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CNCI.py
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CNCI.py
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#!/usr/bin/python
#-*-coding : utf-8-*-
#Copyright(c) 2013 - SunLiang <[email protected]>
import struct
import math
import cmath
import re
import string
import os
import random
import decimal
import sys
import optparse
import subprocess
import time
from multiprocessing import Process
import shutil
######################### define input and output######################################
#FileName = raw_input('Please enter your a file name: ')
parse=optparse.OptionParser()
parse.add_option('-f','--file',dest='file',action='store',metavar='input files',help='enter your transcript (sequence or gtf)')
parse.add_option('-o','--out',dest='outfile',action='store',metavar='output files',help='assign your output file')
parse.add_option('-p','--parallel',dest='parallel',action='store',metavar='prallel numbers',help='please enter your specified speed ratio')
parse.add_option('-m','--model',dest='model',action='store',metavar='model types',default='ve',help='please enter your specified classification model')
parse.add_option('-g','--gtf',dest='gtf',action='store_true',metavar='gtf file name',help='please enter your gtf files')
parse.add_option('-d','--directory',dest='directory',action='store',metavar='',help='if your input file is gtf type please enter RefGenome directory')
(options,args) = parse.parse_args()
inPutFileName = options.file
outPutFileName = options.outfile
Parallel = options.parallel
ClassModel = options.model
FileType = options.gtf
Directory = options.directory
CNCIPATH = os.path.split(os.path.realpath(__file__))[0]
print CNCIPATH
################################################################
if ClassModel == 've':
MatrixPath = CNCIPATH + "/CNCI_Parameters/CNCI_matrix"
inMatrix = open(MatrixPath)
Matrix = inMatrix.read()
inMatrix.close()
if ClassModel == 'pl':
MatrixPath = CNCIPATH + "/CNCI_Parameters/CNCI_matrix"
inMatrix = open(MatrixPath)
Matrix = inMatrix.read()
inMatrix.close()
#################################### 64 alphabet and hash dictionary ############################################
Alphabet = ['ttt','ttc','tta','ttg','tct','tcc','tca','tcg','tat','tac','tgt','tgc','tgg','ctt','ctc','cta','ctg','cct','ccc','cca','ccg','cat','cac','caa','cag','cgt','cgc','cga','cgg','att','atc','ata','atg','act','acc','aca','acg','aat','aac','aaa','aag','agt','agc','aga','agg','gtt','gtc','gta','gtg','gct','gcc','gca','gcg','gat','gac','gaa','gag','ggt','ggc','gga','ggg']
Matrix_hash = {}
Matrix_Arr=Matrix.split('\n')
length = len(Matrix_Arr) - 1
del Matrix_Arr[length]
for line in Matrix_Arr :
each = line.split('\t')
key = each[0]
value = each[1]
Matrix_hash[key] = value
######################################## main method###############################################################3
def count (CNCI_files,number):
File_Counts = []
for i in range(1,int(number)+1):
temp_inPutFileNum = ''+CNCI_files+'/CNCI_file' + str(i)
CNCI_FILES = open(temp_inPutFileNum)
sub_cnci_files = CNCI_FILES.read()
sub_cnci_array = sub_cnci_files.split('\n')
sub_cnci_array_len = int(len(sub_cnci_array) / 2)
File_Counts.append(sub_cnci_array_len)
CNCI_FILES.close()
return File_Counts
def check(ScoreFile,DetilFile):
SUB_SCORE_FILE = open(ScoreFile)
SUB_DETIL_FILE = open(DetilFile)
sub_score_string = SUB_SCORE_FILE.read()
sub_detil_string = SUB_DETIL_FILE.read()
sub_score_array = sub_score_string.split('\n')
sub_score_array_length = int(len(sub_score_array) - 1)
