def top(): g = gt.load_graph(filename) print 'Graph loaded, now calculating top nodes' vblocks = g.vp['blocks'] largest_block = max(range(blocks), key=lambda b: len(gt.find_vertex(g, vblocks, b))) print 'Largest block is %d with %d nodes' % (largest_block, len(gt.find_vertex(g, vblocks, largest_block))) for tup in top_ids(g, largest_block): print tup
def main():
input_fasta = sys.argv[3]
K = int(sys.argv[1])
x = float(sys.argv[2])
ht = khmer.Nodegraph(K, x, 4)
sparse_graph = gt.Graph()
hashes = sparse_graph.new_vertex_property("long long")
for n, record in enumerate(screed.open(input_fasta)):
if n % 1000 == 0:
print('...loaded and tagged {} sequences'.format(n),
file=sys.stderr)
name = record.name
sequence = record.sequence
ht.consume_sequence_and_tag_with_labels(sequence, n)
tags = ht.sweep_tag_neighborhood(sequence, 0)
for i in range(len(tags) - 1):
src = tags[i]
dst = tags[i + 1]
new = False
srcv = gt.find_vertex(sparse_graph, hashes, src)
if not srcv:
srcv = sparse_graph.add_vertex()
hashes[srcv] = src
new = True
else:
srcv = srcv[0]
dstv = gt.find_vertex(sparse_graph, hashes, dst)
if not dstv:
dstv = sparse_graph.add_vertex()
hashes[dstv] = dst
new = True
else:
dstv = dstv[0]
if new:
e = sparse_graph.add_edge(srcv, dstv)
print('Sparse graph has {} nodes, {} edges'.format(
sparse_graph.num_vertices(), sparse_graph.num_edges()))
comp = gt.label_largest_component(sparse_graph, directed=False)
#pos = gt.radial_tree_layout(sparse_graph, sparse_graph.vertex(0))
gt.graph_draw(sparse_graph,
output_size=(5000, 5000),
output=input_fasta + '_sparse.png')
sparse_graph.set_vertex_filter(comp)
gt.graph_draw(sparse_graph,
output_size=(5000, 5000),
output=input_fasta + '_sparse_comp.png')
def closest_vert(self, sigma, zed):
'''Return the vertices closer to a position
`sigma`,`zed`
'''
dist = np.hypot(self.sigmas.fa - sigma, self.zeds.fa - zed)
idx = np.argmin(dist)
sigma, zed = self.sigmas.fa[idx], self.zeds.fa[idx]
s_matches = gt.find_vertex(self.graph, self.sigmas, sigma)
z_matches = gt.find_vertex(self.graph, self.zeds, zed)
log.debug('Number of closest vertices found: %i, %i' %
(len(s_matches), len(z_matches)))
return [v for v in s_matches if v in z_matches][0]
def main():
input_fasta = sys.argv[3]
K = int(sys.argv[1])
x = float(sys.argv[2])
ht = khmer.new_hashbits(K, x, 4)
sparse_graph = gt.Graph()
hashes = sparse_graph.new_vertex_property("long long")
for n, record in enumerate(screed.open(input_fasta)):
if n % 1000 == 0:
print >>sys.stderr, '...loaded and tagged {} sequences'.format(n)
name = record.name
sequence = record.sequence
ht.consume_sequence_and_tag_with_labels(sequence, n)
tags = ht.sweep_tag_neighborhood(sequence, 0)
for i in xrange(len(tags) - 1):
src = tags[i]
dst = tags[i + 1]
new = False
srcv = gt.find_vertex(sparse_graph, hashes, src)
if not srcv:
srcv = sparse_graph.add_vertex()
hashes[srcv] = src
new = True
else:
srcv = srcv[0]
dstv = gt.find_vertex(sparse_graph, hashes, dst)
if not dstv:
dstv = sparse_graph.add_vertex()
hashes[dstv] = dst
new = True
else:
dstv = dstv[0]
if new:
e = sparse_graph.add_edge(srcv, dstv)
print 'Sparse graph has {} nodes, {} edges'.format(sparse_graph.num_vertices(), sparse_graph.num_edges())
comp = gt.label_largest_component(sparse_graph, directed=False)
#pos = gt.radial_tree_layout(sparse_graph, sparse_graph.vertex(0))
gt.graph_draw(sparse_graph, output_size=(
5000, 5000), output=input_fasta + '_sparse.png')
sparse_graph.set_vertex_filter(comp)
gt.graph_draw(sparse_graph, output_size=(
5000, 5000), output=input_fasta + '_sparse_comp.png')
def top(): g = gt.load_graph(filename) print 'Graph loaded, now calculating top nodes' vblocks = g.vp['blocks'] blocks = sorted(range(nblocks), key=lambda b: len(gt.find_vertex(g, vblocks, b)), reverse=True) for block in blocks: print 'Block %d with %d nodes' % (block, len(gt.find_vertex(g, vblocks, block))) tups = top_ids(g, block, n) ids = [t[0] for t in tups] names = get_names(ids) for tup, name in zip(tups, names): print name, tup[0], tup[1]
def closest_vert(self, sigma, zed):
'''Return the vertices closer to a position
`sigma`,`zed`
'''
dist = np.hypot(self.sigmas.fa - sigma,
self.zeds.fa - zed)
idx = np.argmin(dist)
sigma, zed = self.sigmas.fa[idx], self.zeds.fa[idx]
s_matches = gt.find_vertex(self.graph,
self.sigmas, sigma)
z_matches = gt.find_vertex(self.graph,
self.zeds, zed)
log.debug('Number of closest vertices found: %i, %i'
% (len(s_matches), len(z_matches)))
return [v for v in s_matches if v in z_matches][0]
def continue_graph(g, pos, weight, clase, img, actual, padre, enodes, bnodes):
# padre refiere al indice en el grafo de graph_tool
# actual es la posición del nodo en la imagen
global c
hijos = vecinos(img, actual)
for i in hijos:
img, nodo, largo_arista = get_next_node(img, i, actual, hijos, 0)
s = gt.find_vertex(g, pos,nodo)
if s == []:
s = g.add_vertex()
pos[s] = nodo
else:
s = s[0]
arista = g.add_edge(padre, s)
weight[arista] = largo_arista
clase[arista] = [c, 0]
c = c+1
if img[nodo[1],nodo[0]] == 1:
img[nodo[1], nodo[0]] = c
if nodo not in enodes.tolist():
g, pos, weight = continue_graph(g, pos, weight, clase, img, nodo, s, enodes, bnodes)
return g, pos, weight
def _map_vertexs(self, graph, seeds):
components = [[
gt.find_vertex(self._network, self._network.vp['hash'],
graph.vp['hash'][v])[0] for v in seed
] for seed in seeds]
return components
def community():
g = gt.load_graph(filename)
print 'Graph loaded, now finding community'
# state = gt.BlockState(g, B=blocks)
# for i in xrange(iterations):
# if i < iterations / 2:
# gt.mcmc_sweep(state)
# else:
# gt.mcmc_sweep(state, beta=float('inf'))
# g.vp['blocks'] = state.get_blocks()
spins = {}
if 'blocks' in g.vp:
spins = {'spins': g.vp['blocks']}
g.vp['blocks'] = gt.community_structure(g, n_iter=iterations, n_spins=blocks, **spins)
if 'pos' in g.vp:
gt.sfdp_layout(g, groups=g.vp['blocks'], pos=g.vp['pos'])
for i in xrange(blocks):
print '%d nodes in block %d' % (len(gt.find_vertex(g, g.vp['blocks'], i)), i)
g.save(filename)
def generate_graph(arcos_fuertes, arcos_debiles):
'''
A los arcos fuertes se les asigna un peso 100 veces mayor que a los arcos débiles
'''
g = gt.Graph(directed=True)
# etiqueta_nodo=g.new_vertex_property('string')
g.vp['etiqueta_nodo'] = g.new_vertex_property('string')
num_vertices = len(
set(zip(*arcos_fuertes)[0]).union(
set(zip(*arcos_fuertes)[1]).union(
set(zip(*arcos_debiles)[0]).union(set(
zip(*arcos_debiles)[1])))))
for i in range(num_vertices):
u = g.add_vertex()
g.vp['etiqueta_nodo'][u] = str(i)
# etiqueta_arco=g.new_edge_property('string')
g.ep["etiqueta_arco"] = g.new_edge_property('string')
# peso_arco=g.new_edge_property('float')
g.ep["peso_arco"] = g.new_edge_property('float')
for j in arcos_fuertes:
u = gt.find_vertex(g, g.vp['etiqueta_nodo'], str(j[0]))
v = gt.find_vertex(g, g.vp['etiqueta_nodo'], str(j[1]))
e = g.add_edge(u[0], v[0])
a = 100 * np.random.rand()
if (a < 10):
a = 10 + (10 * np.random.rand())
g.ep['etiqueta_arco'][e] = str(round(a, 1))
g.ep['peso_arco'][e] = round(a, 1)
for j in arcos_debiles:
u = gt.find_vertex(g, g.vp['etiqueta_nodo'], str(j[0]))
v = gt.find_vertex(g, g.vp['etiqueta_nodo'], str(j[1]))
e = g.add_edge(u[0], v[0])
a = np.random.rand()
g.ep['etiqueta_arco'][e] = str(round(a, 1))
g.ep['peso_arco'][e] = round(a, 1)
# gt.graph_draw(g,vertex_text=etiqueta_nodo,edge_text=etiqueta_arco,vertex_size=8)
return g
def top_ids(g, block, n=10): '''List the most central node ids in a given block''' vid = g.vp['id'] vrank = g.vp['rank'] vertices = gt.find_vertex(g, g.vp['blocks'], block) sorted_vertices = sorted(vertices, key=lambda v: vrank[v], reverse=True) mapped_vertices = imap(lambda v: (vid[v], vrank[v]), sorted_vertices) return take(n, mapped_vertices)
def find_k_shortest_paths(self, k, verbose=True):
"""
Finds k shortest paths to current target using Yen's Algorithm.
