def test_rdflib_to_graphtool():
try:
from graph_tool import util as gt_util
except ImportError:
raise SkipTest("couldn't find graph_tool")
from rdflib.extras.external_graph_libs import rdflib_to_graphtool
g = Graph()
a, b, l = URIRef('a'), URIRef('b'), Literal('l')
p, q = URIRef('p'), URIRef('q')
edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)]
for t in edges:
g.add(t)
mdg = rdflib_to_graphtool(g)
assert len(list(mdg.edges())) == 4
vpterm = mdg.vertex_properties['term']
va = gt_util.find_vertex(mdg, vpterm, a)[0]
vb = gt_util.find_vertex(mdg, vpterm, b)[0]
vl = gt_util.find_vertex(mdg, vpterm, l)[0]
assert (va, vb) in [(e.source(), e.target()) for e in list(mdg.edges())]
epterm = mdg.edge_properties['term']
assert len(list(gt_util.find_edge(mdg, epterm, p))) == 3
assert len(list(gt_util.find_edge(mdg, epterm, q))) == 1
mdg = rdflib_to_graphtool(
g,
e_prop_names=[text_type('name')],
transform_p=lambda s, p, o: {text_type('name'): text_type(p)})
epterm = mdg.edge_properties['name']
assert len(list(gt_util.find_edge(mdg, epterm, text_type(p)))) == 3
assert len(list(gt_util.find_edge(mdg, epterm, text_type(q)))) == 1
def test_rdflib_to_graphtool():
try:
from graph_tool import util as gt_util
except ImportError:
raise SkipTest("couldn't find graph_tool")
from rdflib.extras.external_graph_libs import rdflib_to_graphtool
g = Graph()
a, b, l = URIRef("a"), URIRef("b"), Literal("l")
p, q = URIRef("p"), URIRef("q")
edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)]
for t in edges:
g.add(t)
mdg = rdflib_to_graphtool(g)
assert len(list(mdg.edges())) == 4
vpterm = mdg.vertex_properties["term"]
va = gt_util.find_vertex(mdg, vpterm, a)[0]
vb = gt_util.find_vertex(mdg, vpterm, b)[0]
vl = gt_util.find_vertex(mdg, vpterm, l)[0]
assert (va, vb) in [(e.source(), e.target()) for e in list(mdg.edges())]
epterm = mdg.edge_properties["term"]
assert len(list(gt_util.find_edge(mdg, epterm, p))) == 3
assert len(list(gt_util.find_edge(mdg, epterm, q))) == 1
mdg = rdflib_to_graphtool(
g,
e_prop_names=[str("name")],
transform_p=lambda s, p, o: {str("name"): str(p)})
epterm = mdg.edge_properties["name"]
assert len(list(gt_util.find_edge(mdg, epterm, str(p)))) == 3
assert len(list(gt_util.find_edge(mdg, epterm, str(q)))) == 1
def run(filename, output, header_bool, sub, obj, pred, props, undirected,
strong):
# import modules locally
import csv
import sys
from graph_tool import load_graph_from_csv
from graph_tool.util import find_edge
from graph_tool.topology import label_components
from kgtk.exceptions import KGTKException
from kgtk.cli_argparse import KGTKArgumentParser
def find_pred_position(sub, pred, obj):
if pred < sub and pred < obj:
return pred
elif (pred > sub and pred < obj) or (pred < sub and pred > obj):
return pred - 1
else:
return pred - 2
try:
header = ['node1', 'label', 'node2']
label = 'c' + str(find_pred_position(sub, pred, obj))
g = load_graph_from_csv(filename,
not (undirected),
skip_first=not (header_bool),
hashed=True,
csv_options={'delimiter': '\t'},
ecols=(sub, obj))
es = []
if props:
properties = props.split(',')
for e in properties:
es += (find_edge(g, g.edge_properties[label], e))
g.clear_edges()
g.add_edge_list(list(set(es)))
comp, hist = label_components(g, directed=strong)
if output:
f = open(output, 'w')
wr = csv.writer(f,
quoting=csv.QUOTE_NONE,
delimiter="\t",
escapechar="\n",
quotechar='')
wr.writerow(header)
for v, c in enumerate(comp):
wr.writerow(
[g.vertex_properties['name'][v], 'connected_component', c])
f.close()
else:
sys.stdout.write('%s\t%s\t%s\n' % ('node1', 'label', 'node2'))
for v, c in enumerate(comp):
sys.stdout.write('%s\t%s\t%s\n' %
