def generate_null_distribution(seq_x, seq_y, scoring_matrix, num_trials):
sdist = { }
# l = list(seq_y)
for i in range(num_trials):
l = list(seq_y)
random.shuffle(l)
rand_y = ''.join(l)
am = student.compute_alignment_matrix(seq_x, rand_y, scoring_matrix, False)
las = student.compute_local_alignment(seq_x, rand_y, scoring_matrix, am)
score = las[0]
if sdist.has_key(score):
sdist[score] = sdist[score] + 1
else:
sdist[score] = 1
return sdist
def part_one():
hseq = read_protein(HUMAN_EYELESS_URL)
fseq = read_protein(FRUITFLY_EYELESS_URL)
pm50 = read_scoring_matrix(PAM50_URL)
am50 = student.compute_alignment_matrix(hseq, fseq, pm50, False)
res = student.compute_local_alignment(hseq, fseq, pm50, am50 )
print_res(res,["Human :", "Fruitfly:"])
cpax = read_protein(CONSENSUS_PAX_URL)
# print 'cpax:', cpax
hdseq = res[1].replace('-', '')
# print 'h no d:', hdseq
amhc = student.compute_alignment_matrix(hdseq, cpax, pm50, True)
gah = student.compute_global_alignment(hdseq, cpax, pm50, amhc)
# gah = student.compute_global_alignment(cpax, hdseq, pm50, am50)
print_res(gah,["Human:", "CPAX :"])
dashh = gah[1].count('-')
dashc = gah[2].count('-')
print "dash h:", dashh, 'dashc:', dashc, 'lenh:', len(gah[1]), 'lenc:', len(gah[2])
print 'Percentage Agree Human:', 100 * (((len(gah[1]) - (dashh + dashc)) * 1.0)/len(gah[1])),"%"
fdseq = res[2].replace('-', '')
amfc = student.compute_alignment_matrix(fdseq, cpax, pm50, True)
gaf = student.compute_global_alignment(fdseq, cpax, pm50, amfc)
print_res(gaf,["Fruitfly:", "CPAX :"])
dashh = gaf[1].count('-')
dashc = gaf[2].count('-')
print "dash h:", dashh, 'dashc:', dashc, 'lenh:', len(gah[1]), 'lenc:', len(gah[2])
print 'Percentage Agree Fruitfly:', 100 * (((len(gah[1]) - (dashh + dashc)) * 1.0)/len(gah[1])),"%"
num_trials = 100
dist = generate_null_distribution(hseq, fseq, pm50, num_trials)
disthf = {}
for i in dist.keys():
disthf[i] = (dist[i] * 1.0) / num_trials
print 'Unnormalized Dist:', dist
print 'Normalized Dist:', disthf
sum = 0
for i in dist.keys():
sum = dist[i] + sum
mean = (sum * 1.0) / len(dist)
sd = 0.0
for i in dist.keys():
sd = math.pow(dist[i] - mean, 2) + sd
sdv = math.sqrt((sd * 1.0)/len(dist))
print "s:", res[0]
print "Mean:", mean
print "Standard Deviation:", sdv
print "z-score:", (res[0] - mean)/sdv
plt.bar(disthf.keys(), disthf.values(), label='statistical hypothesis')
plt.xlabel('Scores')
plt.ylabel('Fraction of trails')
plt.title('Normalized distribution of generate_null_distribution')
plt.grid(True)
# plt.legend(loc='upper right')
plt.show()
