#!/opt/conda/conda-bld/starseqr_1664875695349/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac/bin/python
# encoding: utf-8

from __future__ import (absolute_import, division, print_function)
import os
import sys
import time
import argparse
import pandas as pd
import numpy as np
import logging
from collections import OrderedDict
import starseqr_utils as su


def parse_args():
    class FullPaths(argparse.Action):
        """Expand user- and relative-paths"""

        def __call__(self, parser, namespace, values, option_string=None):
            setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))

    usage = " "
    parser = argparse.ArgumentParser(description="STAR-SEQR Parameters:", epilog=usage)
    # create STAR alignment
    group1 = parser.add_argument_group('Do Alignment', '')
    group1.add_argument('-1', '--fastq1', type=str, required=True, action=FullPaths,
                        help='fastq.gz 1(.gz)')
    group1.add_argument('-2', '--fastq2', type=str, required=True, action=FullPaths,
                        help='fastq.gz 2(.gz)')
    group1.add_argument('-i', '--star_index', type=str, required=False, action=FullPaths,
                        help='path to STAR index folder')
    group1.add_argument('-m', '--mode', type=int, required=False,
                        default=1,
                        choices=[0, 1],
                        help='STAR alignment mode. 0=More-specific, 1=More-Sensitive')

    # existing STAR alignment
    group2 = parser.add_argument_group('Use Existing Alignment', '')
    group2.add_argument('-sj', '--star_jxns', type=str, required=False, action=FullPaths,
                        help='chimeric junctions file produced by STAR')
    group2.add_argument('-ss', '--star_sam', type=str, required=False, action=FullPaths,
                        help='Chimeric.out.sam file produced by STAR. Either use this or -sb')
    group2.add_argument('-sb', '--star_bam', type=str, required=False, action=FullPaths,
                        help='Aligned.sortedByCoord.out.bam file produced by STAR. Must contain chimeric reads with ch tag.')

    # shared args
    parser.add_argument('-p', '--prefix', type=str, required=True, action=FullPaths,
                        help='prefix to name files. Can be string or /path/to/string')
    parser.add_argument('-r', '--fasta', type=str, required=True, action=FullPaths,
                        help='indexed fasta (.fa)')
    parser.add_argument('-g', '--gtf', type=str, required=True, action=FullPaths,
                        help='gtf file. (.gtf)')
    parser.add_argument('-l', '--library', type=str, required=False,
                        default="A",
                        help='salmon library type(A, ISF, ISR, etc)')
    parser.add_argument('-t', '--threads', type=int, required=False,
                        default=8,
                        help='Number of threads to use for STAR and STAR-SEQR. 4-12 recommended.')
    parser.add_argument('-b', '--bed_file', type=str, required=False, action=FullPaths,
                        help='Bed file to subset analysis')
    parser.add_argument('--subset_type', type=str, required=False,
                        default="either",
                        help='allow fusions to pass with either one breakend in bed file or require both. Must use -b.',
                        choices=["either", "both"])
    parser.add_argument('-a', '--as_type', type=str, required=False,
                        default="velvet",
                        help='assembler to use',
                        choices=["velvet"])
    parser.add_argument('--keep_dups', action='store_true',
                        help='keep read duplicates. Use for PCR data or with molecular tags')
    parser.add_argument('--keep_gene_dups', action='store_true',
                        help='allow internal gene duplications to be considered')
    parser.add_argument('--keep_mito', action='store_true',
                        help='allow RNA fusions to contain at least one breakpoint from Mitochondria')
    parser.add_argument('-v', '--verbose', action="count",
                        help="verbose level... repeat up to three times.")
    args = parser.parse_args()

    # check that the correct args have been specified
    align = [args.star_index]
    call = [args.star_jxns, args.star_sam, args.star_bam]
    fqs = [args.fastq1, args.fastq2]
    star_reads = [args.star_sam, args.star_bam]
    if any(align) and any(call):
        print("Error: Please specify either a STAR Index or STAR existing files as input!")
        sys.exit(1)
    if any(align) and None in fqs:
        print("Error: Fastq1, Fastq2, and the STAR index must be specified if doing alignment")
        sys.exit(1)
    if any(call) and not any(star_reads):
        print("Error: The STAR .junctions and .sam|.bam file must be specified if using existing alignment")
        sys.exit(1)
    if all(star_reads):
        print("Error: Please specify only one of -sb/-ss.")
        sys.exit(1)
    if args.fasta.endswith(".gz"):
        print("Error: The genome fasta cannot be compressed!")
        sys.exit(1)
    if args.gtf.endswith(".gz"):
        print("Error: The GTF cannot be compressed!")
        sys.exit(1)
    return args


def set_log_level_from_verbose(verbose_level):
    if not verbose_level:
        return logging.ERROR
    elif verbose_level == 1:
        return logging.WARNING
    elif verbose_level == 2:
        return logging.INFO
    elif verbose_level >= 3:
        return logging.DEBUG


