#edit the file
#1.clean data
#2.align
#3.sv callin





import sys,collections,math,os,os.path,re
import pandas as pd
from pandas.core.frame import DataFrame
import argparse
import numpy as np





def analis_dir(inputpath):
	svdir1 = os.path.join(inputpath, '1SV_varscan_pair')
	if not os.path.exists(svdir1):
		os.mkdir(svdir1)
	svdir2= os.path.join(inputpath, '1SV_vardict_pair')
	if not os.path.exists(svdir2):
		os.mkdir(svdir2)
	# for CNV
	CNVdir = os.path.join(inputpath, '2CNV_cnvkit_pair')
	if not os.path.exists(CNVdir):
		os.mkdir(CNVdir)
	# for MSI
	MSI_dir = os.path.join(inputpath, '3MSI_msisensor2_pair')
	if not os.path.exists(MSI_dir):
		os.mkdir(MSI_dir)
	# for germline result
	gemerlinedir = os.path.join(inputpath, '4Germline_unpair')
	if not os.path.exists(gemerlinedir):
		os.mkdir(gemerlinedir)
	# for HL result
	HLdir = os.path.join(inputpath, '5HL_gatk_unpair')
	if not os.path.exists(HLdir):
		os.mkdir(HLdir)
	# for fusion
	fusion_method1_dir = os.path.join(inputpath, '6Fusion_manta_pair')
	if not os.path.exists(fusion_method1_dir):
		os.mkdir(fusion_method1_dir)
	fusion_method2_dir = os.path.join(inputpath, '6Fusion_genefusion_unpair')
	if not os.path.exists(fusion_method2_dir):
		os.mkdir(fusion_method2_dir)
	#for HLA
	HLA_dir = os.path.join(inputpath, '7HLA-HD_unpair')
	if not os.path.exists(HLA_dir):
		os.mkdir(HLA_dir)
	# for qc
	qc_dir = os.path.join(inputpath, '8Fastqc')
	if not os.path.exists(qc_dir):
		os.mkdir(qc_dir)
	# for ontarget
	ontarget_dir = os.path.join(inputpath, '9Ontarget')
	if not os.path.exists(ontarget_dir):
		os.mkdir(ontarget_dir)
	# for coverage
	coverage_dir = os.path.join(inputpath, '10Coverage')
	if not os.path.exists(coverage_dir):
		os.mkdir(coverage_dir)
	# datasummary
	datasummary_dir = os.path.join(inputpath, 'datasummary')
	if not os.path.exists(datasummary_dir):
		os.mkdir(datasummary_dir)


##### 首先要进行样本的配对，获得同一个样本的tumor和normal
def sampleinfor(inputpath,laneid):
	files = os.listdir(inputpath)
	files1 = [inputpath + '/' + f for f in files if f.endswith(('sample.txt'))]
	samplelist = pd.read_table(files1[0], names=['sampleid'], sep='\t')
	samplenamelist = pd.DataFrame()
	for i in range(len(samplelist)):
		sample = samplelist.loc[i, 'sampleid'][:-2]
		samplenamelist.loc[i, 'samplename'] = sample
	samplenamelist_uq = samplenamelist.drop_duplicates().reset_index(drop=True)
	sample_pair = pd.DataFrame()
	for j in range(len(samplenamelist_uq)):
		sample = samplenamelist_uq.loc[j, 'samplename']
		sample_pair.loc[j, 'samplename'] = sample
		for k in range(len(samplelist)):
			sampleid = samplelist.loc[k, 'sampleid']
			print(sampleid)
			label = sampleid.find(sample)
			if label != -1:
				lib_lable = sampleid[-1]
				print(lib_lable)
				if lib_lable == 'T':
					sample_pair.loc[j, 'tumor'] = sampleid
				elif lib_lable == 'N':
					sample_pair.loc[j, 'normal'] = sampleid
	outputname = inputpath + '/' +laneid+'_'+ 'sample_infor_label.txt'
	sample_pair.to_csv(outputname, index=False, header=True, encoding='gbk', sep='\t')



if __name__=='__main__':
	parser = argparse.ArgumentParser(description='sample infor')
	parser.add_argument('-i', '--inputpath', type=str, help='the path of lane')
	parser.add_argument('-l', '--laneid', type=str, help='laneid')
	args = parser.parse_args()
	Inputpath = args.inputpath
	Laneid = args.laneid
	sampleinfor(Inputpath,Laneid)