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# Source code for pmdtools.pmdtools_mod

    #!/usr/bin/env python3
    # -*- coding: utf-8 -*-

    """
    Calculate and filter post-mortem degeneration score for NGS reads in SAM files.

    Modified version of PMDtools based on v0.55 by Pontus Skoglund:

    cite: P Skoglund, BH Northoff, MV Shunkov, AP Derevianko, S Paabo, J Krause, M
    Jakobsson (2014) Separating endogenous ancient DNA from modern day
    contamination in a Siberian Neandertal, PNAS, advance online 27 January

    Included changes:

    * count and show excluded reads due to unsupported CIGAR operations
    * count and show excluded reads due to % identity filtering
    * port from Python2 to Python3
    * bugfix: ambiguous combinations of CIGAR and MD with deletions lead to shifts
      in reconstructed reference sequence
    * support for all CIGAR operations except padded references (untested)
    * support of IUPAC ambiguity codes
    * optional parameters to adapt % identity calculation to MALT defaults
    * refactoring into main method
    * use of ArgumentParser over deprecated OptionParser
    * improved PEP8 and pylint compliance
    * ReST documentation

    .. moduleauthor:: Florian Aldehoff <faldehoff@student.uni-tuebingen.de>
    """

    import sys
    import argparse
    import math
    import subprocess
    from itertools import repeat

    """ custom libraries """
    from samsifter.util.sam import reconstruct
    from samsifter.util.genetics import reverse_complement, gc, aln_identity

    def phred2prob(Q):
        """Convert PHRED score to probability of sequencing error.

        Parameters
        ----------
        Q : int
            PHRED score.

        Returns
        -------
        float
            The probability of a sequencing error.
        """
        return 10.0 ** (-Q / 10.0)

    def prob2phred(P):
        """Convert probability of sequencing error to PHRED score.

        Parameters
        ----------
        P : float
            The probability of a sequencing error.

        Returns
        -------
        float
            PHRED score.
        """
        return -10.0 * math.log(P, 10)

    def prob2ascii(P, offset=33):
        """Convert probability of sequencing error to ASCII-encoded PHRED score.

        By default uses ASCII offset 33 (Illumina standard).

        Parameters
        ----------
        P : float
            The probability of a sequencing error.
        offset : int, optional
            ASCII encoding offset, defaults to 33.
        """
        return chr(int(prob2phred(P)) + offset)

    def prob(pos, model, qual, poly, match=True):
        """Calculate probability of either match or mismatch under given model.

        Expects distance of base from end of read, a pre-computed distribution of
        damage probabilities, PHRED score of base (probability of sequencing
        error) and probability of true polymorphism.

        Parameters
        ----------
        pos : int
            Position of base from end of read.
        model : list
            Distribution of damage probabilities.
        qual : int
            PHRED score of base.
        poly : float
            Probability of a true polymorphism at this base.
        match : bool, optional
            Switch between calculation for a match (default) or mismatch.

        Returns
        -------
        float
            Probability of either match or mismatch under given model.
        """
        p_damage = float(model[pos])
        p_error = phred2prob((ord(qual) - 33)) / 3.0
        p_poly = poly
        p_match = ((1.0 - p_damage) * (1.0 - p_error) * (1.0 - p_poly)
                   + (p_damage * p_error * (1.0 - p_poly))
                   + (p_error * p_poly * (1.0 - p_damage)))
        p_mismatch = 1.0 - p_match
        if match:
            return p_match
        else:
            return p_mismatch

    def L_match(position, model, qual, poly):
        """Calculate likelihood of a match under given model.

        Parameters
        ----------
        pos : int
            Position of base from end of read.
        model : list
            Distribution of damage probabilities.
        qual : int
            PHRED score of base.
        poly : float
            Probability of a true polymorphism at this base.

        Returns
        -------
        float
            Probability of match under given model.
        """
        return prob(position, model, qual, poly, True)

    def L_mismatch(position, model, qual, poly):
        """Calculate likelihood of a mismatch under given model.

        Parameters
        ----------
        pos : int
            Position of base from end of read.
        model : list
            Distribution of damage probabilities.
        qual : int
            PHRED score of base.
        poly : float
            Probability of a true polymorphism at this base.

        Returns
        -------
        float
            Probability of mismatch under given model.
        """
        return prob(position, model, qual, poly, False)

    def adjust_quality(position, model, qual):
        """Adjust base quality value (sequencing error probability).

        Parameters
        ----------
        position : int
            Position of base from end of read.
        model : list
            Distribution of damage probabilities.
        qual : int
            PHRED score of base.

        Returns
        -------
        float
            Adjusted base quality value.
        """
        p_damage = float(model[position])
        p_error = phred2prob((ord(qual) - 33)) / 3.0
        adjusted = 1.0 - ((1.0 - p_damage) * (1.0 - p_error))
        return adjusted

    def geometric(pval, kval, constant):
        """Calculate geometrically distributed probability.

        Parameters
        ----------
        pval : float
            Parameter p of geometric distribution.
        kval : float
            Parameter k of geometric distribution.
        constant : float
            Constant value of geometric distribution.

        Returns
        -------
        float
            Probability of deamination based on geometric distribution.
        """
        return ((1.0 - pval) ** (kval - 1)) * pval + constant

    def fa_get(ffile, chrom, fstart, fend, samtoolspath, verbose=False):
        """Unused method stub to get reference sequence from supplied FASTA?

        Calls SAMtools in subprocess to create index of FASTA file.

        Parameters
        ----------
        ffile : str
            Path to FASTA file.
        chrom : int
            Chromosome number.
        fstart : int
            Start position.
        fend : int
            End position.
        samtoolspath : str
            Path to samtools.
        verbose : bool, optional
            Enable output of executed commandline, defaults to False.

