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-rw-r--r--Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringHu2006.lua113
1 files changed, 40 insertions, 73 deletions
diff --git a/Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringHu2006.lua b/Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringHu2006.lua
index 51e278b904c..b53bc9bff98 100644
--- a/Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringHu2006.lua
+++ b/Master/texmf-dist/tex/generic/pgf/graphdrawing/lua/pgf/gd/force/SpringHu2006.lua
@@ -25,7 +25,7 @@ local declare = require("pgf.gd.interface.InterfaceToAlgorithms").declare
declare {
key = "spring Hu 2006 layout",
algorithm = SpringHu2006,
-
+
preconditions = {
connected = true,
loop_free = true,
@@ -34,20 +34,20 @@ declare {
old_graph_model = true,
- summary = [["
- Implementation of a spring 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.
+ summary = [["
+ Implementation of a spring 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.
"]]
}
@@ -65,26 +65,26 @@ local lib = require("pgf.gd.lib")
function SpringHu2006:run()
-
+
-- Setup some parameters
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.coarsen = options['coarsen']
self.downsize_ratio = options['downsize ratio']
self.minimum_graph_size = options['minimum coarsening size']
-- Setup
-
+
self.downsize_ratio = math.max(0, math.min(1, tonumber(self.downsize_ratio)))
-
+
self.graph_size = #self.graph.nodes
self.graph_density = (2 * #self.graph.edges) / (#self.graph.nodes * (#self.graph.nodes - 1))
@@ -96,7 +96,7 @@ function SpringHu2006:run()
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.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 = 1
@@ -106,20 +106,20 @@ function SpringHu2006:run()
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
+ -- 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
+ -- 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)
+ while coarse_graph:getSize() > self.minimum_graph_size
+ and coarse_graph:getRatio() <= (1 - self.downsize_ratio)
do
coarse_graph:coarsen()
end
@@ -192,8 +192,8 @@ end
function SpringHu2006: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
+ -- 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
@@ -208,7 +208,7 @@ function SpringHu2006:computeInitialLayout(graph, spring_length)
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
+ graph.nodes[1].pos.y = 0
end
-- position the loose node relative to the fixed node, with
@@ -223,16 +223,16 @@ function SpringHu2006:computeInitialLayout(graph, spring_length)
end
else
-- use a random positioning technique
- local function positioning_func(n)
+ local function positioning_func(n)
local radius = 2 * spring_length * self.graph_density * math.sqrt(self.graph_size) / 2
return lib.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)
+ if not node.fixed then
+ node.pos.x = positioning_func(1)
+ node.pos.y = positioning_func(2)
end
end
end
@@ -253,7 +253,7 @@ function SpringHu2006:computeForceLayout(graph, spring_length, step_update_func)
-- 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
@@ -273,39 +273,6 @@ function SpringHu2006:computeForceLayout(graph, spring_length, step_update_func)
for _,v in ipairs(graph.nodes) do
if not v.fixed then
- -- vector for the displacement of v
- local d = Vector.new(2)
-
- 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 + lib.random() * 0.1 end)
- end
-
- local graph_distance = (distances[u] and distances[u][v]) and distances[u][v] or #graph.nodes + 1
-
- -- compute the repulsive force vector
- local force = repulsive_force(delta:norm(), graph_distance, v.weight)
- local force = delta:normalized():timesScalar(force)
-
- -- move the node v accordingly
- d = d:plus(force)
- end
- 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))
-
- -- update the energy function
- energy = energy + math.pow(d:norm(), 2)
-- vector for the displacement of v
local d = Vector.new(2)
@@ -317,7 +284,7 @@ function SpringHu2006:computeForceLayout(graph, spring_length, step_update_func)
-- 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 + lib.random() * 0.1 end)
+ delta:update(function (n, value) return 0.1 + math.random() * 0.1 end)
end
local graph_distance = (distances[u] and distances[u][v]) and distances[u][v] or #graph.nodes + 1
@@ -351,9 +318,9 @@ function SpringHu2006:computeForceLayout(graph, spring_length, step_update_func)
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
+
+ -- 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