstromLUT in IndexSpotsSet False
Remaining nb of spots to index for grain #0 : 82
**
start to index grain #0 of Material: Ge
**
providing new set of matrices Using Angles LUT template matching
nbspots 82
NBMAXPROBED 10
nbspots 82
set_central_spots_hkl [0, 1, 1]
Central set of exp. spotDistances from spot_index_central_list probed
self.absolute_index [ 0 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]
spot_index_central_list [0, 1, 2, 3, 4]
[0 1 2 3 4]
LUT is None when entering getOrientMatrices()
set_central_spots_hkl [0, 1, 1]
set_central_spots_hkl is not None in getOrientMatrices()
set_central_spots_hkl [0 1 1]
set_central_spots_hkl.shape (3,)
case: 1a
set_central_spots_hkl_list [[0 1 1]
[0 1 1]
[0 1 1]
[0 1 1]
[0 1 1]]
cubicSymmetry True
LUT_tol_angle 0.5
---***------------------------------------------------*
Calculating all possible matrices from exp spot #0 and the 9 other(s)
hkl in getOrientMatrices [0 1 1] <class 'numpy.ndarray'>
using LUTspecific
LUTspecific is None for k_centspot_index 0 in getOrientMatrices()
hkl1 in matrices_from_onespot_hkl() [0 1 1]
Computing hkl2 list for specific or cubic LUT in matrices_from_onespot_hkl()
Calculating LUT in PlanePairs_from2sets()
Looking up planes pairs in LUT from exp. spots (0, 2):
Looking up planes pairs in LUT from exp. spots (0, 6):
Looking up planes pairs in LUT from exp. spots (0, 9):
calculating matching rates of solutions for exp. spots [0, 2]
calculating matching rates of solutions for exp. spots [0, 6]
calculating matching rates of solutions for exp. spots [0, 9]
---***------------------------------------------------*
Calculating all possible matrices from exp spot #1 and the 9 other(s)
hkl in getOrientMatrices [0 1 1] <class 'numpy.ndarray'>
using LUTspecific
LUTspecific is not None for k_centspot_index 1 in getOrientMatrices()
hkl1 in matrices_from_onespot_hkl() [0 1 1]
Using specific LUT in matrices_from_onespot_hkl()
Looking up planes pairs in LUT from exp. spots (1, 2):
Looking up planes pairs in LUT from exp. spots (1, 7):
Looking up planes pairs in LUT from exp. spots (1, 9):
calculating matching rates of solutions for exp. spots [1, 2]
calculating matching rates of solutions for exp. spots [1, 7]
calculating matching rates of solutions for exp. spots [1, 9]
---***------------------------------------------------*
Calculating all possible matrices from exp spot #2 and the 9 other(s)
hkl in getOrientMatrices [0 1 1] <class 'numpy.ndarray'>
using LUTspecific
LUTspecific is not None for k_centspot_index 2 in getOrientMatrices()
hkl1 in matrices_from_onespot_hkl() [0 1 1]
Using specific LUT in matrices_from_onespot_hkl()
Looking up planes pairs in LUT from exp. spots (2, 0):
Looking up planes pairs in LUT from exp. spots (2, 1):
Looking up planes pairs in LUT from exp. spots (2, 9):
calculating matching rates of solutions for exp. spots [2, 0]
calculating matching rates of solutions for exp. spots [2, 1]
calculating matching rates of solutions for exp. spots [2, 9]
---***------------------------------------------------*
Calculating all possible matrices from exp spot #3 and the 9 other(s)
hkl in getOrientMatrices [0 1 1] <class 'numpy.ndarray'>
using LUTspecific
LUTspecific is not None for k_centspot_index 3 in getOrientMatrices()
hkl1 in matrices_from_onespot_hkl() [0 1 1]
Using specific LUT in matrices_from_onespot_hkl()
Looking up planes pairs in LUT from exp. spots (3, 7):
Looking up planes pairs in LUT from exp. spots (3, 8):
Looking up planes pairs in LUT from exp. spots (3, 9):
calculating matching rates of solutions for exp. spots [3, 7]
calculating matching rates of solutions for exp. spots [3, 8]
calculating matching rates of solutions for exp. spots [3, 9]
---***------------------------------------------------*
Calculating all possible matrices from exp spot #4 and the 9 other(s)
hkl in getOrientMatrices [0 1 1] <class 'numpy.ndarray'>
using LUTspecific
LUTspecific is not None for k_centspot_index 4 in getOrientMatrices()
hkl1 in matrices_from_onespot_hkl() [0 1 1]
Using specific LUT in matrices_from_onespot_hkl()
