# Copyright 2013 by Zheng Ruan (zruan1991@gmail.com).
# All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license.  Please see the LICENSE file that should have been included
# as part of this package.

"""Code for dealing with Codon Alignment.
"""
from __future__ import print_function

from Bio import BiopythonWarning
from Bio import BiopythonExperimentalWarning

try:
    from itertools import izip
except ImportError:
    izip = zip
# from itertools import izip

from Bio.SeqRecord import SeqRecord

from Bio.codonalign.codonseq import CodonSeq
from Bio.codonalign.codonalignment import CodonAlignment, mktest
from Bio.codonalign.codonalphabet import CodonAlphabet
from Bio.codonalign.codonalphabet import default_codon_table, default_codon_alphabet
from Bio.codonalign.codonalphabet import get_codon_alphabet as _get_codon_alphabet

import warnings
warnings.warn('Bio.codonalign is an experimental module which may undergo '
              'significant changes prior to its future official release.',
              BiopythonExperimentalWarning)


def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
          codon_table=default_codon_table, alphabet=None,
          complete_protein=False, anchor_len=10, max_score=10):
    """Build a codon alignment from a protein alignment and
    corresponding nucleotide sequences

    Arguments:
        - pro_align  - a protein MultipleSeqAlignment object
        - nucl_align - an object returned by SeqIO.parse or SeqIO.index
          or a collection of SeqRecord.
        - alphabet   - alphabet for the returned codon alignment
        - corr_dict  - a dict that maps protein id to nucleotide id
        - complete_protein - whether the sequence begins with a start
          codon
        - frameshift - whether to apply frameshift detection

    Return a CodonAlignment object

    >>> from Bio.Alphabet import IUPAC
    >>> from Bio.Seq import Seq
    >>> from Bio.SeqRecord import SeqRecord
    >>> from Bio.Align import MultipleSeqAlignment
    >>> seq1 = SeqRecord(Seq('TCAGGGACTGCGAGAACCAAGCTACTGCTGCTGCTGGCTGCGCTCTGCGCCGCAGGTGGGGCGCTGGAG',
    ...     alphabet=IUPAC.IUPACUnambiguousDNA()), id='pro1')
    >>> seq2 = SeqRecord(Seq('TCAGGGACTTCGAGAACCAAGCGCTCCTGCTGCTGGCTGCGCTCGGCGCCGCAGGTGGAGCACTGGAG',
    ...     alphabet=IUPAC.IUPACUnambiguousDNA()), id='pro2')
    >>> pro1 = SeqRecord(Seq('SGTARTKLLLLLAALCAAGGALE', alphabet=IUPAC.protein),id='pro1')
    >>> pro2 = SeqRecord(Seq('SGTSRTKRLLLLAALGAAGGALE', alphabet=IUPAC.protein),id='pro2')
    >>> aln = MultipleSeqAlignment([pro1, pro2])
    >>> codon_aln = build(aln, [seq1, seq2])
    >>> print(codon_aln)
    CodonAlphabet(Standard) CodonAlignment with 2 rows and 69 columns (23 codons)
    TCAGGGACTGCGAGAACCAAGCTACTGCTGCTGCTGGCTGCGCTCTGCGCCGCAGGT...GAG pro1
    TCAGGGACTTCGAGAACCAAGCG-CTCCTGCTGCTGGCTGCGCTCGGCGCCGCAGGT...GAG pro2

    """
    # TODO
    # add an option to allow the user to specify the returned object?

    from Bio.Alphabet import ProteinAlphabet
    from Bio.Align import MultipleSeqAlignment

