# Tessellation Module (-T)

Module -T is the module for generating *tessellations* and *multiscale tessellations* of a finite *domain* of space, in 2D or 3D. The domain is generally convex, although non-convex shapes can also be obtained. Periodicity and semi-periodicity conditions can be prescribed. Module -T also enables the *regularization* of the tessellations for meshing with high quality elements. The tessellations are provided in scalar (vectorial) or raster formats. The scalar format described the tessellation cells using sets of vertices, edges and faces, while the raster format uses a regular raster of voxels (similarly to an EBSD map). Module -T also generates crystal orientations for the cells.

*Tessellations* can be generated from various types of morphological cell properties (option `-morpho`

). Several predefined properties are available, such as those obtained by grain growth in metals (which are described by cell size and sphericity (circularity, in 2D) distributions). Custom properties can be specified using various metrics, including the size and sphericity (circularity, in 2D), the centroid or even the actual shape (using a raster tessellation), in terms of distributions or individual cell values. Standard analytical distributions are included, and custom numerical distributions can be read from a file. Global morphological properties, such as a cell aspect ratio or a columnar axis, can also be specified. The generated *tessellations* are *general convex-cell tessellations* represented as Laguerre (or Voronoi) tessellations whose seed attributes are set by optimization to obtain the desired cell properties [2]. Of course, it is also possible to generate standard tessellations (e.g. Poisson-Voronoi or regular tessellations). Cell *groups* can be defined to represent, for example, the different phases of a multiphased polycrystalline material (option `-group`

).

*Multiscale tessellations* are characterized by the subdivision of the cells of a primary tessellation into secondary tessellations (and so on) and are obtained by combining into one, using `::`

, the option arguments that apply at the successive scales. The same value can be used for defining the tessellations at a given scale, or different values can be loaded using `msfile(<file_name>)`

, where `<file_name>`

is a multiscale cell file). So, all capabilities available for generating a standard (single-scale) tessellations are available for generating the tessellations at the different scales of a multiscale tessellation.

The *domain* of space in which the tessellation is created can be of any convex shape. In 3D, cuboidal, cylindrical and spherical shapes (and a few other, exotic shapes) are directly supported while other morphologies can be defined from a set of planes (option `-domain`

). Non convex domain shapes can be obtained by cutting the tessellation by different geometrical primitives once generated (option `-transform cut`

[1]). Periodicity or semi-periodicity conditions can be applied to the tessellation (option `-periodicity`

).

*Crystal orientations* can be randomly distributed (according to a uniform distribution function), either in the 3D space or along a specific orientation fiber, or uniformly distributed (also according to a uniform distribution function, option `-ori`

). Uniform crystal orientation distributions ensure that all possible crystal orientations are equally represented (no orientation clustering). Crystal orientations can be written according to different descriptors (option `-oridescriptor`

). It is also possible to define an analytical orientation spread for the cells (option `-orispread`

).

*Regularization* can be applied to the tessellations and consists of removing their small edges and faces (option `-regularization`

) which would otherwise be detrimental to generating meshes with high quality elements with module -M).

*Output files* describe the tessellation either at the scalar format (.tess) or at the raster format (.tesr). A *raster tessellation* also has all required fields to describe data obtained by 2D or 3D orientation mapping methods (such as EBSD). Tessellation files are input files of the Meshing Module (-M) and the Visualization Module (-V), and can also be exported as a Simulation Directory (.sim), which is input to the Simulation Module (-S), for post-processing. Third-party software file formats are also available.

The methods implemented for tessellation generation are described in [CMAME2011], [CMAME2018] and [JAC2018].

Here is what a typical run of module -T looks like:

```
$ neper -T -n 10 -reg 1
======================== N e p e r =======================
Info : A software package for polycrystal generation and meshing.
Info : Version 4.0.0
Info : Built with: gsl|muparser|opengjk|openmp|nlopt|libscotch (full)
Info : Running on 8 threads.
Info : <https://neper.info>
Info : Copyright (C) 2003-2024, and GNU GPL'd, by Romain Quey.
Info : No initialization file found (`/home/rquey/.neperrc').
Info : ---------------------------------------------------------------
Info : MODULE -T loaded with arguments:
Info : [ini file] (none)
Info : [com line] -n 10 -reg 1
Info : ---------------------------------------------------------------
Info : Reading input data...
Info : Creating domain...
Info : Creating tessellation...
Info : - Setting seeds...
Info : - Generating crystal orientations...
Info : - Running tessellation...
Info : Regularizing tessellation...
Info : - loop 2/2: 100% del=14
Info : Writing results...
Info : [o] Writing file :data:`n10-id1.tess'...
Info : [o] Wrote file :data:`n10-id1.tess'.
Info : Elapsed time: 0.036 secs.
========================================================================
```

## Arguments

### Input Data

- -n <cell_number>
Specify the number of cells of the tessellation, which can be:

an integer value or any expression based on the Tessellation Keys;

`from_morpho`

to set the value from the morphology (option`-morpho`

).

**Default value**: -.

- -id <identifier>
Specify the identifier of the tessellation, which can be an integer value or any expression based on the Tessellation Keys.

The identifier is used as seed of the random number generator to compute the (initial) seed positions.

**Default value**:`1`

.

- -dim <dimension>
Specify the dimension of the tessellation, which can be

`2`

or`3`

.**Default value**:`3`

.

- -domain <domain_morphology[:transformation]>
Specify the domain morphology and, optionally, a transformation. There are several options to do so:

Simple 3D geometries:

`cube(<size_x>,<size_y>,<size_z>)`

: a cuboidal shape;`cylinder(<height>,<diameter>[,<facet_nb>])`

: a cylindrical shape;`sphere(<diameter>[,<facet_nb>])`

: a spherical shape;For

`cylinder`

and`sphere`

,`<facet_nb>`

is the number of facets used to described a curved surface.

Simple 2D geometries:

`square(<size_x>,<size_y>)`

: a rectangular shape;`circle(<diameter>[,<segment_nb>])`

: a circular shape;For

`circle`

,`<segment_nb>`

is the number of segments used to described the curved line.

Orientation-related geometries:

`rodrigues(<crysym>)`

: a Rodrigues space fundamental region, where`<crysym>`

is the Crystal Symmetry (to enforce periodicity relationships between opposite surfaces, use`-periodicity`

`1`

(only for`-n`

`1`

)) [9];`euler-bunge(<size_x>,<size_y>,<size_z>)`

: the Euler space (Bunge convention), where`<size_x>`

,`<size_y>`

and`<size_z>`

are the space dimensions (in degrees or radians [10]);`stdtriangle(crysym=<crysym>,projection=<projection>,segmentnb=<segment_nb>)`

: a standard stereographic triangle, with the optional arguments:`<crysym>`

: the crystal symmetry (default`cubic`

);`<projection>`

: the projection (default`stereographic`

);`<segment_nb>`

: the number of segments along the curved edge (default`20`

);

Fully custom geometries:

`file(<file_name>)`

: a geometry loaded from a Tessellation File (.tess), a Wavefront OBJ file or an OpenVolumeMesh OVM file. If the geometry contains several cells, use`cell`

as described below to extract a cell.`planes(<file_name>)`

: an arbitrary (non-degenerated) convex 3D shape, where`<file_name>`

contains the total number of planes and then, for each plane, the 4 parameters of its equation:`d`

