def main(args):
with open(args.inputfile) as f:
k = read_k(args.alphabet, args.method, 0)
# Get index_list.
if args.i is not None:
from pse import read_index
ind_list = read_index(args.i)
else:
ind_list = []
default_e = []
# Set Pse default index_list.
if args.alphabet == 'DNA':
args.alphabet = index_list.DNA
if k == 2:
default_e = const.DI_INDS_6_DNA
elif k == 3:
default_e = const.TRI_INDS_DNA
elif args.alphabet == 'RNA':
args.alphabet = index_list.RNA
default_e = const.DI_INDS_RNA
elif args.alphabet == 'Protein':
args.alphabet = index_list.PROTEIN
default_e = const.INDS_3_PROTEIN
theta_type = 1
if args.method in const.METHODS_AC:
theta_type = 1
elif args.method in const.METHODS_CC:
theta_type = 2
elif args.method in const.METHODS_ACC:
theta_type = 3
else:
print("Method error!")
# ACC.
if args.e is None and len(ind_list) == 0 and args.a is False:
# Default Pse.
res = acc(f, k, args.lag, default_e, args.alphabet,
extra_index_file=args.e, all_prop=args.a, theta_type=theta_type)
else:
res = acc(f, k, args.lag, ind_list, args.alphabet,
extra_index_file=args.e, all_prop=args.a, theta_type=theta_type)
# Write correspond res file.
if args.f == 'tab':
from util import write_tab
write_tab(res, args.outputfile)
elif args.f == 'svm':
from util import write_libsvm
write_libsvm(res, [args.l] * len(res), args.outputfile)
elif args.f == 'csv':
from util import write_csv
write_csv(res, args.outputfile)
"Protein: Hydrophobicity, Hydrophilicity, Mass.")
parse.add_argument('-e', help="The user-defined indices file.\n")
parse.add_argument('-all_index', dest='a', action='store_true', help="Choose all physicochemical indices")
parse.add_argument('-no_all_index', dest='a', action='store_false',
help="Do not choose all physicochemical indices, default.")
parse.set_defaults(a=False)
parse.add_argument('-f', default='tab', choices=['tab', 'svm', 'csv'],
help="The output format (default = tab).\n"
"tab -- Simple format, delimited by TAB.\n"
"svm -- The libSVM training data format.\n"
"csv -- The format that can be loaded into a spreadsheet program.")
parse.add_argument('-l', default='+1', choices=['+1', '-1'],
help="The libSVM output file label.")
args = parse.parse_args()
args.k = read_k(args.alphabet, args.method, args.k)
# print(args)
if check_args(args, 'pse.py'):
print("Calculating...")
start_time = time.time()
main(args)
print("Done.")
print("Used time: %ss" % (time.time() - start_time))
# Test dna type1.
# print("Test di_dna, type1.")
# alphabet = index_list.DNA
# res = pseknc(input_data=['GACTGAACTGCACTTTGGTTTCATATTATTTGCTC'], k=2, w=0.5, lamada=1,
# phyche_list=['Tilt', 'Roll', 'Rise', 'Slide', 'Shift'],
# extra_index_file="data/test_ext_dna.txt", alphabet=alphabet)
def main(args):
"""The main process of autocorrelation methods.
:param args: an object of the arguments.
"""
file_list = args.inputfiles
label_list = args.labels
output_format = args.f
if len(file_list) == 0:
print('Input files not found.')
return False
if output_format == 'svm' and len(label_list) == 0:
print('The labels of the input files should be set.')
return False
if output_format == 'svm' and len(file_list) != len(label_list):
print(
'The number of labels should be the same as that of the input files.'
)
return False
if args.out is not None:
outputfile_list = args.out
if len(outputfile_list) != len(file_list):
print(
'The number of output files should be the same as that of input files.'