sub_detil_array = sub_detil_string.split('\n')
sub_detil_array_length = int(len(sub_detil_array) - 1)
SUB_SCORE_FILE.close()
SUB_DETIL_FILE.close()
return (sub_score_array_length,sub_detil_array_length)
########################################################################
def InitCodonSeq(num,length,step,Arr):
TempStrPar = ''
for w in range(num,length,step):
index = w
code1 = Arr[index]
index += 1
code2 = Arr[index]
index += 1
code3 = Arr[index]
Temp = code1+code2+code3
TempStrPar = TempStrPar+Temp+' '
return TempStrPar
##############################################################
def split(files,number,out):
file_num = len(files) / 2
split_step = int(int(file_num) / int(number))
split_step = split_step * 2
title = ''+out+'/CNCI_file'
start = 0
end = split_step
for i in range(1,int(number) + 1):
if i < int(number):
temp_title = title + str(i)
TEMP_FILE = open(temp_title,'w')
for j in range(start,end):
Tmp = files[j]
Tmp = str(Tmp) + '\n'
TEMP_FILE.write(Tmp)
TEMP_FILE.close()
start += split_step
end += split_step
else:
temp_title = title + str(number)
TEMP_FILE = open(temp_title,'w')
for j in range(start,len(files)):
Tmp = files[j]
Tmp = str(Tmp) + '\n'
TEMP_FILE.write(Tmp)
###################################################################################################3
def Add_Svm_Label(rec,FileName):
SVM_arr_store = []
SVM_FILE_ONE = open(FileName,'w')
#SVM_FILE_ONE = open('SVM_FILE','w')
for i in range(len(rec)):
temp_str = rec[i]
temp_arr = temp_str.split(' ')
for j in range(len(temp_arr)):
index = j + 1
temp_arr[j] = str(index)+':'+str(temp_arr[j])
str_temp = ' '.join(temp_arr)
SVM_arr_store.append(str_temp)
str_temp = str_temp + '\n'
SVM_FILE_ONE.write(str_temp)
return SVM_arr_store
SVM_FILE_ONE.close()
###################################################################
def PutResult(detil_array):
File = open(SvmFile)
file_arr_temp = File.read()
File.close()
classify_index = 0
file_Arr = file_arr_temp.split('\n')
index_coding = '1'
index_noncoding = '-1'
Temp_Result_Arr = []
for i in range(len(detil_array)):
temp_label_str = detil_array[i]
temp_label_arr_label = temp_label_str.split(";;;;;")
Label = temp_label_arr_label[0]
temp_label_arr = temp_label_arr_label[1].split(" ")
sub_temp_label_arr = temp_label_arr[1:]
sub_temp_label_str = ' '.join(sub_temp_label_arr)
length = temp_label_arr[1]
score = temp_label_arr[2]
if file_Arr[classify_index] == index_coding:
Label = str(Label) + ';;;;; ' + 'coding'
else:
Label = str(Label) + ';;;;; ' + 'noncoding'
classify_index = classify_index + 1
Temp_Result_str = str(Label) + ' ' + sub_temp_label_str
Temp_Result_Arr.append(Temp_Result_str)
return Temp_Result_Arr
#####################################################################
def PringResult(result,svmfinal):
#OutFile = open('filename','w')
OutFileResult = open(svmfinal,'w')
Tabel = 'Transcript ID' + '\t' + 'index' + '\t' + 'score' + '\t' + 'start' + '\t' + 'end' + '\t' + 'length' + '\n'
OutFileResult.write(Tabel)
Out_Hash = {}
for i in range(len(result)):
out_label = result[i]
out_label_arr_label = out_label.split(';;;;;')
out_label_arr = out_label_arr_label[1].split(' ')
T_label = out_label_arr_label[0]
Tabel_label = T_label[1:]
property = out_label_arr[1]
start_position = out_label_arr[2]
stop_position = out_label_arr[3]
value = out_label_arr[4]
out_value = value[0:5]
out_value = string.atof(out_value)
T_length = out_label_arr[5]
if out_value == 0:
out_value = out_value + 0.001
#if out_value == 0:
# while True:
# out_value = random.randint(0.1,1)
# if Out_Hash[out_value] != 1:
# break
# Out_Hash[out_value] = 1
# out_value = out_value * -1
if property == 'noncoding':