# Wrap function to apply single functions to chunks of a df in parallel.
def apply_cigar_overhang(df):
    df['overhang_len'] = df.apply(lambda x: su.core.cigar_overhang_matches(x['cigar1'], x['cigar2']), axis=1)
    return df


def apply_choose_order(df):
    df['order'] = df.apply(lambda x: su.core.choose_order(x['chrom1'], x['pos1'], x['chrom2'], x['pos2']), axis=1)
    return df


def apply_normalize_jxns(df):
    df['name'] = df.apply(lambda x: su.core.normalize_jxns(x['chrom1'], x['chrom2'], x['pos1'], x['pos2'],
                                                           x['str1'], x['str2'], x['jxnleft'], x['jxnright'],
                                                           x['order']), axis=1)
    return df


def apply_pairs_func(args):
    df, dd = args
    df['spans'], df['spanreads'] = zip(*df.apply(lambda x: su.core.get_pairs_func(x['name'], dd), axis=1))
    return df


def apply_jxn_strand(df):
    _, _, df['test_strand'], _ = zip(*df.apply(lambda x: su.annotate_sv.get_jxnside_anno(x['name'], gtree, 1), axis=1))
    return df


def apply_flip_func(df):
    df['name'], df['flip'] = zip(*df.apply(lambda x: su.core.flip_jxn(x['name'], x['test_strand']), axis=1))
    return df


def wrap_annotate_junctions(df):
    df['ann_format'] = "Symbol:Transcript:Strand:Exon_No:Dist_to_Exon:Frame:CDS_Length"
    left_ann = df.apply(lambda x: su.annotate_sv.get_jxnside_anno(x['name'], gtree, 1), axis=1)
    df['left_symbol'], df['left_annot'], df['left_strand'], df['left_cdslen'] = zip(*left_ann)
    right_ann = df.apply(lambda x: su.annotate_sv.get_jxnside_anno(x['name'], gtree, 2), axis=1)
    df['right_symbol'], df['right_annot'], df['right_strand'], df['right_cdslen'] = zip(*right_ann)
    df['ann'] = df['left_symbol'] + "--" + df['right_symbol']
    return df


def wrap_jxn_info(df):
    df['left_canonical'] = df['left_annot'].str.split(':', expand=True)[4].apply(lambda x: 'CANONICAL_SPLICING' if x == '0' else 'NON-CANONICAL_SPLICING')
    df['right_canonical'] = df['right_annot'].str.split(':', expand=True)[4].apply(lambda x: 'CANONICAL_SPLICING' if x == '0' else 'NON-CANONICAL_SPLICING')
    df['splice_type'] = np.where((df['left_canonical'] == 'CANONICAL_SPLICING') &
                                 (df['right_canonical'] == 'CANONICAL_SPLICING'), 'CANONICAL_SPLICING', 'NON-CANONICAL_SPLICING')
    # get all genes associated to look for overlap for each read later..
    df['left_all'], df['right_all'] = zip(*df.apply(lambda x: su.annotate_sv.get_jxn_genes(x['name'], gtree), axis=1))
    df['txunion'] = [list(set(a).union(set(b))) for a, b in zip(df.left_all, df.right_all)]
    df['txintersection'] = [list(set(a).intersection(set(b))) for a, b in zip(df.left_all, df.right_all)]
    # Get reference dist
    df['dist'] = df.apply(lambda x: su.core.get_distance(x['name']), axis=1)
    return df


def apply_get_rna_support(args):
    df, dbargs = args
    in_bam, chimflag = dbargs
    dict_res = list(df.apply(lambda x: su.support_funcs_rna.get_rna_support(x['name'], x['txunion'], x['supporting_reads'], in_bam, gtree, chimflag), axis=1))
    newdf = pd.DataFrame.from_records(dict_res, index='name')
    return newdf  # not passed back in same df


