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

__author__ = "Elaina Graham"
__license__ = "GPL 3.0"
__maintainer__ = "Elaina Graham"
__email__ = "egraham147@gmail.com"
#########################
def make_tarfile(output_filename, source_dir):
    with tarfile.open(output_filename, "w:gz") as tar:
        tar.add(source_dir, arcname=os.path.sep)
    shutil.rmtree(source_dir)
###################
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 = {}
    inf = open(str(h),"r")
    for line in inf:
        line = line.rstrip()
        cov_data = line.split()
        all_cov_data[cov_data[0]] = cov_data
    inf.close()
    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):
    """Uses affinity propagation to make putative bins"""
    if os.path.isdir(str(output_directory)) is False:
        os.mkdir(output_directory)
    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]
    output_directory = output_directory+"/BINSANITY-INITIAL"
    if os.path.isdir(output_directory) is False:
        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)+"-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
            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"%int(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()
############################
def kmean(array, names, filename, path, clusters, path2, prefix, threads):
    os.mkdir(os.path.join(path, str(prefix)+"-KMEAN-BINS"))
    average_linkage = KMeans(n_clusters=int(
        clusters), n_jobs=threads, algorithm="full", n_init=1000, max_iter=5000)
    apclust = average_linkage.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
    with open(os.path.join(path2, filename), "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(os.path.join(
                    path, str(prefix)+"-KMEAN-BINS"), "%s-kmean-bin_%s.fna" % (prefix, 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(os.path.join(
                        path, str(prefix)+"-KMEAN-BINS"), "%s-kmean-bin_%s.fna" % (prefix, 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(os.path.join(path, str(
                    prefix)+"-KMEAN-BINS"), "%s-kmean-unclustered.fna" % str(prefix)), "w")
                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(os.path.join(path, str(prefix)+"-KMEAN-BINS"), "%s-kmean-unclustered.fna" % str(prefix))) is True:
                count = 0
                for record in SeqIO.parse(os.path.join(os.path.join(path, str(prefix)+"-KMEAN-BINS"), "%s-kmean-unclustered.fna" % str(prefix)), "fasta"):
                    count=count+1
                if count < 2:
                    os.remove(os.path.join(os.path.join(
                        path, str(prefix)+"-KMEAN-BINS"), "%s-kmean-unclustered.fna" % str(prefix)))
            print("          Cluster "+str(k)+": "+str(len(outfile_data[k])))
#        print("         Total Number of Bins: %i"%int(count))
#########################
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))
    reverse_complement = (dna_edit).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(str(key)
                         for row in a.values()
                         for key in row[0]))
    writer = csv.writer(open(os.path.join(location, str(
        prefix)+'-%smer-frequencies.txt' % (str(kmer))), 'w'), 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 = pd.read_csv(GC, sep="\t")
    dataframe = dataframe.set_index("contig")
    dataframe *= 100
    dataframe += 1
    dataframe = np.log10(dataframe)
    tetra = open(os.path.join(location, str(prefix) +
                              "-%smer-frequencies.txt" % (str(kmer))), "r")
    dataframe2 = pd.read_csv(tetra, sep="\t")
    dataframe2 = dataframe2.set_index("contig")
    dataframe2 *= 100
    dataframe2 += 1
    dataframe2 = np.log10(dataframe2)
    cov = open(str(c), "r")
    dataframe3 = pd.read_csv(cov, sep="\t", header=None, index_col=0)
    dataframe3.index.name = 'contig'
    result = pd.concat([dataframe, dataframe2, dataframe3],
                       axis=1, join='inner')
    result.to_csv(path_or_buf=str(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, prefix):
    """Uses affinity propagation to make putative bins"""
    if os.path.isdir(os.path.join(str(output_directory), str(prefix)+"-REFINED-BINS")) is False:
        os.mkdir(os.path.join(str(output_directory),
                              str(prefix)+"-REFINED-BINS"))
    name_of_output_file = os.path.join(
        str(output_directory), str(prefix)+"-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:
                if any((len(fasta_dict[x]) > 50000) for x in outfile_data[k]):
                    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
            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"%int(count))
########################################################################################
def run_checkM(bins, threads, prefix, directory):
    output = str(os.path.join(directory, str(
        prefix)+"_checkm_lineagewf-results.txt"))
    logfile = open(
        str(os.path.join(directory, str(prefix)+".checkm.logfile")), "w")
    subprocess.call(["checkm", "lineage_wf", "--tab_table", "-f", str(output), "-x", "fna", "-t", str(
        threads), str(bins), str(os.path.join(directory, str(prefix)+"_binsanity_checkm"))], stdout=logfile)
    logfile.close()
########################################################################################
def checkm_analysis(file_, fasta, path, prefix):
    df = pd.read_csv(file_, sep="\t")
    highCompletion = list(set(list(df.loc[(df['Completeness'] >= 95) & (df['Contamination'] <= 10), 'Bin Id'].values) + list(df.loc[(df['Completeness'] >= 80) & (
        df['Contamination'] <= 5), 'Bin Id'].values)+list(df.loc[(df['Completeness'] >= 50) & (df['Contamination'] <= 2), 'Bin Id'].values)))
    lowCompletion = list(set(df.loc[(df['Completeness'] <= 50) & (
        df['Contamination'] <= 2), 'Bin Id'].values))
    strainRedundancy = list(set(df.loc[(df['Completeness'] >= 50) & (
        df['Contamination'] >= 10) & (df['Strain heterogeneity'] >= 90), 'Bin Id'].values))
    all = df['Bin Id'].tolist()
    highRedundancy = np.setdiff1d(all, (highCompletion+lowCompletion+strainRedundancy))
    if os.path.isdir("high_completion") is False:
        os.makedirs(os.path.join(path, str(prefix)+"_high_completion"))
    if os.path.isdir("low_completion") is False:
        os.makedirs(os.path.join(path, str(prefix)+"_low_completion"))
    if os.path.isdir("high_redundancy") is False:
