# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------

from warnings import warn
from itertools import product

import numpy as np

from skbio.alignment import TabularMSA
from skbio.alignment._ssw_wrapper import StripedSmithWaterman
from skbio.sequence import DNA, RNA, Protein
from skbio.sequence import GrammaredSequence
from skbio.util import EfficiencyWarning
from skbio.util._decorator import experimental, deprecated

# This is temporary: blosum50 does not exist in skbio yet as per
# issue 161. When the issue is resolved, this should be removed in favor
# of an import.
blosum50 = \
    {
        '*': {'*': 1, 'A': -5, 'C': -5, 'B': -5, 'E': -5, 'D': -5, 'G': -5,
              'F': -5, 'I': -5, 'H': -5, 'K': -5, 'M': -5, 'L': -5,
              'N': -5, 'Q': -5, 'P': -5, 'S': -5, 'R': -5, 'T': -5,
              'W': -5, 'V': -5, 'Y': -5, 'X': -5, 'Z': -5},
        'A': {'*': -5, 'A': 5, 'C': -1, 'B': -2, 'E': -1, 'D': -2, 'G': 0,
              'F': -3, 'I': -1, 'H': -2, 'K': -1, 'M': -1, 'L': -2,
              'N': -1, 'Q': -1, 'P': -1, 'S': 1, 'R': -2, 'T': 0, 'W': -3,
              'V': 0, 'Y': -2, 'X': -1, 'Z': -1},
        'C': {'*': -5, 'A': -1, 'C': 13, 'B': -3, 'E': -3, 'D': -4,
              'G': -3, 'F': -2, 'I': -2, 'H': -3, 'K': -3, 'M': -2,
              'L': -2, 'N': -2, 'Q': -3, 'P': -4, 'S': -1, 'R': -4,
              'T': -1, 'W': -5, 'V': -1, 'Y': -3, 'X': -1, 'Z': -3},
        'B': {'*': -5, 'A': -2, 'C': -3, 'B': 6, 'E': 1, 'D': 6, 'G': -1,
              'F': -4, 'I': -4, 'H': 0, 'K': 0, 'M': -3, 'L': -4, 'N': 5,
              'Q': 0, 'P': -2, 'S': 0, 'R': -1, 'T': 0, 'W': -5, 'V': -3,
              'Y': -3, 'X': -1, 'Z': 1},
        'E': {'*': -5, 'A': -1, 'C': -3, 'B': 1, 'E': 6, 'D': 2, 'G': -3,
              'F': -3, 'I': -4, 'H': 0, 'K': 1, 'M': -2, 'L': -3, 'N': 0,
              'Q': 2, 'P': -1, 'S': -1, 'R': 0, 'T': -1, 'W': -3, 'V': -3,
              'Y': -2, 'X': -1, 'Z': 5},
        'D': {'*': -5, 'A': -2, 'C': -4, 'B': 6, 'E': 2, 'D': 8, 'G': -1,
              'F': -5, 'I': -4, 'H': -1, 'K': -1, 'M': -4, 'L': -4, 'N': 2,
              'Q': 0, 'P': -1, 'S': 0, 'R': -2, 'T': -1, 'W': -5, 'V': -4,
              'Y': -3, 'X': -1, 'Z': 1},
        'G': {'*': -5, 'A': 0, 'C': -3, 'B': -1, 'E': -3, 'D': -1, 'G': 8,
              'F': -4, 'I': -4, 'H': -2, 'K': -2, 'M': -3, 'L': -4, 'N': 0,
              'Q': -2, 'P': -2, 'S': 0, 'R': -3, 'T': -2, 'W': -3, 'V': -4,
              'Y': -3, 'X': -1, 'Z': -2},
        'F': {'*': -5, 'A': -3, 'C': -2, 'B': -4, 'E': -3, 'D': -5,
              'G': -4, 'F': 8, 'I': 0, 'H': -1, 'K': -4, 'M': 0, 'L': 1,
              'N': -4, 'Q': -4, 'P': -4, 'S': -3, 'R': -3, 'T': -2, 'W': 1,
              'V': -1, 'Y': 4, 'X': -1, 'Z': -4},
        'I': {'*': -5, 'A': -1, 'C': -2, 'B': -4, 'E': -4, 'D': -4,
              'G': -4, 'F': 0, 'I': 5, 'H': -4, 'K': -3, 'M': 2, 'L': 2,
              'N': -3, 'Q': -3, 'P': -3, 'S': -3, 'R': -4, 'T': -1,
              'W': -3, 'V': 4, 'Y': -1, 'X': -1, 'Z': -3},
        'H': {'*': -5, 'A': -2, 'C': -3, 'B': 0, 'E': 0, 'D': -1, 'G': -2,
              'F': -1, 'I': -4, 'H': 10, 'K': 0, 'M': -1, 'L': -3, 'N': 1,
              'Q': 1, 'P': -2, 'S': -1, 'R': 0, 'T': -2, 'W': -3, 'V': -4,
              'Y': 2, 'X': -1, 'Z': 0},
        'K': {'*': -5, 'A': -1, 'C': -3, 'B': 0, 'E': 1, 'D': -1, 'G': -2,
              'F': -4, 'I': -3, 'H': 0, 'K': 6, 'M': -2, 'L': -3, 'N': 0,
              'Q': 2, 'P': -1, 'S': 0, 'R': 3, 'T': -1, 'W': -3, 'V': -3,
              'Y': -2, 'X': -1, 'Z': 1},
        'M': {'*': -5, 'A': -1, 'C': -2, 'B': -3, 'E': -2, 'D': -4,
              'G': -3, 'F': 0, 'I': 2, 'H': -1, 'K': -2, 'M': 7, 'L': 3,