Args:
k (int): desired number of shortest pathways (ranked by cost)
verbose (bool): whether to print all identified pathways to the console.
Returns:
[RxnPathway]: list of RxnPathway objects containing reactions traversed on
each path.
"""
g = self._g
paths = []
precursors_v = gt.find_vertex(g, g.vp["type"], 0)[0]
target_v = gt.find_vertex(g, g.vp["type"], 3)[0]
for num, path in enumerate(self._yens_ksp(g, k, precursors_v,
target_v)):
rxns = []
weights = []
for step, v in enumerate(path):
g.vp["path"][v] = True
if (g.vp["type"][v] == 2
): # add rxn step if current node in path is a product
e = g.edge(path[step - 1], v)
g.ep["path"][
e] = True # mark this edge as occurring on a path
rxns.append(g.ep["rxn"][e])
weights.append(g.ep["weight"][e])
rxn_pathway = RxnPathway(rxns, weights)
paths.append(rxn_pathway)
if verbose:
for path in paths:
print(path, "\n")
return paths
def set_target(self, target):
"""
Replaces network's current target phase with new target phase.
Args:
target (ComputedEntry): ComputedEntry-like object for new target phase.
Returns:
None
"""
g = self._g
if target in self._current_target:
return
else:
self._current_target = {target}
g.remove_vertex(gt.find_vertex(g, g.vp["type"], 3))
new_target_entry = RxnEntries(self._current_target, "t")
new_target_v = g.add_vertex()
self._update_vertex_properties(
g,
new_target_v,
{
"entries": new_target_entry,
"type": 3,
"bool": True,
"path": True,
"chemsys": new_target_entry.chemsys,
},
)
new_edges = []
for v in gt.find_vertex(g, g.vp["type"], 2): # search for all products
if self._current_target.issubset(g.vp["entries"][v].entries):
new_edges.append([v, new_target_v, 0, None, True,
False]) # link all products to new target
g.add_edge_list(
new_edges,
eprops=[g.ep["weight"], g.ep["rxn"], g.ep["bool"], g.ep["path"]])
def get_vertex_and_create_if_not_exist(group_name: str, username: str):
v = gt.find_vertex(graph, graph.vp.username, username)
if not v:
v = graph.add_vertex()
graph.vp.group[v] = GROUPS.index(group_name)
graph.vp.group_name[v] = group_name
graph.vp.username[v] = username
return v
else:
return v[0]
def v_id(name, g, v_name):
"""Return integer index of vertex with given `name`
Args:
name - name of vertex (str)
g - graph tool graph
v_name - vertex property array giving names for each vertex
as returned when calling g.add_edge_list with hashed=True
"""
# obtain vertex object
v = gt.find_vertex(g, v_name, name)[0]
# get int index
return g.vertex_index[v]
def make_filter(self, concepts: List[NODE_T], fltr):
fltr.a = False
for n in concepts:
v = gtall.find_vertex(
self.graph,
prop=self.graph.properties[('v', '_graphml_vertex_id')],
match=str(n))[0]
# v = qtqu.get_nodes_by_node_prop(self.graph, '_graphml_vertex_id', n)[0]
fltr[v] = True
for vn in v.out_neighbors():
fltr[vn] = True
return fltr
def citation_success(self,yr,yd,perc):
#create property map
citation_success=self.citation.new_vertex_property("double")
citation_success_perc=self.citation.new_vertex_property("bool")
perc_cuts=[]
for y in yr:
print y,'...'
#find vertices
y1_vertices = gt.find_vertex(self.citation,self.citation.vertex_properties['year'],y)
y1yd_vertices = gt.find_vertex_range(self.citation,self.citation.vertex_properties['year'],[y,y+yd])
#set vertex filter property
print 'Set filter prop...'
y1yd_filter_prop=self.citation.new_vertex_property("bool")
y1_filter_prop=self.citation.new_vertex_property("bool")
y1yd_filter_prop.a=False
y1_filter_prop.a=False
for v in y1yd_vertices:
y1yd_filter_prop[v]=True
for v in y1_vertices:
y1_filter_prop[v]=True
#calculate graph_view of the subgraph of y,y+yd
print 'Calc graph view ...'
sub_cite_degree = self.citation.new_vertex_property("double")
self.citation.set_vertex_filter(y1yd_filter_prop)
sub_cite_degree.fa = self.citation.degree_property_map('out').fa
#there are a lot of zeros ... so the percentile percentage has to be quite high
self.citation.set_vertex_filter(None)
self.citation.set_vertex_filter(y1_filter_prop)
tmp = sub_cite_degree.fa
percentile_cut = numpy.percentile(tmp,perc)
perc_cuts.append(percentile_cut)
print 'Percentile cut is ',percentile_cut
self.citation.set_vertex_filter(None)
print 'Write success ...'