        os.makedirs(os.path.join(path, str(prefix)+"_high_redundancy"))
    if os.path.isdir("strain_redundancy") is False:
        os.makedirs(os.path.join(path, str(prefix)+"_strain_redundancy"))
    for name in highCompletion:
        shutil.move(os.path.join(path, (str(name)+fasta)),
                    os.path.join(path, str(prefix)+"_high_completion"))
    for name in lowCompletion:
        shutil.move(os.path.join(path, (str(name)+fasta)),
                    os.path.join(path, str(prefix)+"_low_completion"))
    for name in highRedundancy:
        shutil.move(os.path.join(path, (str(name)+fasta)),
                    os.path.join(path, str(prefix)+"_high_redundancy"))
    for name in strainRedundancy:
        shutil.move(os.path.join(path, (str(name)+fasta)),
                    os.path.join(path, str(prefix)+"_strain_redundancy"))
########################################################################################
class Logger(object):
    def __init__(self, filename, location):
        self.filename = filename
        out = filename.rsplit(".", 1)[0]
        self.terminal = sys.stdout
        self.log = open(os.path.join(
            location, str(out)+"-BinsanityLC-log.txt"), "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-lc', usage='%(prog)s -f [/path/to/fasta] -l [fastafile] -c [coverage file] -o [output directory]', description="""
    ************************************************************************************************
    **************************************BinSanity*************************************************
    **    Binsanity-lc is a workflow script that will subset assemblies larger than 100,000       ** 
    **    contigs using coverage prior to running Binsanity and Binsanity-refine sequentially.    **
    **    The following is including in the workflow:                                             **
    **       Step 1: Use Coverage to Subsample contigs with K-mean Clustering                     **
    **       STEP 2: Run Binsanity                                                                **
    **       STEP 3: Run CheckM to estimate completeness for Refinement                           **
    **       STEP 4: Run Binsanity-refine                                                         **
    **       STEP 5: Creat Final BinSanity Clusters                                               **
    **                                                                                            **
    ************************************************************************************************
    """, formatter_class=RawTextHelpFormatter)
    parser.add_argument("-c", metavar="CoverageFile", dest="inputCovFile", help="""Specify a Coverage File
    """)
    parser.add_argument("-f", metavar="FastaLocation", dest="inputContigFiles", help="""Specify directory containing Fasta File to be clustered
    """)
    parser.add_argument("-p", type=float, metavar="Preference", 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, metavar="MaximumIterations", dest="maxiter", default=4000, help="""Specify a max number of iterations [Default: 4000]
    """)
    parser.add_argument("-v", type=int, metavar="ConvergenceIterations", dest="conviter", 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, metavar="DampeningFactor", type=float, dest="damp", help="""Specify a damping factor between 0.5 and 1 [Default: 0.95]
    """)
    parser.add_argument("-l", dest="fastafile", metavar="FastaFile Name", help="""Specify the fasta file containing contigs you want to cluster
    """)
    parser.add_argument("-x", dest="ContigSize", type=int, metavar="SizeCutOff", default=1000, help="""Specify the contig size cut-off [Default:1000 bp]
    """)
    parser.add_argument("-o", dest="outputdir", metavar="Output Directory", default="BINSANITY-RESULTS", help="""Give a name to the directory BinSanity results will be output in 
    [Default:'BINSANITY-RESULTS']
    """)
    parser.add_argument("--checkm_threads", dest="threads", metavar="Threads", type=int, default=1, help="""Indicate how many threads you want dedicated to the subprocess CheckM [Default: 1]
    """)
    parser.add_argument("--kmean_threads",dest="kmeanthread",metavar="kmeanThread",type=int,default=1,help="""Indicate how many threads you want dedicated to kmeans clustering
    [Default: 1]""")
    parser.add_argument("--kmer", dest="kmer", metavar="Kmer", type=int, default=4, help="""Indicate a number for the kmer calculation [Default: 4]
    """)
    parser.add_argument("--refine-preference", metavar="", dest="inputrefinedpref", type=float, default=-25, help="""Specify a preference for refinement [Default: -25]
    """)
    parser.add_argument("-C", dest="cluster", metavar="ClusterNumber", type=int, default=100, help="""Indicate a number of initial clusters for kmean [Default:100]
    """)
    parser.add_argument("--Prefix", dest="prefix", metavar="Prefix", default="BinSanityLC", help="""Specify a prefix to append to the start of all directories generated during Binsanity
    """)
    parser.add_argument('--version', action='version',
                        version='%(prog)s v0.4.4')
    args = parser.parse_args()
    if len(sys.argv) is None:
        parser.print_help()
    if os.path.isfile(os.path.join(args.outputdir, "KMEAN-BINS")):
        print("File name 'KMEAN-RESULTS' already exists and Binsanity does not like overwritting files")
    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:
        if os.path.isdir(str(args.outputdir)) is False:
            os.mkdir(args.outputdir)
        sys.stdout = Logger(args.prefix, args.outputdir)
        print("""
        ******************************************************
        *******************BinSanity-lc***********************
        |____________________________________________________|
        |                                                    |
        |             Computing Coverage Array               |
        |____________________________________________________|
        """)
        print("          K-Mean cluster number: " + str(args.cluster))
        print("          Fasta File: " + str(args.fastafile))
        print("          Coverage File: " + str(args.inputCovFile))
        print("          Fasta File: " + str(args.fastafile))
        print("          Output Directory: " + str(args.outputdir))
        print("          Contig Cut-Off: " + str(args.ContigSize))