(g.vertex_properties['name'][v],
'connected_component', str(c)))
except:
raise KGTKException
def _get_edges(self, flows):
edges = []
for flow in flows:
e = util.find_edge(self.graph, self.graph.ep['id'], flow.id)
if len(e) > 0:
edges.append(e[0])
else:
# shouldn't happen if graph is up to date
raise Exception(f'graph is missing flow {flow.id}')
return edges
def translate_to_db(self):
# ToDo: filter for changes
# store edges (flows) to database
strat_flows = []
changed_edges = util.find_edge(self.graph, self.graph.ep['changed'],
True)
for edge in changed_edges:
new_amount = self.graph.ep.amount[edge]
# get the related FractionFlow
flow = FractionFlow.objects.get(id=self.graph.ep.id[edge])
material = self.graph.ep.material[edge]
process = self.graph.ep.process[edge]
if process == -1:
process = None
waste = self.graph.ep.waste[edge]
hazardous = self.graph.ep.hazardous[edge]
# new flow is marked with strategy relation
# (no seperate strategy fraction flow needed)
if flow.strategy is not None:
flow.amount = new_amount
flow.hazardous = hazardous
flow.material_id = material
flow.waste = waste
flow.process_id = process
flow.save()
# changed flow gets a related strategy fraction flow holding changes
else:
ex = StrategyFractionFlow.objects.filter(
fractionflow=flow, strategy=self.strategy)
# if there already was a modification, overwrite it
if len(ex) == 1:
strat_flow = ex[0]
strat_flow.amount = new_amount
strat_flow.material_id = material
strat_flow.waste = waste
strat_flow.hazardous = hazardous
strat_flow.process_id = process
strat_flow.save()
elif len(ex) > 1:
raise Exception('more than StrategyFractionFlow '
'found per flow. This should not happen.')
else:
strat_flow = StrategyFractionFlow(strategy=self.strategy,
amount=new_amount,
fractionflow=flow,
material_id=material,
waste=waste,
hazardous=hazardous,
process_id=process)
strat_flows.append(strat_flow)
StrategyFractionFlow.objects.bulk_create(strat_flows)
def process(self):
input_kr: KgtkReader = KgtkReader.open(
self.input_file_path,
error_file=self.error_file,
who="input",
options=self.input_reader_options,
value_options=self.value_options,
verbose=self.verbose,
very_verbose=self.very_verbose,
)
input_key_columns: typing.List[int] = self.get_key_columns(
input_kr, "input")
label_col_idx = input_key_columns[1]
label = '{}{}'.format('c', label_col_idx)
g = load_graph_from_csv(str(input_kr.file_path),
not (self.undirected),
skip_first=not (self.no_header),
hashed=True,
csv_options={'delimiter': '\t'},
ecols=(input_key_columns[0],
input_key_columns[2]))
es = []
header = ['node1', 'label', 'node2']
if self.properties:
properties = self.properties.split(',')
for e in properties:
es += (find_edge(g, g.edge_properties[label], e))
g.clear_edges()
g.add_edge_list(list(set(es)))
comp, hist = label_components(g, directed=self.strong)
ew: KgtkWriter = KgtkWriter.open(header,
self.output_file_path,
mode=input_kr.mode,
require_all_columns=False,
prohibit_extra_columns=True,
fill_missing_columns=True,
gzip_in_parallel=False,
verbose=self.verbose,
very_verbose=self.very_verbose)
for v, c in enumerate(comp):
ew.write([
g.vertex_properties['name'][v], 'connected_component',
str(c)
])
def _chain_flows(self,
referenced_flows,
possible_new_targets,
formula,
new_material=None,
new_process=None,
prepend=True,
new_waste=-1,
new_hazardous=-1):
'''
creates new flows based on given referenced flows and prepends
(prepend==True) or appends (prepend==False) them
if new flows already exist, changes existing ones instead
returns new/changed flows and deltas in same order as flows
ToDo: almost the same as shift_flows(), generalize!
