# Copyright 2002 by Jeffrey Chang.  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.

"""This package implements pairwise sequence alignment using a dynamic
programming algorithm.

This provides functions to get global and local alignments between two
sequences.  A global alignment finds the best concordance between all
characters in two sequences.  A local alignment finds just the
subsequences that align the best.

When doing alignments, you can specify the match score and gap
penalties.  The match score indicates the compatibility between an
alignment of two characters in the sequences.  Highly compatible
characters should be given positive scores, and incompatible ones
should be given negative scores or 0.  The gap penalties should be
negative.

The names of the alignment functions in this module follow the
convention
<alignment type>XX
where <alignment type> is either "global" or "local" and XX is a 2
character code indicating the parameters it takes.  The first
character indicates the parameters for matches (and mismatches), and
the second indicates the parameters for gap penalties.

The match parameters are::

    CODE  DESCRIPTION
    x     No parameters.  Identical characters have score of 1, otherwise 0.
    m     A match score is the score of identical chars, otherwise mismatch score.
    d     A dictionary returns the score of any pair of characters.
    c     A callback function returns scores.

The gap penalty parameters are::

    CODE  DESCRIPTION
    x     No gap penalties.
    s     Same open and extend gap penalties for both sequences.
    d     The sequences have different open and extend gap penalties.
    c     A callback function returns the gap penalties.

All the different alignment functions are contained in an object
"align".  For example:

    >>> from Bio import pairwise2
    >>> alignments = pairwise2.align.globalxx("ACCGT", "ACG")

will return a list of the alignments between the two strings.  The
parameters of the alignment function depends on the function called.
Some examples::

    # Find the best global alignment between the two sequences.
    # Identical characters are given 1 point.  No points are deducted
    # for mismatches or gaps.
    >>> from Bio.pairwise2 import format_alignment
    >>> for a in pairwise2.align.globalxx("ACCGT", "ACG"):
    ...     print(format_alignment(*a))
    ACCGT
    |||||
    AC-G-
      Score=3
    <BLANKLINE>
    ACCGT
    |||||
    A-CG-
      Score=3
    <BLANKLINE>

    # Same thing as before, but with a local alignment.
    >>> for a in pairwise2.align.localxx("ACCGT", "ACG"):
    ...     print(format_alignment(*a))
    ACCGT
    ||||
    AC-G-
      Score=3
    <BLANKLINE>
    ACCGT
    ||||
    A-CG-
      Score=3
    <BLANKLINE>

    # Do a global alignment.  Identical characters are given 2 points,
    # 1 point is deducted for each non-identical character.
    >>> for a in pairwise2.align.globalmx("ACCGT", "ACG", 2, -1):
    ...     print(format_alignment(*a))
    ACCGT
    |||||
    AC-G-
      Score=6
    <BLANKLINE>
    ACCGT
    |||||
    A-CG-
      Score=6
    <BLANKLINE>

    # Same as above, except now 0.5 points are deducted when opening a
    # gap, and 0.1 points are deducted when extending it.
    >>> for a in pairwise2.align.globalms("ACCGT", "ACG", 2, -1, -.5, -.1):
    ...     print(format_alignment(*a))
    ACCGT
    |||||
    AC-G-
      Score=5
    <BLANKLINE>
    ACCGT
    |||||
    A-CG-
      Score=5
    <BLANKLINE>

The alignment function can also use known matrices already included in
Biopython ( Bio.SubsMat -> MatrixInfo )::

    >>> from Bio.SubsMat import MatrixInfo as matlist
    >>> matrix = matlist.blosum62
    >>> for a in pairwise2.align.globaldx("KEVLA", "EVL", matrix):
    ...     print(format_alignment(*a))
    KEVLA
    |||||
    -EVL-
      Score=13
    <BLANKLINE>

