lpa-xrd

Line profile analysis X-ray diffraction simulator.

https://github.com/dunstanbecht/lpa-xrd

Science Score: 54.0%

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Keywords

automation diffraction gpu opencl python x-ray
Last synced: 6 months ago · JSON representation ·

Repository

Line profile analysis X-ray diffraction simulator.

Basic Info
  • Host: GitHub
  • Owner: DunstanBecht
  • License: cc0-1.0
  • Language: C
  • Default Branch: main
  • Homepage:
  • Size: 1.21 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 4
Topics
automation diffraction gpu opencl python x-ray
Created over 4 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License Citation

README.md

DOI License: CC0-1.0


Mines Saint-Etienne

Line Profile Analysis - X-Ray Diffraction

This project is related to the analysis of crystals containing dislocations by X-ray diffraction. It was developed and used for a study conducted during a research internship at the laboratory of material and structural sciences of the École Nationale Supérieure des Mines de Saint-Étienne. This repository contains the distribution of one of the three published python packages that have been proposed to conduct line profile analyses based on simulation results: * lpa-input (line profile analysis input generator) * lpa-xrd (line profile analysis x-ray diffraction simulation program) * lpa-output (line profile analysis output analyzer)

The repository lpa-workspace contains the parameters and the scripts for the generation of the data used in the study. You can then easily replicate the results obtained or use it as inspiration to take the code in hand and conduct your own calculations. The software is placed in the public domain and you can use it as you wish. However, users are encouraged to contribute to the development and report issues.

Features

The package lpa.xrd can be used to: * make a local copy of the X-ray diffraction simulator code * automate the tasks of compilations and executions

Installation

The package is indexed on PyPI and installable directly via pip: bash pip install -U lpa-xrd

Examples

Output data files

``` 1.0.0 # v: lpa-xrd version 5.00e+13 # d: dislocation density [m^-2] 1 1 0 # z: direction of 'l' (line vector) [uvw] 1 1 0 # b: Burgers vector direction [uvw] 2 0 0 # g: diffraction vector direction (hkl) 0.250000 # C: contrast coefficient [1] 0.404600 # a: cell parameter [nm] 3200 # s: side of the region of interest [nm] 0.345000 # nu: Poisson's number [1] 4608 # nd: number of dislocations in the input file 200000 # np: number of random points

L cos1 errcos1 sin1 errsin1 cos2 errcos2 sin2 errsin2 cos3 errcos3 sin3 errsin3 cos4 errcos4 sin4 errsin4 cos5 errcos5 sin5 errsin5 eps2 nrpo

5.0 9.919780130050152E-01 9.7694405E-05 4.649751351202615E-02 2.4401673E-04 9.718584161805875E-01 1.8592315E-04 9.122464362090398E-02 4.4864260E-04 9.416076235866173E-01 2.8748026E-04 1.329066527551777E-01 6.2921540E-04 9.028444599229754E-01 3.9678622E-04 1.709151124821277E-01 7.8794931E-04 8.573342759258576E-01 5.0992929E-04 2.044546944647581E-01 9.2512636E-04 2.845523399565078E-05 0 10.0 9.710121708440832E-01 2.0278255E-04 9.116575916950630E-02 4.5055666E-04 9.021774943746688E-01 4.0288039E-04 1.707146134209013E-01 7.8889338E-04 8.056001607624746E-01 6.3151908E-04 2.328566641373228E-01 1.0416918E-03 6.927731828272842E-01 8.5946732E-04 2.748280712141710E-01 1.2181959E-03 5.742201959831477E-01 1.0648126E-03 2.962318625026029E-01 1.3337083E-03 2.617600870330373E-05 0 15.0 9.395445834614645E-01 3.1299194E-04 1.327871325064340E-01 6.3257909E-04 8.046734338889502E-01 6.3674889E-04 2.326487626980035E-01 1.0423216E-03 6.327588009897309E-01 9.6786160E-04 2.874622559208860E-01 1.2838090E-03 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Abbreviations

Some abbreviations are used in the column names:

  • cos<j> estimator of the mean cos coefficients of the Fourier transform, harmonic <j>
  • cos<j> estimator of the mean sin coefficients of the Fourier transform, harmonic <j>
  • err_cos<j> estimator of the standard deviation of the cos coefficients mean (standard error), harmonic <j>
  • err_sin<j> estimator of the standard deviation of the sin coefficients mean (standard error), harmonic <j>
  • eps2 estimator of the mean square strain
  • nrpo number of random points outside the region of interest for the current value of L

User guide

It is necessary to install the NVIDIA CUDA Toolkit to compile the OpenCL code on NVIDIA GPUs.

The directory tests/ contains several examples of package module usage. To become familiar with the use of these modules you should go through these scripts in the following order: * test_code.py * test_run.py

In the sources the docstrings are carefully written and it is recommended to refer to the documentation with the help() python command to list the available functions, classes and parameters.

The installation from PyPI does not allow the modification of the code. To edit the package and contribute to the development use the following commands in your working directory. bash pip uninstall lpa-xrd git clone https://github.com/DunstanBecht/lpa-xrd.git pip install -e lpa-xrd cd lpa-xrd git branch <name_of_your_new_branch>

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Aoufi
    given-names: Asdin
    orcid: https://orcid.org/0000-0002-5240-0941
  - family-names: Becht
    given-names: Dunstan
    orcid: https://orcid.org/0000-0002-8524-4527
  - family-names: Borbély
    given-names: András
    orcid: https://orcid.org/0000-0001-7877-5795
title: lpa-xrd
version: 1.0.1
doi: 10.5281/zenodo.5701430

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  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
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  • Average comments per issue: 0
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  • DunstanBecht (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 131 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 27
  • Total maintainers: 1
pypi.org: lpa-xrd

Tools for the simulation of x-ray diffraction.

  • Versions: 27
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 131 Last month
Rankings
Dependent packages count: 10.0%
Dependent repos count: 21.7%
Average: 27.9%
Forks count: 29.8%
Stargazers count: 31.9%
Downloads: 45.8%
Maintainers (1)
Last synced: 6 months ago