Welcome to LaueTools’s DOCUMENTATION!
You have data to analyse? You are at the right place! You can also contact us for more assistance.
Introduction
LaueTools is a python written package aiming at analysing from 1 to few 10000s microdiffraction Laue patterns coming from synchrotron CRG-IF BM32 beamline at ESRF.
With pip installer, it is now relatively easy to install LaueTools and put hands immediately on data. See Installation page.
Tools
LaueTools has got :
Modules to be imported in your own scripts like any scientific library.
LaueTools has got several Graphical User Interfaces to interact graphically with data so that to accelerate the design of future scripts and to set the most finely of parameters for batch processing.
Notebooks to guide the typical way you can handle the data (visualisation, selection, analysis, post processing)
Developers
Developers are welcome to improve the code readability and to add new functionalities. Please contact us.
Browse Modules and Functions
Installation
Some dependencies are rather usual (numpy, scipy, matplotlib) while others are more uncommon but very useful (fabio, networkx).
GUIs are based on wxpython graphical libraries which can be tricky to install (Sorry. We are working on it).
How to Get LaueTools code
Download the very last version of the code at gitlab.esrf.fr (but you are also welcome to fork this project):
or Download last (or older releases) on pypi by means of pip
Build LaueTools Documentation
Documentation can be generated, by installing sphinx and a cool html theme:
pip install sphinx
pip install sphinx-rtd-theme
You may need rinohtype:
pip install RinohType
Then from /LaueTools/Documentation folder which contains Makefile and 2 folders build and source, build the documentation
make html
Files in html format will be browsed in /build/html folder with any web navigator. You can start with index.html.
Getting started
Launch Graphical User Interfaces of LaueTools
start Lauetools GUIs from command line :
To deal with relative import, the package name ‘LaueTools’ must be specified to the python interpreter as following
Examples:
python -m LaueTools.LaueToolsGUI
python -m LaueTools.GUI.LaueSimulatorGUI
python -m LaueTools.GUI.PeaksearchGUI
The first main GUI, LaueToolsGUI can open also the two last GUIs (LaueSimulatorGUI, PeaksearchGUI)
There are additional basic GUIs for batch processing located in FileSeries folder:
python -m LaueTools.FileSeries.Peak_Search
python -m LaueTools.FileSeries.Index_Refine
python -m LaueTools.FileSeries.Build_summary
python -m LaueTools.FileSeries.Plot_Maps2
within interactive python (say, ipython -i), GUI can be started thanks to a start() function:
In [1] : import LaueTools.LaueToolsGUI as LTGUI
In [2] : LTGUI.start()
Note
in the LaueTools folder :
neither > python LaueToolsGUI
nor in >ipython -i : > run LaueToolsGUI will work…
Use LaueTools module as a library
With pip installation, LaueTools package will be included to python packages. Therefore any module will be callable as the following:
-In [1] : import LaueTools.readmccd as rmccd
-In [2] : rmccd.readCCDimage(‘myimage.tif’)
In jupyter-notebook, it is also simple in the same manner:
Conventions
Mathematics and Conventions
Graphical User Interfaces
LaueTools provides two types of GUI:
GUI with graphical interaction when handling data
GUI used as input file parameters for batch processing
Interactive Graphical User Interfaces
The main steps of analysis are Laue peaks search, Laue Pattern indexation and unit Cell Refinement. Detector geometry calibration (DetectorCalibrationBoard) and Laue Pattern of Polycrystals (LaueSimulatorGUI) are also available.
Peak Search (PeaksearchGUI)
PeakSearchGUI
The module PeakSearchGUI.py is made to provide graphical tools to perform peak search and fitting. It enables also tools to get mosaic from a set of images.
Read Images and Binary files
First select the detector you have used for the data collection in the menu Calibration
Choose the correct camera to define the binary image file parameters
View an image by selecting in the Menu File/Open Image and Peak Search.
Then you obtain a board enabling to browse your set of images and to search for peaks
This board is composed by composed by 4 TOP tabs:
parameters of display (View & color)
Digital image processing (Image & Filter)
Browse a set of images and select a ROI (Browse & crop)
Mosaic or image-related monitor from a ROI over a set of images
And 5 tabs at the middle
1 Selection of local maxima search Method
2 Fitting procedure parameters
3 Peaks List Manager
View & Color
This panel helps for viewing images with an appropriate color LUT, getting some infos on pixels intensity on the whole image (distribution) or along line (line profilers)
Color Mapping (LUT) of the displayed image.
ShowHistodisplays the histogram of intensity distribution (nb of pixel as a function of pixel intensity)
intensities limits taken into account by the LUT
Open LineProfiler: 1D plot of pixel intensities along a movable-by-user line. AndEneable X Y Profiler: 1D plot of pixel intensities along X and Y from a clicked pixel position
Image & Filter
This panel supplies digital image processing tools to filter current image and particularly remove background.
Blur Image computes the image filtered by a gaussian kernel (similar to a low pass filter). By checking Substract blur as background the raw image minus the filtered image can be displayed and used for the local maxima (blob) search.
Calculate image with A and B allows with an arithmetical formula with A (current image) and B (additional image to be open) the computation of a new image A’ on which will be performed the local maxima (blob) searchChecking. By default this image will not be used to refine the position and shape of local maxima but the former and initial A image. Check use also for fit to apply the fit procedures on pixel intensities A’.
Save results saves on hard disk the A’ image with the header contains and format of A.
Browse & Crop
One can navigate on a set of images provided the images file name contains a numerical index with a constant prefix (e.g. myimage_0032.ccd). Navigation with button with small step index-1 and index+1 corresponds to consecutive images collected with time or along a line on sample. Navigation with button with larger step (Nb of images per line (step index)) index-10 and index+10 (for instance with larger step equals to 10) permits to look at the images collected along the direction perpendicular to the direction corresponding to the small step.
The Go To index button allows to read directly an image with an other in the same dataset. Auto index+1 button will display the next image and wait for it if it is not already in the folder.
To navigate and display faster the image when browsing on a particular region of interest (ROI) of the images, you can crop the data (CropData) by specifying the half sizes (boxsize) of the cropping box in the two directions.
Mosaic & Monitor
Several counters can be defined from pixel intensities in the same ROI (centered a clicked pixel with half box sizes given by user) over a set of images.
Mosaic: Recompose a 2D raster scan from the selected ROI of each images as a function of image index
Mean Value: plot a 1D graph or a 2D raster scan from the mean pixel value in the selected ROI of each images as a function of image index
Max Value: plot a 1D graph or a 2D raster scan from the maximum pixel value in the selected ROI of each images as a function of image index
Peak-to-peak Value (or ‘peak to valley’): plot a 1D graph or a 2D raster scan from the largest pixel amplitude value in the selected ROI of each images as a function of image index
Peak position: plot two 1D graphes of X and Y peak position of the peak in the selected ROI of each images as a function of image index
Plot Tools
The standard toolbar is provided by the graphical ‘matplotlib’ library with the following features:
A ROI can be set to zoom in the data.
This ROI can be moved easily with the pan button
When hovering the mouse on the image pixel position and corresponding intensity are displayed
Previous selected ROIs are stored and can be recalled by arrows (
Homeicon recalls the initial full image)
To guess the initial parameters fitting, 3 methods leads to a list of local maxima (or blobs)
Method 1: pixels above a threshold on raw image
The basic method consists in considering every pixel higher than intensityThreshold as a local maxima pixel.
If intensityThreshold is too high you will get only few pixels at the submit of laue spots.
If intensityThreshold is too small you will too much pixels that you may stuck the software.
MinimumDistance is the smallest distance separating two local maxima.
A good habit is too check the highest background level (e.g close to the image centre) and set intensityThreshold to a larger value. But even in this case if (fluorescence) background varies a lot, you will miss peaks whose maximum intensities are below the threshold… This is why removing the background is mandatory.
Method 2: hottest pixel in a box (by array shift)
Second Method finds the hottest pixel in a small box given by PixelNearRadius. It shifts the whole data array in 8 directions and determines every pixel hotter than the others lying in these 8 directions.
The thresholding with the IntensityThreshold level is performed on the intensity of these hot pixels with respect to local background level (set to the lowest pixel intensity in the box around the hot pixel).
One drawback of this method is that 2 hot pixels at the top of the peak but with strictly the same intensity are not detected (coincidence or more likely when peak is saturated).
Method 3: Peak enhancement by convolution on raw image
This is the fastest method as soon as you have found the few parameters value (for batch). The Raw image data (unsigned 16 bits integers) are convolved by a 2D gaussian (mexican-hat-like) kernel. The resulting convolved Image (floats) have intense region (called blobs) where pixel intensity 2D profile on raw data is similar to the kernel intensity profile.
A first threshold with the level ThresholdConvolve (float) allows to select enhanced blob above a background. An good estimate of ThresholdConvolve value can be found by means of the pixel intensity histogram (ShowHisto): it corresponds to the (float) intensity that separates the hottest pixel population that belong to the peak (large abscisssa value) from the weakest one that belong to background.
With this blobs list, a second thresholding at Intensity Threshold (raw data) level is performed in the raw data pixel intensity with respect to local background level (like in method 2).
PixelNearRadius value enables the user to reject too much closed spots.
Max.Intensity value is used for the display of the convolved Image by clicking on Show Conv. Image. Thresholding can be visualize by checking Show thresholding.
The goal of the peak search is to have systematic values of Laue peaks and intensities. You can decide to fit or not the blob found. The Button Search All Peaks launches the local maxima chosen method and apply a fitting procedure (or not) to each found blob. A general peak list is built.
By clicking close to a Laue spot in the image and clicking on the Fit Peak button, a gaussian fit is performed. This result can be added to the current general peak list with the button Add Peak. Clicking close to a Laue spot that belongs to the peak list (blue circle marker on top of the image) and pressing Remove Peak removes the laue spot from the list.
Parameters to choose the fitting model or no fit and to reject or not the fitting result of each peak according to deviation from the initial guessed position (FitPixelDeviation).
The next documentation comes from the docstring in the header of function or class definition.
PeakSearchGUI.py (PeakSearchBoard)
Indexation (LaueToolsGUI)
Crystal unit cell refinement (LaueToolsGUI)
Detector Geometry Calibration (DetectorCalibrationBoard)
Laue pattern simulation of assembly of crystals (LaueSimulatorGUI)
Batch Processing Graphical User Interfaces
Peak Search (FileSeries.Peak_Search)
Indexing and Unit Cell Refinement (FileSeries.Index_Refine)
Summary of all refined data (FileSeries.Build_Summary)
Plot of Quantities in Summary file (FileSeries.Plot_Map2)
Tutorials
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
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]])
Indexation of spots data set.
Using Class spotsSet and play with spots considered for indexation and refinement
core function to index a set of spots
by defaut DataSet.getUnIndexedSpotsallData() is called
if use_file = 0, then current non indexed exp. spots will be considered for indexation
if use_file = 1, reimport data from file and reset also spots properties dictionary (i.e. with status unindexed)
DataSet.IndexSpotsSet(fullpathcorfile, key_material, emin, emax, dict_loop, None,
use_file=0, # if 1, reimport data from file and reset also spots properties dictionary
IMM=False,LUT=None,n_LUT=dict_loop['nlutmax'],angletol_list=dict_loop['list matching tol angles'],
nbGrainstoFind=1,
starting_grainindex=0,
MatchingRate_List=[1, 1, 1,1,1,1,1,1],
verbose=0, previousResults=None,
corfilename=corfilename)
self.pixelsize in IndexSpotsSet 0.08057
ResolutionAngstromLUT in IndexSpotsSet False
Remaining nb of spots to index for grain #0 : 12
**
start to index grain #0 of Material: Ge
**
providing new set of matrices Using Angles LUT template matching
nbspots 12
NBMAXPROBED 6
nbspots 12
set_central_spots_hkl None
Computing LUT from material data
Compute LUT for indexing Ge spots in LauePattern
Build angles LUT with latticeparameters
[ 5.657499999999999 5.657499999999999 5.657499999999999
90. 90. 90. ]
and n=3
MaxRadiusHKL False
cubicSymmetry True
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]
spot_index_central_list [0]
[0]
LUT is not None when entering getOrientMatrices()
set_central_spots_hkl None
set_central_spots_hkl_list [None]
cubicSymmetry True
LUT_tol_angle 0.5
---***------------------------------------------------*
Calculating all possible matrices from exp spot #0 and the 5 other(s)
hkl in getOrientMatrices None <class 'NoneType'>
using LUTcubic
LUTcubic is None for k_centspot_index 0 in getOrientMatrices()
hkl1 in matrices_from_onespot_hkl() [[1 0 0]
[1 1 0]
[1 1 1]
[2 1 0]
[2 1 1]
[2 2 1]
[3 1 0]
[3 1 1]
[3 2 1]
[3 2 2]
[3 3 1]
[3 3 2]]
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, 1):
Looking up planes pairs in LUT from exp. spots (0, 2):
Looking up planes pairs in LUT from exp. spots (0, 3):
Looking up planes pairs in LUT from exp. spots (0, 4):
Looking up planes pairs in LUT from exp. spots (0, 5):
calculating matching rates of solutions for exp. spots [0, 1]
calculating matching rates of solutions for exp. spots [0, 2]
calculating matching rates of solutions for exp. spots [0, 3]
calculating matching rates of solutions for exp. spots [0, 4]
return best matrix and matching scores for the one central_spot
-----------------------------------------
results:
matrix: matching results
[-0.211852735694566 0.092255643652867 -0.972937466948891] res: [20.0, 162.0] 0.014 12.35
[-0.775856536468367 0.58951816141498 0.22475536073965 ] spot indices [0 1]
[ 0.594300563948835 0.802473664571131 -0.053318452339475] planes [[1.0, 3.0, 2.0], [1.0, 1.0, 1.0]]
Number of matrices found (nb_sol): 1
set_central_spots_hkl in FindOrientMatrices None
-----------------------------------------
results:
matrix: matching results
[-0.211852735694566 0.092255643652867 -0.972937466948891] res: [20.0, 162.0] 0.014 12.35
[-0.775856536468367 0.58951816141498 0.22475536073965 ] spot indices [0 1]
[ 0.594300563948835 0.802473664571131 -0.053318452339475] planes [[1.0, 3.0, 2.0], [1.0, 1.0, 1.0]]
Nb of potential orientation matrice(s) UB found: 1
[array([[-0.211852735694566, 0.092255643652867, -0.972937466948891],
[-0.775856536468367, 0.58951816141498 , 0.22475536073965 ],
[ 0.594300563948835, 0.802473664571131, -0.053318452339475]])]
Nb of potential UBs 1
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 0x7fb8ec7ddc50>
---------------refining grain orientation and strain #0-----------------
refining grain #0 step -----0
bestUB <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
matrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
For angular tolerance 0.50 deg
Nb of pairs found / nb total of expected spots: 12/176
Matching Rate : 6.82
Nb missing reflections: 164
grain #0 : 12 links to simulated spots have been found
*********mean pixel deviation 0.560750282710606 ******
Initial residues [0.053680370172309 0.013739858524874 0.921977335411896 0.403270956234836
0.919825854310187 0.785969463406447 0.565019172757509 1.127873079813964
0.363514793614926 0.412635402450867 0.711008521607465 0.450488584221994]
---------------------------------------------------
***********************
first error with initial values of: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
*********mean pixel deviation 0.560750282710606 ******
***********************
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 1767
mesg Both actual and predicted relative reductions in the sum of squares
are at most 0.000000
strain_sol [ 1.001128981010799e+00 9.993806401299155e-01 8.449040381845989e-04
-8.486913595751131e-04 3.520626401662759e-04 -2.714612741167435e-02
3.054889720130747e-02 5.311773668297186e-02]
********** End of Fitting - Final errors **************
*********mean pixel deviation 0.3158807195732847 ******
devstrain, lattice_parameter_direct_strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657424223234738 5.651101185787196 5.661104877237695
90.01741959362538 89.9544419036642 90.04747664598754 ]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
devstrain1, lattice_parameter_direct_strain1 [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
final lattice_parameters [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
UB and strain refinement completed
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8c98baa20>
matrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
For angular tolerance 0.50 deg
Nb of pairs found / nb total of expected spots: 12/177
Matching Rate : 6.78
Nb missing reflections: 165
grain #0 : 12 links to simulated spots have been found
GoodRefinement condition is True
nb_updates 12 compared to 6
refining grain #0 step -----1
bestUB <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
matrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
For angular tolerance 0.50 deg
Nb of pairs found / nb total of expected spots: 12/176
Matching Rate : 6.82
Nb missing reflections: 164
grain #0 : 12 links to simulated spots have been found
*********mean pixel deviation 0.560750282710606 ******
Initial residues [0.053680370172309 0.013739858524874 0.921977335411896 0.403270956234836
0.919825854310187 0.785969463406447 0.565019172757509 1.127873079813964
0.363514793614926 0.412635402450867 0.711008521607465 0.450488584221994]
---------------------------------------------------
***********************
first error with initial values of: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
*********mean pixel deviation 0.560750282710606 ******
***********************
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 1767
mesg Both actual and predicted relative reductions in the sum of squares
are at most 0.000000
strain_sol [ 1.001128981010799e+00 9.993806401299155e-01 8.449040381845989e-04
-8.486913595751131e-04 3.520626401662759e-04 -2.714612741167435e-02
3.054889720130747e-02 5.311773668297186e-02]
********** End of Fitting - Final errors **************
*********mean pixel deviation 0.3158807195732847 ******
devstrain, lattice_parameter_direct_strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657424223234738 5.651101185787196 5.661104877237695
90.01741959362538 89.9544419036642 90.04747664598754 ]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
devstrain1, lattice_parameter_direct_strain1 [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
final lattice_parameters [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
UB and strain refinement completed
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8c98ba9b0>
matrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
For angular tolerance 0.50 deg
Nb of pairs found / nb total of expected spots: 12/177
Matching Rate : 6.78
Nb missing reflections: 165
grain #0 : 12 links to simulated spots have been found
GoodRefinement condition is True
nb_updates 12 compared to 6
refining grain #0 step -----2
bestUB <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
matrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
For angular tolerance 0.20 deg
Nb of pairs found / nb total of expected spots: 12/176
Matching Rate : 6.82
Nb missing reflections: 164
grain #0 : 12 links to simulated spots have been found
*********mean pixel deviation 0.560750282710606 ******
Initial residues [0.053680370172309 0.013739858524874 0.921977335411896 0.403270956234836
0.919825854310187 0.785969463406447 0.565019172757509 1.127873079813964
0.363514793614926 0.412635402450867 0.711008521607465 0.450488584221994]
---------------------------------------------------
***********************
first error with initial values of: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
*********mean pixel deviation 0.560750282710606 ******
***********************
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 1767
mesg Both actual and predicted relative reductions in the sum of squares
are at most 0.000000
strain_sol [ 1.001128981010799e+00 9.993806401299155e-01 8.449040381845989e-04
-8.486913595751131e-04 3.520626401662759e-04 -2.714612741167435e-02
3.054889720130747e-02 5.311773668297186e-02]
********** End of Fitting - Final errors **************
*********mean pixel deviation 0.3158807195732847 ******
devstrain, lattice_parameter_direct_strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657424223234738 5.651101185787196 5.661104877237695
90.01741959362538 89.9544419036642 90.04747664598754 ]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
devstrain1, lattice_parameter_direct_strain1 [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
final lattice_parameters [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
UB and strain refinement completed
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8cf573a90>
matrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
For angular tolerance 0.20 deg
Nb of pairs found / nb total of expected spots: 12/177
Matching Rate : 6.78
Nb missing reflections: 165
grain #0 : 12 links to simulated spots have been found
GoodRefinement condition is True
nb_updates 12 compared to 6
refining grain #0 step -----3
bestUB <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7ddc50>
matrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211852735694566 0.092255643652867 -0.972937466948891]
[-0.775856536468367 0.58951816141498 0.22475536073965 ]
[ 0.594300563948835 0.802473664571131 -0.053318452339475]]
For angular tolerance 0.20 deg
Nb of pairs found / nb total of expected spots: 12/176
Matching Rate : 6.82
Nb missing reflections: 164
grain #0 : 12 links to simulated spots have been found
*********mean pixel deviation 0.560750282710606 ******
Initial residues [0.053680370172309 0.013739858524874 0.921977335411896 0.403270956234836
0.919825854310187 0.785969463406447 0.565019172757509 1.127873079813964
0.363514793614926 0.412635402450867 0.711008521607465 0.450488584221994]
---------------------------------------------------
***********************
first error with initial values of: ['b/a', 'c/a', 'a12', 'a13', 'a23', 'theta1', 'theta2', 'theta3']
***********************
*********mean pixel deviation 0.560750282710606 ******
***********************
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 1767
mesg Both actual and predicted relative reductions in the sum of squares
are at most 0.000000
strain_sol [ 1.001128981010799e+00 9.993806401299155e-01 8.449040381845989e-04
-8.486913595751131e-04 3.520626401662759e-04 -2.714612741167435e-02
3.054889720130747e-02 5.311773668297186e-02]
********** End of Fitting - Final errors **************
*********mean pixel deviation 0.3158807195732847 ******
devstrain, lattice_parameter_direct_strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657424223234738 5.651101185787196 5.661104877237695
90.01741959362538 89.9544419036642 90.04747664598754 ]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
devstrain1, lattice_parameter_direct_strain1 [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]] [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
final lattice_parameters [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
UB and strain refinement completed
True it is an OrientMatrix object
Orientation <LaueTools.indexingSpotsSet.OrientMatrix object at 0x7fb8ec7bc128>
matrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
*nb of selected spots in AssignHKL*** 12
UBOrientMatrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
For angular tolerance 0.20 deg
Nb of pairs found / nb total of expected spots: 12/177
Matching Rate : 6.78
Nb missing reflections: 165
grain #0 : 12 links to simulated spots have been found
GoodRefinement condition is True
nb_updates 12 compared to 6
---------------------------------------------
indexing completed for grain #0 with matching rate 6.78
---------------------------------------------
transform matrix to matrix with lowest Euler Angles
start
[[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
final
[[ 0.97230572627369 0.211415207301911 0.091252946262469]
[-0.224552051799525 0.77573594328912 0.590041596352251]
[ 0.054088075690982 -0.594613709497768 0.803610914885283]]
hkl [[2. 6. 4.]