To see a description of the parameters for a function, please look at
the docstring for the function via the help function, e.g.
type help(pairwise2.align.localds) at the Python prompt.
"""
# The alignment functions take some undocumented keyword parameters:
# - penalize_extend_when_opening: boolean
#   Whether to count an extension penalty when opening a gap.  If
#   false, a gap of 1 is only penalize an "open" penalty, otherwise it
#   is penalized "open+extend".
# - penalize_end_gaps: boolean
#   Whether to count the gaps at the ends of an alignment.  By
#   default, they are counted for global alignments but not for local
#   ones. Setting penalize_end_gaps to (boolean, boolean) allows you to
#   specify for the two sequences separately whether gaps at the end of
#   the alignment should be counted.
# - gap_char: string
#   Which character to use as a gap character in the alignment
#   returned.  By default, uses '-'.
# - force_generic: boolean
#   Always use the generic, non-cached, dynamic programming function.
#   For debugging.
# - score_only: boolean
#   Only get the best score, don't recover any alignments.  The return
#   value of the function is the score.
# - one_alignment_only: boolean
#   Only recover one alignment.

from __future__ import print_function

__docformat__ = "restructuredtext en"

MAX_ALIGNMENTS = 1000   # maximum alignments recovered in traceback


class align(object):
    """This class provides functions that do alignments."""

    class alignment_function:
        """This class is callable impersonates an alignment function.
        The constructor takes the name of the function.  This class
        will decode the name of the function to figure out how to
        interpret the parameters.

        """
        # match code -> tuple of (parameters, docstring)
        match2args = {
            'x': ([], ''),
            'm': (['match', 'mismatch'],
"""match is the score to given to identical characters.  mismatch is
the score given to non-identical ones."""),
            'd': (['match_dict'],
"""match_dict is a dictionary where the keys are tuples of pairs of
characters and the values are the scores, e.g. ("A", "C") : 2.5."""),
            'c': (['match_fn'],
"""match_fn is a callback function that takes two characters and
returns the score between them."""),
            }
        # penalty code -> tuple of (parameters, docstring)
        penalty2args = {
            'x': ([], ''),
            's': (['open', 'extend'],
"""open and extend are the gap penalties when a gap is opened and
extended.  They should be negative."""),
            'd': (['openA', 'extendA', 'openB', 'extendB'],
"""openA and extendA are the gap penalties for sequenceA, and openB
and extendB for sequeneB.  The penalties should be negative."""),
            'c': (['gap_A_fn', 'gap_B_fn'],
"""gap_A_fn and gap_B_fn are callback functions that takes 1) the
index where the gap is opened, and 2) the length of the gap.  They
should return a gap penalty."""),
            }

        def __init__(self, name):
            # Check to make sure the name of the function is
            # reasonable.
            if name.startswith("global"):
                if len(name) != 8:
                    raise AttributeError("function should be globalXX")
            elif name.startswith("local"):
                if len(name) != 7:
                    raise AttributeError("function should be localXX")
            else:
                raise AttributeError(name)
            align_type, match_type, penalty_type = \
                        name[:-2], name[-2], name[-1]
            try:
                match_args, match_doc = self.match2args[match_type]
            except KeyError as x:
                raise AttributeError("unknown match type %r" % match_type)
            try:
                penalty_args, penalty_doc = self.penalty2args[penalty_type]
            except KeyError as x:
                raise AttributeError("unknown penalty type %r" % penalty_type)

            # Now get the names of the parameters to this function.
            param_names = ['sequenceA', 'sequenceB']
            param_names.extend(match_args)
            param_names.extend(penalty_args)
            self.function_name = name
            self.align_type = align_type
            self.param_names = param_names

            self.__name__ = self.function_name
            # Set the doc string.
            doc = "%s(%s) -> alignments\n" % (
                self.__name__, ', '.join(self.param_names))
            if match_doc:
                doc += "\n%s\n" % match_doc
            if penalty_doc:
                doc += "\n%s\n" % penalty_doc
            doc += (
"""\nalignments is a list of tuples (seqA, seqB, score, begin, end).
seqA and seqB are strings showing the alignment between the
sequences.  score is the score of the alignment.  begin and end
are indexes into seqA and seqB that indicate the where the
alignment occurs.
""")
            self.__doc__ = doc

