--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Given a sequence of {F|L|R}, each of which is \"move forward one step\", \"turn left, then move forward one step\", \"turn right, then move forward one step\":\n",
+ "1. which tours are closed?\n",
+ "2. what is the area enclosed by the tour?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import collections\n",
+ "import enum\n",
+ "import random\n",
+ "import os\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "class Direction(enum.Enum):\n",
+ " UP = 1\n",
+ " RIGHT = 2\n",
+ " DOWN = 3\n",
+ " LEFT = 4\n",
+ " \n",
+ "turn_lefts = {Direction.UP: Direction.LEFT, Direction.LEFT: Direction.DOWN,\n",
+ " Direction.DOWN: Direction.RIGHT, Direction.RIGHT: Direction.UP}\n",
+ "\n",
+ "turn_rights = {Direction.UP: Direction.RIGHT, Direction.RIGHT: Direction.DOWN,\n",
+ " Direction.DOWN: Direction.LEFT, Direction.LEFT: Direction.UP}\n",
+ "\n",
+ "def turn_left(d):\n",
+ " return turn_lefts[d]\n",
+ "\n",
+ "def turn_right(d):\n",
+ " return turn_rights[d]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "Step = collections.namedtuple('Step', ['x', 'y', 'dir'])\n",
+ "Mistake = collections.namedtuple('Mistake', ['i', 'step'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def advance(step, d):\n",
+ " if d == Direction.UP:\n",
+ " return Step(step.x, step.y+1, d)\n",
+ " elif d == Direction.DOWN:\n",
+ " return Step(step.x, step.y-1, d)\n",
+ " elif d == Direction.LEFT:\n",
+ " return Step(step.x-1, step.y, d)\n",
+ " elif d == Direction.RIGHT:\n",
+ " return Step(step.x+1, step.y, d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def step(s, current):\n",
+ " if s == 'F':\n",
+ " return advance(current, current.dir)\n",
+ " elif s == 'L':\n",
+ " return advance(current, turn_left(current.dir))\n",
+ " elif s == 'R':\n",
+ " return advance(current, turn_right(current.dir))\n",
+ " else:\n",
+ " raise ValueError"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def trace_tour(tour, startx=0, starty=0, startdir=Direction.RIGHT):\n",
+ " current = Step(startx, starty, startdir)\n",
+ " trace = [current]\n",
+ " for s in tour:\n",
+ " current = step(s, current)\n",
+ " trace += [current]\n",
+ " return trace "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def positions(trace):\n",
+ " return [(s.x, s.y) for s in trace]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def valid(trace):\n",
+ " return (trace[-1].x == 0 \n",
+ " and trace[-1].y == 0 \n",
+ " and len(set(positions(trace))) + 1 == len(trace))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def valid_prefix(tour):\n",
+ " current = Step(0, 0, Direction.RIGHT)\n",
+ " prefix = []\n",
+ " posns = []\n",
+ " for s in tour:\n",
+ " current = step(s, current)\n",
+ " prefix += [s]\n",
+ " if (current.x, current.y) in posns:\n",
+ " return ''\n",
+ " elif current.x == 0 and current.y == 0: \n",
+ " return ''.join(prefix)\n",
+ " posns += [(current.x, current.y)]\n",
+ " if current.x == 0 and current.y == 0:\n",
+ " return ''.join(prefix)\n",
+ " else:\n",
+ " return ''"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def mistake_positions(trace, debug=False):\n",
+ " mistakes = []\n",
+ " current = trace[0]\n",
+ " posns = [(0, 0)]\n",
+ " for i, current in enumerate(trace[1:]):\n",
+ " if (current.x, current.y) in posns:\n",
+ " if debug: print(i, current)\n",
+ " mistakes += [Mistake(i+1, current)]\n",
