search-4e.ipynb

    "    f.add(SF); f.add(SR), f.add(SRP), f.add(SRPB); f.add(SFB)\n",
    "    def cs(n): return (n.path_cost, n.state) # cs: cost and state\n",
    "    assert cs(f.pop()) == (0, 'S')\n",
    "    assert cs(f.pop()) == (80, 'R')\n",
    "    assert cs(f.pop()) == (99, 'F')\n",
    "    assert cs(f.pop()) == (177, 'P')\n",
    "    assert cs(f.pop()) == (278, 'B')\n",
    "    return 'test_frontier ok'\n",
    "\n",
    "test_frontier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9r8varwUoYrZVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10465dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "p = plt.plot([i**2 for i in range(10)])\n",
    "plt.savefig('destination_path.eps', format='eps', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAHaCAYAAAApPsHTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAMQ3kDAGAYyhsAAMP8P1qBrT7BINI0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10622af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "import random\n",
    "# http://stackoverflow.com/questions/10194482/custom-matplotlib-plot-chess-board-like-table-with-colored-cells\n",
    "\n",
    "from matplotlib.table import Table\n",
    "\n",
    "def main():\n",
    "    grid_table(8, 8)\n",
    "    plt.axis('scaled')\n",
    "    plt.show()\n",
    "\n",
    "def grid_table(nrows, ncols):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_axis_off()\n",
    "    colors = ['white', 'lightgrey', 'dimgrey']\n",
    "    tb = Table(ax, bbox=[0,0,2,2])\n",
    "    for i,j in itertools.product(range(ncols), range(nrows)):\n",
    "        tb.add_cell(i, j, 2./ncols, 2./nrows, text='{:0.2f}'.format(0.1234), \n",
    "                    loc='center', facecolor=random.choice(colors), edgecolor='grey') # facecolors=\n",
    "    ax.add_table(tb)\n",
    "    #ax.plot([0, .3], [.2, .2])\n",
    "    #ax.add_line(plt.Line2D([0.3, 0.5], [0.7, 0.7], linewidth=2, color='blue'))\n",
    "    return fig\n",
    "\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "class defaultkeydict(collections.defaultdict):\n",
    "    \"\"\"Like defaultdict, but the default_factory is a function of the key.\n",
    "    >>> d = defaultkeydict(abs); d[-42]\n",
    "    42\n",
    "    \"\"\"\n",
    "    def __missing__(self, key):\n",
    "        self[key] = self.default_factory(key)\n",
    "        return self[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  },
  "widgets": {
   "state": {},
   "version": "1.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}