        def decode(self, *args, **keywds):
            # Decode the arguments for the _align function.  keywds
            # will get passed to it, so translate the arguments to
            # this function into forms appropriate for _align.
            keywds = keywds.copy()
            if len(args) != len(self.param_names):
                raise TypeError("%s takes exactly %d argument (%d given)"
                    % (self.function_name, len(self.param_names), len(args)))
            i = 0
            while i < len(self.param_names):
                if self.param_names[i] in [
                   'sequenceA', 'sequenceB',
                   'gap_A_fn', 'gap_B_fn', 'match_fn']:
                    keywds[self.param_names[i]] = args[i]
                    i += 1
                elif self.param_names[i] == 'match':
                    assert self.param_names[i + 1] == 'mismatch'
                    match, mismatch = args[i], args[i + 1]
                    keywds['match_fn'] = identity_match(match, mismatch)
                    i += 2
                elif self.param_names[i] == 'match_dict':
                    keywds['match_fn'] = dictionary_match(args[i])
                    i += 1
                elif self.param_names[i] == 'open':
                    assert self.param_names[i + 1] == 'extend'
                    open, extend = args[i], args[i + 1]
                    pe = keywds.get('penalize_extend_when_opening', 0)
                    keywds['gap_A_fn'] = affine_penalty(open, extend, pe)
                    keywds['gap_B_fn'] = affine_penalty(open, extend, pe)
                    i += 2
                elif self.param_names[i] == 'openA':
                    assert self.param_names[i + 3] == 'extendB'
                    openA, extendA, openB, extendB = args[i:i + 4]
                    pe = keywds.get('penalize_extend_when_opening', 0)
                    keywds['gap_A_fn'] = affine_penalty(openA, extendA, pe)
                    keywds['gap_B_fn'] = affine_penalty(openB, extendB, pe)
                    i += 4
                else:
                    raise ValueError("unknown parameter %r"
                                     % self.param_names[i])

            # Here are the default parameters for _align.  Assign
            # these to keywds, unless already specified.
            pe = keywds.get('penalize_extend_when_opening', 0)
            default_params = [
                ('match_fn', identity_match(1, 0)),
                ('gap_A_fn', affine_penalty(0, 0, pe)),
                ('gap_B_fn', affine_penalty(0, 0, pe)),
                ('penalize_extend_when_opening', 0),
                ('penalize_end_gaps', self.align_type == 'global'),
                ('align_globally', self.align_type == 'global'),
                ('gap_char', '-'),
                ('force_generic', 0),
                ('score_only', 0),
                ('one_alignment_only', 0)
                ]
            for name, default in default_params:
                keywds[name] = keywds.get(name, default)
            value = keywds['penalize_end_gaps']
            try:
                n = len(value)
            except TypeError:
                keywds['penalize_end_gaps'] = tuple([value] * 2)
            else:
                assert n == 2
            return keywds

        def __call__(self, *args, **keywds):
            keywds = self.decode(*args, **keywds)
            return _align(**keywds)

    def __getattr__(self, attr):
        return self.alignment_function(attr)
align = align()


def _align(sequenceA, sequenceB, match_fn, gap_A_fn, gap_B_fn,
           penalize_extend_when_opening, penalize_end_gaps,
           align_globally, gap_char, force_generic, score_only,
           one_alignment_only):
    if not sequenceA or not sequenceB:
        return []

    if (not force_generic) and isinstance(gap_A_fn, affine_penalty) \
    and isinstance(gap_B_fn, affine_penalty):
        open_A, extend_A = gap_A_fn.open, gap_A_fn.extend
        open_B, extend_B = gap_B_fn.open, gap_B_fn.extend
        x = _make_score_matrix_fast(
            sequenceA, sequenceB, match_fn, open_A, extend_A, open_B, extend_B,
            penalize_extend_when_opening, penalize_end_gaps, align_globally,
            score_only)
    else:
        x = _make_score_matrix_generic(
            sequenceA, sequenceB, match_fn, gap_A_fn, gap_B_fn,
            penalize_extend_when_opening, penalize_end_gaps, align_globally,
            score_only)
    score_matrix, trace_matrix = x