)
return False
elif args.out is None:
outputfile_list = []
if output_format == 'svm':
for in_file_name in file_list:
file_elem_list = list(os.path.splitext(in_file_name))
out_name = file_elem_list[0] + '_svm' + file_elem_list[1]
outputfile_list.append(out_name)
elif output_format == 'tab':
for in_file_name in file_list:
file_elem_list = list(os.path.splitext(in_file_name))
out_name = file_elem_list[0] + '_tab' + file_elem_list[1]
outputfile_list.append(out_name)
elif output_format == 'csv':
for in_file_name in file_list:
file_elem_list = list(os.path.splitext(in_file_name))
out_name = file_elem_list[0] + '_csv' + file_elem_list[1]
outputfile_list.append(out_name)
if output_format != 'svm':
label_list = [0] * len(file_list)
if args.method.upper() not in ['MAC', 'GAC', 'NMBAC', 'PDT']:
for input_file, output_file, label in zip(file_list, outputfile_list,
label_list):
with open(input_file) as f:
k = read_k(args.alphabet, args.method, 0)
# Get index_list.
if args.i is not None:
from pse import read_index
ind_list = read_index(args.i)
else:
ind_list = []
default_e = []
# Set Pse default index_list.
if args.alphabet == 'DNA':
alphabet_list = index_list.DNA
if k == 2:
default_e = const.DI_INDS_6_DNA
elif k == 3:
default_e = const.TRI_INDS_DNA
elif args.alphabet == 'RNA':
alphabet_list = index_list.RNA
default_e = const.DI_INDS_RNA
elif args.alphabet == 'Protein':
alphabet_list = index_list.PROTEIN
default_e = const.INDS_3_PROTEIN
else:
print('The alphabet should be DNA, RNA or Protein.')
return False
theta_type = 1
if args.method in const.METHODS_AC:
theta_type = 1
elif args.method in const.METHODS_CC:
theta_type = 2
elif args.method in const.METHODS_ACC:
theta_type = 3
else:
print("Method error!")
# ACC.
if args.e is None and len(ind_list) == 0 and args.a is False:
# Default Pse.
res = acc(f,
k,
args.lag,
default_e,
alphabet_list,
extra_index_file=args.e,
all_prop=args.a,
theta_type=theta_type)
else:
res = acc(f,
k,
args.lag,
ind_list,
alphabet_list,
extra_index_file=args.e,
all_prop=args.a,
theta_type=theta_type)
write_to_file(res, output_format, label, output_file)
if args.method.upper() in ['MAC', 'GAC', 'NMBAC']:
if args.lamada < 0 or args.lamada > 10:
print(
'The value of lamada should be larger than 0 and smaller than 10.'
)
return False
if args.a is None:
args.a == False
elif args.alphabet == 'DNA':
args.alphabet = index_list.DNA
if args.oli == 0:
if args.a == True:
for input_file, output_file, label in zip(
file_list, outputfile_list, label_list):
res = autocorrelation(autoc=args.method,
inputfile=input_file,
props=const.ALL_DI_DNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
write_to_file(res, output_format, label, output_file)
elif args.a == False:
for input_file, output_file, label in zip(
file_list, outputfile_list, label_list):
res = autocorrelation(autoc=args.method,
inputfile=input_file,
props=const.DEFAULT_DI_DNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
write_to_file(res, output_format, label, output_file)
if args.oli == 1:
if args.a == True:
for input_file, output_file, label in zip(
file_list, outputfile_list, label_list):
res = autocorrelation(autoc=args.method,
inputfile=input_file,
props=const.ALL_TRI_DNA_IND,
k=3,
l=args.lamada,
alphabet=args.alphabet)
write_to_file(res, output_format, label, output_file)
elif args.a == False:
for input_file, output_file, label in zip(
file_list, outputfile_list, label_list):
res = autocorrelation(autoc=args.method,
inputfile=input_file,
props=const.DEFAULT_TRI_DNA_IND,
k=3,
l=args.lamada,
alphabet=args.alphabet)
write_to_file(res, output_format, label, output_file)
elif args.alphabet == 'RNA':
args.alphabet = index_list.RNA
if args.a == True:
for input_file, output_file, label in zip(
file_list, outputfile_list, label_list):
res = autocorrelation(autoc=args.method,
inputfile=input_file,
props=const.ALL_RNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
write_to_file(res, output_format, label, output_file)
elif args.a == False:
for input_file, output_file, label in zip(
file_list, outputfile_list, label_list):
res = autocorrelation(autoc=args.method,
inputfile=input_file,
props=const.DEFAULT_RNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
write_to_file(res, output_format, label, output_file)
if args.method.upper() == 'PDT':
if args.alphabet != 'Protein':
print('PDT method is only available for Protein sequences.')
return False
else:
if args.lamada < 1 or args.lamada > 15:
print(
'The value of -lamada should be larger than 0 and smaller than 16.'