,`a`

,`b`

and`c,`

successively for an equation of the form \(a\,x+b\,y+c\,z=d\), and where the plane normal \((a,\,b,\,c)\) is an outgoing vector of the domain. For the unit cube, the file can be as follows:6 0 -1 0 0 0 0 -1 0 0 0 0 -1 1 1 0 0 1 0 1 0 1 0 0 1

The transformation can be:

`translate(<dist_x>,<dist_y>,<dist_z>)`

: translate by distances`<dist_x>`

,`<dist_y>`

and`<dist_z>`

along`x`

,`y`

and`z`

, respectively;`rotate(<axis_x>,<axis_y>,<axis_z>,<angle>)`

: rotate about the center and by an axis/angle pair (angle expressed in degrees);`split(<dir>)`

: split the domain in half along direction`<dir>`

(`x`

,`y`

or`z`

), which can be used to apply symmetries;`cell(<cell_id>)`

: extract a cell from a tessellation.

For a 2D tessellation, the axis parameters can be omitted in

`rotate`

(default`z`

), and the \(z\) component can be omitted in`scale`

(n/a) and`translate`

(default`0`

).An example is

`sphere(1,1,0,0):translate(-0.5-0.5-0.5):scale(0.512)`

.**Default value**:`cube(1,1,1)`

in 3D and`square(1,1)`

in 2D.

- -periodicity <periodicity>
Specify the periodicity conditions that apply to the domain (and therefore to the tessellation), which can be:

`0`

or`none`

: no periodicity;`1`

or`all`

: full periodicity;a list of periodicity directions, among

`x`

,`y`

and`z`

and combined with`,`

: semi-periodicity.

**Default value**:`0`

.

Is it also possible to load a tessellation or a raster tessellation from a file:

- -loadtess <tess_file>
Load a tessellation from a Tessellation File (.tess), a Wavefront OBJ file (

`.obj`

) or an OpenVolumeMesh OVM file (`.ovm`

).Note

It is possible to load an OBJ or OVM file to convert it into a Tessellation File (.tess).

**Default value**: -.

- -loadtesr <tesr_file>[:<transformation1>][:<transformation2>:...]
Load a raster tessellation from a Raster Tessellation File (.tesr) and, optionally, apply transformations.

The transformations can be:

`crop(<xmin>,<xmax>,<ymin>,<ymax>,<zmin>,<zmax>)`

: crop a region of a raster tessellation, where`<xmin>`

,`<xmax>`

,`<ymin>`

,`<ymax>`

,`<zmin>`

and`<zmax>`

are the minimum and maximum positions along`x`

,`y`

and`z`

, respectively. For 2D raster tessellations, the`z`

values can be omitted`rasterscale(<fact>)`

or`rasterscale(<fact_x>,<fact_y>,<fact_z>)`

: : scale the number of voxels of a raster tessellation, where`factor`

is the scaling factor that applies in the three directions, and`<fact_x>`

,`<fact_y>`

and`<fact_z>`

are the scaling factors along`x`

,`y`

and`z`

, respectively. For 2D raster tessellations, the \(z\) value can be omitted.

**Default value**: -*

Finally, it is possible to load a set of points.

- -loadpoint <point_file>
Load points from a file formatted as a Position File.

Important

These points are used only for statistics, in option

`-statpoint`

, and are*not*used for tessellation.**Default value**: -*

### Morphology Options

These options can be used to set the cell morphology.

- -morpho <morphology>
Specify morphological properties of the cells. This can be done in different ways:

**Special morphological properties**can be (mutually-exclusive):`voronoi`

: standard Poisson-Voronoi tessellation;`graingrowth`

or`gg`

: grain-growth morphology, which corresponds to a wider grain size distribution and higher grain sphericities than in a Voronoi tessellation. It actually is an alias for`diameq:lognormal(1,0.35),1-sphericity:lognormal(0.145,0.03)`

in 3D and`diameq:lognormal(1,0.42),1-circularity:lognormal(0.100,0.03)`

in 2D, which are described below. The`graingrowth(<mean>)`

and`gg(<mean>)`

variants can be used to provide an absolute mean grain size,`<mean>`

(in which case`-n from_morpho`

must be used, as described below).`centroidal`

: a centroidal tessellation [3]. It actually is an alias for`centroid:seed`

, which is described below.`cube(<N>)`

or`square(<N>)`

: regular tessellations into cubic or square cells, where`<N>`

is the number of cells along a direction, or`cube(<N1>,<N2>,<N3>)`

/`square(<N1>,<N2>)`

for a regular tessellation into cubic / square cells, where`<N1>`

,`<N2>`

and`<N3>`

are the number of cells along the three directions;`tocta(<N>)`

: regular tessellation into truncated octahedra, where`<N>`

is the number of cells along a direction;`lamellar([w=<width>][,n=<n>][,v=<normal>][,pos=<pos>][,reps=<reps>])`

: lamellar morphology, where`<width>`

is the absolute lamella width or a series of absolute lamella widths combined with`:`

,`<n>`

is the number of lamellae,`<normal>`

is the lamella plane normal,`<pos>`

is the position of the first lamella, and`<reps>`

is a relative tolerance on the width of the last lamella.`width=<width>`

and`n=<n>`

are mutually exclusive. The number of lamellae can also be specified using option`-n`

. Specifying a number of cells (using option`-n`

or`n=<n>`

) enforces`pos=start`

(or equivalently`pos=optimal`

, as described below).`<normal>`

(the lamella plane normal) can be:`random`

: randomly-distributed normals taken from a uniform distribution (the default);`(<dir_x>,<dir_y>,<dir_z>)`

: a specific direction of space, (`dir_x`

,`dir_y`

,`dir_z`

);`crysdir(<crysdir_x>,<crysdir_y>,<crysdir_z>)`

: a specific direction of the parent crystal, (`<crysdir_x>`

,`<crysdir_y>`

,`<crysdir_z>`

).

`<pos>`

(the position of the first lamella) can be:`random`

: random position (the default);`optimal`

: optimal position, i.e. so that lamellae at the start*and*end of the domain are of lengths as close as possible to nominal (along direction`<dir>`

);`start`

: first lamella starting full-width from the start point of the domain (along direction`<dir>`

);`half`

: first lamella starting half-width from the start point of the domain (along direction`<dir>`

).`<factor>`

: lamella starting with a width equal to`<factor>`

times the nominal width (between`0`

and`1`

), from the start point of the domain (along direction`<dir>`

).

`<reps>`

(default`1e-2`

) is so that a lamella is allowed to be larger than nominal, within the specified relative tolerance, to avoid the occurence of unrealistically thin lamella.

In the case of a multiscale tessellation, a multiscale cell file can be provided as value of

`w`

,`n`

,`v`

, and`pos`

.