    # print("SCORE %s" % print_matrix(score_matrix))
    # print("TRACEBACK %s" % print_matrix(trace_matrix))

    # Look for the proper starting point.  Get a list of all possible
    # starting points.
    starts = _find_start(
        score_matrix, sequenceA, sequenceB,
        gap_A_fn, gap_B_fn, penalize_end_gaps, align_globally)
    # Find the highest score.
    best_score = max([x[0] for x in starts])

    # If they only want the score, then return it.
    if score_only:
        return best_score

    tolerance = 0  # XXX do anything with this?
    # Now find all the positions within some tolerance of the best
    # score.
    starts = [
        (score, pos) for score, pos in starts
        if rint(abs(score - best_score)) <= rint(tolerance)
    ]

    # Recover the alignments and return them.
    x = _recover_alignments(
        sequenceA, sequenceB, starts, score_matrix, trace_matrix,
        align_globally, gap_char, one_alignment_only)
    return x


def _make_score_matrix_generic(
        sequenceA, sequenceB, match_fn, gap_A_fn, gap_B_fn,
        penalize_extend_when_opening, penalize_end_gaps, align_globally,
        score_only):
    # This is an implementation of the Needleman-Wunsch dynamic
    # programming algorithm for aligning sequences.

    # Create the score and traceback matrices.  These should be in the
    # shape:
    # sequenceA (down) x sequenceB (across)
    lenA, lenB = len(sequenceA), len(sequenceB)
    score_matrix, trace_matrix = [], []
    for i in range(lenA):
        score_matrix.append([None] * lenB)
        trace_matrix.append([[None]] * lenB)

    # The top and left borders of the matrices are special cases
    # because there are no previously aligned characters.  To simplify
    # the main loop, handle these separately.
    for i in range(lenA):
        # Align the first residue in sequenceB to the ith residue in
        # sequence A.  This is like opening up i gaps at the beginning
        # of sequence B.
        score = match_fn(sequenceA[i], sequenceB[0])
        if penalize_end_gaps[1]:
            score += gap_B_fn(0, i)
        score_matrix[i][0] = score
    for i in range(1, lenB):
        score = match_fn(sequenceA[0], sequenceB[i])
        if penalize_end_gaps[0]:
            score += gap_A_fn(0, i)
        score_matrix[0][i] = score

    # Fill in the score matrix.  Each position in the matrix
    # represents an alignment between a character from sequenceA to
    # one in sequence B.  As I iterate through the matrix, find the
    # alignment by choose the best of:
    #    1) extending a previous alignment without gaps
    #    2) adding a gap in sequenceA
    #    3) adding a gap in sequenceB
    for row in range(1, lenA):
        for col in range(1, lenB):
            # First, calculate the score that would occur by extending
            # the alignment without gaps.
            best_score = score_matrix[row - 1][col - 1]
            best_score_rint = rint(best_score)
            best_indexes = [(row - 1, col - 1)]

            # Try to find a better score by opening gaps in sequenceA.
            # Do this by checking alignments from each column in the
            # previous row.  Each column represents a different
            # character to align from, and thus a different length
            # gap.
            for i in range(0, col - 1):
                score = score_matrix[row - 1][i] + gap_A_fn(row, col - 1 - i)
                score_rint = rint(score)
                if score_rint == best_score_rint:
                    best_score, best_score_rint = score, score_rint
                    best_indexes.append((row - 1, i))
                elif score_rint > best_score_rint:
                    best_score, best_score_rint = score, score_rint
                    best_indexes = [(row - 1, i)]

