# Copyright 2010 Hakan Kjellerstrand hakank@bonetmail.com
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""

  Discrete tomography in Google CP Solver.

  Problem from http://eclipse.crosscoreop.com/examples/tomo.ecl.txt
  '''
  This is a little 'tomography' problem, taken from an old issue
  of Scientific American.

  A matrix which contains zeroes and ones gets "x-rayed" vertically and
  horizontally, giving the total number of ones in each row and column.
  The problem is to reconstruct the contents of the matrix from this
  information. Sample run:

  ?- go.
    0 0 7 1 6 3 4 5 2 7 0 0
 0
 0
 8      * * * * * * * *
 2      *             *
 6      *   * * * *   *
 4      *   *     *   *
 5      *   *   * *   *
 3      *   *         *
 7      *   * * * * * *
 0
 0

 Eclipse solution by Joachim Schimpf, IC-Parc
 '''

 Compare with the following models:
 * Comet: http://www.hakank.org/comet/discrete_tomography.co
 * Gecode: http://www.hakank.org/gecode/discrete_tomography.cpp
 * MiniZinc: http://www.hakank.org/minizinc/tomography.mzn
 * Tailor/Essence': http://www.hakank.org/tailor/tomography.eprime
 * SICStus: http://hakank.org/sicstus/discrete_tomography.pl

  This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
  Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/
"""
import sys
from constraint_solver import pywrapcp


def main(row_sums="", col_sums=""):

    # Create the solver.
    solver = pywrapcp.Solver('n-queens')

    #
    # data
    #
    if row_sums == "":
        print "Using default problem instance"
        row_sums = [0,0,8,2,6,4,5,3,7,0,0]
        col_sums = [0,0,7,1,6,3,4,5,2,7,0,0]


    r = len(row_sums)
    c = len(col_sums)


    # declare variables
    x = []
    for i in range(r):
        t = []
        for j in range(c):
            t.append(solver.IntVar(0,1, 'x[%i,%i]'%(i,j)))
        x.append(t)
    x_flat = [x[i][j] for i in range(r) for j in range(c)]

    #
    # constraints
    #
    [solver.Add(solver.Sum([x[i][j] for j in range(c)]) == row_sums[i]) for i in range(r)]
    [solver.Add(solver.Sum([x[i][j] for i in range(r)]) == col_sums[j]) for j in range(c)]

    #
    # solution and search
    #
    solution = solver.Assignment()
    solution.Add(x_flat)


    # db: DecisionBuilder
    db = solver.Phase(x_flat,
                 solver.INT_VAR_SIMPLE,
                 solver.ASSIGN_MIN_VALUE)

    solver.NewSearch(db)
    num_solutions = 0
    while solver.NextSolution():
        print_solution(x, r, c, row_sums, col_sums)
        print


        num_solutions += 1
    solver.EndSearch()

    print
    print "num_solutions:", num_solutions
    print "failures:", solver.Failures()
    print "branches:", solver.Branches()
    print "WallTime:", solver.WallTime()

#
# Print solution
#
def print_solution(x, rows, cols, row_sums, col_sums):
    print "  ",
    for j in range(cols):
        print col_sums[j],
    print
    for i in range(rows):
        print row_sums[i],
        for j in range(cols):
            if x[i][j].Value() == 1:
                print "#",
            else:
                print ".",
        print ''


#
# Read a problem instance from a file
#
def read_problem(file):
    f = open(file, 'r')
    row_sums = f.readline()
    col_sums = f.readline()
    row_sums = [int(r) for r in (row_sums.rstrip()).split(",")]
    col_sums = [int(c) for c in (col_sums.rstrip()).split(",")]

    return [row_sums, col_sums]

if __name__ == '__main__':
    if len(sys.argv) > 1:
        file = sys.argv[1]
        print "Problem instance from", file
        [row_sums, col_sums] = read_problem(file)
        main(row_sums, col_sums)
    else:
        main()