schiffgeometry(5)
NAME
schiffgeometry  control the shape of soft particlesDESCRIPTION
schiffgeometry is a YAML file [1] that controls the geometry distribution of soft particles. The schiff(1) program relies on this description to generate the shape of the sampled soft particles.A geometry is defined by a type and a set of parameters whose value is controlled by a distribution. Several geometries with their own probability can be declared in the same schiffgeometry file to define a discrete random variate of geometries. This allow to finely tune the shapes of the soft particles with a collection of geometries, each representing a specific subset of shapes of the soft particles to handle.
GRAMMAR
This section describes the schiffgeometry grammar based on the YAML human readable data format [1]. The YAML format provides several ways to define a mapping or a sequence of data. The following grammar always uses the more verbose form but any alternative YAML formatting can be used instead. Refer to the example section for illustrations of such alternatives.When the radius_sphere optional parameter is defined, the relative shape of the geometry must be fixed, i.e. all other parameters must be constants. In this situation, only the volume of the geometry is variable; it is equal to the volume of an equivalent sphere whose radius is controlled by the distribution of the radius_sphere parameter.
The slices optional attribute controls the discretization of the geometries in triangular meshes, i.e. the number of discrete steps around 2PI. When not defined it is assumed to be 64. Note that the helical_pipe geometry exposes 2 discretization parameters: slices_circle and slices_helicoid. The former controls the discretization of the meridian around 2PI while the later defines the total number of discrete steps along the helicoid curve. When not defined slices_circle and slices_helicoid are set to 64 and 128, respectively.
All the geometries have the proba optional attribute that defines the unnormalized probability to sample the geometry. If it is not defined, it is assumed to be equal to 1.

<schiffgeometry> ::= <geometry>  <geometrylist> <geometrylist> ::=  <geometry> [  <geometry> ] <geometry> ::= <cylindergeometry>  <ellipsoidgeometry>  <helicalpipegeometry>  <spheregeometry>  <supershapegeometry> <cylindergeometry> ::= cylinder: radius: <distribution> height: <distribution> [ radius_sphere: <distribution> ] [ slices: INTEGER ] [ proba: REAL ] <ellipsoidgeometry> ::= ellipsoid: a: <distribution> c: <distribution> [ radius_sphere: <distribution> ] [ slices: INTEGER ] [ proba: REAL ] <helicalpipegeometry> ::= helical_pipe: pitch: <distribution> height: <distribution> radius_helicoid: <distribution> radius_circle: <distribution> [ radius_sphere: <distribution> ] [ slices_helicoid: INTEGER ] [ slices_circle: INTEGER ] <spheregeometry> ::= sphere: radius: <distribution> [ slices: INTEGER ] [ proba: REAL ] <supershapegeometry> ::= supershape: formula0: <superformula> formula1: <superformula> [ radius_sphere: <distribution> ] [ slices: INTEGER ] [ proba: REAL ] <superformula> ::= A: <distribution> B: <Idistribution> M: <distribution> N0: <distribution> N1: <distribution> N2: <distribution>  <distribution> ::= <constant>  <gaussian>  <histogram>  <lognormal> <constant> ::= REAL <lognormal> ::= lognormal: mu: REAL sigma: REAL <gaussian> ::= gaussian: mu: REAL sigma: REAL <histogram> ::= histogram: lower: REAL upper: REAL probabilities: <probabilitieslist> <probabilitieslist> ::=  REAL [  <probabilitieslist> ]
GEOMETRY TYPES
cylinder
 A cylinder is simply defined by its height and a its radius.
ellipsoid
 The shape of an ellipsoid geometry is controlled by the length
of its semiprincipal axises a and c used to evaluate
the following equation:

(x/a)^2 + (y/a)^2 + (z/c)^2 = 1

helical_pipe

Helicoid whose meridian shape is a circle that is orthogonal to the helicoid slope. Its pitch defines the width of a complete helicoid turn and its height controls the overall distance between the beginning and the end of the helicoid. Finally, the radius_helicoid and the radius_circle defines the radius of the helicoid and the radius of its meridian, respectively. Let "u" in [0, height * 2PI / pitch] and "t" in [0, 2PI], the "X", "Y" and "Z" 3D coordinates of the helicoid points are computed from the following equations:

X(t, u) = x(t)*cos(u)  y(t)*sin(u) Y(t, u) = x(t)*sin(u) + y(t)*cos(u) Z(t, u) = z(t) + c*u x(t) = radius_helicoid + radius_circle*cos(t) y(t) = radius_circle * c / A * sin(t) z(t) = radius_circle*radius_helicoid / A * sin(t) c = pitch / 2PI A = sqrt(radius_helicoid^2 + c^2)

sphere

A sphere is simply defined by its radius.
supershape

Generalisation of the superellipsoid that is well suited to represent many complex shapes found in the nature. It is controlled by 2 superformulas, each defining a radius "r" for a given angle "a":

r(a) = ( cos(M*a/4)/A)^N1 + sin(M*a/4)/B^N2 )^{1/N0}
Assuming a point with the spherical coordinates {theta, phi}, the corresponding 3D coordinates onto the supershape is obtained by evaluating the following relations:

x = r0(theta)*cos(theta) * r1(phi)*cos(phi) y = r0(theta)*sin(theta) * r1(phi)*cos(phi) z = r1(phi)*sin(phi)