            # Try to find a better score by opening gaps in sequenceB.
            for i in range(0, row - 1):
                score = score_matrix[i][col - 1] + gap_B_fn(col, row - 1 - i)
                score_rint = rint(score)
                if score_rint == best_score_rint:
                    best_score, best_score_rint = score, score_rint
                    best_indexes.append((i, col - 1))
                elif score_rint > best_score_rint:
                    best_score, best_score_rint = score, score_rint
                    best_indexes = [(i, col - 1)]

            score_matrix[row][col] = best_score + \
                                     match_fn(sequenceA[row], sequenceB[col])
            if not align_globally and score_matrix[row][col] < 0:
                score_matrix[row][col] = 0
            trace_matrix[row][col] = best_indexes
    return score_matrix, trace_matrix


def _make_score_matrix_fast(
        sequenceA, sequenceB, match_fn, open_A, extend_A, open_B, extend_B,
        penalize_extend_when_opening, penalize_end_gaps,
        align_globally, score_only):
    first_A_gap = calc_affine_penalty(1, open_A, extend_A,
                                      penalize_extend_when_opening)
    first_B_gap = calc_affine_penalty(1, open_B, extend_B,
                                      penalize_extend_when_opening)

    # Create the score and traceback matrices.  These should be in the
    # shape:
    # sequenceA (down) x sequenceB (across)
    lenA, lenB = len(sequenceA), len(sequenceB)
    score_matrix, trace_matrix = [], []
    for i in range(lenA):
        score_matrix.append([None] * lenB)
        trace_matrix.append([[None]] * lenB)

    # The top and left borders of the matrices are special cases
    # because there are no previously aligned characters.  To simplify
    # the main loop, handle these separately.
    for i in range(lenA):
        # Align the first residue in sequenceB to the ith residue in
        # sequence A.  This is like opening up i gaps at the beginning
        # of sequence B.
        score = match_fn(sequenceA[i], sequenceB[0])
        if penalize_end_gaps[1]:
            score += calc_affine_penalty(
                i, open_B, extend_B, penalize_extend_when_opening)
        score_matrix[i][0] = score
    for i in range(1, lenB):
        score = match_fn(sequenceA[0], sequenceB[i])
        if penalize_end_gaps[0]:
            score += calc_affine_penalty(
                i, open_A, extend_A, penalize_extend_when_opening)
        score_matrix[0][i] = score

    # In the generic algorithm, at each row and column in the score
    # matrix, we had to scan all previous rows and columns to see
    # whether opening a gap might yield a higher score.  Here, since
    # we know the penalties are affine, we can cache just the best
    # score in the previous rows and columns.  Instead of scanning
    # through all the previous rows and cols, we can just look at the
    # cache for the best one.  Whenever the row or col increments, the
    # best cached score just decreases by extending the gap longer.

    # The best score and indexes for each row (goes down all columns).
    # I don't need to store the last row because it's the end of the
    # sequence.
    row_cache_score, row_cache_index = [None] * (lenA - 1), [None] * (lenA - 1)
    # The best score and indexes for each column (goes across rows).
    col_cache_score, col_cache_index = [None] * (lenB - 1), [None] * (lenB - 1)

    for i in range(lenA - 1):
        # Initialize each row to be the alignment of sequenceA[i] to
        # sequenceB[0], plus opening a gap in sequenceA.
        row_cache_score[i] = score_matrix[i][0] + first_A_gap
        row_cache_index[i] = [(i, 0)]
    for i in range(lenB - 1):
        col_cache_score[i] = score_matrix[0][i] + first_B_gap
        col_cache_index[i] = [(0, i)]

    # Fill in the score_matrix.
    for row in range(1, lenA):
        for col in range(1, lenB):
            # Calculate the score that would occur by extending the
            # alignment without gaps.
            nogap_score = score_matrix[row - 1][col - 1]

            # Check the score that would occur if there were a gap in
            # sequence A.
            if col > 1:
                row_score = row_cache_score[row - 1]
            else:
                row_score = nogap_score - 1   # Make sure it's not the best.
            # Check the score that would occur if there were a gap in
            # sequence B.
            if row > 1:
                col_score = col_cache_score[col - 1]
            else:
                col_score = nogap_score - 1

            best_score = max(nogap_score, row_score, col_score)
            best_score_rint = rint(best_score)
            best_index = []
            if best_score_rint == rint(nogap_score):
                best_index.append((row - 1, col - 1))
            if best_score_rint == rint(row_score):
                best_index.extend(row_cache_index[row - 1])
            if best_score_rint == rint(col_score):
                best_index.extend(col_cache_index[col - 1])