        try:
            val1, val2 = cov_array((get_cov_data(
                args.inputCovFile)), args.inputContigFiles, args.fastafile, args.ContigSize)
        except:
            print(
                "The program failed to read in your coverage file :(. Please check it to make sure it is in the right format.")
            with open("error_code.txt", "w") as out_file:
                out_file.write("1")
            sys.exit(1)
        print("""
         ____________________________________________________
        |                                                    |
        |        Initializing clustering via K-means         |
        |____________________________________________________|
         """)
        try:
            kmean(val1, val2, args.fastafile, args.outputdir, args.cluster,args.inputContigFiles, args.prefix, args.threads)
        except:
            print("The program ran into an error and failed to complete the initial K-means clustering. Check to be sure you don't already have existing Binsanity Outputs in your specified output directory. If you do not then Binsanity likely failed due to a memory or computation issue :(")
            with open("error_code.txt", "w") as out_file:
                out_file.write("2")
            sys.exit(1)
        location_kmean = os.path.join(
            args.outputdir, str(args.prefix)+"-KMEAN-BINS")
        path, dirs, files = next(os.walk(location_kmean))
        count = 1
        for clust in os.listdir(location_kmean):
            start_time = time.time()
            print("""
            ____________________________________________________

             Clustering Bin  %s
             via Affinity Propagation
            ____________________________________________________"""%clust)

