
Basics of Laue Pattern peak search and Unit cell Refinement
===========================================================

This Notebook is a part of Tutorials on LaueTools Suite.
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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)

.. code:: ipython3

    LaueToolsCode_Folder = '..'
    import sys,os
    abspathLaueTools =os.path.abspath(LaueToolsCode_Folder)
    print('abspathLaueTools',abspathLaueTools)
    sys.path.append(LaueToolsCode_Folder)


.. parsed-literal::

    abspathLaueTools /home/micha/LaueToolsPy3/LaueTools


.. code:: ipython3

    import LaueTools
    LaueTools.__file__




.. parsed-literal::

    '/home/micha/LaueToolsPy3/LaueTools/__init__.py'



.. code:: ipython3

    #%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



.. parsed-literal::

    LaueToolsProjectFolder /home/micha/LaueToolsPy3/LaueTools


.. parsed-literal::

    /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


.. parsed-literal::

    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)

.. code:: ipython3

    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

.. code:: ipython3

    %%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``

.. code:: ipython3

    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)


.. parsed-literal::

    Displaying Ge_blanc_0000.mccd
    
    nb elements 4194304
    framedim (2048, 2048)
    framedim nb of elements 4194304



.. parsed-literal::

    <IPython.core.display.Javascript object>



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" width="400">




.. parsed-literal::

    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``.

.. code:: ipython3

    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)


.. parsed-literal::

    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)

.. code:: ipython3

    peaklist=res[0]
    print('Digital Spots properties for the 5 most intense spots')
    print(peaklist[:6])


.. parsed-literal::

    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]]


.. code:: ipython3

    print('X, Y pixel refinement positions for the first 5 spots')
    peaklist[:5,:2]


.. parsed-literal::

    X, Y pixel refinement positions for the first 5 spots




.. parsed-literal::

    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*

.. code:: ipython3

    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)



.. parsed-literal::

    <IPython.core.display.Javascript object>



.. raw:: html

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" width="960">


**List of peaks props is written in a file with extension .dat, here the
variable is ``datfilename``**

.. code:: ipython3

    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'


.. parsed-literal::

    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

.. code:: ipython3

    # 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'


.. parsed-literal::

    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 ...)

.. code:: ipython3

    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)


.. parsed-literal::

    Entering CrystalParameters ******---***************************
    
    
    nb of spots and columns in .dat file (82, 3)
    file :../LaueImages/Ge_blanc_0000Notebook.dat
    containing 82 peaks
    (2theta chi X Y I) written in ../LaueImages/Ge_blanc_0000Notebook.cor


create instance of an objet spotsset class
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: ipython3

    import LaueTools.indexingSpotsSet as ISS
    DataSet = ISS.spotsset()
    
    DataSet.importdatafromfile(fullpathcorfile)


.. parsed-literal::

    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}




.. parsed-literal::

    True



.. code:: ipython3

    DataSet.getUnIndexedSpotsallData()[:3]




.. parsed-literal::

    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)

.. code:: ipython3

    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**

.. code:: ipython3

    #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
    ...

.. code:: ipython3

    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


.. parsed-literal::

    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


.. code:: ipython3

    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])


.. parsed-literal::

    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

.. code:: ipython3

    DataSet.B0matrix




.. parsed-literal::

    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]])


