def is_mlst(edge_set):
"""
Get number of leaves if graph is mlst
else return False
"""
import graph
graph = graph.make_graph(edge_set)
graph.search()
return graph.num_leaves if (graph.edges_in_one_component() and len(graph.get_edge_set()) == (graph.num_nodes - 1) and not graph.has_cycle) else False
def search_tweets(update, ctx):
text = update.message.text
text = cutcmd(text)
graf = graph.search(text, title=text)
log(graf, text, 'search', text + ':search')
ctx.bot.send_message(chat_id=update.effective_chat.id,
text="*" + text + "*\n" + graf["url"],
parse_mode=telegram.ParseMode.MARKDOWN)
def find_path_to_end(self, g, start):
'''
Find the start and end points in order for the solution route to be the longest
possible with no backtracking. Uses dynamic programming(???).
Arg: reached is a dictionary whose keys are the vertices that can be reached
the maze start and reached[u] is the vertex that discovered u in the search
Returns the route as a list with the first entry as the start and the last
entry as the end.
'''
paths = [] # store all possible paths from the start
vertices = list(g.vertices())
reached = graph.search(g, start)
for v in vertices:
# find the path from start to v
paths.append(graph.find_path(g, start, v, reached))
# return the longest path
# the last entry is the end of the maze
return max(paths, key=len)