#write number of citations and success bool after yd years
self.citation.set_vertex_filter(y1_filter_prop)
citation_success.fa = tmp.copy()
print 'There are ',numpy.count_nonzero(tmp>percentile_cut),' nodes exceeding the percentile cut.'
citation_success_perc.fa = (tmp > percentile_cut).copy()
self.citation.set_vertex_filter(None)
return citation_success, citation_success_perc,perc_cuts
def build_result_graph(self, reactionlist, product, filename):
g = gt.Graph()
cofactors = [s.id for s in self.cofactor_list]
generic_compound = [s.id for s in self.generic_compound_list]
edges = []
for reaction in self.model.reactions:
for c, val in reaction.reactants.iteritems():
if c not in cofactors and c not in generic_compound:
if val < 0:
edges.append((c, reaction.id))
else:
edges.append((reaction.id, c))
ids = g.add_edge_list(edges, hashed=True, string_vals=True)
g.vertex_properties["ids"] = ids
g.vertex_properties["names"] = g.new_vertex_property("string")
g.vertex_properties["color"] = g.new_vertex_property("string")
g.vertex_properties["size"] = g.new_vertex_property("int")
g.edge_properties["arrows"] = g.new_edge_property("string")
for v in g.vertices():
v_id = g.vp.ids[v]
g.vp.names[v] = v_id
g.vp.color[v] = "red" if v_id in reactionlist else "white"
g.vp.size[v] = 10 if v_id in reactionlist else 3
for e in g.edges():
g.ep.arrows[e] = "none"
root = gt.find_vertex(g, g.vertex_properties["names"], product)[0]
pos = gt.radial_tree_layout(g, g.vertex_index[root])
gt.graph_draw(g,
pos,
vertex_size=g.vp.size,
vertex_text=g.vp.names,
vertex_fill_color=g.vp.color,
edge_pen_width=0.5,
edge_end_marker=g.ep.arrows,
output=filename,
fit_view=True,
output_size=(10000, 10000))
for n, record in enumerate(screed.open(input_fasta)):
if n % 1000 == 0:
print >>sys.stderr, '...loaded and tagged {} sequences'.format(n)
name = record.name
sequence = record.sequence
ht.consume_sequence_and_tag_with_labels(sequence, n)
tags = ht.sweep_tag_neighborhood(sequence, 0)
for i in xrange(len(tags) - 1):
src = tags[i]
dst = tags[i + 1]
new = False
srcv = gt.find_vertex(sparse_graph, hashes, src)
if not srcv:
srcv = sparse_graph.add_vertex()
hashes[srcv] = src
new = True
else:
srcv = srcv[0]
dstv = gt.find_vertex(sparse_graph, hashes, dst)
if not dstv:
dstv = sparse_graph.add_vertex()
hashes[dstv] = dst
new = True
else:
dstv = dstv[0]
def find_system_by_name(name):
systems = gt.find_vertex(g, g.vertex_properties['system_name'], name)
if len(systems) < 1:
bottle.abort(404, "No such system.")
return vtx_to_json(systems[0])
def procuraVerticeNoGrafo(g, nome):
r = gt.find_vertex(g, g.vp.vertex_name, nome)
if (not r):
return r
else:
return r[0]
gprop_vcolour[v] = "blue"
else:
gprop_vcolour[v] = "white"
g.vertex_properties["vcolour"] = gprop_vcolour
# create numLinks edge property for g edges
eprop_numLinks = g.new_edge_property("int")
# creates the edges between nodes
for i in linkDict:
for n in linkDict[i]:
#print(i)
vertex_i = gt.find_vertex(g, gprop_label, i)[0]
#print(n)
try:
vertex_n = gt.find_vertex(g, gprop_label, n)[0]
e = g.add_edge(vertex_i,vertex_n)
eprop_numLinks[e] = linkDict[i][n]
except:
IndexError
###### EXPERIMENTAL SIZE THINGS ######
# gvprop_size = g.new_vertex_property('float')
deleteList = []
for v in g.vertices():
def run(input_file: KGTKFiles, path_file, output_stats, directed, max_hops):
def infer_index(h, options=[]):
for o in options:
if o in h:
return h.index(o)
return -1
def infer_predicate(h, options=[]):
for o in options:
if o in h:
return o
return ''
try:
# import modules locally
from kgtk.exceptions import KGTKException
import socket
from graph_tool import load_graph_from_csv
from graph_tool import centrality
from graph_tool.all import find_vertex
from graph_tool.topology import all_paths
import sys
import csv
from collections import defaultdict
csv.field_size_limit(sys.maxsize)
id_col = 'name'
pairs = []
with open(path_file, 'r') as f:
header = next(f)
for line in f:
src, tgt = line.strip().split('\t')
pairs.append((src, tgt))
filename: Path = KGTKArgumentParser.get_input_file(input_file)
with open(filename, 'r') as f:
header = next(f).strip().split('\t')
subj_index = infer_index(header, options=['node1', 'subject'])
obj_index = infer_index(header,
options=['node2', 'object', 'value'])
predicate = infer_predicate(
header, options=['property', 'predicate', 'label'])
p = []
for i, header_col in enumerate(header):
if i in [subj_index, obj_index]: continue
p.append(header_col)
if 'id' not in p:
raise KGTKException('Error: no id column found')
G = load_graph_from_csv(str(filename),
skip_first=True,
directed=directed,
hashed=True,
ecols=[subj_index, obj_index],
eprop_names=p,
csv_options={'delimiter': '\t'})
sys.stdout.write('node1\tlabel\tnode2\tid\n')
id_count = 0
if not output_stats:
for e in G.edges():
sid, oid = e
lbl = G.ep[predicate][e]
sys.stdout.write(
'%s\t%s\t%s\t%s\n' %
(G.vp[id_col][sid], lbl, G.vp[id_col][oid],
'{}-{}-{}'.format(G.vp[id_col][sid], lbl, id_count)))
id_count += 1
id_count = 0
path_id = 0
paths = defaultdict(set)
for pair in pairs:
source_node, target_node = pair
source_ids = find_vertex(G,
prop=G.properties[('v', id_col)],
match=source_node)
target_ids = find_vertex(G,
prop=G.properties[('v', id_col)],
match=target_node)
if len(source_ids) == 1 and len(target_ids) == 1:
source_id = source_ids[0]
target_id = target_ids[0]
for path in all_paths(G,
source_id,
target_id,
cutoff=max_hops,
edges=True):
for edge_num, an_edge in enumerate(path):
edge_id = G.properties[('e', 'id')][an_edge]
node1 = 'p%d' % path_id
sys.stdout.write(
'%s\t%d\t%s\t%s\n' %
(node1, edge_num, edge_id, '{}-{}-{}'.format(
node1, edge_num, id_count)))
id_count += 1
path_id += 1
except Exception as e:
raise KGTKException('Error: ' + str(e))
def matchGraphs(graph1, graph2):
g1, pos1, weight1, clase1, nodetype1, age1 = graph1
g2, pos2, weight2, clase2, nodetype2, age2 = graph2
vertices1 = g1.get_vertices()
vertices2 = g2.get_vertices()
age = np.zeros_like(vertices1)
for i in range(0, len(vertices1)):
age[i] = age1[vertices1[i]]
vertices1 = np.argsort(age)[::-1]
pos_vertex2 = []
for i in vertices2:
pos_vertex2.append(pos2[i])
pos_vertex2 = np.array(pos_vertex2)
for i in vertices1:
p1 = pos1[i]
v, d = find_nearest_nodes(p1, pos_vertex2, 15)
v = v[np.argsort(d)]
if len(v) == 1:
age2[v[0]] = age1[i] + 1
if nodetype2[v[0]] == "null":
nodetype2[v[0]] = nodetype1[i]
elif len(v) == 2:
n_0 = len(g1.get_out_neighbours(i))
