def dist_abs(self, src, tar, gap_open=1, gap_ext=0.4, sim_func=sim_ident):
        """Return the Gotoh score of two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        gap_open : float
            The cost of an open alignment gap (1 by default)
        gap_ext : float
            The cost of an alignment gap extension (0.4 by default)
        sim_func : function
            A function that returns the similarity of two characters (identity
            similarity by default)

        Returns
        -------
        float
            Gotoh score

        Examples
        --------
        >>> cmp = Gotoh()
        >>> cmp.dist_abs('cat', 'hat')
        2.0
        >>> cmp.dist_abs('Niall', 'Neil')
        1.0
        >>> round(cmp.dist_abs('aluminum', 'Catalan'), 12)
        -0.4
        >>> cmp.dist_abs('cat', 'hat')
        2.0

        """
        d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
        p_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
        q_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)

        d_mat[0, 0] = 0
        p_mat[0, 0] = float('-inf')
        q_mat[0, 0] = float('-inf')
        for i in range(1, len(src) + 1):
            d_mat[i, 0] = float('-inf')
            p_mat[i, 0] = -gap_open - gap_ext * (i - 1)
            q_mat[i, 0] = float('-inf')
            q_mat[i, 1] = -gap_open
        for j in range(1, len(tar) + 1):
            d_mat[0, j] = float('-inf')
            p_mat[0, j] = float('-inf')
            p_mat[1, j] = -gap_open
            q_mat[0, j] = -gap_open - gap_ext * (j - 1)

        for i in range(1, len(src) + 1):
            for j in range(1, len(tar) + 1):
                sim_val = sim_func(src[i - 1], tar[j - 1])
                d_mat[i, j] = max(
                    d_mat[i - 1, j - 1] + sim_val,
                    p_mat[i - 1, j - 1] + sim_val,
                    q_mat[i - 1, j - 1] + sim_val,
                )

                p_mat[i, j] = max(
                    d_mat[i - 1, j] - gap_open, p_mat[i - 1, j] - gap_ext
                )

                q_mat[i, j] = max(
                    d_mat[i, j - 1] - gap_open, q_mat[i, j - 1] - gap_ext
                )

        i, j = (n - 1 for n in d_mat.shape)
        return max(d_mat[i, j], p_mat[i, j], q_mat[i, j])
def dist_abs(self, src, tar, gap_open=1, gap_ext=0.4, sim_func=sim_ident):
        """Return the Gotoh score of two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        gap_open : float
            The cost of an open alignment gap (1 by default)
        gap_ext : float
            The cost of an alignment gap extension (0.4 by default)
        sim_func : function
            A function that returns the similarity of two characters (identity
            similarity by default)

        Returns
        -------
        float
            Gotoh score

        Examples
        --------
        >>> cmp = Gotoh()
        >>> cmp.dist_abs('cat', 'hat')
        2.0
        >>> cmp.dist_abs('Niall', 'Neil')
        1.0
        >>> round(cmp.dist_abs('aluminum', 'Catalan'), 12)
        -0.4
        >>> cmp.dist_abs('cat', 'hat')
        2.0

        """
        d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
        p_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
        q_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)

        d_mat[0, 0] = 0
        p_mat[0, 0] = float('-inf')
        q_mat[0, 0] = float('-inf')
        for i in range(1, len(src) + 1):
            d_mat[i, 0] = float('-inf')
            p_mat[i, 0] = -gap_open - gap_ext * (i - 1)
            q_mat[i, 0] = float('-inf')
            q_mat[i, 1] = -gap_open
        for j in range(1, len(tar) + 1):
            d_mat[0, j] = float('-inf')
            p_mat[0, j] = float('-inf')
            p_mat[1, j] = -gap_open
            q_mat[0, j] = -gap_open - gap_ext * (j - 1)

        for i in range(1, len(src) + 1):
            for j in range(1, len(tar) + 1):
                sim_val = sim_func(src[i - 1], tar[j - 1])
                d_mat[i, j] = max(
                    d_mat[i - 1, j - 1] + sim_val,
                    p_mat[i - 1, j - 1] + sim_val,
                    q_mat[i - 1, j - 1] + sim_val,
                )

                p_mat[i, j] = max(
                    d_mat[i - 1, j] - gap_open, p_mat[i - 1, j] - gap_ext
                )

                q_mat[i, j] = max(
                    d_mat[i, j - 1] - gap_open, q_mat[i, j - 1] - gap_ext
                )

        i, j = (n - 1 for n in d_mat.shape)
        return max(d_mat[i, j], p_mat[i, j], q_mat[i, j])
def dist_abs ( self , src , tar , gap_open = 1 , gap_ext = 0.4 , sim_func = sim_ident ) : d_mat = np_zeros ( ( len ( src ) + 1 , len ( tar ) + 1 ) , dtype = np_float32 ) p_mat = np_zeros ( ( len ( src ) + 1 , len ( tar ) + 1 ) , dtype = np_float32 ) q_mat = np_zeros ( ( len ( src ) + 1 , len ( tar ) + 1 ) , dtype = np_float32 ) d_mat [ 0 , 0 ] = 0 p_mat [ 0 , 0 ] = float ( '-inf' ) q_mat [ 0 , 0 ] = float ( '-inf' ) for i in range ( 1 , len ( src ) + 1 ) : d_mat [ i , 0 ] = float ( '-inf' ) p_mat [ i , 0 ] = - gap_open - gap_ext * ( i - 1 ) q_mat [ i , 0 ] = float ( '-inf' ) q_mat [ i , 1 ] = - gap_open for j in range ( 1 , len ( tar ) + 1 ) : d_mat [ 0 , j ] = float ( '-inf' ) p_mat [ 0 , j ] = float ( '-inf' ) p_mat [ 1 , j ] = - gap_open q_mat [ 0 , j ] = - gap_open - gap_ext * ( j - 1 ) for i in range ( 1 , len ( src ) + 1 ) : for j in range ( 1 , len ( tar ) + 1 ) : sim_val = sim_func ( src [ i - 1 ] , tar [ j - 1 ] ) d_mat [ i , j ] = max ( d_mat [ i - 1 , j - 1 ] + sim_val , p_mat [ i - 1 , j - 1 ] + sim_val , q_mat [ i - 1 , j - 1 ] + sim_val , ) p_mat [ i , j ] = max ( d_mat [ i - 1 , j ] - gap_open , p_mat [ i - 1 , j ] - gap_ext ) q_mat [ i , j ] = max ( d_mat [ i , j - 1 ] - gap_open , q_mat [ i , j - 1 ] - gap_ext ) i , j = ( n - 1 for n in d_mat . shape ) return max ( d_mat [ i , j ] , p_mat [ i , j ] , q_mat [ i , j ] )
Return the Gotoh score of two strings.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        gap_open : float
            The cost of an open alignment gap (1 by default)
        gap_ext : float
            The cost of an alignment gap extension (0.4 by default)
        sim_func : function
            A function that returns the similarity of two characters (identity
            similarity by default)

        Returns
        -------
        float
            Gotoh score

        Examples
        --------
        >>> cmp = Gotoh()
        >>> cmp.dist_abs('cat', 'hat')
        2.0
        >>> cmp.dist_abs('Niall', 'Neil')
        1.0
        >>> round(cmp.dist_abs('aluminum', 'Catalan'), 12)
        -0.4
        >>> cmp.dist_abs('cat', 'hat')
        2.0
Return the Gotoh score of two strings .