        Returns
        -------
        str
            reference sequence
        """
        FNULL = open('/dev/null', 'w')
        chrom = str(chrom)    # 'chr'+str(chrom)
        location = str(chrom) + ':' + str(fstart) + '-' + str(fend)
        cmd_line = [samtoolspath, 'faidx', ffile, location]
        outp_file = subprocess.Popen(cmd_line, stdout=subprocess.PIPE,
                                     stderr=FNULL)
        pileupline = outp_file.stdout.read().split()
        pileupline = ''.join(pileupline[1:])
        if len(pileupline) < 1:
            print('no such reference sequence', cmd_line)
        if verbose:
            print(' '.join(cmd_line))
        return pileupline

    def score_pmd(read, ref, quals, ancient_model_deam, modern_model_deam,
                  adjustment_model_deam=None,
                  polymorphism_contamination=0.001, polymorphism_ancient=0.001,
                  adjustbaseq_all=False,
                  adjustbaseq=False,
                  baseq=0,
                  deamination=False,
                  cpg=False,
                  nocpg=False,
                  UDGhalf=False,
                  PMDSprim=False):
        """Calculate post-mortem degradation score (PMDS).

        Requires full read and reference sequence including clips, skips and gaps
        as recovered from CIGAR and MD to use the correct distance from 5' or 3'
        end in the PMDS calculation. However only the aligned read bases with
        PHRED scores are considered for PMD scoring.

        Optional adjustment of base qualities requires additional adjustment model.

        Parameters
        ----------
        read : str
            Full read sequence.
        ref : str
            Aligned reference sequence including clips, skips and gaps.
        quals : list
            Quality scores for aligned bases.
        ancient_model_deam : list
            Probability distribution for ancient deamination.
        modern_model_deam : list
            Probability distribution for modern deamination.
        adjustment_model_deam : list, optional
            Adjustments to the deamination probabilities, defaults to None.
        polymorphism_contamination : float, optional
            Probability of polymorphism due to contamination, defaults to 0.001.
        polymorphism_ancient : float, optional
            Probability of polymorphism due to age, defaults to 0.001.
        adjustbaseq_all : bool, optional
            Switch to adjust all base quality values, defaults to False.
        adjustbaseq : bool, optional
            Switch to adjust affected base quality values, defaults to False.
        baseq : int, optional
            Minimum quality value for processed bases, defaults to 0
        deamination : bool, optional
            Enable output of base frequencies in deaminated context, defaults to
            False.
        cpg : bool, optional
            Only use Cs and Gs in CpG context, defaults to False.
        nocpg : bool, optional
            Do NOT use Cs and Gs in CpG context, defaults to False.
        UDGhalf : bool, optional
            Only use Cs and Gs in CpG context, the first and last base are used
            regardless of dinucleotide context. Defaults to False.
        PMDSprim : bool, optional
            Defaults to False.

        Returns
        -------
        float
            Likelihood of deamination under ancient DNA model.
        float
            Likelihood of deamination under ancient DNA model.
        float
            Post-mortem degradation score
        list
            Adjusted base qualities.
        """
        # this should never happen if reference was correctly reconstructed
        if len(read) != len(ref):
            raise ValueError("Undefined for sequences of unequal length")

        L_D = 1.0    # likelihood of degradation
        L_M = 1.0    # likelihood of mutation

    #    L_D_max = 1.0
    #    L_M_max = 1.0
    #
    #    L_D_min = 1.0
    #    L_M_min = 1.0

        """
        Check for informative sites (does nothing at the moment)
        """
        '''
        #print ref[-1]
        if cpg:
            if 'CG' not in ref:
                #print 'no informative'
                L_D = 1.0
                L_M = 1.0
        elif UDGhalf:
            if 'CG' not in ref and ref[0] != 'C' and ref[-1] != 'G':
                #print 'no informative'
                L_D = 1.0
                L_M = 1.0
        else:
            if 'C' not in ref and 'G' not in ref:
                #print 'no informative'
                L_D = 1.0
                L_M = 1.0
        '''

        newquals = quals

        qual_idx = 0
        for aln_idx, (readbase, refbase) in enumerate(zip(read, ref)):
            # skipped intron
            if readbase == '-' and refbase == 'X':
                continue

            # hard clipping
            if readbase == refbase == 'X':
                continue

            # soft clipping
            if refbase == 'X':
                qual_idx += 1
                continue

            # gaps in read
            if readbase == '-':
                continue

            # gaps in reference
            if refbase == '-':
                qual_idx += 1
                continue

            # ambiguous bases TODO some of these may actually be informative!
            ambiguous = ('N', 'X', 'B', 'D', 'H', 'V', 'Y', 'R', 'W', 'S')
            if (readbase in ambiguous or refbase in ambiguous):
                qual_idx += 1
                continue

            quality = quals[qual_idx]

            """
            At this point we're dealing with a qualified alignment where each
            position has exactly
            - one informative read base:        readbase       read[aln_idx]
            - one informative reference base:   refbase        ref[aln_idx]
            - one quality score:                quality        quals[qual_idx]
            """

            i = qual_idx                    # distance from 5' end of read
            z = len(quals) - 1 - qual_idx    # distance from 3' end of read

            if adjustbaseq_all and adjustment_model_deam is not None:
                newprob = (adjustment_model_deam[i]
                           + adjustment_model_deam[z]
                           + phred2prob(ord(quality) - 33))
    #            newprob = min(newprob, 1.0)
                newqual = prob2ascii(newprob)
                newquals = quals[0:i] + newqual + quals[(i + 1):]

            if (ord(quality) - 33) < baseq:
                # make sure that quality is adjusted even if baseq is below
                # threshold
                if adjustbaseq:
                    if refbase == 'C' and readbase == 'T':
    #                    if cpg:
    #                        if i + 1 >= readlen:
    #                            break
    #                        if ref[i + 1] != 'G':
    #                            continue
    #                    elif nocpg:
    #                        if i + 1 >= readlen:
    #                            break
    #                        if ref[i + 1] == 'G':
    #                            continue
    #                    elif UDGhalf:
    #                        if i + 1 >= readlen:
    #                            break
    #                        if ref[i + 1] != 'G' and i != 0:
    #                            continue
                        newprob = adjust_quality(i, ancient_model_deam, quality)
                        newqual = prob2ascii(newprob)
                        newquals = quals[0:i] + newqual + quals[(i + 1):]

                    if refbase == 'G' and readbase == 'A':
    #                    if cpg:
    #                        if i - 1 >= readlen:
    #                            break
    #                        if ref[i - 1] != 'C':
    #                            continue
    #                    elif nocpg:
    #                        if i - 1 >= readlen:
    #                            break
    #                        if ref[i - 1] == 'C':
    #                            continue
    #                    elif UDGhalf:
    #                        if ref[i - 1] != 'C' and z != 0:
    #                            continue
                        newprob = adjust_quality(z, ancient_model_deam, quality)
                        newqual = prob2ascii(newprob)
                        newquals = quals[0:i] + newqual + quals[(i + 1):]
                continue

