#####
import pandas as pd
import xlrd
import os,sys
import argparse
import re
#原项目表只有一个sheet的代码
def pancancer_project_v0(inputpath,laneid):
	#inputpath = '/cgdata/liuxiangqiong/work62pancancer/pipelinetest-CGB0158'
	#laneid = 'CGB0158'
	###0.拷贝CNV分级表
	rawcnvdir = '/cgdata/pancancer_report/refdata/CNV.xlsx'
	cnvtable = pd.read_excel(rawcnvdir)
	cnvtable['gene'] = cnvtable['gene'].str.strip()
	cnvtable['CNV_label'] = cnvtable['CNV_label'].str.strip()
	refcnvdir1 = '/cgdata/liuxiangqiong/work62pancancer/pipeline/v0/refdata/CNV_tier_infor.txt'
	biocnvdir2 = '/cgdata/bioproject/pancancer602gene/Project_table/CNV_tier_infor.txt'
	cnvtable.to_csv(refcnvdir1, index=False, header=True, sep='\t')
	cnvtable.to_csv(biocnvdir2, index=False, header=True, sep='\t')
	###1.从项目表提取出当前大panel的样本表
	project = pd.read_excel('/cgdata/bioproject/pancancer602gene/Project_table/阅尔基因项目信息总表-0606.xlsx',engine='openpyxl')
	###2.获得当前的项目，更新当前的样本表
	projectsample1 = project[project['检测项目1'] == '阅全-泛癌种602基因检测']
	projectsample2 = project[project['检测项目1'].str.contains('CPS', na=False)]
	projectsample=projectsample1.append(projectsample2)
	projectsample.reset_index(drop=True,inplace=True)
	outputfile1 = os.path.join('/cgdata/bioproject/pancancer602gene/Project_table/pancancer_602gene_samples.xlsx')
	writer = pd.ExcelWriter(outputfile1)
	projectsample.to_excel(writer, sheet_name='QC', index=False)
	writer.save()
	writer.close()
	####3.提取出当前我们panel分析样本的信息
	selectlist = ['样本编号', '样本类型', '临床诊断']
	selectsample = projectsample[selectlist]
	selectsample.reset_index(drop=True, inplace=True)
	selectsample['样本编号'] = selectsample['样本编号'].str.replace("^A", "Lib")
	###3.1 我们从底层表中获取疾病的名称
	tumordbdir = '/cgdata/liuxiangqiong/work62pancancer/pipeline/v0/refdata/TCGA_tumor_list_20220915.txt'
	tumordblist = pd.read_table(tumordbdir, sep='\t', header=0, encoding='gbk')
	# 添加TCGA的名称
	selectsample.rename(columns={'临床诊断': 'tumor_name', '样本编号': 'sampleid', '样本类型': 'sampletype'}, inplace=True)
	selectsample_TCGA = pd.merge(selectsample, tumordblist, on=['tumor_name'], how='left')
	# 将不在TCGA的癌症名称标为unknown
	selectsample_TCGA['tumor_name'] = selectsample_TCGA['tumor_name'].fillna('unknown')
	# 3.2 获得当前的样本对应的信息
	####提取出当前lane的样本对应的信息
	lane_sampelelist = pd.read_table(os.path.join(inputpath, laneid + '_sample.txt'), sep='\t', header=None, names=['sampleid'])
	lane_sampleinfor = pd.merge(lane_sampelelist, selectsample_TCGA, on=['sampleid'], how='left')
	# 输出原始信息表
	outputfile2 = os.path.join(inputpath, laneid + '_sampleinfor_raw.xlsx')
	writer = pd.ExcelWriter(outputfile2)
	lane_sampleinfor.to_excel(writer, sheet_name='sampleinfor', index=False)
	writer.save()
	writer.close()
	return lane_sampleinfor