n_1 = len(g2.get_out_neighbours(v[0]))
n_2 = len(g2.get_out_neighbours(v[1]))
dif_a = np.abs(n_1 - n_0)
dif_b = np.abs(n_2 - n_0)
if dif_a <= dif_b:
age2[v[0]] = age1[i] + 1
if nodetype2[v[0]] == "null":
nodetype2[v[0]] = nodetype1[i]
else:
age2[v[1]] = age1[i] + 1
if nodetype2[v[1]] == "null":
nodetype2[v[1]] = nodetype1[i]
available = np.ones(pos_vertex2.shape[0])
## If seed is not found => debug
seed = gt.find_vertex(g2, nodetype2, "Ini")
if len(seed) == 1:
seed = seed[0]
else:
seed_prev = gt.find_vertex(g1, nodetype1, "Ini")
p1 = pos1[seed_prev[0]]
v, d = find_nearest_b(p1, pos_vertex2)
if d < 20:
age2[v] = age1[seed_prev[0]] + 1
nodetype2[v] = "Ini"
seed = g2.vertex(v)
available[v] = 0
else:
print('No SEED')
raise Exception("BAD TRACKING")
pos_vertex2[:, 0] = pos_vertex2[:, 0] * available
pos_vertex2[:, 1] = pos_vertex2[:, 1] * available
## If main root tip is not found => debug
end = gt.find_vertex(g2, nodetype2, "FTip")
if len(end) == 1:
end = end[0]
else:
end_prev = gt.find_vertex(g1, nodetype1, "FTip")
p1 = pos1[end_prev[0]]
v, d = find_nearest_b(p1, pos_vertex2)
if d < 20:
age2[v] = age1[end_prev[0]] + 1
nodetype2[v] = "FTip"
end = g2.vertex(v)
else:
v = np.argmax(pos_vertex2[:, 1])
nodetype2[v] = "FTip"
end = g2.vertex(v)
# print('No TIP')
# raise Exception ("BAD TRACKING")
vertices2 = g2.get_vertices()
for i in vertices2:
if age2[i] == 0:
age2[i] == 1
if nodetype2[i] == "null":
vecinos = g2.get_out_neighbours(i)
if len(vecinos) > 1:
nodetype2[i] = "Bif"
else:
if len(vecinos) == 1:
nodetype2[i] = "LTip"
# edge tracking
camino, _ = gt.shortest_path(g2, seed, end, weights=weight2)
l = len(camino)
for k in range(0, l - 1):
arista = g2.edge(camino[k], camino[k + 1])
clase2[arista][1] = 10
return [g2, pos2, weight2, clase2, nodetype2, age2]
def main():
# params
IEXCLOUD_TOKEN = os.getenv("IEXCLOUD_TOKEN")
# 需要事前提供的檔案
# 在執行時會自動產生的檔案
output_folder = './outputs'
downlaods_folder = './downloads'
entities_json = f"{output_folder}/wd_entities.json"
tk_csv = './downloads/bats_symbols_traded_byx.csv'
tk_info_json = "./downloads/iex_ticker_info.json"
urls_json = f"{output_folder}/wiki_urls.json"
mentions_json = f"{output_folder}/wiki_mentions.json"
sent_cooccurs_json = f"{output_folder}/corpus_mentions_sent_cooccurs.json"
atk_cooccurs_json = f"{output_folder}/corpus_mentions_atk_cooccurs.json"
atk_bags_json = f"{output_folder}/corpus_mentions_atk_bags.json"
freqs_json = f"{output_folder}/corpus_mentions_freqs.json"
# Wiki processor requires:
explore_n_wk_depth: int = 2 # 探索wk的層數
adpot_n_wk_depth: int = 1 # 在n層以內的wk-titles會被實際採用（其他用作graph計算）
wkd_dump_json = "./downloads/latest-all.json.bz2"
seeded_wk_titles = []
sp500_csv = f"{downlaods_folder}/s_and_p_500.csv"
# Wiki processor outputs:
wk_titles_graphml = f"{output_folder}/wk_titles.graphml.bz2"
wk_pagerank_json = f"{output_folder}/wk_pagerank.json"
wk_cat_tags_json = f"{output_folder}/wk_cat_tags.json"
# wk_tags_json = f"{output_folder}/wk_tags.json"
wk_tags_pagerank_csv = f"{output_folder}/wk_tags_pagerank.csv"
wkd_filtered_entities_json = f"{output_folder}/wkd_filtered_entities.json"
wk_ranked_titles_json = f"{output_folder}/wk_ranked_titles.json"
wkd_entites_by_ranked_titles_json = f"{output_folder}/wkd_entites_by_ranked_titles.json"
pathlib.Path(output_folder).mkdir(exist_ok=True)
# print(get_matched_wkd_entities(titles, wkd_dump_path=wkd_dump_json))
# entities = load_or_run(wkd_entites_by_ranked_titles_json,
# lambda: get_matched_wkd_entities(titles, wkd_dump_path=wkd_dump_json),
# forcerun=True)
# print("從wikidata取得具有symbol屬性的entities")
# results = load_or_run(
# entities_json, lambda: query_wikidata_by_property())
# comp_wdids = [e['item']['value'].split('/')[-1]
# for e in results['results']['bindings']]
# print("找wikidata-entity對應的wikipage")
# comp_titles = load_or_run(
# comp_titles_json, lambda: query_wikipage_title(comp_wdids))
# return
# print("讀取tickers")
# df = pd.read_csv(tk_csv)
# tickers = list(df['Symbols'])
# # tickers = ['ADBE', 'BA', 'RXT', 'TTOO']
# print(f"載入ticker數量: {len(tickers)}")
# print("從iexcloud抓ticker info")
# infos = load_or_run(
# tk_info_json, lambda: download_ticker_info_from_iexcloud(tickers, IEXCLOUD_TOKEN))
# names = [v['companyName'] for k, v in infos.items()]
# print("找ticker-info中的公司名搜尋對應的wikipage")
# urls = load_or_run(
# urls_json, lambda: search_wikipage(names))
# 掃wikipedia-dump，從company的wiki-page開始抓裡面的mentions
# 將新加入的mentions設為next_entities，重複抓取n次（=爬n層）
# print(f"取得跟公司關聯的mentions - {depth}階層")
# titles = [v.split('/')[-1].replace("_", " ")
# for _, v in urls.items() if v is not None]
print(f"# 連線elasticsearch（用於存放wiki-page, news-corpus）")
es.connect(["es:9200"])
print(f"# 以S&P500 wikipage為起點，爬'{explore_n_wk_depth}階層'的titles，建立graph")
# seedtitles = ["List of S&P 500 companies"]
seedtitles = ["Wilson (company)"]
try:
# raise FileNotFoundError
g = gt.load_graph(wk_titles_graphml)
print(f"File loaded: {wk_titles_graphml}")
except FileNotFoundError:
print(f"File not found, create new one")
g = get_wktitles_graph(seedtitles, n_depth=explore_n_wk_depth)
for n in g:
g.nodes[n]['mentions'] = json.dumps(g.nodes[n]['mentions'],
ensure_ascii=False,
default=serialize_sets)
nx.write_graphml_lxml(g, wk_titles_graphml)
g = gt.load_graph(wk_titles_graphml)
print("# 使用完整的graph跑pagerank（為避免記憶體不足，改用graph-tool庫）")
ranks = load_or_run(wk_pagerank_json,
lambda: calc_pagerank(g),
forcerun=True)
print(f"# 挑出graph中的wiki-category，再找主要描述此category的wiki-title")
def _cat_tags() -> Iterable[str]:
_, wk_title, _ = zip(*ranks)
cats = filter(lambda e: "Category:" in e, wk_title)
# print(list(cats))
# print([c for c in cats])
tags = [es.get_corresponded_wktitles(cat_title=c) for c in cats]
tags = set(itertools.chain(*tags))
# tags &= set(tags)
return tags
cat_tags = load_or_run(wk_cat_tags_json,
lambda: _cat_tags(),
forcerun=True)
print(f"# 依照wk-title找尋對應的wkd-entity")
# tags = ["Technology", "Internet", "Metal"]
cattag_entity = get_matched_wkd_entities(cat_tags)
ranks_by_tags = []
for _, wk_title, pagerank in ranks:
try:
e = cattag_entity[wk_title]
ranks_by_tags.append((e.entity_id, e.get_enwiki_title(),
e.get_label("zh"), pagerank))
except KeyError:
pass
print("# 將ranks存成csv格式")
wkd_id, wk_title, zh_label, pagerank = zip(*ranks_by_tags)
tags = wk_title
df = pd.DataFrame({
'wkd_id': wkd_id,
'wk_title': wk_title,
'zh_label': zh_label,
'pagerank': pagerank
})
df.to_csv(wk_tags_pagerank_csv, index=False)
return
print("# 找一個ticker的tags")