              'N': -2, 'Q': 0, 'P': -3, 'S': -2, 'R': -2, 'T': -1, 'W': -1,
              'V': 1, 'Y': 0, 'X': -1, 'Z': -1},
        'L': {'*': -5, 'A': -2, 'C': -2, 'B': -4, 'E': -3, 'D': -4,
              'G': -4, 'F': 1, 'I': 2, 'H': -3, 'K': -3, 'M': 3, 'L': 5,
              'N': -4, 'Q': -2, 'P': -4, 'S': -3, 'R': -3, 'T': -1,
              'W': -2, 'V': 1, 'Y': -1, 'X': -1, 'Z': -3},
        'N': {'*': -5, 'A': -1, 'C': -2, 'B': 5, 'E': 0, 'D': 2, 'G': 0,
              'F': -4, 'I': -3, 'H': 1, 'K': 0, 'M': -2, 'L': -4, 'N': 7,
              'Q': 0, 'P': -2, 'S': 1, 'R': -1, 'T': 0, 'W': -4, 'V': -3,
              'Y': -2, 'X': -1, 'Z': 0},
        'Q': {'*': -5, 'A': -1, 'C': -3, 'B': 0, 'E': 2, 'D': 0, 'G': -2,
              'F': -4, 'I': -3, 'H': 1, 'K': 2, 'M': 0, 'L': -2, 'N': 0,
              'Q': 7, 'P': -1, 'S': 0, 'R': 1, 'T': -1, 'W': -1, 'V': -3,
              'Y': -1, 'X': -1, 'Z': 4},
        'P': {'*': -5, 'A': -1, 'C': -4, 'B': -2, 'E': -1, 'D': -1,
              'G': -2, 'F': -4, 'I': -3, 'H': -2, 'K': -1, 'M': -3,
              'L': -4, 'N': -2, 'Q': -1, 'P': 10, 'S': -1, 'R': -3,
              'T': -1, 'W': -4, 'V': -3, 'Y': -3, 'X': -1, 'Z': -1},
        'S': {'*': -5, 'A': 1, 'C': -1, 'B': 0, 'E': -1, 'D': 0, 'G': 0,
              'F': -3, 'I': -3, 'H': -1, 'K': 0, 'M': -2, 'L': -3, 'N': 1,
              'Q': 0, 'P': -1, 'S': 5, 'R': -1, 'T': 2, 'W': -4, 'V': -2,
              'Y': -2, 'X': -1, 'Z': 0},
        'R': {'*': -5, 'A': -2, 'C': -4, 'B': -1, 'E': 0, 'D': -2, 'G': -3,
              'F': -3, 'I': -4, 'H': 0, 'K': 3, 'M': -2, 'L': -3, 'N': -1,
              'Q': 1, 'P': -3, 'S': -1, 'R': 7, 'T': -1, 'W': -3, 'V': -3,
              'Y': -1, 'X': -1, 'Z': 0},
        'T': {'*': -5, 'A': 0, 'C': -1, 'B': 0, 'E': -1, 'D': -1, 'G': -2,
              'F': -2, 'I': -1, 'H': -2, 'K': -1, 'M': -1, 'L': -1, 'N': 0,
              'Q': -1, 'P': -1, 'S': 2, 'R': -1, 'T': 5, 'W': -3, 'V': 0,
              'Y': -2, 'X': -1, 'Z': -1},
        'W': {'*': -5, 'A': -3, 'C': -5, 'B': -5, 'E': -3, 'D': -5,
              'G': -3, 'F': 1, 'I': -3, 'H': -3, 'K': -3, 'M': -1, 'L': -2,
              'N': -4, 'Q': -1, 'P': -4, 'S': -4, 'R': -3, 'T': -3,
              'W': 15, 'V': -3, 'Y': 2, 'X': -1, 'Z': -2},
        'V': {'*': -5, 'A': 0, 'C': -1, 'B': -3, 'E': -3, 'D': -4, 'G': -4,
              'F': -1, 'I': 4, 'H': -4, 'K': -3, 'M': 1, 'L': 1, 'N': -3,
              'Q': -3, 'P': -3, 'S': -2, 'R': -3, 'T': 0, 'W': -3, 'V': 5,
              'Y': -1, 'X': -1, 'Z': -3},
        'Y': {'*': -5, 'A': -2, 'C': -3, 'B': -3, 'E': -2, 'D': -3,
              'G': -3, 'F': 4, 'I': -1, 'H': 2, 'K': -2, 'M': 0, 'L': -1,
              'N': -2, 'Q': -1, 'P': -3, 'S': -2, 'R': -1, 'T': -2, 'W': 2,
              'V': -1, 'Y': 8, 'X': -1, 'Z': -2},
        'X': {'*': -5, 'A': -1, 'C': -1, 'B': -1, 'E': -1, 'D': -1,
              'G': -1, 'F': -1, 'I': -1, 'H': -1, 'K': -1, 'M': -1,
              'L': -1, 'N': -1, 'Q': -1, 'P': -1, 'S': -1, 'R': -1,
              'T': -1, 'W': -1, 'V': -1, 'Y': -1, 'X': -1, 'Z': -1},
        'Z': {'*': -5, 'A': -1, 'C': -3, 'B': 1, 'E': 5, 'D': 1, 'G': -2,
              'F': -4, 'I': -3, 'H': 0, 'K': 1, 'M': -1, 'L': -3, 'N': 0,
              'Q': 4, 'P': -1, 'S': 0, 'R': 0, 'T': -1, 'W': -2, 'V': -3,
              'Y': -2, 'X': -1, 'Z': 5}}


@experimental(as_of="0.4.0")
def local_pairwise_align_nucleotide(seq1, seq2, gap_open_penalty=5,
                                    gap_extend_penalty=2,
                                    match_score=2, mismatch_score=-3,
                                    substitution_matrix=None):
    """Locally align exactly two nucleotide seqs with Smith-Waterman