    #        if deamination:
    #            if refbase == 'C':
    #                if cpg:
    #                    if i + 1 >= readlen:
    #                        break
    #                    if read[i + 1] != 'G':
    #                        continue
    #                elif nocpg:
    #                    if i + 1 >= readlen:
    #                        break
    #                    if read[i + 1] == 'G':
    #                        continue
    #                elif UDGhalf:
    #                    if i + 1 >= readlen:
    #                        break
    #                    if ref[i + 1] != 'G' and i != 0:
    #                        continue
    #
    #                thekey = refbase + readbase + str(i)
    #                if thekey in mismatch_dict.keys():
    #                    addition = mismatch_dict[thekey]
    #                    addition += 1
    #                    mismatch_dict[thekey] = addition
    #                else:
    #                    mismatch_dict[thekey] = 1
    #
    #            if refbase == 'G':
    #                if cpg:
    #                    if i - 1 >= readlen:
    #                        break
    #                    if ref[i - 1] != 'C':
    #                        continue
    #                elif nocpg:
    #                    if i - 1 >= readlen:
    #                        break
    #                    if ref[i - 1] == 'C':
    #                        continue
    #                elif UDGhalf:
    #                    if i - 1 >= readlen:
    #                        break
    #                    if ref[i - 1] != 'C' and z != 0:
    #                        continue
    #                thekey = refbase + readbase + str(z)
    #                if thekey in mismatch_dict_rev.keys():
    #                    addition = mismatch_dict_rev[thekey]
    #                    addition += 1
    #                    mismatch_dict_rev[thekey] = addition
    #                else:
    #                    mismatch_dict_rev[thekey] = 1
    #            continue

            """
            compute degradation score
            """
    #        if i >= readlen:continue
            if refbase == 'C':
    #            if cpg:
    #                if i + 1 >= readlen:
    #                    break
    #                if ref[i + 1] != 'G':
    #                    continue
    #            elif cpg:
    #                if i + 1 >= readlen:
    #                    break
    #                if ref[i + 1] == 'G':
    #                    continue
    #            elif UDGhalf:
    #                if i + 1 >= readlen:
    #                    break
    #                if ref[i + 1] != 'G' and i != 0:
    #                    continue

                if readbase == 'T':
                    L_D = L_D * L_mismatch(i, ancient_model_deam, quality,
                                           polymorphism_ancient)
                    L_M = L_M * L_mismatch(i, modern_model_deam, quality,
                                           polymorphism_contamination)

                    if adjustbaseq:
                        newprob = adjust_quality(i, ancient_model_deam, quality)
                        newqual = prob2ascii(newprob)
                        """
                        print phred2prob(ord(quality)),newprob
                        print ord(quality),newphred
                        print quality,newqual
                        print quals[0:i],quality,quals[(i+1):]
                        print quals
                        """
                        quals = quals[0:i] + newqual + quals[(i + 1):]

                elif readbase == 'C':
                    L_D = L_D * L_match(i, ancient_model_deam, quality,
                                        polymorphism_ancient)
                    L_M = L_M * L_match(i, modern_model_deam, quality,
                                        polymorphism_contamination)

    #            if PMDSprim and readbase in ['C', 'T', 'c', 't']:
    #                L_D_max = L_D_max * L_mismatch(i, ancient_model_deam, quality,
    #                                               polymorphism_ancient)
    #                L_M_max = L_M_max * L_mismatch(i, modern_model_deam, quality,
    #                                               polymorphism_ancient)
    #                L_D_min = L_D_min * L_match(i, ancient_model_deam, quality,
    #                                            polymorphism_ancient)
    #                L_M_min = L_M_min * L_match(i, modern_model_deam, quality,
    #                                            polymorphism_ancient)

            if refbase == 'G':
                if cpg:
                    if ref[i - 1] != 'C':
                        continue
                elif nocpg:
                    if ref[i - 1] == 'C':
                        continue
                elif UDGhalf:
                    if ref[i - 1] != 'C' and z != 0:
                        continue

                if readbase == 'A':
                    L_D = L_D * L_mismatch(z, ancient_model_deam, quality,
                                           polymorphism_ancient)
                    L_M = L_M * L_mismatch(z, modern_model_deam, quality,
                                           polymorphism_contamination)

                    if adjustbaseq:
                        newprob = adjust_quality(z, ancient_model_deam, quality)
                        newqual = prob2ascii(newprob)
                        newquals = quals[0:i] + newqual + quals[(i + 1):]

                elif readbase == 'G':
                    L_D = L_D * L_match(z, ancient_model_deam, quality,
                                        polymorphism_ancient)
                    L_M = L_M * L_match(z, modern_model_deam, quality,
                                        polymorphism_contamination)

    #            if PMDSprim and readbase in ['G', 'A', 'g', 'a']:
    #                L_D_max = L_D_max * L_mismatch(z, ancient_model_deam, quality,
    #                                               polymorphism_ancient)
    #                L_M_max = L_M_max * L_mismatch(z, modern_model_deam, quality,
    #                                               polymorphism_ancient)
    #                L_D_min = L_D_min * L_match(z, ancient_model_deam, quality,
    #                                            polymorphism_ancient)
    #                L_M_min = L_M_min * L_match(z, modern_model_deam, quality,
    #                                            polymorphism_ancient)

        # calculate log-likelihood ratio as final PMD score
        pmds = math.log(L_D / L_M)

        return (L_D, L_M, pmds, newquals)

    def main():
        """Executable to calculate post-mortem degradation scores for SAM files.