def get_neighbors(v: gt.Vertex, n_expands: int = 2):
seeds = set([v])
traveled = set()
for i in range(n_expands):
nextseeds = set()
for v in seeds:
nextseeds |= set(v.out_neighbors())
nextseeds -= seeds
traveled |= seeds
seeds = nextseeds
return traveled
# tags = set(["joint venture"])
tickers = ["Wilson (company)"]
tags_by_tickers = []
for tk in tickers:
v = gt.find_vertex(g, g.vp['_graphml_vertex_id'], tk)[0]
neighbors = get_neighbors(v, n_expands=2)
neighbors = set([g.vp['_graphml_vertex_id'][v] for v in neighbors])
tags_by_tickers.append((tk, tags & neighbors))
print(tags_by_tickers)
return
print(f"tag的排序、重要度、重複性（用max_flow、n_path之類的方式）")
# for tk in tickers:
# neighbors = get_neighbors(tk)
print(f"TODO:巡所有的news，計算mentions的詞頻")
# print(f"巡所有的news，計算mentions的詞頻")
# TODO: 擴展同義詞（用於flashtext）
# print(f"載入S&P500，做為seed-wk-titles")
# df = pd.read_csv(sp500_csv)
# seedtitles = list(df['Name'])
# print(f"以seed-wk-titles為起點，爬'{explore_n_wk_depth}階層'的wk-titles，建立graph")
# try:
# # raise FileNotFoundError
# g = gt.load_graph(wk_titles_graphml)
# print(f"File loaded: {wk_titles_graphml}")
# except FileNotFoundError:
# print(f"File not found, create new one")
# g = get_wktitles_graph(seedtitles, n_depth=explore_n_wk_depth)
# for n in g:
# g.nodes[n]['mentions'] = json.dumps(
# g.nodes[n]['mentions'], ensure_ascii=False, default=serialize_sets)
# nx.write_graphml_lxml(g, wk_titles_graphml)
# g = gt.load_graph(wk_titles_graphml)
# print(f"僅採用{adpot_n_wk_depth}-depth的wk-titles")
# vp_label = g.vp['_graphml_vertex_id']
# vp_depth = g.vp['depth']
# wktitles = [vp_label[v]
# for v in g.vertices() if vp_depth[v] <= adpot_n_wk_depth]
# print("掃wkd-dump，將沒有中文名、有位置claim（很可能是地點）、是人的wk-titles排除")
# try:
# raise FileNotFoundError
# entities = WikidataJsonDump(wkd_filtered_entities_json)
# filtered_wktitles = set([e.get_enwiki_title() for e in entities])
# print(f"File loaded: {wkd_filtered_entities_json}")
# except FileNotFoundError:
# print(f"File not found, create new one")
# entities = get_matched_wkd_entities(
# wktitles, wkd_dump_path=wkd_dump_json)
# dump_entities_to_json(entities, wkd_filtered_entities_json)
# filtered_wktitles = set([e.get_enwiki_title() for e in entities])
# print("使用完整的graph跑pagerank（為避免記憶體不足，改用graph-tool庫）")
# load_or_run(wk_filtered_pagerank_json,
# lambda: calc_pagerank(g, included_wktitles=filtered_wktitles), forcerun=True)
return
'~/project/KB_dump/conceptnet/conceptnet-en.csv')
g = load_graph_from_csv(conceptnet_path,
directed=False,
eprop_types=['string', 'string'],
string_vals=True)
prefix = '/c/en/'
entities = [
['capoeira', 'hand', 'cartwheel', 'shirt', 'handstand'],
['sunscreen', 'skateboarding', 'soccer', 'tan', 'rubbing'],
['cream', 'mascara', 'writing', 'lifting', 'dictaphone'],
]
blackListVertex = set([
find_vertex(g, prop=g.properties[('v', 'name')], match=prefix + b)[0]
for b in ['object', 'thing']
])
blackListEdge = set(['/r/DerivedFrom', '/r/RelatedTo'])
print('#' * 20)
for elist in entities:
print(elist)
qid = find_vertex(g,
prop=g.properties[('v', 'name')],
match=prefix + elist[0])[0]
for a in elist[1:]:
aid = find_vertex(g,
prop=g.properties[('v', 'name')],
match=prefix + a)[0]
for vp, ep in zip(
def run(input_file: KGTKFiles, directed, max_hops, source_nodes, target_nodes):
def infer_index(h, options=[]):
for o in options:
if o in h:
return h.index(o)
return -1
def infer_predicate(h, options=[]):
for o in options:
if o in h:
return o
return ''
try:
# import modules locally
from kgtk.exceptions import KGTKException
import socket
from graph_tool import load_graph_from_csv
from graph_tool import centrality
from graph_tool.all import find_vertex
from graph_tool.topology import all_paths
import sys
from collections import defaultdict
id_col = 'name'
graph_edge = 'graph'
filename: Path = KGTKArgumentParser.get_input_file(input_file)
filename = str(filename)
with open(filename, 'r') as f:
header = next(f).split('\t')
subj_index = infer_index(header, options=['node1', 'subject'])
obj_index = infer_index(header,
options=['node2', 'object', 'value'])
predicate = infer_predicate(
header, options=['property', 'predicate', 'label'])
p = []
for i, header_col in enumerate(header):
if i in [subj_index, obj_index]: continue
p.append(header_col)
if 'id' not in p:
raise KGTKException('Error: no id column found')
G = load_graph_from_csv(filename,
skip_first=True,
directed=directed,
hashed=True,
ecols=[subj_index, obj_index],
eprop_names=p,
csv_options={'delimiter': '\t'})
graph_id = 1
paths = defaultdict(set)
for source_node in source_nodes:
source_ids = find_vertex(G,
prop=G.properties[('v', id_col)],
match=source_node)
if len(source_ids) == 1:
source_id = source_ids[0]
for target_node in target_nodes:
target_ids = find_vertex(G,
prop=G.properties[('v', id_col)],
match=target_node)
if len(target_ids) == 1:
target_id = target_ids[0]
for path in all_paths(G,
source_id,
target_id,
cutoff=max_hops,
edges=True):
for an_edge in path:
edge_id = G.properties[('e', 'id')][an_edge]
paths[edge_id].add(str(graph_id))
graph_id += 1
sys.stdout.write('node1\tlabel\tnode2\tid\t%s\n' % graph_edge)
for e in G.edges():
sid, oid = e
edge_id = G.properties[('e', 'id')][e]
lbl = G.ep[predicate][e]
graph_id = '|'.join(list(paths[edge_id]))
sys.stdout.write(
'%s\t%s\t%s\t%s\t%s\n' %
(G.vp[id_col][sid], lbl, G.vp[id_col][oid], edge_id, graph_id))
except Exception as e:
raise KGTKException('Error: ' + str(e))
def neighbor_graphs(self):
for o in self.organisms:
builder = self.builder[o]
cofactors = set([c.id for c in builder.cofactor_list])
g = self.builder[o].arcgraph
model_reactions = {
r.id: r
for r in self.builder[o].model.reactions
}
has_start_metabolite_in_graph = list()
for nf in self.not_found[o]:
g.set_reversed(True)
root = gt.find_vertex(g, g.vertex_properties["compound_ids"],
nf)[0]
helper.add_properties(g)
g.vp.size[root] = 100
old_color = g.vp.color[root]
g.vp.color[root] = "red"
g.vp.besucht[root] = True
visitor = helper.NeighborVisitor(g)
gt.bfs_search(g, root, visitor)
g.set_reversed(False)
g.set_vertex_filter(g.vp.besucht)
g.vp.size.a = 90 * 0.9**g.vp.dist.a + 10
if any([v for v in g.vertices() if g.vp.color[v] == "yellow"]):
has_start_metabolite_in_graph.append(nf)
# reset graph
g.clear_filters()
helper.remove_properties(g)
g.vp.color[root] = old_color
feasible_reactions = self.builder[o].feasible_reaction_list
self.really_not_found[o] = has_start_metabolite_in_graph