    Parameters
    ----------
    seq1 : DNA or RNA
        The first unaligned sequence.
    seq2 : DNA or RNA
        The second unaligned sequence.
    gap_open_penalty : int or float, optional
        Penalty for opening a gap (this is substracted from previous best
        alignment score, so is typically positive).
    gap_extend_penalty : int or float, optional
        Penalty for extending a gap (this is substracted from previous best
        alignment score, so is typically positive).
    match_score : int or float, optional
        The score to add for a match between a pair of bases (this is added
        to the previous best alignment score, so is typically positive).
    mismatch_score : int or float, optional
        The score to add for a mismatch between a pair of bases (this is
        added to the previous best alignment score, so is typically
        negative).
    substitution_matrix: 2D dict (or similar)
        Lookup for substitution scores (these values are added to the
        previous best alignment score). If provided, this overrides
        ``match_score`` and ``mismatch_score``.

    Returns
    -------
    tuple
        ``TabularMSA`` object containing the aligned sequences, alignment score
        (float), and start/end positions of each input sequence (iterable
        of two-item tuples). Note that start/end positions are indexes into the
        unaligned sequences.

    See Also
    --------
    local_pairwise_align
    local_pairwise_align_protein
    skbio.alignment.local_pairwise_align_ssw
    global_pairwise_align
    global_pairwise_align_protein
    global_pairwise_align_nucelotide

    Notes
    -----
    Default ``match_score``, ``mismatch_score``, ``gap_open_penalty`` and
    ``gap_extend_penalty`` parameters are derived from the NCBI BLAST
    Server [1]_.

    References
    ----------
    .. [1] http://blast.ncbi.nlm.nih.gov/Blast.cgi

    """
    for seq in seq1, seq2:
        if not isinstance(seq, (DNA, RNA)):
            raise TypeError(
                "`seq1` and `seq2` must be DNA or RNA, not type %r"
                % type(seq).__name__)

    # use the substitution matrix provided by the user, or compute from
    # match_score and mismatch_score if a substitution matrix was not provided
    if substitution_matrix is None:
        substitution_matrix = \
            make_identity_substitution_matrix(match_score, mismatch_score)

    return local_pairwise_align(seq1, seq2, gap_open_penalty,
                                gap_extend_penalty, substitution_matrix)


@experimental(as_of="0.4.0")
def local_pairwise_align_protein(seq1, seq2, gap_open_penalty=11,
                                 gap_extend_penalty=1,
                                 substitution_matrix=None):
    """Locally align exactly two protein seqs with Smith-Waterman

    Parameters
    ----------
    seq1 : Protein
        The first unaligned sequence.
    seq2 : Protein
        The second unaligned sequence.
    gap_open_penalty : int or float, optional
        Penalty for opening a gap (this is substracted from previous best
        alignment score, so is typically positive).
    gap_extend_penalty : int or float, optional
        Penalty for extending a gap (this is substracted from previous best
        alignment score, so is typically positive).
    substitution_matrix: 2D dict (or similar), optional
        Lookup for substitution scores (these values are added to the
        previous best alignment score); default is BLOSUM 50.

    Returns
    -------
    tuple
        ``TabularMSA`` object containing the aligned sequences, alignment score
        (float), and start/end positions of each input sequence (iterable
        of two-item tuples). Note that start/end positions are indexes into the
        unaligned sequences.

    See Also
    --------
    local_pairwise_align
    local_pairwise_align_nucleotide
    skbio.alignment.local_pairwise_align_ssw
    global_pairwise_align
    global_pairwise_align_protein
    global_pairwise_align_nucelotide

    Notes
    -----
    Default ``gap_open_penalty`` and ``gap_extend_penalty`` parameters are
    derived from the NCBI BLAST Server [1]_.

    The BLOSUM (blocks substitution matrices) amino acid substitution matrices
    were originally defined in [2]_.

    References
    ----------
    .. [1] http://blast.ncbi.nlm.nih.gov/Blast.cgi
    .. [2] Amino acid substitution matrices from protein blocks.
       S Henikoff and J G Henikoff.
       Proc Natl Acad Sci U S A. Nov 15, 1992; 89(22): 10915-10919.

    """
    for seq in seq1, seq2:
        if not isinstance(seq, Protein):
            raise TypeError(
                "`seq1` and `seq2` must be Protein, not type %r"
                % type(seq).__name__)

    if substitution_matrix is None:
        substitution_matrix = blosum50

    return local_pairwise_align(seq1, seq2, gap_open_penalty,
                                gap_extend_penalty, substitution_matrix)


@experimental(as_of="0.4.0")
def local_pairwise_align(seq1, seq2, gap_open_penalty,
                         gap_extend_penalty, substitution_matrix):
    """Locally align exactly two seqs with Smith-Waterman

    Parameters
    ----------
    seq1 : GrammaredSequence
        The first unaligned sequence.
    seq2 : GrammaredSequence
        The second unaligned sequence.
    gap_open_penalty : int or float
        Penalty for opening a gap (this is substracted from previous best
        alignment score, so is typically positive).
    gap_extend_penalty : int or float
        Penalty for extending a gap (this is substracted from previous best
        alignment score, so is typically positive).
    substitution_matrix: 2D dict (or similar)
        Lookup for substitution scores (these values are added to the
        previous best alignment score).

    Returns
    -------
    tuple
        ``TabularMSA`` object containing the aligned sequences, alignment score
        (float), and start/end positions of each input sequence (iterable
        of two-item tuples). Note that start/end positions are indexes into the
        unaligned sequences.