out_value = string.atof(0.64) * out_value
out_value = 0.64 * out_value
if out_value > 0:
if out_value > 1:
out_value = -1 / out_value
temp_out_str = str(Tabel_label)+'\t'+str(property)+'\t'+str(out_value)+'\t'+str(start_position)+'\t'+str(stop_position)+'\t'+str(T_length)
temp_out_str = temp_out_str + '\n'
OutFileResult.write(temp_out_str)
else:
out_value = -1 * out_value
temp_out_str = str(Tabel_label)+'\t'+str(property)+'\t'+str(out_value)+'\t'+str(start_position)+'\t'+str(stop_position)+'\t'+str(T_length)
temp_out_str = temp_out_str + '\n'
OutFileResult.write(temp_out_str)
else:
temp_out_str = str(Tabel_label)+'\t'+str(property)+'\t'+str(out_value)+'\t'+str(start_position)+'\t'+str(stop_position)+'\t'+str(T_length)
temp_out_str = temp_out_str + '\n'
OutFileResult.write(temp_out_str)
if property == 'coding':
if out_value <= 0:
if out_value <= - 1:
out_value = -1 / out_value
temp_out_str = str(Tabel_label)+'\t'+str(property)+'\t'+str(out_value)+'\t'+str(start_position)+'\t'+str(stop_position)+'\t'+str(T_length)
temp_out_str = temp_out_str + '\n'
OutFileResult.write(temp_out_str)
else:
out_value = -1 * out_value
temp_out_str = str(Tabel_label)+'\t'+str(property)+'\t'+str(out_value)+'\t'+str(start_position)+'\t'+str(stop_position)+'\t'+str(T_length)
temp_out_str = temp_out_str + '\n'
OutFileResult.write(temp_out_str)
else:
temp_out_str = str(Tabel_label)+'\t'+str(property)+'\t'+str(out_value)+'\t'+str(start_position)+'\t'+str(stop_position)+'\t'+str(T_length)
temp_out_str = temp_out_str + '\n'
OutFileResult.write(temp_out_str)
def TwoLineFasta (Seq_Array):
Tmp_sequence_Arr = []
Tmp_trans_str = ''
for i in range(len(Seq_Array)):
if '>' in Seq_Array[i]:
if i == 0:
Tmp_sequence_Arr.append(Seq_Array[i])
else:
Tmp_sequence_Arr.append(Tmp_trans_str)
Tmp_sequence_Arr.append(Seq_Array[i])
Tmp_trans_str = ''
else:
if i == len(Seq_Array) - 1:
Tmp_trans_str = Tmp_trans_str + str(Seq_Array[i])
Tmp_sequence_Arr.append(Tmp_trans_str)
else:
Tmp_trans_str = Tmp_trans_str + str(Seq_Array[i])
return Tmp_sequence_Arr
def Tran_checkSeq (input_arr,Temp_Log):
label_Arr = []
FastA_seq_Arr = []
for n in range(len(input_arr)):
if n == 0 or n % 2 == 0:
label = input_arr[n]
label_Arr.append(label)
else :
seq = input_arr[n]
FastA_seq_Arr.append(seq)
LogResult = Temp_Log + '.log'
LOG_FILE = open(LogResult,'w')
num = 0
for i in range(len(label_Arr)):
Label = label_Arr[num]
Seq = FastA_seq_Arr[num]
tran_fir_seq = Seq.lower()
tran_sec_seq_one = tran_fir_seq.replace('u','t')
tran_sec_seq = tran_sec_seq_one.replace('\r','')
if 'n' in tran_sec_seq:
LogString = Label + ' ' + 'contain unknow nucleotide (n),please checkout your sequence again' + '\n'
LOG_FILE.write(LogString)
del label_Arr[num]
del FastA_seq_Arr[num]
continue
if 'w' in tran_sec_seq:
LogString = Label + ' ' + 'contain unknow nucleotide (w),please checkout your sequence again' + '\n'
LOG_FILE.write(LogString)
del label_Arr[num]
del FastA_seq_Arr[num]
continue
if 'd' in tran_sec_seq:
LogString = Label + ' ' + 'contain unknow nucleotide (d),please checkout your sequence again' + '\n'
LOG_FILE.write(LogString)
del label_Arr[num]
del FastA_seq_Arr[num]
continue
if 'r' in tran_sec_seq:
LogString = Label + ' ' + 'contain unknow nucleotide (r),please checkout your sequence again' + '\n'
LOG_FILE.write(LogString)
del label_Arr[num]
del FastA_seq_Arr[num]
continue
if 's' in tran_sec_seq:
LogString = Label + ' ' + 'contain unknow nucleotide (s),please checkout your sequence again' + '\n'
LOG_FILE.write(LogString)
