#for the fusion summary by the Genefusion and Factera method
#date:20220217
import json
import pandas as pd
import sys,collections,math,os,os.path,re
import xlrd
import gzip
from bs4 import BeautifulSoup
import re
import argparse


#######acquire the transcrip strand form html
#->mean the original strand;<-mean the recov

pd.set_option('display.max_columns',6)
def infro_html(htmlPath):
	'''
	extract the transcript strand form html
	→ means original read, ← means reverse complement
	:param htmlPath: the genefuse html
	:return: the trans strand
	'''
	with open(htmlPath,"r",encoding="utf-8") as file:
		file1 = file.read()
		f1 = re.findall("<a name='fusion_id_.+'>.+, Fusion: (.+?) .+?<", file1)
		f2 = re.findall("<tr><td width='.+?%'>(.*?)</td>",file1)
		f3 = re.findall("</td><td width='.+?%'>(.*?)</td>", file1)
		f3_str = "<tr><td class='exon_left' width='.+?%'>.+" +\
								"|<tr><td class='intron_left' width='.+?%'>.+" +\
								"|<tr></tr></table>.+"
		f4 = re.findall(f3_str, file1)
		f5_str = "<tr><td class='exon_right' width='.+?%'>.+Supporting" +\
								"|<tr><td class='intron_right' width='.+?%'>.+Supporting" +\
								"|<tr></tr></table>.+Supporting"
		f5 = re.findall(f5_str, file1)
		print(len(f1),len(f2),len(f3),len(f4),len(f5))
		for i in f4:
			pass
		f4_1 = []
		f5_1 = []
		for i in range(len(f4)):
			if "←" in f4[i]:
				f4_1.append("3_5")
			elif "→" in f4[i]:
				f4_1.append("5_3")
			else:
				f4_1.append("no")
			if "←" in f5[i]:
				f5_1.append("3_5")
			elif "→" in f5[i]:
				f5_1.append("5_3")
			else:
				f5_1.append("no")
	data = pd.DataFrame([f1,f2,f4_1,f3,f5_1])
	data = data.T
	data.columns = ["Fusion","Gene_left","Trans_left","Gene_right","Trans_right"]
	return data


####

def infro_json(filename):
	'''
	extract the information form json
	:param filename: the genefuse json
	:return: all the fusion information
	'''
	with open(filename) as f:
		fusionlist = json.load(f)
	result = pd.DataFrame()
	fusionlist_data = fusionlist['fusions']
	if fusionlist_data:
		for key, value in fusionlist_data.items():
			# for left
			df_left = pd.DataFrame.from_dict(value['left'], orient='index', columns=['values'])
			# change row index
			df_left_renamed = df_left.rename(
				index={'gene_name': 'Gene_left', 'gene_chr': 'chr_left', 'position': 'position_left',
					   'reference': 'reference_left', 'ref_ext': 'ref_ext_left', 'pos_str': 'pos_str_left',
					   'exon_or_intron': 'exon_or_intron_left', 'exon_or_intron_id': 'exon_or_intron_id_left',
					   'strand': 'strand_left'})
			# for right
			df_right = pd.DataFrame.from_dict(value['right'], orient='index', columns=['values'])
			# change the row index
			df_right_renamed = df_right.rename(
				index={'gene_name': 'Gene_right', 'gene_chr': 'chr_right', 'position': 'position_right',
					   'reference': 'reference_right', 'ref_ext': 'ref_ext_right', 'pos_str': 'pos_str_right',
					   'exon_or_intron': 'exon_or_intron_right', 'exon_or_intron_id': 'exon_or_intron_id_right',
					   'strand': 'strand_right'})
			# extract unique reads
			uniquereads = pd.DataFrame(columns=['values'])
			uniquereads.loc['Unique_reads'] = value['unique']
			# merge
			fusioninfor1 = df_left_renamed.append(df_right_renamed)
			fusioninfor2 = fusioninfor1.append(uniquereads)
			# trans the result
			fusioninfor3 = pd.DataFrame(fusioninfor2.values.T, index=fusioninfor2.columns,
										columns=fusioninfor2.index)
			fusioninfor3.insert(0, 'Fusion1', key)
			result = result.append(fusioninfor3)
		result = result.reset_index()
		del result['index']
		result['Total_reads'] = (
			(result['Fusion1'].str.split(' ', expand=True)[4]).str.split(',', expand=True)[0]).astype("int")
		result.insert(0, 'Fusion', result['Fusion1'].str.split(' ', expand=True)[1])
		del result['Fusion1']
	else:
		namelist=['Fusion', 'Gene_left', 'chr_left', 'position_left', 'reference_left','ref_ext_left', 'pos_str_left', 'exon_or_intron_left',
				  'exon_or_intron_id_left', 'strand_left', 'Gene_right', 'chr_right','position_right', 'reference_right', 'ref_ext_right',
				  'pos_str_right','exon_or_intron_right', 'exon_or_intron_id_right', 'strand_right', 'Unique_reads', 'Total_reads']
		result=pd.DataFrame(columns=namelist)
	return result