'''
if formula.is_absolute:
raise ValueError(
'Formula for PrependFlow and AppendFlow must be relative')
new_flows = []
deltas = []
ids = referenced_flows.values_list('destination') if prepend\
else referenced_flows.values_list('origin')
actors_kept = Actor.objects.filter(id__in=ids)
closest_dict = self.find_closest_actor(actors_kept,
possible_new_targets)
# create new flows and add corresponding edges
for flow in referenced_flows:
kept_id = flow.destination_id if prepend \
else flow.origin_id
# no target actor found within range
if kept_id not in closest_dict:
continue
# get new target out of dictionary
new_id = closest_dict[kept_id]
new_vertex = self._get_vertex(new_id)
delta = formula.calculate_delta(flow.strategy_amount)
# the edge corresponding to the referenced flow
edges = util.find_edge(self.graph, self.graph.ep['id'], flow.id)
if len(edges) > 1:
raise ValueError("FractionFlow.id ", flow.id,
" is not unique in the graph")
elif len(edges) == 0:
print("Cannot find FractionFlow.id ", flow.id, " in the graph")
continue
edge = edges[0]
new_edge_args = [new_vertex, edge.source()] if prepend \
else [edge.target(), new_vertex]
new_edge = self.graph.edge(*new_edge_args)
# create a new fractionflow for the implementation flow in db,
# setting id to None creates new one when saving
# while keeping attributes of original model;
# the new flow is added with zero amount and to be changed
# by calculated delta
new_flow = copy_django_model(flow)
new_flow.id = None
new_flow.amount = 0
if prepend:
new_flow.destination_id = new_flow.origin_id
new_flow.origin_id = new_id
else:
new_flow.origin_id = new_flow.destination_id
new_flow.destination_id = new_id
if new_material:
new_flow.material = new_material
if new_process:
new_flow.process = new_process
if new_waste >= 0:
new_flow.waste = new_waste == 1
if new_hazardous >= 0:
new_flow.hazardous = new_hazardous == 1
# strategy marks flow as new flow
new_flow.strategy = self.strategy
new_flow.save()
# create the edge in the graph
new_edge = self.graph.add_edge(*new_edge_args)
self.graph.ep.id[new_edge] = new_flow.id
self.graph.ep.amount[new_edge] = 0
self.graph.ep.material[new_edge] = new_flow.material.id
# process doesn't have to be set, missing attributes
# are marked with -1 in graph (if i remember correctly?)
self.graph.ep.process[new_edge] = \
new_flow.process.id if new_flow.process is not None else - 1
self.graph.ep.waste[new_edge] = new_flow.waste
self.graph.ep.hazardous[new_edge] = new_flow.hazardous
new_flows.append(new_flow)
deltas.append(delta)
return new_flows, deltas
def _shift_flows(self,
referenced_flows,
possible_new_targets,
formula,
new_material=None,
new_process=None,
shift_origin=True,
reduce_reference=True,
new_waste=-1,
new_hazardous=-1):
'''
creates new flows based on given referenced flows and redirects them
to target actor (either origin or destinations are changing)
referenced_flows are reduced by amout of new flows if reduce_reference
is True, otherwise they stay untouched
returns flows to be changed in order of change and the deltas added to
be to each flow in walker algorithm in same order as flows
'''
changed_ref_flows = []
new_flows = []
changed_ref_deltas = []
new_deltas = []
# the actors to keep (not shifted)
ids = referenced_flows.values_list('destination') if shift_origin\
else referenced_flows.values_list('origin')
actors_kept = Actor.objects.filter(id__in=ids)
# actors in possible new targets that are closest
closest_dict = self.find_closest_actor(actors_kept,
possible_new_targets)
if formula.is_absolute:
formula.set_total(referenced_flows)
# create new flows and add corresponding edges
for flow in referenced_flows:
kept_id = flow.destination_id if shift_origin \
else flow.origin_id
# no target actor found within range
if kept_id not in closest_dict:
continue
# get new target out of dictionary
new_id = closest_dict[kept_id]
new_vertex = self._get_vertex(new_id)
delta = formula.calculate_delta(flow.strategy_amount)
delta = min(delta, flow.strategy_amount)
