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Manual for ModelScanning3DXRD.py v. 1.0 -a casual guide for a general use case Axel Henningsson This is a casual manual to use and understand the code for ModelScanning3DXRD.py. University of Lund Sweden November 16, 2018 November 16, 2018- LTH, Lund University N. A. Henningsson Background The central ideas of Scanning-3DXRD is described in Hayashi et al. (2015) and Hayashi et al. (2017). The goal of the here provided algorithm is to forward model a diffraction pattern based on a per-voxel definition of the sample. This means that the sample is built up from a number of cubic voxels with side equal to the beam size. Each voxel can carry position, orientation and strain. Currently, the main use case for the forward model is to evaluate the accuracy of various reconstruction techniques. The model that exists in this repository is called ModelScanning3DXRD and was derived from PolyXSim which is documented at sourceforge.net/p/fable/wiki/PolyXSim/. The documentation for PolyXSim applies in general to ModelScanning3DXRD. In general, the reader will have to know her or his way with python, Linux, the general theory of 3DXRD and file formats used for 3DXRD at the ESRF. The code is, and was, developed for scientific use and is not the most user friendly so to speak. This means that apart from reading this handy dandy manual, you will most likely end up doing some trial and error runs. ModelScanning3DXRD has been used , by the author, with success as described in Henningsson (2018). In the thesis the reader may find more details regarding the background and use case. The thesis can be found at git-hub (https://github.com/FABLE-3DXRD/ S3DXRD) at the same location where this document can be found. If not, it is intended to be included in the repository as soon as possible. Getting Started To forward model a diffraction pattern the user must supply an input file to ModelScanning3DXRD.py. The input file is a text file that defines the sample and experimental setup. The format of the input file is mostly described at (sourceforge.net/p/fable/ wiki/PolyXSim%20-%20input/). The main differences are that the keyword "voxel" is used instead of "grain" and that the user must supply a map that maps the voxels together and defines the grains. The reader should be aware of that not all (read: quite few) features originally included in PolyXSim has been tested in ModelScanning3DXRD, and thus you are expected to encounter bugs moving away from the standard use cases described in the example below. In order to run ModelScanning3DXRD the user must set up their environment in the same fashion as is done for ImageD11, PolyXSim, etc. Documentation for this can be found at the git-hub page. After this is done the user must also install xfab, ImageD11 and FitAllB. Potentially only xfab is needed but you might as well get all the goodness while your at it. Assuming that you have a conda version of python installed (https://conda.io/docs/ user-guide/install/linux.html) the following series of commands will hopefully work (tested with a Ubuntu distribution). sudo apt-get install git-core conda create -n fable python=2.7.15 source activate fable conda install numpy scipy pillow matplotlib ipython scikit-image h5py pip install fabio --no-deps pip install cython pip install pmw pip install pyopengltk cd .. mkdir fable cd fable/ git clone http://github.com/FABLE-3DXRD/xfab git clone https://github.com/FABLE-3DXRD/FitAllB.git 1 November 16, 2018- LTH, Lund University N. A. Henningsson git clone http://github.com/FABLE-3DXRD/PolyXSim git clone http://github.com/FABLE-3DXRD/ImageD11 git clone http://github.com/FABLE-3DXRD/fabian git clone https://github.com/FABLE-3DXRD/S3DXRD.git cd xfab python setup.py build install cd ../FitAllB/ python setup.py build install cd ../PolyXSim/ python setup.py build install cd ../fabian/ python setup.py build install cd ../ImageD11/ python setup.py build install cd ../S3DXRD/ModelScanning3DXRD/ python setup.py build install You could try and run the tests.py file located in S3DXRD/ModelScanning3DXRD/test_ voxelated/ to see if everything is healthy. python tests.py The test will take approximately 60 seconds to run and should output all OK for three tests at the end. Example We will now simulate the diffraction pattern of a single grain located at the origin. The grain is composed of five by five voxels which all have the same orientation. The strain is varied to simulate a gradient across the grain. The input file always defines a slice in the sample, and to simulate a three dimensional structure several input files have to be produced and simulations run for each of them. The first thing to put in the input file is information of how we want to save our data direc ’Tin’ stem ’Sn_diffrac’ make_image 0 output ’.flt’ ’.gve’ ’.par’ ’.ubi’ This will save the output in a folder named ’Tin’ with the base file name ’Sn_diffrac’. Files of the types ’.flt’’.gve’’.par’’.ubi’ will be saved. We may now specify the nature of the studied material via a .cif file as structure_phase_0 ’’ For more information on crystallographic input files the reader is refereed to google :). Also the reader may find one or two examples of crystallographic input files in the repository for reference. They are basically text files that specify unit cell parameters atomic type etc. Now it is time to specify the experimental setup. For information on the below keywords go to the PolyXSim documentation at (https://sourceforge.net/p/fable/ wiki/PolyXSim%20-%20input/) y_size 0.0500 z_size 0.0500 dety_size 2048 detz_size 2048 distance 162.888383321 tilt_x -0.00260053595757 tilt_y -0.00366923010272 tilt_z 0.00463564130438 2 November 16, 2018- LTH, Lund University N. A. Henningsson o11 1 o12 0 o21 0 o22 -1 noise 0 intensity_const 1 lorentz_apply 1 beampol_apply 1 peakshape 0 wavelength 0.21878 beamflux 1e-12 beampol_factor 1 beampol_direct 0 dety_center 1048.20100238 detz_center 1041.55049636 omega_start 0 omega_end 180 omega_step 1.0 omega_sign 1 wedge -0.0142439915706 gen_size 1 -0.001240713 0.00000100.000000000 sample_xyz 0.006000.006000.003000000 gen_U 0 We also specify the width of the beam as beam_width 0.001000000 This means that the beam has a square cross section with the side of 0.001 mm. Now we must define the sample on a per-voxel basis. This is done by specifying each voxel position and state as: pos_voxels_0 -0.00200-0.002000.000000000 U_voxels_0 1.