    See Also
    --------
    local_pairwise_align_protein
    local_pairwise_align_nucleotide
    skbio.alignment.local_pairwise_align_ssw
    global_pairwise_align
    global_pairwise_align_protein
    global_pairwise_align_nucelotide

    Notes
    -----
    This algorithm was originally described in [1]_. The scikit-bio
    implementation was validated against the EMBOSS water web server [2]_.

    References
    ----------
    .. [1] Identification of common molecular subsequences.
       Smith TF, Waterman MS.
       J Mol Biol. 1981 Mar 25;147(1):195-7.
    .. [2] http://www.ebi.ac.uk/Tools/psa/emboss_water/

    """
    warn("You're using skbio's python implementation of Smith-Waterman "
         "alignment. This will be very slow (e.g., thousands of times slower) "
         "than skbio.alignment.local_pairwise_align_ssw.",
         EfficiencyWarning)

    for seq in seq1, seq2:
        if not isinstance(seq, GrammaredSequence):
            raise TypeError(
                "`seq1` and `seq2` must be %r subclasses, not type %r" %
                (GrammaredSequence.__name__, type(seq).__name__))

    if type(seq1) is not type(seq2):
        raise TypeError(
            "`seq1` and `seq2` must be the same type: %r != %r"
            % (type(seq1).__name__, type(seq2).__name__))

    seq1 = _coerce_alignment_input_type(seq1)
    seq2 = _coerce_alignment_input_type(seq2)

    score_matrix, traceback_matrix = _compute_score_and_traceback_matrices(
        seq1, seq2, gap_open_penalty, gap_extend_penalty,
        substitution_matrix, new_alignment_score=0.0,
        init_matrices_f=_init_matrices_sw)

    end_row_position, end_col_position =\
        np.unravel_index(np.argmax(score_matrix), score_matrix.shape)

    aligned1, aligned2, score, seq1_start_position, seq2_start_position = \
        _traceback(traceback_matrix, score_matrix, seq1, seq2,
                   end_row_position, end_col_position)
    start_end_positions = [(seq1_start_position, end_col_position-1),
                           (seq2_start_position, end_row_position-1)]

    msa = TabularMSA(aligned1 + aligned2)

    return msa, score, start_end_positions


@experimental(as_of="0.4.0")
def global_pairwise_align_nucleotide(seq1, seq2, gap_open_penalty=5,
                                     gap_extend_penalty=2,
                                     match_score=1, mismatch_score=-2,
                                     substitution_matrix=None,
                                     penalize_terminal_gaps=False):
    """Globally align nucleotide seqs or alignments with Needleman-Wunsch

    Parameters
    ----------
    seq1 : DNA, RNA, or TabularMSA[DNA|RNA]
        The first unaligned sequence(s).
    seq2 : DNA, RNA, or TabularMSA[DNA|RNA]
        The second unaligned sequence(s).
    gap_open_penalty : int or float, optional
        Penalty for opening a gap (this is substracted from previous best
        alignment score, so is typically positive).
    gap_extend_penalty : int or float, optional
        Penalty for extending a gap (this is substracted from previous best
        alignment score, so is typically positive).
    match_score : int or float, optional
        The score to add for a match between a pair of bases (this is added
        to the previous best alignment score, so is typically positive).
    mismatch_score : int or float, optional
        The score to add for a mismatch between a pair of bases (this is
        added to the previous best alignment score, so is typically
        negative).
    substitution_matrix: 2D dict (or similar)
        Lookup for substitution scores (these values are added to the
        previous best alignment score). If provided, this overrides
        ``match_score`` and ``mismatch_score``.
    penalize_terminal_gaps: bool, optional
        If True, will continue to penalize gaps even after one sequence has
        been aligned through its end. This behavior is true Needleman-Wunsch
        alignment, but results in (biologically irrelevant) artifacts when
        the sequences being aligned are of different length. This is ``False``
        by default, which is very likely to be the behavior you want in all or
        nearly all cases.

    Returns
    -------
    tuple
        ``TabularMSA`` object containing the aligned sequences, alignment score
        (float), and start/end positions of each input sequence (iterable
        of two-item tuples). Note that start/end positions are indexes into the
        unaligned sequences.

    See Also
    --------
    local_pairwise_align
    local_pairwise_align_protein
    local_pairwise_align_nucleotide
    skbio.alignment.local_pairwise_align_ssw
    global_pairwise_align
    global_pairwise_align_protein

    Notes
    -----
    Default ``match_score``, ``mismatch_score``, ``gap_open_penalty`` and
    ``gap_extend_penalty`` parameters are derived from the NCBI BLAST
    Server [1]_.

    This function can be use to align either a pair of sequences, a pair of
    alignments, or a sequence and an alignment.

    References
    ----------
    .. [1] http://blast.ncbi.nlm.nih.gov/Blast.cgi

    """
    for seq in seq1, seq2:
        if not isinstance(seq, (DNA, RNA, TabularMSA)):
            raise TypeError(
                "`seq1` and `seq2` must be DNA, RNA, or TabularMSA, not type "
                "%r" % type(seq).__name__)
        if isinstance(seq, TabularMSA) and not issubclass(seq.dtype,
                                                          (DNA, RNA)):
            raise TypeError(
                "`seq1` and `seq2` must be TabularMSA with DNA or RNA dtype, "
                "not dtype %r" % seq.dtype.__name__)

    # use the substitution matrix provided by the user, or compute from
    # match_score and mismatch_score if a substitution matrix was not provided
    if substitution_matrix is None:
        substitution_matrix = \
            make_identity_substitution_matrix(match_score, mismatch_score)

    return global_pairwise_align(seq1, seq2, gap_open_penalty,
                                 gap_extend_penalty, substitution_matrix,
                                 penalize_terminal_gaps=penalize_terminal_gaps)


