<gh_stars>0
# -*- coding: utf-8 -*-
from modules import *
import argparse
import math
import numpy as np
import matplotlib.pyplot as plt


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-b', '--board', type=int, choices=[0, 1],
                        help="Specify which board to use (1 or 2), default is print 1")
    parser.add_argument('-n', '--netlist', type=int, choices=[0, 1, 2, 3, 4, 5, 6],
                        help="Specify which netlist to use (1-7), default is netlist 1")
    parser.add_argument('-a', '--algorithm', type=str,
                        choices=['astar', 'dijkstra'],
                        help="Specify which algorithm to use, default is astar")
    parser.add_argument('-v', '--visualization', action='store_true',
                        help="3d visualization, default is off")
    parser.add_argument('-r', '--recursion', action='store_true',
                        help="Enable recursion on net order, default is off")
    parser.add_argument('-dfs', '--depthfirstsearch', action='store_true',
                        help="Enable depth first search on net order, default is off")
    parser.add_argument('-l', '--layers', type=int,
                        help="Specify the maximum number of layers on the board, default is 10")
    parser.add_argument('-G', '--genetic', action='store_true',
                        help="Specify if genetic optimization is wanted")
    parser.add_argument('-g', '--generations', type=int,
                        help='specify how many generation')
    parser.add_argument('-p', '--population', type=int,
                        help='specify how big the population should be')
    parser.add_argument('-R', '--read', action='store_true',
                        help='specify if you want to check for an existing board')
    args = parser.parse_args()

    netlists = readNetlists()
    if args.netlist != None:
        n_id = args.netlist
        netlist = netlists[n_id]
    else:
        n_id = 0
        netlist = netlists[n_id]

    if args.board != None:
        b_id = args.board
    else:
        b_id = 0

    if args.layers != None:
        l = args.layers
    else:
        l = 10

    if args.algorithm != None:
        alg = args.algorithm
    else:
        alg = 'astar'

    if args.genetic:
        default = int(
            math.ceil(len(netlist)*math.log(len(netlist)) + len(netlist)))
        if args.generations == None:
            gen = default
        else:
            gen = args.generations
        if args.population == None:
            pop = default
        else:
            pop = args.population
        print 'Using genetic optimization with', pop, \
            'population and', gen, 'generations.'

    board = None
    if args.read:
        board = findBoard(alg, b_id + 1, n_id + 1, l)

        if board:
            print "Found board"

    if board == None:
        print 'Running', alg, 'on board', b_id, 'with netlist', n_id, \
            'and maximum number of layers:', l
        if args.depthfirstsearch:
            board = createBoard(b_id, l)
            dfs = DepthFirst(board, netlist)
            board, netlist = dfs.solve()
        else:
            board = runAlgorithm(alg, b_id, netlist, l, args.recursion)
            print '\nSolved', board.getScore()[0], 'nets with a total path \
            	length of:', board.getScore()[1]

        dumpBoard(board, alg)
    if args.genetic:
        gen = GeneticOpt('astar', board, gen, pop)
        genBoard = gen.run()

    if args.visualization:
        if args.genetic:
            v = Visualizer(board, genBoard)
        else:
            v = Visualizer(board)

        v.start()
