--- /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": 1,
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "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": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "Step = collections.namedtuple('Step', ['x', 'y', 'dir'])\n",
+ "Mistake = collections.namedtuple('Mistake', ['i', 'step'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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": 5,
+ "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",
+ " if s == 'F':\n",
+ " current = advance(current, current.dir)\n",
+ " elif s == 'L':\n",
+ " current = advance(current, turn_left(current.dir))\n",
+ " elif s == 'R':\n",
+ " current = advance(current, turn_right(current.dir))\n",
+ " trace += [current]\n",
+ " return trace "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def positions(trace):\n",
+ " return [(s.x, s.y) for s in trace]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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": 8,
+ "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": "markdown",
+ "metadata": {},
+ "source": [
+ "Using the [Shoelace formula](https://en.wikipedia.org/wiki/Shoelace_formula)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def shoelace(trace):\n",
+ " return abs(sum(s.x * t.y - t.x * s.y for s, t in chunks(trace, 2))) // 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "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": 11,
+ "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": 12,
+ "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": 13,
+ "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": 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": [
+ "def plot_trace(trace, colour='k', xybounds=None, fig=None, subplot_details=None, filename=None):\n",
+ " plt.axis('on')\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": 17,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def trim_loop(tour):\n",
+ " trace = trace_tour(tour)\n",
+ " mistakes = mistake_positions(trace)\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": 18,
+ "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": 19,
+ "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": 20,
+ "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": 21,
+ "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": 22,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def guided_walk(loci, locus_limit=5, wander_limit=10, seek_step_limit=20):\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:\n",
+ " return ''\n",
+ " else:\n",
+ " return trail"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def square_tour(a=80):\n",
+ " \"a is width of square\"\n",
+ " return ('F' * a + 'L') * 4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "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",
+ "def heart_tour_func(): return heart_tour"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# success_count = 0\n",
+ "# while success_count <= 20:\n",
+ "# lc = trace_tour(square_tour(a=10))\n",
+ "# rw = guided_walk(lc, wander_limit=4, locus_limit=2)\n",
+ "# if rw:\n",
+ "# rw_trimmed = trim_all_loops(rw)\n",
+ "# if len(rw_trimmed) > 10:\n",
+ "# with open('small-squares.txt', 'a') as f:\n",
+ "# f.write(rw_trimmed + '\\n')\n",
+ "# success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# success_count = 0\n",
+ "# while success_count <= 20:\n",
+ "# lc = trace_tour(square_tour())\n",
+ "# rw = guided_walk(lc)\n",
+ "# if rw:\n",
+ "# rw_trimmed = trim_all_loops(rw)\n",
+ "# if len(rw_trimmed) > 10:\n",
+ "# with open('large-squares.txt', 'a') as f:\n",
+ "# f.write(rw_trimmed + '\\n')\n",
+ "# success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# success_count = 0\n",
+ "# while success_count <= 20:\n",
+ "# lc = trace_tour(cross_tour())\n",
+ "# rw = guided_walk(lc)\n",
+ "# if rw:\n",
+ "# rw_trimmed = trim_all_loops(rw)\n",
+ "# if len(rw_trimmed) > 10:\n",
+ "# with open('cross.txt', 'a') as f:\n",
+ "# f.write(rw_trimmed + '\\n')\n",