            # Set the score and traceback matrices.
            score = best_score + match_fn(sequenceA[row], sequenceB[col])
            if not align_globally and score < 0:
                score_matrix[row][col] = 0
            else:
                score_matrix[row][col] = score
            trace_matrix[row][col] = best_index

            # Update the cached column scores.  The best score for
            # this can come from either extending the gap in the
            # previous cached score, or opening a new gap from the
            # most previously seen character.  Compare the two scores
            # and keep the best one.
            open_score = score_matrix[row - 1][col - 1] + first_B_gap
            extend_score = col_cache_score[col - 1] + extend_B
            open_score_rint, extend_score_rint = \
                             rint(open_score), rint(extend_score)
            if open_score_rint > extend_score_rint:
                col_cache_score[col - 1] = open_score
                col_cache_index[col - 1] = [(row - 1, col - 1)]
            elif extend_score_rint > open_score_rint:
                col_cache_score[col - 1] = extend_score
            else:
                col_cache_score[col - 1] = open_score
                if (row - 1, col - 1) not in col_cache_index[col - 1]:
                    col_cache_index[col - 1] = col_cache_index[col - 1] + \
                                             [(row - 1, col - 1)]

            # Update the cached row scores.
            open_score = score_matrix[row - 1][col - 1] + first_A_gap
            extend_score = row_cache_score[row - 1] + extend_A
            open_score_rint, extend_score_rint = \
                             rint(open_score), rint(extend_score)
            if open_score_rint > extend_score_rint:
                row_cache_score[row - 1] = open_score
                row_cache_index[row - 1] = [(row - 1, col - 1)]
            elif extend_score_rint > open_score_rint:
                row_cache_score[row - 1] = extend_score
            else:
                row_cache_score[row - 1] = open_score
                if (row - 1, col - 1) not in row_cache_index[row - 1]:
                    row_cache_index[row - 1] = row_cache_index[row - 1] + \
                                             [(row - 1, col - 1)]

    return score_matrix, trace_matrix


def _recover_alignments(sequenceA, sequenceB, starts,
                        score_matrix, trace_matrix, align_globally,
                        gap_char, one_alignment_only):
    # Recover the alignments by following the traceback matrix.  This
    # is a recursive procedure, but it's implemented here iteratively
    # with a stack.
    lenA, lenB = len(sequenceA), len(sequenceB)
    tracebacks = []  # list of (seq1, seq2, score, begin, end)
    in_process = []  # list of ([same as tracebacks], prev_pos, next_pos)

    # sequenceA and sequenceB may be sequences, including strings,
    # lists, or list-like objects.  In order to preserve the type of
    # the object, we need to use slices on the sequences instead of
    # indexes.  For example, sequenceA[row] may return a type that's
    # not compatible with sequenceA, e.g. if sequenceA is a list and
    # sequenceA[row] is a string.  Thus, avoid using indexes and use
    # slices, e.g. sequenceA[row:row+1].  Assume that client-defined
    # sequence classes preserve these semantics.

    # Initialize the in_process stack
    for score, (row, col) in starts:
        if align_globally:
            begin, end = None, None
        else:
            begin, end = None, -max(lenA - row, lenB - col) + 1
            if not end:
                end = None
        # Initialize the in_process list with empty sequences of the
        # same type as sequenceA.  To do this, take empty slices of
        # the sequences.
        in_process.append(
            (sequenceA[0:0], sequenceB[0:0], score, begin, end,
             (lenA, lenB), (row, col)))
        if one_alignment_only:
            break
    while in_process and len(tracebacks) < MAX_ALIGNMENTS:
        seqA, seqB, score, begin, end, prev_pos, next_pos = in_process.pop()
        prevA, prevB = prev_pos
        if next_pos is None:
            prevlen = len(seqA)
            # add the rest of the sequences
            seqA = sequenceA[:prevA] + seqA
            seqB = sequenceB[:prevB] + seqB
            # add the rest of the gaps
            seqA, seqB = _lpad_until_equal(seqA, seqB, gap_char)