del label_Arr[num]
del FastA_seq_Arr[num]
continue
if 'y' in tran_sec_seq:
LogString = Label + ' ' + 'contain unknow nucleotide (y),please checkout your sequence again' + '\n'
LOG_FILE.write(LogString)
del label_Arr[num]
del FastA_seq_Arr[num]
continue
if 'm' in tran_sec_seq:
LogString = Label + ' ' + 'contain unknow nucleotide (m),please checkout your sequence again' + '\n'
LOG_FILE.write(LogString)
del label_Arr[num]
del FastA_seq_Arr[num]
continue
num = int(num) + int(1)
LOG_FILE.close()
return (label_Arr,FastA_seq_Arr)
######################################################################
def mainProcess(input,codonArr,hash_matrix,output,number):
temp_score = ''+output+'/CNCI_file_score' + str(number)
temp_detil = ''+output+'/CNCI_file_detil' + str(number)
SCORE = open(temp_score,'w')
DETIL = open(temp_detil,'w')
sequence_Arr = input.split('\n')
sLen = len(sequence_Arr) - 1
del sequence_Arr[sLen]
######################################################################
label_Arr_tmp = []
FastA_seq_Arr_tmp = []
for n in range(len(sequence_Arr)):
if n == 0 or n % 2 == 0:
label = sequence_Arr[n]
label_Arr_tmp.append(label)
else :
seq = sequence_Arr[n]
FastA_seq_Arr_tmp.append(seq)
###########################################################
PROPERTY_ARR = []
DETIL_ARR = []
for i in range(len(label_Arr_tmp)):
Label = label_Arr_tmp[i]
Seq = FastA_seq_Arr_tmp[i]
Detil_len = len(Seq)
tran_fir_seq = Seq.lower()
tran_sec_seq = tran_fir_seq.replace('u','t')
sequence_process_Arr = list(tran_sec_seq)
r_sequence_process_Arr = sequence_process_Arr[:]
r_sequence_process_Arr.reverse()
Seq_len = len(sequence_process_Arr) - 1
max_Value = []
max_String = []
score_array = []
length_store_array = []
Pos = []
for o in range(0,6): #six kinds of open reading frame of each sequence
CodonScore = [] # coden information array
RelScore = [] # relate acid information array /
TempStr = ''
if o < 3 :
TempStr = InitCodonSeq(o,Seq_len-1,3,sequence_process_Arr)
if 2 < o < 6 :
TempStr = InitCodonSeq(o-3,Seq_len-1,3,r_sequence_process_Arr)
TempArray = TempStr.split(' ') # construct codon array
TempArray.pop()
seqLength = len(TempArray)
WindowStep = 50
WinLen = seqLength - WindowStep
if seqLength > WindowStep:
for EachCodon in range(WinLen):
num = 0
SingleArray = []
for t in range(EachCodon,WindowStep+EachCodon):
SingleArray.append(TempArray[t])
SinLen = len(SingleArray) - 1
for n in range(0,SinLen):
temp1 = SingleArray[n]+SingleArray[n+1]
temple1 = re.compile('[atcg]{6}')
if temple1.match(temp1):
num = string.atof(num) + string.atof(hash_matrix[temp1])
num = num / WindowStep
CodonScore.append(num)
Start = 0
End = 0
Max = 0
Position = ''
for r in range(len(CodonScore)):
sum = 0
CodonLength = len(CodonScore)
for e in range(r,CodonLength):
sum = sum + string.atof(CodonScore[e])
if sum > Max:
Start = r
End = e
Max = sum
OutStr = ''
for out in range(Start,End+1):
OutStr = OutStr+TempArray[out]+' '
Start = Start * 3
End = End *3
Position = str(Start)+' '+str(End)
Pos.append(Position)
max_Value.append(Max)
max_String.append(OutStr)
OutParray = OutStr.split(' ')
max_length = len(OutParray) - 1
Onum = 0
for n in range(max_length):
temp1 = OutParray[n]+OutParray[n+1]
temple = re.compile('[atcg]{6}')
if temple.match(temp1):
Onum = string.atof(Onum) + string.atof(hash_matrix[temp1])
score_array.append(Onum)
length_store_array.append(max_length)
else:
num = 0
for n in range(seqLength-1):
temp1 = TempArray[n]+TempArray[n+1]
temple2 = re.compile('[atcg{6}]')
if temple2.match(temp1):
num = string.atof(num) + string.atof(hash_matrix[temp1])
OutStr = ' '.join(TempArray)
