#! /usr/bin/env python
from argparse import RawTextHelpFormatter
import glob,os, sys, time, argparse,shutil,re,csv
import numpy as np
import subprocess
from Bio import SeqIO
from sklearn.cluster import AffinityPropagation
import collections
from Bio.SeqUtils import GC
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
import pandas

def create_fasta_dict(fastafile):
    """makes dictionary using fasta files to be binned"""
    fasta_dict = {}
    for record in SeqIO.parse(fastafile, "fasta"):
        fasta_dict[record.id] = record.seq
    return fasta_dict

def get_cov_data(h):
    """Makes a dictionary of coverage values for affinity propagation"""
    all_cov_data = {}
    for line in open(str(h), "r"):
        line = line.rstrip()
        cov_data = line.split()
        all_cov_data[cov_data[0]] = cov_data
    return all_cov_data

def cov_array(a,path,file_name,size):
    """Computes a coverage array based on the contigs in files and the contig names associated with the coverage file"""
    count = 0
    names = []
    c = 0
    cov_array = []
    for record in SeqIO.parse(os.path.join(path, file_name), "fasta"):
        count = count + 1
        if len(record.seq) >= int(size):
            if record.id in a.keys():
                data = a[record.id]
                names.append(data[0])
                data.remove(data[0])
                line = " ".join(data)
                if c == 1:
                    temparray = np.fromstring(line, dtype=float, sep=' ')
                    cov_array = np.vstack((cov_array, temparray))
                if c == 0:
                    cov_array = np.fromstring(line, dtype=float, sep=' ')
                    c += 1 
    print "          %s" % (cov_array.shape,)
    return cov_array, names  

def affinity_propagation(array,names,file_name,damping,iterations,convergence,preference,path,output_directory,binname):
    """Uses affinity propagation to make putative bins"""
    apclust = AffinityPropagation(damping=float(damping), max_iter=int(iterations), convergence_iter=int(convergence), copy=True, preference=int(preference), affinity='euclidean', verbose=False).fit_predict(array)
    outfile_data = {}
    i = 0
    while i < len(names):
        if apclust[i] in outfile_data.keys():
            outfile_data[apclust[i]].append(names[i])
        if apclust[i] not in outfile_data.keys():
            outfile_data[apclust[i]] = [names[i]]
        i += 1
    if binname is None:
    	out_name = file_name.rsplit(".",1)[0]+"_Bin"
    else:
    	out_name = binname+"_Bin"
    output_directory=output_directory+"/BINSANITY-INITIAL"
    os.mkdir(output_directory)
    with open(os.path.join(path,file_name),"r") as input2_file: 
        fasta_dict = create_fasta_dict(input2_file)                
        count = 0                
        for k in outfile_data:
            if len(outfile_data[k]) >= 5:
                output_file = open(os.path.join(output_directory,str(out_name)+"-%s.fna" % (k)), "w")
                for x in outfile_data[k]:
                    output_file.write(">"+str(x)+"\n"+str(fasta_dict[x])+"\n")
                output_file.close()
                count = count + 1
            elif len(outfile_data[k]) < 5 and len(outfile_data[k]) > 1:
                if any((len(fasta_dict[x])>50000) for x in outfile_data[k]):
                    output_file = open(os.path.join(output_directory,str(out_name)+"-bin_%s.fna" % (k)), "w")
                    for x in outfile_data[k]:
                        output_file.write(">"+str(x)+"\n"+str(fasta_dict[x])+"\n")
                    output_file.close()
                    count = count +1
            else:
                output_file=open(os.path.join(output_directory,'unclustered.fna'),'a')
                for x in outfile_data[k]:
                    output_file.write(">"+str(x)+"\n"+str(fasta_dict[x])+"\n")
                output_file.close()
            if os.path.isfile(os.path.join(output_directory,'unclustered.fna')) is True:
                contig_number = 0
                for record in SeqIO.parse(os.path.join(output_directory,'unclustered.fna'),"fasta"):
                        contig_number +=1
                if contig_number < 2:
                        os.remove(os.path.join(output_directory,'unclustered.fna'))
            print "          Cluster "+str(k)+": "+str(len(outfile_data[k]))
        print ("""          Total Number of Bins: %i""" % count)
    