Looking up planes pairs in LUT from exp. spots (4, 6):
Looking up planes pairs in LUT from exp. spots (4, 8):
Looking up planes pairs in LUT from exp. spots (4, 9):
calculating matching rates of solutions for exp. spots [4, 6]
calculating matching rates of solutions for exp. spots [4, 8]
calculating matching rates of solutions for exp. spots [4, 9]
-----------------------------------------
results:
matrix: matching results
[ 0.071442298339536 -0.056791122889477 -0.995826674863109] res: [94.0, 135.0] 0.005 69.63
[-0.720597705663885 -0.693247631822884 -0.012110638459969] spot indices [0 9]
[-0.689608632382347 0.718518569419943 -0.090393581312322] planes [[-2.0, 1.0, 1.0], [0.0, 1.0, 1.0]]
[ 0.071284911945937 -0.056791915954001 -0.995837908301915] res: [93.0, 134.0] 0.006 69.40
[-0.720581964957976 -0.693265307713712 -0.012035152592104] spot indices [1 9]
[-0.68968586701208 0.718453369664079 -0.09032253573791 ] planes [[-1.0, 2.0, 1.0], [0.0, 1.0, 1.0]]
[-0.071331310042019 -0.995834915200135 -0.056786141584281] res: [93.0, 134.0] 0.005 69.40
[ 0.720561755804718 -0.012058289359476 -0.693285910522741] spot indices [2 9]
[ 0.689652960030445 -0.090345399841628 0.718482082900264] planes [[1.0, 1.0, 1.0], [0.0, 1.0, 1.0]]
[-0.071391558888759 -0.995830258190095 -0.056792096214888] res: [94.0, 135.0] 0.006 69.63
[ 0.720671448854468 -0.012140732995851 -0.693170444701969] spot indices [3 9]
[ 0.689588625514858 -0.090412962995124 0.718535332243983] planes [[2.0, 3.0, 1.0], [0.0, 1.0, 1.0]]
[ 0.071340651481789 -0.056823338834159 -0.995832124210648] res: [94.0, 135.0] 0.005 69.63
[-0.720605825766681 -0.693235936569283 -0.012295532523216] spot indices [4 9]
[-0.689563524109094 0.718557247109645 -0.090430242974657] planes [[-1.0, 2.0, 3.0], [0.0, 1.0, 1.0]]
Number of matrices found (nb_sol): 5
set_central_spots_hkl in FindOrientMatrices [0, 1, 1]
Merging matrices
keep_only_equivalent = False
sorting according to rank
rank [0 4 3 2 1]
-----------------------------------------
results:
matrix: matching results
[ 0.071442298339536 -0.056791122889477 -0.995826674863109] res: [ 94. 135.] 0.005 69.63
[-0.720597705663885 -0.693247631822884 -0.012110638459969]
[-0.689608632382347 0.718518569419943 -0.090393581312322]
[ 0.071340651481789 -0.056823338834159 -0.995832124210648] res: [ 94. 135.] 0.005 69.63
[-0.720605825766681 -0.693235936569283 -0.012295532523216]
[-0.689563524109094 0.718557247109645 -0.090430242974657]
[-0.071391558888759 -0.995830258190095 -0.056792096214888] res: [ 94. 135.] 0.006 69.63
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]
[-0.071331310042019 -0.995834915200135 -0.056786141584281] res: [ 93. 134.] 0.005 69.40
[ 0.720561755804718 -0.012058289359476 -0.693285910522741]
[ 0.689652960030445 -0.090345399841628 0.718482082900264]
[ 0.071284911945937 -0.056791915954001 -0.995837908301915] res: [ 93. 134.] 0.006 69.40
[-0.720581964957976 -0.693265307713712 -0.012035152592104]
[-0.68968586701208 0.718453369664079 -0.09032253573791 ]
Nb of potential orientation matrice(s) UB found: 5
[[[ 0.071442298339536 -0.056791122889477 -0.995826674863109]
[-0.720597705663885 -0.693247631822884 -0.012110638459969]
[-0.689608632382347 0.718518569419943 -0.090393581312322]]
[[ 0.071340651481789 -0.056823338834159 -0.995832124210648]
[-0.720605825766681 -0.693235936569283 -0.012295532523216]
[-0.689563524109094 0.718557247109645 -0.090430242974657]]
[[-0.071391558888759 -0.995830258190095 -0.056792096214888]
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]]
[[-0.071331310042019 -0.995834915200135 -0.056786141584281]
[ 0.720561755804718 -0.012058289359476 -0.693285910522741]
[ 0.689652960030445 -0.090345399841628 0.718482082900264]]
[[ 0.071284911945937 -0.056791915954001 -0.995837908301915]
[-0.720581964957976 -0.693265307713712 -0.012035152592104]
[-0.68968586701208 0.718453369664079 -0.09032253573791 ]]]
Nb of potential UBs 5
Working with a new stack of orientation matrices
MATCHINGRATE_THRESHOLD_IAL= 100.0
has not been reached! All potential solutions have been calculated
taking the first one only.
