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// Voro++, a 3D cell-based Voronoi library
//
// Author : Chris H. Rycroft (LBL / UC Berkeley)
// Email : chr@alum.mit.edu
// Date : August 30th 2011
/** \file voro++.hh
* \brief A file that loads all of the Voro++ header files. */
/** \mainpage Voro++ class reference manual
* \section intro Introduction
* Voro++ is a software library for carrying out three-dimensional computations
* of the Voronoi tessellation. A distinguishing feature of the Voro++ library
* is that it carries out cell-based calculations, computing the Voronoi cell
* for each particle individually, rather than computing the Voronoi
* tessellation as a global network of vertices and edges. It is particularly
* well-suited for applications that rely on cell-based statistics, where
* features of Voronoi cells (eg. volume, centroid, number of faces) can be
* used to analyze a system of particles.
*
* Voro++ is written in C++ and can be built as a static library that can be
* linked to. This manual provides a reference for every function in the class
* structure. For a general overview of the program, see the Voro++ website at
* http://math.lbl.gov/voro++/ and in particular the example programs at
* http://math.lbl.gov/voro++/examples/ that demonstrate many of the library's
* features.
*
* \section class C++ class structure
* The code is structured around several C++ classes. The voronoicell_base
* class contains all of the routines for constructing a single Voronoi cell.
* It represents the cell as a collection of vertices that are connected by
* edges, and there are routines for initializing, making, and outputting the
* cell. The voronoicell_base class form the base of the voronoicell and
* voronoicell_neighbor classes, which add specialized routines depending on
* whether neighboring particle ID information for each face must be tracked or
* not. Collectively, these classes are referred to as "voronoicell classes"
* within the documentation.
*
* There is a hierarchy of classes that represent three-dimensional particle
* systems. All of these are derived from the voro_base class, which contains
* constants that divide a three-dimensional system into a rectangular grid of
* equally-sized rectangular blocks; this grid is used for computational
* efficiency during the Voronoi calculations.
*
* The container_base, container, and container_poly are then derived from the
* voro_base class to represent a particle system in a specific
* three-dimensional rectangular box using both periodic and non-periodic
* boundary conditions. In addition, the container_periodic_base,
* container_periodic, and container_periodic_poly classes represent
* a particle system in a three-dimensional non-orthogonal periodic domain,
* defined by three periodicity vectors that represent a parallelepiped.
* Collectively, these classes are referred to as "container classes" within
* the documentation.
*
* The voro_compute template encapsulates all of the routines for computing
* Voronoi cells. Each container class has a voro_compute template within
* it, that accesses the container's particle system, and computes the Voronoi
* cells.
*
* There are several wall classes that can be used to apply certain boundary
* conditions using additional plane cuts during the Voronoi cell compution.
* The code also contains a number of small loop classes, c_loop_all,
* c_loop_subset, c_loop_all_periodic, and c_loop_order that can be used to
* iterate over a certain subset of particles in a container. The latter class
* makes use of a special particle_order class that stores a specific order of
* particles within the container. The library also contains the classes
* pre_container_base, pre_container, and pre_container_poly, that can be used
* as temporary storage when importing data of unknown size.
*
* \section voronoicell The voronoicell classes
* The voronoicell class represents a single Voronoi cell as a convex
* polyhedron, with a set of vertices that are connected by edges. The class
* contains a variety of functions that can be used to compute and output the
* Voronoi cell corresponding to a particular particle. The command init()
* can be used to initialize a cell as a large rectangular box. The Voronoi cell
* can then be computed by repeatedly cutting it with planes that correspond to
* the perpendicular bisectors between that particle and its neighbors.
*
* This is achieved by using the plane() routine, which will recompute the
* cell's vertices and edges after cutting it with a single plane. This is the
* key routine in voronoicell class. It begins by exploiting the convexity
* of the underlying cell, tracing between edges to work out if the cell
* intersects the cutting plane. If it does not intersect, then the routine
* immediately exits. Otherwise, it finds an edge or vertex that intersects
* the plane, and from there, traces out a new face on the cell, recomputing
* the edge and vertex structure accordingly.
