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."