bestUB object <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c954f5e80>
---------------refining grain orientation and strain #0-----------------
refining grain #0 step -----0
bestUB <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c954f5e80>
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c954f5e80>
matrix [[-0.071391558888759 -0.995830258190095 -0.056792096214888]
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]]
*nb of selected spots in AssignHKL*** 82
UBOrientMatrix [[-0.071391558888759 -0.995830258190095 -0.056792096214888]
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]]
For angular tolerance 0.50 deg
Nb of pairs found / nb total of expected spots: 81/147
Matching Rate : 55.10
Nb missing reflections: 66
grain #0 : 81 links to simulated spots have been found
*********mean pixel deviation 0.2522039400422887 ******
Initial residues [0.191356096913678 0.158479888122211 0.125984922997516 0.007464331088751
0.220920883122328 0.065197410024489 0.405464259132319 0.079769518806149
0.309835172580193 0.024815180122634 0.146635771529913 0.197926454567734
0.253815574123594 0.241517985294699 0.301875442673439 0.217498144625921
0.186026257638361 0.152430964466482 0.022468745875909 0.372498387808433
0.225884815274198 0.155682523936061 0.308213213363587 0.354423361607117
0.237793437184287 0.344146246502948 0.117700835663451 0.22732103372742
0.263538267437741 0.133994037769124 0.091015982918167 0.367309714380722
0.359174426753832 0.281533512444384 0.191021625928391 0.219461033259323
0.371983339466526 0.3512796731268 0.298580209240117 0.447936020775024
0.160438308161376 0.631208433750478 0.420060120050684 0.195238104695171
0.051118832992816 0.159003375870547 0.354123955360538 0.049380652521924
0.301744705672337 0.127112320459672 0.082920786835417 0.19281838475986
0.130182524243209 0.332360152782496 0.533160923855596 0.276782907418236
0.125265672564509 0.184320173657227 0.238789408490181 0.149666955002009
0.473603697641706 0.235878500572685 0.425250385266374 0.445829965130009
0.255853120078437 0.271987274130697 0.298711159306184 0.310382741777609
0.228936459666657 0.374245425300233 0.100039587285176 0.096572087547537
0.196098380129156 0.140612883827292 0.338936919946618 0.526244208385607
0.190627233723994 0.629487817359844 0.233333060530866 0.316852495355672
0.61336433904477 ]
---------------------------------------------------
***********************
first error with initial values of: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
*********mean pixel deviation 0.2522039400422887 ******
***********************
Fitting parameters: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
With initial values [1. 1. 0. 0. 0. 0. 0. 0.]