        See ``--help`` for details on expected arguments. Takes input only on
        STDIN. Logs messages to STDERR and writes processed SAM file to
        STDOUT.
        """
        parser = argparse.ArgumentParser(
            usage=("python %(prog)s <SAM formatted data with MD field present "
                   "from stdin> [options]")
        )

        # options to control PMD score calculation
        pmd_options = parser.add_argument_group(
            title="PMD parameters",
            description=("influence the geometric distribution used for PMD score "
                         "calculation by changing these parameters")
        )
        pmd_options.add_argument("--PMDpparam",
                                 action="store",
                                 type=float,
                                 dest="PMDpparam",
                                 help="parameter p in geometric probability \
                                 distribution of PMD",
                                 default=0.3)
        pmd_options.add_argument("--PMDconstant",
                                 action="store",
                                 type=float,
                                 dest="PMDconstant",
                                 help="constant C in geometric probability \
                                 distribution of PMD",
                                 default=0.01)
        pmd_options.add_argument("--polymorphism_ancient",
                                 action="store",
                                 type=float,
                                 dest="polymorphism_ancient",
                                 help="True biological polymorphism between the \
                                 ancient individual and the reference sequence",
                                 default=0.001)
        pmd_options.add_argument("--polymorphism_contamination",
                                 action="store",
                                 type=float,
                                 dest="polymorphism_contamination",
                                 help="True biological polymorphism between the \
                                 contaminants and the reference sequence",
                                 default=0.001)

        # options to filter SAM file
        filter_options = parser.add_argument_group(
            title="filters",
            description=("restrict analysis to a subset of reads by setting these "
                         "thresholds")
        )
        filter_options.add_argument("--dry",
                                    action="store_true",
                                    dest="dry",
                                    help="print SAM input without any filters",
                                    default=False)
        filter_options.add_argument("-n", "--number",
                                    action="store",
                                    type=int,
                                    dest="maxreads",
                                    help="stop after these many reads have been \
                                    processed",
                                    default=(10 ** 20))
        filter_options.add_argument("-c", "--chromosome",
                                    action="store",
                                    type=str,
                                    dest="chromosome",
                                    help="only process data from this chromosome",
                                    default=False)
        filter_options.add_argument("--perc_identity",
                                    type=float,
                                    dest="perc_identity",
                                    help="only output sequences with percent \
                                    identity above this threshold",
                                    default=0.0)
        filter_options.add_argument("-m", "--requiremapq",
                                    action="store",
                                    type=int,
                                    dest="mapq",
                                    help="only process sequences with mapping \
                                    quality at least this great",
                                    default=0)
        filter_options.add_argument("--readlength",
                                    action="store",
                                    type=int,
                                    dest="readlength",
                                    help="only process sequences with this read \
                                    length",
                                    default=0)
        filter_options.add_argument("--maxlength",
                                    action="store",
                                    type=int,
                                    dest="maxlength",
                                    help="only process sequences with max this \
                                    read length",
                                    default=300)
        filter_options.add_argument("--minlength",
                                    action="store",
                                    type=int,
                                    dest="minlength",
                                    help="only process sequences with min this \
                                    read length",
                                    default=0)
        filter_options.add_argument("--maxGC",
                                    action="store",
                                    type=float,
                                    dest="maxGC",
                                    help="only process sequences with max this \
                                    GC content of the aligning reference sequence",
                                    default=1.0)
        filter_options.add_argument("--minGC",
                                    action="store",
                                    type=float,
                                    dest="minGC",
                                    help="only process sequences with min this GC \
                                    content of the aligning reference sequence",
                                    default=0.0)
        filter_options.add_argument("-q", "--requirebaseq",
                                    action="store",
                                    type=int,
                                    dest="baseq",
                                    help="only process bases with base quality at \
                                    least this great",
                                    default=0)
        filter_options.add_argument("-t", "--threshold",
                                    type=float,
                                    dest="threshold",
                                    help="only output sequences with PMD score \
                                    above this threshold",
                                    default=(-20000.0))
        filter_options.add_argument("--upperthreshold",
                                    type=float,
                                    dest="upperthreshold",
                                    help="only output sequences with PMD score \
                                    below this threshold",
                                    default=(1000000.0))

        # options controlling handling of CpG contexts
        cpg_options = parser.add_argument_group(
            title="CpG context",
            description=("options controlling the handling of deamination in CpG "
                         "contexts")
        )
        # --CpG, --noCpG and --UDGhalf exclude each other
        cpg_excl = cpg_options.add_mutually_exclusive_group(required=False)
        cpg_excl.add_argument("--CpG", "--UDGplus",
                              action="store_true",
                              dest="cpg",
                              help="only use Cs and Gs in CpG context",
                              default=False)
        cpg_excl.add_argument("--noCpG",
                              action="store_true",
                              dest="nocpg",
                              help="dont use Cs and Gs in CpG context",
                              default=False)
        cpg_excl.add_argument("--UDGhalf",
                              action="store_true",
                              dest="UDGhalf",
                              help=("only use Cs and Gs in CpG context, the first "
                                    "and last base are used regardless of "
                                    "dinucleotide context"),
                              default=False)
    #    cpg_options.add_argument("--UDGplus",
    #                             action="store_true",
    #                             dest="UDGplus",
    #                             help="only use Cs and Gs in CpG context \
    #                             (synonymous with option --CpG)",
    #                             default=False)
        cpg_options.add_argument("--UDGminus",
                                 action="store_true",
                                 dest="UDGminus",
                                 help="use all bases (placeholder)",
                                 default=False)