    # check the type of object of pro_align
    if not isinstance(pro_align, MultipleSeqAlignment):
        raise TypeError("the first argument should be a MultipleSeqAlignment "
                        "object")
    # check the alphabet of pro_align
    for pro in pro_align:
        if not isinstance(pro.seq.alphabet, ProteinAlphabet):
            raise TypeError("Alphabet Error!\nThe input alignment should be "
                            "a *PROTEIN* alignment")
    if alphabet is None:
        alphabet = _get_codon_alphabet(codon_table, gap_char=gap_char)
    # check whether the number of seqs in pro_align and nucl_seqs is
    # the same
    pro_num = len(pro_align)
    if corr_dict is None:
        if nucl_seqs.__class__.__name__ == "generator":
            # nucl_seqs will be a tuple if read by SeqIO.parse()
            nucl_seqs = tuple(nucl_seqs)
        nucl_num = len(nucl_seqs)
        if pro_num > nucl_num:
            raise ValueError("Higher Number of SeqRecords in Protein Alignment "
                             "({0}) than the Number of Nucleotide SeqRecords "
                             "({1}) are found!".format(pro_num, nucl_num))

        # Determine the protein sequences and nucl sequences
        # correspondence. If nucl_seqs is a list, tuple or read by
        # SeqIO.parse(), we assume the order of sequences in pro_align
        # and nucl_seqs are the same. If nucl_seqs is a dict or read by
        # SeqIO.index(), we match seqs in pro_align and those in
        # nucl_seq by their id.
        if nucl_seqs.__class__.__name__ in ("_IndexedSeqFileDict", "dict"):
            corr_method = 1
        elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
            corr_method = 0
        else:
            raise TypeError("Nucl Sequences Error, Unknown type to assign "
                            "correspondence method")
    else:
        if not isinstance(corr_dict, dict):
            raise TypeError("corr_dict should be a dict that corresponds "
                            "protein id to nucleotide id!")
        if len(corr_dict) >= pro_num:
            # read by SeqIO.parse()
            if nucl_seqs.__class__.__name__ == "generator":
                from Bio import SeqIO
                nucl_seqs = SeqIO.to_dict(nucl_seqs)
            elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
                nucl_seqs = dict((i.id, i) for i in nucl_seqs)
                # nucl_seqs = {i.id: i for i in nucl_seqs}
            elif nucl_seqs.__class__.__name__ in \
                    ("_IndexedSeqFileDict", "dict"):
                pass
            else:
                raise TypeError("Nucl Sequences Error, Unknown type of "
                                "Nucleotide Records!")
            corr_method = 2
        else:
            raise RuntimeError("Number of items in corr_dict ({0}) is less "
                               "than number of protein records "
                               "({1})".format(len(corr_dict), pro_num))

    # set up pro-nucl correspondence based on corr_method
    # corr_method = 0, consecutive pairing
    if corr_method == 0:
        pro_nucl_pair = izip(pro_align, nucl_seqs)
    # corr_method = 1, keyword pairing
    elif corr_method == 1:
        nucl_id = set(nucl_seqs.keys())
        pro_id = set(i.id for i in pro_align)
        # check if there is pro_id that does not have a nucleotide match
        if pro_id - nucl_id:
            diff = pro_id - nucl_id
            raise ValueError("Protein Record {0} cannot find a nucleotide "
                             "sequence match, please check the "
                             "id".format(', '.join(diff)))
        else:
            pro_nucl_pair = []
            for pro_rec in pro_align:
                pro_nucl_pair.append((pro_rec, nucl_seqs[pro_rec.id]))
    # corr_method = 2, dict pairing
    elif corr_method == 2:
        pro_nucl_pair = []
        for pro_rec in pro_align:
            try:
                nucl_id = corr_dict[pro_rec.id]
            except KeyError:
                print("Protein record (%s) is not in corr_dict!" % pro_rec.id)
                exit(1)
            pro_nucl_pair.append((pro_rec, nucl_seqs[nucl_id]))