code results 1
nb iterations 189
mesg Both actual and predicted relative reductions in the sum of squares
are at most 0.000000
strain_sol [ 9.999862096544356e-01 9.999941276097474e-01 -7.144700897145182e-06
5.728542645493929e-05 1.242229382006964e-05 2.977984275077042e-05
-1.953472667313407e-03 6.919002586889341e-03]
********** End of Fitting - Final errors **************
*********mean pixel deviation 0.18215488925149526 ******
devstrain, lattice_parameter_direct_strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]] [ 5.657509728355469 5.657578574250457 5.657522951317707
90.0010541881893 90.00328909016345 89.99939828842365 ]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
devstrain1, lattice_parameter_direct_strain1 [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]] [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
final lattice_parameters [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
UB and strain refinement completed
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c851edeb8>
matrix [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
*nb of selected spots in AssignHKL*** 82
UBOrientMatrix [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
For angular tolerance 0.50 deg
Nb of pairs found / nb total of expected spots: 81/147
Matching Rate : 55.10
Nb missing reflections: 66
grain #0 : 81 links to simulated spots have been found
GoodRefinement condition is True
nb_updates 81 compared to 6
refining grain #0 step -----1
bestUB <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c954f5e80>
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c954f5e80>
matrix [[-0.071391558888759 -0.995830258190095 -0.056792096214888]
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]]
*nb of selected spots in AssignHKL*** 82
UBOrientMatrix [[-0.071391558888759 -0.995830258190095 -0.056792096214888]
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]]
For angular tolerance 0.20 deg
Nb of pairs found / nb total of expected spots: 81/147
Matching Rate : 55.10
Nb missing reflections: 66
grain #0 : 81 links to simulated spots have been found
*********mean pixel deviation 0.2522039400422887 ******
Initial residues [0.191356096913678 0.158479888122211 0.125984922997516 0.007464331088751
0.220920883122328 0.065197410024489 0.405464259132319 0.079769518806149
0.309835172580193 0.024815180122634 0.146635771529913 0.197926454567734
0.253815574123594 0.241517985294699 0.301875442673439 0.217498144625921
0.186026257638361 0.152430964466482 0.022468745875909 0.372498387808433
0.225884815274198 0.155682523936061 0.308213213363587 0.354423361607117
0.237793437184287 0.344146246502948 0.117700835663451 0.22732103372742
0.263538267437741 0.133994037769124 0.091015982918167 0.367309714380722
0.359174426753832 0.281533512444384 0.191021625928391 0.219461033259323
0.371983339466526 0.3512796731268 0.298580209240117 0.447936020775024
0.160438308161376 0.631208433750478 0.420060120050684 0.195238104695171
0.051118832992816 0.159003375870547 0.354123955360538 0.049380652521924
0.301744705672337 0.127112320459672 0.082920786835417 0.19281838475986
0.130182524243209 0.332360152782496 0.533160923855596 0.276782907418236
0.125265672564509 0.184320173657227 0.238789408490181 0.149666955002009
0.473603697641706 0.235878500572685 0.425250385266374 0.445829965130009
0.255853120078437 0.271987274130697 0.298711159306184 0.310382741777609
0.228936459666657 0.374245425300233 0.100039587285176 0.096572087547537
0.196098380129156 0.140612883827292 0.338936919946618 0.526244208385607
0.190627233723994 0.629487817359844 0.233333060530866 0.316852495355672
0.61336433904477 ]
---------------------------------------------------
***********************
first error with initial values of: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
*********mean pixel deviation 0.2522039400422887 ******
***********************
Fitting parameters: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
With initial values [1. 1. 0. 0. 0. 0. 0. 0.]
code results 1
nb iterations 189
mesg Both actual and predicted relative reductions in the sum of squares
are at most 0.000000
strain_sol [ 9.999862096544356e-01 9.999941276097474e-01 -7.144700897145182e-06
5.728542645493929e-05 1.242229382006964e-05 2.977984275077042e-05
-1.953472667313407e-03 6.919002586889341e-03]
********** End of Fitting - Final errors **************
*********mean pixel deviation 0.18215488925149526 ******
devstrain, lattice_parameter_direct_strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]] [ 5.657509728355469 5.657578574250457 5.657522951317707
90.0010541881893 90.00328909016345 89.99939828842365 ]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
devstrain1, lattice_parameter_direct_strain1 [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]] [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
final lattice_parameters [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
UB and strain refinement completed
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c84338898>
matrix [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
*nb of selected spots in AssignHKL*** 82
UBOrientMatrix [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
For angular tolerance 0.20 deg
Nb of pairs found / nb total of expected spots: 81/147
Matching Rate : 55.10
Nb missing reflections: 66
grain #0 : 81 links to simulated spots have been found
GoodRefinement condition is True
nb_updates 81 compared to 6
refining grain #0 step -----2
bestUB <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c954f5e80>
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c954f5e80>
