def printMetrics(G):
'''
Set inital values before optimization
'''
global initial_st
global all_pairs_sp
global initial_cr
global initial_ar
global initial_asp
# Do some preliminary stuff
# Stress will be normalized considering the first value as max
# To speed up ST precompute all pairs shortest paths
initial_st = 1
if compute_st:
initial_st = stress.stress(G, all_sp=all_pairs_sp)
print("ST:", initial_st, end=" - ")
if all_pairs_sp is None:
all_pairs_sp = nx.shortest_path(G)
# To speed up NP precompute all pairs shortest paths
if compute_np:
if all_pairs_sp is None:
all_pairs_sp = nx.shortest_path(G)
initial_np = neighbors_preservation.compute_neig_preservation(
G, all_sp=all_pairs_sp)
print("NP:", initial_np, end=" - ")
initial_sym = 0
if compute_sym:
initial_sym = ksymmetry.get_symmetric_score(G)
print("Sym:", abs(initial_sym), end=" - ")
initial_cr = 1
if compute_cr:
initial_cr = len(crossings.count_crossings(G))
print("CR:", initial_cr, end=" - ")
initial_ue = 0
if compute_ue:
initial_ue = uniformity_edge_length.uniformity_edge_length(G)
print("UE:", initial_ue, end=" - ")
initial_ar = 1
if compute_ar:
initial_ar = areafunctions.areaerror(G)
print("AR:", initial_ar, end=" - ")
initial_asp = 1
if compute_asp:
initial_asp = areafunctions.aspectRatioerror(G)
print("ASP:", initial_asp, end=" - ")
print("")
return
def metrics_evaluator(X):
'''
Evaluates the metrics of the given layout and weights them
'''
global log
global G
global all_pairs_sp
n = nx.number_of_nodes(G)
#Reshape the 1D array to a n*2 matrix
X = X.reshape((n, 2))
return_val = 0.0
G = writeSPXPositiontoNetworkXGraph(G, X)
ue = 0
if compute_ue:
ue = uniformity_edge_length.uniformity_edge_length(G)
if log % 100 == 0:
print("UE:", ue, end=" - ")
ue *= abs(compute_ue)
st = 0
if compute_st:
st = stress.stress(G, all_sp=all_pairs_sp)
if log % 100 == 0:
print("ST:", st, end=" - ")
st *= abs(compute_st) / initial_st
sym = 0
if compute_sym:
sym = ksymmetry.get_symmetric_score(G)
if log % 100 == 0:
print("Sym:", abs(sym), end=" - ")
sym = 1 - sym
sym *= abs(compute_sym)
np = 0
if compute_np:
np = neighbors_preservation.compute_neig_preservation(
G, all_sp=all_pairs_sp)
if log % 100 == 0:
print("NP:", abs(np), end=" - ")
np = 1 - np
np *= abs(compute_np)
cr = 0
if compute_cr:
cr = len(crossings.count_crossings(G))
if log % 100 == 0:
print("cr", cr, end="-")
cr *= abs(compute_cr) / initial_cr
return_val = ue + st + sym + np + cr
if log % 100 == 0:
print("ret", return_val)
write_dot(G, 'output/' + graph_name + '_running.dot')
log += 1
return return_val
def metrics_evaluator(X, print_val=False):
'''
Evaluates the metrics of the given layout and weights them
'''
global G
global all_pairs_sp
# Add some additional global variables
global OUTPUT_FOLDER
global graph_name
global cnvs, cnvs_size, cnvs_padding, draw_counter
n = nx.number_of_nodes(G)
#Reshape the 1D array to a n*2 matrix
X = X.reshape((n, 2))
return_val = 0.0
G = writeSPXPositiontoNetworkXGraph(G, X)
ue = 0
ue_count = 0
if compute_ue:
ue = uniformity_edge_length.uniformity_edge_length(G)