+ "# success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# success_count = 0\n",
+ "# while success_count <= 20:\n",
+ "# lc = trace_tour(quincunx_tour())\n",
+ "# rw = guided_walk(lc)\n",
+ "# if rw:\n",
+ "# rw_trimmed = trim_all_loops(rw)\n",
+ "# if len(rw_trimmed) > 10:\n",
+ "# with open('quincunx.txt', 'a') as f:\n",
+ "# f.write(rw_trimmed + '\\n')\n",
+ "# success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "patterns = [square_tour, cross_tour, quincunx_tour, heart_tour_func]\n",
+ "tours_filename = 'tours.txt'\n",
+ "\n",
+ "try:\n",
+ " os.remove(tours_filename)\n",
+ "except OSError:\n",
+ " pass\n",
+ "\n",
+ "success_count = 0\n",
+ "while success_count < 100:\n",
+ " lc = trace_tour(random.choice(patterns)())\n",
+ " rw = guided_walk(lc)\n",
+ " if rw:\n",
+ " rw_trimmed = trim_all_loops(rw)\n",
+ " if len(rw_trimmed) > 10:\n",
+ " with open(tours_filename, 'a') as f:\n",
+ " f.write(rw_trimmed + '\\n')\n",
+ " success_count += 1"
+ ]
+ },
+ {
+ "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
+}
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 1301,
"metadata": {
"collapsed": true
},
"source": [
"import collections\n",
"import enum\n",
- "import random"
+ "import random\n",
+ "import os\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n"
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 2,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
- "Step = collections.namedtuple('Step', ['x', 'y', 'dir'])"
+ "Step = collections.namedtuple('Step', ['x', 'y', 'dir'])\n",
+ "Mistake = collections.namedtuple('Mistake', ['i', 'step'])"
]
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 4,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 489,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
- "def trace_tour(tour):\n",
- " current = Step(0, 0, Direction.RIGHT)\n",
+ "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",
" if s == 'F':\n",
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 490,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 491,
"metadata": {},
"outputs": [
{
"Step(x=1, y=2, dir=3)"
]
},
- "execution_count": 10,
+ "execution_count": 491,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"__main__.Step"
]
},
- "execution_count": 11,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"<Direction.UP: 1>"
]
},
- "execution_count": 22,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"<Direction.RIGHT: 2>"
]
},
- "execution_count": 23,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{<Direction.LEFT: 4>: <Direction.UP: 1>,\n",
- " <Direction.UP: 1>: <Direction.RIGHT: 2>,\n",
+ "{<Direction.RIGHT: 2>: <Direction.DOWN: 3>,\n",
" <Direction.DOWN: 3>: <Direction.LEFT: 4>,\n",
- " <Direction.RIGHT: 2>: <Direction.DOWN: 3>}"
+ " <Direction.LEFT: 4>: <Direction.UP: 1>,\n",
+ " <Direction.UP: 1>: <Direction.RIGHT: 2>}"
]
},
- "execution_count": 21,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 494,
"metadata": {},
"outputs": [
{
" Step(x=2, y=2, dir=<Direction.UP: 1>)]"
]
},
- "execution_count": 31,
+ "execution_count": 494,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
" Step(x=0, y=0, dir=<Direction.DOWN: 3>)]"
]
},
- "execution_count": 33,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
" Step(x=0, y=0, dir=<Direction.RIGHT: 2>)]"
]
},
- "execution_count": 34,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
" Step(x=0, y=0, dir=<Direction.UP: 1>)]"
]
},
- "execution_count": 35,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 112,
+ "execution_count": 16,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 113,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
" (0, 0)]"
]
},
- "execution_count": 113,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 114,
+ "execution_count": 18,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"True"
]
},
- "execution_count": 115,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 116,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"False"
]
},
- "execution_count": 116,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