+ " posns += [(current.x, current.y)]\n",
+ " if (current.x, current.y) == (0, 0):\n",
+ " return mistakes[:-1]\n",
+ " else:\n",
+ " return mistakes + [Mistake(len(trace)+1, current)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def returns_to_origin(mistake_positions):\n",
+ " return [i for i, m in mistake_positions\n",
+ " if (m.x, m.y) == (0, 0)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "sample_tours = ['FFLRLLFLRL', 'FLLFFLFFFLFFLFLLRRFR', 'FFRLLFRLLFFFRFLLRLLRRLLRLL']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def random_walk(steps=1000):\n",
+ " return ''.join(random.choice('FFLR') for _ in range(steps))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def bounds(trace):\n",
+ " return (max(s.x for s in trace),\n",
+ " max(s.y for s in trace),\n",
+ " min(s.x for s in trace),\n",
+ " min(s.y for s in trace))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "plot_wh = {Direction.UP: (0, 1), Direction.LEFT: (-1, 0),\n",
+ " Direction.DOWN: (0, -1), Direction.RIGHT: (1, 0)}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def chunks(items, n=2):\n",
+ " return [items[i:i+n] for i in range(len(items) - n + 1)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def plot_trace(trace, colour='k', xybounds=None, fig=None, subplot_details=None, filename=None, show_axis=False):\n",
+ " if show_axis:\n",
+ " plt.axis('on')\n",
+ " else:\n",
+ " plt.axis('off')\n",
+ " plt.axes().set_aspect('equal')\n",
+ " for s, t in chunks(trace, 2):\n",
+ " w, h = plot_wh[t.dir]\n",
+ " plt.arrow(s.x, s.y, w, h, head_width=0.1, head_length=0.1, fc=colour, ec=colour, length_includes_head=True)\n",
+ " xh, yh, xl, yl = bounds(trace)\n",
+ " if xybounds is not None: \n",
+ " bxh, byh, bxl, byl = xybounds\n",
+ " plt.xlim([min(xl, bxl)-1, max(xh, bxh)+1])\n",
+ " plt.ylim([min(yl, byl)-1, max(yh, byh)+1])\n",
+ " else:\n",
+ " plt.xlim([xl-1, xh+1])\n",
+ " plt.ylim([yl-1, yh+1])\n",
+ " if filename:\n",
+ " plt.savefig(filename)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def trim_loop(tour, random_mistake=False):\n",
+ " trace = trace_tour(tour)\n",
+ " mistakes = mistake_positions(trace)\n",
+ " if random_mistake:\n",
+ " end_mistake_index = random.randrange(len(mistakes))\n",
+ " else:\n",
+ " end_mistake_index = 0\n",
+ "# print('end_mistake_index {} pointing to trace position {}; {} mistakes and {} in trace; {}'.format(end_mistake_index, mistakes[end_mistake_index].i, len(mistakes), len(trace), mistakes))\n",
+ " # while this mistake extends to the next step in the trace...\n",
+ " while (mistakes[end_mistake_index].i + 1 < len(trace) and \n",
+ " end_mistake_index + 1 < len(mistakes) and\n",
+ " mistakes[end_mistake_index].i + 1 == \n",
+ " mistakes[end_mistake_index + 1].i):\n",
+ "# print('end_mistake_index {} pointing to trace position {}; {} mistakes and {} in trace'.format(end_mistake_index, mistakes[end_mistake_index].i, len(mistakes), len(trace), mistakes))\n",
+ " # push this mistake finish point later\n",
+ " end_mistake_index += 1\n",
+ " mistake = mistakes[end_mistake_index]\n",
+ " \n",
+ " # find the first location that mentions where this mistake ends (which the point where the loop starts)\n",
+ " mistake_loop_start = max(i for i, loc in enumerate(trace[:mistake.i])\n",
+ " if (loc.x, loc.y) == (mistake.step.x, mistake.step.y))\n",
+ "# print('Dealing with mistake from', mistake_loop_start, 'to', mistake.i, ', trace has len', len(trace))\n",