###t提取genefuse的序列
def infro_txt(Path):
	'''
	extract the transcript strand form html
	→ means original read, ← means reverse complement
	:param htmlPath: the genefuse html
	:return: the trans strand
	'''
	with open(Path,"r",encoding="utf-8") as file:
		file1 = file.read()
		t1 = re.findall("#Fusion: (.+?) .+?total: (.+?),.+", file1)
		num=0
		for i in t1:
			num = num+int(i[1])
		if num!=0:
			t2 = re.findall("name: (.+)", file1)
			if num==len(t2):
				t3 = re.findall("\n[ATCG].+", file1)
				t1_1 = []
				for i in t1:
					for i1 in range(int(i[1])):
						t1_1.append(i[0])
				data = pd.DataFrame([t1_1,t2,t3])
				data = data.T
				data.columns = ["Fusion","Name",""]
				return data
			else:
				data = pd.DataFrame(columns=["Fusion", "Name", "Seq"])
				print(os.path.basename(Path)+" error:The number of fusion-names is inconsistent")
				return data
		else:
			data = pd.DataFrame(columns=["Fusion", "Name", "Seq"])
			print(os.path.basename(Path)+":File no result")
			return data



######对VAF的计算
#1.获得genefuse的readid
#2.我们将断点区域上下各扩展50bp
##3.提取genefuse与bam中断点位置overlap的reads
def VAF_fusion(laneid,sampleid,fusion_chr,fusion_position,inputpath):
	GeneFusiondir = os.path.join(inputpath, '6Fusion_genefusion_unpair')
	# acqure the read in fusion region
	fusion_loc_ex50=str(fusion_chr) + ':' + str(fusion_position-50) + '-' + str(fusion_position+ 50)
	pandir = os.path.join('/cgdata/pancancer/analyse',laneid+'/bamfile')
	bamdir =os.path.join(pandir, sampleid + '_clean.bam')
	bed_file = GeneFusiondir + '/' + sampleid + '.' + fusion_loc_ex50 + '.bed'
	read_bedcmd = 'samtools view -h ' + bamdir + ' ' + fusion_loc_ex50 + ' | ' + 'samtools view -bS - | bedtools bamtobed -i - ' + '>' + bed_file
	os.system(read_bedcmd)
	# readin the result
	read_region = pd.read_table(bed_file, sep='\t', names=['chr', 'start', 'end', 'readid', 'len', 'strand'])
	##3.提取genefuse与bam中断点位置overlap的reads
	txtfile = GeneFusiondir + '/' + sampleid + '_fusion_GeneFusion.txt'
	genefuse_result = infro_txt(txtfile)
	# rename
	readinfor = genefuse_result['Name'].str.split(' ', expand=True)
	gfusereadid = readinfor[0] + '/' + (readinfor[1].str.split(':', expand=True))[0]
	gfusereadid1 = pd.DataFrame(gfusereadid)
	gfusereadid1.rename(columns={0: 'readid'}, inplace=True)
	# merge
	read_overlap = pd.merge(gfusereadid1, read_region, on='readid', how='inner')
	###acquire the fusion depths
	depthoutdir = GeneFusiondir + '/' + sampleid + '_' + fusion_loc_ex50 + '.txt'
	depthcomd = 'samtools depth -r ' + fusion_loc_ex50+ ' ' + bamdir + '>' + depthoutdir
	os.system(depthcomd)
	depthdata = pd.read_table(depthoutdir, sep='\t', names=['chr', 'positon', 'total_reads'])
	#filter the depth
	depthdata['chr'] = depthdata['chr'].astype(str)
	filter1 = depthdata[(depthdata['chr'] == fusion_chr) & (depthdata['positon'] == fusion_position)]
	filter1.reset_index(drop=True, inplace=True)
	if filter1.shape[0]!=0:
		Total_depth = filter1.loc[0, 'total_reads']
		fusion_readcounts = len(read_overlap)
		VAF = round(fusion_readcounts / Total_depth, 4)
		print('round4VAF '+str(VAF))
	else:
		VAF=0
	os.remove(bed_file)
	os.remove(depthoutdir)
	return VAF