# the edge corresponding to the referenced flow
# (the one to be shifted)
edges = util.find_edge(self.graph, self.graph.ep['id'], flow.id)
if len(edges) > 1:
raise ValueError("FractionFlow.id ", flow.id,
" is not unique in the graph")
elif len(edges) == 0:
print("Cannot find FractionFlow.id ", flow.id, " in the graph")
continue
edge = edges[0]
new_edge_args = [new_vertex, edge.target()] if shift_origin \
else [edge.source(), new_vertex]
new_edge = self.graph.edge(*new_edge_args)
# create a new fractionflow for the implementation flow in db,
# setting id to None creates new one when saving
# while keeping attributes of original model;
# the new flow is added with zero amount and to be changed
# by calculated delta
new_flow = copy_django_model(flow)
new_flow.id = None
new_flow.amount = 0
if shift_origin:
new_flow.origin_id = new_id
else:
new_flow.destination_id = new_id
if new_material:
new_flow.material = new_material
if new_process:
new_flow.process = new_process
if new_waste >= 0:
new_flow.waste = new_waste == 1
if new_hazardous >= 0:
new_flow.hazardous = new_hazardous == 1
# strategy marks flow as new flow
new_flow.strategy = self.strategy
new_flow.save()
# create the edge in the graph
new_edge = self.graph.add_edge(*new_edge_args)
self.graph.ep.id[new_edge] = new_flow.id
self.graph.ep.amount[new_edge] = 0
self.graph.ep.material[new_edge] = new_flow.material.id
# process doesn't have to be set, missing attributes
# are marked with -1 in graph (if i remember correctly?)
self.graph.ep.process[new_edge] = \
new_flow.process.id if new_flow.process is not None else - 1
self.graph.ep.waste[new_edge] = new_flow.waste
self.graph.ep.hazardous[new_edge] = new_flow.hazardous
new_flows.append(new_flow)
new_deltas.append(delta)
# reduce (resp. increase) the referenced flow by the same amount
if reduce_reference:
changed_ref_flows.append(flow)
changed_ref_deltas.append(-delta)
# new flows shall be created before modifying the existing ones
return new_flows + changed_ref_flows, new_deltas + changed_ref_deltas
def get_edges_by_edge_prop(g, p, v):
return find_edge(g, prop=g.properties[('e', p)], match=v)
def makeGraph(self,img,dia,xScale,yScale):
print 'Building Graph Data Structure'
start=time.time()
G = Graph(directed=False)
vprop=G.new_vertex_property('object')
eprop=G.new_edge_property('object')
epropW=G.new_edge_property("int32_t")
avgScale=(xScale+yScale)/2
test=np.where(img==True)
ss = np.shape(test)
cccc=0
percentOld=0.0
print str(np.round(percentOld,1))+'%'
for (i,j) in zip(test[1],test[0]):
cccc+=1
percent=(float(cccc)/float(ss[1]))*100
if percentOld+10< percent:
print str(np.round(percent,1))+'%'
percentOld=percent
nodeNumber1 = (float(i)*yScale,float(j)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j,i),'coord':nodeNumber1, 'nrOfPaths':0, 'diameter':float(dia[j][i])*avgScale}):
v1=gu.find_vertex(G, vprop, {'imgIdx':(j,i),'coord':nodeNumber1, 'nrOfPaths':0, 'diameter':float(dia[j][i])*avgScale})[0]
else:
v1=G.add_vertex()
vprop[G.vertex(v1)]={'imgIdx':(j,i),'coord':nodeNumber1, 'nrOfPaths':0, 'diameter':float(dia[j][i])*avgScale}
try:
if img[j,i+1] == True:
nodeNumber2 = (float(i+1)*yScale,float(j)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j,i+1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i+1])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j,i+1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i+1])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j,i+1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i+1])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except:
pass
try:
if img[j,i-1] == True:
nodeNumber2 = (float(i-1)*yScale,float(j)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j,i-1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i-1])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j,i-1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i-1])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j,i-1),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j][i-1])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except:pass
