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Diffstat (limited to 'Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringElectricalHu2006.lua')
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diff --git a/Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringElectricalHu2006.lua b/Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringElectricalHu2006.lua new file mode 100644 index 00000000000..54ceddcc25a --- /dev/null +++ b/Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringElectricalHu2006.lua @@ -0,0 +1,521 @@ +-- Copyright 2011 by Jannis Pohlmann +-- +-- This file may be distributed and/or modified +-- +-- 1. under the LaTeX Project Public License and/or +-- 2. under the GNU Public License +-- +-- See the file doc/generic/pgf/licenses/LICENSE for more information + +-- @release $Header: /cvsroot/pgf/pgf/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringElectricalHu2006.lua,v 1.1 2012/11/27 17:24:25 tantau Exp $ + + +local SpringElectricalHu2006 = {} + +-- Imports +local declare = require("pgf.gd.interface.InterfaceToAlgorithms").declare + + + + +--- + +declare { + key = "spring electrical Hu 2006 layout", + algorithm = SpringElectricalHu2006, + + preconditions = { + connected = true, + loop_free = true, + simple = true, + }, + + old_graph_model = true, + + summary = [[" + Implementation of a spring electrical graph drawing algorithm based on + a paper by Hu. + "]], + documentation = [[" + \begin{itemize} + \item + Y. Hu. + \newblock Efficient, high-quality force-directed graph drawing. + \newblock \emph{The Mathematica Journal}, 2006. + \end{itemize} + + There are some modifications compared to the original algorithm, + see the Diploma thesis of Pohlmann for details. + "]] +} + +-- Imports + +local PathLengths = require "pgf.gd.lib.PathLengths" +local Vector = require "pgf.gd.deprecated.Vector" + +local QuadTree = require "pgf.gd.force.QuadTree" +local CoarseGraph = require "pgf.gd.force.CoarseGraph" + +local lib = require "pgf.gd.lib" + + +function SpringElectricalHu2006:run() + + -- Setup properties + local options = self.digraph.options + + self.iterations = options['iterations'] + self.cooling_factor = options['cooling factor'] + self.initial_step_length = options['initial step length'] + self.convergence_tolerance = options['convergence tolerance'] + + self.natural_spring_length = options['node distance'] + self.spring_constant = options['spring constant'] + + self.approximate_repulsive_forces = options['approximate remote forces'] + self.repulsive_force_order = options['electric force order'] + + self.coarsen = options['coarsen'] + self.downsize_ratio = options['downsize ratio'] + self.minimum_graph_size = options['minimum coarsening size'] + + -- Adjust types + self.downsize_ratio = math.max(0, math.min(1, self.downsize_ratio)) + self.graph_size = #self.graph.nodes + self.graph_density = (2 * #self.graph.edges) / (#self.graph.nodes * (#self.graph.nodes - 1)) + + -- validate input parameters + assert(self.iterations >= 0, 'iterations (value: ' .. self.iterations .. ') need to be greater than 0') + assert(self.cooling_factor >= 0 and self.cooling_factor <= 1, 'the cooling factor (value: ' .. self.cooling_factor .. ') needs to be between 0 and 1') + assert(self.initial_step_length >= 0, 'the initial step length (value: ' .. self.initial_step_length .. ') needs to be greater than or equal to 0') + assert(self.convergence_tolerance >= 0, 'the convergence tolerance (value: ' .. self.convergence_tolerance .. ') needs to be greater than or equal to 0') + assert(self.natural_spring_length >= 0, 'the natural spring dimension (value: ' .. self.natural_spring_length .. ') needs to be greater than or equal to 0') + assert(self.spring_constant >= 0, 'the spring constant (value: ' .. self.spring_constant .. ') needs to be greater or equal to 0') + assert(self.downsize_ratio >= 0 and self.downsize_ratio <= 1, 'the downsize ratio (value: ' .. self.downsize_ratio .. ') needs to be between 0 and 1') + assert(self.minimum_graph_size >= 2, 'the minimum coarsening size of coarse graphs (value: ' .. self.minimum_graph_size .. ') needs to be greater than or equal to 2') + + -- initialize node weights + for _,node in ipairs(self.graph.nodes) do + node.weight = node:getOption('electric charge') + end + + -- initialize edge weights + for _,edge in ipairs(self.graph.edges) do + edge.weight = 1 + end + + -- initialize the coarse graph data structure. note that the algorithm + -- is the same regardless whether coarsening is used, except that the + -- number of coarsening steps without coarsening