+ " \n",
+ " # direction before entering the loop\n",
+ " direction_before = trace[mistake_loop_start].dir\n",
+ " \n",
+ " # find the new instruction to turn from heading before the loop to heading after the loop\n",
+ " new_instruction = 'F'\n",
+ " if (mistake.i + 1) < len(trace):\n",
+ " if turn_left(direction_before) == trace[mistake.i + 1].dir:\n",
+ " new_instruction = 'L'\n",
+ " if turn_right(direction_before) == trace[mistake.i + 1].dir:\n",
+ " new_instruction = 'R'\n",
+ "# if (mistake.i + 1) < len(trace):\n",
+ "# print('turning from', direction_before, 'to', trace[mistake.i + 1].dir, 'with', new_instruction )\n",
+ "# else:\n",
+ "# print('turning from', direction_before, 'to BEYOND END', 'with', new_instruction )\n",
+ " return tour[:mistake_loop_start] + new_instruction + tour[mistake.i+1:]\n",
+ "# return mistake, mistake_loop_start, trace[mistake_loop_start-2:mistake_loop_start+8]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def trim_all_loops(tour, mistake_reduction_attempt_limit=10):\n",
+ " trace = trace_tour(tour)\n",
+ " mistake_limit = 1\n",
+ " if trace[-1].x == 0 and trace[-1].y == 0:\n",
+ " mistake_limit = 0\n",
+ " mistakes = mistake_positions(trace)\n",
+ " \n",
+ " old_mistake_count = len(mistakes)\n",
+ " mistake_reduction_tries = 0\n",
+ " \n",
+ " while len(mistakes) > mistake_limit and mistake_reduction_tries < mistake_reduction_attempt_limit:\n",
+ " tour = trim_loop(tour)\n",
+ " trace = trace_tour(tour)\n",
+ " mistakes = mistake_positions(trace)\n",
+ " if len(mistakes) < old_mistake_count:\n",
+ " old_mistake_count = len(mistakes)\n",
+ " mistake_reduction_tries = 0\n",
+ " else:\n",
+ " mistake_reduction_tries += 1\n",
+ " if mistake_reduction_tries >= mistake_reduction_attempt_limit:\n",
+ " return ''\n",
+ " else:\n",
+ " return tour"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def reverse_tour(tour):\n",
+ " def swap(tour_step):\n",
+ " if tour_step == 'R':\n",
+ " return 'L'\n",
+ " elif tour_step == 'L':\n",
+ " return 'R'\n",
+ " else:\n",
+ " return tour_step\n",
+ " \n",
+ " return ''.join(swap(s) for s in reversed(tour))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def wander_near(locus, current, limit=10):\n",
+ " valid_proposal = False\n",
+ " while not valid_proposal:\n",
+ " s = random.choice('FFFRL')\n",
+ " if s == 'F':\n",
+ " proposed = advance(current, current.dir)\n",
+ " elif s == 'L':\n",
+ " proposed = advance(current, turn_left(current.dir))\n",
+ " elif s == 'R':\n",
+ " proposed = advance(current, turn_right(current.dir))\n",
+ " if abs(proposed.x - locus.x) < limit and abs(proposed.y - locus.y) < limit:\n",
+ " valid_proposal = True\n",
+ "# print('At {} going to {} by step {} to {}'.format(current, locus, s, proposed))\n",
+ " return s, proposed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def seek(goal, current):\n",
+ " dx = current.x - goal.x\n",
+ " dy = current.y - goal.y\n",
+ "\n",
+ " if dx < 0 and abs(dx) > abs(dy): # to the left\n",
+ " side = 'left'\n",
+ " if current.dir == Direction.RIGHT:\n",
+ " s = 'F'\n",
+ " elif current.dir == Direction.UP:\n",
+ " s = 'R'\n",
+ " else:\n",
+ " s = 'L'\n",
+ " elif dx > 0 and abs(dx) > abs(dy): # to the right\n",
+ " side = 'right'\n",
+ " if current.dir == Direction.LEFT:\n",
+ " s = 'F'\n",
+ " elif current.dir == Direction.UP:\n",
+ " s = 'L'\n",