try:
if img[j + 1,i] == True:
nodeNumber2 = (float(i)*yScale,float(j+1)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j+1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j+1][i])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j+1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j+1][i])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j+1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j+1][i])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except:pass
try:
if img[j - 1,i] == True:
nodeNumber2 = (float(i)*yScale,float(j-1)*xScale)
if gu.find_vertex(G, vprop, {'imgIdx':(j-1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j-1][i])*avgScale}):
v2=gu.find_vertex(G, vprop, {'imgIdx':(j-1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j-1][i])*avgScale})[0]
if gu.find_edge(G, eprop, {'coord1':vprop[v2]['coord'], 'coord2':vprop[v1]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
else:
v2=G.add_vertex()
vprop[G.vertex(v2)]={'imgIdx':(j-1,i),'coord':nodeNumber2, 'nrOfPaths':0, 'diameter':float(dia[j-1][i])*avgScale}
e = G.add_edge(v1, v2)
epropW[e]=(((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)/avgScale)**4
eprop[e]={'coord1':vprop[v1]['coord'], 'coord2':vprop[v2]['coord'],'weight':((vprop[v1]['diameter']+vprop[v2]['diameter'])/2)**4,'RTP':False}
except: pass
#
print '100.0%'
print 'selecting largest connected component'
G.edge_properties["ep"] = eprop
G.edge_properties["w"] = epropW
G.vertex_properties["vp"] = vprop
l = gt.label_largest_component(G)
print(l.a)
u = gt.GraphView(G, vfilt=l)
print '# vertices'
print(u.num_vertices())
print(G.num_vertices())
print '# edges'
print(u.num_edges())
print 'building graph finished in: '+str(time.time()-start)+'s'
return u
def gn(g, odir, focus=None):
"""
Takes graph and uses Girvan Newman to slowly break graph down.
Can operate in faster mode that constrains view to single graph.
Creates new output_directory "odir" in which JSON graphs are placed,
as well as names of clusters, and index mapping tests to clusters.
-----------------------------------------
g: graph_tool graph
odir: output_directory
focus: if True, only looks at clusters with vertex named "focus"
"""
total_edges = g.num_edges()
vprint(INFO,
'Applying Girvan Newman algorithm on %i edges...' % (g.num_edges()))
if focus: vprint(INFO, 'Focused on %s' % (focus))
### Pull some properties of the graph out
weight = g.ep['weight']
name = g.vp['name']
### If focus, make sure focus actually there!
if focus:
if focus not in name:
raise KeyboardInterrupt
### Initialize output
vprint(INFO, 'Output: %s' % (os.path.abspath(odir)))
if not os.path.exists(odir):
vprint(INFO, '\tDirectory did not exist! Created.')
os.mkdir(odir)
json_name = os.path.join(odir, "%i_%i.json")
text_name = os.path.join(odir, "%i_%i.txt")
idx_name = os.path.join(odir, "index.csv")
### Create new property for graph & configure for fast
g.ep["ebc"] = g.new_edge_property("float")
g.set_fast_edge_removal(True)
### Initialize variables for tracking connected components
### and indexing
if focus: cc_cts = [g.num_vertices()]
else: cc_cts = [0]
index = dict([(name[v], [(0, 0)]) for v in g.vertices()])
### Begin Girvan Newman algorithm
_, _ = gt_bt(g, eprop=g.ep["ebc"], weight=weight, norm=False)
while g.num_edges() != 0:
### Get & remove edge of max() betweenness; recalc betweenness
maxedge = find_edge(g, g.ep["ebc"], g.ep["ebc"].a.max())[0]
g.ep["ebc"] = g.new_edge_property("float")
g.remove_edge(maxedge)
_, _ = gt_bt(g, eprop=g.ep["ebc"], weight=weight, norm=False)
### If we're in a focused situation, find relevant cc
if focus:
cc_lbl = gt_cc_out(g, focus)
cc_ct = cc_lbl.a.sum()
g.set_vertex_filter(cc_lbl) # Mask all other edge/verts
### If edge removed creates new clusters
if cc_ct != cc_cts[-1]:
iter_num = len(cc_cts)
cc_cts.append(cc_ct)
### Write sections
gt_to_json(g, json_name % (iter_num, 0))
out = open(text_name % (i - iter_num, 0), 'w')
for v in g.vertices():
if cc_lbl[v]:
out.write(name[v] + '\n')
index[name[v]].append((iter_num, 0))