#fusion_chr=6
#fusion_position=117647036
#VAF_fusion(sampleid,fusion_chr,fusion_position,inputpath)
#####修改，转录本方向改变的时候为3-5时，对应的left和right也需要同时进行修改


def fusion_extract(inputpath,laneid,jsonfile,htmlfile,tumorname):
	'''
	####extract the fusion information form the json and html
	###and calculate the VAF
	:param jsonfile: the json file of genefuse
	:param htmlfile: the html file of genefuse
	:return: the information of fusion
	'''
	headlist = [ 'Fusion_new', 'VAF', 'Gene_left', 'chr_left', 'position_left', 'exon_or_intron_left',
				'exon_or_intron_id_left', 'Gene_right', 'chr_right', 'position_right', 'exon_or_intron_right',
				'exon_or_intron_id_right', 'Unique_reads', 'Total_reads']
	#extract the json file
	json_data = infro_json(jsonfile)
	#extract the html file
	html_data = pd.DataFrame(infro_html(htmlfile))
	if (json_data.shape[0]!=0) & (html_data.shape[0]!=0) :
		#获得结果均有转录本，且转录本方向一致的结果
		#html_data01 = html_data[html_data['Trans_left'] == html_data['Trans_right']]
		#当其中一个的转录本方向不定，结果为空，
		#html_data02 = html_data01[html_data01['Trans_left'] != 'no']
		#html_data03 = html_data02[html_data01['Trans_right'] != 'no']
		#html_data03.reset_index(drop=True, inplace=True)
		# merge the data
		fusion_infor1 = pd.merge(json_data, html_data, on=["Fusion", "Gene_left", "Gene_right"], how='right')
		#fusion_infor1 = pd.merge(json_data, html_data03, on=["Fusion", "Gene_left", "Gene_right"], how='right')
		fusion_infor1['Gene_right'] = fusion_infor1['Gene_right'].str.split('_', expand=True)[0]
		fusion_infor1['Gene_left'] = fusion_infor1['Gene_left'].str.split('_', expand=True)[0]
		for j in range(len(fusion_infor1)):
			print(j)
			#print(fusion_infor1.loc[j, :])
			unique_reads = fusion_infor1.loc[j, 'Unique_reads']
			right_gene = fusion_infor1.loc[j, 'Gene_right']
			right_exonintron = fusion_infor1.loc[j, 'exon_or_intron_right']
			right_exonintron_id = fusion_infor1.loc[j, 'exon_or_intron_id_right']
			right_chr = fusion_infor1.loc[j, 'chr_right']
			right_position = fusion_infor1.loc[j, 'position_right']
			left_gene = fusion_infor1.loc[j, 'Gene_left']
			left_exonintron = fusion_infor1.loc[j, 'exon_or_intron_left']
			left_exonintron_id = fusion_infor1.loc[j, 'exon_or_intron_id_left']
			left_chr = fusion_infor1.loc[j, 'chr_left']
			left_position = fusion_infor1.loc[j, 'position_left']
			#cal the VAF
			print(tumorname)
			VAF_right = VAF_fusion(laneid,tumorname, right_chr, abs(int(right_position)), inputpath)
			VAF_left= VAF_fusion(laneid,tumorname, left_chr, abs(int(left_position)), inputpath)
			VAF=max(VAF_right,VAF_left)
			print(VAF)
			if VAF<1:
