https://multigrain.texture.rocks/ 2026-04-15T08:07:46+00:00 https://multigrain.texture.rocks/ https://multigrain.texture.rocks/lib/exe/fetch.php?media=wiki:logo.png text/html 2023-12-09T22:18:42+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:advanced_better_crystallography&rev=1702160322&do=diff Advanced indexing: better crystal structure In the previous section, we described how to perform grain indexing with GrainSpotter or grain indexing with ImageD11. After some time, you might realize that you are not satisfied with the results and need more advanced tuning of the process. Crystal structure As this point, you probably text/html 2023-12-09T22:30:51+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:advanced_indexing_with_grainspotter&rev=1702161051&do=diff Advanced GrainSpotter indexing: indexing loops You probably noticed that, after some time, GrainSpotter does not find any new grains. At this point, you should * use the TIMEleSS tool to remove the G-vectors which have been assigned, * re-run GrainSpotter with the same of less restrictive tolerances, text/html 2023-12-09T22:33:04+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:advanced_tricks&rev=1702161184&do=diff Advanced (and not so advanced) tricks, in random order If your dataset is complex, here is a list of tricks to get it indexed. This page is being improved over time. Trick 1: Plot intensity vs. 2theta from peak histogram in ImageD11 from ImageD11 import columnfile c = columnfile.columnfile('peaks_t100.flt') % if 'expection: problem interpreting your colfile' check the extention of your file. Should be .flt c.titles % to see what variables you have in your columnfile c.parameters.loadpara… text/html 2019-09-04T16:40:33+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:backgrounddac&rev=1567615233&do=diff Evaluate background One necessary step of the data processing is the subtraction of the background. One way to do it is to create an average or a median image of the whole file series and then subtract each single image by this average/median. This method has the advantage that you not only get rid of the background noise but also all those reflections which are visible homogeneously in every image, such as pressure media, artefacts on the detector or some diamond spots. text/html 2019-09-05T12:55:56+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:backgroundgeneral&rev=1567688156&do=diff Background evaluation and subtraction Diffraction data typically include multiple contributions. These include * noise, from the detector, the source, etc * diffraction from anything within the path of the x-ray beam, which is not your sample, text/html 2019-09-03T16:13:48+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:clean-diamond-anvil-cell-data&rev=1567527228&do=diff Clean up diamond anvil cell data Typical diamond anvil cell data for multigrain XRD has to be pre-processed and cleaned. Elements that should be addressed are: * shadows from the tungsten carbide diamond seats, * single-crystal diffraction spots from the diamond anvils, text/html 2019-12-03T13:39:59+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:compare-grain-orientations&rev=1575380399&do=diff Compare grain orientations Sometimes, you might want to compare the orientations of grains between two collections, to make sure that they are the same, for instance. A typical case when you perform this operation is during training. If you generated a synthetic dataset and indexed this data, you will want to make sure that the grains you indexed actually existed at start. text/html 2023-12-10T11:11:43+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:complex-multi-phase&rev=1702206703&do=diff In the works: indexing a complex dataset with many phases When you have sample with multiple phases, your list of extracted experimental g-gvectors (the bottom section of your GVE file, see example at the link) includes vectors that could originate from all phases in your sample. text/html 2023-03-14T14:29:39+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:compute_gvectors&rev=1678804179&do=diff Compute G-vectors G-vectors are the scattering vector associated with the diffraction. The are nodes of the reciprocal space or, in direct space, normal to the diffracting plane. For a given reflection in the crystal, Ghkl is perpendicular to the diffracting plane (hkl) and its norm is 2π/d text/html 2019-06-04T13:32:30+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:extracteulerangles&rev=1559655150&do=diff ExtractEulerAngles.py The script timelessExtractEulerAngles.py is part of the TIMEleSS tools. It converts results from GrainSpotter to a format which MTeX can read. Therefore, it extracts the Euler angles (phi1, Phi, phi2) from the .log file created by GrainSpotter and writes them into a text file. text/html 2019-07-22T15:37:14+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:extractgvegrain&rev=1563809834&do=diff Extract the peaks for a given grain This tool is useful if you want to see which peak is assigned to grains or a given grain. You can use it once you have run an indexing. The input files are * the output file from the indexing (with GrainSpotter at present, the code could be improved to also deal with text/html 2020-11-10T08:37:41+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:fixgsoutput&rev=1604997461&do=diff Fix bogus GrainSpotter output files Description The script timelessFixGSOutput is part of the TIMEleSS tools. Sometimes, GrainSpotter does not work properly and produces grains with 0 assigned peaks. This utility can be used to generate a new output file with those bogus grains removed. text/html 2020-11-10T08:38:48+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:grainspotter-merge&rev=1604997528&do=diff Merge GrainSpotter output files If you run GrainSpotter multiple times on the same dataset, you will want to merge the different output files into one and end up with a single collection of unique grains. This operation is performed using the timelessGrainSpotterMerge text/html 2023-03-16T12:10:35+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:indexing_with_grainspotter&rev=1678968635&do=diff Grain indexing with GrainSpotter At this point, you should have a computed all experimental G-vectors. You are now ready for indexing grains. This page shows you the indexing procedure with GrainSpotter. For an alternative way of indexing, check out Indexing with ImageD11. GrainSpotter first generates a number of random grain orientations and, for each, calculates the corresponding theoretical G-vectors. For each grain orientation, GrainSpotter looks