    codon_aln = []
    shift = False
    for pair in pro_nucl_pair:
        # Beware that the following span corresponds to an ungapped
        # nucleotide sequence.
        corr_span = _check_corr(pair[0], pair[1], gap_char=gap_char,
                                codon_table=codon_table,
                                complete_protein=complete_protein,
                                anchor_len=anchor_len)
        if not corr_span:
            raise ValueError("Protein Record {0} and Nucleotide Record {1} do"
                             " not match!".format((pair[0].id, pair[1].id)))
        else:
            codon_rec = _get_codon_rec(pair[0], pair[1], corr_span,
                                       alphabet=alphabet,
                                       complete_protein=False,
                                       codon_table=codon_table,
                                       max_score=max_score)
            codon_aln.append(codon_rec)
            if corr_span[1] == 2:
                shift = True
    if shift:
        return CodonAlignment(_align_shift_recs(codon_aln), alphabet=alphabet)
    else:
        return CodonAlignment(codon_aln, alphabet=alphabet)


def _codons2re(codons):
    """Generate regular expression based on a given list of codons
    """
    reg = ''
    for i in izip(*codons):
        if len(set(i)) == 1:
            reg += ''.join(set(i))
        else:
            reg += '[' + ''.join(set(i)) + ']'
    return reg


def _get_aa_regex(codon_table, stop='*', unknown='X'):
    """Set up the regular expression of a given CodonTable for
    further use.

    >>> from Bio.Data.CodonTable import generic_by_id
    >>> p = generic_by_id[1]
    >>> t = _get_aa_regex(p)
    >>> print(t['A'][0])
    G
    >>> print(t['A'][1])
    C
    >>> print(sorted(list(t['A'][2:])))
    ['A', 'C', 'G', 'T', 'U', '[', ']']
    >>> print(sorted(list(t['L'][:5])))
    ['C', 'T', 'U', '[', ']']
    >>> print(sorted(list(t['L'][5:9])))
    ['T', 'U', '[', ']']
    >>> print(sorted(list(t['L'][9:])))
    ['A', 'C', 'G', 'T', 'U', '[', ']']

    """
    from Bio.Data.CodonTable import CodonTable
    if not isinstance(codon_table, CodonTable):
        raise TypeError("Input table is not a instance of "
                        "Bio.Data.CodonTable object")
    aa2codon = {}
    for codon, aa in codon_table.forward_table.items():
        aa2codon.setdefault(aa, []).append(codon)
    for aa, codons in aa2codon.items():
        aa2codon[aa] = _codons2re(codons)
    aa2codon[stop] = _codons2re(codon_table.stop_codons)
    aa2codon[unknown] = '...'
    return aa2codon


def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
                complete_protein=False, anchor_len=10):
    """check if a give protein SeqRecord can be translated by another
    nucleotide SeqRecord.
    """
    import re
    from Bio.Alphabet import NucleotideAlphabet

    if not isinstance(pro, SeqRecord) or not isinstance(nucl, SeqRecord):
        raise TypeError("_check_corr accepts two SeqRecord object. Please "
                        "check your input.")

    def get_alpha(alpha):
        if hasattr(alpha, 'alphabet'):
            return get_alpha(alpha.alphabet)
        else:
            return alpha

    if not isinstance(get_alpha(nucl.seq.alphabet), NucleotideAlphabet):
        raise TypeError("Alphabet for nucl should be an instance of "
                        "NucleotideAlphabet, {0} "
                        "detected".format(str(nucl.seq.alphabet)))

    aa2re = _get_aa_regex(codon_table)
    pro_re = ""
    for aa in pro.seq:
        if aa != gap_char:
            pro_re += aa2re[aa]

    nucl_seq = str(nucl.seq.upper().ungap(gap_char))
    match = re.search(pro_re, nucl_seq)
    if match:
        # mode = 0, direct match
        return (match.span(), 0)
    else:
        # Might caused by mismatches or frameshift, using anchors to
        # have a try
        # anchor_len = 10 # adjust this value to test performance
        pro_seq = str(pro.seq).replace(gap_char, "")
        anchors = [pro_seq[i:(i + anchor_len)] for i in
                   range(0, len(pro_seq), anchor_len)]
        # if the last anchor is less than the specified anchor
        # size, we combine the penultimate and the last anchor
        # together as the last one.
        # TODO: modify this to deal with short sequence with only
        # one anchor.
        if len(anchors[-1]) < anchor_len:
            anchors[-1] = anchors[-2] + anchors[-1]