[3. 3. 3.]
[5. 3. 3.]
[1. 3. 5.]
[6. 2. 4.]
[5. 1. 3.]
[1. 3. 3.]
[6. 4. 6.]
[4. 2. 6.]
[4. 2. 2.]
[3. 5. 3.]
[2. 2. 4.]]
new hkl (min euler angles) [[-4. -2. 6.]
[-3. -3. 3.]
[-3. -5. 3.]
[-5. -1. 3.]
[-4. -6. 2.]
[-3. -5. 1.]
[-3. -1. 3.]
[-6. -6. 4.]
[-6. -4. 2.]
[-2. -4. 2.]
[-3. -3. 5.]
[-4. -2. 2.]]
UB before [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
new UB (min euler angles) [[ 0.97230572627369 0.211415207301911 0.091252946262469]
[-0.224552051799525 0.77573594328912 0.590041596352251]
[ 0.054088075690982 -0.594613709497768 0.803610914885283]]
writing fit file -------------------------
for grainindex= 0
self.dict_grain_matrix[grain_index] [[ 0.97230572627369 0.211415207301911 0.091252946262469]
[-0.224552051799525 0.77573594328912 0.590041596352251]
[ 0.054088075690982 -0.594613709497768 0.803610914885283]]
self.refinedUBmatrix [[-0.211415207301911 0.091252946262469 -0.97230572627369 ]
[-0.77573594328912 0.590041596352251 0.224552051799525]
[ 0.594613709497768 0.803610914885283 -0.054088075690982]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
new UBs matrix in q= UBs G (s for strain)
strain_direct [[-1.339403716515974e-05 -4.138432477238585e-04 3.978226745502020e-04]
[-4.138432477238585e-04 -1.131375039302607e-03 -1.517818975573709e-04]
[ 3.978226745502020e-04 -1.517818975573709e-04 6.255721962393768e-04]]
deviatoric strain [[ 0.000159671589578 -0.000413843247724 0.00039782267455 ]
[-0.000413843247724 -0.00095830941256 -0.000151781897557]
[ 0.00039782267455 -0.000151781897557 0.000798637822982]]
For comparison: a,b,c are rescaled with respect to the reference value of a = 5.657500 Angstroms
lattice_parameter_direct_strain [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
final lattice_parameters [ 5.657499999999999 5.651176877860324 5.661180703302433
90.01741959362538 89.9544419036642 90.04747664598754 ]
File : Ge0001_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 : 0
12 spots have been indexed over 12
indexing rate is --- : 100.0 percents
indexation of short_Ge0001.cor is completed
for the 1 grain(s) that has(ve) been indexed as requested
Leaving Index and Refine procedures...
index_grain_retrieve=0
print("number of indexed spots", len(DataSet.getallIndexedSpotsallData()[index_grain_retrieve]))
number of indexed spots 12
Results of indexation can be found in attributes or through methods
spotsdata=DataSet.getSummaryallData()
print("first 2 indexed spots properties\n")
print('#spot #grain 2theta chi X Y I h k l Energy')
print(spotsdata[:2])
first 2 indexed spots properties
#spot #grain 2theta chi X Y I h k l Energy
[[ 0.000000000000000e+00 0.000000000000000e+00 6.035945800000000e+01
2.648319100000000e+01 6.261200000000000e+02 1.661280000000000e+03
1.582539000000000e+04 -4.000000000000000e+00 -2.000000000000000e+00
6.000000000000000e+00 1.632366988464985e+01]
[ 1.000000000000000e+00 0.000000000000000e+00 7.821582100000001e+01
1.638153000000000e+00 1.027110000000000e+03 1.293280000000000e+03
7.093127000000000e+04 -3.000000000000000e+00 -3.000000000000000e+00
3.000000000000000e+00 9.031851548397370e+00]]
print('#grain : [Npairs = Nb pairs with tolerance angle %.4f, 100*Npairs/Ndirections theo.]'%dict_loop['list matching tol angles'][-1])
DataSet.dict_grain_matching_rate
#grain : [Npairs = Nb pairs with tolerance angle 0.2000, 100*Npairs/Ndirections theo.]
{0: [12, 6.779661016949152]}
print("#grain : deviatoric strain")
DataSet.dict_grain_devstrain
#grain : deviatoric strain
{0: array([[ 0.000159671589578, -0.000413843247724, 0.00039782267455 ],
[-0.000413843247724, -0.00095830941256 , -0.000151781897557],
[ 0.00039782267455 , -0.000151781897557, 0.000798637822982]])}
#RefinedUB= DataSet.dict_grain_matrix
print("#grain : refined UB matrix")
DataSet.dict_grain_matrix
#grain : refined UB matrix
{0: array([[ 0.97230572627369 , 0.211415207301911, 0.091252946262469],
[-0.224552051799525, 0.77573594328912 , 0.590041596352251],
[ 0.054088075690982, -0.594613709497768, 0.803610914885283]])}
print([DataSet.indexed_spots_dict[k] for k in range(10)])
[[0, 60.359458, 26.483191, 626.12, 1661.28, 15825.39, array([-4., -2., 6.]), 16.323669884649853, 0, 1], [1, 78.215821, 1.638153, 1027.11, 1293.28, 70931.27, array([-3., -3., 3.]), 9.03185154839737, 0, 1], [2, 68.680451, -15.358122, 1288.11, 1460.16, 22795.07, array([-3., -5., 3.]), 12.73794897550435, 0, 1], [3, 108.452917, 37.749461, 383.77, 754.58, 4400.61, array([-5., -1., 3.]), 7.989738078276174, 0, 1], [4, 83.349535, -27.458061, 1497.4, 1224.7, 13145.99, array([-4., -6., 2.]), 12.327936233758937, 0, 1], [5, 82.072076, -35.89243, 1672.67, 1258.62, 13318.81, array([-3., -5., 1.]), 9.870774398536632, 0, 1], [6, 81.771257, 30.38247, 548.25, 1260.32, 6137.87, array([-3., -1., 3.]), 7.2986772558942405, 0, 1], [7, 91.798221, -8.309941, 1176.09, 1086.19, 11799.93, array([-6., -6., 4.]), 14.309911950813975, 0, 1], [8, 120.59561, -8.92066, 1183.27, 598.92, 17182.88, array([-6., -4., 2.]), 9.435284238042113, 0, 1], [9, 64.329767, -20.824155, 1379.17, 1553.58, 51933.84, array([-2., -4., 2.]), 10.087272916663885, 0, 1]]
LaueTools Modules
Browse Modules and Functions
Modules for Laue Pattern Simulation
The following modules are used to compute Laue pattern from grain (or crystal) structural parameters and 2D plane detector geometry:
CrystalParameters.py defines structural parameters describing the crystal. It includes orientation matrix and strain operators.
lauecore.py contains the core procedures to compute all Laue spots properties.
LaueGeometry.py handles the 2D plane geometry set by the detector position and orientation with respect to sample and incoming direction.
multigrainsSimulator.py allows to simulate an assembly of grains, some of them according to a distribution of grains. This module is called by the graphical user interface (LaueSimulatorGUI) which provides all arguments in an intuitive way.
CrystalParameters
This module belong to LaueTools package. It gathers procedures to define crystal lattice parameters and strain calculations
Main authors are JS Micha, O. Robach, S. Tardif June 2019
- LaueTools.CrystalParameters.GrainParameter_from_Material(key_material, dictmaterials={'3H-SiC': ['3H-SiC', [4.3596, 4.3596, 4.3596, 90, 90, 90], 'dia'], '4H-SiC': ['4H-SiC', [3.073, 3.073, 10.053, 90, 90, 120], 'wurtzite'], 'Ag': ['Ag', [4.085, 4.085, 4.085, 90, 90, 90], 'fcc'], 'Al': ['Al', [4.05, 4.05, 4.05, 90, 90, 90], 'fcc'], 'Al2Cu': ['Al2Cu', [6.063, 6.063, 4.872, 90, 90, 90], 'no'], 'Al2O3': ['Al2O3', [4.785, 4.785, 12.991, 90, 90, 120], 'Al2O3'], 'Al2O3_all': ['Al2O3_all', [4.785, 4.785, 12.991, 90, 90, 120], 'no'], 'AlN': ['AlN', [3.11, 3.11, 4.98, 90.0, 90.0, 120.0], 'wurtzite'], 'AmbiguousTriclinic': ['AmbiguousTriclinic', [3.9, 4, 4.1, 89, 90, 91], 'no'], 'Au': ['Au', [4.078, 4.078, 4.078, 90, 90, 90], 'fcc'], 'CCDL1949': ['CCDL1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'h+k=2n'], 'CdHgTe': ['CdHgTe', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'dia'], 'CdHgTe_fcc': ['CdHgTe_fcc', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'fcc'], 'CdTe': ['CdTe', [6.487, 6.487, 6.487, 90, 90, 90], 'fcc'], 'CdTeDiagB': ['CdTeDiagB', [4.5721, 7.9191, 11.1993, 90, 90, 90], 'no'], 'Crocidolite': ['Crocidolite', [9.811, 18.013, 5.326, 90, 103.68, 90], 'no'], 'Crocidolite_2': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 103.6, 90], 'no'], 'Crocidolite_2_72deg': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 76.4, 90], 'no'], 'Crocidolite_small': ['Crocidolite_small', [3.2533333333333334, 5.976666666666667, 1.7833333333333332, 90, 103.6, 90], 'no'], 'Crocidolite_whittaker_1949': ['Crocidolite_whittaker_1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'no'], 'Cu': ['Cu', [3.6, 3.6, 3.6, 90, 90, 90], 'fcc'], 'Cu6Sn5_monoclinic': ['Cu6Sn5_monoclinic', [11.02, 7.28, 9.827, 90, 98.84, 90], 'no'], 'Cu6Sn5_tetra': ['Cu6Sn5_tetra', [3.608, 3.608, 5.037, 90, 90, 90], 'no'], 'DIA': ['DIA', [5.0, 5.0, 5.0, 90, 90, 90], 'dia'], 'DIAs': ['DIAs', [3.56683, 3.56683, 3.56683, 90, 90, 90], 'dia'], 'DarinaMolecule': ['DarinaMolecule', [9.4254, 13.5004, 13.8241, 61.83, 84.555, 75.231], 'no'], 'FCC': ['FCC', [5.0, 5.0, 5.0, 90, 90, 90], 'fcc'], 'Fe': ['Fe', [2.856, 2.856, 2.856, 90, 90, 90], 'bcc'], 'Fe2Ta': ['Fe2Ta', [4.83, 4.83, 0.788, 90, 90, 120], 'no'], 'FeAl': ['FeAl', [5.871, 5.871, 5.871, 90, 90, 90], 'fcc'], 'FePS3': ['FePS3', [5, 10, 7, 90, 107, 90], 'no'], 'GaAs': ['GaAs', [5.65325, 5.65325, 5.65325, 90, 90, 90], 'dia'], 'GaAs_wurtz': ['GaAs_wurtz', [5.65325, 5.65325, 5.9, 90, 90, 90], 'wurtzite'], 'GaN': ['GaN', [3.189, 3.189, 5.185, 90, 90, 120], 'wurtzite'], 'GaN_all': ['GaN_all', [3.189, 3.189, 5.185, 90, 90, 120], 'no'], 'Ge': ['Ge', [5.6575, 5.6575, 5.6575, 90, 90, 90], 'dia'], 'Ge_compressedhydro': ['Ge_compressedhydro', [5.64, 5.64, 5.64, 90, 90, 90.0], 'dia'], 'Ge_s': ['Ge_s', [5.6575, 5.6575, 5.6575, 90, 90, 89.5], 'dia'], 'Getest': ['Getest', [5.6575, 5.6575, 5.6574, 90, 90, 90], 'dia'], 'Hematite': ['Hematite', [5.03459, 5.03459, 13.7533, 90, 90, 120], 'no'], 'In': ['In', [3.2517, 3.2517, 4.9459, 90, 90, 90], 'h+k+l=2n'], 'In2Bi': ['In2Bi', [5.496, 5.496, 6.585, 90, 90, 120], 'no'], 'InGaN': ['InGaN', [3.3609999999999998, 3.3609999999999998, 5.439, 90, 90, 120], 'wurtzite'], 'InN': ['InN', [3.533, 3.533, 5.693, 90, 90, 120], 'wurtzite'], 'In_distorted': ['In_distorted', [3.2517, 3.251133, 4.818608, 89.982926, 90.007213, 95.379102], 'h+k+l=2n'], 'In_epsilon': ['In_epsilon', [3.47, 3.47, 4.49, 90, 90, 90], 'no'], 'Magnetite': ['Magnetite', [8.391, 8.391, 8.391, 90, 90, 90], 'dia'], 'Magnetite_fcc': ['Magnetite_fcc', [8.391, 8.391, 8.391, 90, 90, 90], 'fcc'], 'Magnetite_sc': ['Magnetite_sc', [8.391, 8.391, 8.391, 90, 90, 90], 'no'], 'Nd45': ['Nd45', [5.4884, 5.4884, 5.4884, 90, 90, 90], 'fcc'], 'Ni': ['Ni', [3.5238, 3.5238, 3.5238, 90, 90, 90], 'fcc'], 'NiO': ['NiO', [2.96, 2.96, 7.23, 90, 90, 120], 'no'], 'NiTi': ['NiTi', [3.5506, 3.5506, 3.5506, 90, 90, 90], 'fcc'], 'Olivine_fayalite': ['Olivine_fayalite', [4.8211, 10.4779, 6.0889, 90, 90, 90], 'no'], 'Olivine_forsterite': ['Olivine_forsterite', [4.754, 10.1971, 5.9806, 90, 90, 90], 'no'], 'Olivine_mantle': ['Olivine_mantle', [4.7646, 10.2296, 5.9942, 90, 90, 90], 'no'], 'SC': ['SC', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'SC5': ['SC5', [5.0, 5.0, 5.0, 90, 90, 90], 'no'], 'SC7': ['SC7', [7.0, 7.0, 7.0, 90, 90, 90], 'no'], 'Sb': ['Sb', [4.3, 4.3, 11.3, 90, 90, 120], 'no'], 'Si': ['Si', [5.4309, 5.4309, 5.4309, 90, 90, 90], 'dia'], 'Sn_beta': ['Sn_beta', [5.83, 5.83, 3.18, 90, 90, 90], 'SG141'], 'Sn_beta_all': ['Sn_all', [5.83, 5.83, 3.18, 90, 90, 90], 'no'], 'Ti': ['Ti', [2.95, 2.95, 4.68, 90, 90, 120], 'no'], 'Ti2AlN': ['Ti2AlN', [2.989, 2.989, 13.624, 90, 90, 120], 'Ti2AlN'], 'Ti2AlN_w': ['Ti2AlN_w', [2.989, 2.989, 13.624, 90, 90, 120], 'wurtzite'], 'Ti_beta': ['Ti_beta', [3.2587, 3.2587, 3.2587, 90, 90, 90], 'bcc'], 'Ti_omega': ['Ti_omega', [4.6085, 4.6085, 2.8221, 90, 90, 120], 'no'], 'Ti_s': ['Ti_s', [3.0, 3.0, 4.7, 90.5, 89.5, 120.5], 'no'], 'UO2': ['UO2', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'VO2M1': ['VO2M1', [5.75175, 4.52596, 5.38326, 90.0, 122.6148, 90.0], 'VO2_mono'], 'VO2M2': ['VO2M2', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'no'], 'VO2R': ['VO2R', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'rutile'], 'W': ['W', [3.1652, 3.1652, 3.1652, 90, 90, 90], 'bcc'], 'Y2SiO5': ['Y2SiO5', [10.34, 6.689, 12.38, 90.0, 102.5, 90.0], 'no'], 'YAG': ['YAG', [9.2, 9.2, 9.2, 90, 90, 90], 'no'], 'ZnCuOCl': ['ZnCuOCl', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'SG166'], 'ZnCuOCl_all': ['ZnCuOCl_all', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'no'], 'ZnO': ['ZnO', [3.252, 3.252, 5.213, 90, 90, 120], 'wurtzite'], 'ZrO2': ['ZrO2', [5.1505, 5.2116, 5.3173, 90, 99.23, 90], 'VO2_mono'], 'ZrO2Y2O3': ['ZrO2Y2O3', [5.1378, 5.1378, 5.1378, 90, 90, 90], 'fcc'], 'ZrO2_1200C': ['ZrO2_1200C', [3.6406, 3.6406, 5.278, 90, 90, 90], 'h+k+l=2n'], 'ZrO2fake1': ['ZrO2fake1', [5.1505, 5.048116, 4.988933, 90, 99.23, 90], 'VO2_mono'], 'ZrO2swapac': ['ZrO2swapac', [5.3173, 5.2116, 5.1505, 90, 99.23, 90], 'VO2_mono'], 'ZrUO2_corium': ['ZrUO2_corium', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'alphaQuartz': ['alphaQuartz', [4.9, 4.9, 5.4, 90, 90, 120], 'no'], 'betaQuartznew': ['betaQuartznew', [4.9, 4.9, 6.685, 90, 90, 120], 'no'], 'bigpro': ['bigpro', [112.0, 112.0, 136.0, 90, 90, 90], 'no'], 'dummy': ['dummy', [4.0, 8.0, 2.0, 90, 90, 90], 'no'], 'feldspath': ['feldspath', [8.59, 12.985, 7.213, 90, 116.0, 90], 'no'], 'ferrydrite': ['ferrydrite', [2.96, 2.96, 9.4, 90, 90, 120], 'no'], 'hexagonal': ['hexagonal', [1.0, 1.0, 3.0, 90, 90, 120.0], 'no'], 'inputB': ['inputB', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'quartz_alpha': ['quartz_alpha', [4.913, 4.913, 5.404, 90, 90, 120], 'no'], 'smallpro': ['smallpro', [20.0, 4.8, 49.0, 90, 90, 90], 'no'], 'test_reference': ['test_reference', [3.2, 4.5, 5.2, 83, 92.0, 122], 'wurtzite'], 'test_solution': ['test_solution', [3.252, 4.48, 5.213, 83.2569, 92.125478, 122.364], 'wurtzite'], 'testindex': ['testindex', [2.0, 1.0, 4.0, 90, 90, 90], 'no'], 'testindex2': ['testindex2', [2.0, 1.0, 4.0, 75, 90, 120], 'no']})[source]
create grain parameters list for the Laue pattern simulation
Can handle material defined in dictionary by four elements instead of 6 lattice parameters
- Parameters
key_material (string) – material or structure label
- Returns
grain (4 elements list), contains_U (boolean)