ue_count = ue
# if log%100==0:
# print("UE:", ue, end=" - ")
ue *= abs(compute_ue)
st = 0
st_count = 0
if compute_st:
st = stress.stress(G, all_sp=all_pairs_sp)
st_count = st
# if log%100==0:
# print("ST:", st, end=" - ")
st *= abs(compute_st) / initial_st
sym = 0
sym_count = 0
if compute_sym:
G = scale_graph(G, 1000)
sym = ksymmetry.get_symmetric_score(G)
G = scale_graph(G, 1 / 1000)
sym_count = sym
# if log%100==0:
# print("Sym:", abs(sym), end=" - ")
sym = 1 - sym
sym *= abs(compute_sym)
np = 0
np_count = 0
if compute_np:
np = neighbors_preservation.compute_neig_preservation(
G, all_sp=all_pairs_sp)
np_count = np
np = 1 - np
np *= abs(compute_np)
cr = 0
cr_count = 0
if compute_cr:
cr = len(crossings.count_crossings(G))
cr_count = cr
if not initial_cr == 0: cr *= abs(compute_cr) / initial_cr
else: cr = 0
ar = 0
ar_count = 0
if compute_ar:
ar = areafunctions.areaerror(G)
ar_count = ar
ar = abs(ar - 1)
ar *= abs(compute_ar) / initial_ar
# Aspect ratio
asp = 0
asp_count = 0
if compute_asp:
asp = areafunctions.aspectRatioerror(G)
asp_count = asp
asp = abs(asp - 1)
asp *= abs(compute_asp) / initial_asp
return_val = ue + st + sym + np + cr + ar + asp
if print_val:
print("score: ", return_val)
if mode == "GUI":
if draw_counter % 100 == 0:
min_x, min_y, max_x, max_y = 0, 0, 0, 0
for currVStr in nx.nodes(G):
currV = G.nodes[currVStr]
x = float(currV['pos'].split(",")[0])
y = float(currV['pos'].split(",")[1])
min_x = min(min_x, x)
max_x = max(max_x, x)
min_y = min(min_y, y)
max_y = max(max_y, y)
currV['pos'] = str(x) + "," + str(y)
cnvs.delete("all")
scl = (cnvs_size - cnvs_padding) / (max(max_y - min_y,
max_x - min_x))
tx = cnvs_padding / 2
ty = cnvs_padding / 2
pos_dict = nx.get_node_attributes(G, 'pos')
for edge in nx.edges(G):
(s, t) = edge
x_source = float(pos_dict[s].split(",")[0])
x_target = float(pos_dict[t].split(",")[0])
y_source = float(pos_dict[s].split(",")[1])
y_target = float(pos_dict[t].split(",")[1])
cnvs.create_line((x_source - min_x) * scl + tx,
(y_source - min_y) * scl + ty,
(x_target - min_x) * scl + tx,
(y_target - min_y) * scl + ty)
print((x_source - min_x) * scl, (x_target - min_x) * scl,
(y_source - min_y) * scl, (y_target - min_y) * scl)
cnvs.update()
draw_counter += 1
return return_val
def metrics_evaluator(X, print_val=False):
'''
Evaluates the metrics of the given layout and weights them
'''
global G
global all_pairs_sp
global log
n = nx.number_of_nodes(G)
#Reshape the 1D array to a n*2 matrix
X = X.reshape((n, 2))
return_val = 0.0
G = writeSPXPositiontoNetworkXGraph(G, X)
ue = 0
ue_count = 0
if compute_ue:
ue = uniformity_edge_length.uniformity_edge_length(G)
ue_count = ue
# if log%100==0:
# print("UE:", ue, end=" - ")
ue *= abs(compute_ue)
st = 0
st_count = 0
if compute_st:
st = stress.stress(G, all_sp=all_pairs_sp)
st_count = st
# if log%100==0:
# print("ST:", st, end=" - ")
st *= abs(compute_st) / initial_st
sym = 0
sym_count = 0
if compute_sym:
G = scale_graph(G, 1000)
sym = ksymmetry.get_symmetric_score(G)
G = scale_graph(G, 1 / 1000)
sym_count = sym