        pro_re = []
        anchor_distance = 0
        anchor_pos = []
        for i, anchor in enumerate(anchors):
            this_anchor_len = len(anchor)
            qcodon = ""
            fncodon = ""
            # dirty code to deal with the last anchor
            # as the last anchor is combined in the steps
            # above, we need to get the true last anchor to
            # pro_re
            if this_anchor_len == anchor_len:
                for aa in anchor:
                    if complete_protein and i == 0:
                        qcodon += _codons2re(codon_table.start_codons)
                        fncodon += aa2re['X']
                        continue
                    qcodon += aa2re[aa]
                    fncodon += aa2re['X']
                match = re.search(qcodon, nucl_seq)
            elif this_anchor_len > anchor_len:
                last_qcodon = ""
                last_fcodon = ""
                for j in range(anchor_len, len(anchor)):
                    last_qcodon += aa2re[anchor[j]]
                    last_fcodon += aa2re['X']
                match = re.search(last_qcodon, nucl_seq)
            # build full_pro_re from anchors
            if match:
                anchor_pos.append((match.start(), match.end(), i))
                if this_anchor_len == anchor_len:
                    pro_re.append(qcodon)
                else:
                    pro_re.append(last_qcodon)
            else:
                if this_anchor_len == anchor_len:
                    pro_re.append(fncodon)
                else:
                    pro_re.append(last_fcodon)
        full_pro_re = "".join(pro_re)
        match = re.search(full_pro_re, nucl_seq)
        if match:
            # mode = 1, mismatch
            return (match.span(), 1)
        else:
            # check frames of anchors
            # ten frameshift events are allowed in a sequence
            first_anchor = True
            shift_id_pos = 0
            # check the first anchor
            if first_anchor and anchor_pos[0][2] != 0:
                shift_val_lst = [1, 2, 3 * anchor_len - 2, 3 * anchor_len - 1, 0]
                sh_anc = anchors[0]
                for shift_val in shift_val_lst:
                    if shift_val == 0:
                        qcodon = None
                        break
                    if shift_val in (1, 2):
                        sh_nuc_len = anchor_len * 3 + shift_val
                    elif shift_val in (3 * anchor_len - 2, 3 * anchor_len - 1):
                        sh_nuc_len = anchor_len * 3 - (3 * anchor_len - shift_val)
                    if anchor_pos[0][0] >= sh_nuc_len:
                        sh_nuc = nucl_seq[anchor_pos[0][0] - sh_nuc_len:anchor_pos[0][0]]
                    else:
                        # this is unlikely to produce the correct output
                        sh_nuc = nucl_seq[:anchor_pos[0][0]]
                    qcodon, shift_id_pos = _get_shift_anchor_re(sh_anc, sh_nuc,
                                                                shift_val,
                                                                aa2re,
                                                                anchor_len,
                                                                shift_id_pos)
                    if qcodon is not None and qcodon != -1:
                        # pro_re[0] should be '.'*anchor_len, therefore I
                        # replace it.
                        pro_re[0] = qcodon
                        break
                if qcodon == -1:
                    warnings.warn("first frameshift detection failed for "
                                  "{0}".format(nucl.id), BiopythonWarning)
            # check anchors in the middle
            for i in range(len(anchor_pos) - 1):
                shift_val = (anchor_pos[i + 1][0] - anchor_pos[i][0]) % \
                            (3 * anchor_len)
                sh_anc = "".join(anchors[anchor_pos[i][2]:anchor_pos[i + 1][2]])
                sh_nuc = nucl_seq[anchor_pos[i][0]:anchor_pos[i + 1][0]]
                qcodon = None
                if shift_val != 0:
                    qcodon, shift_id_pos = _get_shift_anchor_re(sh_anc, sh_nuc,
                                                                shift_val,
                                                                aa2re,
                                                                anchor_len,
                                                                shift_id_pos)
                if qcodon is not None and qcodon != -1:
                    pro_re[anchor_pos[i][2]:anchor_pos[i + 1][2]] = [qcodon]
                    qcodon = None
                elif qcodon == -1:
                    warnings.warn("middle frameshift detection failed for "
                                  "{0}".format(nucl.id), BiopythonWarning)
            # check the last anchor
            if anchor_pos[-1][2] + 1 == len(anchors) - 1:
                sh_anc = anchors[-1]
                this_anchor_len = len(sh_anc)
                shift_val_lst = [1, 2, 3 * this_anchor_len - 2, 3 * this_anchor_len - 1, 0]
                for shift_val in shift_val_lst:
                    if shift_val == 0:
                        qcodon = None
                        break
                    if shift_val in (1, 2):
                        sh_nuc_len = this_anchor_len * 3 + shift_val
                    elif shift_val in \
                            (3 * this_anchor_len - 2, 3 * this_anchor_len - 1):
                        sh_nuc_len = this_anchor_len * 3 - (3 * this_anchor_len - shift_val)
                    if len(nucl_seq) - anchor_pos[-1][0] >= sh_nuc_len:
                        sh_nuc = nucl_seq[anchor_pos[-1][0]:anchor_pos[-1][0] + sh_nuc_len]
                    else:
                        # this is unlikely to produce the correct output
                        sh_nuc = nucl_seq[anchor_pos[-1][0]:]
                    qcodon, shift_id_pos = _get_shift_anchor_re(sh_anc, sh_nuc,
                                                                shift_val,
                                                                aa2re,
                                                                this_anchor_len,
                                                                shift_id_pos)
                    if qcodon is not None and qcodon != -1:
                        pro_re.pop()
                        pro_re[-1] = qcodon
                        break
                if qcodon == -1:
                    warnings.warn("last frameshift detection failed for "
                                  "{0}".format(nucl.id), BiopythonWarning)
            # try global match
            full_pro_re = "".join(pro_re)
            match = re.search(full_pro_re, nucl_seq)
            if match:
                return (match.span(), 2, match)
            else:
                raise RuntimeError("Protein SeqRecord ({0}) and Nucleotide "
                                   "SeqRecord ({1}) do not "
                                   "match!".format((pro.id, nucl.id)))