- LaueTools.CrystalParameters.Prepare_Grain(key_material, OrientMatrix, force_extinction=None, dictmaterials={'3H-SiC': ['3H-SiC', [4.3596, 4.3596, 4.3596, 90, 90, 90], 'dia'], '4H-SiC': ['4H-SiC', [3.073, 3.073, 10.053, 90, 90, 120], 'wurtzite'], 'Ag': ['Ag', [4.085, 4.085, 4.085, 90, 90, 90], 'fcc'], 'Al': ['Al', [4.05, 4.05, 4.05, 90, 90, 90], 'fcc'], 'Al2Cu': ['Al2Cu', [6.063, 6.063, 4.872, 90, 90, 90], 'no'], 'Al2O3': ['Al2O3', [4.785, 4.785, 12.991, 90, 90, 120], 'Al2O3'], 'Al2O3_all': ['Al2O3_all', [4.785, 4.785, 12.991, 90, 90, 120], 'no'], 'AlN': ['AlN', [3.11, 3.11, 4.98, 90.0, 90.0, 120.0], 'wurtzite'], 'AmbiguousTriclinic': ['AmbiguousTriclinic', [3.9, 4, 4.1, 89, 90, 91], 'no'], 'Au': ['Au', [4.078, 4.078, 4.078, 90, 90, 90], 'fcc'], 'CCDL1949': ['CCDL1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'h+k=2n'], 'CdHgTe': ['CdHgTe', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'dia'], 'CdHgTe_fcc': ['CdHgTe_fcc', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'fcc'], 'CdTe': ['CdTe', [6.487, 6.487, 6.487, 90, 90, 90], 'fcc'], 'CdTeDiagB': ['CdTeDiagB', [4.5721, 7.9191, 11.1993, 90, 90, 90], 'no'], 'Crocidolite': ['Crocidolite', [9.811, 18.013, 5.326, 90, 103.68, 90], 'no'], 'Crocidolite_2': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 103.6, 90], 'no'], 'Crocidolite_2_72deg': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 76.4, 90], 'no'], 'Crocidolite_small': ['Crocidolite_small', [3.2533333333333334, 5.976666666666667, 1.7833333333333332, 90, 103.6, 90], 'no'], 'Crocidolite_whittaker_1949': ['Crocidolite_whittaker_1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'no'], 'Cu': ['Cu', [3.6, 3.6, 3.6, 90, 90, 90], 'fcc'], 'Cu6Sn5_monoclinic': ['Cu6Sn5_monoclinic', [11.02, 7.28, 9.827, 90, 98.84, 90], 'no'], 'Cu6Sn5_tetra': ['Cu6Sn5_tetra', [3.608, 3.608, 5.037, 90, 90, 90], 'no'], 'DIA': ['DIA', [5.0, 5.0, 5.0, 90, 90, 90], 'dia'], 'DIAs': ['DIAs', [3.56683, 3.56683, 3.56683, 90, 90, 90], 'dia'], 'DarinaMolecule': ['DarinaMolecule', [9.4254, 13.5004, 13.8241, 61.83, 84.555, 75.231], 'no'], 'FCC': ['FCC', [5.0, 5.0, 5.0, 90, 90, 90], 'fcc'], 'Fe': ['Fe', [2.856, 2.856, 2.856, 90, 90, 90], 'bcc'], 'Fe2Ta': ['Fe2Ta', [4.83, 4.83, 0.788, 90, 90, 120], 'no'], 'FeAl': ['FeAl', [5.871, 5.871, 5.871, 90, 90, 90], 'fcc'], 'FePS3': ['FePS3', [5, 10, 7, 90, 107, 90], 'no'], 'GaAs': ['GaAs', [5.65325, 5.65325, 5.65325, 90, 90, 90], 'dia'], 'GaAs_wurtz': ['GaAs_wurtz', [5.65325, 5.65325, 5.9, 90, 90, 90], 'wurtzite'], 'GaN': ['GaN', [3.189, 3.189, 5.185, 90, 90, 120], 'wurtzite'], 'GaN_all': ['GaN_all', [3.189, 3.189, 5.185, 90, 90, 120], 'no'], 'Ge': ['Ge', [5.6575, 5.6575, 5.6575, 90, 90, 90], 'dia'], 'Ge_compressedhydro': ['Ge_compressedhydro', [5.64, 5.64, 5.64, 90, 90, 90.0], 'dia'], 'Ge_s': ['Ge_s', [5.6575, 5.6575, 5.6575, 90, 90, 89.5], 'dia'], 'Getest': ['Getest', [5.6575, 5.6575, 5.6574, 90, 90, 90], 'dia'], 'Hematite': ['Hematite', [5.03459, 5.03459, 13.7533, 90, 90, 120], 'no'], 'In': ['In', [3.2517, 3.2517, 4.9459, 90, 90, 90], 'h+k+l=2n'], 'In2Bi': ['In2Bi', [5.496, 5.496, 6.585, 90, 90, 120], 'no'], 'InGaN': ['InGaN', [3.3609999999999998, 3.3609999999999998, 5.439, 90, 90, 120], 'wurtzite'], 'InN': ['InN', [3.533, 3.533, 5.693, 90, 90, 120], 'wurtzite'], 'In_distorted': ['In_distorted', [3.2517, 3.251133, 4.818608, 89.982926, 90.007213, 95.379102], 'h+k+l=2n'], 'In_epsilon': ['In_epsilon', [3.47, 3.47, 4.49, 90, 90, 90], 'no'], 'Magnetite': ['Magnetite', [8.391, 8.391, 8.391, 90, 90, 90], 'dia'], 'Magnetite_fcc': ['Magnetite_fcc', [8.391, 8.391, 8.391, 90, 90, 90], 'fcc'], 'Magnetite_sc': ['Magnetite_sc', [8.391, 8.391, 8.391, 90, 90, 90], 'no'], 'Nd45': ['Nd45', [5.4884, 5.4884, 5.4884, 90, 90, 90], 'fcc'], 'Ni': ['Ni', [3.5238, 3.5238, 3.5238, 90, 90, 90], 'fcc'], 'NiO': ['NiO', [2.96, 2.96, 7.23, 90, 90, 120], 'no'], 'NiTi': ['NiTi', [3.5506, 3.5506, 3.5506, 90, 90, 90], 'fcc'], 'Olivine_fayalite': ['Olivine_fayalite', [4.8211, 10.4779, 6.0889, 90, 90, 90], 'no'], 'Olivine_forsterite': ['Olivine_forsterite', [4.754, 10.1971, 5.9806, 90, 90, 90], 'no'], 'Olivine_mantle': ['Olivine_mantle', [4.7646, 10.2296, 5.9942, 90, 90, 90], 'no'], 'SC': ['SC', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'SC5': ['SC5', [5.0, 5.0, 5.0, 90, 90, 90], 'no'], 'SC7': ['SC7', [7.0, 7.0, 7.0, 90, 90, 90], 'no'], 'Sb': ['Sb', [4.3, 4.3, 11.3, 90, 90, 120], 'no'], 'Si': ['Si', [5.4309, 5.4309, 5.4309, 90, 90, 90], 'dia'], 'Sn_beta': ['Sn_beta', [5.83, 5.83, 3.18, 90, 90, 90], 'SG141'], 'Sn_beta_all': ['Sn_all', [5.83, 5.83, 3.18, 90, 90, 90], 'no'], 'Ti': ['Ti', [2.95, 2.95, 4.68, 90, 90, 120], 'no'], 'Ti2AlN': ['Ti2AlN', [2.989, 2.989, 13.624, 90, 90, 120], 'Ti2AlN'], 'Ti2AlN_w': ['Ti2AlN_w', [2.989, 2.989, 13.624, 90, 90, 120], 'wurtzite'], 'Ti_beta': ['Ti_beta', [3.2587, 3.2587, 3.2587, 90, 90, 90], 'bcc'], 'Ti_omega': ['Ti_omega', [4.6085, 4.6085, 2.8221, 90, 90, 120], 'no'], 'Ti_s': ['Ti_s', [3.0, 3.0, 4.7, 90.5, 89.5, 120.5], 'no'], 'UO2': ['UO2', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'VO2M1': ['VO2M1', [5.75175, 4.52596, 5.38326, 90.0, 122.6148, 90.0], 'VO2_mono'], 'VO2M2': ['VO2M2', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'no'], 'VO2R': ['VO2R', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'rutile'], 'W': ['W', [3.1652, 3.1652, 3.1652, 90, 90, 90], 'bcc'], 'Y2SiO5': ['Y2SiO5', [10.34, 6.689, 12.38, 90.0, 102.5, 90.0], 'no'], 'YAG': ['YAG', [9.2, 9.2, 9.2, 90, 90, 90], 'no'], 'ZnCuOCl': ['ZnCuOCl', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'SG166'], 'ZnCuOCl_all': ['ZnCuOCl_all', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'no'], 'ZnO': ['ZnO', [3.252, 3.252, 5.213, 90, 90, 120], 'wurtzite'], 'ZrO2': ['ZrO2', [5.1505, 5.2116, 5.3173, 90, 99.23, 90], 'VO2_mono'], 'ZrO2Y2O3': ['ZrO2Y2O3', [5.1378, 5.1378, 5.1378, 90, 90, 90], 'fcc'], 'ZrO2_1200C': ['ZrO2_1200C', [3.6406, 3.6406, 5.278, 90, 90, 90], 'h+k+l=2n'], 'ZrO2fake1': ['ZrO2fake1', [5.1505, 5.048116, 4.988933, 90, 99.23, 90], 'VO2_mono'], 'ZrO2swapac': ['ZrO2swapac', [5.3173, 5.2116, 5.1505, 90, 99.23, 90], 'VO2_mono'], 'ZrUO2_corium': ['ZrUO2_corium', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'alphaQuartz': ['alphaQuartz', [4.9, 4.9, 5.4, 90, 90, 120], 'no'], 'betaQuartznew': ['betaQuartznew', [4.9, 4.9, 6.685, 90, 90, 120], 'no'], 'bigpro': ['bigpro', [112.0, 112.0, 136.0, 90, 90, 90], 'no'], 'dummy': ['dummy', [4.0, 8.0, 2.0, 90, 90, 90], 'no'], 'feldspath': ['feldspath', [8.59, 12.985, 7.213, 90, 116.0, 90], 'no'], 'ferrydrite': ['ferrydrite', [2.96, 2.96, 9.4, 90, 90, 120], 'no'], 'hexagonal': ['hexagonal', [1.0, 1.0, 3.0, 90, 90, 120.0], 'no'], 'inputB': ['inputB', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'quartz_alpha': ['quartz_alpha', [4.913, 4.913, 5.404, 90, 90, 120], 'no'], 'smallpro': ['smallpro', [20.0, 4.8, 49.0, 90, 90, 90], 'no'], 'test_reference': ['test_reference', [3.2, 4.5, 5.2, 83, 92.0, 122], 'wurtzite'], 'test_solution': ['test_solution', [3.252, 4.48, 5.213, 83.2569, 92.125478, 122.364], 'wurtzite'], 'testindex': ['testindex', [2.0, 1.0, 4.0, 90, 90, 90], 'no'], 'testindex2': ['testindex2', [2.0, 1.0, 4.0, 75, 90, 120], 'no']})[source]
Constructor of the grain (crystal) parameters for Laue pattern simulation
if in key_material definition (see dict_Materials) orient matrix is missing (i.e. only lattice parameter are input)
- Parameters
key_material (str) – material label
then list parameter will consider the provided value of the optional OrientMatrix argument
- Parameters
force_extinction (str) – None, use default extinction rules, otherwise use other extinction correspondoing to the label
- LaueTools.CrystalParameters.AngleBetweenNormals(HKL1s, HKL2s, Gstar)[source]
compute pairwise angles (in degrees) between reflections or lattice plane normals of two sets according to unit cell metrics Gstar
- Parameters
HKL1s – list of [H1,K1,L1]
HKL2s – list of [H2,K2,L2]
Gstar – 3*3 matrix corresponding to reciprocal metric tensor of unit cell (as provided by Gstar_from_directlatticeparams())
- Returns
array of pairwise angles between reflections
- LaueTools.CrystalParameters.FilterHarmonics_2(hkl, return_indices_toremove=0)[source]
keep only hkl 3d vectors that are representative of direction nh,nk,nl for any h,k,l signed integers
It removes only parallel vector but KEEPs antiparallel vectors (vec , -n vec) with n>0
- Parameters
hkl – array of 3d hkl indices
return_indices_toremove – 1, returns indices of element in hkl that have been removed
- LaueTools.CrystalParameters.calc_B_RR(latticeparameters, directspace=1, setvolume=False)[source]
Calculate B0 matrix (columns = vectors a*,b*,c*) from direct (real) space lattice parameters (directspace=1)
Calculate a matrix (columns = vectors a,b,c) from direct (real) space lattice parameters (directspace=0)
\(\boldsymbol q_{ortho}=B_0 {\bf G^*}\) where \({\bf G^*}=h{\bf a^*}+k{\bf b^*}+l{\bf c^*}\)
- Parameters
latticeparameters –
[a,b,c, alpha, beta, gamma] (angles are in degrees) if directspace=1
[a*,b*,c*, alpha*, beta*, gamma*] (angles are in degrees) if directspace=0
directspace –
- 1 (default) converts (reciprocal) direct lattice parameters
to (direct) reciprocal space calculates “B” matrix in the reciprocal space of input latticeparameters
- 0 converts (reciprocal) direct lattice parameters to (reciprocal) direct space
calculates “B” matrix in same space of input latticeparameters
setvolume –
False, sets direct unit cell volume to the true volume from lattice parameters
1, sets direct unit cell volume to 1
’a**3’, sets direct unit cell volume to a**3
’b**3’, sets direct unit cell volume to b**3
’c**3’, sets direct unit cell volume to c**3
- Returns
B Matrix (triangular up) from crystal (reciprocal space) frame to orthonormal frame matrix
- Return type
numpy array
- B matrix is used in q=U B G* formula or
as B0 in q= (UB) B0 G*
after Busing Levy, Acta Crysta 22 (1967), p 457
\[\begin{split}\left( \begin{matrix} a^* & b^*\cos \gamma^* & c^*\cos beta^*\\ 0 & b^*\sin \gamma^* &-c^*\sin \beta^*\cos \alpha\\ 0 & 0 & c^*\sin \beta^*\sin \alpha\\ \end{matrix} \right)\end{split}\]with
\[\cos(\alpha)=(\cos \beta^*\cos \gamma^*-\cos \alpha^*)/(\sin \beta^*\sin \gamma^*)\]and
\[c^* \sin \beta^* \sin \alpha = 1/c\]
- LaueTools.CrystalParameters.DeviatoricStrain_LatticeParams(newUBmat, latticeparams, constantlength='a', verbose=0)[source]
Computes deviatoric strain and new direct (real) lattice parameters from matrix newUBmat (rotation and deformation) considering that one lattice length is chosen to be constant
Zero strain corresponds to reference state of input lattice parameters
- Parameters
newUBmat – (3x3) matrix operator including rotation and deformation
latticeparams – 6 lattice parameters [a,b,c,:math:alpha, beta, gamma] in Angstrom and degrees
constantlength – ‘a’,’b’, or ‘c’ to set one length according to the value in latticeparams
- Returns
3x3 deviatoric strain tensor)
lattice_parameter_direct_strain (direct (real) lattice parameters)
- Return type
3x3 numpy array, 6 elements list
Note
q = newUBmat . B0 . G* where B0 (triangular up matrix) comes from lattice parameters input.
equivalently, q = UBstar_s . G*
- LaueTools.CrystalParameters.VolumeCell(latticeparameters)[source]
Computes unit cell volume from lattice parameters (either real or reciprocal)
- Parameters
latticeparameters – 6 lattice parameters
- Returns
scalar volume
- LaueTools.CrystalParameters.VolumeCell(latticeparameters)[source]
Computes unit cell volume from lattice parameters (either real or reciprocal)
- Parameters
latticeparameters – 6 lattice parameters
- Returns
scalar volume
- LaueTools.CrystalParameters.matrix_to_rlat(mat, angles_in_deg=1)[source]
Returns RECIPROCAL lattice parameters of the unit cell a*,b*,c* in columns of mat
- Parameters
mat – matrix where columns are respectively a*,b*,c* coordinates in orthonormal frame
- Returns
[a*,b*,c*, alpha*, beta*, gamma*] (angles are in degrees)
Note
Reciprocal lattice parameters are contained in UB matrix : q = mat G*
Laue Pattern Simulation
Core module to compute Laue Pattern in various geometry
Main author is J. S. Micha: micha [at] esrf [dot] fr
version July 2019 from LaueTools package hosted in
http://sourceforge.net/projects/lauetools/
or
https://gitlab.esrf.fr/micha/lauetools
- LaueTools.lauecore.Quicklist(OrientMatrix, ReciprocBasisVectors, listRSnorm, lambdamin, verbose=0)[source]
return 6 indices min and max boundary values for each Miller index h, k, l to be contained in the largest Ewald Sphere.
- Parameters
OrientMatrix – orientation matrix (3*3 matrix)
ReciprocBasisVectors – list of the three vectors a*,b*,c* in the lab frame before rotation with OrientMatrix
listRSnorm – : list of the three reciprocal space lengthes of a*,b*,c*
lambdamin – : lambdamin (in Angstrom) corresponding to energy max
- Returns
[[hmin,hmax],[kmin,kmax],[lmin,lmax]]
- LaueTools.lauecore.genHKL_np(listn, Extinc)[source]
Generate all Miller indices hkl from indices limits given by listn and taking into account for systematic exctinctions
- Parameters
listn ([[hmin,hmax],[kmin,kmax],[lmin,lmax]]) – Miller indices limits (warning: these lists are used in python range (last index is excluded))
Extinc (string) – label corresponding to systematic exctinction rules on h k and l miller indics such as (‘fcc’, ‘bcc’, ‘dia’, …) or ‘no’ for any rules
- Returns
array of [h,k,l]
Note
node [0,0,0] is excluded
- LaueTools.lauecore.getLaueSpots(wavelmin, wavelmax, crystalsParams, kf_direction='Z>0', OpeningAngleCollection=22.0, fastcompute=0, ResolutionAngstrom=False, verbose=1, dictmaterials=None)[source]
Compute Qxyz vectors and corresponding HKL miller indices for nodes in recicprocal space that can be measured for the given detection geometry and energy bandpass configuration.
- Parameters
wavelmin – smallest wavelength in Angstrom
wavelmax – largest wavelength in Angstrom
crystalsParams –
list of SingleCrystalParams, each of them being a list of 4 elements for crystal orientation and strain properties:
- [0](array): is the B matrix a*,b*,c* vectors are expressed in column
in LaueTools frame in reciprocal angstrom units
[1](str): peak Extinction rules (‘no’,’fcc’,’dia’, etc…)
[2](array): orientation matrix
[3](str): key for material element
kf_direction –
string defining the average geometry, mean value of exit scattered vector: ‘Z>0’ top spots
’Y>0’ one side spots (towards hutch door)
’Y<0’ other side spots
’X>0’ transmission spots
’X<0’ backreflection spots
fastcompute –
1, compute reciprocal space (RS) vector BUT NOT the Miller indices
0, returns both RS vectors (normalised) and Miller indices
ResolutionAngstrom –
scalar, smallest interplanar distance ordered in crystal in angstrom.
None, all reflections will be calculated that can be time-consuming for large unit cell
- Returns
list of [Qx,Qy,Qz]s for each grain, list of [H,K,L]s for each grain (fastcompute = 0)
list of [Qx,Qy,Qz]s for each grain, None (fastcompute = 1)
Caution
This method doesn’t create spot instances.