@experimental(as_of="0.4.0")
def global_pairwise_align_protein(seq1, seq2, gap_open_penalty=11,
                                  gap_extend_penalty=1,
                                  substitution_matrix=None,
                                  penalize_terminal_gaps=False):
    """Globally align pair of protein seqs or alignments with Needleman-Wunsch

    Parameters
    ----------
    seq1 : Protein or TabularMSA[Protein]
        The first unaligned sequence(s).
    seq2 : Protein or TabularMSA[Protein]
        The second unaligned sequence(s).
    gap_open_penalty : int or float, optional
        Penalty for opening a gap (this is substracted from previous best
        alignment score, so is typically positive).
    gap_extend_penalty : int or float, optional
        Penalty for extending a gap (this is substracted from previous best
        alignment score, so is typically positive).
    substitution_matrix: 2D dict (or similar), optional
        Lookup for substitution scores (these values are added to the
        previous best alignment score); default is BLOSUM 50.
    penalize_terminal_gaps: bool, optional
        If True, will continue to penalize gaps even after one sequence has
        been aligned through its end. This behavior is true Needleman-Wunsch
        alignment, but results in (biologically irrelevant) artifacts when
        the sequences being aligned are of different length. This is ``False``
        by default, which is very likely to be the behavior you want in all or
        nearly all cases.

    Returns
    -------
    tuple
        ``TabularMSA`` object containing the aligned sequences, alignment score
        (float), and start/end positions of each input sequence (iterable
        of two-item tuples). Note that start/end positions are indexes into the
        unaligned sequences.

    See Also
    --------
    local_pairwise_align
    local_pairwise_align_protein
    local_pairwise_align_nucleotide
    skbio.alignment.local_pairwise_align_ssw
    global_pairwise_align
    global_pairwise_align_nucelotide

    Notes
    -----
    Default ``gap_open_penalty`` and ``gap_extend_penalty`` parameters are
    derived from the NCBI BLAST Server [1]_.

    The BLOSUM (blocks substitution matrices) amino acid substitution matrices
    were originally defined in [2]_.

    This function can be use to align either a pair of sequences, a pair of
    alignments, or a sequence and an alignment.

    References
    ----------
    .. [1] http://blast.ncbi.nlm.nih.gov/Blast.cgi
    .. [2] Amino acid substitution matrices from protein blocks.
       S Henikoff and J G Henikoff.
       Proc Natl Acad Sci U S A. Nov 15, 1992; 89(22): 10915-10919.

    """
    for seq in seq1, seq2:
        if not isinstance(seq, (Protein, TabularMSA)):
            raise TypeError(
                "`seq1` and `seq2` must be Protein or TabularMSA, not type %r"
                % type(seq).__name__)
        if isinstance(seq, TabularMSA) and not issubclass(seq.dtype, Protein):
            raise TypeError(
                "`seq1` and `seq2` must be TabularMSA with Protein dtype, "
                "not dtype %r" % seq.dtype.__name__)

    if substitution_matrix is None:
        substitution_matrix = blosum50

    return global_pairwise_align(seq1, seq2, gap_open_penalty,
                                 gap_extend_penalty, substitution_matrix,
                                 penalize_terminal_gaps=penalize_terminal_gaps)


@experimental(as_of="0.4.0")
def global_pairwise_align(seq1, seq2, gap_open_penalty, gap_extend_penalty,
                          substitution_matrix, penalize_terminal_gaps=False):
    """Globally align a pair of seqs or alignments with Needleman-Wunsch

    Parameters
    ----------
    seq1 : GrammaredSequence or TabularMSA
        The first unaligned sequence(s).
    seq2 : GrammaredSequence or TabularMSA
        The second unaligned sequence(s).
    gap_open_penalty : int or float
        Penalty for opening a gap (this is substracted from previous best
        alignment score, so is typically positive).
    gap_extend_penalty : int or float
        Penalty for extending a gap (this is substracted from previous best
        alignment score, so is typically positive).
    substitution_matrix: 2D dict (or similar)
        Lookup for substitution scores (these values are added to the
        previous best alignment score).
    penalize_terminal_gaps: bool, optional
        If True, will continue to penalize gaps even after one sequence has
        been aligned through its end. This behavior is true Needleman-Wunsch
        alignment, but results in (biologically irrelevant) artifacts when
        the sequences being aligned are of different length. This is ``False``
        by default, which is very likely to be the behavior you want in all or
        nearly all cases.

    Returns
    -------
    tuple
        ``TabularMSA`` object containing the aligned sequences, alignment score
        (float), and start/end positions of each input sequence (iterable
        of two-item tuples). Note that start/end positions are indexes into the
        unaligned sequences.

    See Also
    --------
    local_pairwise_align
    local_pairwise_align_protein
    local_pairwise_align_nucleotide
    skbio.alignment.local_pairwise_align_ssw
    global_pairwise_align_protein
    global_pairwise_align_nucelotide

    Notes
    -----
    This algorithm (in a slightly more basic form) was originally described
    in [1]_. The scikit-bio implementation was validated against the
    EMBOSS needle web server [2]_.

    This function can be use to align either a pair of sequences, a pair of
    alignments, or a sequence and an alignment.