for a possible match between the theoretica… text/html 2019-07-08T15:19:09+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:indexing_with_imaged11&rev=1562599149&do=diff Grain indexing with ImageD11 ImageD11 can also do indexing, using a completely different philosophy from that of GrainSpotter. The idea is based on the following: * Select 2 diffraction rings from your sample, * For each pair of peaks (one on each ring): * Test whether those 2 peaks could belong to the same grain, text/html 2023-03-14T10:29:25+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:list-peaks-from-cif&rev=1678789765&do=diff timelessPeaksFromCIF Objectives This tools generates a list of peaks based on a cif file. Arguments that are required are * the cif file * the wavelength * the minimum and maximum 2theta values The code will list the following information text/html 2023-12-10T10:59:32+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:minimal_software_intallation&rev=1702205972&do=diff Linux software kit installation There is a whole suite of software required to run multigrain X-ray diffraction, and their installation procedure can be quite tricky if you are not comfortable with computing techniques. The procedure below was tested with a recent Windows laptop. We proceed as follow: text/html 2023-03-16T14:04:05+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:phase_identification&rev=1678975445&do=diff Identify sample phases Before you move on to multigrain indexing, you will need to know: * what phases you are looking for, * their crystal structures and cell parameters. If you have a decent powder diffraction pattern to work with, you can try to do so with typical software people use in DAC experiments: dioptas, maud, etc. text/html 2019-09-02T09:12:15+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:preprocessing&rev=1567415535&do=diff Steps to do before the actual data processing Ideally, the steps listed below should be done only once for every beamtime (as long as the detector is not moved or the wavelength is not changed). The information obtained by following these steps can then be used for any data set acquired at that beamtime. text/html 2023-03-16T13:45:57+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:remove_bad_images&rev=1678974357&do=diff Removal of bad images The removal and exchange of bad images should be one of the first steps in your processing workflow. It is intended to be done once but, however, during the processing it may happen several times that you have to throw out an image which you considered all right in a previous step. text/html 2019-07-12T08:49:02+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:remove-used-flt&rev=1562921342&do=diff Remove used FLT peaks Description The script timelessClearFLTGrains is part of the TIMEleSS tools. It takes a list of peaks (.flt file), compares it with a GrainSpotter output file (.log file), removes all peaks which have already been used and writes the leftover peaks in a new text/html 2023-03-16T12:07:16+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:remove-used-gve-updated&rev=1678968436&do=diff timelessClearGVEGrains Introduction timelessClearGVEGrains compares peaks in a GVE file with output from a GrainSpotter indexing. You have to provide (those arguments are required) * the name of GrainSpotter output file * the name of the G-vector file used for indexing text/html 2020-11-10T08:56:21+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:remove-used-gve&rev=1604998581&do=diff Remove Used G-vectors Description In the process of grain indexing (usually done with GrainSpotter), you won't index all grains in the first try. Usually, one has to perform several indexing runs. To make life easier (and to cut down calculation time), this script removes all G-vectors which were used already in the previous run. Then you can perform another run with the reduced G-vector file. After that, this process can be repeated until GrainSpotter doesn't index anything anymore. text/html 2019-02-20T21:51:00+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:search_for_peaks&rev=1550699460&do=diff Search for peaks Once your processed your images, removed your background, etc, you are ready to extract peaks. Searching for diffraction spots in usually performed using peaksearch. To do so, you should look at some random peaks from several images by zooming in (in text/html 2019-06-07T16:00:27+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:start&rev=1559923227&do=diff Multigrain data processing workflow Here is an outline of a data processing workflow. This assumes that you already have your data collected. Depending on your objectives, follow the matching track. Data collection Usually, the data is collected in synchrotron facilities by stepwise rotating the sample while the detector is acquiring diffraction images. For more information, click text/html 2019-02-23T19:43:23+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:synthetic_dataset&rev=1550951003&do=diff Creation of the synthetic dataset Synthetic data sets are useful to learn how to process data. You can input the experimental parameters you will use in your actual experiment and your actual sample. The software to do so is PolyXSim. For high pressure experiments, the purpose of this step is to simulate the outcome of a DAC experiment (grains with random orientations, random strains etc.). You can check the possible outcome of an actual experiment. You can also check if your workflow for data… text/html 2020-03-25T09:36:31+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:workflow_dac_data&rev=1585128991&do=diff Processing actual multigrain diffraction data from a diamond anvil cell experiment That's it, you have been trained in multigrain XRD data processing. You feel that you are ready for real data from a diamond anvil cell experiments! Tough parts with diamond anvil cell data include * shadows from the DAC itself, text/html 2019-09-04T16:47:10+00:00 Anonymous (anonymous@undisclosed.example.com) https://multigrain.texture.rocks/doku.php?id=processing:workflow_training&rev=1567615630&do=diff Learning multigrain diffraction with a synthetic dataset Multigrain diffraction data processing is tricky, you things can go seriously wrong if you do not understand the various parameters and steps in the data processing workflow. A good strategy is to generate a synthetic dataset, as close as possible as the dataset you will have in you experiment. Generate a dataset from 500 grains if you expect 500 grains. Then, try to go through the data processing strategy and see if you do index the gra…