"False"
]
},
- "execution_count": 117,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 118,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"False"
]
},
- "execution_count": 118,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 119,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"True"
]
},
- "execution_count": 119,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 120,
+ "execution_count": 24,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 121,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
" 'tuvwxyz']"
]
},
- "execution_count": 121,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 122,
+ "execution_count": 26,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 123,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
"4"
]
},
- "execution_count": 123,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 124,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"9"
]
},
- "execution_count": 124,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 125,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
"15"
]
},
- "execution_count": 125,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 329,
- "metadata": {},
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"def step(s, current):\n",
},
{
"cell_type": "code",
- "execution_count": 507,
- "metadata": {},
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"def valid_prefix(tour):\n",
},
{
"cell_type": "code",
- "execution_count": 552,
- "metadata": {},
+ "execution_count": 267,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
- "def mistake_positions(tour, debug=False):\n",
+ "def mistake_positions(trace, debug=False):\n",
" mistakes = []\n",
- " current = Step(0, 0, Direction.RIGHT)\n",
+ " current = trace[0]\n",
" posns = [(0, 0)]\n",
- " for i, s in enumerate(tour):\n",
- " current = step(s, current)\n",
+ " for i, current in enumerate(trace[1:]):\n",
" if (current.x, current.y) in posns:\n",
" if debug: print(i, current)\n",
- " mistakes += [(i+1, 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 + [(len(tour)+1, current)]"
+ " return mistakes + [Mistake(len(trace)+1, current)]"
]
},
{
"cell_type": "code",
- "execution_count": 550,
+ "execution_count": 84,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 492,
+ "execution_count": 34,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 493,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
"'FFLRLLFLRL'"
]
},
- "execution_count": 493,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 494,
+ "execution_count": 36,
"metadata": {
"scrolled": true
},
"True"
]
},
- "execution_count": 494,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 495,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "False"
+ "True"
]
},
- "execution_count": 495,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 526,
+ "execution_count": 89,
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "9 Step(x=0, y=0, dir=<Direction.DOWN: 3>)\n"
- ]
- },
{
"data": {
"text/plain": [
- "[(10, Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
- " (12, Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
+ "[Mistake(i=1, step=Step(x=0, y=0, dir=<Direction.RIGHT: 2>)),\n",
+ " Mistake(i=11, step=Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
+ " (13, Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
]
},
- "execution_count": 526,
+ "execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "mistake_positions(sample_tours[0] + 'F')"
+ "mistake_positions(trace_tour(sample_tours[0] + 'F'))"
]
},
{
"cell_type": "code",
- "execution_count": 527,
+ "execution_count": 90,
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "9 Step(x=0, y=0, dir=<Direction.DOWN: 3>)\n",
- "12 Step(x=1, y=0, dir=<Direction.UP: 1>)\n",
- "13 Step(x=0, y=0, dir=<Direction.LEFT: 4>)\n",
- "14 Step(x=0, y=-1, dir=<Direction.DOWN: 3>)\n"
- ]
- },
{
"data": {
"text/plain": [
- "[(10, Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
- " (13, Step(x=1, y=0, dir=<Direction.UP: 1>)),\n",
- " (14, Step(x=0, y=0, dir=<Direction.LEFT: 4>)),\n",