##项目表有上海和启动两个sheet
def pancancer_project(inputpath,laneid):
	#inputpath = '/cgdata/liuxiangqiong/work62pancancer/pipelinetest-CGB0158'
	#laneid = 'CGB0158'
	###0.拷贝CNV分级表
	rawcnvdir = '/cgdata/pancancer_report/refdata/CNV.xlsx'
	cnvtable = pd.read_excel(rawcnvdir)
	cnvtable['gene'] = cnvtable['gene'].str.strip()
	cnvtable['CNV_label'] = cnvtable['CNV_label'].str.strip()
	refcnvdir1 = '/cgdata/liuxiangqiong/work62pancancer/pipeline/v0/refdata/CNV_tier_infor.txt'
	biocnvdir2 = '/cgdata/bioproject/pancancer602gene/Project_table/CNV_tier_infor.txt'
	cnvtable.to_csv(refcnvdir1, index=False, header=True, sep='\t')
	cnvtable.to_csv(biocnvdir2, index=False, header=True, sep='\t')
	###1.从项目表提取出当前大panel的样本表
	project = pd.read_excel('/cgdata/bioproject/pancancer602gene/Project_table/阅尔基因项目信息总表-0606.xlsx',engine='openpyxl',sheet_name=None)
	#提取出两个sheet的结果并追加合并
	project_SH=project['项目信息表-上海']
	project_QD=project['项目信息表-启东']
	project_all=project_QD.append(project_SH)
	project_all.reset_index(drop=True,inplace=True)
	###2.获得当前的项目，更新当前的样本表
	projectsample1 = project_all[project_all['检测项目1'] == '阅全-泛癌种602基因检测']
	projectsample2 = project_all[project_all['检测项目1'].str.contains('CPS', na=False)]
	projectsample=projectsample1.append(projectsample2)
	projectsample.reset_index(drop=True,inplace=True)
	outputfile1 = os.path.join('/cgdata/bioproject/pancancer602gene/Project_table/pancancer_602gene_samples.xlsx')
	writer = pd.ExcelWriter(outputfile1)
	projectsample.to_excel(writer, sheet_name='QC', index=False)
	writer.save()
	writer.close()
	####3.提取出当前我们panel分析样本的信息
	selectlist = ['样本编号', '样本类型', '临床诊断']
	selectsample = projectsample[selectlist]
	selectsample.reset_index(drop=True, inplace=True)
	selectsample['样本编号'] = selectsample['样本编号'].str.replace("^A", "Lib")
	###3.1 我们从底层表中获取疾病的名称
	tumordbdir = '/cgdata/liuxiangqiong/work62pancancer/pipeline/v0/refdata/TCGA_tumor_list_20220915.txt'
	tumordblist = pd.read_table(tumordbdir, sep='\t', header=0, encoding='gbk')
	# 添加TCGA的名称
	selectsample.rename(columns={'临床诊断': 'tumor_name', '样本编号': 'sampleid', '样本类型': 'sampletype'}, inplace=True)
	selectsample_TCGA = pd.merge(selectsample, tumordblist, on=['tumor_name'], how='left')
	# 将不在TCGA的癌症名称标为unknown
	selectsample_TCGA['tumor_name'] = selectsample_TCGA['tumor_name'].fillna('unknown')
	# 3.2 获得当前的样本对应的信息
	####提取出当前lane的样本对应的信息
	lane_sampelelist = pd.read_table(os.path.join(inputpath, laneid + '_sample.txt'), sep='\t', header=None, names=['sampleid'])
	lane_sampleinfor = pd.merge(lane_sampelelist, selectsample_TCGA, on=['sampleid'], how='left')
	# 输出原始信息表
	outputfile2 = os.path.join(inputpath, laneid + '_sampleinfor_raw.xlsx')
	writer = pd.ExcelWriter(outputfile2)
	lane_sampleinfor.to_excel(writer, sheet_name='sampleinfor', index=False)
	writer.save()
	writer.close()
	return lane_sampleinfor


#####################获得项目表中样本的信息
def project_pair(rawsheet):
	#inputfile = os.path.join(inputpath, laneid + '_sampleinfor_raw.xlsx')
	#rawsheet = pd.read_excel(inputfile)
	##首先获得所有的肿瘤样本
	rawsheet_tumorid = rawsheet[rawsheet['sampleid'].str.contains('T')]
	rawsheet_tumorid.reset_index(drop=True, inplace=True)
	###获得肿瘤样本名,构建配对表，因为不管正常如何命名，最后的normal也是根据肿瘤样本名，所以只需要看肿瘤样本名就可
	sample_pair = pd.DataFrame()
	for i in range(len(rawsheet_tumorid)):
		sample = rawsheet_tumorid.loc[i, 'sampleid']
		sampletype1 = rawsheet_tumorid.loc[i, 'sampletype']
		tumortype = rawsheet_tumorid.loc[i, 'Abbr']
		sample_pair.loc[i, 'samplename'] = sample
		sample_pair.loc[i, 'normal'] = sample + 'CN'
		if sampletype1 == '血浆':
			sample_pair.loc[i, 'tumor'] = sample + 'CT'
			sample_pair.loc[i, 'sampletype'] = 'blood'
			sample_pair.loc[i, 'tumortype'] = tumortype
		elif sampletype1=='外周血':
			sample_pair.loc[i, 'tumor'] = sample + 'CT'
			sample_pair.loc[i, 'sampletype'] = 'blood'
			sample_pair.loc[i, 'tumortype'] = tumortype
		else:
			sample_pair.loc[i, 'tumor'] = sample + 'TT'
			sample_pair.loc[i, 'sampletype'] = 'FFPE'
			sample_pair.loc[i, 'tumortype'] = tumortype
	return sample_pair