### Otherwise...
else:
### First, gather connected components
cc_lbl, _ = gt_cc(g, directed=False)
cc_ct = cc_lbl.a.max()
### If we've generated new clusters...
if cc_ct != cc_cts[-1]:
iter_num = len(cc_cts)
cc_cts.append(cc_ct)
### Create a filter for each cc label
filters = dict([(i, g.new_vertex_property("bool"))
for i in range(cc_lbl.a.max() + 1)])
for v in g.vertices():
lbl = cc_lbl[v]
filters[lbl][v] = True
for (i, f) in filters.iteritems():
if f.a.sum() == 1:
index[name[v]].append((iter_num, i))
continue
g.set_vertex_filter(f)
gt_to_json(g, json_name % (iter_num, i))
out = open(text_name % (iter_num, i), 'w')
for v in g.vertices():
if f[v]:
out.write(name[v] + '\n')
index[name[v]].append((iter_num, i))
g.set_vertex_filter(None)
# Progress bar
progress_bar(total_edges - g.num_edges(), total_edges, 100)
### Final step: break down index into legible file
progress_bar_complete(total_edges)
vprint(INFO, 'Making index file.')
m = np.ones((len(index), len(cc_cts))) * -1
ordered_keys = sorted(index.keys())
for (key, i) in zip(ordered_keys, range(len(index))):
for (iter_num, clust_num) in index[key]:
m[i, iter_num] = clust_num
write_mtx(idx_name, m, ordered_keys, range(len(cc_cts)))
vprint(INFO, 'Girvan Newman Complete!')
return None
def process(self):
input_kr: KgtkReader = KgtkReader.open(
self.input_file_path,
error_file=self.error_file,
who="input",
options=self.input_reader_options,
value_options=self.value_options,
verbose=self.verbose,
very_verbose=self.very_verbose,
)
input_key_columns: typing.List[int] = self.get_key_columns(
input_kr, "input")
label_col_idx = input_key_columns[1]
label = input_kr.column_names[label_col_idx]
g = load_graph_from_kgtk(input_kr, directed=not self.undirected)
es = []
header = ['node1', 'label', 'node2']
if self.properties:
properties = self.properties.split(',')
for e in properties:
es += (find_edge(g, g.edge_properties[label], e))
g.clear_edges()
g.add_edge_list(list(set(es)))
comp, hist = label_components(g, directed=self.strong)
ew: KgtkWriter = KgtkWriter.open(header,
self.output_file_path,
mode=input_kr.mode,
require_all_columns=False,
prohibit_extra_columns=True,
fill_missing_columns=True,
gzip_in_parallel=False,
verbose=self.verbose,
very_verbose=self.very_verbose)
clusters: typing.MutableMapping[str, typing.List[str]] = dict()
cluster_id: str
name: str
v: int
for v, c in enumerate(comp):
name = g.vertex_properties['name'][v]
cluster_id = str(c)
if cluster_id not in clusters:
clusters[cluster_id] = [name]
else:
clusters[cluster_id].append(name)
trimmed_clusters: typing.MutableMapping[str, typing.List[str]] = dict()
for cluster_id in clusters.keys():
if len(clusters[cluster_id]) >= self.minimum_cluster_size:
trimmed_clusters[cluster_id] = clusters[cluster_id]
named_clusters: typing.MutableMapping[
str, typing.List[str]] = self.name_clusters(trimmed_clusters)
for cluster_id in sorted(named_clusters.keys()):
for name in sorted(named_clusters[cluster_id]):
ew.write([name, 'connected_component', cluster_id])
ew.close()
def makeGraph(self, img, dia, xScale, yScale):
print 'Building Graph Data Structure'
start = time.time()
G = Graph(directed=False)
vprop = G.new_vertex_property('object')
eprop = G.new_edge_property('object')
epropW = G.new_edge_property("int32_t")
avgScale = (xScale + yScale) / 2
test = np.where(img == True)
ss = np.shape(test)
cccc = 0
percentOld = 0.0
print str(np.round(percentOld, 1)) + '%'
for (i, j) in zip(test[1], test[0]):
cccc += 1
percent = (float(cccc) / float(ss[1])) * 100
if percentOld + 10 < percent:
print str(np.round(percent, 1)) + '%'
percentOld = percent