is 0 + local coarse_graph = CoarseGraph.new(self.graph) + + -- check if the multilevel approach should be used + if self.coarsen then + -- coarsen the graph repeatedly until only minimum_graph_size nodes + -- are left or until the size of the coarse graph was not reduced by + -- at least the downsize ratio configured by the user + while coarse_graph:getSize() > self.minimum_graph_size + and coarse_graph:getRatio() <= (1 - self.downsize_ratio) + do + coarse_graph:coarsen() + end + end + + if self.coarsen then + -- use the natural spring length as the initial natural spring length + local spring_length = self.natural_spring_length + + -- compute a random initial layout for the coarsest graph + self:computeInitialLayout(coarse_graph.graph, spring_length) + + -- set the spring length to the average edge length of the initial layout + spring_length = 0 + for _,edge in ipairs(coarse_graph.graph.edges) do + spring_length = spring_length + edge.nodes[1].pos:minus(edge.nodes[2].pos):norm() + end + spring_length = spring_length / #coarse_graph.graph.edges + + -- additionally improve the layout with the force-based algorithm + -- if there are more than two nodes in the coarsest graph + if coarse_graph:getSize() > 2 then + self:computeForceLayout(coarse_graph.graph, spring_length, SpringElectricalHu2006.adaptive_step_update) + end + + -- undo coarsening step by step, applying the force-based sub-algorithm + -- to every intermediate coarse graph as well as the original graph + while coarse_graph:getLevel() > 0 do + + -- compute the diameter of the parent coarse graph + local parent_diameter = PathLengths.pseudoDiameter(coarse_graph.graph) + + -- interpolate the previous coarse graph from its parent + coarse_graph:interpolate() + + -- compute the diameter of the current coarse graph + local current_diameter = PathLengths.pseudoDiameter(coarse_graph.graph) + + -- scale node positions by the quotient of the pseudo diameters + for _,node in ipairs(coarse_graph.graph) do + node.pos:update(function (n, value) + return value * (current_diameter / parent_diameter) + end) + end + + -- compute forces in the graph + self:computeForceLayout(coarse_graph.graph, spring_length, SpringElectricalHu2006.conservative_step_update) + end + else + -- compute a random initial layout for the coarsest graph + self:computeInitialLayout(coarse_graph.graph, self.natural_spring_length) + + -- set the spring length to the average edge length of the initial layout + spring_length = 0 + for _,e in ipairs(coarse_graph.graph.edges) do + spring_length = spring_length + edge.nodes[1].pos:minus(edge.nodes[2].pos):norm() + end + spring_length = spring_length / #coarse_graph.graph.edges + + -- improve the layout with the force-based algorithm + self:computeForceLayout(coarse_graph.graph, spring_length, SpringElectricalHu2006.adaptive_step_update) + end +end + + + +function SpringElectricalHu2006:computeInitialLayout(graph, spring_length) + -- TODO how can supernodes and fixed nodes go hand in hand? + -- maybe fix the supernode if at least one of its subnodes is + -- fixated? + + -- fixate all nodes that have a 'desired at' option. this will set the + -- node.fixed member to true and also set node.pos.x and node.pos.y + self:fixateNodes(graph) + + if #graph.nodes == 2 then + if not (graph.nodes[1].fixed and graph.nodes[2].fixed) then + local fixed_index = graph.nodes[2].fixed and 2 or 1 + local loose_index = graph.nodes[2].fixed and 1 or 2 + + if not graph.nodes[1].fixed and not graph.nodes[2].fixed then + -- both nodes can be moved, so we assume node 1 is fixed at (0,0) + graph.nodes[1].pos.x = 0 + graph.nodes[1].pos.y = 0 + end + + -- position the loose node relative to the fixed node, with + -- the displacement (random direction) matching the spring length + local direction = Vector.new{x = math.random(1, spring_length), y = math.random(1, spring_length)} + local distance = 3 * spring_length * self.graph_density * math.sqrt(self.graph_size) / 2 + local displacement = direction:normalized():timesScalar(distance) + + graph.nodes[loose_index].pos = graph.nodes[fixed_index].pos:plus(displacement) + else + -- both nodes are fixed, initial layout may be far from optimal + end + else + + -- use a random positioning technique + local function positioning_func(n) + local radius = 3 * spring_length * self.graph_density * math.sqrt(self.graph_size) / 2 + return math.random(-radius, radius) + end + + -- compute initial layout based on the random positioning technique + for _,node in ipairs(graph.nodes) do + if not node.fixed then + node.pos.x = positioning_func(1) + node.pos.y = positioning_func(2) + end + end + end +end + + + +function SpringElectricalHu2006:computeForceLayout(graph, spring_length, step_update_func) + -- global (=repulsive) force function + function accurate_repulsive_force(distance, weight) + -- note: the weight is taken into the equation here. unlike in the original + -- algorithm different electric charges are allowed for each node in this + -- implementation + return - weight * self.spring_constant * math.pow(spring_length, self.repulsive_force_order + 1) / math.pow(distance, self.repulsive_force_order) + end + + -- global (=repulsive, approximated) force function + function approximated_repulsive_force(distance, mass) + return - mass * self.spring_constant * math.pow(spring_length, self.repulsive_force_order + 1) / math.pow(distance, self.repulsive_force_order) + end + + -- local (spring) force function + function attractive_force(distance) + return (distance * distance) / spring_length + end + + -- define the Barnes-Hut opening criterion + function barnes_hut_criterion(cell, particle) + local distance = particle.pos:minus(cell.center_of_mass):norm() + return cell.width / distance <= 1.2 + end + + -- fixate all nodes that have a 'desired at' option. this will set the + -- node.fixed member to true and also set node.pos.x and node.pos.y + self:fixateNodes(graph) + + -- adjust the initial step length automatically if desired by the user + local step_length = self.initial_step_length == 0 and spring_length or self.initial_step_length + + -- convergence criteria etc. + local converged = false + local energy = math.huge + local iteration = 0 + local progress = 0 + + while not converged and iteration < self.iterations do + -- remember old node positions + local old_positions = lib.map(graph.nodes, function (node) + return node.pos:copy(), node + end) + + -- remember the old system energy and reset it for the current iteration + local old_energy = energy + energy = 0 + + -- build the quadtree for approximating repulsive forces, if desired + local quadtree = nil + if self.approximate_repulsive_forces then + quadtree = self:buildQuadtree(graph) + end + + for _,v in ipairs(graph.nodes) do + if not v.fixed then + -- vector for the displacement of v + local d = Vector.new(2) + + -- compute repulsive forces + if self.approximate_repulsive_forces then + -- determine the cells that have a repulsive influence on v + local cells = quadtree:findInteractionCells(v, barnes_hut_criterion) + + -- compute the repulsive force between these cells and v + for _,cell in ipairs(cells) do + -- check if the cell is a leaf + if #cell.subcells == 0 then + -- compute the forces between the node and all particles in the cell + for _,particle in ipairs(cell.particles) do + local real_particles = lib.copy(particle.subparticles) + table.insert(real_particles, particle) + + for _,real_particle in ipairs(real_particles) do + local delta = real_particle.pos:minus(v.pos) + + -- enforce a small virtual distance if the node and the cell's + -- center of mass are located at (almost) the same position + if delta:norm() < 0.1 then + delta:update(function (n, value) return 0.1 + math.random() * 0.1 end) + end + + -- compute the repulsive force vector + local repulsive_force = approximated_repulsive_force(delta:norm(), real_particle.mass) + local force = delta:normalized():timesScalar(repulsive_force) + + -- move the node v accordingly + d = d:plus(force) + end + end + else + -- compute the distance between the node and the cell's center of mass + local delta = cell.center_of_mass:minus(v.pos) + + -- enforce a small virtual distance if the node and the cell's + -- center of mass are located at (almost) the same position + if delta:norm() < 0.1 then + delta:update(function (n, value) return 0.1 + math.random() * 0.1 end) + end + + -- compute the repulsive force vector + local repulsive_force = approximated_repulsive_force(delta:norm(), cell.mass) + local force = delta:normalized():timesScalar(repulsive_force) + + -- move the node v accordingly + d = d:plus(force) + end + end + else + for _,u in ipairs(graph.nodes) do + if v ~= u then + -- compute the distance between u and v + local delta = u.pos:minus(v.pos) + + -- enforce a small virtual distance if the nodes are + -- located at (almost) the same position + if delta:norm() < 0.1 then + delta:update(function (n, value) return 0.1 + math.random() * 0.1 end) + end + + -- compute the repulsive force vector + local repulsive_force = accurate_repulsive_force(delta:norm(), u.weight) + local force = delta:normalized():timesScalar(repulsive_force) + + -- move the