matrix [[-0.071391558888759 -0.995830258190095 -0.056792096214888]
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]]
*nb of selected spots in AssignHKL*** 82
UBOrientMatrix [[-0.071391558888759 -0.995830258190095 -0.056792096214888]
[ 0.720671448854468 -0.012140732995851 -0.693170444701969]
[ 0.689588625514858 -0.090412962995124 0.718535332243983]]
For angular tolerance 0.10 deg
Nb of pairs found / nb total of expected spots: 81/147
Matching Rate : 55.10
Nb missing reflections: 66
grain #0 : 81 links to simulated spots have been found
*********mean pixel deviation 0.2522039400422887 ******
Initial residues [0.191356096913678 0.158479888122211 0.125984922997516 0.007464331088751
0.220920883122328 0.065197410024489 0.405464259132319 0.079769518806149
0.309835172580193 0.024815180122634 0.146635771529913 0.197926454567734
0.253815574123594 0.241517985294699 0.301875442673439 0.217498144625921
0.186026257638361 0.152430964466482 0.022468745875909 0.372498387808433
0.225884815274198 0.155682523936061 0.308213213363587 0.354423361607117
0.237793437184287 0.344146246502948 0.117700835663451 0.22732103372742
0.263538267437741 0.133994037769124 0.091015982918167 0.367309714380722
0.359174426753832 0.281533512444384 0.191021625928391 0.219461033259323
0.371983339466526 0.3512796731268 0.298580209240117 0.447936020775024
0.160438308161376 0.631208433750478 0.420060120050684 0.195238104695171
0.051118832992816 0.159003375870547 0.354123955360538 0.049380652521924
0.301744705672337 0.127112320459672 0.082920786835417 0.19281838475986
0.130182524243209 0.332360152782496 0.533160923855596 0.276782907418236
0.125265672564509 0.184320173657227 0.238789408490181 0.149666955002009
0.473603697641706 0.235878500572685 0.425250385266374 0.445829965130009
0.255853120078437 0.271987274130697 0.298711159306184 0.310382741777609
0.228936459666657 0.374245425300233 0.100039587285176 0.096572087547537
0.196098380129156 0.140612883827292 0.338936919946618 0.526244208385607
0.190627233723994 0.629487817359844 0.233333060530866 0.316852495355672
0.61336433904477 ]
---------------------------------------------------
***********************
first error with initial values of: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
*********mean pixel deviation 0.2522039400422887 ******
***********************
Fitting parameters: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
With initial values [1. 1. 0. 0. 0. 0. 0. 0.]
code results 1
nb iterations 189
mesg Both actual and predicted relative reductions in the sum of squares
are at most 0.000000
strain_sol [ 9.999862096544356e-01 9.999941276097474e-01 -7.144700897145182e-06
5.728542645493929e-05 1.242229382006964e-05 2.977984275077042e-05
-1.953472667313407e-03 6.919002586889341e-03]
********** End of Fitting - Final errors **************
*********mean pixel deviation 0.18215488925149526 ******
devstrain, lattice_parameter_direct_strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]] [ 5.657509728355469 5.657578574250457 5.657522951317707
90.0010541881893 90.00328909016345 89.99939828842365 ]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
devstrain1, lattice_parameter_direct_strain1 [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]] [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
final lattice_parameters [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
UB and strain refinement completed
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7f7c94485da0>
matrix [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
*nb of selected spots in AssignHKL*** 81
UBOrientMatrix [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
For angular tolerance 0.10 deg
Nb of pairs found / nb total of expected spots: 81/147
Matching Rate : 55.10
Nb missing reflections: 66
grain #0 : 81 links to simulated spots have been found
GoodRefinement condition is True
nb_updates 81 compared to 6
---------------------------------------------
indexing completed for grain #0 with matching rate 55.10
---------------------------------------------
writing fit file -------------------------
for grainindex= 0
self.dict_grain_matrix[grain_index] [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
self.refinedUBmatrix [[-0.071478224945242 -0.995814588672313 -0.056724144676328]
[ 0.720639310822985 -0.012262885888099 -0.693156594892675]
[ 0.689613233174427 -0.090416539541802 0.718545890295648]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[ 1.719550237533340e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 1.388845576189013e-05 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 3.998638790081444e-06]]
deviatoric strain [[-4.815998025634964e-06 5.250997004746657e-06 -2.870284279887629e-05]
[ 5.250997004746657e-06 7.352907498721824e-06 -9.199263307200638e-06]
[-2.870284279887629e-05 -9.199263307200638e-06 -2.536909473086861e-06]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
final lattice_parameters [ 5.6575 5.657568845776604 5.6575132229395
90.0010541881893 90.00328909016345 89.99939828842365 ]
File : Ge_blanc_0000Notebook_g0.fit written in /home/micha/LaueToolsPy3/LaueTools/notebooks
Experimental experimental spots indices which are not indexed []
Missing reflections grainindex is -100 for indexed grainindex 0
within angular tolerance 0.500
Remaining nb of spots to index for grain #1 : 1
81 spots have been indexed over 82
indexing rate is --- : 98.8 percents
indexation of ../LaueImages/Ge_blanc_0000Notebook.cor is completed
for the 1 grain(s) that has(ve) been indexed as requested
Leaving Index and Refine procedures...
