def pruner_bakeoff(nPoints, nSeeds, r0, delta, spread, lumpage, outputNameRoot):
"""
Generate a graph, then use all the CANDIDATE_PRUNERS to prune it, and save the results for later investigation.
"""
from spiralPointDistribution import spiralPointDistribution
from pointsToOutwardDigraph import graphFromPoints
#import matplotlib.pyplot as plot
#import matplotlib.tri as tri
import pygraph.readwrite.dot as dotIO
points = spiralPointDistribution(nPoints, nSeeds, r0, delta, spread, lumpage)
outdir = os.path.dirname(outputNameRoot)
if not os.path.exists(outdir):
os.makedirs(outdir)
#x, y = zip(*points)
#plot.figure()
#plot.gca().set_aspect('equal')
#plot.triplot(tri.Triangulation(x, y), 'r,:')
#plot.scatter(x, y)
#plot.show()
#NOT YET FINISHED
#save png as outputNameRoot.prunerName.png (once I get Python graphviz bindings working)
for pruner in CANDIDATE_PRUNERS:
graph = graphFromPoints(points, nSeeds)
graph = friendly_rename(graph)
graph = pruner.prune(graph)
dotstring = dotIO.write(graph)
dotname = "{0}.{1}.gv".format(outputNameRoot, pruner.__class__.__name__)
with open(dotname, "w") as dotfile:
dotfile.write(dotstring)
def buildRandomModel(
nPoints,
nSeeds,
r0,
delta,
spread,
lumpage,
behaviorPaths,
pruner,
prunerPaths=None,
name_prefix="",
bonus_identity=3,
):
"""
Builds a running, randomly-generated network model from the given parameters.
Parameters:
nPoints - Number of nodes that should be in the resulting network, including seed points.
nSeeds - Number of nodes that are "seeds"- have an in-degree of 0. Must be at least 1.
r0 - A parameter that defines how strongly the first few nodes shape the graph. Large values will result in more separation between generators.
delta - A parameter that, in practice, defines how quickly distances grow. Leads to fewer edges as the graph goes further down.
spread - A parameter that affects how many "clusters" the graph will eventually wind up with.
lumpage - An integer that affects how aggressively points cluster. 0 will result in a graph of a random scattering of points, but
otherwise low values result in stricter clusters.
behaviorPaths - A list of strings representing file paths that yapsy should search for IModelBehavior plugins.
pruner - either a pointsToOutwardDigraph.IPruneEdges to use to prune the graph to its final form,
or the name of a plugin implementing IPruneEdges that can be loaded for the purpose.
prunerPaths - Ignored if pruner is not a string. If pruner is a string, that pruner, as the name of a Yapsy plugin,
will be searched for in these paths.
namePrefix - Prefix for all node names (inserted after @ for non-generator nodes). Used when welding multiple graphs.
bonus_identiy - Number of additional times to add the IdentityBehavior to the pool of IModelBehavior that
is drawn from for noise functions. Used to increase the odds that a column will not be
intentionally semi-randomized or modified before presentation to the column printer.
Use 0 to keep standard equal probabilities.
"""
points = spiralPointDistribution.spiralPointDistribution(nPoints, nSeeds, r0, delta, spread, lumpage)
rawCompleteGraph = pointsToOutwardDigraph.graphFromPoints(points, nSeeds)
rawCompleteGraph = pointsToOutwardDigraph.friendly_rename(rawCompleteGraph, name_prefix)
if isinstance(pruner, str):
# TODO: fix
candidatePruners = pointsToOutwardDigraph.prunerImplementations(prunerPaths)
for pluginInfo in candidatePruners:
if pluginInfo.name == pruner:
pruner = pluginInfo.plugin_object
pruner.activate()
break
raise ValueError("No pruner by name {0} found in specified paths.".format(pruner))
trimmedGraph = pruner.prune(rawCompleteGraph)
function_plugins = modelBehaviorImplementations(behaviorPaths)
functions = [plugin.plugin_object for plugin in function_plugins]
return workingModelFromPygraph(trimmedGraph, functions, bonus_identity)
def buildRandomModel(nPoints, nSeeds, r0, delta, spread, lumpage, behaviorPaths, pruner, prunerPaths = None, name_prefix="", bonus_identity = 3):
"""
Builds a running, randomly-generated network model from the given parameters.