**Custom morphological properties**can be defined using`<property>:<value>`

. The properties and the possible corresponding values can be:`size`

: the size (volume in 3D and area in 2D) [12];`diameq`

: the equivalent diameter [12];`sphericity`

: the sphericity, and`1-sphericity`

: 1 \(-\) the sphericity (or`circularity`

and`1-circularity`

). [4] [5]All of

`size`

,`diameq`

and`sphericity`

(and their variants) can be defined by statistical distributions or cell by cell. If the number of cells is defined using option`-n`

, the`size`

or`diameq`

distribution is scaled to get the specified number of cells. At the opposite, if`-n from_morpho`

is used, the number of cells is determined from the`size`

or`diameq`

distribution. An interval of possible values can also be provided using`interval(<min>,<max>)`

. Cell-by-cell values can be provided using`file(<file_name>)`

, where`<file_name>`

contains the cell values. A unique (numeral) value to be assigned to all cells can also be provided directly.`centroid`

for the centroid;`centroidtol`

for the centroid with a tolerance (see below for the format; centroids with a tolerance more than 1000 times as high as the minimum tolerance are simply disregarded);`centroidsize`

for combined centroid and size, and`centroiddiameq`

for combined centroid and equivalent diameter.All of the

`centroid*`

properties must be defined cell by cell, and provided using`file(<file_name>)`

. For`centroid`

, the file must be a position file while, for more complete properties, the additional data should be provided on the following columns.`tesr`

for cells of a raster tessellation. It must be defined by a raster tessellation, provided using`file(<file_name>)`

. If`-n`

is set to`from_morpho`

, the number of cells is set to the number of cells of the raster tessellation.

**Global cell properties**can be defined as follows (mutually-exclusive):`columnar(<dir>)`

for a columnar morphology along direction`<dir>`

, where`<dir>`

can be`x`

,`y`

or`z`

;`bamboo(<dir>)`

for a bamboo morphology along direction`<dir>`

, where`<dir>`

can be`x`

,`y`

or`z`

;`aspratio(<r_x>,<r_y>,<r_z>)`

, where`r_x`

,`r_y`

and`r_z`

represent relative length along the`x`

,`y`

and`z`

directions. For a 2D tessellation,`r3`

can be omitted. When provided, other properties, such as the equivalent diameter or the sphericity (circularity, in 2D), are considered to apply to the cells as if they had no aspect ratio.

**A tessellation file**(`.tess`

) can be loaded using`file(<file_name>)`

.To specify several properties, combine them with

`,`

(centroids and sizes / equivalent diameters should be seen as one property and specified with`centroidsize`

/`centroiddiameq`

).**Default value**:`voronoi`

.

- -morphooptiini <seed_attributes>
Specify the initial positions, weights, orientations and/or ids of the seeds.

The general form of the argument is

`coo:<coo_definition>,weight:<weight_definition>,...`

. Different values of`<coo_definition>`

and`<weight_definition>`

are available, depending on the value of option`-morpho`

:`<coo_definition>`

can be:`random`

: random positions;`packing`

: positions set by (rough) dense sphere packing using the weights as sphere radii;`centroid`

: cell centroids;`LLLFP2011`

: Lyckegaard et al.’s method [LLLFP2011];`file(<file_name>)`

: values to load from a Position File or a Tessellation File (.tess) (only the seed coordinates are considered).

The default depends on the value of option

`-morpho`

: for`voronoi`

, it is`random`

, for a cell-size statistical distribution, it is`none`

, and for cell-based coordinate values (including`-morpho tesr`

), it is`centroid`

.`<weight_definition>`

can be:a real value or an expression based on the Tessellation Keys, especially

`radeq`

,`diameq`

,`avradeq`

and`avdiameq`

;`file(<file_name>)`

: values from a Data File or a Tessellation File (.tess) (only the seed weights are considered).

The default depends on the value of option

`-morpho`

: for`voronoi`

, it is`0`

, for a cell-size statistical distribution, it is`avradeq`

, and for cell-based size values (including`-morpho tesr`

) , it is`radeq`

.

It is also possible to load orientations or ids using

`ori:<ori_definition>`

and`id:<id_definition>`

:`<ori_definition>`

can be:`file(<file_name>[,des=<descriptor>])`

: discrete orientations to be read from a Data File written using a specific descriptor (see Rotations and Orientations, default`rodrigues`

).`-ori`

`from_morpho`

must also be used.

`<id_definition>`

can be:`file(<file_name>)`

: values from a Data File.

Alternatively,

`file(<file_name>)`

can be used to load the seed coordinates, weights, orientations and ids (if defined) from a unique Tessellation File (.tess) (thereby replicating the tessellation).**Default value**:`default`

.

- -morphooptiobjective <objective_function> (secondary option)
Specify the objective function. The general form of the argument is

`<prop1>:<objective_function1>,<prop2>:<objective_function2>,...`

, where`<prop#>`

are properties as defined in option`-morpho`

, and`<objective_function#>`

are their objective functions. An objective function depends on the property and its value.**Properties defined by a statistical distribution**(which can be`size`

,`diameq`

,`sphericity`

or`1-sphericity`

(or`circularity`

and`1-circularity`

)) can take values of:`chi2`

: Chi-square test;`ks`

: Kolmogorov-Smirnov test;`kuiper`

(Kuiper’s test);`cmv`

(Cramér-von Mises test);`ad`

(Anderson-Darling test);`FL2`

(\(L^2\text{-norm}\) on \(F\));`FL2w`

(weighted \(L^2\)-norm on \(F\)) [CMAME2018];`FL2wu`

(weighted \(L^2\)-norm on \(F\) corresponding to`FL2w`

for a unimodal distribution).

The default value is

`FL2w`

.**The**`centroid`

**property**can take values of a Minkowski distance between the seeds and centroids is used, and can be`L1`

,`L2`

or`Linf`

.**The**`tesr`

**property**accepts an objective function which includes several factors.First, preprocessing operations to the raster tessellation can be applied using

`transform(<operation1>,<operation2>,...)`

, where`<operation#>`

can be:`scale`

to scale the tessellation to correct for a global cell elongation;`rasterscale`

to scale the raster itself to correct for a global voxel elongation (which may result from operation`scale`

).

Second, control points can be defined using

`pts(<def1>,<def2>,...)`

, where`<def#>`

can be:`region=<region>`

, where`<region>`

can be`surf`

for surface voxels or`all`

for all voxels;`res=<res>`

, where`<res>`

is the resolution, i.e. the average number of control points along a direction of a grain.

Third, the expression of the objective function

*per se*can be specified using`val(<expr>)`

, where`expr`

can be (mutually exclusive):`bounddist`

: minimize the distance between the raster tessellation and tessellation cell boundaries;`intervol`

: maximize the volume of intersection between the raster tessellation and tessellation (both provide similar results).

To define the objective function, combine the above factors using

`+`

. The default value is`pts(region=surf,res=5)+val(bounddist)`

. A penalty is automatically added to the objective function in the case where cells are empty (including when`-transform cut`

is used).**The**`general`

**variable**, finally, can be used to specify how the different components of the objective function are combined into the objective function (in the case where several properties are specified), using the syntax`general:<norm>`

, where`norm`

can be`L1`

,`L2`

or`Linf`

; the default is`L2`

.Examples are

`diameq:FL2,1-sphericity:FL2`

,`centroid:L1`

or`tesr:pts(region=all,res=5)+val(intervol)`

.**Default value**:`default`

.