*
* Once the cell is computed, there are many routines for computing features of
* the the Voronoi cell, such as its volume, surface area, or centroid. There
* are also many routines for outputting features of the Voronoi cell, or
* writing its shape in formats that can be read by Gnuplot or POV-Ray.
*
* \subsection internal Internal data representation
* The voronoicell class has a public member p representing the
* number of vertices. The polyhedral structure of the cell is stored
* in the following arrays:
*
* - pts: a one-dimensional array of floating point numbers, that represent the
* position vectors x_0, x_1, ..., x_{p-1} of the polyhedron vertices.
* - nu: the order of each vertex n_0, n_1, ..., n_{p-1}, corresponding to
* the number of other vertices to which each is connected.
* - ed: a two-dimensional table of edges and relations. For the ith vertex,
* ed[i] has 2n_i+1 elements. The first n_i elements are the edges e(j,i),
* where e(j,i) is the jth neighbor of vertex i. The edges are ordered
* according to a right-hand rule with respect to an outward-pointing normal.
* The next n_i elements are the relations l(j,i) which satisfy the property
* e(l(j,i),e(j,i)) = i. The final element of the ed[i] list is a back
* pointer used in memory allocation.
*
* In a very large number of cases, the values of n_i will be 3. This is because
* the only way that a higher-order vertex can be created in the plane()
* routine is if the cutting plane perfectly intersects an existing vertex. For
* random particle arrangements with position vectors specified to double
* precision this should happen very rarely. A preliminary version of this code
* was quite successful with only making use of vertices of order 3. However,
* when calculating millions of cells, it was found that this approach is not
* robust, since a single floating point error can invalidate the computation.
* This can also be a problem for cases featuring crystalline arrangements of
* particles where the corresponding Voronoi cells may have high-order vertices
* by construction.
*
* Because of this, Voro++ takes the approach that it if an existing vertex is
* within a small numerical tolerance of the cutting plane, it is treated as
* being exactly on the plane, and the polyhedral topology is recomputed
* accordingly. However, while this improves robustness, it also adds the
* complexity that n_i may no longer always be 3. This causes memory management
* to be significantly more complicated, as different vertices require a
* different number of elements in the ed[][] array. To accommodate this, the
* voronoicell class allocated edge memory in a different array called mep[][],
* in such a way that all vertices of order k are held in mep[k]. If vertex
* i has order k, then ed[i] points to memory within mep[k]. The array ed[][]
* is never directly initialized as a two-dimensional array itself, but points
* at allocations within mep[][]. To the user, it appears as though each row of
* ed[][] has a different number of elements. When vertices are added or
* deleted, care must be taken to reorder and reassign elements in these
* arrays.
*
* During the plane() routine, the code traces around the vertices of the cell,
* and adds new vertices along edges which intersect the cutting plane to
* create a new face. The values of l(j,i) are used in this computation, as
* when the code is traversing from one vertex on the cell to another, this
* information allows the code to immediately work out which edge of a vertex
* points back to the one it came from. As new vertices are created, the l(j,i)
* are also updated to ensure consistency. To ensure robustness, the plane
* cutting algorithm should work with any possible combination of vertices
* which are inside, outside, or exactly on the cutting plane.
*
* Vertices exactly on the cutting plane create some additional computational
* difficulties. If there are two marginal vertices connected by an existing
* edge, then it would be possible for duplicate edges to be created between
* those two vertices, if the plane routine traces along both sides of this
* edge while constructing the new face. The code recognizes these cases and
* prevents the double edge from being formed. Another possibility is the
* formation of vertices of order two or one. At the end of the plane cutting
* routine, the code checks to see if any of these are present, removing the
* order one vertices by just deleting them, and removing the order two
* vertices by connecting the two neighbors of each vertex together. It is
* possible that the removal of a single low-order vertex could result in the
* creation of additional low-order vertices, so the process is applied
* recursively until no more are left.