This is done in filterLaueSpots with fastcompute = 0
Caution
finer selection of nodes : on camera , without harmonics can be done later with filterLaueSpots()
Note
lauetools laboratory frame is in this case: x// ki (center of ewald sphere has negative x component) z perp to x and belonging to the plane defined by x and dd vectors (where dd vector is the smallest vector joining sample impact point and points on CCD plane) y is perpendicular to x and z
- LaueTools.lauecore.create_spot(pos_vec, miller, detectordistance, allattributes=False, pixelsize=0.08056640625, dim=(2048, 2048))[source]
From reciprocal space position and 3 miller indices create a spot instance (on top camera geometry)
- Parameters
pos_vec (list of 3 float) – 3D vector
miller – list of 3 miller indices
detectordistance – approximate distance detector sample (to compute complementary spots attributes)
allattributes – False or 0 not to compute complementary spot attributes
allattributes – boolean
- Returns
spot instance
Note
spot.Qxyz is a vector expressed in lauetools frame
X along x-ray and Z towards CCD when CCD on top and y towards experimental hutch door
- LaueTools.lauecore.create_spot_np(Qxyz, miller, detectordistance, allattributes=False, pixelsize=0.08056640625, dim=(2048, 2048))[source]
From reciprocal space position and 3 miller indices create a spot instance (on top camera geometry)
- Parameters
pos_vec (list of 3 float) – 3D vector
miller – list of 3 miller indices
detectordistance – approximate distance detector sample (to compute complementary spots attributes)
allattributes – False or 0 not to compute complementary spot attributes
allattributes – boolean
- Returns
spot instance
Note
spot.Qxyz is a vector expressed in lauetools frame
X along x-ray and Z towards CCD when CCD on top and y towards experimental hutch door
- LaueTools.lauecore.filterLaueSpots(vec_and_indices, HarmonicsRemoval=1, fastcompute=0, kf_direction='Z>0', fileOK=0, detectordistance=70.0, detectordiameter=165.0, pixelsize=0.08056640625, dim=(2048, 2048), linestowrite=[['']], verbose=0)[source]
Calculates list of grains spots on camera and without harmonics and on CCD camera from [[spots grain 0],[spots grain 1],etc] => returns [[spots grain 0],[spots grain 1],etc] w / o harmonics and on camera CCD
- Parameters
vec_and_indices –
list of elements corresponding to 1 grain, each element is composed by * [0] array of vector
[1] array of indices
HarmonicsRemoval – 1, removes harmonics according to their miller indices (only for fastcompute = 0)
fastcompute –
- 1, outputs a list for each grain of 2theta spots and a list for each grain of chi spots
(HARMONICS spots are still HERE!)
0, outputs list for each grain of spots with
kf_direction (string) – label for detection geometry (CCD plane with respect to the incoming beam and sample)
- Returns
list of spot instances if fastcompute=0
2theta, chi if fastcompute=1
Note
USED IMPORTANTLY in lauecore.SimulateResults lauecore.SimulateLaue
USED in matchingrate.AngularResidues
USED in ParametricLaueSimulator.dosimulation_parametric
USED in AutoindexationGUI.OnSimulate_S3, DetectorCalibration.Reckon_2pts, and others
Todo
add dim in create_spot in various geometries
- LaueTools.lauecore.get2ThetaChi_geometry(oncam_vec, oncam_HKL, detectordistance=70.0, pixelsize=0.08056640625, dim=(2048, 2048), kf_direction='Z>0')[source]
computes list of spots instances from oncam_vec (q 3D vectors) and oncam_HKL (miller indices 3D vectors)
- Parameters
oncam_vec (array with 3D elements (shape = (n,3))) – q vectors [qx,qy,qz] (corresponding to kf collected on camera)
dim (list or tuple of 2 integers) – CCD frame dimensions (nb pixels, nb pixels)
detectordistance – approximate distance detector sample
detectordistance – float or integer
pixelsize (float) – pixel size in mm
- Param
kf_direction : label for detection geometry (CCD plane with respect to the incoming beam and sample)
- Type
kf_direction: string
- Returns
list of spot instances
Note
USED in lauecore.filterLaueSpots
Todo
to be replaced by something else not using spot class
put this function in LaueGeometry module ?
- LaueTools.lauecore.calcSpots_fromHKLlist(UB, B0, HKL, dictCCD)[source]
computes all Laue Spots properties on 2D detector from a list of hkl (given structure by B0 matrix, orientation by UB matrix, and detector geometry by dictCCD)
- Parameters
UB (3x3 array (or list)) – orientation matrix (rotation -and if any- strain)
B0 (3x3 array (or list)) – initial a*,b*,c* reciprocal unit cell basis vector in Lauetools frame (x// ki))
HKL (array with shape = (n,3)) – array of Miller indices
dictCCD – dictionnary of CCD properties (with key ‘CCDparam’, ‘pixelsize’,’dim’) for ‘ccdparam’ 5 CCD calibration parameters [dd,xcen,ycen,xbet,xgam], pixelsize in mm, and (dim1, dim2)
dictCCD – dict object
- Returns
list of arrays H, K, L, Qx, Qy, Qz, X, Y, twthe, chi, Energy
Fundamental equation \({\bf q} = UB*B0 * {\bf G^*}\) with \({\bf G^*} = h{\bf a^*}+k{\bf b^*}+l{\bf c^*}\)
Note
USED in DetectorCalibration.OnWriteResults, and PlotRefineGUI.onWriteFitFile
- LaueTools.lauecore.SimulateLaue(grain, emin, emax, detectorparameters, kf_direction='Z>0', ResolutionAngstrom=False, removeharmonics=0, pixelsize=0.08056640625, dim=(2048, 2048), detectordiameter=None, force_extinction=None, dictmaterials={'3H-SiC': ['3H-SiC', [4.3596, 4.3596, 4.3596, 90, 90, 90], 'dia'], '4H-SiC': ['4H-SiC', [3.073, 3.073, 10.053, 90, 90, 120], 'wurtzite'], 'Ag': ['Ag', [4.085, 4.085, 4.085, 90, 90, 90], 'fcc'], 'Al': ['Al', [4.05, 4.05, 4.05, 90, 90, 90], 'fcc'], 'Al2Cu': ['Al2Cu', [6.063, 6.063, 4.872, 90, 90, 90], 'no'], 'Al2O3': ['Al2O3', [4.785, 4.785, 12.991, 90, 90, 120], 'Al2O3'], 'Al2O3_all': ['Al2O3_all', [4.785, 4.785, 12.991, 90, 90, 120], 'no'], 'AlN': ['AlN', [3.11, 3.11, 4.98, 90.0, 90.0, 120.0], 'wurtzite'], 'AmbiguousTriclinic': ['AmbiguousTriclinic', [3.9, 4, 4.1, 89, 90, 91], 'no'], 'Au': ['Au', [4.078, 4.078, 4.078, 90, 90, 90], 'fcc'], 'CCDL1949': ['CCDL1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'h+k=2n'], 'CdHgTe': ['CdHgTe', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'dia'], 'CdHgTe_fcc': ['CdHgTe_fcc', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'fcc'], 'CdTe': ['CdTe', [6.487, 6.487, 6.487, 90, 90, 90], 'fcc'], 'CdTeDiagB': ['CdTeDiagB', [4.5721, 7.9191, 11.1993, 90, 90, 90], 'no'], 'Crocidolite': ['Crocidolite', [9.811, 18.013, 5.326, 90, 103.68, 90], 'no'], 'Crocidolite_2': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 103.6, 90], 'no'], 'Crocidolite_2_72deg': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 76.4, 90], 'no'], 'Crocidolite_small': ['Crocidolite_small', [3.2533333333333334, 5.976666666666667, 1.7833333333333332, 90, 103.6, 90], 'no'], 'Crocidolite_whittaker_1949': ['Crocidolite_whittaker_1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'no'], 'Cu': ['Cu', [3.6, 3.6, 3.6, 90, 90, 90], 'fcc'], 'Cu6Sn5_monoclinic': ['Cu6Sn5_monoclinic', [11.02, 7.28, 9.827, 90, 98.84, 90], 'no'], 'Cu6Sn5_tetra': ['Cu6Sn5_tetra', [3.608, 3.608, 5.037, 90, 90, 90], 'no'], 'DIA': ['DIA', [5.0, 5.0, 5.0, 90, 90, 90], 'dia'], 'DIAs': ['DIAs', [3.56683, 3.56683, 3.56683, 90, 90, 90], 'dia'], 'DarinaMolecule': ['DarinaMolecule', [9.4254, 13.5004, 13.8241, 61.83, 84.555, 75.231], 'no'], 'FCC': ['FCC', [5.0, 5.0, 5.0, 90, 90, 90], 'fcc'], 'Fe': ['Fe', [2.856, 2.856, 2.856, 90, 90, 90], 'bcc'], 'Fe2Ta': ['Fe2Ta', [4.83, 4.83, 0.788, 90, 90, 120], 'no'], 'FeAl': ['FeAl', [5.871, 5.871, 5.871, 90, 90, 90], 'fcc'], 'FePS3': ['FePS3', [5, 10, 7, 90, 107, 90], 'no'], 'GaAs': ['GaAs', [5.65325, 5.65325, 5.65325, 90, 90, 90], 'dia'], 'GaAs_wurtz': ['GaAs_wurtz', [5.65325, 5.65325, 5.9, 90, 90, 90], 'wurtzite'], 'GaN': ['GaN', [3.189, 3.189, 5.185, 90, 90, 120], 'wurtzite'], 'GaN_all': ['GaN_all', [3.189, 3.189, 5.185, 90, 90, 120], 'no'], 'Ge': ['Ge', [5.6575, 5.6575, 5.6575, 90, 90, 90], 'dia'], 'Ge_compressedhydro': ['Ge_compressedhydro', [5.64, 5.64, 5.64, 90, 90, 90.0], 'dia'], 'Ge_s': ['Ge_s', [5.6575, 5.6575, 5.6575, 90, 90, 89.5], 'dia'], 'Getest': ['Getest', [5.6575, 5.6575, 5.6574, 90, 90, 90], 'dia'], 'Hematite': ['Hematite', [5.03459, 5.03459, 13.7533, 90, 90, 120], 'no'], 'In': ['In', [3.2517, 3.2517, 4.9459, 90, 90, 90], 'h+k+l=2n'], 'In2Bi': ['In2Bi', [5.496, 5.496, 6.585, 90, 90, 120], 'no'], 'InGaN': ['InGaN', [3.3609999999999998, 3.3609999999999998, 5.439, 90, 90, 120], 'wurtzite'], 'InN': ['InN', [3.533, 3.533, 5.693, 90, 90, 120], 'wurtzite'], 'In_distorted': ['In_distorted', [3.2517, 3.251133, 4.818608, 89.982926, 90.007213, 95.379102], 'h+k+l=2n'], 'In_epsilon': ['In_epsilon', [3.47, 3.47, 4.49, 90, 90, 90], 'no'], 'Magnetite': ['Magnetite', [8.391, 8.391, 8.391, 90, 90, 90], 'dia'], 'Magnetite_fcc': ['Magnetite_fcc', [8.391, 8.391, 8.391, 90, 90, 90], 'fcc'], 'Magnetite_sc': ['Magnetite_sc', [8.391, 8.391, 8.391, 90, 90, 90], 'no'], 'Nd45': ['Nd45', [5.4884, 5.4884, 5.4884, 90, 90, 90], 'fcc'], 'Ni': ['Ni', [3.5238, 3.5238, 3.5238, 90, 90, 90], 'fcc'], 'NiO': ['NiO', [2.96, 2.96, 7.23, 90, 90, 120], 'no'], 'NiTi': ['NiTi', [3.5506, 3.5506, 3.5506, 90, 90, 90], 'fcc'], 'Olivine_fayalite': ['Olivine_fayalite', [4.8211, 10.4779, 6.0889, 90, 90, 90], 'no'], 'Olivine_forsterite': ['Olivine_forsterite', [4.754, 10.1971, 5.9806, 90, 90, 90], 'no'], 'Olivine_mantle': ['Olivine_mantle', [4.7646, 10.2296, 5.9942, 90, 90, 90], 'no'], 'SC': ['SC', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'SC5': ['SC5', [5.0, 5.0, 5.0, 90, 90, 90], 'no'], 'SC7': ['SC7', [7.0, 7.0, 7.0, 90, 90, 90], 'no'], 'Sb': ['Sb', [4.3, 4.3, 11.3, 90, 90, 120], 'no'], 'Si': ['Si', [5.4309, 5.4309, 5.4309, 90, 90, 90], 'dia'], 'Sn_beta': ['Sn_beta', [5.83, 5.83, 3.18, 90, 90, 90], 'SG141'], 'Sn_beta_all': ['Sn_all', [5.83, 5.83, 3.18, 90, 90, 90], 'no'], 'Ti': ['Ti', [2.95, 2.95, 4.68, 90, 90, 120], 'no'], 'Ti2AlN': ['Ti2AlN', [2.989, 2.989, 13.624, 90, 90, 120], 'Ti2AlN'], 'Ti2AlN_w': ['Ti2AlN_w', [2.989, 2.989, 13.624, 90, 90, 120], 'wurtzite'], 'Ti_beta': ['Ti_beta', [3.2587, 3.2587, 3.2587, 90, 90, 90], 'bcc'], 'Ti_omega': ['Ti_omega', [4.6085, 4.6085, 2.8221, 90, 90, 120], 'no'], 'Ti_s': ['Ti_s', [3.0, 3.0, 4.7, 90.5, 89.5, 120.5], 'no'], 'UO2': ['UO2', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'VO2M1': ['VO2M1', [5.75175, 4.52596, 5.38326, 90.0, 122.6148, 90.0], 'VO2_mono'], 'VO2M2': ['VO2M2', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'no'], 'VO2R': ['VO2R', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'rutile'], 'W': ['W', [3.1652, 3.1652, 3.1652, 90, 90, 90], 'bcc'], 'Y2SiO5': ['Y2SiO5', [10.34, 6.689, 12.38, 90.0, 102.5, 90.0], 'no'], 'YAG': ['YAG', [9.2, 9.2, 9.2, 90, 90, 90], 'no'], 'ZnCuOCl': ['ZnCuOCl', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'SG166'], 'ZnCuOCl_all': ['ZnCuOCl_all', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'no'], 'ZnO': ['ZnO', [3.252, 3.252, 5.213, 90, 90, 120], 'wurtzite'], 'ZrO2': ['ZrO2', [5.1505, 5.2116, 5.3173, 90, 99.23, 90], 'VO2_mono'], 'ZrO2Y2O3': ['ZrO2Y2O3', [5.1378, 5.1378, 5.1378, 90, 90, 90], 'fcc'], 'ZrO2_1200C': ['ZrO2_1200C', [3.6406, 3.6406, 5.278, 90, 90, 90], 'h+k+l=2n'], 'ZrO2fake1': ['ZrO2fake1', [5.1505, 5.048116, 4.988933, 90, 99.23, 90], 'VO2_mono'], 'ZrO2swapac': ['ZrO2swapac', [5.3173, 5.2116, 5.1505, 90, 99.23, 90], 'VO2_mono'], 'ZrUO2_corium': ['ZrUO2_corium', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'alphaQuartz': ['alphaQuartz', [4.9, 4.9, 5.4, 90, 90, 120], 'no'], 'betaQuartznew': ['betaQuartznew', [4.9, 4.9, 6.685, 90, 90, 120], 'no'], 'bigpro': ['bigpro', [112.0, 112.0, 136.0, 90, 90, 90], 'no'], 'dummy': ['dummy', [4.0, 8.0, 2.0, 90, 90, 90], 'no'], 'feldspath': ['feldspath', [8.59, 12.985, 7.213, 90, 116.0, 90], 'no'], 'ferrydrite': ['ferrydrite', [2.96, 2.96, 9.4, 90, 90, 120], 'no'], 'hexagonal': ['hexagonal', [1.0, 1.0, 3.0, 90, 90, 120.0], 'no'], 'inputB': ['inputB', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'quartz_alpha': ['quartz_alpha', [4.913, 4.913, 5.404, 90, 90, 120], 'no'], 'smallpro': ['smallpro', [20.0, 4.8, 49.0, 90, 90, 90], 'no'], 'test_reference': ['test_reference', [3.2, 4.5, 5.2, 83, 92.0, 122], 'wurtzite'], 'test_solution': ['test_solution', [3.252, 4.48, 5.213, 83.2569, 92.125478, 122.364], 'wurtzite'], 'testindex': ['testindex', [2.0, 1.0, 4.0, 90, 90, 90], 'no'], 'testindex2': ['testindex2', [2.0, 1.0, 4.0, 75, 90, 120], 'no']}, version=1)[source]
- Computes Laue Pattern spots positions, scattering angles, miller indices
for a SINGLE grain or Xtal
- Parameters
grain – crystal parameters made of a 4 elements list
emin – minimum bandpass energy (keV)
emax – maximum bandpass energy (keV)
removeharmonics –
- 1, removes harmonics spots and keep fondamental spots (or reciprocal direction)
(with lowest Miller indices)
0 keep all spots (including harmonics)
- Returns
single grain data: Twicetheta, Chi, Miller_ind, posx, posy, Energy
Todo
To update to accept kf_direction not only in reflection geometry
Note
USED in detectorCalibration…simulate_theo for non routine geometry (ie except Z>0 (reflection top) X>0 (transmission)