- -morphooptidof <dof1>,<dof2>,... (secondary option)
Specify the degrees of freedom, which can be

`x`

,`y`

and`z`

for the 3 coordinates, and`w`

for the weights.**Default value**:`x,y,z,w`

.

- -morphooptistop <stopping_criterion> (secondary option)
Specify the stopping criterion of the optimization process, as a logical expression of the form

`<var1>=<val1>||<var2>=<val2>||...`

(where`||`

represents the logical OR) and based on the following variables:`eps`

: absolute error on the value of the objective function evaluated on a number of degrees of freedom basis (`nlopt_eps`

or`nlopt_reps`

are the NLopt iteration-based values);`reps`

: relative error on the value of the objective function evaluated on a number of degrees of freedom basis (`nlopt_eps`

or`nlopt_reps`

are the NLopt iteration-based values);`xeps`

: absolute error on the components of the solution vector;`xreps`

: relative error on the components of the solution vector;`val`

: value of the objective function;`iter`

: number of iterations;`time`

: maximum computation time;`loop`

: number of iteration loops (see option`-morphooptialgomaxiter`

).

Optimization stops as soon as one stopping criterion is verified.

Optimization can also be stopped anytime using the

**Ctrl+C**command.**Default value**:`eps<1e-6`

(`val<1e-4||iter>=10000`

for`-morpho centroidal`

).

- -morphooptialgo <algorithm1>,<algorithm2>,... (secondary option)
Specify the optimization algorithm, which can be:

`subplex`

: Subplex (recommend);`praxis`

: Praxis (recommended, except for high numbers of seeds, where it becomes highly memory-intensive) [11] ;`neldermead`

: Nelder-Mead (not recommended);`cobyla`

: Cobyla (not recommended);`bobyqa`

: Bobyqa (not recommended);`newuoa`

: Newuoa (not recommended).`lloyd[(<fact>]`

: Lloyd’s algorithm using a specified factor (default`1.9`

, only for`-morpho centroidal`

).`random(<seednb>,<dimnb>,<min>,<max>,<id>)`

: random perturbations (use only if you know what you are doing). At each odd iteration, for each of the`seednb`

seeds,`dimnb`

of its attributes (among those specified by option`-morphooptidof`

) are randomly perturbed, the norm of the total perturbation vector ranging from`<min>`

to`<max>`

;`id`

is the identifier of the distribution (similarly to option`-id`

). Variables can be any mathematical expression based on`seednb`

(the total number of seeds),`dim`

(the tessellation dimension),`avdiameq`

(the average equivalent cell diameter) and`inistep`

(the value of`-morphooptiinistep`

); at each next (even) iteration, the attributes of the seeds are reverted to their original values.

In several algorithms are provided, the second etc. are used if the previous ones fails.

**Default value**:`subplex,praxis`

(`lloyd`

for`-morpho centroidal`

).

- -morphooptigrid <var1>:<grid1>,<var2>:<grid2>,... (secondary option)
Specify the grids used to discretize the variable distributions. The variables are those defined in

`-morpho`

, and the grid must be`regular(<min>,<max>,<bin_nb>)`

, where`<min>`

and`<max>`

are the minimum and maximum values of the grid interval, respectively, and`<bin_nb>`

is the number of bins.**Default value**:`diameq:regular(-1,10,1100),size:regular(-1,10,1100),sphericity:regular(-0.1,1.1,1200),1-sphericity:regular(-0.1,1.1,1200)`

.

- -morphooptismooth <var1>:<val1>,<var2>:<val2>,... (secondary option)
Specify the standard deviations of the Gaussian distributions which are assigned to each cell data to compute the distributions. The variables are those defined in

`-morpho`

.It is also possible to specify how the convolution functions should be treated, using

`analytical`

for analytical functions or`numerical`

for numerical functions (the default, recommended).**Default value**:`diameq:0.05,size:0.05,sphericity:0.005,numerical`

.

- -morphooptideltamax <deltamax> (secondary option)
Specify the maximal value by which each variable is allowed to change during optimization.

Possible values: any (\(\geq 0\)).

**Default value**:`HUGE_VAL`

.

- -morphooptiinistep <inistep> (secondary option)
Specify the step used to perturb the seed positions and weights when optimization begins. The argument can be a real value of a mathematical expression based on

`avdiameq`

, the average equivalent cell diameter.**Default value**:`avdiameq/10`

.

- -morphooptialgomaxiter <iter_number> (secondary option)
Specify the maximum number of iterations allowed to the optimization algorithm to run without decreasing the objective function. The expression can be any mathematical expression based on variable

`varnb`

(the total number of optimization variables).**Default value**:`max(varnb,1000)`

.

- -morphooptilogtime <variables> (secondary option)
Log the time taken during the optimization process. The variables can be among those provided in Time Keys.

**Default value**: -.**File extension**:`.logtime`

.

- -morphooptilogvar <variables> (secondary option)
Log the variables (seed attributes) during the optimization process. The variables can be among those provided in Variable Keys.

**Default value**: -.**File extension**:`.logvar`

.

- -morphooptilogval <variables> (secondary option)
Log the value of the objective function during the optimization process. The variables can be among those provided in Objective Function Value Keys.

**Default value**: -.**File extension**:`.logval`

.

- -morphooptilogdis <variables> (secondary option)
Log the distributions during the optimization process. The variables can be among those provided in Statistical Distribution Keys.

**Default value**: -.**File extension**:`.logdis#`

.

- -morphooptilogtesr <variables> (secondary option)
Log the raster tessellation voxel data during the optimization process. The variables can be among those provided in Tessellation Optimization Keys.

**Default value**: -.**File extension**:`.logtesr`

.

### Group Options

The following option can be used to define cell groups (each cell is assigned to a group). Groups are computed after tessellation and so can be defined from the cell properties (if scalar and raster tessellations are written in output, groups are computed independently for each of them).

- -group <group_expression>
Specify the groups of the cells. The expression can be:

an integer number or an expression based on the variables defined in Tessellation Keys or Raster Tessellation Keys, for example

`"vol<0.1?1:2"`

;`file(<file_name>)`

: values to load from a Data File.

**Default value**: -.

### Crystal Orientation Options

- -crysym <crysym>
Specify the Crystal Symmetry.

Note

It is used by option

`-orisampling uniform`

, to reduce the domain of definition of the orientation descriptors and by the Visualization Module (-V). See also Rotations and Orientations for the definition of the crystal coordinate system.**Default value**:`triclinic`

.