+ " else:\n",
+ " s = 'R'\n",
+ " elif dy > 0 and abs(dx) <= abs(dy): # above\n",
+ " side = 'above'\n",
+ " if current.dir == Direction.DOWN:\n",
+ " s = 'F'\n",
+ " elif current.dir == Direction.RIGHT:\n",
+ " s = 'R'\n",
+ " else:\n",
+ " s = 'L'\n",
+ " else: # below\n",
+ " side = 'below'\n",
+ " if current.dir == Direction.UP:\n",
+ " s = 'F'\n",
+ " elif current.dir == Direction.LEFT:\n",
+ " s = 'R'\n",
+ " else:\n",
+ " s = 'L'\n",
+ " if s == 'F':\n",
+ " proposed = advance(current, current.dir)\n",
+ " elif s == 'L':\n",
+ " proposed = advance(current, turn_left(current.dir))\n",
+ " elif s == 'R':\n",
+ " proposed = advance(current, turn_right(current.dir))\n",
+ " \n",
+ "# print('At {} going to {}, currently {}, by step {} to {}'.format(current, goal, side, s, proposed))\n",
+ "\n",
+ " return s, proposed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def guided_walk(loci, locus_limit=5, wander_limit=10, seek_step_limit=20, return_anyway=False):\n",
+ " trail = ''\n",
+ " current = Step(0, 0, Direction.RIGHT) \n",
+ " l = 0\n",
+ " finished = False\n",
+ " while not finished:\n",
+ " if abs(current.x - loci[l].x) < locus_limit and abs(current.y - loci[l].y) < locus_limit:\n",
+ " l += 1\n",
+ " if l == len(loci) - 1:\n",
+ " finished = True\n",
+ " s, proposed = wander_near(loci[l], current, limit=wander_limit)\n",
+ " trail += s\n",
+ " current = proposed\n",
+ "# print('!! Finished loci')\n",
+ " seek_steps = 0\n",
+ " while not (current.x == loci[l].x and current.y == loci[l].y) and seek_steps < seek_step_limit:\n",
+ "# error = max(abs(current.x - loci[l].x), abs(current.y - loci[l].y))\n",
+ "# s, proposed = wander_near(loci[l], current, limit=error+1)\n",
+ " s, proposed = seek(loci[l], current)\n",
+ " trail += s\n",
+ " current = proposed\n",
+ " seek_steps += 1\n",
+ " if seek_steps >= seek_step_limit and not return_anyway:\n",
+ " return ''\n",
+ " else:\n",
+ " return trail"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD7CAYAAABqkiE2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ7EMO+IfAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea71b8cc0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def square_tour(a=80):\n",
+ " \"a is width of square\"\n",
+ " return ('F' * a + 'L') * 4\n",
+ "\n",
+ "plot_trace(trace_tour(square_tour()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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t38g6ESeYLYRY/hX4EfDHHFXHt4BzEz8hXWSXSZOprHTbHpAulEOOakMbfH059NFFNumb\n9hsdb9JD0sUG6WJcJl3UF7ttT0nJ5Bnp4p98zWj/yfdrORrd+YfEuklKaHD09x/VDv4ezcwWgqQx\nnzJIGjMhSBozIUgaMyFIGjMhSBozIUgaMyFIGjMhSBozIUgaMyFIGjMhSBozIUgaMyFIGjMhSBoz\nIUgaMyFIGjMhSBozIUgaMyFIGjMhSBozIUga+39B5c93LYMfSQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea71dde10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def cross_tour(a=50, b=40):\n",
+ " \"a is width of cross arm, b is length of cross arm\"\n",
+ " return ('F' * a + 'L' + 'F' * b + 'R' + 'F' * b + 'L') * 4\n",
+ "\n",
+ "plot_trace(trace_tour(cross_tour()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6f8a160>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def quincunx_tour(a=60, b=30, c=50):\n",
+ " \"a is length of indent, b is indent/outdent distance, c is outdent outer length\"\n",
+ " return ('F' * a + 'R' + 'F' * b + 'L' + 'F' * c + 'L' + 'F' * c + 'L' + 'F' * b + 'R') * 4\n",