)
return False
else:
for input_file, output_file, label in zip(
file_list, outputfile_list, label_list):
res = pdt(input_file, args.lamada)
write_to_file(res, output_format, label, output_file)
if len(outputfile_list) != 0:
for index, output_file in enumerate(outputfile_list):
out_with_full_path = os.path.abspath(output_file)
if os.path.isfile(out_with_full_path):
if index == 0:
print('The output file(s) can be found here:')
print(out_with_full_path)
def main(args):
with open(args.inputfile) as f:
k = read_k(args.alphabet, args.method, 0)
# Get index_list.
if args.i is not None:
from pse import read_index
ind_list = read_index(args.i)
else:
ind_list = []
default_e = []
# Set Pse default index_list.
if args.alphabet == "DNA":
args.alphabet = index_list.DNA
if k == 2:
default_e = const.DI_INDS_6_DNA
elif k == 3:
default_e = const.TRI_INDS_DNA
elif args.alphabet == "RNA":
args.alphabet = index_list.RNA
default_e = const.DI_INDS_RNA
elif args.alphabet == "Protein":
args.alphabet = index_list.PROTEIN
default_e = const.INDS_3_PROTEIN
theta_type = 1
if args.method in const.METHODS_AC:
theta_type = 1
elif args.method in const.METHODS_CC:
theta_type = 2
elif args.method in const.METHODS_ACC:
theta_type = 3
else:
print("Method error!")
# ACC.
if args.e is None and len(ind_list) == 0 and args.a is False:
# Default Pse.
res = acc(
f,
k,
args.lag,
default_e,
args.alphabet,
extra_index_file=args.e,
all_prop=args.a,
theta_type=theta_type,
)
else:
res = acc(
f, k, args.lag, ind_list, args.alphabet, extra_index_file=args.e, all_prop=args.a, theta_type=theta_type
)
# Write correspond res file.
if args.f == "tab":
from util import write_tab
write_tab(res, args.outputfile)
elif args.f == "svm":
from util import write_libsvm
write_libsvm(res, [args.l] * len(res), args.outputfile)
elif args.f == "csv":
from util import write_csv
write_csv(res, args.outputfile)
action='store_false',
help="Do not choose all physicochemical indices, default.")
parse.set_defaults(a=False)
parse.add_argument(
'-f',
default='tab',
choices=['tab', 'svm', 'csv'],
help="The output format (default = tab).\n"
"tab -- Simple format, delimited by TAB.\n"
"svm -- The libSVM training data format.\n"
"csv -- The format that can be loaded into a spreadsheet program.")
parse.add_argument(
'-labels',
nargs='*',
help="The labels of the input files.\n"
"For binary classification problem, the labels can only be '+1' or '-1'.\n"
"For multiclass classification problem, the labels can be set as a list of integers."
)
args = parse.parse_args()
args.k = read_k(args.alphabet, args.method, args.k)
# print(args)
if check_args(args, 'pse.py'):
print("Calculating...")
start_time = time.time()
main(args)
print("Done.")
print("Used time: %.2fs" % (time.time() - start_time))
def main(args):
with open(args.inputfile) as f:
if args.method.upper() not in ['MAC', 'GAC', 'NMBAC']:
k = read_k(args.alphabet, args.method, 0)
# Get index_list.
if args.i is not None:
from .pse import read_index
ind_list = read_index(args.i)
else:
ind_list = []
default_e = []
# Set Pse default index_list.
if args.alphabet == 'DNA':
args.alphabet = index_list.DNA
if k == 2:
default_e = const.DI_INDS_6_DNA
elif k == 3:
default_e = const.TRI_INDS_DNA
elif args.alphabet == 'RNA':
args.alphabet = index_list.RNA
default_e = const.DI_INDS_RNA
elif args.alphabet == 'Protein':
args.alphabet = index_list.PROTEIN
default_e = const.INDS_3_PROTEIN
theta_type = 1
if args.method in const.METHODS_AC:
theta_type = 1
elif args.method in const.METHODS_CC:
theta_type = 2
elif args.method in const.METHODS_ACC:
theta_type = 3
else:
print("Method error!")