def _get_shift_anchor_re(sh_anc, sh_nuc, shift_val, aa2re, anchor_len,
                         shift_id_pos):
    """This function tries all the best to come up with an re that
    matches a potentially shifted anchor.

    Arguments:
        - sh_anc    - shifted anchor sequence
        - sh_nuc    - potentially corresponding nucleotide sequence
          of sh_anc
        - shift_val - 1 or 2 indicates forward frame shift, whereas
          3*anchor_len-1 or 3*anchor_len-2 indicates
          backward shift
        - aa2re     - aa to codon re dict
        - anchor_len - length of the anchor
        - shift_id_pos - specify current shift name we are at
    """
    import re
    shift_id = [chr(i) for i in range(97, 107)]
    if 0 < shift_val < 3 * anchor_len - 2:
        # if shift_val in (1, 2):
        for j in range(len(sh_anc)):
            qcodon = "^"
            for k, aa in enumerate(sh_anc):
                if k == j:
                    qcodon += aa2re[aa] + "(?P<" + shift_id[shift_id_pos] + ">..*)"
                else:
                    qcodon += aa2re[aa]
            qcodon += "$"
            match = re.search(qcodon, sh_nuc)
            if match:
                qcodon = qcodon.replace('^', '').replace('$', '')
                shift_id_pos += 1
                return qcodon, shift_id_pos
        if not match:
            # failed to find a match (frameshift)
            return -1, shift_id_pos
    elif shift_val in (3 * anchor_len - 1, 3 * anchor_len - 2):
        shift_val = 3 * anchor_len - shift_val
        # obtain shifted anchor and corresponding nucl
        # first check if the shifted pos is just at the end of the
        # previous anchor.
        for j in range(1, len(sh_anc)):
            qcodon = "^"
            for k, aa in enumerate(sh_anc):
                if k == j - 1:
                    # will be considered in the next step
                    pass
                elif k == j:
                    qcodon += _merge_aa2re(
                            sh_anc[j - 1], sh_anc[j], shift_val, aa2re,
                            shift_id[shift_id_pos].upper())
                else:
                    qcodon += aa2re[aa]
            qcodon += '$'
            match = re.search(qcodon, sh_nuc)
            if match:
                qcodon = qcodon.replace('^', '').replace('$', '')
                shift_id_pos += 1
                return qcodon, shift_id_pos
        if not match:
            # failed to find a match (frameshift)
            return -1, shift_id_pos