            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(clust))
            print("          Output Directory: " + str(args.outputdir))

            try:
                val3, val4 = cov_array(
                    (get_cov_data(args.inputCovFile)), location_kmean, clust, args.ContigSize)
                affinity_propagation(val3, val4, clust, args.damp, args.maxiter,
                                     args.conviter, args.preference, location_kmean, args.outputdir)
                count = + 1
            except:
                print("The program failed to complete clustering with affinity propagation when it reached %s. Check the number of contigs in the following bin: %s. If the number is >100,000 it is likely you ran into a memory error.") % (clust)
                with open("error_code.txt", "w") as out_file:
                    out_file.write("3")
                    out_file.write("%s"% str(clust))
                sys.exit(1)
            print("""
            ___________________________________________________
             Putative Bins Computed in %s seconds
            ____________________________________________________""" % (time.time() - start_time))
##########################Finding Bin Metrics####################################
        print ("""
           ____________________________________________________
          |                                                    |
          |     Evaluating Initial Genome Bins With CheckM     |
          |____________________________________________________|
        """)
        out_1 = args.outputdir+'/BINSANITY-INITIAL'
        try:
            run_checkM(str(out_1), str(args.threads),
                       args.prefix, args.outputdir)
        except:
            print("Binsanity failed to run CheckM. In all liklihood there is an issue with your CheckM installation.")
            with open("error_code.txt", "w") as out_file:
                out_file.write("4")
            sys.exit(1)
        checkm_analysis(os.path.join(args.outputdir, str(
            args.prefix)+"_checkm_lineagewf-results.txt"), ".fna", str(out_1), args.prefix)
##############################Refining Bins#######################################
        start_time = time.time()

        location = os.path.join(out_1, str(args.prefix)+"_high_redundancy")
        location2 = os.path.join(out_1, str(args.prefix)+"_low_completion")
        location4 = os.path.join(out_1, str(args.prefix)+"_high_completion")
        if len(os.listdir(location2)) != 0:
            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)

            shutil.move("low_completion.fna", str(location))
            shutil.rmtree(str(location2))
        location3 = os.path.join(out_1, str(args.prefix)+"_strain_redundancy")
        if len(os.listdir(location3)) != 0:
            for filename in glob.glob(str(location3)+"/*.fna"):
                shutil.move(filename, str(location4))
        if len(os.listdir(location)) != 0:
            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, redundant_bin))
                kmer_time = time.time()
                output(kmer_counts(args.kmer, location, redundant_bin),
                       args.prefix, args.kmer, args.outputdir)
                print("          kmer frequency calculated in %s seconds" %
                      (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))
                try:
                    val1, val2 = cov_array((get_cov_data(os.path.join(
                        args.outputdir, 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, args.prefix)
                except:
                    print("BinSanity failed when refining you genomes :/. The Bin that it failed at was the following bin: %s" % str(redundant_bin))
                    with open("error_code.txt", "w") as out_file:
                        out_file.write("5\n")
                        out_file.write("%s" % str(redundant_bin))
                    sys.exit(1)
                print("""
                     ____________________________________________________
                    	Ammended Bins Computed in %s seconds
                     ____________________________________________________""" % (time.time() - refine_time))
        else:
            print("All bins pass initial quality check and there are no bins to refine.")
        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"))
        try:
            shutil.move(os.path.join(str(args.outputdir), str(
                args.prefix)+"-REFINED-BINS"), os.path.join(str(args.outputdir), "Binsanity-records"))
        except:
            pass
        path1 = os.path.join(
            args.outputdir, "Binsanity-records/"+args.prefix+"_high_completion")
        for files in os.listdir(path1):
            shutil.copy(os.path.join(os.path.join(str(
                args.outputdir), "Binsanity-records/"+args.prefix+"_high_completion"), files), str(final_out))
        try:
            for files in os.listdir(os.path.join(str(args.outputdir), "Binsanity-records/"+str(args.prefix)+"-REFINED-BINS")):
                shutil.copy(os.path.join(os.path.join(str(
                    args.outputdir), "Binsanity-records/"+str(args.prefix)+"-REFINED-BINS"), files), str(final_out))
        except:
            pass
        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(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/"))
        shutil.move(os.path.join(args.outputdir, str(args.prefix)+"-KMEAN-BINS"),
                    os.path.join(str(args.outputdir), "Binsanity-records/"))
        make_tarfile(os.path.join(str(args.outputdir), "Binsanity-records.tar.gz"),
                     os.path.join(str(args.outputdir), "Binsanity-records/"))