def GC_Fasta_file(b,name,prefix,location):
    """Makes a tab-delimeted output indicating the GC count associated with each contig"""
    GC_dict = {}
    input_file = open(os.path.join(b,name), 'r') 
    output_file = open(os.path.join(location,str(prefix)+'_GC_count.txt'),'w')
    output_file.write("%s\t%s\n" % ('contig','GC'))
    for cur_record in SeqIO.parse(input_file, "fasta") :
        gene_name = cur_record.id 
        GC_percent = float(GC(cur_record.seq))
        GC_dict.setdefault(gene_name,[])
        GC_dict[gene_name].append(GC_percent) 
        output_line = '%s\t%i\n' % \
        (gene_name, GC_percent) 
        output_file.write(output_line)    
    output_file.close() 
    input_file.close()
###########################Get tetramer frequencies#####################################

def kmer_list(dna, k):
    """Makes list of k-mers based on input of k and the dna sequence"""
    result = []
    dna = dna.upper()	
    dna_edit = Seq(str(dna),IUPAC.unambiguous_dna)
    reverse_complement = (dna_edit).reverse_complement()
    dna_edit = str(dna_edit)
    reverse_complement = str(reverse_complement)
    for x in range(len(dna)+1-k):
        result.append(dna[x:x+k])
    for x in range(len(reverse_complement)+1-k):
        result.append(reverse_complement[x:x+k])
    result= [s for s in result if not s.strip('AGTC')]
    return result

def kmer_counts(kmer,b,name):
    kmer_dict = {}
    for record in SeqIO.parse(os.path.join(b,name), "fasta"):
        id_=record.id
        seq = record.seq
        length = len(seq)
        tetra = kmer_list(seq,kmer)           
        c = collections.Counter(tetra)
        c = dict(c)
        val = list(c.values())
        val_edit = []
        for freq in val:
            freq= (float(freq)/float(length))
            val_edit.append(freq)
    
        keys = []
        for key in c:
            keys.append(str(key))
        c_edit = dict(zip(keys,val_edit))
        kmer_dict.setdefault(str(id_),[])
        kmer_dict[str(id_)].append(c_edit)
    return kmer_dict
                   
         
def output(a,prefix,kmer,location):
    """Builds tab-delimeted file based on dictionary of tetramers"""
    topleft = 'contig' 
    headers = sorted(set(key
                        for row in a.values()
                        for key in row[0]))
    name = str(kmer)
    writer = csv.writer(open(os.path.join(location,str(prefix)+'_%smer_frequencies.txt'%(name)),'wb'), delimiter='\t')
    writer.writerow([topleft] + headers)
    for key in a:
        row = [key]
        for header in headers:
            row.append(a[key][0].get(header, 0))
        writer.writerow(row)
#########################Combine tetramer frequencies and GC counts in tab delimeted format and normalize################################
def get_contigs(c,prefix,kmer,location):
    GC = open(os.path.join(location,str(prefix)+"_GC_count.txt"),"r")
    dataframe =pandas.read_csv(GC,sep="\t")
    dataframe=dataframe.set_index("contig")
    dataframe *= 100
    dataframe += 1
    dataframe = np.log10(dataframe)
    name = str(kmer)
    tetra = open(os.path.join(location,str(prefix)+'_%smer_frequencies.txt'%(name)),"r")
    dataframe2=pandas.read_csv(tetra,sep="\t")
    dataframe2=dataframe2.set_index("contig")
    dataframe2*=100
    dataframe2+=1
    dataframe2 = np.log10(dataframe2)
    cov = open(str(c),"r")
    reader =list(csv.reader(cov,delimiter='\t'))
    titles = ["contig"]
    reader.insert(0,titles)
    dataframe3=pandas.DataFrame(reader)
    dataframe3.columns = dataframe3.iloc[0]
    dataframe3 = dataframe3[1:]
    dataframe3=dataframe3.set_index("contig")
    result = pandas.concat([dataframe, dataframe2,dataframe3],axis=1,join='inner')
    result.to_csv(path_or_buf=os.path.join(location,str(prefix)+"_kmerGC.txt"),header=None,sep ="\t")

