lpa-input 0.8.3

Tools for the generation and analysis of dislocation distributions.

Project

This repository is related to the analysis of crystals containing dislocations by X-ray diffraction. It is part of a project conducted during a research internship at the laboratory of material and structural sciences of the École Nationale Supérieure des Mines de Saint-Étienne.

Features

The tools developed can be used to:

• generate dislocation distributions according to different models
• export the distributions in standardized files for input to an X-ray diffraction simulation program
• export the distributions in dislocation maps
• export a spatial analysis of the distributions

Physical aspects

A dislocation associates:

• a Burgers vector
• a position

Two geometries are proposed:

• circle (intersection of a plane with a cylinder) centered in (0, 0)
• square (intersection of a plane with a cuboid) bottom left corner in (0, 0)

A distribution is characterized by the following elements:

• the geometry of the region of interest
• the model used for the random generation of dislocations
• the generated dislocations

Abbreviations

Some abbreviations are used in the program:

Models

• urdd: uniformly random dislocation distribution
• rrdd: restrictedly random dislocation distribution
• rcdd: random cell dislocation distribution

Model variants

• r: randomly distributed Burgers vectors
• e: evenly distributed Burgers vectors
• d: dipolar Burgers vectors

Boundary conditions

• pbcg: periodic boundary conditions applied when generating the distribution
• pbcr: periodic boundary conditions applied when runnning the simulation
• idbc: image dislocations boundary conditions

User guide

Installation

The project is indexed on PyPI and installable directly via pip.

pip install lpa-input

Generation

To create a uniformly random dislocation distribution with evenly distributed Burgers vectors in a cylindrical geometry with a radius of 1000 nm:

from lpa.input import sets
r = {'density': 0.03, 'variant': 'e'}
d = sets.Distribution('circle', 1000, 'urdd', r)

To create a sample of 100 uniformly random dislocation distribution with evenly distributed Burgers vectors in a cylindrical geometry with a radius of 1000 nm:

from lpa.input import sets
r = {'density': 0.03, 'variant': 'e'}
s = sets.Sample(500, 'circle', 1000, 'urdd', r)

Export

To export a dislocation map of a distribution d.

from lpa.input import maps
maps.export(d)

To make standardized files for input to an X-ray diffraction simulation program from a sample s:

from lpa.input import data
data.export(s)

To make a spatial analysis of a sample s:

from lpa.input import analyze
analyze.export(s)