def calculate(graph):
if nx.is_connected(graph):
return bool(
Utils.approx_to_int(
la.det(inv_other.DistanceMatrix.calculate(graph))) != 0)
else:
return False
def calculate(graph):
if nx.is_connected(graph):
return len(
Utils.MainEigenvalue(
inv_other.DistanceMatrix.calculate(graph)))
else:
return 0
def update_table(self, invariants_selected):
self.model.removeRows(0, self.model.rowCount())
column_max_width = 0
for key, value in invariants_selected.items():
row = []
invariant_name = QtGui.QStandardItem(str(key))
invariant_name.setEditable(False)
invariant_value = QtGui.QStandardItem(value)
invariant_value.setEditable(False)
current_column_max_width = 0
current_column_max_width = max(
UtilsToInvariants.max_line_of_string(value),
current_column_max_width)
if current_column_max_width > column_max_width:
column_max_width = current_column_max_width
row.append(invariant_name)
row.append(invariant_value)
self.model.appendRow(row)
self.table.resizeColumnToContents(1)
if 5.1 * column_max_width < self.table.columnWidth(1):
self.table.horizontalHeader().setSectionResizeMode(
1, QHeaderView.Stretch)
self.table.horizontalHeader().setSectionResizeMode(
0, QHeaderView.Interactive)
self.table.setColumnWidth(0, 200)
elif 5.1 * column_max_width > self.table.columnWidth(1):
self.table.horizontalHeader().setSectionResizeMode(
0, QHeaderView.Interactive)
self.table.horizontalHeader().setSectionResizeMode(
1, QHeaderView.Interactive)
self.table.setColumnWidth(0, 200)
def calculate(graph):
if nx.is_connected(graph):
return Utils.is_integer(
inv_other.SignlessLaplacianDistanceSpectrum.calculate(graph)[
nx.number_of_nodes(graph) - 1])
else:
return False
def calculate(graph):
if nx.number_of_nodes(graph) < 2:
return 0
elif nx.is_connected(graph):
return Utils.SecondLargestEigen(
inv_other.DistanceMatrix.calculate(graph))
else:
return 10**10
def calculate(graph):
if nx.is_connected(graph):
return Utils.approx_to_int(
la.det(
inv_other.SignlessLaplacianDistanceMatrix.calculate(
graph)))
else:
return 10**10
def calculate(graph):
return len(
Utils.MainEigenvalue(
inv_other.SignlessLaplacianMatrix.calculate(graph)))
def calculate(graph):
return len(
Utils.MainEigenvalue(inv_other.AdjacencyMatrix.calculate(graph)))
def print(graph, precision):
return Utils.print_numeric(NumberSpanningTree.calculate(graph),
precision)
def print(graph, precision):
return Utils.print_numeric(EstradaIndex.calculate(graph), precision)
def print(graph, precision):
return Utils.print_numeric(WienerIndex.calculate(graph), precision)
def print(graph, precision):
return Utils.print_numeric(SeidelEnergy.calculate(graph), precision)
def calculate(graph):
return Utils.Energy(inv_other.SeidelMatrix.calculate(graph))
def print(graph, precision):
return Utils.print_numeric(NormalizedLaplacianEnergy.calculate(graph),
precision)
def calculate(graph):
return Utils.Energy(
inv_other.NormalizedLaplacianMatrix.calculate(graph))
def print(graph, precision):
return Utils.print_numeric(NumberOfTriangles.calculate(graph),
precision)
def calculate(graph):
if nx.is_connected(graph):
return Utils.approx_to_int(nx.wiener_index(graph))
else:
return 10**10
def print(graph, precision):
return Utils.print_numeric(DistanceEnergy.calculate(graph), precision)
def calculate(graph):
return Utils.approx_to_int(nx.estrada_index(graph))
def calculate(graph):
if nx.is_connected(graph):
return Utils.Energy(
inv_other.LaplacianDistanceMatrix.calculate(graph))
else:
return 10**10
def print(graph, precision):
return Utils.print_set(
set(nx.max_weight_clique(graph, weight=None)[0]), precision)
def calculate(graph):
if nx.is_connected(graph):
return Utils.Energy(
inv_other.SignlessLaplacianMatrix.calculate(graph))
else:
return 10**10
def print(graph, precision):
return Utils.print_numeric(Density.calculate(graph), precision)
def print(graph, precision):
return Utils.print_numeric(
SignlessLaplacianDistanceEnergy.calculate(graph), precision)
def print(graph, precision):
return Utils.print_numeric(MainEigenvalueAdjacency.calculate(graph),
precision)
def calculate(graph):
if nx.number_of_nodes(graph) > 1:
return Utils.approx_to_int(
inv_other.LaplacianSpectrum.calculate(graph)[1])
else:
return 0
def print(graph, precision):
return Utils.print_numeric(MainEigenvalueDistance.calculate(graph),
precision)
def print(graph, precision):
return Utils.print_numeric(EdgeConnectivity.calculate(graph),
precision)
def print(graph, precision):
return Utils.print_numeric(
MainEigenvalueSignlessLaplacian.calculate(graph), precision)
def print(graph, precision):
if nx.number_of_isolates(graph) < 1:
return Utils.print_set(
nx.algorithms.covering.min_edge_cover(graph), precision)
else:
return "Graph has isolate vertice."