node v accordingly + d = d:plus(force) + end + end + end + + -- compute attractive forces between v and its neighbours + for _,edge in ipairs(v.edges) do + local u = edge:getNeighbour(v) + + -- compute the distance between u and v + local delta = u.pos:minus(v.pos) + + -- enforce a small virtual distance if the nodes are + -- located at (almost) the same position + if delta:norm() < 0.1 then + delta:update(function (n, value) return 0.1 + math.random() * 0.1 end) + end + + -- compute the spring force vector between u and v + local attr_force = attractive_force(delta:norm()) + local force = delta:normalized():timesScalar(attr_force) + + -- move the node v accordingly + d = d:plus(force) + end + + -- really move the node now + -- TODO note how all nodes are moved by the same amount (step_length) + -- while Walshaw multiplies the normalized force with min(step_length, + -- d:norm()). could that improve this algorithm even further? + v.pos = v.pos:plus(d:normalized():timesScalar(step_length)) + + -- TODO Hu doesn't mention this but the energy of a particle is + -- typically considered as the product of its mass and the square of + -- its forces. This means we should probably take the weight of + -- the node v into the equation, doesn't it? + -- + -- update the energy function + energy = energy + math.pow(d:norm(), 2) + end + end + + -- update the step length and progress counter + step_length, progress = step_update_func(step_length, self.cooling_factor, energy, old_energy, progress) + + -- compute the maximum node movement in this iteration + local max_movement = 0 + for _,x in ipairs(graph.nodes) do + local delta = x.pos:minus(old_positions[x]) + max_movement = math.max(delta:norm(), max_movement) + end + + -- the algorithm will converge if the maximum movement is below a + -- threshold depending on the spring length and the convergence + -- tolerance + if max_movement < spring_length * self.convergence_tolerance then + converged = true + end + + -- increment the iteration counter + iteration = iteration + 1 + end +end + + + +-- Fixes nodes at their specified positions. +-- +function SpringElectricalHu2006:fixateNodes(graph) + local number_of_fixed_nodes = 0 + + for _,node in ipairs(graph.nodes) do + -- read the 'desired at' option of the node + local coordinate = node:getOption('desired at') + + if coordinate then + -- apply the coordinate + node.pos.x = coordinate.x + node.pos.y = coordinate.y + + -- mark the node as fixed + node.fixed = true + + number_of_fixed_nodes = number_of_fixed_nodes + 1 + end + end + if number_of_fixed_nodes > 1 then + self.growth_direction = "fixed" -- do not grow, orientation is now fixed + end +end + + + +function SpringElectricalHu2006:buildQuadtree(graph) + -- compute the minimum x and y coordinates of all nodes + local min_pos = graph.nodes[1].pos + for _,node in ipairs(graph.nodes) do + min_pos = Vector.new(2, function (n) return math.min(min_pos[n], node.pos[n]) end) + end + + -- compute maximum x and y coordinates of all nodes + local max_pos = graph.nodes[1].pos + for _,node in ipairs(graph.nodes) do + max_pos = Vector.new(2, function (n) return math.max(max_pos[n], node.pos[n]) end) + end + + -- make sure the maximum position is at least a tiny bit + -- larger than the minimum position + if min_pos:equals(max_pos) then + max_pos = max_pos:plus(Vector.new(2, function (n) + return 0.1 + math.random() * 0.1 + end)) + end + + -- make sure to make the quadtree area slightly larger than required + -- in theory; for some reason Lua will otherwise think that nodes with + -- min/max x/y coordinates are outside the box... weird? yes. + min_pos = min_pos:minus({1,1}) + max_pos = max_pos:plus({1,1}) + + -- create the quadtree + quadtree = QuadTree.new(min_pos.x, min_pos.y, + max_pos.x - min_pos.x, + max_pos.y - min_pos.y) + + -- insert nodes into the quadtree + for _,node in ipairs(graph.nodes) do + local particle = QuadTree.Particle.new(node.pos, node.weight) + particle.node = node + quadtree:insert(particle) + end + + return quadtree +end + + + +function SpringElectricalHu2006.conservative_step_update(step, cooling_factor) + return cooling_factor * step, nil +end + + + +function SpringElectricalHu2006.adaptive_step_update(step, cooling_factor, energy, old_energy, progress) + if energy < old_energy then + progress = progress + 1 + if progress >= 5 then + progress = 0 + step = step / cooling_factor + end + else + progress = 0 + step = cooling_factor * step + end + return step, progress +end + + +-- done + +return SpringElectricalHu2006
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