def _merge_aa2re(aa1, aa2, shift_val, aa2re, reid):
    """Function to merge two amino acids based on detected frame shift
    value.
    """
    def get_aa_from_codonre(re_aa):
        aas = []
        m = 0
        for i in re_aa:
            if i == '[':
                m = -1
                aas.append('')
            elif i == ']':
                m = 0
                continue
            elif m == -1:
                aas[-1] = aas[-1] + i
            elif m == 0:
                aas.append(i)
        return aas
    scodon = list(map(get_aa_from_codonre, (aa2re[aa1], aa2re[aa2])))
    if shift_val == 1:
        intersect = ''.join(set(scodon[0][2]) & set(scodon[1][0]))
        scodonre = '(?P<' + reid + '>'
        scodonre += '[' + scodon[0][0] + ']' + \
                    '[' + scodon[0][1] + ']' + \
                    '[' + intersect + ']' + \
                    '[' + scodon[1][1] + ']' + \
                    '[' + scodon[1][2] + ']'
    elif shift_val == 2:
        intersect1 = ''.join(set(scodon[0][1]) & set(scodon[1][0]))
        intersect2 = ''.join(set(scodon[0][2]) & set(scodon[1][1]))
        scodonre = '(?P<' + reid + '>'
        scodonre += '[' + scodon[0][0] + ']' + \
                    '[' + intersect1 + ']' + \
                    '[' + intersect2 + ']' + \
                    '[' + scodon[1][2] + ']'
    scodonre += ')'
    return scodonre


def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
                   codon_table=default_codon_table, complete_protein=False,
                   max_score=10):
    """Generate codon alignment based on regular re match (PRIVATE)

    span_mode is a tuple returned by _check_corr. The first element
    is the span of a re search, and the second element is the mode
    for the match.

    mode
        - 0: direct match
        - 1: mismatch (no indels)
        - 2: frameshift