        # options controlling output
        output_options = parser.add_argument_group(
            title="output",
            description="control type and detail of output"
        )
        output_options.add_argument("--verbose",
                                    action="store_true",
                                    dest="verbose",
                                    help="verbose",
                                    default=False)
        output_options.add_argument("--header",
                                    action="store_true",
                                    dest="header",
                                    help="output the SAM header",
                                    default=False)
        output_options.add_argument("--writesamfield",
                                    action="store_true",
                                    dest="writesamfield",
                                    help="add 'DS:Z:<PMDS>' field to SAM output, \
                                    will overwrite if already present",
                                    default=False)
        output_options.add_argument("--stats",
                                    action="store_true",
                                    dest="stats",
                                    help="output summarizing statistics to stderr",
                                    default=False)
        output_options.add_argument("--debug",
                                    action="store_true",
                                    dest="debug",
                                    help="show additional information for \
                                    debugging",
                                    default=False)
        output_options.add_argument("-p", "--printDS",
                                    action="store_true",
                                    dest="printDS",
                                    help="print PMD scores",
                                    default=False)
        output_options.add_argument("--printalignments",
                                    action="store_true",
                                    dest="printalignments",
                                    help="print human readable alignments",
                                    default=False)
        output_options.add_argument("--platypus",
                                    action="store_true",
                                    dest="platypus",
                                    help="output big list of base frequencies for \
                                    platypus",
                                    default=False)
        output_options.add_argument("-d", "--deamination",
                                    action="store_true",
                                    dest="deamination",
                                    help="output base frequencies in the read at \
                                    positions where there are C or G in the \
                                    reference",
                                    default=False)

        # options to modify calculation of percent identity
        perc_id_options = parser.add_argument_group(
            title="percent identity",
            description="options to modify calculation of percent identity"
        )
        perc_id_options.add_argument("--include_deamination",
                                     action="store_true",
                                     dest="include_deamination",
                                     help="treat possibly deaminated T>C and A>G \
                                     pairs as mismatches in calculation of \
                                     percent identity",
                                     default=False)
        perc_id_options.add_argument("--include_indels",
                                     action="store_true",
                                     dest="include_indels",
                                     help="treat insertions and deletions as \
                                     mismatches in calculation of percent \
                                     identity",
                                     default=False)
        perc_id_options.add_argument("--include_unknown",
                                     action="store_true",
                                     dest="include_unknown",
                                     help="treat Ns in either read or reference \
                                     as mismatch in calculation of percent \
                                     identity",
                                     default=False)

        # remaining options
        parser.add_argument('--version',
                            action='version',
                            version='%(prog)s v0.55 (modified)')
        parser.add_argument("--PMDSprim",
                            action="store_true",
                            dest="PMDSprim",
                            help="PMDSprim",
                            default=False)
        parser.add_argument("--PMDSprimthreshold",
                            action="store",
                            type=float,
                            dest="PMDSprimthreshold",
                            help="PMDSprimthreshold",
                            default=False)
        parser.add_argument("--first",
                            action="store_true",
                            dest="first",
                            help="outputs the deamination rate at the first \
                            position only, but with a standard error",
                            default=False)
        parser.add_argument("--range",
                            action="store",
                            type=int,
                            dest="range",
                            help="output deamination patterns for this many \
                            positions from the sequence terminus (default=30)",
                            default=30)
        parser.add_argument("--noclips",
                            action="store_true",
                            dest="noclips",
                            help="no clips",
                            default=False)
        parser.add_argument("--noindels",
                            action="store_true",
                            dest="noindels",
                            help="no indels",
                            default=False)
        parser.add_argument("--onlyclips",
                            action="store_true",
                            dest="onlyclips",
                            help="only clips",
                            default=False)
        parser.add_argument("--onlydeletions",
                            action="store_true",
                            dest="onlydeletions",
                            help="only deletions",
                            default=False)
        parser.add_argument("--onlyinsertions",
                            action="store_true",
                            dest="onlyinsertions",
                            help="only insertions",
                            default=False)
        parser.add_argument("--nodeletions",
                            action="store_true",
                            dest="nodeletions",
                            help="no deletions",
                            default=False)
        parser.add_argument("--noinsertions",
                            action="store_true",
                            dest="noinsertions",
                            help="no insertions",
                            default=False)
        parser.add_argument("--notreverse",
                            action="store_true",
                            dest="notreverse",
                            help="no reverse complement alignments",
                            default=False)
        parser.add_argument("-a", "--adjustbaseq",
                            action="store_true",
                            dest="adjustbaseq",
                            help="apply PMD-aware adjustment of base quality \
                            scores specific to C>T and G>A mismatches to the \
                            reference",
                            default=False)
        parser.add_argument("--adjustbaseq_all",
                            action="store_true",
                            dest="adjustbaseq_all",
                            help="apply PMD-aware adjustment of base quality \
                            scores regardless of observed bases",
                            default=False)
        parser.add_argument("--samtoolspath",
                            action="store",
                            dest="samtoolspath",
                            help="full path to samtools",
                            default='samtools')
        parser.add_argument("--basecomposition",
                            action="store_true",
                            dest="basecomposition",
                            help="basecomposition",
                            default=False)
        parser.add_argument("-r", "--refseq",
                            action="store",
                            dest="refseq",
                            help="refseq",
                            default=False)
        parser.add_argument("--estimate",
                            action="store_true",
                            dest="estimate",
                            help="two-terminus estimate of contamination",
                            default=False)
        parser.add_argument("--estimatebase",
                            action="store",
                            type=int,
                            dest="estimatebase",
                            help="position of base used fortwo-terminus estimate \
                            of contamination",
                            default=0)
        parser.add_argument("-b", "--basic",
                            action="store",
                            type=int,
                            dest="basic",
                            help="only output reads with a C>T mismatch this \
                            many basepairs from the 5' end",
                            default=0)

        (options, args) = parser.parse_known_args()

    #    if options.UDGplus:
    #        options.CpG = True

    #    maxlen = options.maxlength

        # pre-compute probabilities of deamination for 1000 bases under modern and
        # ancient models
        modern_model_deam = [i for i in repeat(0.001, 1000)]
        ancient_model_deam = [geometric(options.PMDpparam, i, options.PMDconstant)
                              for i in range(1, 1000)]
        adjustment_model_deam = None
        if options.adjustbaseq_all:
            # constant is 0.0 here in contrast to model used to compute PMD scores
            adjustment_model_deam = [geometric(options.PMDpparam, i, 0.0)
                                     for i in range(1, 1000)]

