Utilities

extract_edges(array, filter, mode)

Perform edge extraction in the same way as in surfsurf and surfvoid functions from Map and Directional modules. array may be a CUDA array or an ordinary array. filter is a value of AbstractKernel type which selects an edge extraction algorithm. Boundary conditions are affected by mode. Periodic boundary conditions are assumed if mode is Periodic() and reflection from the boundaries is used if mode is NonPeriodic().

NB: We use reflection instead of zero-padding with NonPeriodic() to ensure that $F_{ss}^{(0)} == F_{ss}^{(1)}$ for two-phase media.

See also: AbstractKernel, AbstractMode.

AbstractKernel

Abstract type for one of edge detecting filters.

See also: ConvKernel, ErosionKernel.

ConvKernel(n)

Convolution kernel of width n used in edge detection. Values 5 and 7 are possible values for n. Using n = 7 works best for the most cases (lowfreq_energy_ratio(array) > 0.97).

See also: AbstractKernel, extract_edges.

ErosionKernel(n)

Erosion kernel of width n used in edge detection. Used in three-point surface correlation functions.

See also: AbstractKernel, extract_edges.

right_triangles(array, plane)

Make a set of points for calculation of correlation functions based on three-point statistics. The created set is based of a right triangle with varying lengths of catheti. The second argument defines alignment of the pattern with one of the planes.

See also: AbstractPlane, PlaneXY, PlaneXZ, PlaneYZ.

PlaneXY()

A designator for a plane defined by vectors [1, 0] and [0, 1] (2D case) or [1, 0, 0] and [0, 1, 0] (3D case).

See also: AbstractPlane.

PlaneYZ()

A designator for a plane defined by vectors [0, 1, 0] and [0, 0, 1].

See also: AbstractPlane.

PlaneXZ()

A designator for a plane defined by vectors [1, 0, 0] and [0, 0, 1].

See also: AbstractPlane.

AbstractPlane

Subtypes of AbstractPlane serve as a plane designators for three-point correlation functions.

See also: PlaneXY, PlaneXZ, PlaneYZ.

read_cuboid(datapath :: String, side, dim)

Read 3D array from a disk. The data on the disk must be in binary format, one octet per sample. Totally, there must be $side^{dim}$ octets which are read into $side \times side \times \dots \times side$ array.

lowfreq_energy_ratio(array, fraction = 0.5)

Calculate a ratio $E_a/E$ where $E$ is a total energy of a signal array and $E_a$ is the energy concentrated in frequencies $[0, af/2]$ where $f$ is the sampling rate and $a$ is set via parameter fraction. mean(array) is subtracted from the array before calculations.

This function can be helpful in estimating if array is suitable for calculating surface-surface or surface-void function. An empirical criterion is that if this function returns a value greater than 0.97, the array is good.