def wrap_exons2seq(args):
    df, vargs = args
    fa_object, fa_dir = vargs
    # get exon bounds
    df['left_trx_exons'] = df['name'].apply(lambda x: su.annotate_sv.get_all_exons(x, gtree, 1, exon_type="trx"))
    df['right_trx_exons'] = df['name'].apply(lambda x: su.annotate_sv.get_all_exons(x, gtree, 2, exon_type="trx"))
    df['left_fusion_exons'] = df['name'].apply(lambda x: su.annotate_sv.get_all_exons(x, gtree, 1, exon_type="fusion"))
    df['right_fusion_exons'] = df['name'].apply(lambda x: su.annotate_sv.get_all_exons(x, gtree, 2, exon_type="fusion"))
    # write to file
    df.apply(lambda x: su.core.exons2seq(fa_object, x['left_trx_exons'], x['name'], "left", out_dir=fa_dir), axis=1)
    df.apply(lambda x: su.core.exons2seq(fa_object, x['right_trx_exons'], x['name'], "right", out_dir=fa_dir), axis=1)
    df.apply(lambda x: su.core.exons2seq(fa_object, x['left_fusion_exons'], x['name'], "fusion", x['right_fusion_exons'], out_dir=fa_dir), axis=1)
    df['write_seq'] = "Finished"
    return df['write_seq']  # sequences are written to fasta not passed, but need to pass something


def apply_primers_func(args):
    df, chim_dir = args
    df['primers'] = df.apply(lambda x: su.run_primer3.wrap_runp3(x['name'], x['Max_Trx_Fusion'], chim_dir), axis=1).apply(lambda x: ",".join(x))
    return df


def apply_get_cross_homology(args):
    df, chim_dir = args
    df['span_homology_score'], df['jxn_homology_score'] = zip(*df.apply(lambda x: su.cross_homology.get_cross_homology(x['name'], chim_dir), axis=1))
    return df


def apply_get_diversity(df):
    div_res = df.apply(lambda x: su.overhang_diversity.get_diversity(x['name']), axis=1)
    df['overhang_diversity'], df['minfrag20'], df['minfrag35'] = zip(*div_res)
    return df


def apply_get_assembly_info(args):
    df, as_type = args
    df['assembly'], df['assembly_len'], df['assembly_cross_fusions'] = zip(
        *df.apply(lambda x: su.run_assembly.get_assembly_info(x['name'], as_type), axis=1))
    return df


def apply_get_fusion_class(df):
    df['Fusion_Class'] = df.apply(lambda x: su.core.get_fusion_class(x['name'], x['txintersection']), axis=1)
    return df


def apply_get_annot_db(args):
    df, dbargs = args
    chimerdb3, fuca = dbargs
    df['ChimerDB_ann'] = df.apply(lambda x: su.annotate_db.get_chimerdb(x['name'], chimerdb3), axis=1)
    df['FusionCancer_ann'] = df.apply(lambda x: su.annotate_db.get_fusioncancerdb(x['name'], fuca), axis=1)
    return df


def main():
    start = time.time()
    args = parse_args()

    # file log
    console_level = set_log_level_from_verbose(args.verbose)
    logger = su.common.init_log("STAR-SEQR", logfile=args.prefix + '_STAR-SEQR.log', consoleLevel=console_level)

    # start analysis
    logger.info("***************STAR-SEQR******************")
    logger.info("CMD = " + str(' '.join(sys.argv)))
    logger.info("STAR-SEQR_version = " + str(su.__version__))
    logger.info('Starting to work on sample: ' + args.prefix)

    # check dependent software can be found
    depend_tools = ['samtools', 'bamfilternames', 'velveth', 'gtfToGenePred', 'salmon', 'gffread']
    if args.star_index:
        depend_tools.extend(['STAR'])
    for tool in depend_tools:
        if not su.common.which(tool):
            logger.error(tool + "exe not found on path! Quitting.")
            sys.exit(1)

    # check files exist and get abs paths. Necessary since we switch folders.
    depend_paths = [args.fastq1, args.fastq2, args.fasta, args.gtf,
                    args.bed_file, args.star_jxns, args.star_sam, args.star_bam]
    for f_item in depend_paths:
        if f_item:
            su.common.check_file_exists(f_item)
            logger.info("Found input: %s", f_item)

    # make sample folder
    prefix_dir = args.prefix + "_STAR-SEQR"
    if not os.path.isdir(prefix_dir):
        os.makedirs(prefix_dir)
    os.chdir(prefix_dir)
    new_prefix = os.path.join(prefix_dir, os.path.basename(args.prefix))

    # Do alignment if fastqs
    if not args.star_jxns:
        su.star_funcs.run_star(new_prefix, args.fastq1, args.fastq2, args.star_index, args.threads, args.mode)
    else:  # symlink existing files into folder if provided
        su.common.force_symlink(args.star_jxns, new_prefix + ".Chimeric.out.junction")
        if args.star_sam:
            su.common.force_symlink(args.star_sam, new_prefix + ".Chimeric.out.sam")
        elif args.star_bam:
            su.common.force_symlink(args.star_bam, new_prefix + ".Chimeric.out.bam")

