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data prepare.py
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data prepare.py
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import sys
import numpy as np
import h5py
import heapq
filename = 'dm3.kc167.example.h5'
f = h5py.File(filename, 'r')
x_train = np.array(f['x_train'])
x_test = np.array(f['x_test'])
x_val = np.array(f['x_val'])
y_train = np.array(f['y_train'])
y_test = np.array(f['y_test'])
y_val= np.array(f['y_val'])
#np.set_printoptions(threshold=sys.maxsize)
#one-hot reverse encoding,Sequence in numbers
x = [[0] * 1000] * 30127
for i in range(28127):
a = x_train[i,:,:]
x[i] = np.argmax(a, axis=1)
for i in range(28127,29127):
a = x_val[i-28127,:,:]
x[i] = np.argmax(a, axis=1)
for i in range(29127,30127):
a = x_test[i-29127,:,:]
x[i] = np.argmax(a, axis=1)
x = np.array(x)
#np.savetxt('data.txt',x)
np.savetxt('data.txt',x)
#Sequence in bases
data = np.loadtxt('data.txt')
outf = "data_base.txt"
out=open(outf,'w')
for i in range(len(data)):
for j in range(len(data[0])):
if data[i][j] == 0 :#A
out.writelines('A')
elif data[i][j] == 1 :#T
out.writelines('T')
elif data[i][j] == 2 :#G
out.writelines('G')
else :#C
out.writelines('C')
out.writelines('\n')
out.close()
#Convert .txt files to .fa files
inf = "data_base.txt"
outf= "data_base.fasta"
def readwrite(inf,outf):
f=open(inf,'r')
out=open(outf,'w')
i=1
for line in f.readlines():
list_line = line.strip().split()
x=list_line[0]+"\n"
y=">Chr"+str(i)+"\n"
out.writelines(y)
out.writelines(x)
i=i+1
f.close()
out.close()
readwrite(inf,outf)