- LaueTools.lauecore.SimulateLaue_full_np(grain, emin, emax, detectorparameters, kf_direction='Z>0', ResolutionAngstrom=False, removeharmonics=0, pixelsize=0.08056640625, dim=(2048, 2048), detectordiameter=None, force_extinction=None, dictmaterials={'3H-SiC': ['3H-SiC', [4.3596, 4.3596, 4.3596, 90, 90, 90], 'dia'], '4H-SiC': ['4H-SiC', [3.073, 3.073, 10.053, 90, 90, 120], 'wurtzite'], 'Ag': ['Ag', [4.085, 4.085, 4.085, 90, 90, 90], 'fcc'], 'Al': ['Al', [4.05, 4.05, 4.05, 90, 90, 90], 'fcc'], 'Al2Cu': ['Al2Cu', [6.063, 6.063, 4.872, 90, 90, 90], 'no'], 'Al2O3': ['Al2O3', [4.785, 4.785, 12.991, 90, 90, 120], 'Al2O3'], 'Al2O3_all': ['Al2O3_all', [4.785, 4.785, 12.991, 90, 90, 120], 'no'], 'AlN': ['AlN', [3.11, 3.11, 4.98, 90.0, 90.0, 120.0], 'wurtzite'], 'AmbiguousTriclinic': ['AmbiguousTriclinic', [3.9, 4, 4.1, 89, 90, 91], 'no'], 'Au': ['Au', [4.078, 4.078, 4.078, 90, 90, 90], 'fcc'], 'CCDL1949': ['CCDL1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'h+k=2n'], 'CdHgTe': ['CdHgTe', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'dia'], 'CdHgTe_fcc': ['CdHgTe_fcc', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'fcc'], 'CdTe': ['CdTe', [6.487, 6.487, 6.487, 90, 90, 90], 'fcc'], 'CdTeDiagB': ['CdTeDiagB', [4.5721, 7.9191, 11.1993, 90, 90, 90], 'no'], 'Crocidolite': ['Crocidolite', [9.811, 18.013, 5.326, 90, 103.68, 90], 'no'], 'Crocidolite_2': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 103.6, 90], 'no'], 'Crocidolite_2_72deg': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 76.4, 90], 'no'], 'Crocidolite_small': ['Crocidolite_small', [3.2533333333333334, 5.976666666666667, 1.7833333333333332, 90, 103.6, 90], 'no'], 'Crocidolite_whittaker_1949': ['Crocidolite_whittaker_1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'no'], 'Cu': ['Cu', [3.6, 3.6, 3.6, 90, 90, 90], 'fcc'], 'Cu6Sn5_monoclinic': ['Cu6Sn5_monoclinic', [11.02, 7.28, 9.827, 90, 98.84, 90], 'no'], 'Cu6Sn5_tetra': ['Cu6Sn5_tetra', [3.608, 3.608, 5.037, 90, 90, 90], 'no'], 'DIA': ['DIA', [5.0, 5.0, 5.0, 90, 90, 90], 'dia'], 'DIAs': ['DIAs', [3.56683, 3.56683, 3.56683, 90, 90, 90], 'dia'], 'DarinaMolecule': ['DarinaMolecule', [9.4254, 13.5004, 13.8241, 61.83, 84.555, 75.231], 'no'], 'FCC': ['FCC', [5.0, 5.0, 5.0, 90, 90, 90], 'fcc'], 'Fe': ['Fe', [2.856, 2.856, 2.856, 90, 90, 90], 'bcc'], 'Fe2Ta': ['Fe2Ta', [4.83, 4.83, 0.788, 90, 90, 120], 'no'], 'FeAl': ['FeAl', [5.871, 5.871, 5.871, 90, 90, 90], 'fcc'], 'FePS3': ['FePS3', [5, 10, 7, 90, 107, 90], 'no'], 'GaAs': ['GaAs', [5.65325, 5.65325, 5.65325, 90, 90, 90], 'dia'], 'GaAs_wurtz': ['GaAs_wurtz', [5.65325, 5.65325, 5.9, 90, 90, 90], 'wurtzite'], 'GaN': ['GaN', [3.189, 3.189, 5.185, 90, 90, 120], 'wurtzite'], 'GaN_all': ['GaN_all', [3.189, 3.189, 5.185, 90, 90, 120], 'no'], 'Ge': ['Ge', [5.6575, 5.6575, 5.6575, 90, 90, 90], 'dia'], 'Ge_compressedhydro': ['Ge_compressedhydro', [5.64, 5.64, 5.64, 90, 90, 90.0], 'dia'], 'Ge_s': ['Ge_s', [5.6575, 5.6575, 5.6575, 90, 90, 89.5], 'dia'], 'Getest': ['Getest', [5.6575, 5.6575, 5.6574, 90, 90, 90], 'dia'], 'Hematite': ['Hematite', [5.03459, 5.03459, 13.7533, 90, 90, 120], 'no'], 'In': ['In', [3.2517, 3.2517, 4.9459, 90, 90, 90], 'h+k+l=2n'], 'In2Bi': ['In2Bi', [5.496, 5.496, 6.585, 90, 90, 120], 'no'], 'InGaN': ['InGaN', [3.3609999999999998, 3.3609999999999998, 5.439, 90, 90, 120], 'wurtzite'], 'InN': ['InN', [3.533, 3.533, 5.693, 90, 90, 120], 'wurtzite'], 'In_distorted': ['In_distorted', [3.2517, 3.251133, 4.818608, 89.982926, 90.007213, 95.379102], 'h+k+l=2n'], 'In_epsilon': ['In_epsilon', [3.47, 3.47, 4.49, 90, 90, 90], 'no'], 'Magnetite': ['Magnetite', [8.391, 8.391, 8.391, 90, 90, 90], 'dia'], 'Magnetite_fcc': ['Magnetite_fcc', [8.391, 8.391, 8.391, 90, 90, 90], 'fcc'], 'Magnetite_sc': ['Magnetite_sc', [8.391, 8.391, 8.391, 90, 90, 90], 'no'], 'Nd45': ['Nd45', [5.4884, 5.4884, 5.4884, 90, 90, 90], 'fcc'], 'Ni': ['Ni', [3.5238, 3.5238, 3.5238, 90, 90, 90], 'fcc'], 'NiO': ['NiO', [2.96, 2.96, 7.23, 90, 90, 120], 'no'], 'NiTi': ['NiTi', [3.5506, 3.5506, 3.5506, 90, 90, 90], 'fcc'], 'Olivine_fayalite': ['Olivine_fayalite', [4.8211, 10.4779, 6.0889, 90, 90, 90], 'no'], 'Olivine_forsterite': ['Olivine_forsterite', [4.754, 10.1971, 5.9806, 90, 90, 90], 'no'], 'Olivine_mantle': ['Olivine_mantle', [4.7646, 10.2296, 5.9942, 90, 90, 90], 'no'], 'SC': ['SC', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'SC5': ['SC5', [5.0, 5.0, 5.0, 90, 90, 90], 'no'], 'SC7': ['SC7', [7.0, 7.0, 7.0, 90, 90, 90], 'no'], 'Sb': ['Sb', [4.3, 4.3, 11.3, 90, 90, 120], 'no'], 'Si': ['Si', [5.4309, 5.4309, 5.4309, 90, 90, 90], 'dia'], 'Sn_beta': ['Sn_beta', [5.83, 5.83, 3.18, 90, 90, 90], 'SG141'], 'Sn_beta_all': ['Sn_all', [5.83, 5.83, 3.18, 90, 90, 90], 'no'], 'Ti': ['Ti', [2.95, 2.95, 4.68, 90, 90, 120], 'no'], 'Ti2AlN': ['Ti2AlN', [2.989, 2.989, 13.624, 90, 90, 120], 'Ti2AlN'], 'Ti2AlN_w': ['Ti2AlN_w', [2.989, 2.989, 13.624, 90, 90, 120], 'wurtzite'], 'Ti_beta': ['Ti_beta', [3.2587, 3.2587, 3.2587, 90, 90, 90], 'bcc'], 'Ti_omega': ['Ti_omega', [4.6085, 4.6085, 2.8221, 90, 90, 120], 'no'], 'Ti_s': ['Ti_s', [3.0, 3.0, 4.7, 90.5, 89.5, 120.5], 'no'], 'UO2': ['UO2', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'VO2M1': ['VO2M1', [5.75175, 4.52596, 5.38326, 90.0, 122.6148, 90.0], 'VO2_mono'], 'VO2M2': ['VO2M2', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'no'], 'VO2R': ['VO2R', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'rutile'], 'W': ['W', [3.1652, 3.1652, 3.1652, 90, 90, 90], 'bcc'], 'Y2SiO5': ['Y2SiO5', [10.34, 6.689, 12.38, 90.0, 102.5, 90.0], 'no'], 'YAG': ['YAG', [9.2, 9.2, 9.2, 90, 90, 90], 'no'], 'ZnCuOCl': ['ZnCuOCl', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'SG166'], 'ZnCuOCl_all': ['ZnCuOCl_all', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'no'], 'ZnO': ['ZnO', [3.252, 3.252, 5.213, 90, 90, 120], 'wurtzite'], 'ZrO2': ['ZrO2', [5.1505, 5.2116, 5.3173, 90, 99.23, 90], 'VO2_mono'], 'ZrO2Y2O3': ['ZrO2Y2O3', [5.1378, 5.1378, 5.1378, 90, 90, 90], 'fcc'], 'ZrO2_1200C': ['ZrO2_1200C', [3.6406, 3.6406, 5.278, 90, 90, 90], 'h+k+l=2n'], 'ZrO2fake1': ['ZrO2fake1', [5.1505, 5.048116, 4.988933, 90, 99.23, 90], 'VO2_mono'], 'ZrO2swapac': ['ZrO2swapac', [5.3173, 5.2116, 5.1505, 90, 99.23, 90], 'VO2_mono'], 'ZrUO2_corium': ['ZrUO2_corium', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'alphaQuartz': ['alphaQuartz', [4.9, 4.9, 5.4, 90, 90, 120], 'no'], 'betaQuartznew': ['betaQuartznew', [4.9, 4.9, 6.685, 90, 90, 120], 'no'], 'bigpro': ['bigpro', [112.0, 112.0, 136.0, 90, 90, 90], 'no'], 'dummy': ['dummy', [4.0, 8.0, 2.0, 90, 90, 90], 'no'], 'feldspath': ['feldspath', [8.59, 12.985, 7.213, 90, 116.0, 90], 'no'], 'ferrydrite': ['ferrydrite', [2.96, 2.96, 9.4, 90, 90, 120], 'no'], 'hexagonal': ['hexagonal', [1.0, 1.0, 3.0, 90, 90, 120.0], 'no'], 'inputB': ['inputB', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'quartz_alpha': ['quartz_alpha', [4.913, 4.913, 5.404, 90, 90, 120], 'no'], 'smallpro': ['smallpro', [20.0, 4.8, 49.0, 90, 90, 90], 'no'], 'test_reference': ['test_reference', [3.2, 4.5, 5.2, 83, 92.0, 122], 'wurtzite'], 'test_solution': ['test_solution', [3.252, 4.48, 5.213, 83.2569, 92.125478, 122.364], 'wurtzite'], 'testindex': ['testindex', [2.0, 1.0, 4.0, 90, 90, 90], 'no'], 'testindex2': ['testindex2', [2.0, 1.0, 4.0, 75, 90, 120], 'no']}, verbose=0, depth=None)[source]
- Compute Laue Pattern spots positions, scattering angles, miller indices
for a SINGLE grain or Xtal using numpy vectorization
- Parameters
grain – crystal parameters in a 4 elements list
emin – minimum bandpass energy (keV)
emax – maximum bandpass energy (keV)
removeharmonics – 1, remove harmonics spots and keep fondamental spots (with lowest Miller indices)
depth – depth (in microns) of the sample point that produces Laue pattern. Default = 0 (impact point at sample surface). Positive depth towards inside the sample (// k_i)
- Returns
single grain data: Twicetheta, Chi, Miller_ind, posx, posy, Energy
Todo
update to accept kf_direction not only in reflection geometry
Note
USED in detectorCalibration…simulate_theo for routine geometry Z>0 (reflection top) X>0 (transmission)
- LaueTools.lauecore.SimulateResult(grain, emin, emax, simulparameters, fastcompute=1, ResolutionAngstrom=False, dictmaterials={'3H-SiC': ['3H-SiC', [4.3596, 4.3596, 4.3596, 90, 90, 90], 'dia'], '4H-SiC': ['4H-SiC', [3.073, 3.073, 10.053, 90, 90, 120], 'wurtzite'], 'Ag': ['Ag', [4.085, 4.085, 4.085, 90, 90, 90], 'fcc'], 'Al': ['Al', [4.05, 4.05, 4.05, 90, 90, 90], 'fcc'], 'Al2Cu': ['Al2Cu', [6.063, 6.063, 4.872, 90, 90, 90], 'no'], 'Al2O3': ['Al2O3', [4.785, 4.785, 12.991, 90, 90, 120], 'Al2O3'], 'Al2O3_all': ['Al2O3_all', [4.785, 4.785, 12.991, 90, 90, 120], 'no'], 'AlN': ['AlN', [3.11, 3.11, 4.98, 90.0, 90.0, 120.0], 'wurtzite'], 'AmbiguousTriclinic': ['AmbiguousTriclinic', [3.9, 4, 4.1, 89, 90, 91], 'no'], 'Au': ['Au', [4.078, 4.078, 4.078, 90, 90, 90], 'fcc'], 'CCDL1949': ['CCDL1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'h+k=2n'], 'CdHgTe': ['CdHgTe', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'dia'], 'CdHgTe_fcc': ['CdHgTe_fcc', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'fcc'], 'CdTe': ['CdTe', [6.487, 6.487, 6.487, 90, 90, 90], 'fcc'], 'CdTeDiagB': ['CdTeDiagB', [4.5721, 7.9191, 11.1993, 90, 90, 90], 'no'], 'Crocidolite': ['Crocidolite', [9.811, 18.013, 5.326, 90, 103.68, 90], 'no'], 'Crocidolite_2': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 103.6, 90], 'no'], 'Crocidolite_2_72deg': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 76.4, 90], 'no'], 'Crocidolite_small': ['Crocidolite_small', [3.2533333333333334, 5.976666666666667, 1.7833333333333332, 90, 103.6, 90], 'no'], 'Crocidolite_whittaker_1949': ['Crocidolite_whittaker_1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'no'], 'Cu': ['Cu', [3.6, 3.6, 3.6, 90, 90, 90], 'fcc'], 'Cu6Sn5_monoclinic': ['Cu6Sn5_monoclinic', [11.02, 7.28, 9.827, 90, 98.84, 90], 'no'], 'Cu6Sn5_tetra': ['Cu6Sn5_tetra', [3.608, 3.608, 5.037, 90, 90, 90], 'no'], 'DIA': ['DIA', [5.0, 5.0, 5.0, 90, 90, 90], 'dia'], 'DIAs': ['DIAs', [3.56683, 3.56683, 3.56683, 90, 90, 90], 'dia'], 'DarinaMolecule': ['DarinaMolecule', [9.4254, 13.5004, 13.8241, 61.83, 84.555, 75.231], 'no'], 'FCC': ['FCC', [5.0, 5.0, 5.0, 90, 90, 90], 'fcc'], 'Fe': ['Fe', [2.856, 2.856, 2.856, 90, 90, 90], 'bcc'], 'Fe2Ta': ['Fe2Ta', [4.83, 4.83, 0.788, 90, 90, 120], 'no'], 'FeAl': ['FeAl', [5.871, 5.871, 5.871, 90, 90, 90], 'fcc'], 'FePS3': ['FePS3', [5, 10, 7, 90, 107, 90], 'no'], 'GaAs': ['GaAs', [5.65325, 5.65325, 5.65325, 90, 90, 90], 'dia'], 'GaAs_wurtz': ['GaAs_wurtz', [5.65325, 5.65325, 5.9, 90, 90, 90], 'wurtzite'], 'GaN': ['GaN', [3.189, 3.189, 5.185, 90, 90, 120], 'wurtzite'], 'GaN_all': ['GaN_all', [3.189, 3.189, 5.185, 90, 90, 120], 'no'], 'Ge': ['Ge', [5.6575, 5.6575, 5.6575, 90, 90, 90], 'dia'], 'Ge_compressedhydro': ['Ge_compressedhydro', [5.64, 5.64, 5.64, 90, 90, 90.0], 'dia'], 'Ge_s': ['Ge_s', [5.6575, 5.6575, 5.6575, 90, 90, 89.5], 'dia'], 'Getest': ['Getest', [5.6575, 5.6575, 5.6574, 90, 90, 90], 'dia'], 'Hematite': ['Hematite', [5.03459, 5.03459, 13.7533, 90, 90, 120], 'no'], 'In': ['In', [3.2517, 3.2517, 4.9459, 90, 90, 90], 'h+k+l=2n'], 'In2Bi': ['In2Bi', [5.496, 5.496, 6.585, 90, 90, 120], 'no'], 'InGaN': ['InGaN', [3.3609999999999998, 3.3609999999999998, 5.439, 90, 90, 120], 'wurtzite'], 'InN': ['InN', [3.533, 3.533, 5.693, 90, 90, 120], 'wurtzite'], 'In_distorted': ['In_distorted', [3.2517, 3.251133, 4.818608, 89.982926, 90.007213, 95.379102], 'h+k+l=2n'], 'In_epsilon': ['In_epsilon', [3.47, 3.47, 4.49, 90, 90, 90], 'no'], 'Magnetite': ['Magnetite', [8.391, 8.391, 8.391, 90, 90, 90], 'dia'], 'Magnetite_fcc': ['Magnetite_fcc', [8.391, 8.391, 8.391, 90, 90, 90], 'fcc'], 'Magnetite_sc': ['Magnetite_sc', [8.391, 8.391, 8.391, 90, 90, 90], 'no'], 'Nd45': ['Nd45', [5.4884, 5.4884, 5.4884, 90, 90, 90], 'fcc'], 'Ni': ['Ni', [3.5238, 3.5238, 3.5238, 90, 90, 90], 'fcc'], 'NiO': ['NiO', [2.96, 2.96, 7.23, 90, 90, 120], 'no'], 'NiTi': ['NiTi', [3.5506, 3.5506, 3.5506, 90, 90, 90], 'fcc'], 'Olivine_fayalite': ['Olivine_fayalite', [4.8211, 10.4779, 6.0889, 90, 90, 90], 'no'], 'Olivine_forsterite': ['Olivine_forsterite', [4.754, 10.1971, 5.9806, 90, 90, 90], 'no'], 'Olivine_mantle': ['Olivine_mantle', [4.7646, 10.2296, 5.9942, 90, 90, 90], 'no'], 'SC': ['SC', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'SC5': ['SC5', [5.0, 5.0, 5.0, 90, 90, 90], 'no'], 'SC7': ['SC7', [7.0, 7.0, 7.0, 90, 90, 90], 'no'], 'Sb': ['Sb', [4.3, 4.3, 11.3, 90, 90, 120], 'no'], 'Si': ['Si', [5.4309, 5.4309, 5.4309, 90, 90, 90], 'dia'], 'Sn_beta': ['Sn_beta', [5.83, 5.83, 3.18, 90, 90, 90], 'SG141'], 'Sn_beta_all': ['Sn_all', [5.83, 5.83, 3.18, 90, 90, 90], 'no'], 'Ti': ['Ti', [2.95, 2.95, 4.68, 90, 90, 120], 'no'], 'Ti2AlN': ['Ti2AlN', [2.989, 2.989, 13.624, 90, 90, 120], 'Ti2AlN'], 'Ti2AlN_w': ['Ti2AlN_w', [2.989, 2.989, 13.624, 90, 90, 120], 'wurtzite'], 'Ti_beta': ['Ti_beta', [3.2587, 3.2587, 3.2587, 90, 90, 90], 'bcc'], 'Ti_omega': ['Ti_omega', [4.6085, 4.6085, 2.8221, 90, 90, 120], 'no'], 'Ti_s': ['Ti_s', [3.0, 3.0, 4.7, 90.5, 89.5, 120.5], 'no'], 'UO2': ['UO2', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'VO2M1': ['VO2M1', [5.75175, 4.52596, 5.38326, 90.0, 122.6148, 90.0], 'VO2_mono'], 'VO2M2': ['VO2M2', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'no'], 'VO2R': ['VO2R', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'rutile'], 'W': ['W', [3.1652, 3.1652, 3.1652, 90, 90, 90], 'bcc'], 'Y2SiO5': ['Y2SiO5', [10.34, 6.689, 12.38, 90.0, 102.5, 90.0], 'no'], 'YAG': ['YAG', [9.2, 9.2, 9.2, 90, 90, 90], 'no'], 'ZnCuOCl': ['ZnCuOCl', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'SG166'], 'ZnCuOCl_all': ['ZnCuOCl_all', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'no'], 'ZnO': ['ZnO', [3.252, 3.252, 5.213, 90, 90, 120], 'wurtzite'], 'ZrO2': ['ZrO2', [5.1505, 5.2116, 5.3173, 90, 99.23, 90], 'VO2_mono'], 'ZrO2Y2O3': ['ZrO2Y2O3', [5.1378, 5.1378, 5.1378, 90, 90, 90], 'fcc'], 'ZrO2_1200C': ['ZrO2_1200C', [3.6406, 3.6406, 5.278, 90, 90, 90], 'h+k+l=2n'], 'ZrO2fake1': ['ZrO2fake1', [5.1505, 5.048116, 4.988933, 90, 99.23, 90], 'VO2_mono'], 'ZrO2swapac': ['ZrO2swapac', [5.3173, 5.2116, 5.1505, 90, 99.23, 90], 'VO2_mono'], 'ZrUO2_corium': ['ZrUO2_corium', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'alphaQuartz': ['alphaQuartz', [4.9, 4.9, 5.4, 90, 90, 120], 'no'], 'betaQuartznew': ['betaQuartznew', [4.9, 4.9, 6.685, 90, 90, 120], 'no'], 'bigpro': ['bigpro', [112.0, 112.0, 136.0, 90, 90, 90], 'no'], 'dummy': ['dummy', [4.0, 8.0, 2.0, 90, 90, 90], 'no'], 'feldspath': ['feldspath', [8.59, 12.985, 7.213, 90, 116.0, 90], 'no'], 'ferrydrite': ['ferrydrite', [2.96, 2.96, 9.4, 90, 90, 120], 'no'], 'hexagonal': ['hexagonal', [1.0, 1.0, 3.0, 90, 90, 120.0], 'no'], 'inputB': ['inputB', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'quartz_alpha': ['quartz_alpha', [4.913, 4.913, 5.404, 90, 90, 120], 'no'], 'smallpro': ['smallpro', [20.0, 4.8, 49.0, 90, 90, 90], 'no'], 'test_reference': ['test_reference', [3.2, 4.5, 5.2, 83, 92.0, 122], 'wurtzite'], 'test_solution': ['test_solution', [3.252, 4.48, 5.213, 83.2569, 92.125478, 122.364], 'wurtzite'], 'testindex': ['testindex', [2.0, 1.0, 4.0, 90, 90, 90], 'no'], 'testindex2': ['testindex2', [2.0, 1.0, 4.0, 75, 90, 120], 'no']})[source]
Simulates 2theta chi of Laue Pattern spots for ONE SINGLE grain
- Parameters
grain – crystal parameters in a 4 elements list
emin – minimum bandpass energy (keV)
emax – maximum bandpass energy (keV)
- Returns
2theta, chi
Warning
Need of approximate detector distance and diameter to restrict simulation to a limited solid angle
Note
USED: in AutoindexationGUI.OnStart, LaueToolsGUI.OnCheckOrientationMatrix
USED also IndexingImageMatching, lauecore.SimulateLaue_merge
2D Detection Geometry
Module of lauetools project to compute Laue spots position on CCD camera. It handles detection and source geometry.