    # import all jxns
    rawdf = su.core.import_starjxns(new_prefix + ".Chimeric.out.junction", args.keep_dups, args.keep_mito)

    # get overhang match len
    rawdf = su.common.pandas_parallel(rawdf, apply_cigar_overhang, args.threads, "map_async", "")

    # get jxns only
    jxns = rawdf[rawdf['jxntype'] >= 0].reset_index()  # junctions can be either 0, 1, 2

    # Prepare Annotation
    global gtree  # necessary to make global for multiprocessing at the moment
    # symlink gtf locally to get around staging issues
    su.common.force_symlink(args.gtf, os.path.basename(args.gtf))
    gtree = su.gtf_convert.gtf2tree(os.path.basename(args.gtf))

    # start output files
    breakpoints_fh = open(new_prefix + "_STAR-SEQR_breakpoints.txt", 'w')
    breakpoint_cols = ["ann", "span_first", "jxn_left", "jxn_right",
                       "Fusion_Class", "splice_type", "breakpoint_left", "breakpoint_right",
                       "left_symbol", "right_symbol", "ann_format", "left_annot", "right_annot",
                       "dist", "assembly", "assembly_cross_disp", "primers", "name",
                       "span_homology_score", "jxn_homology_score", "overhang_diversity",
                       "minfrag20", "minfrag35",
                       "avg_overhang_BQ", "avg_span_BQ", "avg_jxn_BQ",
                       "overhang_BQ", "span_BQ", "jxn_BQ",
                       "overhang_mm", "span_mm", "jxn_mm",
                       "overhang_meanlen", "span_meanlen", "jxn_meanlen",
                       "TPM_Fusion", "TPM_Left", "TPM_Right",
                       "Max_Trx_Fusion", "disposition"]
    breakpoint_header = ["NAME", "NREAD_SPANS", "NREAD_JXNLEFT", "NREAD_JXNRIGHT",
                         "FUSION_CLASS", "SPLICE_TYPE", "BRKPT_LEFT", "BRKPT_RIGHT",
                         "LEFT_SYMBOL", "RIGHT_SYMBOL", "ANNOT_FORMAT", "LEFT_ANNOT", "RIGHT_ANNOT",
                         "DISTANCE", "ASSEMBLED_CONTIGS", "ASSEMBLY_CROSS_JXN", "PRIMERS", "ID",
                         "SPAN_CROSSHOM_SCORE", "JXN_CROSSHOM_SCORE", "OVERHANG_DIVERSITY",
                         "MINFRAG20", "MINFRAG35",
                         "OVERHANG_MEANBQ", "SPAN_MEANBQ", "JXN_MEANBQ",
                         "OVERHANG_BQ15", "SPAN_BQ15", "JXN_BQ15",
                         "OVERHANG_MM", "SPAN_MM", "JXN_MM",
                         "OVERHANG_MEANLEN", "SPAN_MEANLEN", "JXN_MEANLEN",
                         "TPM_FUSION", "TPM_LEFT", "TPM_RIGHT",
                         "MAX_TRX_FUSION", "DISPOSITION"]
    print('\t'.join(map(str, breakpoint_header)), file=breakpoints_fh)

    # stats dict
    stats_res = OrderedDict([('All_Breakpoints', 0), ('Candidate_Breakpoints', 0), ('Passing_Breakpoints', 0)])

    if len(jxns.index) == 0:
        logger.info("No junctions found in the input file")
        su.core.rna_closeout(new_prefix, stats_res, breakpoints_fh)
        sys.exit(0)

    # Order, Normalize and Aggregate
    logger.info("Ordering junctions")
    jxns['order'] = 1
    logger.info('Normalizing junctions')
    jxns = su.common.pandas_parallel(jxns, apply_normalize_jxns, args.threads, "map_async", "")
    logger.info("Getting gene strand and flipping info as necessary")
    jxns = su.common.pandas_parallel(jxns, apply_jxn_strand, args.threads, "map_async", "")
    jxns = su.common.pandas_parallel(jxns, apply_flip_func, args.threads, "map_async", "")
    logger.info("Aggregating junctions")
    jxn_summary = su.core.count_jxns(jxns)

    # write stats
    stats_res['All_Breakpoints'] = len(jxn_summary.index)
    logger.info('Raw Breakpoints:' + str(stats_res['All_Breakpoints']))