- " (15, Step(x=0, y=-1, dir=<Direction.DOWN: 3>)),\n",
- " (16, Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
+ "[Mistake(i=1, step=Step(x=0, y=0, dir=<Direction.RIGHT: 2>)),\n",
+ " Mistake(i=11, step=Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
+ " Mistake(i=14, step=Step(x=1, y=0, dir=<Direction.UP: 1>)),\n",
+ " Mistake(i=15, step=Step(x=0, y=0, dir=<Direction.LEFT: 4>)),\n",
+ " Mistake(i=16, step=Step(x=0, y=-1, dir=<Direction.DOWN: 3>)),\n",
+ " (17, Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
]
},
- "execution_count": 527,
+ "execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "mistake_positions(sample_tours[0] + 'FLLLL')"
+ "mistake_positions(trace_tour(sample_tours[0] + 'FLLLL'))"
]
},
{
"cell_type": "code",
- "execution_count": 528,
+ "execution_count": 91,
"metadata": {},
"outputs": [
{
"'FFLRLLFLRL'"
]
},
- "execution_count": 528,
+ "execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 375,
+ "execution_count": 574,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def random_walk(steps=1000):\n",
- " return ''.join(random.choice('FLR') for _ in range(steps))"
+ " return ''.join(random.choice('FFLR') for _ in range(steps))"
]
},
{
"cell_type": "code",
- "execution_count": 548,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
"''"
]
},
- "execution_count": 548,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 342,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'FLFFLRFRLFRRRR'"
+ "''"
]
},
- "execution_count": 342,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "valid_prefix(random_walk(1000), allowed_mistakes=1)"
+ "valid_prefix(random_walk(1000))"
]
},
{
"cell_type": "code",
- "execution_count": 440,
+ "execution_count": 45,
"metadata": {
+ "collapsed": true,
"scrolled": true
},
"outputs": [],
},
{
"cell_type": "code",
- "execution_count": 441,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "['LFRRFLLRFLRLLRFLRLFLRFLFFFRLLRLL',\n",
- " 'LRFFLFFFRLLRFRFFLRRFRLFLRFRLFRLRFFFF',\n",
- " 'FFFLRFRLRLFLRLFLFFFFFRFFRLLFFFLRLFFLRF',\n",
- " 'LFRLFFFFRLLRFFLLFFFRFFLLRFRLFLRFLF',\n",
- " 'LFLRRFLLRRFRFLFFRFFFFRRLLRFLRFRRLF',\n",
- " 'FLRLRLRFRLRFRLLFRFFRRLFRFLFLRRLR',\n",
- " 'RFRRFFFFRLRFLFRRLRFFFFRRFFLLFFRF',\n",
- " 'FFLFLRLFRLFFRFFLLFFRLLFRRLLRLFFF',\n",
- " 'RFFLFRRLRRLRLLFRFRLLRRLRFRLLRRFFRLFFLR',\n",
- " 'RFLFFLFRLFLRLFLRRLFFLRFLFLFRLLRF']"
+ "['RFLFFLFFFFFFLRRLLRLFFFLFFFLRFRLLFFRR',\n",
+ " 'FLRLFLFFLFRLLRRFLFRFLLFFFLLRRFLF',\n",
+ " 'LRFLFRFRLLRLRLRRLRRFLFRLRLFRLRRFLLRRLFFRFRRL',\n",
+ " 'RFLLRFLFFFFLRFFLFLFLRFRFLRRLLFLFRL',\n",
+ " 'LFFRLFRLLRFFLLRFLFFLRFRFFLLRLRLF',\n",
+ " 'LRFRLFRFFLRRLRLFFRFFFRFLRFFFFRLR',\n",
+ " 'LRFLFFFRRLLRFFFRRLFRFFRLLFLRFRFLRRLR',\n",
+ " 'LLRRLFLFRLFFFFFFLLRLFRLRFLFFRFRLRFLLRFLFLRFLFFFF',\n",
+ " 'FLFRFFRFLRFLLRFRFRLFRRLLRRFLFLRRFFLR',\n",
+ " 'FRFFFRFFRRLLFRFLRFRLFLRRFRFLFFRFRL']"
]
},
- "execution_count": 441,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 350,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
" Step(x=0, y=0, dir=<Direction.DOWN: 3>)]"
]
},
- "execution_count": 350,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 147,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
"(-1, 3)"
]
},
- "execution_count": 147,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ef4c240>"
+ "<matplotlib.figure.Figure at 0x7f40411df278>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 135,
+ "execution_count": 49,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 136,
+ "execution_count": 50,
"metadata": {},
"outputs": [
{
"(0, 2, -13, -13)"
]
},
- "execution_count": 136,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 148,
+ "execution_count": 51,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 214,
+ "execution_count": 1198,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
- "def plot_trace(trace):\n",
+ "def plot_trace(trace, colour='k', xybounds=None, fig=None, subplot_details=None, filename=None):\n",
" plt.axis('on')\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='k', ec='k', length_includes_head=True)\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",