    """
    import re
    from Bio.Seq import Seq

    nucl_seq = nucl.seq.ungap(gap_char)
    codon_seq = ""
    span = span_mode[0]
    mode = span_mode[1]
    aa2re = _get_aa_regex(codon_table)
    if mode in (0, 1):
        if len(pro.seq.ungap(gap_char)) * 3 != (span[1] - span[0]):
            raise ValueError("Protein Record {0} and Nucleotide Record {1} "
                             "do not match!".format((pro.id, nucl.id)))
        aa_num = 0
        for aa in pro.seq:
            if aa == "-":
                codon_seq += "---"
            elif complete_protein and aa_num == 0:
                this_codon = nucl_seq._data[span[0]:span[0] + 3]
                if not re.search(_codons2re[codon_table.start_codons],
                                 this_codon.upper()):
                    max_score -= 1
                    warnings.warn("start codon of {0} ({1} {2}) does not "
                                  "correspond to {3} "
                                  "({4})".format(pro.id, aa, aa_num,
                                                 nucl.id, this_codon),
                                  BiopythonWarning)
                if max_score == 0:
                    raise RuntimeError("max_score reached for {0}! Please "
                                       "raise up the tolerance to get an "
                                       "alignment in anyway".format(nucl.id))
                codon_seq += this_codon
                aa_num += 1
            else:
                this_codon = nucl_seq._data[(span[0] + 3 * aa_num):
                                            (span[0] + 3 * (aa_num + 1))]
                if not str(Seq(this_codon.upper()).translate(table=codon_table)) == aa:
                    max_score -= 1
                    warnings.warn("%s(%s %d) does not correspond to %s(%s)"
                                  % (pro.id, aa, aa_num, nucl.id, this_codon),
                                  BiopythonWarning)
                if max_score == 0:
                    raise RuntimeError("max_score reached for {0}! Please "
                                       "raise up the tolerance to get an "
                                       "alignment in anyway".format(nucl.id))
                codon_seq += this_codon
                aa_num += 1
        return SeqRecord(CodonSeq(codon_seq, alphabet=alphabet), id=nucl.id)
    elif mode == 2:
        from collections import deque
        shift_pos = deque([])
        shift_start = []
        match = span_mode[2]
        m_groupdict = list(match.groupdict().keys())
        # backward frameshift
        for i in m_groupdict:
            shift_pos.append(match.span(i))
            shift_start.append(match.start(i))
        rf_table = []
        i = match.start()
        while True:
            rf_table.append(i)
            i += 3
            if i in shift_start and \
                    m_groupdict[shift_start.index(i)].isupper():
                shift_index = shift_start.index(i)
                shift_val = 6 - (shift_pos[shift_index][1] -
                            shift_pos[shift_index][0])
                rf_table.append(i)
                rf_table.append(i + 3 - shift_val)
                i = shift_pos[shift_index][1]
            elif i in shift_start and \
                    m_groupdict[shift_start.index(i)].islower():
                i = shift_pos[shift_start.index(i)][1]
            if i >= match.end():
                break
        aa_num = 0
        for aa in pro.seq:
            if aa == "-":
                codon_seq += "---"
            elif complete_protein and aa_num == 0:
                this_codon = nucl_seq._data[rf_table[0]:rf_table[0] + 3]
                if not re.search(_codons2re[codon_table.start_codons],
                                 this_codon.upper()):
                    max_score -= 1
                    warnings.warn("start codon of {0}({1} {2}) does not "
                                  "correspond to {3}({4})".format(
                                      pro.id, aa, aa_num, nucl.id, this_codon),
                                  BiopythonWarning)
                    codon_seq += this_codon
                    aa_num += 1
            else:
                if aa_num < len(pro.seq.ungap('-')) - 1 and \
                        rf_table[aa_num + 1] - rf_table[aa_num] - 3 < 0:
                    max_score -= 1
                    start = rf_table[aa_num]
                    end = start + (3 - shift_val)
                    ngap = shift_val
                    this_codon = nucl_seq._data[start:end] + '-' * ngap
                elif rf_table[aa_num] - rf_table[aa_num - 1] - 3 > 0:
                    max_score -= 1
                    start = rf_table[aa_num - 1] + 3
                    end = rf_table[aa_num]
                    ngap = 3 - (rf_table[aa_num] - rf_table[aa_num - 1] - 3)
                    this_codon = nucl_seq._data[start:end] + '-' * ngap + \
                            nucl_seq._data[rf_table[aa_num]:rf_table[aa_num] + 3]
                else:
                    start = rf_table[aa_num]
                    end = start + 3
                    this_codon = nucl_seq._data[start:end]
                    if not str(Seq(this_codon.upper()).translate(table=codon_table)) == aa:
                        max_score -= 1
                        warnings.warn("Codon of {0}({1} {2}) does not "
                                      "correspond to {3}({4})".format(
                                          pro.id, aa, aa_num, nucl.id,
                                          this_codon),
                                      BiopythonWarning)
                if max_score == 0:
                    raise RuntimeError("max_score reached for {0}! Please "
                                       "raise up the tolerance to get an "
                                       "alignment in anyway".format(nucl.id))
                codon_seq += this_codon
                aa_num += 1
        return SeqRecord(CodonSeq(codon_seq, alphabet=alphabet,
                         rf_table=rf_table), id=nucl.id)


def _align_shift_recs(recs):
    """This function is useful to build alignment according to the
    frameshift detected by _check_corr.