nodeNumber1 = (float(i) * yScale, float(j) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j, i),
'coord': nodeNumber1,
'nrOfPaths': 0,
'diameter': float(dia[j][i]) * avgScale
}):
v1 = gu.find_vertex(
G, vprop, {
'imgIdx': (j, i),
'coord': nodeNumber1,
'nrOfPaths': 0,
'diameter': float(dia[j][i]) * avgScale
})[0]
else:
v1 = G.add_vertex()
vprop[G.vertex(v1)] = {
'imgIdx': (j, i),
'coord': nodeNumber1,
'nrOfPaths': 0,
'diameter': float(dia[j][i]) * avgScale
}
try:
if img[j, i + 1] == True:
nodeNumber2 = (float(i + 1) * yScale, float(j) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j, i + 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i + 1]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j, i + 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i + 1]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j, i + 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i + 1]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
try:
if img[j, i - 1] == True:
nodeNumber2 = (float(i - 1) * yScale, float(j) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j, i - 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i - 1]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j, i - 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i - 1]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j, i - 1),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j][i - 1]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
try:
if img[j + 1, i] == True:
nodeNumber2 = (float(i) * yScale, float(j + 1) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j + 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j + 1][i]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j + 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j + 1][i]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j + 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j + 1][i]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
try:
if img[j - 1, i] == True:
nodeNumber2 = (float(i) * yScale, float(j - 1) * xScale)
if gu.find_vertex(
G, vprop, {
'imgIdx': (j - 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j - 1][i]) * avgScale
}):
v2 = gu.find_vertex(
G, vprop, {
'imgIdx': (j - 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j - 1][i]) * avgScale
})[0]
if gu.find_edge(
G, eprop, {
'coord1':
vprop[v2]['coord'],
'coord2':
vprop[v1]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}):
pass
else:
e = G.add_edge(v1, v2)
epropW[e] = (((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2) /
avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight': ((vprop[v1]['diameter'] +
vprop[v2]['diameter']) / 2)**4,
'RTP':
False
}
else:
v2 = G.add_vertex()
vprop[G.vertex(v2)] = {
'imgIdx': (j - 1, i),
'coord': nodeNumber2,
'nrOfPaths': 0,
'diameter': float(dia[j - 1][i]) * avgScale
}
e = G.add_edge(v1, v2)
epropW[e] = (
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2) / avgScale)**4
eprop[e] = {
'coord1':
vprop[v1]['coord'],
'coord2':
vprop[v2]['coord'],
'weight':
((vprop[v1]['diameter'] + vprop[v2]['diameter']) /
2)**4,
'RTP':
False
}
except:
pass
#
print '100.0%'
print 'selecting largest connected component'
G.edge_properties["ep"] = eprop
G.edge_properties["w"] = epropW
G.vertex_properties["vp"] = vprop
l = gt.label_largest_component(G)
print(l.a)
u = gt.GraphView(G, vfilt=l)
print '# vertices'
print(u.num_vertices())
print(G.num_vertices())
print '# edges'
print(u.num_edges())
print 'building graph finished in: ' + str(time.time() - start) + 's'
return u
def gn(g, odir, focus = None):
"""
Takes graph and uses Girvan Newman to slowly break graph down.
Can operate in faster mode that constrains view to single graph.