Parameters:
nPoints - Number of nodes that should be in the resulting network, including seed points.
nSeeds - Number of nodes that are "seeds"- have an in-degree of 0. Must be at least 1.
r0 - A parameter that defines how strongly the first few nodes shape the graph. Large values will result in more separation between generators.
delta - A parameter that, in practice, defines how quickly distances grow. Leads to fewer edges as the graph goes further down.
spread - A parameter that affects how many "clusters" the graph will eventually wind up with.
lumpage - An integer that affects how aggressively points cluster. 0 will result in a graph of a random scattering of points, but
otherwise low values result in stricter clusters.
behaviorPaths - A list of strings representing file paths that yapsy should search for IModelBehavior plugins.
pruner - either a pointsToOutwardDigraph.IPruneEdges to use to prune the graph to its final form,
or the name of a plugin implementing IPruneEdges that can be loaded for the purpose.
prunerPaths - Ignored if pruner is not a string. If pruner is a string, that pruner, as the name of a Yapsy plugin,
will be searched for in these paths.
namePrefix - Prefix for all node names (inserted after @ for non-generator nodes). Used when welding multiple graphs.
bonus_identiy - Number of additional times to add the IdentityBehavior to the pool of IModelBehavior that
is drawn from for noise functions. Used to increase the odds that a column will not be
intentionally semi-randomized or modified before presentation to the column printer.
Use 0 to keep standard equal probabilities.
"""
points = spiralPointDistribution.spiralPointDistribution(nPoints, nSeeds, r0, delta, spread, lumpage)
rawCompleteGraph = pointsToOutwardDigraph.graphFromPoints(points, nSeeds)
rawCompleteGraph = pointsToOutwardDigraph.friendly_rename(rawCompleteGraph, name_prefix)
if isinstance(pruner, str):
#TODO: fix
candidatePruners = pointsToOutwardDigraph.prunerImplementations(prunerPaths)
for pluginInfo in candidatePruners:
if pluginInfo.name == pruner:
pruner = pluginInfo.plugin_object
pruner.activate()
break
raise ValueError("No pruner by name {0} found in specified paths.".format(pruner))
trimmedGraph = pruner.prune(rawCompleteGraph)
function_plugins =modelBehaviorImplementations(behaviorPaths)
functions = [plugin.plugin_object for plugin in function_plugins]
return workingModelFromPygraph(trimmedGraph, functions, bonus_identity)
def pruner_bakeoff(nPoints, nSeeds, r0, delta, spread, lumpage,
outputNameRoot):
"""
Generate a graph, then use all the CANDIDATE_PRUNERS to prune it, and save the results for later investigation.
"""
from spiralPointDistribution import spiralPointDistribution
from pointsToOutwardDigraph import graphFromPoints
#import matplotlib.pyplot as plot
#import matplotlib.tri as tri
import pygraph.readwrite.dot as dotIO
points = spiralPointDistribution(nPoints, nSeeds, r0, delta, spread,
lumpage)
outdir = os.path.dirname(outputNameRoot)
if not os.path.exists(outdir):
os.makedirs(outdir)
#x, y = zip(*points)
#plot.figure()
#plot.gca().set_aspect('equal')
#plot.triplot(tri.Triangulation(x, y), 'r,:')
#plot.scatter(x, y)
#plot.show()
#NOT YET FINISHED
#save png as outputNameRoot.prunerName.png (once I get Python graphviz bindings working)
for pruner in CANDIDATE_PRUNERS:
graph = graphFromPoints(points, nSeeds)
graph = friendly_rename(graph)
graph = pruner.prune(graph)
dotstring = dotIO.write(graph)
dotname = "{0}.{1}.gv".format(outputNameRoot,
pruner.__class__.__name__)
with open(dotname, "w") as dotfile:
dotfile.write(dotstring)