            # Now make sure begin is set.
            if begin is None:
                if align_globally:
                    begin = 0
                else:
                    begin = len(seqA) - prevlen
            tracebacks.append((seqA, seqB, score, begin, end))
        else:
            nextA, nextB = next_pos
            nseqA, nseqB = prevA - nextA, prevB - nextB
            maxseq = max(nseqA, nseqB)
            ngapA, ngapB = maxseq - nseqA, maxseq - nseqB
            seqA = sequenceA[nextA:nextA + nseqA] + gap_char * ngapA + seqA
            seqB = sequenceB[nextB:nextB + nseqB] + gap_char * ngapB + seqB
            prev_pos = next_pos
            # local alignment stops early if score falls < 0
            if not align_globally and score_matrix[nextA][nextB] <= 0:
                begin = max(prevA, prevB)
                in_process.append(
                    (seqA, seqB, score, begin, end, prev_pos, None))
            else:
                for next_pos in trace_matrix[nextA][nextB]:
                    in_process.append(
                        (seqA, seqB, score, begin, end, prev_pos, next_pos))
                    if one_alignment_only:
                        break

    return _clean_alignments(tracebacks)


def _find_start(score_matrix, sequenceA, sequenceB, gap_A_fn, gap_B_fn,
                penalize_end_gaps, align_globally):
    # Return a list of (score, (row, col)) indicating every possible
    # place to start the tracebacks.
    if align_globally:
        starts = _find_global_start(
            sequenceA, sequenceB, score_matrix, gap_A_fn, gap_B_fn, penalize_end_gaps)
    else:
        starts = _find_local_start(score_matrix)
    return starts


def _find_global_start(sequenceA, sequenceB,
                       score_matrix, gap_A_fn, gap_B_fn, penalize_end_gaps):
    # The whole sequence should be aligned, so return the positions at
    # the end of either one of the sequences.
    nrows, ncols = len(score_matrix), len(score_matrix[0])
    positions = []
    # Search all rows in the last column.
    for row in range(nrows):
        # Find the score, penalizing end gaps if necessary.
        score = score_matrix[row][ncols - 1]
        if penalize_end_gaps[1]:
            score += gap_B_fn(ncols, nrows - row - 1)
        positions.append((score, (row, ncols - 1)))
    # Search all columns in the last row.
    for col in range(ncols - 1):
        score = score_matrix[nrows - 1][col]
        if penalize_end_gaps[0]:
            score += gap_A_fn(nrows, ncols - col - 1)
        positions.append((score, (nrows - 1, col)))
    return positions


def _find_local_start(score_matrix):
    # Return every position in the matrix.
    positions = []
    nrows, ncols = len(score_matrix), len(score_matrix[0])
    for row in range(nrows):
        for col in range(ncols):
            score = score_matrix[row][col]
            positions.append((score, (row, col)))
    return positions


def _clean_alignments(alignments):
    # Take a list of alignments and return a cleaned version.  Remove
    # duplicates, make sure begin and end are set correctly, remove
    # empty alignments.
    unique_alignments = []
    for align in alignments:
        if align not in unique_alignments:
            unique_alignments.append(align)
    i = 0
    while i < len(unique_alignments):
        seqA, seqB, score, begin, end = unique_alignments[i]
        # Make sure end is set reasonably.
        if end is None:   # global alignment
            end = len(seqA)
        elif end < 0:
            end = end + len(seqA)
        # If there's no alignment here, get rid of it.
        if begin >= end:
            del unique_alignments[i]
            continue
        unique_alignments[i] = seqA, seqB, score, begin, end
        i += 1
    return unique_alignments


def _pad_until_equal(s1, s2, char):
    # Add char to the end of s1 or s2 until they are equal length.
    ls1, ls2 = len(s1), len(s2)
    if ls1 < ls2:
        s1 = _pad(s1, char, ls2 - ls1)
    elif ls2 < ls1:
        s2 = _pad(s2, char, ls1 - ls2)
    return s1, s2


def _lpad_until_equal(s1, s2, char):
    # Add char to the beginning of s1 or s2 until they are equal
    # length.
    ls1, ls2 = len(s1), len(s2)
    if ls1 < ls2:
        s1 = _lpad(s1, char, ls2 - ls1)
    elif ls2 < ls1:
        s2 = _lpad(s2, char, ls1 - ls2)
    return s1, s2


def _pad(s, char, n):
    # Append n chars to the end of s.
    return s + char * n


def _lpad(s, char, n):
    # Prepend n chars to the beginning of s.
    return char * n + s

