class block(object):
def __init__(self, N):
self.N = N; self.l, self.r, self.u, self.d = [None]*4
def move_lower(self):
res = []
for x in (self.u, self.d, self.l, self.r):
if x != None and x.N < self.N:
res.append(x)
return res
def move_lower_chain(self, res = [], history = []):
temp = self.move_lower()
if len(temp) == 0:
res.append(history + [self.N])
else:
for x in temp:
x.move_lower_chain(res, history + [x.N])
return res
I tried to change the 'move_lower_chain' function to Non-recursive function, but i couldn't and even didn't know how to solve. most difficult part was below:
for x in temp:
x.move_lower_chain(res, history + [x.N])
Is there anyone who can help me?
Related
AttributeError: module 'collections' has no attribute 'Sequence'
i get this error everything i try to run my code but there isn't any information about how to use Mothur except for the the documentation.
`# python3
import sys
import queue
import itertools
from collections import deque
from mothur_py import Mothur
import collections.abc as collections
class KmerIdMgmt:
def __init__(self):
self.id = 0
self.ids_map = {}
self.kmers = {}
def insert(self, kmer):
if kmer not in self.ids_map:
self.ids_map[kmer] = self.id
self.kmers[self.id] = kmer
self.id += 1
return self.ids_map[kmer]
class DeBruijnGraph(object):
def __init__(self, k, reads):
self.k = k
self.threshold = self.k + 1
self.kmer_ids = KmerIdMgmt()
self.coverage = {}
self.graph = {}
self.outgoing_num = lambda k: len(self.graph[k][0])
self.incoming_num = lambda k: self.graph[k][1]
self.make_deBruijn_graph(self.break_reads_into_kmers(reads))
def break_reads_into_kmers(self, reads):
break_read = lambda read: [ read[j:j + self.k] for j in range(len(read) - self.k + 1) ]
return [ kmer for read in reads for kmer in break_read(read) ]
def make_deBruijn_graph(self, kmers):
def add_edge(graph, coverage, left, right):
graph.setdefault(left, [set(), 0])
graph.setdefault(right, [set(), 0])
coverage.setdefault((left, right), 0)
coverage[(left, right)] += 1
if right not in graph[left][0]:
graph[left][0].add(right)
graph[right][1] += 1
for kmer in kmers:
left = self.kmer_ids.insert(kmer[:-1])
right = self.kmer_ids.insert(kmer[1:])
if left != right:
add_edge(self.graph, self.coverage, left, right)
def remove_leaves(self):
removable = [ k for k, v in self.graph.items() if len(v[0]) == 0 ]
for k in removable:
del self.graph[k]
def print_graph(self):
for k, v in self.graph.items():
print(k, v)
class TipRemoval(DeBruijnGraph):
def __init__(self, k, reads):
DeBruijnGraph.__init__(self, k, reads)
def remove_tips(self):
for k, v in self.graph.items():
find_and_remove = None
if self.outgoing_num(k) == 1 and self.incoming_num(k) == 0:
find_and_remove = self.find_and_remove_incoming
elif self.outgoing_num(k) > 1:
find_and_remove = self.find_and_remove_outgoing
else: continue
condition = True
while condition:
condition = False
for edge in v[0]:
if find_and_remove(edge, 0):
v[0].remove(edge)
condition = True
break
def find_and_remove_outgoing(self, current, depth):
if self.outgoing_num(current) > 1 or self.incoming_num(current) > 1:
return False
if depth == self.threshold:
return False
if self.outgoing_num(current) == 0:
return True
if self.find_and_remove_outgoing(next(iter(self.graph[current][0])), depth + 1):
to = next(iter(self.graph[current][0]))
self.graph[current][0].pop()
self.graph[to][1] -= 1
return True
return False
def find_and_remove_incoming(self, current, depth):
if self.outgoing_num(current) == 0 or self.incoming_num(current) > 1:
return True
if depth == self.threshold:
return False
if self.find_and_remove_incoming(next(iter(self.graph[current][0])), depth + 1):
to = next(iter(self.graph[current][0]))
self.graph[current][0].pop()
self.graph[to][1] -= 1
return True
return False
class BubbleRemoval(TipRemoval):
def __init__(self, k, reads):
TipRemoval.__init__(self, k, reads)
self.paths = {}
def remove_bubbles(self):
for k, v in self.graph.items():
if self.outgoing_num(k) > 1:
self.dfs(path=[k], current=k, depth=0)
for pair, candidates_list in self.paths.items():
source, target = pair[0], pair[1]
best_path = max(candidates_list, key=lambda item: item[1])[0]
for path, _ in candidates_list:
if best_path == path or not self.bubble_possible(source, target):
continue
if self.paths_disjoint(best_path, path) and self.path_exists(path):
self.remove_path(path)
def bubble_possible(self, source, target):
return len(self.graph[source][0]) > 1 and self.graph[target][1] > 1
def path_exists(self, path):
for j in range(len(path) -1):
if path[j +1] not in self.graph[path[j]][0]:
return False