with open(join(const.MECAT_BASE, 'Targets '+o,'has_start_metabolite_in_graph.txt'),'w') as f1, \
open(join(const.MECAT_BASE, 'Targets ' + o, 'reactions_for_really_not_found.txt'), 'w') as f2:
rrnf = list()
substrates_not_found = set(has_start_metabolite_in_graph +
self.not_found[o] +
self.really_not_found[o])
for c in has_start_metabolite_in_graph:
f1.write('{}\t{}\n'.format(c, self.compounds[c].names[0]))
reactions_for_target = self.__reactions_for_target(
c, model_reactions)
fr = set(reactions_for_target) & feasible_reactions
if len(fr) > 0:
rrnf.append(c)
f2.write('{}\t{}:\n'.format(c, self.compounds[c].names[0]))
for reac_id in fr:
t = ''
# test if the substrates of the reaction are all cofactors
substrates = set(
next(
itertools.ifilter(
lambda x: x.id == reac_id,
builder.model.reactions)).substrates())
if substrates <= cofactors:
t += ' keine Kante (Cofactors)'
# test if the reaction has arcs that end with the target
rpairs = builder.reaction_rpair[reac_id]
if not any((x[1] == c) for x in rpairs):
t += ' keine Kante zu Target'
# test if any substrate is also in list of not found
if substrates & substrates_not_found:
t += ' Substrat auch nicht gefunden'
fx = re.findall('R\d{5}', reac_id)[0]
f2.write('\t{}\t{}\t{}\n'.format(
t, reac_id, self.reactions[fx].definition))
self.really_really_not_found[o] = rrnf
self.__dump_statistics(
self.organisms, join(const.MECAT_BASE, 'targets_in_organisms.csv'))
def find_system_by_name(name):
systems = gt.find_vertex(g, g.vertex_properties['system_name'], name)
if len(systems) < 1:
bottle.abort(404, "No such system.")
return vtx_to_json(systems[0])
def local_cells(self):
for vertex in gt.find_vertex(self.eptm.graph, self.eptm.is_local_vert,
1):
if self.eptm.is_cell_vert[vertex]:
yield vertex
def set_precursors(self, precursors=None, complex_loopback=True):
"""
Replaces network's previous precursor node with provided new precursors.
Finds new edges that link products back to reactants as dependent on the
complex_loopback parameter.
Args:
precursors ([ComputedEntry]): list of new precursor entries
complex_loopback (bool): if True, adds zero-weight edges which "loop back"
to allow for multi-step or autocatalytic-like reactions, i.e. original
precursors can reappear many times and in different steps.
Returns:
None
"""
g = self._g
self._precursors = set(precursors) if precursors else None
if not self._precursors:
precursors_entries = RxnEntries(None,
"d") # use dummy precursors node
if complex_loopback:
raise ValueError(
"Complex loopback can't be enabled when using a dummy precursors "
"node!")
else:
precursors_entries = RxnEntries(precursors, "s")
g.remove_vertex(gt.find_vertex(g, g.vp["type"], 0))
new_precursors_v = g.add_vertex()
self._update_vertex_properties(
g,
new_precursors_v,
{
"entries": precursors_entries,
"type": 0,
"bool": True,
"path": True,
"chemsys": precursors_entries.chemsys,
},
)
new_edges = []
remove_edges = []
for v in gt.find_vertex(g, g.vp["type"],
1): # iterate over all reactants
phases = g.vp["entries"][v].entries
remove_edges.extend(list(v.in_edges()))
if precursors_entries.description == "D" or phases.issubset(
self._precursors):
new_edges.append([new_precursors_v, v, 0, None, True, False])
for v in gt.find_vertex(g, g.vp["type"],
2): # iterate over all products
phases = g.vp["entries"][v].entries
if complex_loopback:
combos = generate_all_combos(phases.union(self._precursors),
self._max_num_phases)
else:
combos = generate_all_combos(phases, self._max_num_phases)
for c in combos:
combo_phases = set(c)
if complex_loopback and combo_phases.issubset(
self._precursors):
continue
combo_entry = RxnEntries(combo_phases, "R")
loopback_v = gt.find_vertex(g, g.vp["entries"], combo_entry)[0]
new_edges.append([v, loopback_v, 0, None, True, False])
for e in remove_edges:
g.remove_edge(e)
g.add_edge_list(
new_edges,
eprops=[g.ep["weight"], g.ep["rxn"], g.ep["bool"], g.ep["path"]])
def run(
input_file: KGTKFiles,
path_file: KGTKFiles,
output_file: KGTKFiles,
statistics_only: bool,
undirected: bool,
max_hops: int,
source_column_name: typing.Optional[str],
target_column_name: typing.Optional[str],
shortest_path: bool,
errors_to_stdout: bool,
errors_to_stderr: bool,
show_options: bool,
verbose: bool,
very_verbose: bool,
**kwargs, # Whatever KgtkFileOptions and KgtkValueOptions want.
):
# import modules locally
from pathlib import Path
import sys
from graph_tool.all import find_vertex
from graph_tool.topology import all_paths
from graph_tool.topology import all_shortest_paths
from kgtk.gt.gt_load import load_graph_from_kgtk
from kgtk.io.kgtkreader import KgtkReader, KgtkReaderOptions
from kgtk.io.kgtkwriter import KgtkWriter
from kgtk.value.kgtkvalueoptions import KgtkValueOptions
from kgtk.exceptions import KGTKException
try:
# Select where to send error messages, defaulting to stderr.
error_file: typing.TextIO = sys.stdout if errors_to_stdout else sys.stderr
# Build the option structures.
input_reader_options: KgtkReaderOptions = KgtkReaderOptions.from_dict(
kwargs, who="input", fallback=True)
path_reader_options: KgtkReaderOptions = KgtkReaderOptions.from_dict(
kwargs, who="path", fallback=True)
value_options: KgtkValueOptions = KgtkValueOptions.from_dict(kwargs)
input_kgtk_file: Path = KGTKArgumentParser.get_input_file(input_file)
path_kgtk_file: Path = KGTKArgumentParser.get_input_file(path_file)
output_kgtk_file: Path = KGTKArgumentParser.get_output_file(
output_file)
id_col = 'name'
if verbose:
print("Reading the path file: %s" % str(path_kgtk_file),
file=error_file,
flush=True)
pairs = []
pkr: KgtkReader = KgtkReader.open(
path_kgtk_file,
error_file=error_file,
options=path_reader_options,
value_options=value_options,
verbose=verbose,
very_verbose=very_verbose,
)
path_source_idx: int = pkr.get_node1_column_index(source_column_name)
if path_source_idx < 0:
print("Missing node1 (source) column name in the path file.",
file=error_file,
flush=True)
path_target_idx: int = pkr.get_node2_column_index(target_column_name)
if path_target_idx < 0:
print("Missing node1 (target) column name in the path file.",
file=error_file,
flush=True)
if path_source_idx < 0 or path_target_idx < 0:
pkr.close()
raise KGTKException("Exiting due to missing columns.")
paths_read: int = 0
path_row: typing.List[str]
for path_row in pkr:
paths_read += 1
if len(path_row) != pkr.column_count:
raise KGTKException(
"Exiting because line %d in the path file (%s) is the wrong length: %d columns expected, %d were read."