        # base composition
    #    start_count = 0
    #    rev_start_count = 0
    #    not_counted = 0
    #    imperfect = 0

    #    start_dict= {}
    #    start_dict_rev = {}
        mismatch_dict = {}
        mismatch_dict_rev = {}
    #    mismatch_dict_CpG = {}
    #    mismatch_dict_CpG_rev = {}

        firstC = 0
        firstT = 0

        clipexcluded = 0
        indelexcluded = 0
        cigarextexcluded = 0  # reads excluded for extended CIGAR (N, H, P, not S)
        identityexcluded = 0  # reads excluded due to percent identity filter
        noMD = 0
        noGCexcluded = 0
        excluded_threshold = 0
        passed = 0
        noquals = 0
    #    maskings = 0
    #
    #    CCandCC = 0
    #    CTandCC = 0
    #    CCandCT = 0
    #    CTandCT = 0
    #
    #    estimator_list = []

        composition_dict = {}
        composition_dict_rev = {}

        line_counter = 0
        for line in sys.stdin:
            if '@' in line[0]:
                if options.header:
                    print(line.rstrip('\n'))
                continue
            line_counter += 1
            line = line.rstrip('\n')
            col = line.split('\t')

            if options.debug:
                print(col)

    #        readname = col[0]
            position = int(col[3])
            chromosome = col[2]

            if options.chromosome:
                if chromosome != options.chromosome:
                    continue
            MAPQ = int(col[4])
            read = col[9]
            readlen = len(read)
            quals = col[10]
            flag = col[1]
            position = int(col[3])
            cigar = col[5]

            if len(quals) < 2:
                noquals += 1
                continue

            if options.noinsertions:
                if 'I' in cigar:
                    continue
            if options.nodeletions:
                if 'D' in cigar:
                    continue
            if options.onlyinsertions:
                if 'I' not in cigar:
                    continue
            if options.onlydeletions:
                if 'D' not in cigar:
                    continue
            if options.noindels:
                if 'I' in cigar or 'D' in cigar:
                    indelexcluded += 1
                    continue
            if options.noclips:
                if 'S' in cigar or 'H' in cigar or 'N' in cigar or 'P' in cigar:
                    clipexcluded += 1
                    continue
            if options.onlyclips:
                if 'S' not in cigar:
                    continue
            if 'H' in cigar or 'P' in cigar or 'N' in cigar:
                print("cigar found: %s" % (cigar,),
                      "PMDtools only supports cigar operations M, I, S and D,",
                      "the alignment has been excluded",
                      file=sys.stderr)
                cigarextexcluded += 1
                continue
            if MAPQ < options.mapq:
                continue

            # read length filter
            # TODO check for default values unnecessary, just remove defaults and
            # check for args directly
            if options.readlength > 0:
                if options.readlength != len(read):
                    continue
            if options.minlength > 0:
                if options.minlength > len(read):
                    continue
            if options.maxlength != 300:
                if options.maxlength < len(read):
                    continue

            # chromosome filter
            if options.chromosome:
                if chromosome != options.chromosome:
                    continue

            # check orientation of read
            if flag.isdigit():
                if int(flag) & 16:
                    reverse = True
                else:
                    reverse = False
            else:
                if 'r' in flag:
                    reverse = True
                else:
                    reverse = False

            # filter reverse reads
            if options.notreverse:
                if reverse:
                    continue

            # check for previously calculated PMD score
            DSfield = False
            if 'DS:Z:' in line:
                DSfield = True
                PMDS = float(line.split('DS:Z:')[1].rstrip('\n').split()[0])
                LR = PMDS
    #            print PMDS

            """
            Recreate reference sequence from MD field
            """
            if (DSfield is False
               or options.writesamfield
               or options.basic > 0
               or options.perc_identity > 0.01
               or options.printalignments
               or options.adjustbaseq
               or options.adjustbaseq_all
               or options.deamination
               or options.dry
               or options.estimate
               or options.first):
                read = col[9]
                try:
                    MD = line.split('MD:Z:')[1].split()[0].rstrip('\n')
                except IndexError:
                    noMD += 1
                    continue

                # using external library to reconstruct reference from CIGAR and MD
                rec = reconstruct(read, cigar, MD)
                (rec_ref_full, rec_read_full, rec_ref_aln, rec_read_aln) = rec

                if reverse:
                    read = reverse_complement(read)
                    # don't use raw read beyond here...
                    rec_ref_full = reverse_complement(rec_ref_full)
                    rec_read_full = reverse_complement(rec_read_full)
                    rec_ref_aln = reverse_complement(rec_ref_aln)
                    rec_read_aln = reverse_complement(rec_read_aln)
                    quals = quals[::-1]

                # this assumption previously lead to trouble because of missing
                # gaps in aligned read!
                real_read = read
                real_ref_seq = rec_ref_aln

            # debugging the reference sequence reconstruction
            if options.debug:
                print("%s\tread\n%s\tref" % (real_read, real_ref_seq))
                print("CIGAR:\t%s" % (cigar,))
                print("MD:\t%s" % (MD,))

            # GC content filter
            # FA: Read and aligned reference can have different lengths!
            # See genetics.gc() for a faster, case-unaware alternative considering
            # all ambiguity codes.
    #        GCcontent = (1.0 * (real_ref_seq.count('G') + real_ref_seq.count('C'))
    #                     / readlen)
            GCcontent = gc(real_ref_seq)
            if GCcontent > options.maxGC:
                continue
            elif GCcontent < options.minGC:
                continue

            # test for empty reference sequence TODO include in reconstruction
            # method and throw exception?
            if ('G' not in real_ref_seq
               and 'C' not in real_ref_seq
               and 'T' not in real_ref_seq
               and 'A' not in real_ref_seq):
                print('bad reference sequence reconstruction: %s' % real_ref_seq,
                      file=sys.stderr)
                print('SAM line: %s' % (line,))
                exit(1)

            if options.basecomposition:
                backoffset = 10
                if reverse:
                    endpos = position
                    startpos = position + len(real_read)
                else:
                    startpos = position
                    endpos = position + len(real_read)