Warning
The frame (LT2) considered in this package (with y axis parallel to the incoming beam) in not the LaueTools frame (for which x is parallel to the incoming beam)
JS Micha June 2019
- Vectors Definitions
q momentum transfer vector from resp. incoming and outgoing wave vector ki and kf, \(q=kf-ki\)
When a Laue spot exists, q is equal to the one node of the reciprocal lattice given by G* vector
- G* is perpendicular to atomic planes defined by the three Miller indices h,k,l
such as G***=h**a* + k**b*** +l**c*** where a*, b*, and c* are the unit cell lattice basis vectors.
kf: scattered beam vector whose corresponding unit vector is uf
ki incoming beam vector, ui corresponding unit vector
- Laboratory Frame LT2
I: origin
z vertical up perpendicular to CCD plane (top camera geometry)
y along X-ray horizontal
x towards wall behind horizontal
O: origin of pixel CCD frame in detecting plane
j // ui incoming beam unit vector
- z axis is defined by the CCD camera position. z axis is perpendicular to CCD plane
such as IO belongs to the plane Iyz
bet: angle between IO and k
- i**= **j**^**k (when entering the BM32 hutch i is approximately towards the wall
(in CCD on top geometry and beam coming from the right)
M: point lying in CCD plane corresponding to Laue spot
uf is the unit vector relative to vector IM
kf is also a vector collinear to IM with a length of R=1/wavelength=E/12.398 [keV] with wavelength and Energy of the corresponding bragg’s reflections.
I is the point from which calibration parameters (CCD position) are deduced (from a perfectly known crystal structure Laue pattern) Iprime is an other source of emission (posI or offset in functions)
\(2 \theta\) is the scattering angle between ui and uf, i.e.
\(\cos(2 \theta)=u_i.u_f\)
Energy= 12.398* q**2/(2* q.**ui**)=12.398 * q**2/ (-2 sin theta)
- Calibration parameters (CCD position and detection geometry)
calib: list of the 5 calibration parameters [dd,xcen,ycen,xbet,xgam]
dd: norm of IO [mm]
- xcen,ycen [pixel unit]: pixels values in CCD frame of point O with respect to Oprime where
Oprime is the origin of CCD pixels frame (at a corner of the CCD array)
xbet: angle between IO and k [degree]
- xgam: azimutal rotation angle around z axis. Angle between CCD array axes
and (i,**j**) after rotation by xbet [degree].
sample frame
Origin is I and unit frame vectors (is, js, ks) are derived from absolute frame by the rotation (axis= - i, angle= wo) where wo is the angle between js and j
- LaueTools.LaueGeometry.calc_uflab(xcam, ycam, detectorplaneparameters, offset=0, returnAngles=1, verbose=0, pixelsize=0.08056640625, rectpix=0, kf_direction='Z>0', version=1)[source]
Computes scattered unit vector \({\bf u_f}=\frac{\bf k_f}{\|k_f\|}\) in laboratory frame corresponding to \(k_f\) (angle scattering angles 2theta and chi) from X, Y pixel Laue spot position
Unit vector uf correspond to normalized kf vector: q = kf - ki from lists of X and Y Laue spots pixels positions on detector
- Parameters
- Returns
if returnAngles=1 : twicetheta, chi (default)
if returnAngles!=1 : uflab, IMlab
- LaueTools.LaueGeometry.calc_uflab_trans(xcam, ycam, calib, returnAngles=1, verbose=0, pixelsize=0.08056640625, rectpix=0)[source]
compute \(2 \theta\) and \(\chi\) scattering angles or uf and kf vectors from lists of X and Y Laue spots positions in TRANSMISSION geometry
- Parameters
- Returns
if returnAngles=1 : twicetheta, chi (default)
if returnAngles!=1 : uflab, IMlab
# TODO: add offset like in reflection geometry
- LaueTools.LaueGeometry.calc_xycam(uflab, calib, energy=0, offset=None, verbose=0, returnIpM=False, pixelsize=0.08056640625, rectpix=0)[source]
Computes Laue spots position x and y in pixels units in CCD frame from unit scattered vector uf expressed in Lab. frame
computes coordinates of point M on CCD from point source and uflab. Point Ip (source Iprime of x-ray scattered beams) (for each Laue spot uflab is the unit vector of IpM) Point Ip is shifted by offset (if not None) from the default point I (used to calibrate the CCD camera and 2theta chi determination)
th0 (theta in degrees) Energy (energy in keV)
- Parameters
uflab (list or array (length must > 1)) – list or array of [kf_x,kf_y,kf_z] (kf or uf unit vector)
calib (list of floats) – list 5 detector calibration parameters
offset (list of floats ([x,y,z])) – offset (in mm) in the scattering source (origin of Laue spots) position with respect to the position which has been used for the calibration of the CCD detector plane. Offset is positive when in the same direction as incident beam (i.e. in sample depth) (incident beam direction remains constant)
- Returns
xcam: list of pixel X coordinates
ycam: list of pixel Y coordinates
theta: list half scattering angle “theta” (in degree)
optionally energy=1: add in output list of spot energies (in keV)
if returnIpM and offset not None: return list of vectors IprimeM
- LaueTools.LaueGeometry.calc_xycam_transmission(uflab, calib, energy=0, offset=None, verbose=0, returnIpM=False, pixelsize=0.08056640625, rectpix=0)[source]
Computes Laue spots position x and y in pixels units (in CCD frame) from scattered vector uf or kf As calc_xycam() but in TRANSMISSION geometry
- LaueTools.LaueGeometry.uflab_from2thetachi(twicetheta, chi, verbose=0)[source]
Computes \({\bf u_f}\) vectors coordinates in lauetools LT2 frame from \({\bf k_f}\) scattering angles \(2 \theta\) and \(2 \chi\) angles
- Parameters
twicetheta – (list) \(2 \theta\) angle(s) ( in degree)
chi – (list) \(2 \chi\) angle(s) ( in degree)
- Returns
list of {bf u_f} = [\(uf_x,uf_y,uf_z\)]
- Return type
- LaueTools.LaueGeometry.from_twchi_to_qunit(Angles)[source]
from kf 2theta, chi to q unit in LaueTools frame (xx// ki) q=kf-ki returns array = (all x’s, all y’s, all z’s)
Angles in degrees !! Angles[0] 2theta deg values, Angles[1] chi values in deg
this is the inverse function of from_qunit_to_twchi(), useful to check it
- LaueTools.LaueGeometry.from_twchi_to_q(Angles)[source]
From kf 2theta,chi to q (arbitrary lenght) in lab frame (xx// ki) q=kf-ki returns array = (all qx’s, all qy’s, all qz’s)
Angles in degrees !! Angles[0] 2theta deg values, Angles[1] chi values in deg
- LaueTools.LaueGeometry.from_qunit_to_twchi(arrayXYZ, labXMAS=0)[source]
Returns 2theta chi from a q unit vector (defining a direction) expressed in LaueTools frame (xx// ki) q=kf-ki
\[\begin{split}\left [ \begin{matrix} -\sin \theta \\ \cos \theta \sin \chi \\ \cos \theta \cos \chi \end{matrix} \right ]\end{split}\]Note
in LaueTools frame
\[ \begin{align}\begin{aligned}\begin{split}kf = \left [ \begin{matrix} \cos 2\theta \\ \sin 2\theta \sin \chi \\ \sin 2\theta \cos \chi \end{matrix} \right ]\end{split}\\\begin{split}q = 2 \sin \theta \left [ \begin{matrix} -\sin \theta \\ \cos \theta \sin \chi \\ \cos \theta \cos \chi \end{matrix} \right ]\end{split}\end{aligned}\end{align} \]In LT2 Frame labXMAS=1
\[ \begin{align}\begin{aligned}\begin{split}kf = \left [ \begin{matrix} \sin 2\theta \sin \chi \\ \cos 2\theta \\ \sin 2\theta \cos \chi \end{matrix} \right ]\end{split}\\\begin{split}q = 2 \sin \theta \left [ \begin{matrix} \cos \theta \sin \chi \\ -\sin \theta \\ \cos \theta \cos \chi \end{matrix} \right ]\end{split}\end{aligned}\end{align} \]
- LaueTools.LaueGeometry.qvector_from_xy_E(xcamList, ycamList, energy, detectorplaneparameters, pixelsize)[source]
Returns q vectors in Lauetools frame given x and y pixel positions on detector for a given Energy (keV)
- Parameters
xcamList – list pixel x postions
ycamList – list pixel y postions
energy – list pf energies
detectorplaneparameters – list of 5 calibration parameters
pixelsize – pixel size in mm
- LaueTools.LaueGeometry.unit_q(ttheta, chi, frame='lauetools', anglesample=40.0)[source]
Returns unit q vector from 2theta,chi coordinates
- Parameters
ttheta – list of 2theta angles (in degrees)
chi – list of chi angles (in degrees)
anglesample – incidence angle of beam to surface plane (degrees)
frame – frame to express vectors in: ‘lauetools’ , ‘XMASlab’ (LT2 frame),’XMASsample’
- Returns
list of 3D u_f (unit vector of scattering transfer q)
- LaueTools.LaueGeometry.Compute_data2thetachi(filename, sorting_intensity='yes', detectorparams=None, kf_direction='Z>0', verbose=1, pixelsize=0.08056640625, dim=(2048, 2048), saturation=0, forceextension_lines_to_extract=None, col_isbadspot=None, alpha_xray_incidence_correction=None)[source]
Read a file and convert spot positions x,y to scattering angles 2theta, chi according to detector parameters
- Parameters
filename (string) – fullpath to peaks list ASCII file
detectorparams – list of CCD calibration parameters [det, xcen, ycen, xbet, xgam]
pixelsize – pixelsize in mm
dim – (nb pixels x, nb pixels y)
kf_direction (string) – label of detection geometry (CCD position): ‘Z>0’,’X>0’,…
sorting_intensity – ‘yes’ sort spots list by decreasing intensity
saturation = 0 : do not read Ipixmax column of DAT file from LaueTools peaksearch saturation > 0 : read Ipixmax column and create data_sat list data_sat[i] = 1 if Ipixmax[i]> saturation, =0 otherwise
col_Ipixmax = 10 for .dat from LT peak search using method “Local Maxima” (TODO : bug in Ipixmax for method “convolve”)
- Returns
twicetheta, chi, dataintensity, data_x, data_y [, other data]
- LaueTools.LaueGeometry.convert2corfile(filename, calibparam, dirname_in=None, dirname_out=None, pixelsize=0.08056640625, CCDCalibdict=None, add_props=False)[source]
Convert .dat (peaks list from peaksearch procedure) to .cor (adding scattering angles 2theta chi)
From X,Y pixel positions in peak list file (x,y,I,…) and detector plane geometry comptues scattering angles 2theta chi and creates a .cor file (ascii peaks list (2theta chi X Y int …))
- Parameters
calibparam – list of 5 CCD calibration parameters (used if CCDCalibdict is None or CCDCalibdict[‘CCDCalibPameters’] is missing)
pixelsize – CCD pixelsize (in mm) (used if CCDCalibdict is None or CCDCalibdict[‘pixelsize’] is missing)
CCDCalibdict – dictionary of CCD file and calibration parameters
add_props – add all peaks properties to .cor file instead of the 5 columns
- LaueTools.LaueGeometry.convert2corfile_fileseries(fileindexrange, filenameprefix, calibparam, suffix='', nbdigits=4, dirname_in=None, dirname_out=None, pixelsize=0.08056640625, fliprot='no')[source]
convert a serie of peaks list ascii files to .cor files (adding scattering angles).
Filename is decomposed as following for incrementing file index in ####: prefix####suffix example: myimage_0025.myccd => prefix=myimage_ nbdigits=4 suffix=.myccd
- Parameters
nbdigits – nb of digits of file index in filename (with zero padding) (example: for myimage_0002.ccd nbdigits = 4
calibparam – list of 5 CCD cakibration parameters
- LaueTools.LaueGeometry.convert2corfile_multiprocessing(fileindexrange, filenameprefix, calibparam, dirname_in=None, suffix='', nbdigits=4, dirname_out=None, pixelsize=0.08056640625, fliprot='no', nb_of_cpu=6)[source]
launch several processes in parallel to convert .dat file to .cor file
- LaueTools.LaueGeometry.vec_normalTosurface(mat_labframe)[source]
solve Mat * X = (0,0,1) for X for pure rotation invMat = transpose(Mat)
TODO: add option sample angle and axis
- LaueTools.LaueGeometry.vec_onsurface_alongys(mat_labframe)[source]
solve Mat * X = (0,1,0) for X for pure rotation invMat = transpose(Mat)
- LaueTools.LaueGeometry.convert_xycam_from_sourceshift(OMs, IIp, calib, verbose=0)[source]
From x,y on CCD camera (OMs) and source shift (IIprime) compute modified x,y values for the SAME calibration (calib)(for further analysis)
return new value of x,y
- LaueTools.LaueGeometry.lengthInSample(depth, twtheta, chi, omega, verbose=False)[source]
compute geometrical lengthes in sample from impact point (I) at the surface to a point (B) where xray are scattered (or fluorescence is emitted) and finally escape from inside at point (C) lying at the sample surface (intersection of line with unit vector u with sample surface plane tilted by omega)
Warning
twtheta and chi angles can be misleading. Assumption is made that angles of unit vector from B to C (or to detector frame pixel) are \(2 \theta\) and \(\chi\). For large depth D, unit vector scattered beam direction is not given by \(2 \theta\) and \(\chi\) angles as they are used for describing the scattering direction from point I and a given detector frame position (you should then compute the two angles correction , actually \(\chi\) is unchanged, and the \(2 \theta\) change is approx d/ distance .i.e. 3 10-4 for d=20 µm and CCD at 70 mm)
Note
- incoming beam coming from the right positive x direction with
IB = (-D,0,0)
BC =(xc+D,yc,zc)
and length BC is proportional to the depth D
Multiple Grains and Strain/orientation Distribution
Module to compute Laue Patterns from several crystals in various geometry
Main author is J. S. Micha: micha [at] esrf [dot] fr
version July 2019 from LaueTools package for python2 hosted in
http://sourceforge.net/projects/lauetools/
or for python3 and 2 in
https://gitlab.esrf.fr/micha/lauetools
- LaueTools.multigrainsSimulator.Read_GrainListparameter(param)[source]
Read dictionary of input key parameters for simulation
- LaueTools.multigrainsSimulator.Construct_GrainsParameters_parametric(SelectGrains_parametric)[source]
return list of simulation parameters for each grain set (mother and children grains)
- LaueTools.multigrainsSimulator.dosimulation_parametric(_list_param, Transform_params=None, SelectGrains=None, emax=25.0, emin=5.0, detectordistance=68.7, detectordiameter=165.0, posCEN=(1024.0, 1024.0), cameraAngles=(0.0, 0.0), gauge=None, kf_direction='Z>0', pixelsize=0.08056640625, dictmaterials={'3H-SiC': ['3H-SiC', [4.3596, 4.3596, 4.3596, 90, 90, 90], 'dia'], '4H-SiC': ['4H-SiC', [3.073, 3.073, 10.053, 90, 90, 120], 'wurtzite'], 'Ag': ['Ag', [4.085, 4.085, 4.085, 90, 90, 90], 'fcc'], 'Al': ['Al', [4.05, 4.05, 4.05, 90, 90, 90], 'fcc'], 'Al2Cu': ['Al2Cu', [6.063, 6.063, 4.872, 90, 90, 90], 'no'], 'Al2O3': ['Al2O3', [4.785, 4.785, 12.991, 90, 90, 120], 'Al2O3'], 'Al2O3_all': ['Al2O3_all', [4.785, 4.785, 12.991, 90, 90, 120], 'no'], 'AlN': ['AlN', [3.11, 3.11, 4.98, 90.0, 90.0, 120.0], 'wurtzite'], 'AmbiguousTriclinic': ['AmbiguousTriclinic', [3.9, 4, 4.1, 89, 90, 91], 'no'], 'Au': ['Au', [4.078, 4.078, 4.078, 90, 90, 90], 'fcc'], 'CCDL1949': ['CCDL1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'h+k=2n'], 'CdHgTe': ['CdHgTe', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'dia'], 'CdHgTe_fcc': ['CdHgTe_fcc', [6.46678, 6.46678, 6.46678, 90, 90, 90], 'fcc'], 'CdTe': ['CdTe', [6.487, 6.487, 6.487, 90, 90, 90], 'fcc'], 'CdTeDiagB': ['CdTeDiagB', [4.5721, 7.9191, 11.1993, 90, 90, 90], 'no'], 'Crocidolite': ['Crocidolite', [9.811, 18.013, 5.326, 90, 103.68, 90], 'no'], 'Crocidolite_2': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 103.6, 90], 'no'], 'Crocidolite_2_72deg': ['Crocidolite_2', [9.76, 17.93, 5.35, 90, 76.4, 90], 'no'], 'Crocidolite_small': ['Crocidolite_small', [3.2533333333333334, 5.976666666666667, 1.7833333333333332, 90, 103.6, 90], 'no'], 'Crocidolite_whittaker_1949': ['Crocidolite_whittaker_1949', [9.89, 17.85, 5.31, 90, 107.5, 90], 'no'], 'Cu': ['Cu', [3.6, 3.6, 3.6, 90, 90, 90], 'fcc'], 'Cu6Sn5_monoclinic': ['Cu6Sn5_monoclinic', [11.02, 7.28, 9.827, 90, 98.84, 90], 'no'], 'Cu6Sn5_tetra': ['Cu6Sn5_tetra', [3.608, 3.608, 5.037, 90, 90, 90], 'no'], 'DIA': ['DIA', [5.0, 5.0, 5.0, 90, 90, 90], 'dia'], 'DIAs': ['DIAs', [3.56683, 3.56683, 3.56683, 90, 90, 90], 'dia'], 'DarinaMolecule': ['DarinaMolecule', [9.4254, 13.5004, 13.8241, 61.83, 84.555, 75.231], 'no'], 'FCC': ['FCC', [5.0, 5.0, 5.0, 90, 90, 90], 'fcc'], 'Fe': ['Fe', [2.856, 2.856, 2.856, 90, 90, 90], 'bcc'], 'Fe2Ta': ['Fe2Ta', [4.83, 4.83, 0.788, 90, 90, 120], 'no'], 'FeAl': ['FeAl', [5.871, 5.871, 5.871, 90, 90, 90], 'fcc'], 'FePS3': ['FePS3', [5, 10, 7, 90, 107, 90], 'no'], 'GaAs': ['GaAs', [5.65325, 5.65325, 5.65325, 90, 90, 90], 'dia'], 'GaAs_wurtz': ['GaAs_wurtz', [5.65325, 5.65325, 5.9, 90, 90, 90], 'wurtzite'], 'GaN': ['GaN', [3.189, 3.189, 5.185, 90, 90, 120], 'wurtzite'], 'GaN_all': ['GaN_all', [3.189, 3.189, 5.185, 90, 90, 120], 'no'], 'Ge': ['Ge', [5.6575, 5.6575, 5.6575, 90, 90, 90], 'dia'], 'Ge_compressedhydro': ['Ge_compressedhydro', [5.64, 5.64, 5.64, 90, 90, 90.0], 'dia'], 'Ge_s': ['Ge_s', [5.6575, 5.6575, 5.6575, 90, 90, 89.5], 'dia'], 'Getest': ['Getest', [5.6575, 5.6575, 5.6574, 90, 90, 90], 'dia'], 'Hematite': ['Hematite', [5.03459, 5.03459, 13.7533, 90, 90, 120], 'no'], 'In': ['In', [3.2517, 3.2517, 4.9459, 90, 90, 90], 'h+k+l=2n'], 'In2Bi': ['In2Bi', [5.496, 5.496, 6.585, 90, 90, 120], 'no'], 'InGaN': ['InGaN', [3.3609999999999998, 3.3609999999999998, 5.439, 90, 90, 120], 'wurtzite'], 'InN': ['InN', [3.533, 3.533, 5.693, 90, 90, 120], 'wurtzite'], 'In_distorted': ['In_distorted', [3.2517, 3.251133, 4.818608, 89.982926, 90.007213, 95.379102], 'h+k+l=2n'], 'In_epsilon': ['In_epsilon', [3.47, 3.47, 4.49, 90, 90, 90], 'no'], 'Magnetite': ['Magnetite', [8.391, 8.391, 8.391, 90, 90, 90], 'dia'], 'Magnetite_fcc': ['Magnetite_fcc', [8.391, 8.391, 8.391, 90, 90, 90], 'fcc'], 'Magnetite_sc': ['Magnetite_sc', [8.391, 8.391, 8.391, 90, 90, 90], 'no'], 'Nd45': ['Nd45', [5.4884, 5.4884, 5.4884, 90, 90, 90], 'fcc'], 'Ni': ['Ni', [3.5238, 3.5238, 3.5238, 90, 90, 90], 'fcc'], 'NiO': ['NiO', [2.96, 2.96, 7.23, 90, 90, 120], 'no'], 'NiTi': ['NiTi', [3.5506, 3.5506, 3.5506, 90, 90, 90], 'fcc'], 'Olivine_fayalite': ['Olivine_fayalite', [4.8211, 10.4779, 6.0889, 90, 90, 90], 'no'], 'Olivine_forsterite': ['Olivine_forsterite', [4.754, 10.1971, 5.9806, 90, 90, 90], 'no'], 'Olivine_mantle': ['Olivine_mantle', [4.7646, 10.2296, 5.9942, 90, 90, 90], 'no'], 'SC': ['SC', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'SC5': ['SC5', [5.0, 5.0, 5.0, 90, 90, 90], 'no'], 'SC7': ['SC7', [7.0, 7.0, 7.0, 90, 90, 90], 'no'], 'Sb': ['Sb', [4.3, 4.3, 11.3, 90, 90, 120], 'no'], 'Si': ['Si', [5.4309, 5.4309, 5.4309, 90, 90, 90], 'dia'], 'Sn_beta': ['Sn_beta', [5.83, 5.83, 3.18, 90, 90, 90], 'SG141'], 'Sn_beta_all': ['Sn_all', [5.83, 5.83, 3.18, 90, 90, 90], 'no'], 'Ti': ['Ti', [2.95, 2.95, 4.68, 90, 90, 120], 'no'], 'Ti2AlN': ['Ti2AlN', [2.989, 2.989, 13.624, 90, 90, 120], 'Ti2AlN'], 'Ti2AlN_w': ['Ti2AlN_w', [2.989, 2.989, 13.624, 90, 90, 120], 'wurtzite'], 'Ti_beta': ['Ti_beta', [3.2587, 3.2587, 3.2587, 90, 90, 90], 'bcc'], 'Ti_omega': ['Ti_omega', [4.6085, 4.6085, 2.8221, 90, 90, 120], 'no'], 'Ti_s': ['Ti_s', [3.0, 3.0, 4.7, 90.5, 89.5, 120.5], 'no'], 'UO2': ['UO2', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'VO2M1': ['VO2M1', [5.75175, 4.52596, 5.38326, 90.0, 122.6148, 90.0], 'VO2_mono'], 'VO2M2': ['VO2M2', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'no'], 'VO2R': ['VO2R', [4.5546, 4.5546, 2.8514, 90.0, 90, 90.0], 'rutile'], 'W': ['W', [3.1652, 3.1652, 3.1652, 90, 90, 90], 'bcc'], 'Y2SiO5': ['Y2SiO5', [10.34, 6.689, 12.38, 90.0, 102.5, 90.0], 'no'], 'YAG': ['YAG', [9.2, 9.2, 9.2, 90, 90, 90], 'no'], 'ZnCuOCl': ['ZnCuOCl', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'SG166'], 'ZnCuOCl_all': ['ZnCuOCl_all', [6.839, 6.839, 14.08, 90.0, 90, 120.0], 'no'], 'ZnO': ['ZnO', [3.252, 3.252, 5.213, 90, 90, 120], 'wurtzite'], 'ZrO2': ['ZrO2', [5.1505, 5.2116, 5.3173, 90, 99.23, 90], 'VO2_mono'], 'ZrO2Y2O3': ['ZrO2Y2O3', [5.1378, 5.1378, 5.1378, 90, 90, 90], 'fcc'], 'ZrO2_1200C': ['ZrO2_1200C', [3.6406, 3.6406, 5.278, 90, 90, 90], 'h+k+l=2n'], 'ZrO2fake1': ['ZrO2fake1', [5.1505, 5.048116, 4.988933, 90, 99.23, 90], 'VO2_mono'], 'ZrO2swapac': ['ZrO2swapac', [5.3173, 5.2116, 5.1505, 90, 99.23, 90], 'VO2_mono'], 'ZrUO2_corium': ['ZrUO2_corium', [5.47, 5.47, 5.47, 90, 90, 90], 'fcc'], 'alphaQuartz': ['alphaQuartz', [4.9, 4.9, 5.4, 90, 90, 120], 'no'], 'betaQuartznew': ['betaQuartznew', [4.9, 4.9, 6.685, 90, 90, 120], 'no'], 'bigpro': ['bigpro', [112.0, 112.0, 136.0, 90, 90, 90], 'no'], 'dummy': ['dummy', [4.0, 8.0, 2.0, 90, 90, 90], 'no'], 'feldspath': ['feldspath', [8.59, 12.985, 7.213, 90, 116.0, 90], 'no'], 'ferrydrite': ['ferrydrite', [2.96, 2.96, 9.4, 90, 90, 120], 'no'], 'hexagonal': ['hexagonal', [1.0, 1.0, 3.0, 90, 90, 120.0], 'no'], 'inputB': ['inputB', [1.0, 1.0, 1.0, 90, 90, 90], 'no'], 'quartz_alpha': ['quartz_alpha', [4.913, 4.913, 5.404, 90, 90, 120], 'no'], 'smallpro': ['smallpro', [20.0, 4.8, 49.0, 90, 90, 90], 'no'], 'test_reference': ['test_reference', [3.2, 4.5, 5.2, 83, 92.0, 122], 'wurtzite'], 'test_solution': ['test_solution', [3.252, 4.48, 5.213, 83.2569, 92.125478, 122.364], 'wurtzite'], 'testindex': ['testindex', [2.0, 1.0, 4.0, 90, 90, 90], 'no'], 'testindex2': ['testindex2', [2.0, 1.0, 4.0, 75, 90, 120], 'no']})[source]
Simulation of orientation or deformation gradient. From parent grain simulate a list of transformations (deduced by a parametric variation)
_list_param : list of parameters for each grain [grain parameters, grain name]
posCEN =(Xcen, Ycen) cameraAngles =(Xbet, Xgam)
- Returns
(list_twicetheta, list_chi, list_energy, list_Miller, list_posX, list_posY, ParentGrainName_list, list_ParentGrain_transforms, calib, total_nb_grains)
TODO:simulate for any camera position TODO: simulate spatial distribution of laue pattern origin
Modules for Digital Image processing, Peak Search & Fitting
Peak Search and Fit (readmccd.py)
Module functions
The next documentation comes from the docstring in the header of function or class definition.