def refined_ap(array,names,file_name,damping,iterations,convergence,preference,path,output_directory):
    """Uses affinity propagation to make putative bins"""    
    if os.path.isdir(os.path.join(str(output_directory),"REFINED-BINS")) is False:
        os.mkdir(os.path.join(str(output_directory),"REFINED-BINS"))
    name_of_output_file = os.path.join(str(output_directory),"REFINED-BINS")
    apclust = AffinityPropagation(damping=float(damping), max_iter=int(iterations), convergence_iter=int(convergence), copy=True, preference=int(preference), affinity='euclidean', verbose=False).fit_predict(array)
    outfile_data = {}
    i = 0
    while i < len(names):
        if apclust[i] in outfile_data.keys():
            outfile_data[apclust[i]].append(names[i])
        if apclust[i] not in outfile_data.keys():
            outfile_data[apclust[i]] = [names[i]]
        i += 1
    out_name = file_name.rsplit(".",1)[0]
    with open(os.path.join(path,file_name),"r") as input2_file: 
        fasta_dict = create_fasta_dict(input2_file)                
        count = 0                
        for k in outfile_data:
            if len(outfile_data[k]) >= 5:
                output_file = open(os.path.join(name_of_output_file,str(out_name)+"-refined_%s.fna" % (k)), "w" )
                for x in outfile_data[k]:
                    output_file.write(">"+str(x)+"\n"+str(fasta_dict[x])+"\n")
                output_file.close()
                count = count + 1
            elif len(outfile_data[k]) < 5 and len(outfile_data[k]) > 1:
                if any((len(fasta_dict[x])>50000) for x in outfile_data[k]):
                    output_file = open(os.path.join(output_directory,str(out_name)+"-bin_%s.fna" % (k)), "w")
                    for x in outfile_data[k]:
                        output_file.write(">"+str(x)+"\n"+str(fasta_dict[x])+"\n")
                    output_file.close()
                    count = count +1
            else:
                output_file=open(os.path.join(output_directory,'unclustered.fna'),'a')
                for x in outfile_data[k]:
                    output_file.write(">"+str(x)+"\n"+str(fasta_dict[x])+"\n")
                output_file.close()
            if os.path.isfile(os.path.join(output_directory,'unclustered.fna')) is True:
                contig_number = 0
                for record in SeqIO.parse(os.path.join(output_directory,'unclustered.fna'),"fasta"):
                        contig_number +=1
                if contig_number < 2:
                        os.remove(os.path.join(output_directory,'unclustered.fna'))
            print "Cluster "+str(k)+": "+str(len(outfile_data[k]))
        print ("""Total Number of Bins: %i""" % count)
    
########################################################################################
def run_checkM(bins,threads,prefix,directory):
    output = open(os.path.join(directory,str(prefix)+"_checkm_lineagewf-results.txt"),"w")   
    subprocess.call(["checkm","lineage_wf","-x","fna","-t",str(threads),str(bins),os.path.join(directory,str(prefix)+"_binsanity_checkm")],stdout=output)
########################################################################################
def checkm_analysis(file_,fasta,path):
    checkm = list(csv.reader(open(file_,'rb')))
    new = []
    for list_ in checkm:
        for string in list_:
            x = re.sub(' +',' ',str(re.split(r'\t+', string.rstrip('\t'))))
            new.append(x)
    
    del new[0], new[1], new[(len(new)-1)]
    new_2 = []
    for list_ in new:
        x = list_.strip("['']")
        x_2 = x.split()
        new_2.append(x_2)
    del new_2[0]
    High_completion = []
    Low_completion = []
    High_redundancy = []
    Strain_variation = []
    for list_ in new_2:
        if float(list_[12]) >95 and (float(list_[13])<10):
            High_completion.append(list_[0])
        elif float(list_[12]) > 80 and (float(list_[13])<=5):
            High_completion.append(list_[0])
        elif float(list_[12]) > 50 and (float(list_[13])<=2):
            High_completion.append(list_[0])
        elif float(list_[12]) < 50:
            Low_completion.append(list_[0])
        elif float(list_[12])>80 and (float(list_[13])>10) and (float(list_[13])<=50):
            Low_completion.append(list_[0])
        elif float(list_[13])>50 and float(list_[14])>90:
            Strain_variation.append(list_[0])
        else:
            High_redundancy.append(list_[0])