				VAFnew=VAF
			else:
				VAFnew=0
			fusion_infor1.loc[j, 'VAF'] = str(round(VAFnew*100,4)) + '%'
			if (fusion_infor1.loc[j, 'Trans_left'] == fusion_infor1.loc[j, 'Trans_right']) & (
					fusion_infor1.loc[j, 'Trans_left'] == '3_5'):
				# for new name
				fusion_new = right_gene + '_' + right_exonintron + str(
					right_exonintron_id) + '-' + left_gene + '_' + left_exonintron + str(left_exonintron_id)
				fusion_infor1.loc[j, 'Fusion_new'] = fusion_new
				#change the left and right information
				fusion_infor1.loc[j, 'Gene_left'] = right_gene
				fusion_infor1.loc[j, 'chr_left'] = right_chr
				fusion_infor1.loc[j, 'position_left'] = right_position
				fusion_infor1.loc[j, 'exon_or_intron_left'] = right_exonintron
				fusion_infor1.loc[j, 'exon_or_intron_id_left'] = right_exonintron_id
				fusion_infor1.loc[j, 'Gene_right'] = left_gene
				fusion_infor1.loc[j, 'chr_right'] = left_chr
				fusion_infor1.loc[j, 'position_right'] = left_position
				fusion_infor1.loc[j, 'exon_or_intron_right'] = left_exonintron
				fusion_infor1.loc[j, 'exon_or_intron_id_right'] = left_exonintron_id
			elif (fusion_infor1.loc[j, 'Trans_left'] == fusion_infor1.loc[j, 'Trans_right']) & (
					fusion_infor1.loc[j, 'Trans_left'] == '5_3'):
				# for new name
				fusion_new = left_gene + '_' + left_exonintron + str(
					left_exonintron_id) + '-' + right_gene + '_' + right_exonintron + str(right_exonintron_id)
				fusion_infor1.loc[j, 'Fusion_new'] = fusion_new
			elif fusion_infor1.loc[j, 'Trans_left'] != fusion_infor1.loc[j, 'Trans_right']:
				fusion_infor1.loc[j, 'Fusion_new'] = 'Neg'
			else:
				fusion_infor1.loc[j, 'Fusion_new'] = 'Neg'
	else:
		fusion_infor1=pd.DataFrame(columns=headlist)
	# output the data
	fusion_infor2 = fusion_infor1[headlist]
	fusion_infor2.rename(columns={'Fusion_new': 'Fusion'}, inplace=True)
	return fusion_infor2

###过滤假阳性

def filter_black_fusion(fusionresult2):
	fusion_black_dir='/cgdata/liuxiangqiong/work62pancancer/Client/v0/script/refdata/fusion_blacklist_20230403.txt'
	fusion_black=pd.read_table(fusion_black_dir,sep='\t',header=0)
	del fusion_black['Fusion']
	del fusion_black['samplecounts']
	fusionresult2['loc']=fusionresult2['chr_left'].astype('str')+'_'+fusionresult2['position_left'].astype('str')+'_'+fusionresult2['chr_right'].astype('str')+'_'+fusionresult2['position_right'].astype('str')
	fusionlabel=pd.merge(fusionresult2,fusion_black,on=['loc'],how='left')
	fusion_filter=fusionlabel[fusionlabel['label']!='black']
	fusion_filter.reset_index(drop=True,inplace=True)
	del fusion_filter['loc']
	del fusion_filter['label']
	return fusion_filter