    # Get discordant pairs
    logger.info('Getting pair info')
    # break the rawdf into chromosome specific files for quicker lookups
    dd = {}
    for chrom in set(rawdf['chrom1'].unique()) | set(rawdf['chrom2'].unique()):
        dd[chrom] = rawdf[(rawdf['chrom1'] == chrom) & (rawdf['jxntype'] == -1)]
    jxn_filt = su.common.pandas_parallel(jxn_summary, apply_pairs_func, args.threads, "map_async", "", dd)

    # TODO: # Get spans without junctions and do additional processing
    # jxn_filt.to_csv(path_or_buf="All_breakpoints.txt", header=True, sep="\t", mode='w', index=False)

    # Require at least two reads for processing in order to reduce run time
    logger.info('Filtering junctions')
    before_remove = len(jxn_filt.index)
    jxn_filt = jxn_filt[(((jxn_filt['spans'].astype(int) + jxn_filt['jxn_counts'].astype(int)) >= 2))]
    #                     (jxn_filt['max_overhang'].astype(int) >= 30))]  # reads with 1 jxn but sufficient overhang
    logger.info('Number of candidates removed due to read support less than 2: ' +
                str(before_remove - len(jxn_filt.index)))

    if len(jxn_filt.index) >= 1:
        # Get Annotation info for each junction
        logger.info('Annotating junctions')
        jxn_filt = su.common.pandas_parallel(jxn_filt, wrap_annotate_junctions, args.threads, "map_async", "")

        # determine if junction follows canonical splicing at exon junction
        logger.info('Getting junction info')
        jxn_filt = su.common.pandas_parallel(jxn_filt, wrap_jxn_info, args.threads, "map_async", "")

        # subset to ROI using bed file if it exists
        if args.bed_file:
            logger.info('Subsetting junctions using the supplied bed file')
            before_remove = len(jxn_filt.index)
            targets_tree = su.common.bed_to_tree(args.bed_file)
            jxn_filt['subset'] = jxn_filt.apply(lambda x: su.common.subset_bed_func(x['name'], targets_tree, sub_style=args.subset_type), axis=1)
            jxn_filt = jxn_filt[jxn_filt['subset'] >= 1]
            logger.info("Number of candidates removed due to bed filter: " + str(before_remove - len(jxn_filt.index)))

    if len(jxn_filt.index) >= 1:
        # remove internal gene dups unless otherwise requested, also removes overlapping genes
        if not args.keep_gene_dups:
            before_remove = len(jxn_filt.index)
            jxn_filt = jxn_filt[jxn_filt['txintersection'].astype(str).str.len() < 3]  # [] counts as two
            logger.info("Number of candidates removed due to internal gene duplication filter: " + str(before_remove - len(jxn_filt.index)))

    if len(jxn_filt.index) >= 1:
        # remove novel genes unless otherwise requested
        # if not args.keep_novel:
        before_remove = len(jxn_filt.index)
        # jxn_filt.to_csv(path_or_buf="test_debug.txt", header=True, sep="\t", mode='w', index=False)
        jxn_filt = jxn_filt[((jxn_filt['left_symbol'] != "NA") &
                             (jxn_filt['right_symbol'] != "NA") &
                             (jxn_filt['left_annot'].str.split(':', expand=True)[3] != "NA") &  # No Exon number
                             (jxn_filt['right_annot'].str.split(':', expand=True)[3] != "NA"))]
        logger.info("Number of candidates removed due to novel gene filter: " + str(before_remove - len(jxn_filt.index)))

    if len(jxn_filt.index) >= 1:
        # remove non-canonical jxns with less than 3 reads here to reduce run time
        before_remove = len(jxn_filt.index)
        jxn_filt = jxn_filt[~((jxn_filt['splice_type'] == "NON-CANONICAL_SPLICING") &
                              ((jxn_filt["jxn_counts"].astype(int)) < 3))]
        logger.info("Number of candidates removed due to NON-CANONICAL splicing with low support: " + str(before_remove - len(jxn_filt.index)))

    if len(jxn_filt.index) >= 1:
        # combine all supporting reads together.
        jxn_filt['supporting_reads'] = jxn_filt['jxn_reads'] + ',' + jxn_filt['spanreads']

        stats_res['Candidate_Breakpoints'] = len(jxn_filt.index)
        logger.info('Candidate Breakpoints:' + str(stats_res['Candidate_Breakpoints']))