PARAMETER DISTRIBUTIONS
constant

Fixe the value of the parameter.
gaussian

Use the following probability distribution to define the parameter according to the mean value mu and the standard deviation sigma:

P(x) dx = 1 / (sigma*sqrt(2*PI)) * exp(1/2*((xmu)/sigma)^2) dx

histogram

Split the parameter domain [lower, upper] in N intervals of length (upperlower)/N. The list of unnormalized probabilities of the interval bounds are listed in the probabilities array and are used to build the cumulative distribution of the parameter. Let a random number "r" in [0, 1], the corresponding parameter value is computed by retrieving the interval of the parameter from the aforementioned cumulative, before linearly interpolating its bounds with respect to "r";
lognormal

Distribute the parameter with respect to a mean value mu and a standard deviation sigma as follow:

P(x) dx = 1/(log(sigma)*x*sqrt(2*PI) * exp((ln(x)log(mu))^2 / (2*log(sigma)^2)) dx

EXAMPLES
Soft particles are spheres whose radius is distributed according to an histogram:

sphere: radius: histogram: lower: 1.0 # Min radius upper: 2.1 # Max radius probabilities:  2  1  0.4  1.23  3
Soft particles are ellipsoids whose one of its semiprincipal axis is distributed with respect to a lognormal distribution:

ellipsoid: a: 1.0 c: lognormal: sigma: 0.2 mu: 1.3
Soft particles are ellipsoids whose semiprincipal axises are fixed. Its volume is equal to the volume of an equivalent sphere whose radius follows an histogram distribution:

ellipsoid: a: 1.1 b: 0.3 radius_sphere: histogram: lower: 1 upper: 2.5 probabilities: [ 0.5, 2, 1 ]
Soft particles are cylinders. Their radius is constant and their height is distributed according to a gaussian distribution. The cylinder geometry is discretized in 128 slices along 2PI:

cylinder: slices: 128 radius: 1 height: { gaussian: { mu: 1.3, sigma: 0.84 } }
Soft particles are cylinders whose height and radius are fixed. Their volume is equal to the volume of a sphere whose radius is distributed with respect to an histogram:

cylinder: height: 1.2 radius: 3.4 radius_sphere: histogram: lower: 1.24 upper: 4.56 probabilities: [ 2, 1.2, 3, 0.2 ]
Soft particle are helical pipes whose attributes are controlled by several distribution types. Their helicoid curve is split in 256 steps while its meridian is discretized in 128 slices:

helical_pipe: slices_helicoid: 256 slices_circle: 128 height : 4 pitch : { gaussian: { mu: 3, sigma: 1.3} } radius_helicoid: { lognormal: { mu: 2, sigma: 0.4} } radius_circle: histogram: lower: 1 upper: 1.5 probabilities: [ 1, 1.2, 0.2, 0.5, 1.4 ]
Soft particles are supershapes whose 2 parameters of each of its superformulas are controlled by gaussian distributions:

supershape: formula0: A: 1 B: 1 M: { gaussian: { mu: 5, sigma: 1 } } N0: 1 N1: 1 N2: { gaussian: { mu: 3, sigma: 1 } } formula1: A: 1 B: 1 M: { gaussian: { mu: 1.2, sigma: 0.3 } } N0: 1 N1: 1 N2: { gaussian: { mu: 1, sigma: 0.3 } }
Soft particles are supershapes with the same shape. Their volume is controlled by an equivalent sphere whose radius follows a lognormal distribution:

supershape: formula0: { A: 1, B: 1, M: 3, N0: 3, N1: 3, N2: 5 } formula1: { A: 2, B: 1.1, M: 3, N0: 1, N1: 1, N2: 1 } radius_sphere : { lognormal: { mu: 2.2, sigma: 1.3 } }
Soft particles are spheres and cylinders with 2 times more spheres than cylinders. The cylinder parameters are controlled by lognormal distributions and spherical soft particles have a fixed radius:

 sphere: { radius: 1.12, proba: 2.0, slices: 64 }  cylinder: radius: {lognormal: { sigma: 2.3, mu: 0.2 } } height: {lognormal: { mu: 1, sigma: 1.5 } } slices: 32 # Discretisation in 32 slices proba: 1
NOTES
[1] YAML Ain't Markup Language  http://yaml.org