# if log%100==0:
# print("Sym:", abs(sym), end=" - ")
sym = 1 - sym
sym *= abs(compute_sym)
np = 0
np_count = 0
if compute_np:
np = neighbors_preservation.compute_neig_preservation(
G, all_sp=all_pairs_sp)
np_count = np
np = 1 - np
np *= abs(compute_np)
cr = 0
cr_count = 0
if compute_cr:
cr = len(crossings.count_crossings(G))
# if log%100==0:
# print("CR:", abs(cr), end=" - ")
cr_count = cr
cr *= abs(compute_cr) / initial_cr
ar = 0
ar_count = 0
if compute_ar:
ar = areafunctions.areaerror(G)
ar_count = ar
ar = abs(ar - 1)
ar *= abs(compute_ar) / initial_ar
# Aspect ratio
asp = 0
asp_count = 0
if compute_asp:
asp = areafunctions.aspectRatioerror(G)
asp_count = asp
asp = abs(asp - 1)
asp *= abs(compute_asp) / initial_asp
nonintv = 0
if compute_intcoo:
nonintv = intcoord.nonintvalues(G)
nonupward = 0
if compute_upward:
nonupward = intcoord.upwardness(G)
nonoverlapping = 0
if compute_nonoverlapping:
nonoverlapping = intcoord.overlapping(G)
upawardgrid = 0
if compute_upwardgrid:
upwardgrid = intcoord.upwardgrid(G)
print("Grid:", upwardgrid, end=" - ")
return_val = upawardgrid
# return_val = ue+st+sym+np+cr+ar+asp+nonintv+nonupward+nonoverlapping
if print_val:
print("score: ", return_val)
log += 1
return return_val
output_txt += output_line + "\n"
print(output_line)
csv_head_line += "uniformity\_edge\_length&"
csv_line += str(uniedgelen_val) + "&"
if st or all:
stress_val = stress.stress(G, weighted=weighted, all_sp=all_pairs_sp)
output_line = "ST: " + str(stress_val)
output_txt += output_line + "\n"
print(output_line)
csv_head_line += "stress&"
csv_line += str(stress_val) + "&"
if np or all:
neigpres_val = neigpres.compute_neig_preservation(G, weighted=weighted, all_sp=all_pairs_sp)
output_line = "NP: " + str(neigpres_val)
output_txt += output_line + "\n"
print(output_line)
csv_head_line += "neighbors\_preservation&"
csv_line += str(neigpres_val) + "&"
if lblbb or all:
labelsBBRatio_val = labelsmeas.labelsBBRatio(G)
output_line = "lblbb: " + str(labelsBBRatio_val)
output_txt += output_line + "\n"
print(output_line)
csv_head_line += "boundingbox\_ratio\_to\_labels&"
csv_line += str(labelsBBRatio_val) + "&"
if lblarea or all:
import neighbors_preservation as np
s_path = sys.argv[1]
g_path = sys.argv[2]
outputTxtFile = sys.argv[3]
input_file_name = os.path.basename(s_path)
graph_name = input_file_name.split(".")[0]
S = nx_read_dot(s_path)
G = nx_read_dot(g_path)
G = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)[0]
S_induced = nx.Graph(G.subgraph(S))
np_val_S = np.compute_neig_preservation(S)
np_val_SG = np.compute_neig_preservation(S, G)
np_val_SGI = np.compute_neig_preservation(S, S_induced)
output_txt = "Metrics for " + graph_name + "\n"
output_txt += "np_S:" + str(np_val_S) + "\n"
output_txt += "np_SG:" + str(np_val_SG) + "\n"
output_txt += "np_SGI:" + str(np_val_SGI) + "\n"
print(output_txt)
csv_head_line = "filename;np;np(induced);np(complete)\n"
csv_line = graph_name + ";" + str(np_val_S) + ";" + str(
np_val_SGI) + ";" + str(np_val_SG) + "\n"
exists = os.path.isfile(outputTxtFile)