+ "plot_trace(trace_tour(quincunx_tour()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6d354e0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "heart_points = [Step(60, 50, Direction.UP), Step(50, 90, Direction.UP),\n",
+ " Step(20, 70, Direction.UP), \n",
+ " Step(-40, 90, Direction.UP), Step(-60, 80, Direction.UP), \n",
+ " Step(0, 0, Direction.RIGHT)]\n",
+ "\n",
+ "heart_tour = ''\n",
+ "current = Step(0, 0, Direction.RIGHT)\n",
+ "\n",
+ "for hp in heart_points:\n",
+ " while not (current.x == hp.x and current.y == hp.y):\n",
+ " s, proposed = seek(hp, current)\n",
+ " heart_tour += s\n",
+ " current = proposed\n",
+ "\n",
+ "plot_trace(trace_tour(heart_tour))\n",
+ "\n",
+ "def heart_tour_func(): return heart_tour"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def trim_some_mistakes(tour, mistake_limit):\n",
+ " trimmed_tour = rw\n",
+ " mistake_count = len(mistake_positions(trace_tour(trimmed_tour)))\n",
+ " while len(mistake_positions(trace_tour(trimmed_tour))) > mistake_limit:\n",
+ " trimmed_tour = trim_loop(trimmed_tour, random_mistake=True)\n",
+ " return trimmed_tour"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEACAYAAADLIw+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJogA7b/FLob4OhrHyQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6b5b1d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(sample_tours[0]))\n",
+ "len(sample_tours[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAN4AAAEACAYAAADcJMhcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6ff01d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(sample_tours[1]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAASMAAAEACAYAAAD4GBC1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea700c4e0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(sample_tours[2]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['FLRFRFFFRFFFRFFR',\n",
+ " 'FFFRFRFRLR',\n",
+ " 'FFFFRFRFRLFR',\n",
+ " 'FFFLFFLFFLFF',\n",
+ " 'FFRRFLRRFR',\n",
+ " 'RLRFFRFRFFFR',\n",
+ " 'LFRFLLFFFLFFLF',\n",
+ " 'RLFFLFLFLR',\n",
+ " 'RFFFLLRFFFLLRRLLFFFF',\n",
+ " 'FLFFLFFLFLRL']"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "samples = []\n",
+ "while len(samples) < 10:\n",
+ " t = valid_prefix(random_walk())\n",
+ " if len(t) > 8 and len(t) < 25:\n",
+ " samples += [t]\n",
+ "samples"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6ba2c50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(samples[2]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mistakes = [t.strip() for t in open('tours-random-walk.txt').readlines()\n",
+ " if len(t) > 8\n",
+ " if len(t) < 26]\n",
+ "len(mistakes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEACAYAAAC3RRNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEwOgEgNgEwOgEgNgEwOgEgNgEwOgqv8F9Hl5y6i1C9AAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea7040b70>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(mistakes[0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(212,\n",
+ " 94,\n",
+ " 'FFRFLLFFFRLRRFFFLFLRRFLLFFFFFRFLFFFFFRLLFRFRLLFFFFFFLRFFRLLFRFFFLFFLFFRFRRLLFFRLFFFFFLLFFRFRFL')"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEACAYAAACTecuMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea7095780>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lc = trace_tour(square_tour(a=10))\n",
+ "rw = guided_walk(lc, wander_limit=4, locus_limit=2)\n",
+ "rw_trimmed = trim_all_loops(rw)\n",