return True
def remove_path(self, path):
for j in range(len(path) -1):
self.graph[path[j]][0].remove(path[j +1])
self.graph[path[j +1]][1] -= 1
del self.coverage[(path[j], path[j +1])]
def paths_disjoint(self, a, b):
return len(set(a) & set(b)) == 2
def dfs(self, path, current, depth):
if current != path[0] and self.incoming_num(current) > 1:
weight = sum(self.coverage[(path[i], path[i+1])] for i in range(len(path)-1)) / len(path)
self.paths.setdefault((path[0], current), list()).append((path[:], weight))
if depth == self.threshold:
return
for next_ in self.graph[current][0]:
if next_ not in path:
path.append(next_)
self.dfs(path, next_, depth + 1)
path.remove(next_)
class PhiX174GenomeAssembler(BubbleRemoval):
def __init__(self, k, reads):
BubbleRemoval.__init__(self, k, reads)
def make_Euler_cycle(self):
verteces = deque()
path = []
# line 191
current = next(iter(self.graph))
verteces.append(current)
while verteces:
current = verteces[0]
if len(self.graph[current][0]) != 0:
t = next(iter(self.graph[current][0]))
verteces.append(t)
self.graph[current][0].remove(t)
continue
path.append(current)
verteces.popleft()
return path
def assemble(self):
self.remove_tips()
self.remove_leaves()
self.remove_bubbles()
cycle = self.make_Euler_cycle()
circular_genome = self.kmer_ids.kmers[cycle[0]]
for i in range(1, len(cycle) - (self.k - 1)):
circular_genome += self.kmer_ids.kmers[cycle[i]][-1]
return circular_genome
if __name__ == "__main__":
n_kmers = int(input())
for _ in range(n_kmers):
reads = list(input())
reads = str(reads)
with open('reads.fasta', 'w') as read:
read.write(reads)
k = 100
m = Mothur()
contig = m.make.contigs(ffasta = read)
for x in range(n_kmers):
print(">CONTIG", x)
print(contig)
`
In the function of find_shortest_func, i think if now position isn't "T" which is also known as the terminal or exit, then i will try to find for direction and see if it is "T", if not, check if it is space and i can go there. Besides, tell the next state function now output and dic to tell the place where i visited. But some errors occur and I don't know why.
I think the problem may occur where I tried to deepcopy the output list
import copy
def set_symbol(symbol_name):
def set_symbol_decorator(func):
def wrapper(self, symbol):
setattr(self, symbol_name, symbol)
return wrapper
return set_symbol_decorator
class Maze:
space_symbol = " "
obstacle_symbol = "X"
path_symbol = "•"
output = []
dis = 0
def __init__(self, input_string):
self.maze = []
if input_string.endswith("txt"):
with open(input_string) as f:
count = 0
for line in f.readlines():
self.maze.append([])
for j in line:
if j != '\n':
self.maze[count].append(j)
count += 1
else:
count = 0
for i in input_string.split("\n"):
self.maze.append([])
for j in i:
self.maze[count].append(j)
count += 1
def __str__(self):
output_string = ""
for i in range(20):
for j in range(20):
output_string += self.maze[i][j]
output_string += "\n"
return output_string
#set_symbol("space_symbol")
def set_space_symbol(self, change):
pass
#set_symbol("obstacle_symbol")
def set_obstacle_symbol(self, change):
pass
#set_symbol("path_symbol")
def set_path_symbol(self, change):
pass
def find_shortest_func(self, position: tuple, d: dict, out: list, dis: int):
dic = copy.deepcopy(d)
output = copy.deepcopy(out)
dic[(position[0], position[1])] = 1
output.append((position[0], (position[1])))
dis += 1
if self.maze[position[0]][position[1]] != "T":
if position[0]+1 < 20 and self.maze[position[0]+1][position[1]] == self.space_symbol and (position[0]+1, position[1]) not in dic:
self.find_shortest_func(
(position[0]+1, position[1]), dic, output, dis)
if position[1]+1 < 20 and self.maze[position[0]][position[1]+1] == self.space_symbol and (position[0], position[1]+1) not in dic:
self.find_shortest_func(
(position[0], position[1]+1), dic, output, dis)
if position[0]-1 >= 0 and self.maze[position[0]-1][position[1]] == self.space_symbol and (position[0]-1, position[1]) not in dic:
self.find_shortest_func(
(position[0]-1, position[1]), dic, output, dis)
if position[1]-1 >= 0 and self.maze[position[0]][position[1]-1] == self.space_symbol and (position[0], position[1]-1) not in dic:
self.find_shortest_func(
(position[0], position[1]-1), dic, output, dis)
if self.maze[position[0]][position[1]] == "T":
if dis < self.dis:
self.output = copy.deepcopy(output)
self.dis = dis
return
def find_shortest_path(self):
d = dict()
output = []
dis = -1
self.find_shortest_func((1, 0), d, output, dis)
return self.output, self.dis
Here's what I wrote, (yes, it's not the most optimal answer)