###for sample
def fusion_GeneFusion_sample(inputpath,laneid,tumorname,sampleid):
	#inputpath = '/cgdata/liuxiangqiong/work62pancancer/fusiontest/lane1'
	tempfile=os.path.join(inputpath,'tempfile')
	if not os.path.exists(tempfile):
		os.mkdir(tempfile)
	fusion_tem_dir=os.path.join(tempfile,'fusion')
	if not os.path.exists(fusion_tem_dir):
		os.mkdir(fusion_tem_dir)
	GeneFusiondir = os.path.join(inputpath, '6Fusion_genefusion_unpair')
	#sampleid=tumorid[:-2]
	# make the result dir
	result_dir = os.path.join(inputpath, 'resultfile')
	if not os.path.exists(result_dir):
		os.mkdir(result_dir)
	sample_dir = os.path.join(result_dir, sampleid)
	if not os.path.exists(sample_dir):
		os.mkdir(sample_dir)
	tumor_jsonfile = GeneFusiondir + '/' + tumorname + '_report.json'
	tumor_htmlfile = GeneFusiondir + '/' + tumorname + '_report.html'
	if (os.path.exists(tumor_jsonfile)) & (os.path.exists(tumor_jsonfile)):
		fusionresult = fusion_extract(inputpath,laneid,tumor_jsonfile, tumor_htmlfile, tumorname)
		print(fusionresult)
		fusionresult.insert(0, 'sampleid', sampleid)
		#输出未过滤结果
		outputfile0=os.path.join(fusion_tem_dir,laneid + '-' + sampleid + '.fusion_unfilter.txt')
		fusionresult.to_csv(outputfile0,sep='\t',index=False,header=True)
		#设置最后结果
		Fusion_final=pd.DataFrame(columns=list(fusionresult.columns))
		# 选择uniquer reads>=5的fusion，且有突变丰度的
		fusionresult1 = fusionresult[fusionresult['Unique_reads'] >= 5]
		fusionresult2 = fusionresult1[(fusionresult1['VAF'] != '0%') & (fusionresult1['VAF'] != '0.0%')]
		fusionresult2.reset_index(drop=True, inplace=True)
		print(fusionresult2)
		if len(fusionresult2) == 0:
			Fusion_final.loc[0, 'sampleid'] = sampleid
			Fusion_final.loc[0, 'Fusion'] = 'no fusion'
		else:
			###对fusion的表示进行修改CD74-ROS1 (intron6:intron33)
			#当有intron-1时，将其修改为基因间区
			fusionresult2['Fusion']=fusionresult2['Fusion'].str.replace('intron-1', 'intergenic')
			fusionsplit1 = fusionresult2['Fusion'].str.split('-', expand=True)
			fusionsplit1_left = fusionsplit1[0].str.split('_', expand=True)
			fusionsplit1_right = fusionsplit1[1].str.split('_', expand=True)
			fusion_new = fusionsplit1_left[0] + '-' + fusionsplit1_right[0] + '(' + fusionsplit1_left[1] + ':' + \
						 fusionsplit1_right[1] + ')'
			fusionresult2.insert(1, 'Fusion_new', fusion_new)
			del fusionresult2['Fusion']
			fusionresult2.rename(columns={'Fusion_new': 'Fusion'}, inplace=True)
			#将intron=-1的修改为基因间区
			fusionresult2.reset_index(drop=True, inplace=True)
			for i in range(len(fusionresult2)):
				if fusionresult2.loc[i, 'exon_or_intron_id_left'] == -1:
					fusionresult2.loc[i, 'exon_or_intron_id_left'] = 'intergenic'
				elif fusionresult2.loc[i, 'exon_or_intron_id_right'] == -1:
					fusionresult2.loc[i, 'exon_or_intron_id_right'] = 'intergenic'
			#去除假阳性
			fusionresult2_1=filter_black_fusion(fusionresult2)
			if len(fusionresult2_1)!=0:
				Fusion_final=fusionresult2_1
			else:
				Fusion_final.loc[0, 'sampleid'] = sampleid
				Fusion_final.loc[0, 'Fusion'] = 'no fusion'
		outputfile1 = os.path.join(sample_dir, laneid + '-' + sampleid + '.fusion.xlsx')
		writer = pd.ExcelWriter(outputfile1)
		Fusion_final.to_excel(writer, sheet_name='fusion', index=False)
		writer.save()
		writer.close()
	else:
		print(tumorid+' is no data')
		Fusion_final=pd.DataFrame()
		Fusion_final.loc[0,'sampleid']=sampleid
		Fusion_final.loc[0,'Fusion']='No fusion file'
		# make the temp dir
		temp_dir = os.path.join(inputpath, 'tempfile')
		if not os.path.exists(temp_dir):
			os.mkdir(temp_dir)
		bugfile_dir = os.path.join(temp_dir, 'bugfile')
		if not os.path.exists(bugfile_dir):
			os.mkdir(bugfile_dir)
		outputfile2 = os.path.join(bugfile_dir, laneid + '-' + sampleid + '.fusion.nofile.log.txt')
		Fusion_final.to_csv(outputfile2, index=False, header=True, encoding='gbk', sep='\t')