readmccd.py
readmccd module is made for reading data contained in binary image file fully or partially. It can process a peak or blob search by various methods and refine the peak by a gaussian or lorentzian 2D model
More tools can be found in LaueTools package at sourceforge.net and gitlab.esrf.fr March 2020
- LaueTools.readmccd.readoneimage_multiROIfit(filename, centers, boxsize, stackimageindex=-1, CCDLabel='PRINCETON', baseline='auto', startangles=0.0, start_sigma1=1.0, start_sigma2=1.0, position_start='max', fitfunc='gaussian', showfitresults=1, offsetposition=0, verbose=0, xtol=1e-08, addImax=False, use_data_corrected=None)[source]
Fit several peaks in one image
- Parameters
filename – string, full path to image file
centers – list or array like with shape=(n,2) list of centers of selected ROI
boxsize – (Truly HALF boxsize: fuill boxsize= 2(halfboxsize) +1), iterable 2 elements or integer boxsizes [in x, in y] direction or integer to set a square ROI
baseline – string, ‘auto’ (ie minimum intensity in ROI) or array of floats
startangles – float or iterable of 2 floats, elliptic gaussian angle (major axis with respect to X direction), one value or array of values
start_sigma2 (start_sigma1,) – floats, gaussian standard deviation (major and minor axis) in pixel,
position_start – string, starting gaussian center:’max’ (position of maximum intensity in ROI), “centers” (centre of each ROI)
offsetposition – integer, 0 for no offset, 1 XMAS compatible, since XMAS consider first pixel as index 1 (in array, index starts with 0), 2 fit2d, since fit2d for peaksearch put pixel labelled n at the position n+0.5 (between n and n+1)
use_data_corrected – tuple of 3 elements, Enter data instead of reading data from file: fulldata, framedim, fliprot where fulldata is a 2D ndarray
- Returns
list of results: bkg, amp (gaussian height-bkg), X , Y, major axis standard deviation, minor axis standard deviation, major axis tilt angle / Ox
Todo
setting list of initial guesses can be improve with scipy.ndimages of a concatenate array of multiple slices?
- LaueTools.readmccd.fitPeakMultiROIs(Data, centers, FittingParametersDict, showfitresults=True, verbose=False)[source]
refine all peaks guessed to be at center of several ROIs
- Parameters
Data – list of Data array centered on peaks
centers – list of pixels (x,y) positions of ROI centers
FittingParametersDict – dict of fitting parameters
- Returns
RES_params, RES_cov, RES_infodict, RES_errmsg, start_baseline which are all list of refinement results
- LaueTools.readmccd.getIntegratedIntensities(fullpathimagefile, list_centers, boxsize, CCDLabel='MARCCD165', thresholdlevel=0.2, flipxycenter=True)[source]
read binary image file and compute integrated intensities of peaks whose center is given in list_centers
- Returns
array whose columns are: - integrated intensity - absolute minimum intensity threshold - nb of pixels composing the peak
- LaueTools.readmccd.writepeaklist(tabpeaks, output_filename, outputfolder=None, comments=None, initialfilename=None)[source]
write peaks properties and comments in file with extension .dat added
- LaueTools.readmccd.fitoneimage_manypeaks(filename, peaklist, boxsize, stackimageindex=-1, CCDLabel='PRINCETON', dirname=None, position_start='max', type_of_function='gaussian', guessed_peaksize=(1.0, 1.0), xtol=0.001, FitPixelDev=2.0, Ipixmax=None, MaxIntensity=100000000000, MinIntensity=0, PeakSizeRange=(0, 200), verbose=0, position_definition=1, NumberMaxofFits=500, ComputeIpixmax=False, use_data_corrected=None, reject_negative_baseline=True, purgeDuplicates=True)[source]
fit multiple ROI data to get peaks position in a single image
Ipixmax : highest intensity above background in every ROI centered on element of peaklist
- use_data_correctedenter data instead of reading data from file
must be a tuple of 3 elements: fulldata, framedim, fliprot where fulldata is an ndarray
purgeDuplicates : True remove duplicates that are close within pixel distance of ‘boxsize’ and keep the most intense peak
- use_data_correctedenter data instead of reading data from file
must be a tuple of 3 elements: fulldata, framedim, fliprot where fulldata ndarray
Note
used in PeakSearchGUI
- LaueTools.readmccd.PeakSearch(filename, stackimageindex=-1, CCDLabel='PRINCETON', center=None, boxsizeROI=(200, 200), PixelNearRadius=5, removeedge=2, IntensityThreshold=400, thresholdConvolve=200, paramsHat=(4, 5, 2), boxsize=15, verbose=0, position_definition=1, local_maxima_search_method=1, peakposition_definition='max', fit_peaks_gaussian=1, xtol=1e-05, return_histo=1, FitPixelDev=25, write_execution_time=1, Saturation_value=65535, Saturation_value_flatpeak=65535, MinIntensity=0, PeakSizeRange=(0, 200), Data_for_localMaxima=None, Fit_with_Data_for_localMaxima=False, Remove_BlackListedPeaks_fromfile=None, maxPixelDistanceRejection=15.0, NumberMaxofFits=5000, reject_negative_baseline=True, formulaexpression='A-1.1*B', listrois=None, outputIpixmax=True)[source]
Find local intensity maxima as starting position for fittinng and return peaklist.
- Parameters
filename – string, full path to image data file
stackimageindex – integer, index corresponding to the position of image data on a stacked images file if -1 means single image data w/o stacking
CCDLabel – string, label for CCD 2D detector used to read the image data file see dict_LaueTools.py
center – position
Todo
to be removed: position of the ROI center in CCD frame
- Parameters
boxsizeROI – dimensions of the ROI to crop the data array only used if center != None
boxsize – half length of the selected ROI array centered on each peak, used for: - fitting a peak - estimating the background around a peak - shifting array in second method of local maxima search (shifted arrays)
IntensityThreshold – integer, pixel intensity level above which potential peaks are kept for fitting position procedure. For local maxima method 0 and 1, this level is relative to zero intensity. For local maxima method 2, this level is relative to lowest intensity in the ROI (local background).
Note
Start with high value, because if too high, few peaks are found (only the most important), and if too low, too many local maxima are found leading to time consuming fitting procedure.
- Parameters
thresholdConvolve – integer, pixel intensity level in convolved image above which potential peaks are kept for fitting position procedure. This threshold step on convolved image is applied prior to the local threshold step with IntensityThreshold on initial image (with respect to the local background)
paramsHat – mexican hat kernel parameters (see
LocalMaxima_ndimage())PixelNearRadius – integer, pixel distance between two regions considered as peaks.
Note
Start rather with a large value. If too low, there are very much peaks duplicates and this is very time consuming.
- Parameters
local_maxima_search_method – integer, Select method for find the local maxima, each of them will fitted - 0 extract all pixel above intensity threshold - 1 find pixels are highest than their neighbours in horizontal, vertica and diagonal direction (up to a given pixel distance) - 2 find local hot pixels which after numerical convolution give high intensity above threshold (thresholdConvolve) then threshold (IntensityThreshold) on raw data
peakposition_definition – ‘max’ or ‘center’ for local_maxima_search_method == 2 to assign to the blob position its hottest pixel position or its center (no weight)
Saturation_value_flatpeak – saturation value of detector for local maxima search method 1
Remove_BlackListedPeaks_fromfile –
None
file fullpath, str, to a peaklist file containing peaks that will be deleted in peak list resulting from
the local maxima search procedure (prior to peak refinement) - ndarray of nx2 X Y pixels cooordinates (avoid reading file in peaksearch series)
maxPixelDistanceRejection – maximum distance between black listed peaks and current peaks (found by peak search) to be rejected
NumberMaxofFits – highest acceptable number of local maxima peak to be refined with a 2D modelPeakSearch
fit_peaks_gaussian –
0 no position and shape refinement procedure performed from local maxima (or blob) result
1 2D gaussian peak refinement
2 2D lorentzian peak refinement
xtol – relative error on solution (x vector) see args for leastsq in scipy.optimize
FitPixelDev – largest pixel distance between initial (from local maxima search) and refined peak position
position_definition – due to various conventional habits when reading array, add some offset to fitdata XMAS or fit2d peak search values: - 0 no offset (python numpy convention) - 1 XMAS offset (first pixel is counted as located at 1 instead of 0) - 2 fit2d offset (obsolete)
return_histo –
0 3 output elements
1 4 elemts, last one is histogram of data
2 4 elemts, last one is the nb of raw blob found after convolution and threshold
Data_for_localMaxima –
- object to be used only for initial step of finding local maxima (blobs) search
(and not necessarly for peaks fitting procedure):
ndarray = array data
’auto_background’ = calculate and remove background computed from image data itself (read in file ‘filename’)
path to image file (string) = B image to be used in a mathematical operation with Ato current image
Fit_with_Data_for_localMaxima – use ‘Data_for_localMaxima’ object as image when refining peaks position and shape with initial peak position guess from local maxima search
formulaexpression – string containing A (raw data array image) and B (other data array image) expressing mathematical operation,e.g: ‘A-3.2*B+10000’ for simple background substraction (with B as background data): ‘A-B’ or ‘A-alpha*B’ with alpha > 1.
reject_negative_baseline – True reject refined peak result if intensity baseline (local background) is negative (2D model is maybe not suitable)
outputIpixmax – compute maximal pixel intensity for all peaks found
- Returns
peak list sorted by decreasing (integrated intensity - fitted bkg)
-peak_X,peak_Y,peak_I,peak_fwaxmaj,peak_fwaxmin,peak_inclination,Xdev,Ydev,peak_bkg
- for fit_peaks_gaussian == 0 (no fitdata) and local_maxima_search_method==2 (convolution)
if peakposition_definition =’max’ then X,Y,I are from the hottest pixels if peakposition_definition =’center’ then X,Y are blob center and I the hottest blob pixel
Warning
nb of output elements depends on ‘return_histo’ argument
- LaueTools.readmccd.peaksearch_on_Image(filename_in, pspfile, background_flag='no', blacklistpeaklist=None, dictPeakSearch={}, CCDLabel='MARCCD165', outputfilename=None, psdict_Convolve={'Data_for_localMaxima': 'auto_background', 'FitPixelDev': 2.0, 'IntensityThreshold': 10, 'NumberMaxofFits': 5000, 'PixelNearRadius': 10, 'boxsize': 15, 'fit_peaks_gaussian': 1, 'local_maxima_search_method': 2, 'position_definition': 1, 'removeedge': 2, 'return_histo': 0, 'thresholdConvolve': 500, 'verbose': 0, 'write_execution_time': 0, 'xtol': 0.001}, verbose=0)[source]
Perform a peaksearch by using .psp file
# still not very used and checked? # missing dictPeakSearch as function argument for formulaexpression or dict_param??
- LaueTools.readmccd.savePeakSearchConfigFile(dict_param, outputfilename=None)[source]
save peak search parameters in .psp file
- LaueTools.readmccd.readPeakSearchConfigFile(filename)[source]
read peak search parameters in .psp file
- LaueTools.readmccd.read_background_flag(background_flag, verbose=0)[source]
interpret the background flag (field used in FileSeries/Peak_Search.py)
return two values to put in dict_param of peaksearch_series
- LaueTools.readmccd.peaksearch_fileseries(fileindexrange, filenameprefix='', suffix='', nbdigits=4, dirname_in='/home/micha/LaueProjects/AxelUO2', outputname=None, dirname_out=None, CCDLABEL='MARCCD165', KF_DIRECTION='Z>0', dictPeakSearch=None, verbose=0, writeResultDicts=0, computetime=0)[source]
peaksearch function to be called for multi or single processing
- LaueTools.readmccd.peaksearch_multiprocessing(fileindexrange, filenameprefix, suffix='', nbdigits=4, dirname_in='/home/micha/LaueProjects/AxelUO2', outputname=None, dirname_out=None, CCDLABEL='MARCCD165', KF_DIRECTION='Z>0', dictPeakSearch=None, nb_of_cpu=2, verbose=0, writeResultDicts=0)[source]
launch several processes in parallel
- LaueTools.readmccd.purgePeaksListFile(filename1, blacklisted_XY, dist_tolerance=0.5, dirname=None)[source]
remove in peaklist .dat file peaks that are in blacklist
- Parameters
blacklisted_XY – [X1,Y1],[X2,Y2]
- LaueTools.readmccd.write_PurgedPeakListFile(filename1, blacklisted_XY, outputfilename, dist_tolerance=0.5, dirname=None)[source]
write a new .dat file where peaks in blacklist are omitted
- LaueTools.readmccd.removePeaks_inPeakList(PeakListfilename, BlackListed_PeakListfilename, outputfilename, dist_tolerance=0.5, dirname=None)[source]
read peaks PeakListfilename and remove those in BlackListed_PeakListfilename and write a new peak list file
Note
Not used ??
IOimagefile.py
IOimagefile module is made for reading data contained in binary image file fully or partially.
More tools can be found in LaueTools package at sourceforge.net and gitlab.esrf.fr March 2020
- LaueTools.IOimagefile.stringint(k, n)[source]
returns string of integer k with n zeros padding (by placing zeros before to have n characters)
- Parameters
k – integer to convert
n – nb of digits for zero padding
- Returns
string of length n containing integer k
Example: 1 -> ‘0001’ 15 -> ‘0015’
- LaueTools.IOimagefile.setfilename(imagefilename, imageindex, nbdigits=4, CCDLabel=None, verbose=0)[source]
reconstruct filename string from imagefilename and update filename index with imageindex
- Parameters
imagefilename – filename string (full path or not)
imageindex (string) – index in filename
- Return filename
input filename with index replaced by input imageindex
- Return type
string
- LaueTools.IOimagefile.getIndex_fromfilename(imagefilename, nbdigits=4, CCDLabel=None, stackimageindex=-1, verbose=0)[source]
get integer index from imagefilename string
- Parameters
imagefilename – filename string (full path or not)
- Returns
file index
- LaueTools.IOimagefile.getfilename(dirname, imfileprefix, imfilesuffix=None, numim=None, nbdigits_filename=4)[source]
to get the global file name (name+path) for given components of the name put %4d instead of stringint
- LaueTools.IOimagefile.getwildcardstring(CCDlabel)[source]
return smart wildcard to open binary CCD image file with priority of CCD type of CCDlabel
- LaueTools.IOimagefile.getpixelValue(filename, x, y, ccdtypegeometry='edf')[source]
return pixel value at x,y
Warning
Very old function. To be checked. Use better readpixelvalue in plotdip.py
- LaueTools.IOimagefile.readheader(filename, offset=4096, CCDLabel='MARCCD165')[source]
return header in a raw format
default offset for marccd image
- LaueTools.IOimagefile.read_header_marccd(filename)[source]
return string of parameters found in header in marccd image file .mccd
print allsentences displays the header
use allsentences.split(’n’) to get a list
- LaueTools.IOimagefile.read_header_marccd2(filename)[source]
return string of parameters comments and exposure time found in header in marccd image file .mccd
print allsentences displays the header
use allsentences.split(’n’) to get a list
- LaueTools.IOimagefile.read_header_scmos(filename, verbose=0)[source]
return string of parameters comments and exposure time found in header in scmis image file .tif
print allsentences displays the header
use allsentences.split(’n’) to get a list
- LaueTools.IOimagefile.read_motorsposition_fromheader(filename, CCDLabel='MARCCD165')[source]
return xyzpositions, expo_time from image file header available for “MARCCD165”, “sCMOS”, “sCMOS_fliplr”
- LaueTools.IOimagefile.readoneimage_full(filename, frametype='mccd', dirname=None)[source]
too SLOW! reads 1 entire image (marCCD format) :return: PILimage, image object of PIL module (16 bits integer) and arrayofdata: 2D array of intensity #TODO: manage framedim like readoneimage() just below
- LaueTools.IOimagefile.readCCDimage(filename, CCDLabel='MARCCD165', dirname=None, stackimageindex=-1, verbose=0)[source]
general function to read raw data binary (Laue pattern) image file recorder on 2D detector.