Creates new output_directory "odir" in which JSON graphs are placed,
as well as names of clusters, and index mapping tests to clusters.
-----------------------------------------
g: graph_tool graph
odir: output_directory
focus: if True, only looks at clusters with vertex named "focus"
"""
total_edges = g.num_edges()
vprint(INFO, 'Applying Girvan Newman algorithm on %i edges...'%(g.num_edges()))
if focus: vprint(INFO, 'Focused on %s'%(focus))
### Pull some properties of the graph out
weight = g.ep['weight']
name = g.vp['name']
### If focus, make sure focus actually there!
if focus:
if focus not in name:
raise KeyboardInterrupt
### Initialize output
vprint(INFO, 'Output: %s'%(os.path.abspath(odir)))
if not os.path.exists(odir):
vprint(INFO, '\tDirectory did not exist! Created.')
os.mkdir(odir)
json_name = os.path.join(odir, "%i_%i.json")
text_name = os.path.join(odir, "%i_%i.txt")
idx_name = os.path.join(odir, "index.csv")
### Create new property for graph & configure for fast
g.ep["ebc"] = g.new_edge_property("float")
g.set_fast_edge_removal(True)
### Initialize variables for tracking connected components
### and indexing
if focus: cc_cts = [g.num_vertices()]
else: cc_cts = [0]
index = dict( [(name[v], [(0,0)]) for v in g.vertices()] )
### Begin Girvan Newman algorithm
_, _ = gt_bt(g, eprop = g.ep["ebc"], weight = weight, norm = False)
while g.num_edges() != 0:
### Get & remove edge of max() betweenness; recalc betweenness
maxedge = find_edge(g, g.ep["ebc"], g.ep["ebc"].a.max())[0]
g.ep["ebc"] = g.new_edge_property("float")
g.remove_edge(maxedge)
_, _ = gt_bt(g, eprop = g.ep["ebc"], weight = weight, norm = False)
### If we're in a focused situation, find relevant cc
if focus:
cc_lbl = gt_cc_out(g, focus)
cc_ct = cc_lbl.a.sum()
g.set_vertex_filter(cc_lbl) # Mask all other edge/verts
### If edge removed creates new clusters
if cc_ct != cc_cts[-1]:
iter_num = len(cc_cts)
cc_cts.append(cc_ct)
### Write sections
gt_to_json(g, json_name%(iter_num, 0))
out = open(text_name%(i - iter_num, 0), 'w')
for v in g.vertices():
if cc_lbl[v]:
out.write(name[v] + '\n')
index[name[v]].append((iter_num, 0))
### Otherwise...
else:
### First, gather connected components
cc_lbl, _ = gt_cc(g, directed = False)
cc_ct = cc_lbl.a.max()
### If we've generated new clusters...
if cc_ct != cc_cts[-1]:
iter_num = len(cc_cts)
cc_cts.append(cc_ct)
### Create a filter for each cc label
filters = dict([(i, g.new_vertex_property("bool")) for i in range(cc_lbl.a.max()+1)])
for v in g.vertices():
lbl = cc_lbl[v]
filters[lbl][v] = True
for (i, f) in filters.iteritems():
if f.a.sum() == 1:
index[name[v]].append((iter_num,i))
continue
g.set_vertex_filter(f)
gt_to_json(g, json_name%(iter_num, i))
out = open(text_name%(iter_num, i), 'w')
for v in g.vertices():
if f[v]:
out.write(name[v] + '\n')
index[name[v]].append((iter_num,i))
g.set_vertex_filter(None)
# Progress bar
progress_bar(total_edges - g.num_edges(), total_edges, 100)
### Final step: break down index into legible file
progress_bar_complete(total_edges)
vprint(INFO, 'Making index file.')
m = np.ones( (len(index), len(cc_cts) ) ) * -1
ordered_keys = sorted(index.keys())
for (key, i) in zip(ordered_keys, range(len(index))):
for (iter_num, clust_num) in index[key]:
m[i, iter_num] = clust_num
write_mtx(idx_name, m, ordered_keys, range(len(cc_cts)))
vprint(INFO, 'Girvan Newman Complete!')
return None