Pos.append('Full Length')
max_Value.append(num)
max_String.append(OutStr)
score_array.append(num)
length_store_array.append(seqLength)
r_max_Value = max_Value[:]
r_max_Value.sort(reverse=True)
M = r_max_Value[0] # property number one
orf_index = 0
for o in range(len(max_Value)):
temp = max_Value[o]
if temp == M:
orf_index = o
detil_index = orf_index # The selected reading frame
o_arr = max_String[orf_index].split(' ') ## MLCDS sequence
o_arr.pop()
SequenceLen = len(o_arr) - 1
M_score = 0
for j in range(SequenceLen):
temp_trip = o_arr[j]+o_arr[j+1]
temple3 = re.compile('[atcg]{6}')
if temple3.match(temp_trip):
M_score = string.atof(M_score) + string.atof(hash_matrix[temp_trip])
SequenceLen = SequenceLen + 2
M_score = M_score / SequenceLen ### M_score
MLCDS_str = ''.join(o_arr)
MLCDS_sequence = list(MLCDS_str)
rMLCDS_sequence = MLCDS_sequence[:]
rMLCDS_sequence.reverse()
MLCDS_seq_length = len(MLCDS_sequence) - 1
other_CDS_array = []
for o in range(1,6):
MLCDS_TempStr = ''
if o < 3:
MLCDS_TempStr = InitCodonSeq(o,MLCDS_seq_length-1,3,MLCDS_sequence)
if 2 < o < 6 :
MLCDS_TempStr = InitCodonSeq(o,MLCDS_seq_length-1,3,rMLCDS_sequence)
MLCDS_array = MLCDS_TempStr.split(' ') ## codon array
MLCDS_array.pop()
other_num = 0
MLCDS_array_Len = len(MLCDS_array) - 1
for j in range(MLCDS_array_Len):
temp2 = MLCDS_array[j]+MLCDS_array[j+1]
temple4 = re.compile('[atcg]{6}')
if temple4.match(temp2):
other_num = string.atof(other_num) + string.atof(hash_matrix[temp2])
MLCDS_array_Len = MLCDS_array_Len + 2
other_num = other_num / MLCDS_array_Len
other_CDS_array.append(other_num)
score_distance = 0
for m in range(len(other_CDS_array)):
score_distance += M_score - other_CDS_array[m]
score_distance = score_distance / 5 ####### score_distance
########################################################################################
out_pos = Pos[orf_index]
M_length = length_store_array[orf_index]
length_total_score = 0
for p in range(len(length_store_array)):
length_total_score = string.atof(length_total_score) + string.atof(length_store_array[p])
length_precent = string.atof(M_length) / string.atof(length_total_score) ## length_precent
########################################################################################3
detil_other_length_array = []
for p in range(len(length_store_array)):
temp = length_store_array[p]
if temp != M_length:
detil_other_length_array.append(temp)
r_detil_other_length_array = detil_other_length_array[:]
r_detil_other_length_array.sort(reverse=True)
############################################################################################3
dicodon_hash = {}
Coding_Array_one = []
keys_Coding_Array = []
for i in range(len(codonArr)):
temp = codonArr[i]
dicodon_hash[temp] = 0
for n in range(len(o_arr)):
temp1 = o_arr[n]
tep1 = re.compile('[atcg{3}]')
if tep1.match(temp1) and temp1 != 'taa' and temp1 != 'tag' and temp1 != 'tga':
dicodon_hash[temp1] = dicodon_hash[temp1] + 1
for (key,value) in dicodon_hash.items():
Coding_Array_one.append(value)
keys_Coding_Array.append(key)
#############################################################################################
C_num1 = 0
for i in range(len(Coding_Array_one)):
C_num1 = string.atof(C_num1) + string.atof(Coding_Array_one[i])
if C_num1 == 0:
C_num1 = 1
for i in range(len(Coding_Array_one)):
Coding_Array_one[i] = str(Coding_Array_one[i] / C_num1)
Array_Str = ' '.join(Coding_Array_one)
GC_string = ' '.join(o_arr)
GC_array = GC_string.split(' ')
GC_number = 0
for c in range(len(GC_array)):
temp = GC_array[c]
if temp == 'g' or temp == 'c':