###########获得下机数据的样本表信息
def subdir_list(dirname):
	"""获取目录下所有子目录名
	@param dirname: str 目录的完整路径
	@return: list(str) 所有子目录完整路径组成的列表
	"""
	return list(filter(os.path.isdir,
		map(lambda filename: os.path.join(dirname, filename),
			os.listdir(dirname))))

def file_list(dirname, ext='.gz'):
	"""获取目录下所有特定后缀的文件
	@param dirname: str 目录的完整路径
	@param ext: str 后缀名, 以点号开头
	@return: list(str) 所有子文件名(不包含路径)组成的列表
	"""
	return list(filter(
		lambda filename: os.path.splitext(filename)[1] == ext,
		os.listdir(dirname)))

def fastq_pair(bclpath):
	file0 = subdir_list(bclpath)
	fastq_sample = pd.DataFrame()
	for i in range(len(file0)):
		split0 = file0[i].split('/')[-1]
		sampleid0 = split0.split('-')[-1]
		corsampleid = re.findall(r"[Lib]+[0-9]+", sampleid0)
		fastq_sample.loc[i, 'corname'] = corsampleid[0]
		fastq_sample.loc[i, 'fastq_name'] = sampleid0
	# 制作下机数据的配对表
	core_samplelist = pd.DataFrame(fastq_sample.loc[:, 'corname'].drop_duplicates(keep='first'))
	core_samplelist.reset_index(drop=True, inplace=True)
	pair_fastq = pd.DataFrame()
	k = 0
	for i in range(len(core_samplelist)):
		corname = core_samplelist.loc[i, 'corname']
		# 从下机数据里面获得对应的样本编号
		fastqsample = fastq_sample[fastq_sample['corname'] == corname]
		fastqsample.reset_index(drop=True, inplace=True)
		fastqsample.rename(columns={'fastq_name': 'sampleid'}, inplace=True)
		###提取出肿瘤的样本
		fastqsample_tumor = fastqsample[fastqsample['sampleid'].str.contains('T')]
		fastqsample_normal = fastqsample[fastqsample['sampleid'].str.contains('N')]
		fastqsample_tumor.reset_index(drop=True, inplace=True)
		fastqsample_normal.reset_index(drop=True, inplace=True)
		for j in range(len(fastqsample_tumor)):
			pair_fastq.loc[k, 'samplename'] = fastqsample_tumor.loc[j, 'sampleid']
			pair_fastq.loc[k, 'fastq_tumor'] = fastqsample_tumor.loc[j, 'sampleid']
			for m in range(len(fastqsample_normal)):
				pair_fastq.loc[k, 'fastq_normal'] = fastqsample_normal.loc[m, 'sampleid']
				k = k + 1
	return pair_fastq

def sheetrunmain(inputpath,laneid,bclpath):
	lane_sampleinfor=pancancer_project(inputpath,laneid)
	project_sample=project_pair(lane_sampleinfor)
	fastq_sample=fastq_pair(bclpath)
	#merge the project and fastq
	print('project_sample')
	print(project_sample)
	print('fastq_sample')
	print(fastq_sample)
	sample_infor_pair = pd.merge(project_sample, fastq_sample, on=['samplename'], how='outer')
	print('mergedata')
	print(sample_infor_pair)
	outputfile = os.path.join(inputpath, laneid + '_sample_infor_label.txt')
	sample_infor_pair.to_csv(outputfile,sep='\t',header=True, index=False)
	print('finish')



#inputpath='/cgdata/liuxiangqiong/work62pancancer/pipelinetest-CGB0158'
#laneid='CGB0158'
#sheetrun(inputpath,laneid)

if __name__=='__main__':
	parser = argparse.ArgumentParser(description='sample pair')
	parser.add_argument('-i', '--inputpath', type=str, help='the path of lane')
	parser.add_argument('-l', '--laneid', type=str, help='laneid')
	parser.add_argument('-f', '--bclpath', type=str, help='the fastq file')
	args = parser.parse_args()
	Inputpath = args.inputpath
	Laneid = args.laneid
	Bclpath=args.bclpath
	sheetrunmain(Inputpath,Laneid,Bclpath)