- -ori <ori_distrib>
Specify the crystal orientation distribution function (ODF). By default, the crystal orientations are sampled randomly from the distribution function (ODF). For uniform sampling, see

`-orisampling`

. The ODF can be:`random`

: ODF = 1, i.e. no or “random” texture (standard case);`odf(mesh=file(<mesh_file>),val=file(<value_file>)[,theta=<theta>)`

: ODF described by`<mesh_file>`

(a mesh of the fundamental region of orientation space),`<value_file>`

(a Data File containing the ODF values at the mesh elements) and`<theta>`

is the (optional, Neper-style) size of the kernel used to general the ODF (1-D standard deviation expressed in degrees, if any).`<orientation>[:<distribution>]`

: a continuous distribution about a discrete orientation (the distribution itself is optional, see below);`fiber(<dirc_x>,<dirc_y>,<dirc_z>,<dirs_x>,<dirs_y>,<dirs_z>)[:normal(<var>=<val>)]`

: orientations along a fiber (see Orientation Fibers), with an optional continuous distribution about the fiber (see below);`parent[:<distribution>]`

: orientations inherited from the ones of the parent cells, with an optional continuous distribution about the nominal orientations (see below);`ks[:<distribution>]`

: orientations inherited from the ones of the parent cells using the Kurdjumov-Sachs (KS) orientation relationship (austenite–ferrite transformation), with an optional continuous distribution about the nominal orientations (see below);`ti_beta2alpha[:<distribution>]`

: orientations inherited from the ones of the parent cells using the \(\beta\rightarrow\alpha\) transformation orientation relationship in titanium, with an optional continuous distribution about the nominal orientations (see below);`file(<file_name>[,des=<descriptor>])`

: discrete orientations to be read from a Data File written using a specific descriptor (see Rotations and Orientations, default`rodrigues`

).`from_morpho`

: discrete orientations read from`-morphooptiini`

`ori`

.

For

`-ori`

`<orientation>`

and`-ori`

`parent`

, the optional distributions are:`normal(<var>=<val>)`

: a 3-variate normal distribution, where`<var>`

can be:`theta`

: the 1D standard deviation;`theta1`

: the standard deviation about direction 1 (\(x\));`theta2`

: the standard deviation about direction 2 (\(y\));`theta3`

: the standard deviation about direction 3 (\(z\));`thetam`

: the average angle of the distribution;

and the value is provided in degrees.

`flat(theta=<val>)`

: a flat (orientation) distribution, where`theta`

is the maximum disorientation angle (in degrees).

An example is

`cube:normal(thetam=5)`

;Finally, a sum of distributions can be provided; e.g.,

`0.5*random+0.1*cube`

.**Default value**:`random`

.

- -orisampling <sampling>
Specify the type of sampling of the orientation distribution. It can be:

`random`

: random sampling;`uniform`

: uniform sampling [7].

Uniform sampling is only available for

`-ori random`

(done according to [JAC2018]).**Default value**:`random`

.

- -orioptialgo <algorithm1>,<algorithm2>,... (secondary option)
Specify the optimization algorithm, which can be:

`subplex`

: Subplex (recommend);`praxis`

: Praxis (recommended, except for high numbers of seeds, where it becomes highly memory-intensive) [11] ;`neldermead`

: Nelder-Mead (not recommended);`cobyla`

: Cobyla (not recommended);`bobyqa`

: Bobyqa (not recommended);`newuoa`

: Newuoa (not recommended).

In several algorithms are provided, the second etc. are used if the previous ones fails.

**Default value**:`subplex,praxis`

.

- -orioptidof <dof1>,<dof2>,... (secondary option)
Specify the degrees of freedom, which can be

`r1`

,`r2`

and`r3`

for the 3 components of the orientation (Rodrigues) vector,`rw`

for the weights, and`rt`

for the orientation spreads. Use this option only if you really now what you are doing.**Default value**:`r1,r2,r3`

.

- -orioptiini <ori_attributes> (secondary option)
Specify the initial crystal orientations and/or their weights and distributions (theta parameter).

`random`

: random orientations;`file(<file_name>[,des=<descriptor>])`

: orientations to be read from a Data File written using a specific descriptor (see Rotations and Orientations, default`rodrigues`

).

**Default value**:`random`

.

- -orioptifix <orientations> (secondary option)
Specify some orientations to fix during optimization. The argument can be:

`file(<file_name>)`

: logical values to load from a Data File;`none`

: none.

**Default value**:`none`

.

- -orioptistop <stopping_criterion> (secondary option)
Specify the stopping criterion of the optimization process, as a logical expression based on the following variables. Depending on the problem (and on the algorithm to solve it), different criteria are available.

For

`-ori random -orisampling uniform`

, the Thomson problem is solved using gradient descent, and the following criteria are available:`reps`

: relative error on the forces at orientations;`iter`

: iteration number.

In other situations, general minimization operates, and the following criteria are available:

`eps`

: absolute error on the value of the objective function evaluated on a number of degrees of freedom basis (`nlopt_eps`

or`nlopt_reps`

are the NLopt iteration-based values);`reps`

: relative error on the value of the objective function evaluated on a number of degrees of freedom basis (`nlopt_eps`

or`nlopt_reps`

are the NLopt iteration-based values);`xeps`

: absolute error on the components of the solution vector;`xreps`

: relative error on the components of the solution vector;`val`

: value of the objective function;`iter`

: number of iterations;`time`

: maximum computation time;`loop`

: number of iteration loops.

Different criteria can be defined for the different algorithms (either

`Thomson`

or`general`

) using the syntax`<algorithm1>:<criterion1>,<algorithm2>:<criterion2>,...`

. The algorithm may be omitted in the case of a universal criterion (or when only one algorithm is used).**Default value**:`"thomson:reps<1e-3||iter>=1e3,general:eps<1e-6"`

.

- -orioptineigh <neighborhood_radius> (secondary option)
Specify the radius of the neighborhood of orientations to be used to compute their forces (for

`-orisampling uniform`

), which can be any mathematical or logical expression based on:`dr`

: average radius of an orientation;`Nstar`

: grand number of orientations (i.e., taking crystal symmetry into account).

**Default value**:`"Nstar<10000?pi:20*dr"`

.

- -orioptilogvar <variables> (secondary option)
Log the variables (the orientations) during the optimization process. The variables can be among those provided in Orientation Optimization Keys.

**Default value**: -.**File extension**:`.logorivar`

.

- -orispread <spread>
Specify the type of (in-cell) orientation spreads. It can be:

`normal(<thetam>)`

: a 3-variate normal distribution corresponding to an average misorientation angle (with respect to the average orientation) of`<thetam>`

(expressed in degree), to be applied to all cells.`file(<file_name>)`

: different cell distributions (of the type`normal...`

), to load from a Data File.`none`

: none.

**Default value**:`none`

.

### Transformation Options

- -transform <transformation1>,<transformation2>,...
Apply successive transformations to a tessellation (if scalar and raster tessellations are written in output, they are transformed independently from each other).