GC_number = GC_number + 1
GC_precent = GC_number / len(GC_array)# GC precent
###############################################################################################
PROPERTY_STR = str(M)+' '+str(M_length)+' '+str(M_score)+' '+str(length_precent)+' '+str(score_distance)+' '+str(Array_Str) + '\n'
DETIL_STR = str(Label)+';;;;; '+str(out_pos)+' '+str(M_score) + ' ' + str(Detil_len) + '\n'
SCORE.write(PROPERTY_STR)
DETIL.write(DETIL_STR)
PROPERTY_ARR.append(PROPERTY_STR)
DETIL_ARR.append(DETIL_STR)
return(PROPERTY_ARR,DETIL_ARR)
#######################################################################################################################
if FileType:
inGtfFiles = inPutFileName + '.bed'
fastaFiles = inPutFileName + '.fa'
os.system('perl ' + CNCIPATH + '/gtf2Bed.pl '+inPutFileName+' > '+inGtfFiles+'')
time.sleep(10)
os.system('twoBitToFa -bed='+inGtfFiles+' '+Directory+' '+fastaFiles+' ')
GtfInFiles = open(fastaFiles)
inFilesArr = GtfInFiles.read()
inFileNum = inFilesArr.split('\n')
inFileLen = len(inFileNum) - 1
GtfInFiles.close()
else:
inFiles = open(inPutFileName)
inFilesArr = inFiles.read()
inFileNum = inFilesArr.split('\n')
inFileLen = len(inFileNum) - 1
inFiles.close()
#################################################################
Compute_time = time.time()
sequence_Arr = inFilesArr.split('\n')
sLen = len(sequence_Arr) - 1
del sequence_Arr[sLen]
ARRAY = TwoLineFasta(sequence_Arr)
temp_log = outPutFileName
Label_Array,FastA_Seq_Array = Tran_checkSeq(ARRAY,temp_log)
inFileLength = len(Label_Array)
TOT_STRING = []
for i in range(len(Label_Array)):
tmp_label_one = Label_Array[i]
tmp_label = tmp_label_one.replace('\r','')
tmp_seq = FastA_Seq_Array[i]
Temp_Seq = tmp_seq.replace('\r','')
TOT_STRING.append(tmp_label)
TOT_STRING.append(Temp_Seq)
############################################################
if Parallel == 1:
(Result_Pro,Result_Detil) = mainProcess(inFilesArr,Alphabet,Matrix_hash,outPutFileName,1,)
Temp_Dir = outPutFileName + '_Temp_Dir'
subprocess.call('mkdir '+Temp_Dir+'' , shell=True)
SvmoutPutFile = Temp_Dir + '/pro'
SvmPutFileName = Temp_Dir + '/svm'
SvmFile = Temp_Dir + '/file'
svm_tmp = Temp_Dir + '/temp'
SVM_STORE = Add_Svm_Label(Result_Pro,SvmoutPutFile)
################################################################################################
if ClassModel == 've':
os.system(CNCIPATH + '/libsvm-3.0/svm-scale -r '+ CNCIPATH + '/CNCI_Parameters/python_scale '+SvmoutPutFile+' > '+SvmPutFileName+'')
os.system(CNCIPATH + '/libsvm-3.0/svm-predict '+SvmPutFileName+' ' + CNCIPATH + '/CNCI_Parameters/python_model '+SvmFile+' > '+svm_tmp+'')
if ClassModel == 'pl':
os.system(CNCIPATH + '/libsvm-3.0/svm-scale -r ' + CNCIPATH + '/CNCI_Parameters/plant_scale '+SvmoutPutFile+' > '+SvmPutFileName+'')
os.system(CNCIPATH + '/libsvm-3.0/svm-predict '+SvmPutFileName+' ' + CNCIPATH + '/CNCI_Parameters/plant_model '+SvmFile+' > '+svm_tmp+'')
##########################################################################################################
FirResult = PutResult(Result_Detil)
Out_Dir = outPutFileName
subprocess.call('mkdir '+Out_Dir+'' , shell=True)
SvmFinalResutl = ''+Out_Dir+'/CNCI.index'
PringResult(FirResult,SvmFinalResutl)
#subprocess.call('rm '+SvmPutFileName+' ', shell=True)
#subprocess.call('rm '+SvmFile+' ', shell=True)
#subprocess.call('rm '+svm_tmp+' ', shell=True)
#subprocess.call('rm '+outPutFileName+' ', shell=True)
print 'CNCI classification were completely done!'
print "%f second for" % (time.time() - Compute_time) + ' ' + str(inFileLength) + ' ' + "transcript's computation."