    os.makedirs(os.path.join(path,"high_completion"))
    os.makedirs(os.path.join(path,"low_completion"))
    os.makedirs(os.path.join(path,"high_redundancy"))
    os.makedirs(os.path.join(path,"strain_redundancy"))
    
    for name in High_completion:
        shutil.move(os.path.join(path,(str(name)+fasta)), os.path.join(path,"high_completion"))
    for name in Low_completion:
        shutil.move(os.path.join(path,(str(name)+fasta)),os.path.join(path,"low_completion"))
    for name in High_redundancy:
        shutil.move(os.path.join(path,(str(name)+fasta)),os.path.join(path,"high_redundancy"))
    for name in Strain_variation:
        shutil.move(os.path.join(path,(str(name)+fasta)),os.path.join(path,"strain_redundancy"))
########################################################################################     
class Logger(object):
    def __init__(self,logfile,location):
        self.terminal = sys.stdout
        if logfile is None:
        	self.log = open(os.path.join(location,"Binsanity-wf.log"), "a")
	else:
		self.log = open(os.path.join(location,logfile+".log"), "a")
    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)

    def flush(self):
        pass
###################################################################

if __name__ == '__main__':
    parser = argparse.ArgumentParser(prog='Binsanity-wf',usage='%(prog)s -f [/path/to/fasta] -l [FastaFile] -c [CoverageFile] -o [OutputDirectory]', description="""
    ************************************************************************************************
    **************************************BinSanity*************************************************
    **  Binsanity-wf is a workflow script that runs Binsanity and Binsanity-refine sequentially.  **
    **  The following is including in the workflow:                                               ** 
    **  STEP 1. Run Binsanity                                                                     **
    **  STEP 2: Run CheckM to estimate completeness for Refinement                                ** 
    **  STEP 3: Run Binsanity-refine                                                              **
    **  STEP 4: Create Final BinSanity Clusters                                                   **
    **                                                                                            **  
    ************************************************************************************************
    """,formatter_class=RawTextHelpFormatter)     
    parser.add_argument("-c", dest="inputCovFile", help="""
    Specify a Transformed Coverage File
    e.g Log transformed
    """)
    parser.add_argument("-f", dest="inputContigFiles",metavar="FastaLocation", help="""Specify directory containing your contigs""")
    parser.add_argument("-p", type=float, dest="preference", default=-3, help="""Specify a preference [Default: -3]
    Note: decreasing the preference leads to more lumping, 
    increasing will lead to more splitting. If your range
    of coverages are low you will want to decrease the
    preference, if you have 10 or less replicates increasing
    the preference could benefit you.""")
    parser.add_argument("-m", type=int, dest="maxiter", default=4000, help="""
    Specify a max number of iterations [Default: 4000]""")
    parser.add_argument("-v", type=int, dest="conviter",metavar="ConvergenceIteration",default=400, help="""Specify the convergence iteration number [Default: 400]
    e.g Number of iterations with no change in the number 
    of estimated clusters that stops the convergence.""")
    parser.add_argument("-d",default=0.95, type=float, dest="damp",metavar="DampeningFactor", help="""Specify a damping factor between 0.5 and 1, [Default: 0.95]""")
    parser.add_argument("-l",dest="fastafile",metavar="FastaFile", help="""Specify the fasta file containing contigs you want to cluster""")
    parser.add_argument("-x",dest="ContigSize", type=int,metavar="SizeThreshold", default=1000,help="""Specify the contig size cut-off [Default: 1000 bp]""")
    parser.add_argument("-o",dest="outputdir", default="BINSANITY-RESULTS", metavar="OutputDirectory", help="""Give a name to the directory BinSanity results will be output in 
    [Default: 'BINSANITY-RESULTS']""")
    parser.add_argument("--threads",dest="threads",type=int,default=1, help="""Indicate how many threads you want dedicated to the subprocess CheckM. [Default=1]""")
    parser.add_argument("--kmer",dest="kmer",type=int,default=4,help="""Indicate a number for the kmer calculation, the [Default: 4]""")
    parser.add_argument("--Prefix",dest="prefix",default="BinSanityWf", help="""Specify a prefix to append to the start of all files generated during Binsanity""")
    parser.add_argument("--refine-preference", dest="inputrefinedpref", type=float,default=-25,help="""Specify a preference for refinement. [Default: -25]""")
    parser.add_argument("--binPrefix",dest="binprefix",default=None,metavar="BinPrefix",help="""Sepcify what prefix you want appended to final Bins {optional}""")
    parser.add_argument('--version', action='version', version='%(prog)s v0.2.6.2')
    args = parser.parse_args()
    if len(sys.argv) is None:
        parser.print_help()
    if os.path.isfile("high_completion"):
        print "File name 'high_completion' already exists and Binsanity does not like overwritting files"      
    if os.path.isfile("low_completion"):
        print "File name 'low_completion' already exists and Binsanity does not like overwritting files"
    if os.path.isfile("high_redundancy"):
        print "File name 'high_redundancy' already exists and Binsanity does not like overwritting files"
    if os.path.isfile("strain_redundancy"):
        print " File name 'strain_redundancy' already exists and Binsanity does not like overwritting files"
    elif len(sys.argv)<4:
        parser.print_help()
    elif (args.inputCovFile is None):
        print "Please indicate -c coverage file"
    elif args.inputContigFiles is None:
        print "Please indicate -f directory containing your contigs"
    elif args.inputContigFiles and not args.fastafile:
        parser.error('-l Need to identify file to be clustered')
    else:
        start_time = time.time()