- " plt.xlim([xl-1, xh+1])\n",
- " plt.ylim([yl-1, yh+1])"
+ " 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": 995,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# def plot_trace(trace, colour='k', xybounds=None, fig=None, subplot_details=None):\n",
+ "# plt.ioff()\n",
+ "# if fig is None:\n",
+ "# fig = plt.figure()\n",
+ "# ax = fig.gca()\n",
+ "# if subplot_details is not None:\n",
+ "# ax = fig.add_subplot(*subplot_details)\n",
+ "# plt.axis('on')\n",
+ "# # plt.axes().set_aspect('equal')\n",
+ "# ax.set_aspect('equal')\n",
+ "# for s, t in chunks(trace, 2):\n",
+ "# w, h = plot_wh[t.dir]\n",
+ "# ax.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",
+ "# ax.set_xlim([min(xl, bxl)-1, max(xh, bxh)+1])\n",
+ "# ax.set_ylim([min(yl, byl)-1, max(yh, byh)+1])\n",
+ "# else:\n",
+ "# ax.set_xlim([xl-1, xh+1])\n",
+ "# ax.set_ylim([yl-1, yh+1])\n",
+ "# plt.close(fig)\n",
+ "# return fig"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 967,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def pqr(p, q, r):\n",
+ " return '{}:{};{}'.format(p, q, r)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 968,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'1:2;3'"
+ ]
+ },
+ "execution_count": 968,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pqrt = (1, 2, 3)\n",
+ "pqr(*pqrt)"
]
},
{
"cell_type": "code",
- "execution_count": 514,
+ "execution_count": 1002,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ef71c50>"
+ "<matplotlib.figure.Figure at 0x7f4026af4b00>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 215,
+ "execution_count": 1003,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4026adc278>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(sample_tours[0] + 'FLLLL'), colour='g')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1004,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ef505f8>"
+ "<matplotlib.figure.Figure at 0x7f4027cd51d0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 216,
+ "execution_count": 1005,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Step(x=0, y=0, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=1, y=0, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=1, y=-1, dir=<Direction.DOWN: 3>),\n",
- " Step(x=1, y=-2, dir=<Direction.DOWN: 3>),\n",
- " Step(x=0, y=-2, dir=<Direction.LEFT: 4>),\n",
- " Step(x=-1, y=-2, dir=<Direction.LEFT: 4>),\n",
- " Step(x=-1, y=-3, dir=<Direction.DOWN: 3>),\n",
- " Step(x=-2, y=-3, dir=<Direction.LEFT: 4>),\n",
- " Step(x=-2, y=-2, dir=<Direction.UP: 1>),\n",
- " Step(x=-2, y=-1, dir=<Direction.UP: 1>),\n",
- " Step(x=-1, y=-1, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=-1, y=0, dir=<Direction.UP: 1>),\n",
+ " Step(x=0, y=-1, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=0, y=-2, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=1, y=-2, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=1, y=-1, dir=<Direction.UP: 1>),\n",
+ " Step(x=2, y=-1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=3, y=-1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=3, y=0, dir=<Direction.UP: 1>),\n",
+ " Step(x=3, y=1, dir=<Direction.UP: 1>),\n",
+ " Step(x=3, y=2, dir=<Direction.UP: 1>),\n",
+ " Step(x=3, y=3, dir=<Direction.UP: 1>),\n",
+ " Step(x=3, y=4, dir=<Direction.UP: 1>),\n",
+ " Step(x=2, y=4, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=2, y=5, dir=<Direction.UP: 1>),\n",
+ " Step(x=2, y=6, dir=<Direction.UP: 1>),\n",
+ " Step(x=2, y=7, dir=<Direction.UP: 1>),\n",
+ " Step(x=1, y=7, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=0, y=7, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=0, y=6, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=0, y=5, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=1, y=5, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=1, y=4, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=1, y=3, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=0, y=3, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-1, y=3, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-1, y=2, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=-2, y=2, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-2, y=3, dir=<Direction.UP: 1>),\n",