% (paths_read, str(path_kgtk_file), pkr.column_count,
len(path_row)))
src: str = path_row[path_source_idx]
tgt: str = path_row[path_target_idx]
pairs.append((src, tgt))
pkr.close()
if verbose:
print("%d path rows read" % paths_read,
file=error_file,
flush=True)
if len(pairs) == 0:
print("No path pairs found, the output will be empty.",
file=error_file,
flush=True)
elif verbose:
print("%d path pairs found" % len(pairs),
file=error_file,
flush=True)
if verbose:
print("Reading the input file: %s" % str(input_kgtk_file),
file=error_file,
flush=True)
kr: KgtkReader = KgtkReader.open(
input_kgtk_file,
error_file=error_file,
options=input_reader_options,
value_options=value_options,
verbose=verbose,
very_verbose=very_verbose,
)
sub_index: int = kr.get_node1_column_index()
if sub_index < 0:
print("Missing node1 (subject) column.",
file=error_file,
flush=True)
pred_index: int = kr.get_label_column_index()
if pred_index < 0:
print("Missing label (predicate) column.",
file=error_file,
flush=True)
obj_index: int = kr.get_node2_column_index()
if obj_index < 0:
print("Missing node2 (object) column", file=error_file, flush=True)
id_index: int = kr.get_id_column_index()
if id_index < 0:
print("Missing id column", file=error_file, flush=True)
if sub_index < 0 or pred_index < 0 or obj_index < 0 or id_index < 0:
kr.close()
raise KGTKException("Exiting due to missing columns.")
predicate: str = kr.column_names[pred_index]
id_col_name: str = kr.column_names[id_index]
G = load_graph_from_kgtk(kr,
directed=not undirected,
ecols=(sub_index, obj_index),
verbose=verbose,
out=error_file)
output_columns: typing.List[str] = ['node1', 'label', 'node2', 'id']
kw: KgtkWriter = KgtkWriter.open(output_columns,
output_kgtk_file,
mode=KgtkWriter.Mode.EDGE,
require_all_columns=True,
prohibit_extra_columns=True,
fill_missing_columns=False,
verbose=verbose,
very_verbose=very_verbose)
id_count = 0
if not statistics_only:
for e in G.edges():
sid, oid = e
lbl = G.ep[predicate][e]
kw.write([
G.vp[id_col][sid], lbl, G.vp[id_col][oid],
'{}-{}-{}'.format(G.vp[id_col][sid], lbl, id_count)
])
id_count += 1
if verbose:
print("%d edges found." % id_count,
file=error_file,
flush=True)
id_count = 0
path_id = 0
for pair in pairs:
source_node, target_node = pair
source_ids = find_vertex(G,
prop=G.properties[('v', id_col)],
match=source_node)
target_ids = find_vertex(G,
prop=G.properties[('v', id_col)],
match=target_node)
if len(source_ids) == 1 and len(target_ids) == 1:
source_id = source_ids[0]
target_id = target_ids[0]
if shortest_path:
_all_paths = all_shortest_paths(G,
source_id,
target_id,
edges=True)
else:
_all_paths = all_paths(G,
source_id,
target_id,
cutoff=max_hops,
edges=True)
for path in _all_paths:
for edge_num, an_edge in enumerate(path):
edge_id = G.properties[('e', 'id')][an_edge]
node1: str = 'p%d' % path_id
kw.write([
node1,
str(edge_num), edge_id,
'{}-{}-{}'.format(node1, edge_num, id_count)
])
id_count += 1
path_id += 1
if verbose:
print("%d paths contining %d edges found." % (path_id, id_count),
file=error_file,
flush=True)
kw.close()
kr.close()
except Exception as e:
raise KGTKException('Error: ' + str(e))
def local_cells(self):
for vertex in gt.find_vertex(self.eptm.graph,
self.eptm.is_local_vert, 1):
if self.eptm.is_cell_vert[vertex]:
yield vertex
def Stochastic():
import pandas as pd
import numpy as np
import pprint as pp
import locale
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
import graph_tool.all as gt
import math
# Need to drag this out into the real world
from GAC_Graph_Builder import findEdges
t = gt.Graph(directed=True)
tprop_label = t.new_vertex_property("string")
tprop_instType = t.new_vertex_property("string")
linkDict, instSet = findEdges()
# ingest our university checking lists [this is sloppy, TBI]
foreignUniTxt = open('Workaround txts/Foreign Unis.txt', 'r')
UKUniTxt = open('Workaround txts/UK Unis.txt', 'r')
forerignUniVals = foreignUniTxt.read().splitlines()
UKUniVals = UKUniTxt.read().splitlines()
# add vertices and label them based on their names.
######## FILTERING BASED ON CORDIS RESIDENCY ##########
dfCordisNames = pd.read_pickle('Pickles/CORDIS_Countries.pickle')
eligiblenames = dfCordisNames.name.values.tolist()
veryDirtyWorkaround = ['FOCUS', 'FLUOR', 'GE', 'NI', 'OTE', 'ROKE']
for inst in instSet:
nameCheck = inst.upper()
firstFound = next((x for x in eligiblenames if nameCheck in x), None)
if inst in forerignUniVals:
del (linkDict[inst])
elif nameCheck in veryDirtyWorkaround:
del (linkDict[inst])
elif firstFound is None:
del (linkDict[inst])
else:
vert = t.add_vertex()
tprop_label[vert] = str(inst)
del (linkDict[''])
# internalise property map
t.vertex_properties["label"] = tprop_label
# explicitly declare the hierarchy defining vertices and edges, the sequencing here matters.
for_uni = t.add_vertex()
UK_uni = t.add_vertex()
other = t.add_vertex()
root = t.add_vertex()
edgeList = [(root, for_uni), (root, UK_uni), (root, other)]
t.add_edge_list(edgeList)
# use label name to add edges to hierarchy
for i in range(t.num_vertices())[:-4]:
if tprop_label[i] in forerignUniVals:
t.add_edge(for_uni, t.vertex(i))
tprop_instType[i] = "Foreign Uni"
elif tprop_label[i] in UKUniVals:
t.add_edge(UK_uni, t.vertex(i))
tprop_instType[i] = "UK Uni"
else:
t.add_edge(other, t.vertex(i))
tprop_instType[i] = "Other Institution"
t.vertex_properties["instType"] = tprop_instType
tpos = gt.radial_tree_layout(t,
t.vertex(t.num_vertices() - 1),
rel_order_leaf=True)
######### MAIN GRAPH DRAWING ################
g = gt.Graph(directed=False)
# creates graph g, using the same nodes (with the same index!)
for v in t.vertices():
gv = g.add_vertex()
# we remove: root, for_uni, uk_uni or 'other' vertices
lower = g.num_vertices() - 5
current = g.num_vertices() - 1
while current > lower:
g.remove_vertex(current)
current -= 1
# Pull vertex properties from t
labelDict = t.vertex_properties["label"]
instTypeDict = t.vertex_properties["instType"]
# create properties for g vertices
gprop_label = g.new_vertex_property("string")
gprop_instType = g.new_vertex_property("string")
# match labels between g and t
for v in g.vertices():
gprop_label[v] = labelDict[v]
gprop_instType[v] = instTypeDict[v]
# make property map internal to graph g
g.vertex_properties["label"] = gprop_label
g.vertex_properties["instType"] = gprop_instType
###### COLOUR VERTICES #########
# Reclaim variable names because lazy
gprop_vcolour = g.new_vertex_property("string")
for v in g.vertices():
if gprop_instType[v] == "Foreign Uni":
gprop_vcolour[v] = "red"
elif gprop_instType[v] == "UK Uni":
gprop_vcolour[v] = "blue"
else:
gprop_vcolour[v] = "white"
g.vertex_properties["vcolour"] = gprop_vcolour
# create numLinks edge property for g edges
eprop_numLinks = g.new_edge_property("int")
# creates the edges between nodes
for i in linkDict:
for n in linkDict[i]:
#print(i)
vertex_i = gt.find_vertex(g, gprop_label, i)[0]
#print(n)
try:
vertex_n = gt.find_vertex(g, gprop_label, n)[0]
e = g.add_edge(vertex_i, vertex_n)
eprop_numLinks[e] = linkDict[i][n]
except:
IndexError
##### EXPERIMENTAL SIZE THINGS ######
#gvprop_size = g.new_vertex_property('float')
deleteList = []
for v in g.vertices():