                """
                5' end
                """
                largerefseq = fa_get(options.refseq,
                                     chromosome,
                                     startpos - backoffset,
                                     startpos + options.range)
                if len(largerefseq) < 1:
                    continue
    #            print largerefseq
                if reverse:
                    largerefseq = reverse_complement(largerefseq)
    #            print(largerefseq, real_read)
                for i in range(-backoffset, options.range):
    #                print(i+backoffset, len(largerefseq))
                    base = largerefseq[min([i + backoffset, len(largerefseq)])]
                    thekey = '5' + base + str(i)
                    if thekey in composition_dict.keys():
                        addition = composition_dict[thekey]
                        addition += 1
                        composition_dict[thekey] = addition
                    else:
                        composition_dict[thekey] = 1

                """
                3' end
                """
                largerefseq = fa_get(options.refseq,
                                     chromosome,
                                     endpos - options.range,
                                     endpos + backoffset)
                if len(largerefseq) < 1:
                    continue
                if reverse:
                    largerefseq = reverse_complement(largerefseq)

                for i in range(-backoffset, options.range):
                    base = largerefseq[min([i + backoffset, len(largerefseq)])]
                    thekey = '3' + base + str(i)
                    if thekey in composition_dict_rev.keys():
                        addition = composition_dict_rev[thekey]
                        addition += 1
                        composition_dict_rev[thekey] = addition
                    else:
                        composition_dict_rev[thekey] = 1
                continue    # useless?

            """
            basic filter
            prints the SAM line if a C>T mismatch with sufficient base quality is
            observed in the first n bases, where n is specified
            """
            if options.basic > 0:
    #            start_position = len(real_read) - len(real_read.lstrip('-'))
                for a, b, x in zip(real_read, real_ref_seq,
                                   range(0, len(real_ref_seq))):
                    if a == 'N':
                        break
                    elif b == 'N':
                        break
                    elif a == '-':
                        break
                    elif b == '-':
                        break
                    i = x                       # - start_position
                    if i >= readlen:            # 20
                        break
                    if i >= options.basic:
                        break
    #                print(a, b, i)
                    if options.cpg:
                        if (b == 'C' and a == 'T'
                           and ord(quals[i] - 33) > options.baseq
                           and real_ref_seq[i + 1] == 'G'):
                            print(line.rstrip('\n'))
                            break

                    elif (b == 'C' and a == 'T'
                          and ord(quals[i]) - 33 > options.baseq):
                        print(line.rstrip('\n'))
                        break

            """
            first base
            prints the deamination rate at the first base and a standard error
            computed by jackknife over reads
            """
            if options.first:
                b = real_ref_seq[0]
                a = real_read[0]
                if options.cpg:
                    if (b == 'C' and a == 'T'
                       and ((ord(quals[0])-33) > options.baseq)
                            and (real_ref_seq[1] == 'G')):
                        firstT += 1
                    if (b == 'C' and a == 'C'
                       and ((ord(quals[0])-33) > options.baseq)
                            and (real_ref_seq[1] == 'G')):
                        firstC += 1
                else:
                    if (b == 'C' and a == 'T'
                       and ord(quals[0]) - 33 > options.baseq):
                        firstT += 1
                    if (b == 'C' and a == 'C'
                       and ord(quals[0]) - 33 > options.baseq):
                        firstC += 1

            if options.perc_identity > 0.01 or options.printalignments:
                """
                divergence filter
                """
                (perc_identity, mismatch_string) = aln_identity(
                    rec_read_aln,
                    rec_ref_aln,
                    options.include_indels,
                    options.include_deamination,
                    options.include_unknown
                )
                if perc_identity < options.perc_identity:
                    identityexcluded += 1
                    continue

            """
            start PMD score computations
            """
            if (DSfield is False
               or (DSfield is True and options.writesamfield is True)
               or options.basic > 0
               or options.adjustbaseq
               or options.adjustbaseq_all
               or options.deamination
               or options.dry):

                (L_D, L_M, LR, newquals) = score_pmd(
                    rec_read_full,  # was: real_read
                    rec_ref_full,   # was: real_ref_seq
                    quals,
                    ancient_model_deam,
                    modern_model_deam,
                    adjustment_model_deam,
                    options.polymorphism_contamination,
                    options.polymorphism_ancient,
                    options.adjustbaseq_all,
                    options.adjustbaseq,
                    options.baseq
                )
                # TODO re-enable these options step by step...
    #                options.deamination,
    #                options.cpg,
    #                options.nocpg,
    #                options.UDGhalf,
    #                options.PMDSprim)

    #            if options.PMDSprim:
    #                maxPMDSval = math.log(L_D_max / L_M_max)
    #                maxPMDSval = maxPMDSval / readlen
    #                if LR > 0.0:
    #                    LRnumerator = math.log(L_D_max / L_M_max)
    #                elif LR < 0.0:
    #                    LRnumerator = math.log(L_D_max / L_M_max)
    #                if options.PMDSprimthreshold:
    #                    if maxPMDSval < options.PMDSprimthreshold:
    #                        continue
    #
    #                if options.printDS:
    #                    print(LR, maxPMDSval, maxPMDSval, maxPMDSval * readlen,
    #                          readlen)
    ##                LR = LR / LRnumerator
    #            quals = newquals

            if options.adjustbaseq:
                if reverse:
                    qualsp = quals[::-1]
                else:
                    qualsp = quals
                line = ('\t'.join(col[0:10]) + '\t'
                        + qualsp + '\t'
                        + '\t'.join(col[11:]))

