# 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.
#

"""Generalized N-Point Crossover.

For even values of N, perform N point crossover
(select N/2 points each in the two genomes, and alternate)

For odd values of N, perform symmetric N+1 point crossover
(select N/2 points for both genomes)

N-Point introduction (my notation)::

    | genome 1:    A-----B-----C-----D-----E-----F-----G
    | genome 2:    a=====b=====c=====d=====e=====f=====g
    |
    | 2-point, symmetric (points=1):
    |              A-----B-----C- 1 -D-----E-----F-----G
    |              a=====b=====c= 2 =d=====e=====f=====g
    | returns: (ABCdefg, abcDEFG)
    |
    | 2-point, asymmetric (points=2):
    |              A-----B- 1 -C-----D-----E-----F-----G
    |              a=====b=====c=====d=====e= 2 =f=====g
    | returns: (ABfg, abcdeCDEFG)

and for the drastic (n can be arbitrary to the length of the genome!)::

    | 12-point, symmetric (points=11):
    |              A- 1 -B- 2 -C- 3 -D- 4 -E- 5 -F- 6 -G
    |              a= 7 =b= 8 =c= 9 =d= 10 e= 11 f= 12 g
    | returns: (AbCdEfG, aBcDeFg)
    | (note that points=12 will yield the same result, but 11
    |  may be somewhat faster)

"""
# standard modules
import random

from Bio._py3k import range


class GeneralPointCrossover(object):
    """Perform n-point crossover between genomes at some defined rates.

    Ideas on how to use this class:
     - Call it directly ( construct, do_crossover )
     - Use one of the provided subclasses
     - Inherit from it:

       * replace _generate_locs with a more domain specific technique
       * replace _crossover with a more efficient technique for your
         point-count

    """

    def __init__(self, points, crossover_prob=.1):
        """Initialize to do crossovers at the specified probability."""
        self._crossover_prob = crossover_prob

        self._sym = points % 2  # odd n, gets a symmetry flag
        self._npoints = (points + self._sym) // 2  # (N or N+1)//2

    def do_crossover(self, org_1, org_2):
        """Potentially do a crossover between the two organisms."""
        new_org = (org_1.copy(), org_2.copy())

        # determine if we have a crossover
        crossover_chance = random.random()
        if crossover_chance <= self._crossover_prob:

            # pre-compute bounds (len(genome))
            bound = (len(new_org[0].genome), len(new_org[1].genome))

            mbound = min(bound)
            # can't have more than 0,x_0...x_n,bound locations
            if (self._npoints == 0 or self._npoints > mbound - 2):
                self._npoints = mbound - 2

            y_locs = []
            # generate list for the shortest of the genomes
            x_locs = self._generate_locs(mbound)

            if (self._sym != 0):
                y_locs = x_locs
            else:
                # figure out which list we've generated
                # for, and generate the other
                if (mbound == bound[0]):
                    y_locs = self._generate_locs(bound[1])
                else:
                    y_locs = x_locs
                    xlocs = self._generate_locs(bound[0])

            # copy new genome strings over
            tmp = self._crossover(0, new_org, (x_locs, y_locs))
            new_org[1].genome = self._crossover(1, new_org, (x_locs, y_locs))
            new_org[0].genome = tmp

        return new_org

    def _generate_locs(self, bound):
        """Generalized Location Generator.

        Arguments:
         - bound (int)   - upper bound

        Returns: [0]+x_0...x_n+[bound] where n=self._npoints-1
        and 0 < x_0 < x_1 ... < bound

        """
        results = []
        for increment in range(self._npoints):
            x = random.randint(1, bound - 1)
            while (x in results):  # uniqueness
                x = random.randint(1, bound - 1)
            results.append(x)
        results.sort()             # sorted
        return [0] + results + [bound]  # [0, +n points+, bound]

    def _crossover(self, x, no, locs):
        """Generalized Crossover Function.

        Arguments:
         - x (int) - genome number [0|1]
         - no (organism, organism)

           - new organisms
           - locs (int list, int list)

         - lists of locations,
           [0, +n points+, bound]
           for each genome (sync'd with x)

        Return type: sequence (to replace no[x])
        """
        # TODO: The above docstring is unclear
        s = no[x].genome[:locs[x][1]]
        for n in range(1, self._npoints):
            # flipflop between genome_0 and genome_1
            mode = (x + n) % 2
            # _generate_locs gives us [0, +n points+, bound]
            #  so we can iterate: { 0:loc(1) ... loc(n):bound }
            t = no[mode].genome[locs[mode][n]:locs[mode][n + 1]]
            if (s):
                s = s + t
            else:
                s = t
        return s


class TwoCrossover(GeneralPointCrossover):
    """Helper class for Two Point crossovers.

    Offers more efficient replacements to the GeneralPoint framework
    for single pivot crossovers
    """

    def _generate_locs(self, bound):
        """Generate replacement (PRIVATE).

        See GeneralPoint._generate_locs documentation for details
        """
        return [0, random.randint(1, bound - 1), bound]

    def _crossover(self, x, no, locs):
        """Crossover replacement (PRIVATE)..

        See GeneralPoint._crossover documentation for details
        """
        y = (x + 1) % 2
        return no[x].genome[:locs[x][1]] + no[y].genome[locs[y][1]:]


class InterleaveCrossover(GeneralPointCrossover):
    """Demonstration class for Interleaving crossover.

    Interleaving:  AbCdEfG, aBcDeFg
    """

    def __init__(self, crossover_prob=0.1):
        GeneralPointCrossover.__init__(self, 0, crossover_prob)

    def _generate_locs(self, bound):
        return list(range(-1, bound + 1))

    def _crossover(self, x, no, locs):
        s = no[x].genome[0:1]
        for n in range(1, self._npoints + 2):
            mode = (x + n) % 2
            s += no[mode].genome[n:n + 1]
        return s + no[mode].genome[self._npoints + 3:]