Saving unindexed fit file: ../LaueImages/Ge_blanc_0000Notebook_unindexed.cor
File : ../LaueImages/Ge_blanc_0000Notebook_g0.fit written in /home/micha/LaueToolsPy3/LaueTools/notebooks
print('Indexation time %.3f second(s) \n\n'%tf)
print('Spots properties of the 10 first spots that have been indexed (sorted by intensity)')
print('#spot 2theta chi X, Y intensity h k l energy')
print(DataSet.getSpotsFamilyallData(0)[:10])
Indexation time 2.487 second(s)
Spots properties of the 10 first spots that have been indexed (sorted by intensity)
#spot 2theta chi X, Y intensity h k l energy
[[ 0.000000000000000e+00 5.842691500000000e+01 2.013003500000000e+01
6.231300000000000e+02 1.657730000000000e+03 2.979938000000000e+04
4.000000000000000e+00 2.000000000000000e+00 2.000000000000000e+00
1.099874517758171e+01]
[ 1.000000000000000e+00 5.763467200000000e+01 -1.841552300000000e+01
1.244330000000000e+03 1.662150000000000e+03 2.242563000000000e+04
2.000000000000000e+00 2.000000000000000e+00 4.000000000000000e+00
1.113626245226060e+01]
[ 2.000000000000000e+00 8.091984600000001e+01 6.615810000000000e-01
9.330400000000000e+02 1.215440000000000e+03 2.219754000000000e+04
3.000000000000000e+00 3.000000000000000e+00 3.000000000000000e+00
8.773642495456929e+00]
[ 3.000000000000000e+00 1.168117220000000e+02 2.128975200000000e+01
5.852300000000000e+02 5.888000000000000e+02 9.528830000000000e+03
4.000000000000000e+00 6.000000000000000e+00 2.000000000000000e+00
9.626187155122841e+00]
[ 4.000000000000000e+00 1.159585970000000e+02 -2.073868400000000e+01
1.276610000000000e+03 6.003000000000000e+02 9.153969999999999e+03
2.000000000000000e+00 6.000000000000000e+00 4.000000000000000e+00
9.671066779127555e+00]
[ 5.000000000000000e+00 1.097624120000000e+02 3.270820000000000e-01
9.326600000000000e+02 7.500800000000000e+02 6.940080000000000e+03
3.000000000000000e+00 5.000000000000000e+00 3.000000000000000e+00
8.784305505382406e+00]
[ 6.000000000000000e+00 9.664626300000000e+01 -3.623969200000000e+01
1.596620000000000e+03 9.353600000000000e+02 6.136730000000000e+03
1.000000000000000e+00 5.000000000000000e+00 5.000000000000000e+00
1.047621498150968e+01]
[ 7.000000000000000e+00 9.807526100000000e+01 3.749168800000000e+01
2.523600000000000e+02 9.206000000000000e+02 5.635550000000000e+03
5.000000000000000e+00 5.000000000000000e+00 1.000000000000000e+00
1.036136430057910e+01]
[ 8.000000000000000e+00 6.421410100000000e+01 -1.397936600000000e+01
1.168360000000000e+03 1.512820000000000e+03 5.570320000000000e+03
3.000000000000000e+00 3.000000000000000e+00 5.000000000000000e+00
1.351813316448898e+01]
[ 9.000000000000000e+00 9.620103100000000e+01 -4.831242900000000e+01
1.945880000000000e+03 9.142200000000000e+02 5.317320000000000e+03
0.000000000000000e+00 4.000000000000000e+00 4.000000000000000e+00
8.328162135695869e+00]]
DataSet is an object with many attributes and methods related to spots properties (indexed or not, belonging to grains counted from zero). By press Tab key after having typed DataSet. can show you infos about spots
DataSet.B0matrix
array([[ 1.767565178965975e-01, -2.842461599074922e-17,
-2.842461599074922e-17],
[ 0.000000000000000e+00, 1.767565178965975e-01,
-1.082321519352500e-17],
[ 0.000000000000000e+00, 0.000000000000000e+00,
1.767565178965975e-01]])