shutil.rmtree(Temp_Dir,True)
if Parallel > 1:
Proc_Thread = []
Temp_Dir = outPutFileName + '_Tmp_Dir'
Out_Dir = outPutFileName
subprocess.call('mkdir '+Temp_Dir+'' , shell=True)
subprocess.call('mkdir '+Out_Dir+'' , shell=True)
split(TOT_STRING,Parallel,Temp_Dir)
Con_ARRAY = count(Temp_Dir,Parallel)
for i in range(1,int(Parallel)+1):
temp_inPutFileName = ''+Temp_Dir+'/CNCI_file' + str(i)
temp_inFiles = open(temp_inPutFileName)
temp_inFilesArr = temp_inFiles.read()
Proc_Thread.append(Process(target=mainProcess, args=(temp_inFilesArr,Alphabet,Matrix_hash,Temp_Dir,str(i))))
for p in Proc_Thread:
p.start()
for i in Proc_Thread:
p.join()
for i in range(1,int(Parallel)+1):
n = int(i) - 1
Score_string = ''+Temp_Dir+'/CNCI_file_score' + str(i)
Detil_string = ''+Temp_Dir+'/CNCI_file_detil' + str(i)
SUB_SCORE_LEN = 0
SUB_DETIL_LEN = 0
#########################################################################
SUB_SCORE_LEN,SUB_DETIL_LEN = check(Score_string,Detil_string)
while int(SUB_SCORE_LEN) < Con_ARRAY[n] or int(SUB_DETIL_LEN) < Con_ARRAY[n]:
SUB_SCORE_LEN,SUB_DETIL_LEN = check(Score_string,Detil_string)
#######################################################################
subprocess.call('cat '+Score_string+' >> '+Temp_Dir+'/CNCI_score', shell=True)
subprocess.call('cat '+Detil_string+' >> '+Temp_Dir+'/CNCI_detil', shell=True)
Score_File_Path = ''+Temp_Dir+'/CNCI_score'
Detil_File_Path = ''+Temp_Dir+'/CNCI_detil'
SCORE_FILE = open(Score_File_Path)
DETIL_FILE = open(Detil_File_Path)
score_string = SCORE_FILE.read()
detil_string = DETIL_FILE.read()
score_array = score_string.split('\n')
scoreSLength = int(len(score_array) - 1)
del score_array[scoreSLength]
detil_array = detil_string.split('\n')
detilSLength = int(len(detil_array) - 1)
del detil_array[detilSLength]
#######################################################################
SvmoutPutFile = Temp_Dir + '/pro'
SvmPutFileName = Temp_Dir + '/svm'
SvmFile = Temp_Dir + '/file'
svm_tmp = Temp_Dir + '/temp'
SVM_STORE = Add_Svm_Label(score_array,SvmoutPutFile)
#################################################################################################
if ClassModel == 've':
os.system(CNCIPATH + '/libsvm-3.0/svm-scale -r ' + CNCIPATH + '/CNCI_Parameters/python_scale '+SvmoutPutFile+' > '+SvmPutFileName+'')
os.system(CNCIPATH + '/libsvm-3.0/svm-predict '+SvmPutFileName+' ' + CNCIPATH + '/CNCI_Parameters/python_model '+SvmFile+' > '+svm_tmp+'')
if ClassModel == 'pl':
os.system(CNCIPATH + '/libsvm-3.0/svm-scale -r ' + CNCIPATH + '/CNCI_Parameters/plant_scale '+SvmoutPutFile+' > '+SvmPutFileName+'')
os.system(CNCIPATH + '/libsvm-3.0/svm-predict '+SvmPutFileName+' ' + CNCIPATH + '/CNCI_Parameters/plant_model '+SvmFile+' > '+svm_tmp+'')
##########################################################################################################
FirResult = PutResult(detil_array)
SvmFinalResutl = Out_Dir + '/CNCI.index'
PringResult(FirResult,SvmFinalResutl)
#subprocess.call('rm '+SvmPutFileName+' ', shell=True)
#subprocess.call('rm '+SvmFile+' ', shell=True)
#subprocess.call('rm '+svm_tmp+' ', shell=True)
#subprocess.call('rm '+outPutFileName+' ', shell=True)
#subprocess.call('rm '+inGtfFiles+' ', shell=True)
#subprocess.call('rm '+fastaFiles+' ', shell=True)
##############################################################################################################
print 'CNCI classification were completely done!'
print "%f second for" % (time.time() - Compute_time) + ' ' + str(inFileLength) + ' ' + "transcript's computation."
shutil.rmtree(Temp_Dir,True)