+ "plot_trace(trace_tour(rw_trimmed))\n",
+ "len(rw), len(rw_trimmed), rw_trimmed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['LFLLFFFFLLLFFRFRFF',\n",
+ " 'LFFLLFFL',\n",
+ " 'FRFFRRFFFFFFLLRLRLRLLFFF',\n",
+ " 'RFFRRRFLFFFFLFLFRLLF',\n",
+ " 'LFFRFRFFFFFRFFRLRFRF',\n",
+ " 'LFFRFRFRFL',\n",
+ " 'FLFFLLFFRLLL',\n",
+ " 'FLLFFLLF',\n",
+ " 'FFLLRLRLLFLR',\n",
+ " 'FRRLLFLFFFFLFFFLLFFRRFLL',\n",
+ " 'FRFRRFFF',\n",
+ " 'FFFRFRRFFFFFFLFLFFFLRR',\n",
+ " 'FFRRRFFLRLFRLFLFLFRFLF',\n",
+ " 'RFFRRFFR',\n",
+ " 'LFFFFRRRFLFFFL',\n",
+ " 'FFLLLRRLLL',\n",
+ " 'FFRFFRFRFRLL',\n",
+ " 'RFLFFFFRRFFFFFRFFLLLLR',\n",
+ " 'RRLLRRFFRFRFLR',\n",
+ " 'FFRFRFFRFR']"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mistakes = []\n",
+ "while len(mistakes) < 20:\n",
+ " lc = trace_tour(square_tour(a=8))\n",
+ " rw = guided_walk(lc, wander_limit=4, locus_limit=2)\n",
+ " rw_trimmed = trim_some_mistakes(rw, 2)\n",
+ " if len(rw_trimmed) > 6 and len(rw_trimmed) < 25:\n",
+ " mistakes += [rw_trimmed]\n",
+ "mistakes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "317"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "open('mistakes.txt', 'w').write('\\n'.join(mistakes))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "137"
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "open('samples.txt', 'w').write('\\n'.join(samples))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('FFRRFLRRFR', 10)"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "samples[4], len(samples[4])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('RRLLRRFFRFRFLR', 14)"
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mistakes[18], len(mistakes[18])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'RRFFLLFFFFLF'"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAADgCAYAAAAANN1GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6e5cac8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "t = trim_all_loops(random_walk(20))\n",
+ "plot_trace(trace_tour(t))\n",
+ "t"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "t2 = 'FFRFLLFFFRLRRFFFLFLRRFLLFFFFFRFLFFFFFRLLFRFRLLFFFFFFLRFFRLLFRFFFLFFLFFRFRRLLFFRLFFFFFLLFFRFRFL'\n",
+ "t2a = t2[:51]\n",
+ "t2b = t2[51:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'FFRFLLFFFRLRRFFFLFLRRFLLFFFFFRFLFFFFFRLLFRFRLLFFFFF'"
+ ]
+ },
+ "execution_count": 80,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMwAAAEACAYAAAD/f5mJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6fc12e8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(t2a))\n",
+ "t2a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'FLRFFRLLFRFFFLFFLFFRFRRLLFFRLFFFFFLLFFRFRFL'"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAALUAAAEACAYAAAD1IzfbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACLlJREFUeJzt3VvIZWUdx/HvXxsnzyMNeQQTJEJC8FChlVBgSoEU3dhd\nRndFpIREEXVhN3ph0YEgujCIbgqvSqRuCpSi0CTToNKQ1GzwMDoe5uS/i3e/ta1R1toza717fvP9\nwLD3xXp8noGvyyX74VnV3UhJjtvqBUhHmlErjlErjlErjlErjlErjlErjlErjlErjlErjlErjlEr\njlErjlErjlErjlErjlErjlErjlErjlErjlErjlErjlFrJVW1s6ou3up1HIpRa7SquhbYBTxQVT3i\nz52zrM/DbDRWVd0DXAm8BHxt4LCbgZ3Axd39x4mWBsCbpvyHK1t3nzz02qq6no2oH51uRRt8/NBc\nfg7Q3XumnsioFceoFceoFceoFceoFceoFceoFceoFceo11hVnV1V6/ir75WwsalpqxdyKEa9pqrq\nBuAJYP/ITUPvm2F5vwSeA54dMebARGv5P+t4F9CGh5a+3z3g+m3AB4GfAGdNsqLXzrWjuw+OGPMh\ngKq6sLv/Os2yNhj1muru3wI19PqqOgl4EThzskX917YVxpy3+Nx9JBdyKD5+hOjulxZf79rShby+\nHwJ0966pJzJqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqrWJzQ9PpW72QQzHqPC/PMMeDi8+9M8w1\nmlGHqKrNg2UumGG6JwG6+5URYy6Cje20k6xoiVHn2L74vGSGuQafzLTkmsXn9je86ggw6hDd/czi\n67puaLodoLv/PvVERq04Rq04Rq04Rq04Rq04Rq04Rq04Rq04Rq1VbG5oOnGrF3IonvuR5+qqauCe\ngdfvAc4H3jFizH42zv7YxvANVIPPMDlcRp3lZuDWxff3rjB+zJjPdffzI65/C0BV7eju58Ytaxzf\noximqnYAr4zZQbd4jNg2MtKx69oM7aLufniqecBn6jjd/dzILaF098tTBr1wy2KuSYMGo1Ygo1Yc\no1Yco1Yco1Yco1Yco1Yco1Yco1Yco1Yco9ZcCtgzx3ZVNzRpFksbmi7p7j9MOZd3as3lNoCpgwaj\n1nxmOyHVqBXHqBXHqBXHqBXHqBXHqBXHqBXHqBXHqBXHqDWXvcCuqpr8VDA3NGkWSxua3t3dv5ty\nLu/Umsv3F5+/n3oio9ZcngLoGR4NjFpxjFpxjFpxjFpxjFpxjFpxjFpxjFpxjFqTq6pLgffMNZ+v\nnNNoVfUl4OsrDH3sSK/lUNzQpNGWNiedDewaMORy4Crg29099GWiKzNqjVZVLwIndfdsb7Edw2dq\nrWLyo8MOh1ErjlErjlErjlErjlErjlErjlErjlErjlErjhuatIpHgXO3ehGvx70fGm1pQ9OZ3f2v\nLV3MIRi1RluK+rg5DqcZy2dqreJemOe0pVUYteIYteIYteIYteIYteIYteIYteIYteIY9TGsqi6o\nqrdu9TqONKMOUlXfrKoe+gd4BHiqqm4YOdUTwDNH/m9wZLj3I0RVnQU8CRwEbhs47GY2bmwvdPdp\nI+bajGZndz89aqEzMOoQVXUCGy/gZMzJSYtA93X39pFjALZ194FRC52Bjx8hunvf4utdM0y3uaFp\n7YIGo1Ygo1Yco1Yco1Yco1Yco1Yco1Yco1Ycoz6GVdXmr4gnVNWgg42q6kTWvBtPaMrz/Ihr9wH/\nAM4D9leNei/Rq2MunpNRh6iqMxZfrxg6pru7qj4AfA8YuqHpXcCzwMfGrXA+bmgKUVWnAC/AuA1N\nidb62UjDdfeexdc5NjStNaNWHKNWHKNWHKNWHKNWHKNWHKNWHKNWHKMOVCM3cdSG41eYZy23Wfgz\neZCl8zgAXhw47AngLODUEWN2A3uAt48Ysw+4sbvvGHj9yow6SFV9CvjByGFPA2cw7r/aexfjzhk5\nF8zwmjqjDrM4qelAdw/eGrp49Dh+6UCcIWMK2N7drwy8fi9wAnBhd/9t6Dyr8Jk6THfvGxP0YszB\nMUEvxvTQoBduXYybNGgwagUyasUxasUxasUxasUxasUxasUxasUxasUxasUxas3lFOBAVQ1+td2q\n3NCkWSxti31nd/9pyrm8U2sutwBMHTQYtQIZteIYteIYteIYteIYteIYteIYteIYteIYteIYteby\nKrB76YWkk3FDk2axtKHp0u6+f8q5vFNrLrcDTB00GLXmM/R01MNm1Ipj1Ipj1Ipj1Ipj1Ipj1Ipj\n1Ipj1Iqzlu/BE1TVqcDbRgz5IvAz4EFg6N6HTwOPAL8GDgwc81E2Xkh0J7B/4Jj3A9cOvPawufdj\nDVXVNuAxNt5vmOS+7r5s6km8U6+nK9gIend37xgyoKo+Djzc3Q8NnaSqrgZe6O7fjBhzObATuLsH\n3hGr6kLgUuCnQ+c5HN6p11BVHQccBL7R3Tdu9XqONv6P4hpaeg/i41u6kKOUUSuOUSuOUSuOUSuO\nUSuOUSuOUSuOUSvOMRl1VV1QVZ+tqhNHjNlZVTdV1Rkjxpy8GHPOaivVKiJ+Jq+q64Efb/U63sA+\n4Pzu/ueQixc/kz8HfLm7vzXpygKlRL35l7gK+POAIRcBHwG+A7w0cJpzgRuA7wLPDBxzOvB54DPA\nZd1935BBVXUV8Cvg8e4+b+BcWkiJ+l42drYdv7RvYm0s/qUbE/U2Nu7ud3T3J6dcW6KUZ+r74TUb\ngY5q3b25+f7BLV3IUSolauk/jFpxjFpxjFpxjFpxjFpxjFpxjHpiVbV5DMXJW7qQY8hRH3VVnQuc\nvdXreAOfWHx+YcjFteGaCdcTb+0Os6mqi4EHVhg6dA/H3H4B7AWuW9qjMtRfJlhPvLXb+7HYoXYd\nMPR9ex8GTgNu6u5HJ1vYYVicUDT0uK03A18BvtrdP5puVbnWLmrpcB31z9TS/zJqxTFqxTFqxTFq\nxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFq\nxTFqxTFqxTFqxTFqxTFqxTFqxTFqxTFqxfk3Gpchq3+xOKsAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6c4e550>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(t2b))\n",
+ "t2b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(94,\n",
+ " 'FFRFLLFFFRLRRFFFLFLRRFLLFFFFFRFLFFFFFRLLFRFRLLFFFFFFLRFFRLLFRFFFLFFLFFRFRRLLFFRLFFFFFLLFFRFRFL')"
+ ]
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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N4AeQHghylqSv1HrKzTJIS4svkfSP7v5463pqIvhAQPzjHhAQwQcCIvhAQAQfCIjgAwER\nfCAggg8ERPCBgAg+EBDBBwIi+EBABB8IiOADARF8ICCCDwRE8IGACD4QEMEHAiL4QEAEHwiI4AMB\nEXwgIIIPBETwgYAIPhAQwQcCIvhAQAQfCIjgAwERfCAggg8E9H9g3ys8YjIz8gAAAABJRU5ErkJg\ngg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fdea6e6ca20>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(t2a + t2b))\n",
+ "len(t2a + t2b), t2a + t2b"
+ ]
+ },
+ {
+ "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": "3.5.2+"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
projects / ou-summer-of-code-2017.git / commitdiff