_PRECISION = 1000


def rint(x, precision=_PRECISION):
    return int(x * precision + 0.5)


class identity_match(object):
    """identity_match([match][, mismatch]) -> match_fn

    Create a match function for use in an alignment.  match and
    mismatch are the scores to give when two residues are equal or
    unequal.  By default, match is 1 and mismatch is 0.

    """
    def __init__(self, match=1, mismatch=0):
        self.match = match
        self.mismatch = mismatch

    def __call__(self, charA, charB):
        if charA == charB:
            return self.match
        return self.mismatch


class dictionary_match(object):
    """dictionary_match(score_dict[, symmetric]) -> match_fn

    Create a match function for use in an alignment.  score_dict is a
    dictionary where the keys are tuples (residue 1, residue 2) and
    the values are the match scores between those residues.  symmetric
    is a flag that indicates whether the scores are symmetric.  If
    true, then if (res 1, res 2) doesn't exist, I will use the score
    at (res 2, res 1).

    """
    def __init__(self, score_dict, symmetric=1):
        self.score_dict = score_dict
        self.symmetric = symmetric

    def __call__(self, charA, charB):
        if self.symmetric and (charA, charB) not in self.score_dict:
            # If the score dictionary is symmetric, then look up the
            # score both ways.
            charB, charA = charA, charB
        return self.score_dict[(charA, charB)]


class affine_penalty(object):
    """affine_penalty(open, extend[, penalize_extend_when_opening]) -> gap_fn

    Create a gap function for use in an alignment.

    """
    def __init__(self, open, extend, penalize_extend_when_opening=0):
        if open > 0 or extend > 0:
            raise ValueError("Gap penalties should be non-positive.")
        self.open, self.extend = open, extend
        self.penalize_extend_when_opening = penalize_extend_when_opening

    def __call__(self, index, length):
        return calc_affine_penalty(
            length, self.open, self.extend, self.penalize_extend_when_opening)


def calc_affine_penalty(length, open, extend, penalize_extend_when_opening):
    if length <= 0:
        return 0
    penalty = open + extend * length
    if not penalize_extend_when_opening:
        penalty -= extend
    return penalty


def print_matrix(matrix):
    """print_matrix(matrix)

    Print out a matrix.  For debugging purposes.

    """
    # Transpose the matrix and get the length of the values in each column.
    matrixT = [[] for x in range(len(matrix[0]))]
    for i in range(len(matrix)):
        for j in range(len(matrix[i])):
            matrixT[j].append(len(str(matrix[i][j])))
    ndigits = [max(x) for x in matrixT]
    for i in range(len(matrix)):
        # Using string formatting trick to add leading spaces,
        print(" ".join("%*s " % (ndigits[j], matrix[i][j])
                       for j in range(len(matrix[i]))))


def format_alignment(align1, align2, score, begin, end):
    """format_alignment(align1, align2, score, begin, end) -> string

    Format the alignment prettily into a string.

    """
    s = []
    s.append("%s\n" % align1)
    s.append("%s%s\n" % (" " * begin, "|" * (end - begin)))
    s.append("%s\n" % align2)
    s.append("  Score=%g\n" % score)
    return ''.join(s)


# Try and load C implementations of functions.  If I can't,
# then just ignore and use the pure python implementations.
try:
    from .cpairwise2 import rint, _make_score_matrix_fast
except ImportError:
    pass


def _test():
    """Run the module's doctests (PRIVATE)."""
    print("Running doctests...")
    import doctest
    doctest.testmod(optionflags=doctest.IGNORE_EXCEPTION_DETAIL)
    print("Done")

if __name__ == "__main__":
    _test()