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 1.000 eps_voxels_0 -0.0200 0.000 0.000 0.000 0.000 0.000 After listing all the voxels from zero to the total number of voxels we specify the total number of voxels as no_voxels 25 Then we map each voxel to a grain by inputting the dictionary as voxel_grain_map {0:0,1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:0,9:0,10:0,11:0,1 2:0,13:0,14:0,15:0,16:0,17:0,18:0,19:0,20:0,21:0,22:0,23:0,24:0} the format is here: {voxel_number:grain_number, voxel_number:grain_number, voxel_number:grain_number,...} Such that the above example maps all 25 voxels to a single grain labelled with 0. We may now run the forward model with the input file as python ModelScanning3DXRD.py --input This will run the simulation and printout the progress to the active terminal window. You should see something like 3 November 16, 2018- LTH, Lund University N. A. Henningsson As the simulation finished it will save any output files to the directory specified in the input file. The files are of the same format as those which can be retrived at beamline ID11 at the ESRF. After deploying some preferred reconstruction algorithms it is possible to receive geometries that reassembles the input sample state. The below reconstruction was retrieved with FBP and a per-voxel local refinement. Extra Features For large samples it is recommend to use the "parallel" keyword as an additional input to ModelScanning3DXRD. python ModelScanning3DXRD.py --input --parallel ModelScanning3DXRD will then start several parallel threads for computing the diffraction patterns. In general, for any sample with say 100+ voxels, it is worth while to use the parallel keyword. You will see which voxels are treated by which thread ModelScanning3DXRD also has a profiler mode for anyone who wants to try and make the algorithm run faster. You can run in profiler mode as python ModelScanning3DXRD.py --input --profile This will run the algorithm and save a file with statistics. The file will look something like this 2030420 function calls in 6.512 seconds Ordered by: cumulative time 4 November 16, 2018- LTH, Lund University ncalls 1 36450 82182 35800 689978 416389 tottime 1.361 1.395 0.832 0.746 0.806 0.597 percall 1.361 0.000 0.000 0.000 0.000 0.000 cumtime 6.512 1.917 1.396 0.970 0.806 0.597 percall 6.512 0.000 0.000 0.000 0.000 0.000 N. A. Henningsson filename:lineno(function) find_refl.py:69(run) find_refl.py:350(find_omega_general) find_refl.py:422(det_coor) find_refl.py:447(calc_int_mod) {numpy.core.multiarray.dot} {numpy.core.multiarray.array} The relevant colon is here the cumtime, which states how much time was spent executing respective function in total. Pitfalls/Limitations/Bugs Like mentioned earlier, not all of the extensions used in PolyXSim has been tested with the modified code. The model used at the writing time (November 16, 2018) is fairly simple. Especially the calculation of the intensity is heavily simplified, basically the structure factors and voxel beam overlap is used. Future versions should take more physics into account. Another simplification is the fact that peaks are assumed to never overlap and that the detector is assumed to have an infinite resolution. This means that the .flt file gets huge since what would normally be a single peak might be many many many delta peaks clustered together. Work is currently put into taking detector pixel size into account and computing peaks as centre of mass of continuous regions of intensity. Hopefully this feature will arrive in a near future. If the .flt files get to big, the reader might want to throw some python code together to parse and split the .flt into several files in order to be a able to read them into matrix format without running out of RAM. In general more details on the used assumptions and how the algorithm can be used is found in the thesis (Henningsson 2018). The thesis will be made available at the git-hub page as soon as possible. Most likely it will be up before the end of January 2019. Finally a general disclaimer is announced, as the user may very well find flaws (and hopefully will) and bugs throughout the code. Together we can build something that keeps getting better and better, both from a physics perspective, discussing how these models should be built, as well as from a coding perspective, writing better and more solid code together. Develop and Adapt In reality any one who reads this will probably need to modify the code for their specific scientific use. To run the altered code you will need to first run the python setup.py build command, followed by the python setup.py install command. This make sure that the python interpreter is importing the updated modules and not uses the old ones. Mostly it is the find_refl.py script that contains the diffraction model algorithm while the other scripts deal with file handling, parallel runs, input parsing etc. If you would like to improve the model/adapt it this is most likely the place to add some code. 5 November 16, 2018- LTH, Lund University N. A. Henningsson References Hayashi, Y., Hirose, Y., and Seno, Y. (2015). Polycrystal orientation mapping using scanning three-dimensional X-ray diffraction microscopy. Journal of Applied Crystallography, 48(4):1094–1101. Hayashi, Y., Setoyama, D., and Seno, Y. (2017). Scanning three-dimensional x-ray diffraction microscopy with a high-energy microbeam at spring-8. Materials Science Forum, 905:157–164. Henningsson, N. A. (2018). 3dxrd reconstructions for intragranular resolution - a forward model for scanning-3dxr. 6
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