But I get a timeout exception sometimes.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def rotateRight(self, head: ListNode, k: int) -> ListNode:
if (head != None):
while (k > 0):
cur = head
if (head.next == None):
prev = head
else:
while (head.next != None):
prev = head
head = head.next
prev.next = None
head.next = cur
k -= 1
return head```
We can use Floyd's Tortoise and Hare algorithm for this question, is a bit more efficient:
class Solution:
def rotateRight(self, head, k):
if not head:
return
if not head.next:
return head
curr, length = head, 1
while curr.next:
curr = curr.next
length += 1
k %= length
if k == 0:
return head
slow = fast = head
for _ in range(k):
fast = fast.next
while fast.next:
slow, fast = slow.next, fast.next
temp = slow.next
slow.next = None
fast.next = head
head = temp
return head
import heapq
from collections import defaultdict
a = list(map(int, input().split()))
nodes = a[0]
disjoint_set = [-1]*(nodes+1)
rank_set = [0]*(nodes+1)
edges = a[1]
heap = []
def get_parent(u):
if disjoint_set[u] == -1:
return u
return get_parent(disjoint_set[u])
def make_union(x, y):
x_parent = get_parent(x)
y_parent = get_parent(y)
if rank_set[x_parent] == rank_set[y_parent]:
disjoint_set[x_parent] = y_parent
rank_set[x_parent] +=1
elif rank_set[x_parent] > rank_set[y_parent]:
disjoint_set[x_parent] = y_parent
else:
disjoint_set[y_parent] = x_parent
def not_cycle(*item):
x_parent = get_parent(item[1])
y_parent = get_parent(item[2])
if x_parent == y_parent:
return False;
make_union(x_parent, y_parent)
return True
while(edges!=0):
edge = list(map(int, input().split()))
heapq.heappush(heap, [edge[2], edge[0], edge[1]])
edges-=1
cnt = 0
total = 0
while(cnt!=nodes-1):
item = heapq.heappop(heap)
if(not_cycle(*item) is True):
total+= item[0]
cnt+=1
print(total)
I implemented the kruskal algorthm in python. I am getting RecursionError:maximum recursion depth exceeded in comparison error. make_union and get_parent are method of disjoint set algorithm. I am getting the error in get_parent method. How to solve this?
In not_cycle you are passing the parents to make_union but then in make_union you are trying to get the parents again. After the first change the parents will no longer be -1 and you will recurse "forever"[1]
[1] "forever" in this case is until the maximum depth of your stack.
-
aa,bb=list(map(int,input().split()))
c=[] for i in range(bb):
z=list(map(int,input().split()))
c.append(z) c.sort(key=lambda x: x[2])
a=[]
b=[]
for i in c:
a.append((i[0]-1,i[1]-1))
b.append(i[2])
arr=[]
size=[]
for i in range(len(b)):
arr.append(i)
size.append(1)
def root(i):
while arr[i]!=i:
i=arr[i]
return i
def unions(arr,size,p,q):
root_a=root(p)
root_b=root(q)
if size[root_a]>size[root_b]:
arr[root_b]=arr[root_a]
size[root_a]+=size[root_b]
else:
arr[root_a]=arr[root_b]
size[root_b]+=size[root_a]
def kruskals(b,a,aa):
te=[]
i=0
while (len(te))<aa-1:
(p,q)=a[i]
if root(p)!=root(q):
te.append(b[i])
unions(arr,size,p,q)
i+=1
return sum(te)
print(kruskals(b,a,aa))
I'm basically trying to implement this Heap Structure in Python and I've editing the portions under def heap-iffy and def add but I'm not sure how to how to use the current initialize with a key function. This function will be used to extract a value from each element added to the heap; these values, in turn, will be used to order the elements. f no key function is provided, the default max-heap behavior should be used — the "lambda x:x" default value for the initialize method does just that.
class Heap:
def __init__(self, key=lambda x:x):
self.data = []
self.key = key
#staticmethod
def _parent(idx):
return (idx-1)//2
#staticmethod
def _left(idx):
return idx*2+1
#staticmethod
def _right(idx):
return idx*2+2
def _heapify(self, idx=0):
enter code here
while True:
l = Heap._left(idx)
r = Heap._right(idx)
maxidx = idx
if l < len(self) and self.data[l] > self.data[idx]:
maxidx = l
if r < len(self) and self.data[r] > self.data[maxidx]:
maxidx = r
if maxidx != idx:
self.data[idx], self.data[maxidx] = self.data[maxidx], self.data[idx]
idx = maxidx
else:
break
def add(self, x):
enter code here
self.data.append(x)
i = len(self.data) - 1
p = Heap._parent(i)
while i > 0 and self.data[p] < self.data[i]:
self.data[p], self.data[i] = self.data[i], self.data[p]
i = p
p = Heap._parent(i)
def peek(self):
return self.data[0]
def pop(self):
ret = self.data[0]
self.data[0] = self.data[len(self.data)-1]
del self.data[len(self.data)-1]
self._heapify()
return ret
def __bool__(self):
return len(self.data) > 0
def __len__(self):
return len(self.data)
def __repr__(self):
return repr(self.data)