    References
    ----------
    .. [1] A general method applicable to the search for similarities in
       the amino acid sequence of two proteins.
       Needleman SB, Wunsch CD.
       J Mol Biol. 1970 Mar;48(3):443-53.
    .. [2] http://www.ebi.ac.uk/Tools/psa/emboss_needle/

    """
    warn("You're using skbio's python implementation of Needleman-Wunsch "
         "alignment. This is known to be very slow (e.g., thousands of times "
         "slower than a native C implementation). We'll be adding a faster "
         "version soon (see https://github.com/biocore/scikit-bio/issues/254 "
         "to track progress on this).", EfficiencyWarning)

    for seq in seq1, seq2:
        # We don't need to check the case where `seq` is a `TabularMSA` with a
        # dtype that isn't a subclass of `GrammaredSequence`, this is
        # guaranteed by `TabularMSA`.
        if not isinstance(seq, (GrammaredSequence, TabularMSA)):
            raise TypeError(
                "`seq1` and `seq2` must be GrammaredSequence subclasses or "
                "TabularMSA, not type %r" % type(seq).__name__)

    seq1 = _coerce_alignment_input_type(seq1)
    seq2 = _coerce_alignment_input_type(seq2)

    if seq1.dtype is not seq2.dtype:
        raise TypeError(
            "`seq1` and `seq2` must have the same dtype: %r != %r"
            % (seq1.dtype.__name__, seq2.dtype.__name__))

    if penalize_terminal_gaps:
        init_matrices_f = _init_matrices_nw
    else:
        init_matrices_f = _init_matrices_nw_no_terminal_gap_penalty

    score_matrix, traceback_matrix = \
        _compute_score_and_traceback_matrices(
            seq1, seq2, gap_open_penalty, gap_extend_penalty,
            substitution_matrix, new_alignment_score=-np.inf,
            init_matrices_f=init_matrices_f,
            penalize_terminal_gaps=penalize_terminal_gaps)

    end_row_position = traceback_matrix.shape[0] - 1
    end_col_position = traceback_matrix.shape[1] - 1

    aligned1, aligned2, score, seq1_start_position, seq2_start_position = \
        _traceback(traceback_matrix, score_matrix, seq1, seq2,
                   end_row_position, end_col_position)
    start_end_positions = [(seq1_start_position, end_col_position-1),
                           (seq2_start_position, end_row_position-1)]

    msa = TabularMSA(aligned1 + aligned2)

    return msa, score, start_end_positions


@experimental(as_of="0.4.0")
def local_pairwise_align_ssw(sequence1, sequence2, **kwargs):
    """Align query and target sequences with Striped Smith-Waterman.

    Parameters
    ----------
    sequence1 : DNA, RNA, or Protein
        The first unaligned sequence
    sequence2 : DNA, RNA, or Protein
        The second unaligned sequence

    Returns
    -------
    tuple
        ``TabularMSA`` object containing the aligned sequences, alignment score
        (float), and start/end positions of each input sequence (iterable
        of two-item tuples). Note that start/end positions are indexes into the
        unaligned sequences.

    Notes
    -----
    This is a wrapper for the SSW package [1]_.

    For a complete list of optional keyword-arguments that can be provided,
    see ``skbio.alignment.StripedSmithWaterman``.

    The following kwargs will not have any effect: `suppress_sequences`,
    `zero_index`, and `protein`

    If an alignment does not meet a provided filter, `None` will be returned.

    References
    ----------
    .. [1] Zhao, Mengyao, Wan-Ping Lee, Erik P. Garrison, & Gabor T.
       Marth. "SSW Library: An SIMD Smith-Waterman C/C++ Library for
       Applications". PLOS ONE (2013). Web. 11 July 2014.
       http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082138

    See Also
    --------
    skbio.alignment.StripedSmithWaterman

    """
    for seq in sequence1, sequence2:
        if not isinstance(seq, (DNA, RNA, Protein)):
            raise TypeError(
                "`sequence1` and `sequence2` must be DNA, RNA, or Protein, "
                "not type %r" % type(seq).__name__)

    if type(sequence1) is not type(sequence2):
        raise TypeError(
            "`sequence1` and `sequence2` must be the same type: %r != %r"
            % (type(sequence1).__name__, type(sequence2).__name__))

    # We need the sequences for `TabularMSA` to make sense, so don't let the
    # user suppress them.
    kwargs['suppress_sequences'] = False
    kwargs['zero_index'] = True

    kwargs['protein'] = False
    if isinstance(sequence1, Protein):
        kwargs['protein'] = True

    query = StripedSmithWaterman(str(sequence1), **kwargs)
    alignment = query(str(sequence2))

    # If there is no cigar, then it has failed a filter. Return None.
    if not alignment.cigar:
        return None

    start_end = None
    if alignment.query_begin != -1:
        start_end = [
            (alignment.query_begin, alignment.query_end),
            (alignment.target_begin, alignment.target_end_optimal)
        ]

    metadata1 = metadata2 = None
    if sequence1.has_metadata():
        metadata1 = sequence1.metadata
    if sequence2.has_metadata():
        metadata2 = sequence2.metadata

    constructor = type(sequence1)
    msa = TabularMSA([
        constructor(alignment.aligned_query_sequence, metadata=metadata1,
                    validate=False),
        constructor(alignment.aligned_target_sequence, metadata=metadata2,
                    validate=False)
    ])

    return msa, alignment.optimal_alignment_score, start_end


@deprecated(as_of="0.4.0", until="0.5.1",
            reason="Will be replaced by a SubstitutionMatrix class. To track "
                   "progress, see [#161]"
                   "(https://github.com/biocore/scikit-bio/issues/161).")
def make_identity_substitution_matrix(match_score, mismatch_score,
                                      alphabet='ACGTU'):
    """Generate substitution matrix where all matches are scored equally

    Parameters
    ----------
    match_score : int, float
        The score that should be assigned for all matches. This value is
        typically positive.
    mismatch_score : int, float
        The score that should be assigned for all mismatches. This value is
        typically negative.
    alphabet : iterable of str, optional
        The characters that should be included in the substitution matrix.

    Returns
    -------
    dict of dicts
        All characters in alphabet are keys in both dictionaries, so that any
        pair of characters can be looked up to get their match or mismatch
        score.

