def test_topological_figure(self):
DG = nx.DiGraph()
DG.add_edges_from([('a', 'b'), ('a', 'c'), ('b', 'c'), ('b', 'd')])
print(list(nx.topological_sort(DG)))
from ch07.digraph_search import topological_sort
print(list(topological_sort(DG)))
DG = nx.DiGraph()
from ch07.book import make_sample_directed_graph
DG = make_sample_directed_graph()
print(list(topological_sort(DG)))
def test_topological_example(self):
from ch07.book import topological_example
DG = nx.DiGraph()
topological_example(DG, 5)
print(list(nx.topological_sort(DG)))
from ch07.digraph_search import topological_sort
print(list(topological_sort(DG)))
def topological_sp(DAG, src):
"""Given a DAG, compute shortest path from src."""
from ch07.digraph_search import topological_sort
from ch07.single_source_sp import WEIGHT
inf = float('inf')
dist_to = {v:inf for v in DAG.nodes()}
dist_to[src] = 0
edge_to = {}
def relax(e):
n, v, weight = e[0], e[1], e[2][WEIGHT]
if dist_to[n] + weight < dist_to[v]:
dist_to[v] = dist_to[n] + weight
edge_to[v] = e
for n in topological_sort(DAG):
for e in DAG.edges(n, data=True):
relax(e)
return (dist_to, edge_to)
# 'B7': '=(B5+B6)',
# 'B8': '=(B6+B7)',
'C2': '=B2',
'C3': '=(B3+C2)',
'C4': '=(B4+C3)',
'C5': '=(B5+C4)',
# 'C6': '=(B6+C5)',
# 'C7': '=(B7+C6)',
# 'C8': '=(B8+C7)'
}
for k in entries:
spreadsheet.set(k, entries[k])
#######################################################################
if __name__ == '__main__':
if tkinter_error:
print(
'tkinter is not installed so unable to launch spreadsheet application'
)
else:
import tkinter
from ch07.spreadsheet import Spreadsheet
root = tkinter.Tk()
ss = Spreadsheet(root, nx.DiGraph())
fibonacci_example(ss)
from ch07.digraph_search import topological_sort
print(list(topological_sort(ss.digraph)))
root.mainloop()
def print_sample_linear_ordering():
"""Produce sample linear ordering for spreadsheet example."""
from ch07.digraph_search import topological_sort
DG = make_sample_directed_graph(output=False)
linear = topological_sort(DG)
print(list(linear))