**For a scalar tessellation**, the transformations can be:`translate(<dist_x>,<dist_y>,<dist_z>)`

: translate by distances`<dist_x>`

,`<dist_y>`

and`<dist_z>`

along`x`

,`y`

and`z`

, respectively;`rotate(<axis_x>,<axis_y>,<axis_z>,<angle>)`

: rotate about the center and by an axis/angle pair (angle expressed in degrees);`scale(<fact_x>,<fact_y>,<fact_z>)`

: scale by`<fact_x>`

,`<fact_y>`

and`<fact_z>`

along`x`

,`y`

and`z`

, respectively. For a 2D tessellation,`<fact_z>`

can be omitted.`cut(<primitive1>,<primitive2>,...)`

: cut by a series of geometrical primitives (experimental). The region interior to the primitives is removed from the tessellation. Append`i`

to a primitive name (as in`spherei`

, etc.) for the outer region.The primitives can be:

`hspace[i](<d>,<a>,<b>,<c>)`

: the half-space of equation \(a\,x+b\,y+c\,z \geq d\);`sphere[i](<center_x>,<center_y>,<center_z>,<rad>)`

: a sphere of center (`<center_x>`

,`<center_y>`

,`<center_z>`

) and radius`<rad>`

;`cylinder[i](<basis_x>,<basis_y>,<basis_z>,<axis_x>,<axis_y>,<axis_z>,<rad>)`

: a cylinder of basis point (`<basis_x>`

,`<basis_y>`

,`<basis_z>`

), axis (`<axis_x>`

,`<axis_y>`

,`<axis_z>`

) and radius`<rad>`

;`ecylinder[i](<basis_x>,<basis_y>,<basis_z>,<axis_x>,<axis_y>,<axis_z>,<esaxis1_x>,<esaxis1_y>,<esaxis1_z>,<esaxis2_x>,<esaxis2_y>,<esaxis2_z>,<srad1>,<srad2>)`

: an elliptic cylinder of basis point (`<basis_x>`

,`<basis_y>`

,`<basis_z>`

), axis (`<axis_x>`

,`<axis_y>`

,`<axis_z>`

), ellipse section first axis (`<esaxis1_x>`

,`<esaxis1_y>`

,`<esaxis1_z>`

), ellipse section second axis (`<esaxis2_x>`

,`<esaxis2_y>`

,`<esaxis2_z>`

), ellipse section first radius`<esrad1>`

and ellipse section second radius`<esrad2>`

;`torus[i](<basis_x>,<basis_y>,<basis_z>,<axis_x>,<axis_y>,<axis_z>,<rad>,<srad>)`

: a torus of basis point (`<basis_x>`

,`<basis_y>`

,`<basis_z>`

), axis (`<axis_x>`

,`<axis_y>`

,`<axis_z>`

), radius`<rad>`

and section radius`<srad>`

;`cube[i](<xmin>,<xmax>,<ymin>,<ymax>,<zmin>,<zmax>,<rad>)`

: a cube of \(x\), \(y\) and \(z\) coordinates in the specified ranges, with corners of radius`<rad>`

; the radius can take any value, but should typically be non-zero (and larger than the cell size).

`planecut(<d>,<a>,<b>,<c>)`

: cut by the (oriented) plane of equation \(a\,x+b\,y+c\,z=d\).`crop(<primitive>)`

: crop by a primitive. The primitive can be:`cube(<xmin>,<xmax>,<ymin>,<ymax>,<zmin>,<zmax>)`

for a cube defined by its \(x\), \(y\) and \(z\) bounds.

`slice(<d>,<a>,<b>,<c>)`

for slicing a 3D tessellation by the (oriented) plane of equation \(a\,x+b\,y+c\,z=d\) (yielding to a 2D tessellation).`mergecell(<expr1>,<expr2>,...)`

: merge cells matching successive expressions`<expr1>`

,`<expr2>`

, etc., where expressions are based on the variables defined in Tessellation Keys.`rmcell(<expr1>,<expr2>,...)`

remove cells matching successive expressions`<expr1>`

,`<expr2>`

, etc., where expressions are based on the variables defined in Tessellation Keys.`resetcellid`

: reset cell ids to get a contiguous numbering starting from 1.`resetlabels`

: reset the domain face, edge and vertex labels.`ori(<file_name>[,des=<descriptor>])`

: override cell orientations with ones defined in a Data File written using a specific descriptor (see Rotations and Orientations, default`rodrigues`

).

**For a raster tessellation**, the transformations can be:`translate(<dist_x>,<dist_y>,<dist_z>)`

: translate by distances`<dist_x>`

,`<dist_y>`

and`<dist_z>`

along`x`

,`y`

and`z`

, respectively;`rotate(<axis_x>,<axis_y>,<axis_z>,<angle>)`

: rotate about the center and by an axis/angle pair (angle expressed in degrees);`scale(<fact_x>,<fact_y>,<fact_z>)`

: scale by`<fact_x>`

,`<fact_y>`

and`<fact_z>`

along`x`

,`y`

and`z`

, respectively. For a 2D tessellation,`<fact_z>`

can be omitted.`resetorigin`

: set origin to \((0,\,0,\,0)\).`renumber`

: renumber cells to remove those that are empty or have a zero id.`unindex`

: assign a zero cell id to voxels of orientation \((0,\,0,\,0)\) (in Rodrigues vector).`oriaverage`

: set the cell orientations (field`**cell/ori`

) as the averages of the cell voxel orientations (field`**oridata`

).`crop(<primitive>)`

: crop the raster tessellation by a primitive. The primitive can be:

`cube(<xmin>,<xmax>,<ymin>,<ymax>,<zmin>,<zmax>)`

: cube defined by its \(x\), \(y\) and \(z\) bounds;`cylinder(<center_x>,<center_y>,<diameter>)`

: cylinder of center (\(<center_x>\), \(<center_y>\)) of \(z\) axis,`square(<xmin>,<xmax>,<ymin>,<ymax>)`

: square defined by its \(x\) and \(y\) bounds (2D tessellation only),`circle(<center_x>,<center_y>,<diameter>)`

: circle (2D tessellation only);

`autocrop`

: reduce the raster to its minimal size.`rasterscale(<fact_x>,<fact_y>,<fact_z>)`

: scale the number of voxels of the raster by factors`<fact_x>`

,`<fact_y>`

and`<fact_z>`

along`x`

,`y`

and`z`

, respectively. For a 2D tessellation,`<fact_z>`

can be omitted.`rmsat`

: remove the cell*satellites*, i.e. parts disconnected from the cell bulk.`grow`

: grow the cells to fill the domain.`tessinter(<tess_file>)`

: intersect with tessellation`tess_file`

.`addbuffer(<buff_x>,<buff_y>,<buff_z>)`

: add a buffer of`<buff_x>`

void voxels on both sides in the x direction,`<buff_y>`

void voxels on both sides in the y direction and`<buff_z>`

void voxels on both sides in the z direction.`2d`

: transform a 3D tessellation with 1 voxel along z into a 2D tessellation.

**Default value**: -.

- -sort <sort_expression> (secondary option)
Sort the tessellation cells (typically to facilitate data post-processing) following a mathematical expression based on the tessellation variables (see Tessellation Keys). Sorting is done in ascending order.

Note

Sorting acts on the cell

*ids*, see Tessellation File (.tess).**Default value**: -.

### Regularization Options

- -regularization <logical>
Regularize a tessellation, that is, removes the small edges and (indirectly) faces. Regularization enables meshing with higher-quality elements and generates some slightly non-planar internal faces (in 3D).

Control parameters can be set using options

`-fmax`

,`-sel`

and`-mloop`

.**Default value**:`0`

.

- -fmax <maximum_angle>
Specify the maximum allowed face flatness fault (in degrees). The flatness fault is the maximum angle between the normals at two locations on a face.

**Default value**:`20`

.