    # Process candidates
    if len(jxn_filt.index) >= 1:
        # Get potential fusion transcripts
        logger.info('Writing chimeric transcripts')
        chim_trx_dir = "chim_transcripts"
        su.common.pandas_parallel(jxn_filt, wrap_exons2seq, args.threads, "map_async", "", args.fasta, chim_trx_dir)

        # Get salmon quant for left, right, fusion transcripts
        ref_transcripts = "ref_transcripts.fa"
        su.gtf_convert.gtf2trxfasta(args.gtf, args.fasta, ref_transcripts, cds=False)
        salmon_df = su.salmon_quant.wrap_salmon(chim_trx_dir, args.fastq1, args.fastq2, args.library, args.threads, ref_transcripts)

        # merge salmon results
        logger.info("Merging salmon results with other metrics")
        jxn_filt = pd.merge(jxn_filt, salmon_df, how='left', left_on="name", right_on="Jxn", left_index=False,
                            right_index=False, sort=True, suffixes=('_x', '_y'), copy=True, indicator=False)

        logger.info("Preparing BAM to identify read support")
        if args.star_sam:
            logger.info("Converting Alignment to ChimericOnly.bam")
            chimflag = 256
            star_bam_local = new_prefix + ".Chimeric.out.bam"
            su.common.sam_2_coord_bam(new_prefix + ".Chimeric.out.sam", star_bam_local, args.threads)
        elif args.star_bam:
            chimflag = 2048
            star_bam_local = new_prefix + ".Chimeric.out.bam"
            su.common.sam_2_coord_bam(new_prefix + ".Chimeric.out.sam", star_bam_local, args.threads)
        else:  # alignment
            chimflag = 256
            star_bam_local = new_prefix + ".Chimeric.out.bam"
            su.common.sam_2_coord_bam(new_prefix + ".Chimeric.out.sam", star_bam_local, args.threads)
        su.common.check_file_exists(star_bam_local)
        su.common.index_bam(star_bam_local)

        # Gather unique read support
        logger.info("Getting read support from BAM for each candidate junction:")
        su.common.make_new_dir('support')
        support_df = su.common.pandas_parallel(jxn_filt, apply_get_rna_support, args.threads, "map_async", "", star_bam_local, chimflag)

        finaldf = pd.merge(jxn_filt, support_df, how='inner', left_on="name", right_on="name", left_index=False,
                           right_index=True, sort=True, suffixes=('_x', '_y'), copy=True, indicator=False)

        # collapse read info for brevity but keep here in case useful later on
        finaldf['jxn_left'] = finaldf["jxnleft_for_first"] + finaldf["jxnleft_rev_first"]
        finaldf['jxn_right'] = finaldf["jxnright_for_first"] + finaldf["jxnright_rev_first"]
        finaldf['jxn_first'] = finaldf["jxnleft_for_first"] + finaldf["jxnleft_rev_first"] + \
            finaldf["jxnright_for_first"] + finaldf["jxnright_rev_first"]
        finaldf['jxn_second'] = finaldf["jxnleft_for_second"] + finaldf["jxnleft_rev_second"] + \
            finaldf["jxnright_for_second"] + finaldf["jxnright_rev_second"]
        finaldf['span_first'] = finaldf["spanleft_for_first"] + finaldf["spanleft_rev_first"] + \
            finaldf["spanright_for_first"] + finaldf["spanright_rev_first"]
        finaldf['span_second'] = finaldf["spanleft_for_second"] + finaldf["spanleft_rev_second"] + \
            finaldf["spanright_for_second"] + finaldf["spanright_rev_second"]
        finaldf['spans_disc'] = finaldf['span_first']

        # Get all overlapping transcript seqs into one fasta per side
        # writes fasta for each transcript per side(left, right, fusion) in support folder
        su.common.pandas_parallel(finaldf, wrap_exons2seq, args.threads, "map_async", "", args.fasta, "support")

        # get homology mapping scores
        logger.info("Getting read homology mapping scores")
        finaldf = su.common.pandas_parallel(finaldf, apply_get_cross_homology, args.threads, "map_async", "", chim_trx_dir)

        # get multimapping homologous names to mark
        logger.info("Getting fusions homology mapping scores")
        homologous_remove = su.homology_graph.prune_homology_graph(finaldf, chim_trx_dir)

        # get overhang read diversity
        logger.info("Getting overhang read diversity")
        finaldf = su.common.pandas_parallel(finaldf, apply_get_diversity, args.threads, "map_async", "")