Read raw data binary image file and return pixel intensity 2D array such as to fit the data (2theta, chi) scattering angles representation convention.
- Parameters
filename (str) – path to image file (fullpath if ` dirname` =None)
CCDLabel (str, optional) – label, defaults to “MARCCD165”
dirname (str, optional) – folder path, defaults to None
stackimageindex (int, optional) – index of images bunch, defaults to -1
verbose (int, optional) – 0 or 1, defaults to 0
- Raises
ValueError – if data format and CCD parameters from label are not compatible
- Returns
dataimage, 2D array image data pixel intensity properly oriented
framedim, iterable of 2 integers shape of dataimage
fliprot : string, key for CCD frame transform to orient image
- Return type
tuple of 3 elements
- LaueTools.IOimagefile.readoneimage(filename, framedim=(2048, 2048), dirname=None, offset=4096, formatdata='uint16')[source]
crude way to open binary image. it returns a 1d array of integers from a binary image file (full data)
- Parameters
filename (str) – image file name (full path if dirname=0)
framedim (tuple of 2 integers, optional) – detector dimensions, defaults to (2048, 2048)
dirname (str, optional) – folder path, defaults to None
offset (int, optional) – file header in byte (octet), defaults to 4096
formatdata (str, optional) – numpy format of raw binary image pixel value, defaults to “uint16”
- Returns
dataimage : image data pixel intensity
- Return type
1D array
- LaueTools.IOimagefile.readoneimage_band(filename, framedim=(2048, 2048), dirname=None, offset=4096, line_startindex=0, line_finalindex=2047, formatdata='uint16')[source]
returns a 1d array of integers from a binary image file. Data located in band according shape of data (framedim)
- Parameters
filename – string path to image file (fullpath if `dirname`=None)
offset – integer nb of file header bytes
framedim – iterable of 2 integers shape of expected 2D data
formatdata – string key for numpy dtype to decode binary file
- Returns
dataimage, 1D array, image data pixel intensity
- LaueTools.IOimagefile.readoneimage_crop_fast(filename, dirname=None, CCDLabel='MARCCD165', firstElemIndex=0, lastElemIndex=2047, verbose=0)[source]
Returns a 2d array of integers from a binary image file. Data are taken only from a rectangle
with respect to firstElemIndex and lastElemIndex.
- Parameters
filename – string, path to image file (fullpath if ` dirname`=None)
offset – integer, nb of file header bytes
framedim – iterable of 2 integers, shape of expected 2D data
formatdata – string, key for numpy dtype to decode binary file
- Returns
dataimage : 1D array image data pixel intensity
- LaueTools.IOimagefile.readrectangle_in_image(filename, pixx, pixy, halfboxx, halfboxy, dirname=None, CCDLabel='MARCCD165', verbose=0)[source]
returns a 2d array of integers from a binary image file. Data are taken only from a rectangle centered on pixx, pixy
- Returns
dataimage : 2D array, image data pixel intensity
- LaueTools.IOimagefile.readoneimage_crop(filename, center, halfboxsize, CCDLabel='PRINCETON', dirname=None)[source]
return a cropped array of data read in an image file
- Parameters
filename – string, path to image file (fullpath if ` dirname`=None)
center – iterable of 2 integers, (x,y) pixel coordinates
halfboxsize – integer or iterable of 2 integers, ROI half size in both directions
- Returns
dataimage : 1D array, image data pixel intensity
Todo
useless?
- LaueTools.IOimagefile.readoneimage_manycrops(filename, centers, boxsize, stackimageindex=-1, CCDLabel='MARCCD165', addImax=False, use_data_corrected=None, verbose=0)[source]
reads 1 image and extract many regions centered on center_pixel with xyboxsize dimensions in pixel unit
- Parameters
filename – string,fullpath to image file
centers – list or array of [int,int] centers (x,y) pixel coordinates
use_data_corrected – enter data instead of reading data from file must be a tuple of 3 elements: fulldata, framedim, fliprot where fulldata is a numpy.ndarray as output by
readCCDimage()boxsize – iterable 2 elements or integer boxsizes [in x, in y] direction or integer to set a square ROI
- Returns
Data, list of 2D array pixel intensity or Data and Imax
- LaueTools.IOimagefile.writeimage(outputname, _header, data, dataformat=<class 'numpy.uint16'>, verbose=0)[source]
from data 1d array of integers with header coming from a f.open(‘imagefile’); f.read(headersize);f.close() .. warning:: header contain dimensions for subsequent data. Check before the compatibility of data with header infos(nb of byte per pixel and array dimensions
- LaueTools.IOimagefile.write_rawbinary(outputname, data, dataformat=<class 'numpy.uint16'>, verbose=0)[source]
write a binary file without header of a 2D array
used ?
- LaueTools.IOimagefile.SumImages(prefixname, suffixname, ind_start, ind_end, dirname=None, plot=0, output_filename=None, CCDLabel=None, nbdigits=0)[source]
sum images and write image with 32 bits per pixel format (4 bytes)
used?
- LaueTools.IOimagefile.Add_Images2(prefixname, ind_start, ind_end, plot=0, writefilename=None, CCDLabel='MARCCD165', average=True)[source]
in dev
- LaueTools.IOimagefile.Add_Images(prefixname, ind_start, ind_end, plot=0, writefilename=None)[source]
Add continuous sequence of images
Note
Add_Images2 exists
- Parameters
prefixname – string, prefix common part of name of files
ind_start – int, starting image index
ind_end – int, final image index
writefilename – string, new image filename where to write datastart (with last image file header read)
- Returns
datastart, array accumulation of 2D data from each image
imageprocessing.py
imageprocessing module is made to modify filter data array
More tools can be found in LaueTools package at sourceforge.net and gitlab.esrf.fr March 2020
- LaueTools.imageprocessing.getindices2cropArray(center, halfboxsizeROI, arrayshape, flipxycenter=False)[source]
return array indices limits to crop array data
- Parameters
center – iterable of 2 elements (x,y) pixel center of the ROI
halfboxsizeROI – integer or iterable of 2 elements half boxsize ROI in two dimensions
arrayshape – iterable of 2 integers maximal number of pixels in both directions
flipxycenter – boolean True: swap x and y of center with respect to others parameters that remain fixed
- Returns
imin, imax, jmin, jmax : 4 integers 4 indices allowing to slice a 2D np.ndarray
Todo
merge with check_array_indices()
- LaueTools.imageprocessing.check_array_indices(imin, imax, jmin, jmax, framedim=None)[source]
Return 4 indices for array slice compatible with framedim
- Parameters
jmax (imin, imax, jmin,) – 4 integers mini. and maxi. indices in both directions
framedim – iterable of 2 integers shape of the array to be sliced by means of the 4 indices
- Returns
imin, imax, jmin, jmax: 4 integers mini. and maxi. indices in both directions
Todo
merge with getindices2cropArray()
- LaueTools.imageprocessing.to8bits(PILimage, normalization_value=None)[source]
convert PIL image (16 bits) in 8 bits PIL image
- Returns
[0] 8 bits image
[1] corresponding pixels value array
Todo
since not used, may be deleted
- LaueTools.imageprocessing.diff_pix(pix, array_pix, radius=1)[source]
returns index in array_pix which is the closest to pix if below the tolerance radius
array_pix: array of 2d pixel points pix: one 2elements pixel point
- LaueTools.imageprocessing.minmax(D_array, center, boxsize, framedim=(2048, 2048), withmaxpos=False)[source]
extract min and max from a 2d array in a ROI
Obsolete? Still used in LocalMaxima_ShiftArrays()
Parameters D_array : 2D array
data array
- centeriterable of 2 integers
(x,y) pixel center
- boxsizeinteger or iterable of 2 integers
full boxsize defined in both directions
- framedimiterable of 2 integers
shape of D_array
Return [min, max]: minimium and maximum pixel internsity in ROI [min, max],absolute_max_pos : if withmaxpos is True add in output
the absolute position of the largest pixel
#TODO: replace by scipy.ndimage.extrema # see next functions below # framedim = from dictionary of CCDs D_array shape is flip(framedim)
- LaueTools.imageprocessing.getExtrema(data2d, center, boxsize, framedim, ROIcoords=0, flipxycenter=True, verbose=0)[source]
return min max XYposmin, XYposmax values in ROI
- Parameters
ROIcoords – 1 in local array indices coordinates 0 in X,Y pixel CCD coordinates
flipxycenter – boolean like swap input center coordinates
data2d – 2D array data array as read by
readCCDimage()
- Returns
min, max, XYposmin, XYposmax: - min : minimum pixel intensity - max : maximum pixel intensity - XYposmin : list of absolute pixel coordinates of lowest pixel - XYposmax : list of absolute pixel coordinates of largest pixel
- LaueTools.imageprocessing.getIntegratedIntensity(data2d, center, boxsize, framedim, thresholdlevel=0.2, flipxycenter=True)[source]
return crude estimate of integrated intensity of peak above a given relative threshold
- Parameters
ROIcoords – 1 in local array indices coordinates 0 in X,Y pixel CCD coordinates
flipxycenter – boolean like swap input center coordinates
data2d – 2D array data array as read by
readCCDimage()Thresholdlevel – relative level above which pixel intensity must be taken into account I(p)- minimum> Thresholdlevel* (maximum-minimum)
- Returns
integrated intensity, minimum absolute intensity, nbpixels used for the summation
- LaueTools.imageprocessing.getMinMax(data2d, center, boxsize, framedim)[source]
return min and max values in ROI
Parameters:
- data2d2D array
array as read by readCCDimage
- LaueTools.imageprocessing.minmax_fast(D_array, centers, boxsize=(25, 25))[source]
extract min (considered as background in boxsize) and intensity at center from a 2d array at different places (centers)
centers is tuple a two array ( array([slow indices]), array([fast indices]))
return:
[0] background values [1] intensity value
used?
- LaueTools.imageprocessing.normalize_shape(shape)[source]
return shape in case a scalar was given: return (shape,)
- LaueTools.imageprocessing.LoG(r, sigma=None, dim=1, r0=None, peakVal=None)[source]
note: returns negative Laplacian-of-Gaussian (aka. mexican hat) zero-point will be at sqrt(dim)*sigma integral is _always_ 0 if peakVal is None: uses “mathematical” “gaussian derived” norm if r0 is not None: specify radius of zero-point (IGNORE sigma !!)
- LaueTools.imageprocessing.LoGArr(shape=(256, 256), r0=None, sigma=None, peakVal=None, orig=None, wrap=0, dtype=<class 'numpy.float32'>)[source]
returns n-dim Laplacian-of-Gaussian (aka. mexican hat) if peakVal is not None
result max is peakVal
if r0 is not None: specify radius of zero-point (IGNORE sigma !!)
credits: “Sebastian Haase <haase@msg.ucsf.edu>”
- LaueTools.imageprocessing.radialArr(shape, func, orig=None, wrap=False, dtype=<class 'numpy.float32'>)[source]
generates and returns radially symmetric function sampled in volume(image) of shape shape if orig is None the origin defaults to the center func is a 1D function with 1 paramater: r
if shape is a scalar uses implicitely (shape,) wrap tells if functions is continued wrapping around image boundaries wrap can be True or False or a tuple same length as shape:
then wrap is given for each axis sperately
- LaueTools.imageprocessing.LocalMaxima_ndimage(Data, peakVal=4, boxsize=5, central_radius=2, threshold=1000, connectivity=1, returnfloatmeanpos=0, autothresholdpercentage=None)[source]
returns (float) i,j positions in array of each blob (peak, spot, assembly of hot pixels or whatever)
Note
used only in LocalMaxima_KernelConvolution
inputs
- peakVal, boxsize, central_radius:
parameters for numerical convolution with a mexican-hat-like kernel
- threshold:
intensity threshold of filtered Data (by convolution with the kernel) above which blob signal will be considered if = 0 : take all blobs at the expense of processing time
- connectivity :
1 for filled square 3*3 connectivity 0 for 3*3 star like connectivity
- autothresholdpercentage :
threshold in filtered image with respect to the maximum intensity in filtered image
output: array (n,2): array of 2 indices
- LaueTools.imageprocessing.ConvolvebyKernel(Data, peakVal=4, boxsize=5, central_radius=2)[source]
Convolve Data array witn mexican-hat kernel
inputs: Data : 2D array containing pixel intensities peakVal > central_radius : defines pixel distance from box center where weights are positive
(in the middle) and negative farther to converge back to zero
boxsize : size of the box
ouput: array (same shape as Data)
- LaueTools.imageprocessing.LocalMaxima_KernelConvolution(Data, framedim=(2048, 2048), peakValConvolve=4, boxsizeConvolve=5, central_radiusConvolve=2, thresholdConvolve=1000, connectivity=1, IntensityThreshold=500, boxsize_for_probing_minimal_value_background=30, return_nb_raw_blobs=0, peakposition_definition='max')[source]
return local maxima (blobs) position and amplitude in Data by using convolution with a mexican hat like kernel.
- Two Thresholds are used sequently:
thresholdConvolve : level under which intensity of kernel-convolved array is discarded
IntensityThreshold : level under which blob whose local intensity amplitude in raw array is discarded
- Parameters
Data – 2D array containing pixel intensities
central_radiusConvolve (peakValConvolve, boxsizeConvolve,) – convolution kernel parameters
thresholdConvolve – minimum threshold (expressed in unit of convolved array intensity) under which convoluted blob is rejected.It can be zero (all blobs are accepted but time consuming)
connectivity –
shape of connectivity pattern to consider pixels belonging to the same blob.
1: filled square (1 pixel connected to 8 neighbours)
0: star (4 neighbours in vertical and horizontal direction)
IntensityThreshold – minimum local blob amplitude to accept
boxsize_for_probing_minimal_value_background – boxsize to evaluate the background and the blob amplitude
peakposition_definition – string (‘max’ or ‘center’) key to assign to the blob position its hottest pixel position or its center (no weight)
- Returns
- peakslistarray like (n,2)
list of peaks position (pixel)
- Ipixmaxarray like (n,1) of integer
list of highest pixel intensity in the vicinity of each peak
- npeaksinteger
nb of peaks (if return_nb_raw_blobs =1)
- LaueTools.imageprocessing.LocalMaxima_ShiftArrays(Data, framedim=(2048, 2048), IntensityThreshold=500, Saturation_value=65535, boxsize_for_probing_minimal_value_background=30, nb_of_shift=25, pixeldistance_remove_duplicates=25, verbose=0)[source]
blob search or local maxima search by shift array method (kind of derivative)
Warning
Flat peak (= two neighbouring pixel with rigourouslty the same intensity) is not detected
- LaueTools.imageprocessing.shiftarrays_accum(Data_array, n, dimensions=1, diags=0)[source]
idem than shiftarrays() but with all intermediate shifted arrays 1D returns 3 arrays corresponding to shifted arrays by n in two directions and original one 2D returns 5 arrays corresponding to shifted arrays by n in two directions and original one
these arrays are ready for comparison with eg np.greater
Data_array must have shape (slowdim,fastdim) so that slowdim-2*n>=1 and fastdim-2*n>=1 (ie central array with zero shift has some elements)
TODO: replace append by a pre allocated array
Note
readmccd.localmaxima is better
- LaueTools.imageprocessing.LocalMaxima_from_thresholdarray(Data, IntensityThreshold=400, rois=None, framedim=None, verbose=False, outputIpixmax=False)[source]
return center of mass of each blobs composes by pixels above IntensityThreshold
if Centers = list of (x,y, halfboxsizex, halfboxsizey) perform only blob search in theses ROIs
Warning
center of mass of blob where all intensities are set to 1
- LaueTools.imageprocessing.localmaxima(DataArray, n, diags=1, verbose=0)[source]
from DataArray 2D returns (array([i1,i2,…,ip]),array([j1,j2,…,jp])) of indices where pixels value is higher in two direction up to n pixels
this tuple can be easily used after in the following manner: DataArray[tupleresult] is an array of the intensity of the hottest pixels in array
in similar way with only four cardinal directions neighbouring (found in the web): import numpy as N def local_minima(array2d):
- return ((array2d <= np.roll(array2d, 1, 0)) &
(array2d <= np.roll(array2d, -1, 0)) & (array2d <= np.roll(array2d, 1, 1)) & (array2d <= np.roll(array2d, -1, 1)))
WARNING: flat top peak are not detected !!
- LaueTools.imageprocessing.gauss_kern(size, sizey=None)[source]
Returns a normalized 2D gauss kernel array for convolutions
- LaueTools.imageprocessing.blur_image(im, n, ny=None)[source]
- blurs the image by convolving with a gaussian kernel of typical
size n. The optional keyword argument ny allows for a different size in the y direction.
- LaueTools.imageprocessing.circularMask(center, radius, arrayshape)[source]
return a boolean ndarray of elem in array inside a mask
- LaueTools.imageprocessing.compute_autobackground_image(dataimage, boxsizefilter=10)[source]
return 2D array of filtered data array :param dataimage: array of image data :type dataimage: 2D array
- LaueTools.imageprocessing.computefilteredimage(dataimage, bkg_image, CCDlabel, kernelsize=5, formulaexpression='A-B', usemask=True, verbose=0)[source]
return 2D array of initial image data without background given by bkg_image data
- usemaskTrue then substract bkg image on masked raw data
False apply formula on all pixels (no mask)
- Parameters
dataimage (2D array) – array of image data
bkg_image (2D array) – array of filtered image data (background)
CCDlabel (string) – key for CCD dictionary
- LaueTools.imageprocessing.filterimage(image_array, framedim, blurredimage=None, kernelsize=5, mask_parameters=None, clipvalues=None, imageformat=<class 'numpy.uint16'>)[source]
compute a difference of images inside a region defined by a mask
- Parameters
blurredimage – ndarray image to substract to image_array
kernelsize – pixel size of gaussian kernel if blurredimage is None
mask_parameters – circular mask parameter: center=(x,y), radius, value outside mask
- LaueTools.imageprocessing.rebin2Darray(inputarray, bin_dims=(2, 2), operator='mean')[source]
rebin 2D array by applying an operator to define the value of one element from the other
- Parameters
operator – mean, min, max, sum
bin_dims – side sizes of binning. (2,3) means 2X3
- LaueTools.imageprocessing.blurCCD_with_binning(im, n, binsize=(2, 2))[source]
blur the array by rebinning before and after aplying the filter
- LaueTools.imageprocessing.filter_minimum(im, boxsize=10)[source]
return filtered image using minimum filter
- LaueTools.imageprocessing.remove_minimum_background(im, boxsize=10)[source]
remove to image array the array resulting from minimum_filter
- LaueTools.imageprocessing.plot_image_markers(image, markerpos, position_definition=1)[source]
plot 2D array (image) with markers at first two columns of (markerpos)
Note
used in LaueHDF5. Could be better implementation in some notebooks
- LaueTools.imageprocessing.applyformula_on_images(A, B, formulaexpression='A-B', SaturationLevel=None, clipintensities=True)[source]
calculate image data array from math expression
- Parameters
B (A,) – ndarray of the same shape
SaturationLevel – saturation level of intensity
clipintensities – clip resulting intensities to zero and saturation value