/
utils.py
182 lines (144 loc) · 5.21 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
from __future__ import print_function
from func import compose_all, compose, starcompose, dictzip, ilen, pmap
from fn import F, _ as X
from itertools import groupby, starmap, imap, ifilter, izip, ifilterfalse
from operator import methodcaller as mc, ne
import numpy as np
import networkx as nx
from matplotlib import pyplot as plt
def to_adj_list(G, edgekey='weight', bothways=True, as_float=False):
edges = sorted(ifilterfalse(lambda x: x[0] ==x[1], G.edges(data=True)))
if as_float:
res = map(lambda x: (x[0], x[1], float(x[-1]['weight'])), edges)
form = "{0}->{1}:{2:.3f}\n{1}->{0}:{2:.3f}".format
else:
res = map(lambda x: (x[0], x[1], int(x[-1]['weight'])), edges)
form="{0}->{1}:{2}".format if not bothways else "{0}->{1}:{2}\n{1}->{0}:{2}".format
return starmap(form, res)
adj_str = compose('\n'.join, to_adj_list)
def parseM(raw):
'''parse & return a space-seperated matrix.'''
_in = filter(bool, raw.split('\n'))
return np.matrix(map(pmap(float), map(str.split, _in)))
def quantify(iterable, pred=bool):
'''https://docs.python.org/2/library/itertools.html#recipes
"Count how many times the predicate is true"'''
return sum(imap(pred, iterable))
def drawgraph(G, edgekey='weight', big=False, **kwargs):
if big: fig = plt.figure(figsize = (15, 10))
pos=nx.spring_layout(G)
nx.draw_networkx(G, pos=pos, **kwargs)
if edgekey:
edge_labels=dict([((u,v,),d.get(edgekey, ''))
for u,v,d in G.edges(data=True)])
nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)#, **kwargs)
plt.show()
def hamming(s1, s2):
assert len(s1) == len(s2)
return ilen(ifilter(bool, imap(ne, s1, s2)))
def logcall(func):
def wrap(*args, **kwargs):
print( func.__name__)
print( args, kwargs)
#print formatAllArgs(args, kwargs)
return func(*args, **kwargs)
return wrap
def slider(seq, window, start=0):#, stop=None):
'''assert list(slider([0, 1, 2], 2)) == [ [0,1], [1,2] ]
assert list(slider('ABCDE', 4)) == [ 'ABCD', 'BCDE' ]
assert list(slider('ABCDE', 1)) == list('ABCDE')'''
N = len(seq)
for idx in xrange(N-window+1):
yield seq[idx:idx+window]
filterfst = compose(next, ifilter)
composition = compose_all(sorted, list, slider)
def fromstr(_in):
lines = filter(str.strip, _in.split('\n'))
k = int(lines[0])
s = ''.join(lines[1:])
return s, k
cfromstr = starcompose(composition, fromstr)
#assert ["AATCC", "ATCCA", "CAATC", "CCAAC", "TCCAA"] == cfromstr(r_in)
#neighobrs = filter(X[:k] == sfx, prefixg)
#NOTE: using generators over lists makes a huge difference.
def make_ovrlp_graph(kmers):
N = len(kmers)
ov = len(kmers[0]) - 1
M = np.zeros((N, N))
D = dictzip(kmers, xrange(N))
def update(M, pre, suff):
sfx, suffg = suff
nbrs = imap(X[0], ifilter(X[1]==sfx, pre))
nbr_idxs = map(D.__getitem__, nbrs)
s_idxs = map(D.__getitem__, suffg)
M[ s_idxs, nbr_idxs] = 1
pre, suff = groupby(kmers, X[:ov]), groupby(kmers, X[-ov:])
pre = izip(kmers, imap(X[:ov], kmers))
Mupdate = F(update, M, pre)
#NOTE: starmap drains itertools.groupby
map(Mupdate, suff)
return D, M
form = '{0} -> {1}'.format
def printgraph(D, M):
with open('overlap.txt', 'w') as out:
idx_kmer_map = dict(map(reversed, D.items()))
for kmer, idx in D.items():
for nbr in M[idx].nonzero()[0]:
print(form(kmer, idx_kmer_map[nbr]), file=out)
getkmers = compose(F(filter, str.strip), mc('split', '\n'))
slv_overlap=compose(starcompose(printgraph, make_ovrlp_graph), getkmers)
exp='''
AGGCA -> GGCAT
CATGC -> ATGCG
GCATG -> CATGC
GGCAT -> GCATG
'''
_in= '''ATGCG
GCATG
CATGC
AGGCA
GGCAT
'''
def allbut(end, excluding, start=0):
''' get range(0, end) excluding one element '''
if excluding < end: return range(start or 0, excluding) + range(excluding + 1, end)
else: return range(0, end)
def emptygraph(N):
G = nx.Graph()
''' add blank nodes to ease the construction of the graph.'''
#G.add_nodes_from(zip(xrange(N), [dict(weight=0) for i in xrange(N)]))
G.add_nodes_from(xrange(N))
return G
#def prepmatrix(m):
# M = doublematrix(m)
# M[np.diag_indices(len(M))] = np.inf
# return M
def min_matrix_ij(M):
''' :return indices (i, j) for the minimum element in the matrix.
assumes diagonals != 0 or are masked. '''
return np.unravel_index(M.argmin(), M.shape)
def doublematrix(m):
n = m.shape[0]
#M = np.full((n*2, n*2), 0)
M = np.zeros((n*2, n*2), 0)
M[0:n, 0:n] = m
return M
import traceback
def pvar(__x):
print (traceback.extract_stack(limit=2)[0][3][6:][:-1],"=",__x)
def write_graph(inputf, outf, func):
extraM = open(inputf).read()
eM = parseM(extraM)
AG = func(eM)
_actual = adj_str(AG, as_float=True).strip()
with open(outf, 'w') as out:
print(_actual, file=out)
def test_graph(inputf, expf, func):
extraM = open(inputf).read()
eM = parseM(extraM)
AG = func(eM)
_actual = adj_str(AG, as_float=True).strip()
_exp = open(expf).read().strip()
exp, act = sorted(_exp.split()), sorted(_actual.split())
assert exp == act, 'failed'
print( 'passed')