        if os.path.isdir(str(args.outputdir)) is False:
        	os.mkdir(args.outputdir)
        sys.stdout = Logger(args.prefix,args.outputdir)
        print """
        ******************************************************
        **********************BinSanity***********************
        |____________________________________________________|
        |                                                    | 
        |             Computing Coverage Array               | 
        |____________________________________________________|
        """
        print "          Preference: " + str(args.preference)
        print "          Maximum Iterations: " + str(args.maxiter)
        print "          Convergence Iterations: " + str(args.conviter)
        print "          Contig Cut-Off: " + str(args.ContigSize)
        print "          Damping Factor: " + str(args.damp)
        print "          Coverage File: " + str(args.inputCovFile)
        print "          Fasta File: " + str(args.fastafile)
        print "          Output Directory: " + str(args.outputdir)
        
        val1, val2 = cov_array((get_cov_data(args.inputCovFile)), args.inputContigFiles, args.fastafile,args.ContigSize)

        print """
         ______________________________________________________   
        |                                                      |
        |                 Clustering Contigs                   |
        |______________________________________________________|
        
        """ 
        affinity_propagation(val1,val2,args.fastafile,args.damp,args.maxiter,args.conviter,args.preference,args.inputContigFiles,args.outputdir,args.binprefix)
        print
        """
         *|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*
         _____________________________________________________
        |                                                     |
        |                   Creating Bins                     |
        |_____________________________________________________|
        """  

        print("""
         _____________________________________________________
                                                               
                       Putative Bins Computed                 
                       in %s seconds                          
         _____________________________________________________""" % (time.time() - start_time))
##########################Finding Bin Metrics####################################
    
        print ("""
         _____________________________________________________
        |                                                     |
        |       Evaluating Genome With CheckM Lineage_wf      |
        |_____________________________________________________|
        """)
        out_1= args.outputdir+'/BINSANITY-INITIAL'
        run_checkM(str(out_1),str(args.threads),args.prefix,args.outputdir)
        checkm_analysis(os.path.join(args.outputdir,str(args.prefix)+"_checkm_lineagewf-results.txt"),".fna",str(out_1))
##############################Refining Bins#######################################
        start_time = time.time()

        location = os.path.join(out_1,"high_redundancy")
        location2 = os.path.join(out_1,"low_completion")
        with open("low_completion.fna","wb") as outfile:
            for filename in glob.glob(str(location2)+"/*.fna"):
                if filename == "low_completion.fna":
                    continue
                else:
                    with open(filename,"rb") as readfile:
                        shutil.copyfileobj(readfile,outfile)
        if os.stat("low_completion.fna").st_size ==0:
            os.remove("low_completion.fna")
        else:
            shutil.move("low_completion.fna", str(location))
            shutil.rmtree(str(location2))
	print """
        *|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*|*
    	 _____________________________________________________
    	|                                                     |
    	|                Refining Inital Bins                 |
    	|_____________________________________________________|"""
        for redundant_bin in os.listdir(location):
            print """
             ______________________________________________________
                                                                   