*
* \section container The container classes
* There are four container classes available for general usage: container,
* container_poly, container_periodic, and container_periodic_poly. Each of
* these represent a system of particles in a specific three-dimensional
* geometry. They contain routines for importing particles from a text file,
* and adding particles individually. They also contain a large number of
* analyzing and outputting the particle system. Internally, the routines that
* compute Voronoi cells do so by making use of the voro_compute template.
* Each container class contains routines that tell the voro_compute template
* about the specific geometry of this container.
*
* \section voro_compute The voro_compute template
* The voro_compute template encapsulates the routines for carrying out the
* Voronoi cell computations. It contains data structures suchs as a mask and a
* queue that are used in the computations. The voro_compute template is
* associated with a specific container class, and during the computation, it
* calls routines in the container class to access the particle positions that
* are stored there.
*
* The key routine in this class is compute_cell(), which makes use of a
* voronoicell class to construct a Voronoi cell for a specific particle in the
* container. The basic approach that this function takes is to repeatedly cut
* the Voronoi cell by planes corresponding neighboring particles, and stop
* when it recognizes that all the remaining particles in the container are too
* far away to possibly influence cell's shape. The code makes use of two
* possible methods for working out when a cell computation is complete:
*
* - Radius test: if the maximum distance of a Voronoi cell
* vertex from the cell center is R, then no particles more than a distance
* 2R away can possibly influence the cell. This a very fast computation to
* do, but it has no directionality: if the cell extends a long way in one
* direction then particles a long distance in other directions will still
* need to be tested.
* - Region test: it is possible to test whether a specific region can
* possibly influence the cell by applying a series of plane tests at the
* point on the region which is closest to the Voronoi cell center. This is a
* slower computation to do, but it has directionality.
*
* Another useful observation is that the regions that need to be tested are
* simply connected, meaning that if a particular region does not need to be
* tested, then neighboring regions which are further away do not need to be
* tested.
*
* For maximum efficiency, it was found that a hybrid approach making use of
* both of the above tests worked well in practice. Radius tests work well for
* the first few blocks, but switching to region tests after then prevent the
* code from becoming extremely slow, due to testing over very large spherical
* shells of particles. The compute_cell() routine therefore takes the
* following approach:
*
* - Initialize the voronoicell class to fill the entire computational domain.
* - Cut the cell by any wall objects that have been added to the container.
* - Apply plane cuts to the cell corresponding to the other particles which
* are within the current particle's region.
* - Test over a pre-computed worklist of neighboring regions, that have been
* ordered according to the minimum distance away from the particle's
* position. Apply radius tests after every few regions to see if the
* calculation can terminate.
* - If the code reaches the end of the worklist, add all the neighboring
* regions to a new list.
* - Carry out a region test on the first item of the list. If the region needs
* to be tested, apply the plane() routine for all of its particles, and then
* add any neighboring regions to the end of the list that need to be tested.
* Continue until the list has no elements left.
*
* The compute_cell() routine forms the basis of many other routines, such as
* store_cell_volumes() and draw_cells_gnuplot() that can be used to calculate
* and draw the cells in a container.
*
* \section walls Wall computation
* Wall computations are handled by making use of a pure virtual wall class.
* Specific wall types are derived from this class, and require the
* specification of two routines: point_inside() that tests to see if a point
* is inside a wall or not, and cut_cell() that cuts a cell according to the
* wall's position. The walls can be added to the container using the
* add_wall() command, and these are called each time a compute_cell() command
* is carried out. At present, wall types for planes, spheres, cylinders, and
* cones are provided, although custom walls can be added by creating new
* classes derived from the pure virtual class. Currently all wall types
* approximate the wall surface with a single plane, which produces some small
* errors, but generally gives good results for dense particle packings in
* direct contact with a wall surface. It would be possible to create more
* accurate walls by making cut_cell() routines that approximate the curved
* surface with multiple plane cuts.