# sum the num edges and the number of links they correspond to
# use this to find a ratio and scale size off of this.
numEdges = sum(1 for _ in v.all_edges())
numLinks = 0
for e in v.all_edges():
numLinks += eprop_numLinks[e]
#print(gprop_label[v])
print("NumEdges = " + str(numEdges) + " NumLinks = " + str(numLinks))
# create a delete list
try:
ratio = (numLinks / numEdges) * 5 * 2
except:
ZeroDivisionError
deleteList.append(v)
#gvprop_size[v] = ratio
#g.vertex_properties['size'] = gvprop_size
#### Delete linkless vertices #######
for v in reversed(sorted(deleteList)):
g.remove_vertex(v)
for v in reversed(sorted(deleteList)):
t.remove_vertex(v)
tpos = gt.radial_tree_layout(t,
t.vertex(t.num_vertices() - 1),
rel_order_leaf=True)
#######
############ stochastic BLOCK MODEL ####################
state = gt.minimize_nested_blockmodel_dl(g, deg_corr=True, verbose=True)
t = gt.get_hierarchy_tree(state)[0]
tpos = pos = gt.radial_tree_layout(t,
t.vertex(t.num_vertices() - 1),
weighted=True)
# in order to make sure labels fit in the image we have to manually adjust the
# co-ordinates of each vertex.
x, y = gt.ungroup_vector_property(tpos, [0, 1])
x.a = (x.a - x.a.min()) / (x.a.max() - x.a.min()) * 1400 + 400
y.a = (y.a - y.a.min()) / (y.a.max() - y.a.min()) * 1400 + 400
tpos = gt.group_vector_property([x, y])
# This draws the 'Bezier spline control points' for edges
# it draws the edges directed in graph g, but uses the hierarchy / positioning of graph t.
cts = gt.get_hierarchy_control_points(g, t, tpos)
pos = g.own_property(tpos)
gt.graph_draw(
g,
vertex_text_position="centered",
vertex_text=g.vertex_properties["label"],
vertex_font_size=14,
vertex_anchor=0,
vertex_aspect=1,
vertex_shape="square",
vertex_fill_color=g.vertex_properties["vcolour"],
vertex_size=10,
fit_view=False,
# edge_color=g.edge_properties["colour"],
# edge_pen_width=g.edge_properties["thickness"],
edge_end_marker="none",
edge_pen_width=0.2,
edge_color="white",
bg_color=[0, 0, 0, 1],
output_size=[2000, 2000],
output='UK_ONLY_RELATIONSHIPS_stochastic.png',
pos=pos,
edge_control_points=cts)
if __name__ == '__main__':
pyjd.setup("Hello.html")
def find_nodes(self,prop,match): return gt.find_vertex(self.graph,prop,match)
def topology(self):
g = self.arcgraph.copy()
components, chist = gt.label_components(
g, directed=False
) # directed = False because True would look for strongly connected components
self.__plot_component_hist(chist, 'componenthist')
start_components = set()
number_compounds_in_start_components = 0
for c in self.start_compounds:
for v in gt.find_vertex(g, g.vp.compound_ids, c):
start_components.add(components[v])
cg = gt.Graph()
cg.vertex_properties["size"] = cg.new_vertex_property("int", val=10)
for c in start_components:
v = cg.add_vertex()
cg.vp.size[v] = chist[c]
number_compounds_in_start_components += chist[c]
satellites = set()
clustering_coefficient = gt.global_clustering(g)
with open(join(self.statistics_path, "clustering_coefficient.txt"),
'w') as f:
f.write(
str(clustering_coefficient[0]) + '\t' +
str(clustering_coefficient[1]) + '\n')
with open(join(self.statistics_path, "compounds_components.txt"), 'w') as f, \
open(join(self.statistics_path, "component_hist.txt"), 'w') as f2:
for componentid, elem in enumerate(chist):
u = gt.GraphView(g, vfilt=components.a == componentid)
u = gt.Graph(u, prune=True)
f2.write(str(componentid + 1) + '\t' + str(elem) + '\n')
for v in u.vertices():
f.write(
str(componentid + 1) + '\t' + u.vp.compound_ids[v] +
'\t' + u.vp.name[v] + '\n')
if componentid not in start_components:
satellites.add(u.vp.compound_ids[v])
# gt.graph_draw(u, output=join(self.statistics_path, "component{i}.pdf".format(i=componentid)))
targets_in_main_component = self.targets - satellites
targets_in_satellites = self.targets & satellites
with open(join(self.statistics_path, "targets_in_main_component.txt"),
'w') as f:
for c in targets_in_main_component:
compound = self.builder.compounds[c]
f.write(c + '\t' + compound.names[0] + '\n')
with open(join(self.statistics_path, "targets_in_satellites.txt"),
'w') as f:
for c in targets_in_satellites:
compound = self.builder.compounds[c]
f.write(c + '\t' + compound.names[0] + '\n')
with open(
join(self.statistics_path,
"components_with_start_metabolites.txt"), 'w') as f:
for cid in start_components:
f.write(str(cid) + '\n')
p = number_compounds_in_start_components / g.num_vertices() * 100
with open(join(const.MECAT_BASE, "component_table.txt"), 'a') as f:
f.write(self.name + ' & ' + str(len(chist)) + ' & ' +
str(np.amax(chist)) + ' & ' + str(len(start_components)) +
' & ' + str(int(number_compounds_in_start_components)) +
' & ' + str(int(round(p, 0))) + '\%' + '\\\\ \n')
#largest = gt.label_largest_component(g, directed=False)
#gt.graph_draw(g, vertex_fill_color=largest, output=join(self.statistics_path,"largest_component.pdf"))
g.vertex_properties["start_components"] = g.new_vertex_property(
"string", val='white')
for v in g.vertices():
if components[v] in start_components:
g.vp.start_components[v] = 'red'
else:
g.vp.start_components[v] = 'blue'
gt.graph_draw(g,
vertex_fill_color=g.vp.start_components,
output=join('/mnt', 'g', 'LisaDaten', 'Paper2',
'figures', 'arcgraph' + self.name + '.pdf'))
#add username property map
name_prop = g.new_vertex_property("string")
g.vertex_properties['name'] = name_prop
#add names to each vertex
for v in g.vertices():
g.vp.name[v] = cursor[g.vertex_index[v]]['user']
#print(g.vertex_properties['name'][v])
cursor.rewind()
#create all edges
for user in cursor:
#v1 is the vertex where name = cursor['user']
v1 = gt.find_vertex(g, g.vp.name, user['user'])[0]
for mention in user['user_mentions']:
try:
# v2 is the vertex where name = mention
v2 = gt.find_vertex(g, g.vp.name, mention)[0]
except IndexError:
print("Error: " + mention + " is not in the collection")
continue
if g.vp.name[v1] != g.vp.name[v2]:
print("adding edge between " + g.vp.name[v1] + " and " +
g.vp.name[v2])
edge = g.add_edge(v1, v2)
#attempt at weighting the graph
pos = gt.sfdp_layout(g)