# ACC.
if args.e is None and len(ind_list) == 0 and args.a is False:
# Default Pse.
res = acc(f,
k,
args.lag,
default_e,
args.alphabet,
extra_index_file=args.e,
all_prop=args.a,
theta_type=theta_type)
else:
res = acc(f,
k,
args.lag,
ind_list,
args.alphabet,
extra_index_file=args.e,
all_prop=args.a,
theta_type=theta_type)
if args.method.upper() in ['MAC', 'GAC', 'NMBAC']:
if args.lamada < 0 or args.lamada > 10:
print(
'The value of lamada should be larger than 0 and smaller than 10.'
)
return False
if args.a is None:
args.a == False
elif args.alphabet == 'DNA':
args.alphabet = index_list.DNA
if args.oli == 0:
if args.a == True:
res = autocorrelation(autoc=args.method,
inputfile=args.inputfile,
props=const.ALL_DI_DNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
elif args.a == False:
res = autocorrelation(autoc=args.method,
inputfile=args.inputfile,
props=const.DEFAULT_DI_DNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
if args.oli == 1:
if args.a == True:
res = autocorrelation(autoc=args.method,
inputfile=args.inputfile,
props=const.ALL_TRI_DNA_IND,
k=3,
l=args.lamada,
alphabet=args.alphabet)
elif args.a == False:
res = autocorrelation(autoc=args.method,
inputfile=args.inputfile,
props=const.DEFAULT_TRI_DNA_IND,
k=3,
l=args.lamada,
alphabet=args.alphabet)
elif args.alphabet == 'RNA':
args.alphabet = index_list.RNA
if args.a == True:
res = autocorrelation(autoc=args.method,
inputfile=args.inputfile,
props=const.ALL_RNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
elif args.a == False:
res = autocorrelation(autoc=args.method,
inputfile=args.inputfile,
props=const.DEFAULT_RNA_IND,
k=2,
l=args.lamada,
alphabet=args.alphabet)
#print res
# Write correspond res file.
if args.f == 'tab':
from util import write_tab
write_tab(res, args.outputfile)
elif args.f == 'svm':
if args.multi == 0 and args.l is None:
args.l = '+1'
elif args.multi == 0 and (args.l != '+1' and args.l != '-1'):
print(
"For binary classification, the label should be either '+1' or '-1'."
)
return False
elif args.multi == 1 and args.l is None:
args.l = '0'
elif args.multi == 1 and args.l is not None:
try:
label = int(args.l)
except ValueError:
print('The labels should be integer.')
return False
from util import write_libsvm
write_libsvm(res, [args.l] * len(res), args.outputfile)
elif args.f == 'csv':
from util import write_csv
write_csv(res, args.outputfile)
def main(args):
names=[]
with open(args.inputfile) as af:
seq_list=read_fasta(af)
for e in seq_list:
names.append(e.name)
#kmer.py -f tab -l +1 -r 1 -k ',num2str(k),' TAIR10_DHSs.fas TAIR10_DHSs_reckmer_',num2str(k),'.txt DNA'
res_kmer = make_kmer_vector(k=2, alphabet=index_list.DNA, filename=args.inputfile, revcomp=True)
#acc.py -e user_indices.txt -f svm -l +1 -lag ',num2str(lag),' TAIR10_DHSs.fas TAIR10_DHSs_dac_',num2str(lag),'.txt DNA DAC'
if args.s==0:
model_file='pDHSdata_TAIR_model.txt'
lag=3
else:
model_file='pDHSdata_TIGR_model.txt'
lag=8
with open(args.inputfile) as f:
k = read_k('DNA', 'DAC', 0)
ind_list=[]
res_acc = acc(f, k, lag, ind_list, index_list.DNA, extra_index_file='user_indices.txt', all_prop=False, theta_type=1)
# features= revckmer+dac,formed by add each row
res=[]
for i in range(len(res_kmer)):
res.append(res_kmer[i]+res_acc[i])
featuresfile=args.inputfile+'_tmp_features.txt'