- -sel <length> or -rsel <relative_length> [secondary option]
Specify the absolute,

`sel`

, or relative,`rsel`

, (maximum) small edge length.`rsel`

is defined relative to the average cell size (volume in 3D and area in 2D), and a value of`1`

corresponds to a length of 0.25 for a unit volume cell in 3D and 0.125 for a unit area cell in 2D. The value can be:a real value that applies to all cells;

an expression of the form

`<default_sel>,<cell_expr1>:<cell_sel1>,<cell_expr2>:<cell_sel2>...`

to define different cell values, where`<default_sel>`

is the default small edge length,`<cell_expr#>`

is an expression defining the #th set of cells and`<cell_sel#>`

is the corresponding small edge length.`<cell_expr#>`

can be any expression based on variables provided in Tessellation Keys. Expressions are processed successively.`file(<file_name>)`

to load values from a Data File.

The default value allows one to avoid mesh refinement with the default meshing parameters (see option

`-rcl`

), but the value should typically be the same as the one of data`-rcl`

.**Default value**:`-rsel 1`

.

- -mloop <loop_number> [secondary option]
Specify the maximum number of regularization loops. During each loop, the small edges are considered for removal in turn from the shortest to the longest. Regularization stops when the maximum number of loops is reached or no edges are deleted during a loop.

**Default value**:`2`

.

### Output Options

- -o <file_name>
Specify the output file name (no extension).

**Default value**:`n<n>-id<id>`

.

- -format <format1>,<format2>,...
Specify the format(s) of the output file(s), which can be:

tessellation:

`tess`

,`sim`

,`geo`

,`ply`

,`stl[:bycell]`

,`obj`

,`3dec`

,`fe`

,`svg[(unit=<unit>)]`

;raster tessellation:

`tesr`

,`sim`

,`vtk`

;orientations:

`ori`

.

`svg[(unit=<unit>)]`

applies only to 2D tessellations, and`<unit>`

is an optional physical unit (e.g.`mm`

). See Output Files for details on the file formats.**Default value**:`tess`

.

- -tesrformat <format1>,<format2>,...
Specify the format(s) of the raster output file(s), which can be:

`ascii`

: ASCII;`binary8`

: 8-bit binary / unsigned char-type;`binary16`

or`binary16_big`

: 16-bit binary / short-type;`binary32`

or`binary32_big`

: 32-bit binary / int-type.

`binary16`

and`binary32`

correspond to little endianness while`binary16_big`

and`binary32_big`

correspond to big endianness. [6]**Default value**:`binary16`

or`binary_big`

(depending on the system).

- -tesrsize <number_of_voxels>
Specify the number of voxels of a raster tessellation along a direction of the domain, which can be:

`<number>`

: a single integer value (in the case of a domain of different lengths along the different directions, the value is considered as the geometrical average of the number of voxels along the different directions, so that the voxels are as cubic as possible);`<number_x>:<number_y>:<number_z>`

: integer values along the`x`

,`y`

and`z`

directions.

**Default value**:`20`

.

- -oridescriptor <descriptor[:convention]>
Specify the orientation descriptor and (optionally) the orientation convention used in the

`.tess`

,`.tesr`

and`.ori`

files. See Rotations and Orientations for possible values.**Default value**:`rodrigues:passive`

.

- -oriformat <format1>,<format2>,...
Specify the format(s) of the

`.ori`

output file(s), which can be:`plain`

: plain format, for which descriptors are provided on successive lines;`geof`

: Z-set format (Euler angles in Bunge convention are written).

If several formats are specified, the format is appended to the file name as in

`<file_name>.ori-plain`

and`<file_name>.ori-geof`

.**Default value**:`plain`

.

### Post-Processing Options

The following two options provide general statistics on tessellations.

- -stattess <key1>,<key2>,...
Provide statistics on the tessellation. Available keys are described in Tessellation Keys.

**Default value**: -.**File extension**:`.sttess`

.

- -stattesr <key1>,<key2>,...
Provide statistics on the raster tessellation. Available keys are described in Raster Tessellation Keys.

**Default value**: -.**File extension**:`.sttesr`

.

The following options apply to the cells, seeds or cell groups of a tessellation or a raster tessellation, independently of its dimension.

- -statcell <key1>,<key2>,...
Provide statistics on the tessellation cells. Available keys are described in Tessellation Keys and Raster Tessellation Keys.

**Default value**: -.**File extension**:`.stcell`

.

- -statseed <key1>,<key2>,...
Provide statistics on the tessellation seeds. Available keys are described in Tessellation Keys and Raster Tessellation Keys.

**Default value**: -.**File extension**:`.stseed`

.

- -statgroup <key1>,<key2>,...
Provide statistics on the tessellation cell groups. Available keys are described in Tessellation Keys and Raster Tessellation Keys.

**Default value**: -.**File extension**:`.stgroup`

.For a tessellation, it is also possible to get statistics on an per-entity basis using the following options.

- -statver <key1>,<key2>,...
Provide statistics on the tessellation vertices. Available keys are described in Tessellation Keys.

**Default value**: -.**File extension**:`.stver`

.

- -statedge <key1>,<key2>,...
Provide statistics on the tessellation edges. Available keys are described in Tessellation Keys.

**Default value**: -.**File extension**:`.stedge`

.

- -statface <key1>,<key2>,...
Provide statistics on the tessellation faces. Available keys are described in Tessellation Keys.

**Default value**: -.**File extension**:`.stface`

.

- -statpoly <key1>,<key2>,...
Provide statistics on the tessellation polyhedra. Available keys are described in Tessellation Keys.

**Default value**: -.**File extension**:`.stpoly`

.

For a raster tessellation, it is also possible to get statistics on an per-voxel basis,

- -statvox <key1>,<key2>,...
Provide statistics on the tessellation voxels. Available keys are described in Raster Tessellation Keys.

**Default value**: -.**File extension**:`.stvox`

.

Finally, it is possible to get statistics for a particular set of points.

- -statpoint <key1>,<key2>,...
Provide statistics on the points loaded with option

`-loadpoint`

. Available keys are described in Point Keys.**Default value**: -.**File extension**:`.stpoint`

.

### Debugging Options

## Output Files

### Tessellation

`.tess`

: Neper (scalar) tessellation file (see Tessellation File (.tess));`.tesr`

: Neper raster tessellation file (see Raster Tessellation File (.tesr));`.sim`

: Neper simulation directory (see Simulation Directory (.sim));`.geo`

: Gmsh geometry file describing (under a minimal form) the tessellation and can be used for interactive visualization with Gmsh;`.ply`

: Ply (“Polygon File Format”) file describing the tessellation;`.stl`

: STL (“STereoLithography”) file describing the tessellation. If`-format stl:bycell`

is used, a separate file is written for each cell, whose name ends in`-<id>.stl`

, where`id`

is the cell identifier written with leading zeros;`.obj`

: Wavefront geometry file describing the tessellation;`.3dec`

: Itasca 3DEC file describing the tessellation;`.svg`

: SVG file describing the tessellation;`.vtk`

: VTK file describing the raster tessellation and that is supported by Amitex_ffpt. Binary data are always written using big endians;`.ori`

: orientation file describing the crystal orientations of the tessellation cells. The orientations are written on successive lines, using the descriptor specified by option`-oridescriptor`

(see also Rotations and Orientations) and the writing convention specified by option`-oriformat`

.