def cp_fusionhtml(inputpath,sampleid,tumorname,laneid):
	fusiondir = os.path.join(inputpath, '6Fusion_genefusion_unpair')
	rawhtml = os.path.join(fusiondir, tumorname + '_report.html')
	result_dir = os.path.join(inputpath, 'resultfile')
	if not os.path.exists(result_dir):
		os.mkdir(result_dir)
	sample_dir = os.path.join(result_dir, sampleid)
	if not os.path.exists(sample_dir):
		os.mkdir(sample_dir)
	fusionhtml = os.path.join(sample_dir, laneid + '-' + sampleid + '.fusion.html')
	copyfile='cp  '+rawhtml+' '+fusionhtml
	os.system(copyfile)



def fusion_runmain(inputpath,tumorname):
	laneid = inputpath.split('/')[-1].split('-')[-1]
	datasummarydir = os.path.join(inputpath, 'datasummary')
	isExists = os.path.exists(datasummarydir)
	if not isExists:
		os.makedirs(datasummarydir)
	sampledir = os.path.join(inputpath, laneid + '_sample_infor_label.txt')
	samplelist = pd.read_table(sampledir, sep='\t', header=0)
	sampledata = samplelist[samplelist['tumor'] ==tumorname]
	sampledata.reset_index(drop=True, inplace=True)
	sampleid = sampledata.loc[0, 'samplename']
	fusion_GeneFusion_sample(inputpath, laneid, tumorname, sampleid)
	cp_fusionhtml(inputpath, sampleid, tumorname, laneid)



if __name__=='__main__':
	parser = argparse.ArgumentParser(description='filter the chemothereapy_runmain')
	parser.add_argument('-i', '--inputpath', type=str, help='the path of lane')
	parser.add_argument('-s', '--tumorname', type=str, help='the tumor name of sample')
	args = parser.parse_args()
	Inputpath = args.inputpath
	Tumorname = args.tumorname
	fusion_runmain(Inputpath,Tumorname)


#for the result of GeneFusion
def fusion_GeneFusion_sum(inputpath,laneid):
	datasummarydir = os.path.join(inputpath, 'datasummary')
	isExists = os.path.exists(datasummarydir)
	if not isExists:
		os.makedirs(datasummarydir)
	sampledir = os.path.join(inputpath, laneid + '_sample_infor_label.txt')
	samplelist = pd.read_table(sampledir, sep='\t', header=0)
	genefusionsummary_last = pd.DataFrame()
	for i in range(len(samplelist)):
		sampleid = samplelist.loc[i,'samplename']
		tumorname = samplelist.loc[i, 'tumor']
		print(sampleid)
		fusionresult2=fusion_GeneFusion_sample(inputpath,laneid,tumorname,sampleid)
		genefusionsummary_last = genefusionsummary_last.append(fusionresult2)
		cp_fusionhtml(inputpath, sampleid, tumorname, laneid)
	outputname = datasummarydir + '/' + laneid+'_table8_fusion_datasummary.txt'
	genefusionsummary_last.to_csv(outputname, index=False, header=True, encoding='gbk', sep='\t')