from BalanceNearlyBalancedGraph import balance_graph
from Graph import Graph
from WalkRandomEulerianCycle import walk_eulerian_cycle
with open('/home/user/Downloads/dataset_240261_6(1).txt',
mode='r',
encoding='utf-8') as f:
data = f.read()
lines = data.split('\n')
adjacency_list = lines[:]
adjacency_list = [l.strip() for l in adjacency_list] # get rid of whitespace
adjacency_list = [l for l in adjacency_list
if len(l) > 0] # get rid of empty lines
adjacency_list = [l.split(' -> ') for l in adjacency_list]
adjacency_list = [(l[0], l[1].split(',')) for l in adjacency_list]
graph = Graph()
[
graph.insert_edge(from_node, to_node)
for from_node, to_nodes in adjacency_list for to_node in to_nodes
]
graph, roots, tails = balance_graph(graph)
path = walk_eulerian_cycle(graph, roots.pop())
path.pop(
) # last conn in cycle is artificial -- it was created from balancing so generating this path would be fast
print(f'{"->".join(path)}')
for i in range(len(cycle_path) - 1):
kmer = cycle_path[i].data + cycle_path[i + 1].data[-1]
out.append(Read(kmer))
return out
def do_kmers_cycle(reads: List[Read]) -> bool:
for i in range(len(reads) - 1):
if reads[i].suffix() != reads[i + 1].prefix():
return False
if reads[-1].suffix() != reads[0].prefix():
return False
return True
cycle_path = walk_eulerian_cycle(graph, Read('00'))
cycle_path_as_kmers = eularian_path_to_kmers(cycle_path)
print(f'{cycle_path_as_kmers}')
print(f'{cycle_path_as_kmers[0].stitch(cycle_path_as_kmers)}')
print(f'{do_kmers_cycle(cycle_path_as_kmers)}')
cycle_path = walk_eulerian_cycle(graph, Read('01'))
cycle_path_as_kmers = eularian_path_to_kmers(cycle_path)
print(f'{cycle_path_as_kmers}')
print(f'{cycle_path_as_kmers[0].stitch(cycle_path_as_kmers)}')
print(f'{do_kmers_cycle(cycle_path_as_kmers)}')
cycle_path = walk_eulerian_cycle(graph, Read('10'))
cycle_path_as_kmers = eularian_path_to_kmers(cycle_path)
print(f'{cycle_path_as_kmers}')
print(f'{cycle_path_as_kmers[0].stitch(cycle_path_as_kmers)}')
print(f'{do_kmers_cycle(cycle_path_as_kmers)}')
cycle_path = walk_eulerian_cycle(graph, Read('11'))
from Graph import Graph
from WalkRandomEulerianCycle import walk_eulerian_cycle
with open('/home/user/Downloads/dataset_240261_2(2).txt',
mode='r',
encoding='utf-8') as f:
data = f.read()
lines = data.split('\n')
adjacency_list = lines[:]
adjacency_list = [l.strip() for l in adjacency_list] # get rid of whitespace
adjacency_list = [l for l in adjacency_list
if len(l) > 0] # get rid of empty lines
adjacency_list = [l.split(' -> ') for l in adjacency_list]
adjacency_list = [(l[0], l[1].split(',')) for l in adjacency_list]
graph = Graph()
[
graph.insert_edge(from_node, to_node)
for from_node, to_nodes in adjacency_list for to_node in to_nodes
]
cycle_path = walk_eulerian_cycle(graph, next(graph.get_nodes()))
print(f'{"->".join(cycle_path)}')
from BalanceNearlyBalancedGraph import balance_graph
from ReadPair import ReadPair
from Kdmer import Kdmer
from ToOverlapGraphHash import to_overlap_graph
from WalkRandomEulerianCycle import walk_eulerian_cycle
with open('/home/user/Downloads/dataset_240262_16(1).txt', mode='r', encoding='utf-8') as f:
data = f.read()
lines = data.split('\n')
lines = [l.strip() for l in lines] # get rid of whitespace
lines = [l for l in lines if len(l) > 0] # get rid of empty li
k, d = [int(s) for s in lines[0].split(' ')]
kdmers = [tuple(s.split('|', maxsplit=2)) for s in lines[1:]]
kdmers = [Kdmer(k1, k2, d) for k1, k2 in kdmers]
read_pairs = [ReadPair(kdmer) for kdmer in kdmers]
graph = to_overlap_graph(read_pairs)
graph, roots, tails = balance_graph(graph)
path = walk_eulerian_cycle(
graph,
list(roots)[0]
)
path.pop() # remove last kdmer because the cycle we created when balancing the graph is artificial -- we just did it so
# we can get the path using eularian cycles, which is an efficient way of reconstructing the string.
genome = path[0].stitch(path)
print(f'{genome}')