        # get assembly seq and confirm breakpoint
        logger.info("doing assembly")
        finaldf = su.common.pandas_parallel(finaldf, apply_get_assembly_info, args.threads, "map_async", "", args.as_type)
        finaldf['assembly_cross_disp'] = finaldf['assembly_cross_fusions'].apply(lambda x: True if len(str(x)) > 1 else False)

        # Generate Primers
        # finaldf.to_csv(path_or_buf="test_debug.txt", header=True, sep="\t", mode='w', index=True)
        logger.info("Generating primers using indexed fasta")
        finaldf = su.common.pandas_parallel(finaldf, apply_primers_func, args.threads, "map_async", "", chim_trx_dir)

        # Get normalized breakpoint locations
        logger.info("Getting normalized breakpoint locations")
        finaldf['breakpoint_left'], finaldf['breakpoint_right'] = zip(*finaldf.apply(lambda x: su.core.get_fusion_locations(x['name']), axis=1))

        # get fusion class
        logger.info("Getting fusion classes")
        finaldf = su.common.pandas_parallel(finaldf, apply_get_fusion_class, args.threads, "map_async", "")

        # Extract BaseQualities
        finaldf['avg_overhang_BQ'] = su.core.mean_from_cols(finaldf, '^hang.*meanBQ')
        finaldf['avg_jxn_BQ'] = su.core.mean_from_cols(finaldf, '^jxn.*meanBQ')
        finaldf['avg_span_BQ'] = su.core.mean_from_cols(finaldf, '^span.*meanBQ')
        # get bq thresh counts
        finaldf['overhang_BQ'] = su.core.minvalcnts_from_cols(finaldf, '^hang.*meanBQ', 15)
        finaldf['jxn_BQ'] = su.core.minvalcnts_from_cols(finaldf, '^jxn.*meanBQ', 15)
        finaldf['span_BQ'] = su.core.minvalcnts_from_cols(finaldf, '^span.*meanBQ', 15)
        # Extract mean mismatches
        finaldf['overhang_mm'] = su.core.mean_from_cols(finaldf, '^hang.*mismatches')
        finaldf['jxn_mm'] = su.core.mean_from_cols(finaldf, '^jxn.*mismatches')
        finaldf['span_mm'] = su.core.mean_from_cols(finaldf, '^span.*mismatches')
        # Extract mean read len
        finaldf['overhang_meanlen'] = su.core.mean_from_cols(finaldf, '^hang.*seqlen')
        finaldf['jxn_meanlen'] = su.core.mean_from_cols(finaldf, '^jxn.*seqlen')
        finaldf['span_meanlen'] = su.core.mean_from_cols(finaldf, '^span.*seqlen')

        # Get total chimeric counts
        finaldf['Chimeric_Counts'] = finaldf['jxn_left'] + finaldf['jxn_right'] + finaldf['spans_disc']

        # Get number of breakpoints partners per junction
        finaldf['breakpoint_left_rep'] = finaldf['breakpoint_left'].apply(lambda x: finaldf['breakpoint_left'].value_counts()[x])
        finaldf['breakpoint_right_rep'] = finaldf['breakpoint_right'].apply(lambda x: finaldf['breakpoint_right'].value_counts()[x])

        # HARD FILTERING - Change this once a probabilistic module is ready.
        # Hard filter on read counts after accounting for transcript info.
        finaldf['filter_minreads'] = (finaldf["jxn_right"] >= 1) | (finaldf["jxn_left"] >= 1)
        finaldf['filter_minreads'].replace(to_replace=[False], value='minreads', inplace=True, method=None)

        # Hard filter on homology for discordant pairs and jxn.
        finaldf['filter_homology'] = ((finaldf['span_homology_score'] <= .50) &
                                      (finaldf['jxn_homology_score'] <= .50))
        finaldf['filter_homology'].replace(to_replace=[False], value='homology', inplace=True, method=None)

        # Hard filter on multimapping-homologus duplicate fusions
        finaldf['homology_collapse'] = (~finaldf['name'].isin(homologous_remove))
        finaldf['homology_collapse'].replace(to_replace=[False], value='homology_collapse', inplace=True, method=None)

        # Hard filter on unique overhangs. Requre at least 20% of overhangs to be unique if less than 10
        finaldf['diversity1'] = (finaldf['jxn_left'] + 1) / (finaldf['overhang_diversity'] + 1)
        finaldf['diversity2'] = (finaldf['jxn_left'] + 1) / (finaldf['overhang_diversity'] + 1) ** 2
        finaldf['filter_diversity'] = (((finaldf['diversity1'] <= 4) | (finaldf['overhang_diversity'] >= 15)) &
                                       ((finaldf['diversity2'] < .75) | (finaldf['overhang_diversity'] >= 15)))
        finaldf['filter_diversity'].replace(to_replace=[False], value='diversity', inplace=True, method=None)

