def get_edit_distance(seq_x, seq_y, scoring_matrix):
'''
compute the seq_x and seq_y global alignment with scoring matrix
return the edit distance can be expressed in term of:
|x| + |y| - score(x, y)
'''
alignment_matrix = compute_alignment_matrix(seq_x, seq_y, scoring_matrix, True)
score, align_x, align_y = compute_global_alignment(seq_x, seq_y, scoring_matrix, alignment_matrix)
return len(seq_x) + len(seq_y) - score
def dosomething(t): seq_x, seq_y, scoring_matrix=t[0],t[1],t[2] # seq_x_1=list(seq_x) # random.shuffle(seq_x_1) # seq_x=''.join(seq_x_1) seq_y_1=list(seq_y) random.shuffle(seq_y_1) seq_y=''.join(seq_y_1) ss=fiel1.compute_alignment_matrix(seq_x,seq_y,scoring_matrix,False) s=fiel1.compute_local_alignment(seq_x, seq_y, scoring_matrix, ss) return s[0]
plt1=plt.bar(x, y_1, alpha = 0.5, color = 'g',width = 0.8,align="center")
ax=plt.gca()
plt.xticks(x,x)
# ax.set_xticklabels( list(range(100)))
# plt2,=plt.plot( x, f2, 'b',linewidth=2,label='hierarchical clustering')
# plt3,=plt.plot( x, f3, 'g', linewidth=2,label='13')
# print 'a'
# plt.axis([-4, 4, -0.5, 8])
# plt.text(1, 7.5, r'$10^x$', )
# plt.text(2.2, 7.5, r'$e^x$')
# plt.text(3.2, 7.5, r'$2^x$')
# plt.legend([plt1,plt2], ["k-means",'hierarchical clustering'],loc=1)
plt.legend([plt1], ["ratio"],loc=1)
plt.xlabel('the ratio')
plt.xlabel('score')
plt.title('normalized version of this distribution')
plt.show()
import math
mu=float(sum(result))/len(result)
s=0
for x in result:
s+=(x-mu)*(x-mu)
# s+=(x-mu)**(2)
sigma=math.sqrt(s/len(result))
print mu,sigma
m=fiel1.compute_alignment_matrix(alg_HumanEyelessProtein,alg_FruitflyEyelessProtein,get_sc(),False)
s=fiel1.compute_local_alignment(alg_HumanEyelessProtein,alg_FruitflyEyelessProtein,get_sc(),m)
print (s[0]-mu)/sigma