# Write correspond res file.
from util import write_libsvm
write_libsvm(res, ['+1'] * len(res), featuresfile)
#predict the result
tmp_predict_result_file=args.inputfile+'_tmp_result.txt'
if sys.platform == 'win32':
options='svm-predict -b 1 -q '+featuresfile+' '+model_file+' '+ tmp_predict_result_file
else:
options='./svm-predict -b 1 -q '+featuresfile+' '+model_file+' '+ tmp_predict_result_file
os.system(options)
pf=open(args.outputfile,'w')
with open(tmp_predict_result_file) as nf:
label, TrueProb, FalseProb= '', '',''
count = 0
while True:
line = nf.readline().strip()
if not line:
break
if count>len(names):
break
if 0==count:
pf.write('ID\t\tLabel\t\tProb\n')
count+=1
continue
label=int(line.split()[0])
TrueProb=line.split()[1]
FalseProb=line.split()[2]
if label==-1:
pf.write(names[count-1]+'\t\t'+'Non DHS'+'\t\t'+str(FalseProb)+'\n')
else:
pf.write(names[count-1]+'\t\t'+'DHS'+'\t\t'+str(TrueProb)+'\n')
count+=1
pf.close()
cwd = os.getcwd()
files = [x for x in os.listdir(os.getcwd()) if os.path.isfile(os.path.join(cwd,x))]
#print files
for file in files:
if -1 != file.find('tmp'):
os.remove(file)
def GetVariousClassFeatures(samples_file, path):
isExists = os.path.exists(path)
if not isExists:
os.makedirs(path)
fp = open(samples_file, 'r')
sample = GetSequences(fp, 'ACGT')
instances = array(sample)
print('The number of samples: %d' % (len(sample)))
# 1 Spectrum Profile for k=1,2,3,4,5
for k in range(1, 6):
tic = time.clock()
X = GetSpectrumProfile(k, samples_file)
np.savetxt(path + str(k) + '-SpectrumProfile.txt', X)
toc = time.clock()
print('Coding time for ' + str(k) + '-Spectrum Profile:%.3f minutes' % ((toc - tic) / 60))
# 2 Mismatch Profile for (k,m)=(3,1),(4,1),(5,1)
for (k, m) in [(3, 1), (4, 1), (5, 1)]:
tic = time.clock()
X = GetMismatchProfile(instances, alphabet, k, m)
np.savetxt(path + str((k, m)) + '-MismatchProfile.txt', X)
toc = time.clock()
print('Coding time for ' + str((k, m)) + '-Mismatch Profile:%.3f minutes' % ((toc - tic) / 60))
# 3 Reverse Compliment Kmer for k=1,2,3,4,5
for k in range(1, 6):
tic = time.clock()
X = GetRevcKmer(k)
np.savetxt(path + str(k) + '-RevcKmer.txt', X)
toc = time.clock()
print('Coding time for ' + str(k) + '-RevcKmer:%.3f minutes' % ((toc - tic) / 60))
# 4 Parallel Correlation Pseudo Dinucleotide Composition
tic = time.clock()
X = GetPCPseDNC(3, 0.9) #(2, 0.2)
np.savetxt(path + 'PCPseDNC.txt', X)
toc = time.clock()
print('Coding time for PCPseDNC:%.3f minutes' % ((toc - tic) / 60))
# 5 Parallel Correlation Pseudo Trinucleotide Composition
tic = time.clock()
X = GetPCPseTNC(3, 0.5) #(6, 0.1)
np.savetxt(path + 'PCPseTNC.txt', X)
toc = time.clock()
print('Coding time for PCPseTNC:%.3f minutes' % ((toc - tic) / 60))
# 6 Series Correlation Pseudo Dinucleotide Composition
tic = time.clock()
X = GetSCPseDNC(5, 0.1) #(1, 0.1)
np.savetxt(path + 'SCPseDNC.txt', X)
toc = time.clock()
print('Coding time for SCPseDNC:%.3f minutes' % ((toc - tic) / 60))