                          Calculating GC content for               
                          redundant bin %s                    
             ______________________________________________________""" % str(redundant_bin)
            GC_time = time.time()
            GC_Fasta_file(location, redundant_bin,args.prefix,args.outputdir)
            print "          GC content calculated in %s seconds" % (time.time() - GC_time)
            
            print """
             ______________________________________________________
                                                                   
                       Calculating %smer frequencies for              
                       redundant bin %s                            
             ______________________________________________________"""% (args.kmer,str(redundant_bin))
            kmer_time = time.time()
            output(kmer_counts(args.kmer,location,redundant_bin),args.prefix,args.kmer,args.outputdir)
            print "          %smer frequency calculated in %s seconds" % (args.kmer,time.time()- kmer_time)
         
            print """
             ______________________________________________________
                                                                  
                          Creating Profile for                     
                          redundant bin %s                         
             ______________________________________________________""" % str(redundant_bin)
            combine_time = time.time()
            get_contigs(args.inputCovFile,args.prefix,args.kmer,args.outputdir)
            print "          Combined profile created in %s seconds" % (time.time()- combine_time)
            refine_time=time.time()
  	    print """ 
             ______________________________________________________ 
	                                                           
               Reclustering redundant bin %s                       
             ______________________________________________________"""% str(redundant_bin)
            print "          Preference: " + str(args.inputrefinedpref)
            print "          Maximum Iterations: " + str(args.maxiter)
            print "          Convergence Iterations: " + str(args.conviter)
            print "          Contig Cut-Off: " + str(args.ContigSize)
            print "          Damping Factor: " + str(args.damp)
            print "          Coverage File: " + str(args.inputCovFile)
            print "          Fasta File: " + str(redundant_bin)
            print "          Kmer: " + str(args.kmer)
    
            val1, val2 = cov_array((get_cov_data(os.path.join(args.outputdir,str(args.prefix)+'_kmerGC.txt'))), location, redundant_bin,args.ContigSize)
    
            refined_ap(val1, val2, redundant_bin, args.damp, args.maxiter, args.conviter, args.inputrefinedpref,location,args.outputdir)
    
            print("""
             ______________________________________________________
                                                                   
              Ammended Bins Computed in %s seconds                 
            ______________________________________________________""" % (time.time() - refine_time))
        final_out = os.path.join(str(args.outputdir),"BinSanity-Final-bins")
        initial_out = os.path.join(str(args.outputdir),"BINSANITY-INITIAL")
        os.mkdir(str(final_out))
        os.rename(initial_out,os.path.join(str(args.outputdir),"Binsanity-records"))
        shutil.move(os.path.join(str(args.outputdir),"REFINED-BINS"),os.path.join(str(args.outputdir),"Binsanity-records"))
        path1 = os.path.join(args.outputdir,"Binsanity-records/high_completion")
        for files in os.listdir(path1):
            shutil.copy(os.path.join(os.path.join(str(args.outputdir),"Binsanity-records/high_completion"),files),str(final_out))
        for files in os.listdir(os.path.join(str(args.outputdir),"Binsanity-records/REFINED-BINS")):
            shutil.copy(os.path.join(os.path.join(str(args.outputdir),"Binsanity-records/REFINED-BINS"),files),str(final_out))
	shutil.move(os.path.join(args.outputdir,str(args.prefix)+"_kmerGC.txt"),os.path.join(str(args.outputdir),"Binsanity-records/"))
        shutil.move(os.path.join(args.outputdir,str(args.prefix)+"_%smer_frequencies.txt"%(args.kmer)),os.path.join(str(args.outputdir),"Binsanity-records/"))
        shutil.move(os.path.join(args.outputdir,str(args.prefix)+"_GC_count.txt"),os.path.join(str(args.outputdir),"Binsanity-records/"))
        shutil.move(os.path.join(args.outputdir,str(args.prefix)+"_checkm_lineagewf-results.txt"),os.path.join(str(args.outputdir),"Binsanity-records/"))
        shutil.move(os.path.join(args.outputdir,str(args.prefix)+"_binsanity_checkm"),os.path.join(str(args.outputdir),"Binsanity-records/"))
        