            """
            add PMDS tag
            """
            if options.writesamfield is True:
                # remove DS field if present
                if DSfield is True:
                    newline = ''
                    for col in line.split('\t'):
                        if 'DS:Z:' in col:
                            continue
                        else:
                            newline += col + '\t'
                    line = newline.rstrip('\t')

                line = line.rstrip('\n') + '\t' + 'DS:Z:' + str(round(LR, 3))

            if options.printDS:
                print(L_D, '\t', L_M, '\t', L_D / L_M, '\t', LR)
    #            print(L_D, '\t', L_M, '\t', L_D / L_M, '\t', LR, '\t',
    #                  readlen, '\t',
    #                  perc_identity, '\t',
    #                  perc_identity * math.log(L_D / L_M))

            if options.dry:
                if len(line) < 1:
                    continue
                print(line.rstrip('\n'))
                continue

            if options.threshold > (-10000) or options.upperthreshold < (1000000):
                if LR >= options.threshold and LR < options.upperthreshold:
                    print(line.rstrip('\n'))
                else:
                    excluded_threshold += 1

            if options.printalignments:
                if (options.threshold > -10000
                   or options.upperthreshold < 1000000):
                    try:
                        LR = math.log(L_D / L_M)
                    except:
                        continue
                    if (LR < options.threshold
                       or LR > options.upperthreshold < 1000000):
                        continue

                quals1 = ''
                quals2 = ''
                for q in quals:
                    qnum = ord(q) - 33
                    if qnum < 10:
                        quals1 += '0'
                        quals2 += str(qnum)
                    else:
                        quals1 += str(qnum)[0]
                        quals2 += str(qnum)[1]
    #            print(MD, cigar, reverse)
    #            print(col[9])
                print(real_read)
                print(mismatch_string)
                print(real_ref_seq)
                print(quals)
    #            print(quals1)
    #            print(quals2)
    #            print(col[10])
                print('')
            passed += 1
            if passed >= options.maxreads:
                break

        if options.first:
            n = firstC + firstT
            freq = 1.0 * firstT / n
            SE = math.sqrt((freq * (1.0 - freq)) / n)
            if freq == 0.0:
                SE = 'NA'
            print('C>T at first position and SE:', freq, '\t', SE)
    #        print('C>T at first position and SE:', freq, '\t', SE, '\t',
    #              n, firstC, firstT)

        if options.stats:
            print('""""""""""""""""""""""""""""""""', file=sys.stderr)

            # added stats
            print("excluded due to extended CIGAR (H, N, P):\t%i"
                  % (cigarextexcluded,), file=sys.stderr)
            print("excluded due percent identity filter:\t%i"
                  % (identityexcluded,), file=sys.stderr)

            print("excluded due to clipping:\t%i" % (clipexcluded,),
                  file=sys.stderr)
            print("excluded due to indels:\t%i" % (indelexcluded,),
                  file=sys.stderr)
            print("no MD field:\t%i" % (noMD,), file=sys.stderr)
            print("no G or C in ref:\t%i" % (noGCexcluded,), file=sys.stderr)
            print("total seqs:\t%i" % (passed,), file=sys.stderr)
            print("excluded due to PMD score < %i:\t%i"
                  % (int(options.threshold), excluded_threshold), file=sys.stderr)
            print("passed seqs:\t%i" % (passed - excluded_threshold,),
                  file=sys.stderr)

            print('""""""""""""""""""""""""""""""""', file=sys.stderr)

        if options.deamination:
            if True:
                pairs = ['CT', 'CA', 'CG', 'CC', 'GA', 'GT', 'GC', 'GG']
                itotaldict = {}
                ztotaldict = {}
                for i in range(0, options.range):
                    itotal = 0
                    ztotal = 0
                    for p in pairs:
                        thekey = p + str(i)
                        try:
                            itotal += mismatch_dict[thekey]
                        except KeyError:
                            pass
                        try:
                            ztotal += mismatch_dict_rev[thekey]
                        except KeyError:
                            pass
                    itotaldict[i] = itotal
                    ztotaldict[i] = ztotal

                print('z\t', '\t'.join(pairs))

                for i in range(0, options.range):
                    print(str(i) + '\t')
                    for p in pairs:
                        thekey = p + str(i)
                        if 'C' in p[0]:
                            try:
                                thecount = mismatch_dict[thekey]
                            except KeyError:
                                print('0.00000\t')
                                continue
                            thetotal = itotaldict[i]
                            frac = 1.0 * thecount / thetotal
                        if 'G' in p[0]:
                            try:
                                thecount = mismatch_dict_rev[thekey]
                            except KeyError:
                                print('0.00000\t')
                                continue
                            thetotal = ztotaldict[i]
                            frac = 1.0 * thecount / thetotal
                        print(str(round(frac, 5)) + '\t')
                    print('')

        if options.basecomposition:
            print(composition_dict)
            print(composition_dict_rev)
            if True:
                pairs = ['5T', '5A', '5G', '5C', '3T', '3A', '3G', '3C']
                itotaldict = {}
                ztotaldict = {}
                for i in range(-backoffset, options.range):
                    itotal = 0
                    ztotal = 0
                    for p in pairs:
                        thekey = p + str(i)
                        try:
                            itotal += composition_dict[thekey]
                        except KeyError:
                            pass
                        try:
                            ztotal += composition_dict_rev[thekey]
                        except KeyError:
                            pass
                    itotaldict[i] = itotal
                    ztotaldict[i] = ztotal

                print('z\t', '\t'.join(pairs))

                for i in range(-backoffset, options.range):
                    print(str(i) + '\t')
                    for p in pairs:
                        thekey = p + str(i)
                        if '5' in p[0]:
                            try:
                                thecount = composition_dict[thekey]
                            except KeyError:
                                print('0.00000\t')
                                continue
                            thetotal = itotaldict[i]
                            frac = 1.0 * thecount / thetotal
                        if '3' in p[0]:
                            try:
                                thecount = composition_dict_rev[thekey]
                            except KeyError:
                                print('0.00000\t')
                                continue
                            thetotal = ztotaldict[i]
                            frac = 1.0 * thecount / thetotal
                        print(str(round(frac, 5)) + '\t')
                    print('')

        exit()

    if __name__ == "__main__":
        main()

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