    Argument:
        - recs - a list of SeqRecords containing a CodonSeq dictated
          by a rf_table (with frameshift in some of them).
    """
    def find_next_int(k, lst):
        idx = lst.index(k)
        p = 0
        while True:
            if isinstance(lst[idx + p], int):
                return lst[idx + p], p
            p += 1
    full_rf_table_lst = [rec.seq.get_full_rf_table() for rec in recs]
    rf_num = [0] * len(recs)
    for k, rec in enumerate(recs):
        for i in rec.seq.get_full_rf_table():
            if isinstance(i, int):
                rf_num[k] += 1
            # isinstance(i, float) should be True
            elif rec.seq._data[int(i):int(i) + 3] == "---":
                rf_num[k] += 1
    if len(set(rf_num)) != 1:
        raise RuntimeError("Number alignable codons unequal in given records")
    i = 0
    rec_num = len(recs)
    while True:
        add_lst = []
        try:
            col_rf_lst = [k[i] for k in full_rf_table_lst]
        except IndexError:
            # we probably reached the last codon
            break
        for j, k in enumerate(col_rf_lst):
            add_lst.append((j, int(k)))
            if isinstance(k, float) and \
                    recs[j].seq._data[int(k):int(k) + 3] != "---":
                m, p = find_next_int(k, full_rf_table_lst[j])
                if (m - k) % 3 != 0:
                    gap_num = 3 - (m - k) % 3
                else:
                    gap_num = 0
                if gap_num != 0:
                    gaps = '-' * int(gap_num)
                    seq = recs[j].seq._data[:int(k)] + gaps + \
                            recs[j].seq._data[int(k):]
                    full_rf_table = full_rf_table_lst[j]
                    bp = full_rf_table.index(k)
                    full_rf_table = full_rf_table[:bp] + \
                            [v + int(gap_num) for v in full_rf_table[bp + 1:]]
                    full_rf_table_lst[j] = full_rf_table
                    recs[j].seq = CodonSeq(seq,
                                           rf_table=recs[j].seq.rf_table,
                                           alphabet=recs[j].seq.alphabet)
                add_lst.pop()
                gap_num += m - k
                i += p - 1
        if len(add_lst) != rec_num:
            for j, k in add_lst:
                gaps = "-" * int(gap_num)
                seq = recs[j].seq._data[:int(k)] + gaps + \
                        recs[j].seq._data[int(k):]
                full_rf_table = full_rf_table_lst[j]
                bp = full_rf_table.index(k)
                inter_rf = []
                for t in filter(lambda x: x % 3 == 0, range(len(gaps))):
                    inter_rf.append(k + t + 3.0)
                full_rf_table = full_rf_table[:bp] + inter_rf + \
                        [v + int(gap_num) for v in full_rf_table[bp:]]
                full_rf_table_lst[j] = full_rf_table
                recs[j].seq = CodonSeq(seq,
                                       rf_table=recs[j].seq.rf_table,
                                       alphabet=recs[j].seq.alphabet)
        i += 1
    return recs


# def toCodonAlignment(align, alphabet=default_codon_alphabet):
#    """Function to convert a MultipleSeqAlignment to CodonAlignment.
#    It is the user's responsibility to ensure all the requirement
#    needed by CodonAlignment is met.
#
#    """
#    rec = [SeqRecord(CodonSeq(str(i.seq), alphabet=alphabet), id=i.id) \
#            for i in align._records]
#    return CodonAlignment(rec, alphabet=align._alphabet)


if __name__ == "__main__":
    from Bio._utils import run_doctest
    run_doctest()