### Statistics

Statistics files are first provided for the tessellation and raster tessellation. Each file contains the data specified to the corresponding `-stat`

option and as described in Tessellation Keys and Raster Tessellation Keys.

`.sttess`

: tessellation statistics file;`.sttesr`

: raster tessellation statistics file.

Statistics files are also provided for cells, seeds, vertices, edges, faces, polyhedra and points. They are formatted with one line per entity. Each line contains the data specified to the corresponding `-stat`

option and described in Tessellation Keys and Raster Tessellation Keys.

`.stcell`

: tessellation cell statistics file;

`.stseed`

: tessellation seed statistics file;

`.stgroup`

: tessellation cell group statistics file;

`.stver`

: tessellation vertex statistics file;

`.stedge`

: tessellation edge statistics file;

`.stface`

: tessellation face statistics file;

`.stpoly`

: tessellation polyhedron statistics file;

`.stpoint`

: point statistics file.

### Tessellation Optimization Log Files

Log files are provided for the time, variables, statistical distributions and objective function value. The files contain the data specified to the corresponding `-morphooptilog`

option and described in Tessellation Optimization Keys.

`.logtime`

: time file;

`.logvar`

: variables (seed attributes) file;

`.logdis#`

: statistical distribution files;

`.logval`

: objective function value file;

`-obj.tesr`

: target raster tessellation file.

### Orientation Optimization Log Files

A log file is provided for the orientation variables.
The files contain the data specified to the
`-orioptilogvar`

option and described in Orientation Optimization Keys.

`.logorivar`

: variables (orientations) file.

## Examples

Below are some examples of use of neper -T.

Generate a Voronoi tessellation containing 100 cells:

$ neper -T -n 100

Generate a different Voronoi tessellation containing 100 cells (identifier = 2):

$ neper -T -n 100 -id 2

Use an elongated domain and generate a Voronoi tessellation containing 100 cells:

$ neper -T -n 100 -domain "cube(3,1,0.33)"

Generate a Voronoi tessellation containing 100 cells and apply regularization:

$ neper -T -n 100 -reg 1

Generate a 2D Voronoi tessellation containing 100 cells:

$ neper -T -n 100 -dim 2

Generate a tessellation containing 100 cells with an

`x`

columnar axis:$ neper -T -n 100 -morpho "columnar(x)"

Generate a tessellation containing 100 cells with a bamboo structure along

`x`

:$ neper -T -n 100 -morpho "bamboo(x)"

Generate a tessellation containing 100 cells with experimental grain-growth morphological properties:

$ neper -T -n 100 -morpho gg

Generate a tessellation containing 100 cells with experimental grain-growth morphological properties and define groups by splitting cells based on their ids:

$ neper -T -n 100 -morpho gg -group "id<=50?1:2"

Generate a tessellation containing 100 cells with experimental grain-growth morphological properties and an aspect ratio of 2:1:0.5:

$ neper -T -n 100 -morpho "gg,aspratio(2,1,0.5)"

Generate a tessellation containing 100 cells with experimental grain-growth morphological properties, and get the equivalent diameters and sphericities of the cells:

$ neper -T -n 100 -morpho gg -statcell diameq,sphericity

Generate a tessellation of specified absolute grain size distribution (the number of cells is determined accordingly):

$ neper -T -n from_morpho -morpho "diameq:lognormal(0.1,0.03),1-sphericity:lognormal(0.145,0.03)"

Generate a tessellation in a non-convex domain (by cutting the tessellation once generated):

$ neper -T -n 100 -morpho gg -transform "cut(cylinder(1.2,0.5,0.5,0,1,0,0.4))"

Generate a 2-scale Voronoi tessellation containing 100 x 10 cells:

$ neper -T -n 100::10

Generate a 2-scale Voronoi tessellation containing 100 x 10 cells, with different tessellations at scale 2 (identifier = 2) (identifier = 2):

$ neper -T -n 100::10 -id 1::2

Generate a 2-scale tessellation containing 10 primary cells with grain-growth morphological properties, each one divided into lamellae` of width 0.1:

$ neper -T -n 10::from_morpho -morpho "gg::lamellar(w=0.1)"

Generate a 2-scale Voronoi tessellation containing 10 primary cells with grain-growth morphological properties, each one divided into lamellae` of widths loaded from file

`lam_width`

and plane normals loaded from file`lam_normal`

:$ neper -T -n 10::from_morpho -morpho "gg::lamellar(w=msfile(lam_width),v=msfile(lam_normal))" lam_width: 1 0.05 2 0.10 3 0.05 4 0.10 5 0.05 6 0.10 7 0.05 8 0.10 9 0.05 10 0.10 lam_normal: 1 1.000000 0.000000 0.000000 2 0.000000 1.000000 0.000000 3 1.000000 0.000000 0.000000 4 0.000000 1.000000 0.000000 5 1.000000 0.000000 0.000000 6 0.000000 1.000000 0.000000 7 1.000000 0.000000 0.000000 8 0.000000 1.000000 0.000000 9 1.000000 0.000000 0.000000 10 0.000000 1.000000 0.000000

Generate a 2-scale Voronoi tessellation containing 3 primary cells divided into 1, 10 and 100 secondary cells, respectively:

$ neper -T -n "3::msfile(myfile)" -id 1::1 myfile: 1 1 2 10 3 100

Generate a 2-scale Voronoi tessellation containing 2 x 3 cells with specific seed coordinates at both scales (files

`coo1`

and`coo2`

):$ neper -T -n 2::3 -id 1::1 -morphooptiini "coo,file(coo1),weight,0::coo,msfile(coo2),weight,0" -morpho voronoi coo1: 0.25 0.50 0.50 0.75 0.50 0.50 coo2: 1 0.25 0.10 0.50 1 0.25 0.50 0.50 1 0.25 0.90 0.50 2 0.75 0.50 0.10 2 0.75 0.50 0.50 2 0.75 0.50 0.90

Note

`coo1`

is a simple position file (Position File) while`coo2`

is a multiscale cell file (Multiscale Cell File).Generate a Voronoi tessellation containing 100 cells with uniformly distributed crystal orientations and cubic crystal symmetry:

$ neper -T -n 100 -crysym cubic -orisampling uniform

Generate 100 uniformly distributed crystal orientations with cubic crystal symmetry (no tessellation):

$ neper -T -n 100 -crysym cubic -orisampling uniform -for ori

## References

Quey, P.R. Dawson and F. Barbe, Large-scale 3D random polycrystals for the finite element method: Generation, meshing and remeshing, Comput. Methods Appl. Mech. Engrg., vol. 200, pp. 1729-1745, 2011.

Quey and L. Renversade, Optimal polyhedral description of 3D polycrystals: Method and application to statistical and synchrotron X-ray diffraction data, Comput. Methods Appl. Mech. Engrg., vol. 330, pp. 308-333, 2018.

Quey, A. Villani and C. Maurice, Nearly uniform sampling of crystal orientations. J. Appl. Crystallogr., vol. 51, pp. 1162-1173, 2018.

Lyckegaard, E.M. Lauridsen, W. Ludwig, R.W. Fonda, and H.F. Poulsen. On the Use of Laguerre Tessellations for Representations of 3D Grain Structures. Advanced Engineering Materials, vol. 13, pp. 165–170, 2011.