*
* The wall objects can used for periodic calculations, although to obtain
* valid results, the walls should also be periodic as well. For example, in a
* domain that is periodic in the x direction, a cylinder aligned along the x
* axis could be added. At present, the interior of all wall objects are convex
* domains, and consequently any superposition of them will be a convex domain
* also. Carrying out computations in non-convex domains poses some problems,
* since this could theoretically lead to non-convex Voronoi cells, which the
* internal data representation of the voronoicell class does not support. For
* non-convex cases where the wall surfaces feature just a small amount of
* negative curvature (eg. a torus) approximating the curved surface with a
* single plane cut may give an acceptable level of accuracy. For non-convex
* cases that feature internal angles, the best strategy may be to decompose
* the domain into several convex subdomains, carry out a calculation in each,
* and then add the results together. The voronoicell class cannot be easily
* modified to handle non-convex cells as this would fundamentally alter the
* algorithms that it uses, and cases could arise where a single plane cut
* could create several new faces as opposed to just one.
*
* \section loops Loop classes
* The container classes have a number of simple routines for calculating
* Voronoi cells for all particles within them. However, in some situations it
* is desirable to iterate over a specific subset of particles. This can be
* achieved with the c_loop classes that are all derived from the c_loop_base
* class. Each class can iterate over a specific subset of particles in a
* container. There are three loop classes for use with the container and
* container_poly classes:
*
* - c_loop_all will loop over all of the particles in a container.
* - c_loop_subset will loop over a subset of particles in a container that lie
* within some geometrical region. It can loop over particles in a
* rectangular box, particles in a sphere, or particles that lie within
* specific internal computational blocks.
* - c_loop_order will loop over a specific list of particles that were
* previously stored in a particle_order class.
*
* Several of the key routines within the container classes (such as
* draw_cells_gnuplot and print_custom) have versions where they can be passed
* a loop class to use. Loop classes can also be used directly and there are
* some examples on the library website that demonstrate this. It is also
* possible to write custom loop classes.
*
* In addition to the loop classes mentioned above, there is also a
* c_loop_all_periodic class, that is specifically for use with the
* container_periodic and container_periodic_poly classes. Since the data
* structures of these containers differ considerably, it requires a different
* loop class that is not interoperable with the others.
*
* \section pre_container The pre_container classes
* Voro++ makes use of internal computational grid of blocks that are used to
* configure the code for maximum efficiency. As discussed on the library
* website, the best performance is achieved for around 5 particles per block,
* with anything in the range from 3 to 12 giving good performance. Usually
* the size of the grid can be chosen by ensuring that the number of blocks is
* equal to the number of particles divided by 5.
*
* However, this can be difficult to choose in cases when the number of
* particles is not known a priori, and in thes cases the pre_container classes
* can be used. They can import an arbitrary number of particle positions from
* a file, dynamically allocating memory in chunks as necessary. Once particles
* are imported, they can guess an optimal block arrangement to use for the
* container class, and then transfer the particles to the container. By
* default, this procedure is used by the command-line utility to enable it to
* work well with arbitrary sizes of input data.
*
* The pre_container class can be used when no particle radius information is
* available, and the pre_container_poly class can be used when radius
* information is available. At present, the pre_container classes can only be
* used with the container and container_poly classes. They do not support
* the container_periodic and container_periodic_poly classes. */
#ifndef VOROPP_HH
#define VOROPP_HH
#include "config.hh"
#include "common.hh"
#include "cell.hh"
#include "v_base.hh"
#include "rad_option.hh"
#include "container.hh"
#include "unitcell.hh"
#include "container_prd.hh"
#include "pre_container.hh"
#include "v_compute.hh"
#include "c_loops.hh"
#include "wall.hh"
#endif
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