# 7 Series Correlation Pseudo Trinucleotide Composition
tic = time.clock()
X = GetSCPseTNC(10, 0.1) #(6, 0.1)
np.savetxt(path + 'SCPseTNC.txt', X)
toc = time.clock()
print('Coding time for SCPseTNC:%.3f minutes' % ((toc - tic) / 60))
# 8 Dinucleotide-based auto covariance
tic = time.clock()
k = read_k('DNA', 'DAC', 0)
# X = GetDAC(instances, k, 3, alphabet)
X = GetDAC(instances, k, 8, alphabet)
np.savetxt(path + 'DAC.txt', X)
toc = time.clock()
print('Coding time for DAC:%.3f minutes' % ((toc - tic) / 60))
f0 = open((path + 'SpectrumProfile.txt'), 'w+')
f1 = open((path + '1-SpectrumProfile.txt'), 'r')
f2 = open((path + '2-SpectrumProfile.txt'), 'r')
f3 = open((path + '3-SpectrumProfile.txt'), 'r')
f4 = open((path + '4-SpectrumProfile.txt'), 'r')
f5 = open((path + '5-SpectrumProfile.txt'), 'r')
while True:
F = []
s1 = f1.readline()
s1 = s1.strip('\n')
F.extend(s1 + ' ')
s2 = f2.readline()
s2 = s2.strip('\n')
F.extend(s2 + ' ')
s3 = f3.readline()
s3 = s3.strip('\n')
F.extend(s3 + ' ')
s4 = f4.readline()
s4 = s4.strip('\n')
F.extend(s4 + ' ')
s5 = f5.readline()
if not s5:
break
s5 = s5.strip('\n')
F.extend(s5 + ' ')
result = ''.join(F)
f0.write(result + '\n')
f1.close()
f2.close()
f3.close()
f4.close()
f5.close()
f0.close()
f0 = open((path + 'MismatchProfile.txt'), 'w+')
f1 = open((path + '(3, 1)-MismatchProfile.txt'), 'r')
f2 = open((path + '(4, 1)-MismatchProfile.txt'), 'r')
f3 = open((path + '(5, 1)-MismatchProfile.txt'), 'r')
while True:
F = []
s1 = f1.readline()
s1 = s1.strip('\n')
F.extend(s1 + ' ')
s2 = f2.readline()
s2 = s2.strip('\n')
F.extend(s2 + ' ')
s3 = f3.readline()
if not s3:
break
s3 = s3.strip('\n')
F.extend(s3 + ' ')
result = ''.join(F)
f0.write(result + '\n')
f1.close()
f2.close()
f3.close()
f0.close()
f0 = open((path + 'RevcKmer.txt'), 'w+')
f1 = open((path + '1-RevcKmer.txt'), 'r')
f2 = open((path + '2-RevcKmer.txt'), 'r')
f3 = open((path + '3-RevcKmer.txt'), 'r')
f4 = open((path + '4-RevcKmer.txt'), 'r')
f5 = open((path + '5-RevcKmer.txt'), 'r')
while True:
F = []
s1 = f1.readline()
s1 = s1.strip('\n')
F.extend(s1 + ' ')
s2 = f2.readline()
s2 = s2.strip('\n')
F.extend(s2 + ' ')
s3 = f3.readline()
s3 = s3.strip('\n')
F.extend(s3 + ' ')
s4 = f4.readline()
s4 = s4.strip('\n')
F.extend(s4 + ' ')
s5 = f5.readline()
if not s5:
break
s5 = s5.strip('\n')
F.extend(s5 + ' ')
result = ''.join(F)
f0.write(result + '\n')
f1.close()
f2.close()
f3.close()
f4.close()
f5.close()
f0.close()
f0 = open((path + 'Pse.txt'), 'w+')
f1 = open((path + 'PCPseDNC.txt'), 'r')
f2 = open((path + 'PCPseTNC.txt'), 'r')
f3 = open((path + 'SCPseDNC.txt'), 'r')
f4 = open((path + 'SCPseTNC.txt'), 'r')
while True:
F = []
s1 = f1.readline()
s1 = s1.strip('\n')
F.extend(s1 + ' ')
s2 = f2.readline()
s2 = s2.strip('\n')
F.extend(s2 + ' ')
s3 = f3.readline()
s3 = s3.strip('\n')
F.extend(s3 + ' ')
s4 = f4.readline()
if not s4:
break
s4 = s4.strip('\n')
F.extend(s4 + ' ')
result = ''.join(F)
f0.write(result + '\n')
f1.close()
f2.close()
f3.close()
f4.close()
f0.close()