    """
    result = {}
    for c1 in alphabet:
        row = {}
        for c2 in alphabet:
            if c1 == c2:
                row[c2] = match_score
            else:
                row[c2] = mismatch_score
        result[c1] = row
    return result


# Functions from here allow for generalized (global or local) alignment. I
# will likely want to put these in a single object to make the naming a little
# less clunky.


def _coerce_alignment_input_type(seq):
    if isinstance(seq, GrammaredSequence):
        return TabularMSA([seq])
    else:
        return seq


_traceback_encoding = {'match': 1, 'vertical-gap': 2, 'horizontal-gap': 3,
                       'uninitialized': -1, 'alignment-end': 0}


def _init_matrices_sw(aln1, aln2, gap_open_penalty, gap_extend_penalty):
    shape = (aln2.shape.position+1, aln1.shape.position+1)
    score_matrix = np.zeros(shape)
    traceback_matrix = np.zeros(shape, dtype=np.int)
    traceback_matrix += _traceback_encoding['uninitialized']
    traceback_matrix[0, :] = _traceback_encoding['alignment-end']
    traceback_matrix[:, 0] = _traceback_encoding['alignment-end']
    return score_matrix, traceback_matrix


def _init_matrices_nw(aln1, aln2, gap_open_penalty, gap_extend_penalty):
    shape = (aln2.shape.position+1, aln1.shape.position+1)
    score_matrix = np.zeros(shape)
    traceback_matrix = np.zeros(shape, dtype=np.int)
    traceback_matrix += _traceback_encoding['uninitialized']
    traceback_matrix[0, 0] = _traceback_encoding['alignment-end']

    # cache some values for quicker access
    vgap = _traceback_encoding['vertical-gap']
    hgap = _traceback_encoding['horizontal-gap']

    for i in range(1, shape[0]):
        score_matrix[i, 0] = -gap_open_penalty - ((i-1) * gap_extend_penalty)
        traceback_matrix[i, 0] = vgap

    for i in range(1, shape[1]):
        score_matrix[0, i] = -gap_open_penalty - ((i-1) * gap_extend_penalty)
        traceback_matrix[0, i] = hgap

    return score_matrix, traceback_matrix


def _init_matrices_nw_no_terminal_gap_penalty(
        aln1, aln2, gap_open_penalty, gap_extend_penalty):
    shape = (aln2.shape.position+1, aln1.shape.position+1)
    score_matrix = np.zeros(shape)
    traceback_matrix = np.zeros(shape, dtype=np.int)
    traceback_matrix += _traceback_encoding['uninitialized']
    traceback_matrix[0, 0] = _traceback_encoding['alignment-end']

    # cache some values for quicker access
    vgap = _traceback_encoding['vertical-gap']
    hgap = _traceback_encoding['horizontal-gap']

    for i in range(1, shape[0]):
        traceback_matrix[i, 0] = vgap

    for i in range(1, shape[1]):
        traceback_matrix[0, i] = hgap

    return score_matrix, traceback_matrix


def _compute_substitution_score(aln1_chars, aln2_chars, substitution_matrix,
                                gap_substitution_score, gap_chars):
    substitution_score = 0
    for aln1_char, aln2_char in product(aln1_chars, aln2_chars):
        if aln1_char in gap_chars or aln2_char in gap_chars:
                substitution_score += gap_substitution_score
        else:
            try:
                substitution_score += \
                    substitution_matrix[aln1_char][aln2_char]
            except KeyError:
                offending_chars = \
                    [c for c in (aln1_char, aln2_char)
                     if c not in substitution_matrix]
                raise ValueError(
                    "One of the sequences contains a character that is "
                    "not contained in the substitution matrix. Are you "
                    "using an appropriate substitution matrix for your "
                    "sequence type (e.g., a nucleotide substitution "
                    "matrix does not make sense for aligning protein "
                    "sequences)? Does your sequence contain invalid "
                    "characters? The offending character(s) is: "
                    " %s." % ', '.join(offending_chars))
    substitution_score /= (len(aln1_chars) * len(aln2_chars))
    return substitution_score


def _compute_score_and_traceback_matrices(
        aln1, aln2, gap_open_penalty, gap_extend_penalty, substitution_matrix,
        new_alignment_score=-np.inf, init_matrices_f=_init_matrices_nw,
        penalize_terminal_gaps=True, gap_substitution_score=0):
    """Return dynamic programming (score) and traceback matrices.

    A note on the ``penalize_terminal_gaps`` parameter. When this value is
    ``False``, this function is no longer true Smith-Waterman/Needleman-Wunsch
    scoring, but when ``True`` it can result in biologically irrelevant
    artifacts in Needleman-Wunsch (global) alignments. Specifically, if one
    sequence is longer than the other (e.g., if aligning a primer sequence to
    an amplification product, or searching for a gene in a genome) the shorter
    sequence will have a long gap inserted. The parameter is ``True`` by
    default (so that this function computes the score and traceback matrices as
    described by the original authors) but the global alignment wrappers pass
    ``False`` by default, so that the global alignment API returns the result
    that users are most likely to be looking for.