        # Hard filter on basequalities. Require 20% and at least 1 of reads to have meanbq>10.
        finaldf['filter_BQ'] = (((finaldf['overhang_BQ'] >= finaldf['jxn_first'] * .2) & (finaldf['overhang_BQ'] >= 1)) &
                                ((finaldf['jxn_BQ'] >= finaldf['jxn_first'] * .2) & (finaldf['jxn_BQ'] >= 1)) &
                                (finaldf['span_BQ'] >= finaldf['spans_disc'] * .2))
        finaldf['filter_BQ'].replace(to_replace=[False], value='bq10', inplace=True, method=None)

        # Hard filter to require non-canonical splicing events to have greater read support and at least 10% with minfrag35
        noncan_mask = finaldf[finaldf['splice_type'] == "NON-CANONICAL_SPLICING"]
        finaldf['filter_noncanonical'] = ((noncan_mask['jxn_first'] >= 5) &
                                          (noncan_mask['minfrag35'] >= noncan_mask['jxn_first'] * .1))
        finaldf['filter_noncanonical'].replace(to_replace=[False], value='noncanonical_support', inplace=True, method=None)

        # Hard filter to require at least 10% of reads to pass minfrag20 if span reads == 0
        nospan_mask = finaldf[finaldf["span_first"] == 0]
        finaldf['filter_nospanminfrag'] = ((nospan_mask['minfrag20'] >= nospan_mask['jxn_first'] * .1) &
                                           (nospan_mask['minfrag20'] >= 1))
        finaldf['filter_nospanminfrag'].replace(to_replace=[False], value='nospan_minfrag', inplace=True, method=None)

        # Hard filter to require ratio of  minfrag20 of jxn reads
        finaldf['jxnminfragratio'] = (finaldf['jxn_left'] + 1) / (finaldf['minfrag20'] + 1)
        finaldf['filter_minfrag'] = ((finaldf['jxnminfragratio'] < 4) | (finaldf['overhang_diversity'] >= 5))
        finaldf['filter_minfrag'].replace(to_replace=[False], value='minfrag', inplace=True, method=None)

        # Hard filter to require a > 0 TPM value for the fusion
        finaldf['filter_expression'] = (finaldf['TPM_Fusion'] > 0)
        finaldf['filter_expression'].replace(to_replace=[False], value='expression', inplace=True, method=None)

        # Get the final disposition of filtering
        finaldf['filter_all'] = finaldf[['filter_minreads', 'filter_homology',
                                         'filter_diversity', 'filter_noncanonical',
                                         'filter_nospanminfrag', 'filter_minfrag',
                                         'filter_expression', 'homology_collapse',
                                         'filter_BQ']].values.tolist()
        finaldf['disposition'] = finaldf['filter_all'].apply(lambda x: ','.join(x for x in list(map(str, x)) if x not in ['True', 'nan']))
        finaldf['disposition'].replace('', 'PASS', inplace=True)

        # write candidates and info to file
        candid_fh = open(new_prefix + "_STAR-SEQR_candidates.txt", 'w')
        print('\t'.join(map(str, breakpoint_header)), file=candid_fh)
        finaldf.to_csv(path_or_buf=candid_fh, header=False, sep="\t", columns=breakpoint_cols, mode='w', index=False)
        candid_fh.close()

        # dump all values
        # finaldf.to_csv(path_or_buf="data_dump.txt", header=True, sep="\t", mode='w', index=False)  # DEBUG

        resultsdf = finaldf[finaldf['disposition'] == 'PASS'].sort_values(['jxn_first', "span_first"], ascending=[False, False])

        # Write passing fusions to file
        resultsdf.to_csv(path_or_buf=breakpoints_fh, header=False, sep="\t",
                         columns=breakpoint_cols, mode='w', index=False)

        # Log Stats
        stats_res['Passing_Breakpoints'] = len(resultsdf.index)
        logger.info('Passing Breakpoints:' + str(stats_res['Passing_Breakpoints']))

        # closeout
        su.core.rna_closeout(new_prefix, stats_res, breakpoints_fh)

    else:  # there were no candidates to process but still want result files produced
        # closeout
        logger.info('No candidates left to process.')
        su.core.rna_closeout(new_prefix, stats_res, breakpoints_fh)

    # Finish
    logger.info("Program took  %g seconds" % (time.time() - start))


if __name__ == "__main__":
    sys.exit(main())