    """
    aln1_length = aln1.shape.position
    aln2_length = aln2.shape.position
    # cache some values for quicker/simpler access
    aend = _traceback_encoding['alignment-end']
    match = _traceback_encoding['match']
    vgap = _traceback_encoding['vertical-gap']
    hgap = _traceback_encoding['horizontal-gap']

    new_alignment_score = (new_alignment_score, aend)

    # Initialize a matrix to use for scoring the alignment and for tracing
    # back the best alignment
    score_matrix, traceback_matrix = init_matrices_f(
        aln1, aln2, gap_open_penalty, gap_extend_penalty)

    # Iterate over the characters in aln2 (which corresponds to the vertical
    # sequence in the matrix)
    for aln2_pos, aln2_chars in enumerate(aln2.iter_positions(
            ignore_metadata=True), 1):
        aln2_chars = str(aln2_chars)

        # Iterate over the characters in aln1 (which corresponds to the
        # horizontal sequence in the matrix)
        for aln1_pos, aln1_chars in enumerate(aln1.iter_positions(
                ignore_metadata=True), 1):
            aln1_chars = str(aln1_chars)

            # compute the score for a match/mismatch
            substitution_score = _compute_substitution_score(
                aln1_chars, aln2_chars, substitution_matrix,
                gap_substitution_score, aln1.dtype.gap_chars)

            diag_score = \
                (score_matrix[aln2_pos-1, aln1_pos-1] + substitution_score,
                 match)

            # compute the score for adding a gap in aln2 (vertical)
            if not penalize_terminal_gaps and (aln1_pos == aln1_length):
                # we've reached the end of aln1, so adding vertical gaps
                # (which become gaps in aln1) should no longer
                # be penalized (if penalize_terminal_gaps == False)
                up_score = (score_matrix[aln2_pos-1, aln1_pos], vgap)
            elif traceback_matrix[aln2_pos-1, aln1_pos] == vgap:
                # gap extend, because the cell above was also a gap
                up_score = \
                    (score_matrix[aln2_pos-1, aln1_pos] - gap_extend_penalty,
                     vgap)
            else:
                # gap open, because the cell above was not a gap
                up_score = \
                    (score_matrix[aln2_pos-1, aln1_pos] - gap_open_penalty,
                     vgap)

            # compute the score for adding a gap in aln1 (horizontal)
            if not penalize_terminal_gaps and (aln2_pos == aln2_length):
                # we've reached the end of aln2, so adding horizontal gaps
                # (which become gaps in aln2) should no longer
                # be penalized (if penalize_terminal_gaps == False)
                left_score = (score_matrix[aln2_pos, aln1_pos-1], hgap)
            elif traceback_matrix[aln2_pos, aln1_pos-1] == hgap:
                # gap extend, because the cell to the left was also a gap
                left_score = \
                    (score_matrix[aln2_pos, aln1_pos-1] - gap_extend_penalty,
                     hgap)
            else:
                # gap open, because the cell to the left was not a gap
                left_score = \
                    (score_matrix[aln2_pos, aln1_pos-1] - gap_open_penalty,
                     hgap)

            # identify the largest score, and use that information to populate
            # the score and traceback matrices
            best_score = _first_largest([new_alignment_score, left_score,
                                         diag_score, up_score])
            score_matrix[aln2_pos, aln1_pos] = best_score[0]
            traceback_matrix[aln2_pos, aln1_pos] = best_score[1]

    return score_matrix, traceback_matrix


def _traceback(traceback_matrix, score_matrix, aln1, aln2, start_row,
               start_col):
    # cache some values for simpler reference
    aend = _traceback_encoding['alignment-end']
    match = _traceback_encoding['match']
    vgap = _traceback_encoding['vertical-gap']
    hgap = _traceback_encoding['horizontal-gap']
    gap_character = aln1.dtype.default_gap_char

    # initialize the result alignments
    aln1_sequence_count = aln1.shape.sequence
    aligned_seqs1 = [[] for e in range(aln1_sequence_count)]

    aln2_sequence_count = aln2.shape.sequence
    aligned_seqs2 = [[] for e in range(aln2_sequence_count)]

    current_row = start_row
    current_col = start_col

    best_score = score_matrix[current_row, current_col]
    current_value = None

    while current_value != aend:
        current_value = traceback_matrix[current_row, current_col]

        if current_value == match:
            for aligned_seq, input_seq in zip(aligned_seqs1, aln1):
                aligned_seq.append(str(input_seq[current_col-1]))
            for aligned_seq, input_seq in zip(aligned_seqs2, aln2):
                aligned_seq.append(str(input_seq[current_row-1]))
            current_row -= 1
            current_col -= 1
        elif current_value == vgap:
            for aligned_seq in aligned_seqs1:
                aligned_seq.append(gap_character)
            for aligned_seq, input_seq in zip(aligned_seqs2, aln2):
                aligned_seq.append(str(input_seq[current_row-1]))
            current_row -= 1
        elif current_value == hgap:
            for aligned_seq, input_seq in zip(aligned_seqs1, aln1):
                aligned_seq.append(str(input_seq[current_col-1]))
            for aligned_seq in aligned_seqs2:
                aligned_seq.append(gap_character)
            current_col -= 1
        elif current_value == aend:
            continue
        else:
            raise ValueError(
                "Invalid value in traceback matrix: %s" % current_value)

    for i, (aligned_seq, original) in enumerate(zip(aligned_seqs1, aln1)):
        aligned_seq = ''.join(aligned_seq)[::-1]
        constructor = aln1.dtype
        metadata = None
        if original.has_metadata():
            metadata = original.metadata
        aligned_seqs1[i] = constructor(aligned_seq, metadata=metadata,
                                       validate=False)

    for i, (aligned_seq, original) in enumerate(zip(aligned_seqs2, aln2)):
        aligned_seq = ''.join(aligned_seq)[::-1]
        constructor = aln2.dtype
        metadata = None
        if original.has_metadata():
            metadata = original.metadata
        aligned_seqs2[i] = constructor(aligned_seq, metadata=metadata,
                                       validate=False)

    return aligned_seqs1, aligned_seqs2, best_score, current_col, current_row


def _first_largest(scores):
    """ Similar to max, but returns the first element achieving the high score

    If max receives a tuple, it will break a tie for the highest value
    of entry[i] with entry[i+1]. We don't want that here - to better match
    with the results of other tools, we want to be able to define which
    entry is returned in the case of a tie.
    """
    result = scores[0]
    for score, direction in scores[1:]:
        if score > result[0]:
            result = (score, direction)
    return result