+ " Step(x=-3, y=3, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-3, y=2, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=-3, y=1, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=-2, y=1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=-1, y=1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=-1, y=0, dir=<Direction.DOWN: 3>),\n",
" Step(x=0, y=0, dir=<Direction.RIGHT: 2>)]"
]
},
- "execution_count": 216,
+ "execution_count": 1005,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 405,
+ "execution_count": 1006,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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HOHDgQOIsd+fVV19l+vTplJaWcvvtt0f5nJtKZ4x2yN05//zz2bFjBxdccEHivClTpkQY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\nFUMk4P8ANAvxgu/mStQAAAAASUVORK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85eaef0f0>"
+ "<matplotlib.figure.Figure at 0x7f4027bb3e80>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 442,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
"10"
]
},
- "execution_count": 442,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 443,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ebd9f28>"
+ "<matplotlib.figure.Figure at 0x7f4027d30588>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 444,
+ "execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc8640290b8>"
+ "<matplotlib.figure.Figure at 0x7f4027d86518>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 445,
+ "execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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DoH///uzduzfhjqS5tIqQnD17NmPGjIk+tnXlypWMGjWKsrKyqHW3bt3K2LFjef7556PW\nzaVly5YxatQoysvLk24lEQUFBfsnZ3bs2JHCwsKEO5Jm4+7NsmV2Fde0adMc8A4dOjiZCY3Rtu7d\nu3thYWH0uoAXFxe7mUWvO2bMmOj3sbv7ueee60VFRTm5LxYsWJCTnnNh8eLFDng6nU66FWmi+hxq\nVHZlPQisoWIPAoNMwI8ZM4YZM2awa9cuvve970WbI9O9e3fmzZtHeXk5vXr1ijYzu2PHjmzYsIE3\n33wTgCuvvDJK3bfeeouNGzeybt06evfuHaXmPmbG17/+dTZv3swpp5zCmWeemXXNPXv28Kc//Ym+\nffuyYsWKvJn/s28QWHFxcdKtSBM0ZRBYXq8k96msrPTFixdHr1tbW+sLFizwPXv2RK+9bNkyLy8v\nj1Jr9erV+1dm1113XZSaBwK8rKzMFyxY4DU1NVFqjh07dn/P+bQyA3Ly8yDNg7a2kpSMnTt30rNn\nT6qqqnjhhReiziCHzG/f8vJyevToEa3mwoULGTx4MAAVFRV069YtWu1c0koyvyUyUlaSV1JSwo9/\n/GOA6AGZKxdccAGQmZmeLwEpbZNCUkQkQCEpIhKgkBQRCVBIiogEKCRFRAIUkiIiAQpJEZEAhaSI\nSIBCUkQkQCEpIhKgkBQRCVBIiogEKCRFRAIUkiIiAVmFpJk9ZGarzexdM3vBzI6J1ZiISEuQ7Ury\ndeAMd+8HrAXGZd+StCSPPPIIAJMnT44+aC3fPPjgg1/6KG1DViHp7m+4e139xbeBXtm3JNnYvHkz\nS5YsiVLL3Xn88ccBmDlzJjU1NVHqNpeNGzeydOnSKLXq6up47LHHAHj88cc1UrYNKYpYaxQwK2I9\naYR9g7ROPPFEdu/eHW3AWJcuXSgrK2PChAmkUqkoNQ+Ui9Gs1dXVjB49mueff57q6upo90WPHj3Y\ntGkTkyZN0kjZNuSIM27MbAHQ9cCryAxwGu/uL9ffZjzQ393/T6COT5w4cf/l0tJSSktLm965fElF\nRQXf+ta32LVrF7t3745au0+fPixfvpyOHTtGq1lbW0vXrl0577zzePHFF6PVBRg3bhyTJ0+mXbt2\nVFdXR6397W9/m3feeYd27dpFrSu5kU6nSafT+y/fd999jZ5xk/UgMDMbAfw/4AJ3P+xPpAaB5d7e\nvXuZNWsWS5cu5dFHH026naBnn32W0aNHU1dXx9q1aznppJOi1R45ciTTp0/nt7/9LWvWrGHKlCnR\nakt+a8ogsKwOt81sKHAnMCgUkNI8CgsLGT58OMOHD0+6lSMqKyvbv8qrqKiIGpL7jBw5MnpNaXuy\nPbv9OFACLDCz5Wb2VISepA24++67gcy0xHPOOSfhbkQOL6uVpLv/j1iNiIi0RHrHjYhIgEJSRCRA\nISkiEqCQFBEJUEiKiAQoJEVEAhSSIiIBCkkRkQCFpIhIgEJSRCRAISkiEqCQFBEJUEiKiAQoJEVE\nAhSS0qrU1dXx97//Hcj8MV+RbCkkJXG1tbXRxtUuXrx4/4TE22+/PUpNadtiTksUabTy8nK6d+/O\niBEjuP/++7Oud+aZZ9KtWze2bNnCjTfeGKFDaeuyHgTW4B1pEJgc5LzzzuOvf/1rTmr369ePFStW\n5KS25K+mDALT4bYkJp1O87vf/Y6ePXtyzz334O7RNgWkxKKVpCTO3TFr1C93kSbRSlLykgJSWjKF\npIhIgEJSRCRAISkiEqCQFBEJUEiKiAQoJEVEAhSSIiIBCkkRkQCFpIhIgEJSRCRAISkiEqCQFBEJ\nUEiKiAQoJEVEAqKEpJn9zMzqzOy4GPVERFqKrEPSzHoBFwJl2bfTeOl0OondZkU9516+9Qv513O+\n9dtUMVaSjwJ3RKjTJPn4jVLPuZdv/UL+9Zxv/TZVViFpZpcBG9z9vUj9iIi0KEccKWtmC4CuB14F\nOHA38HPgXw/6mohIq9HkQWBm9i3gDWAXmXDsBZQDA9z9vw5xe00BE5HENXYQWLRpiWa2Hujv7p9F\nKSgi0gLEfJ2ko8NtEWllmm3utohIPmoV77gxs4fMbLWZvWtmL5jZMUn3dChmNtTM1pjZh2Y2Nul+\njsTMepnZQjNbZWbvmdktSffUUGZWYGbLzeylpHs5EjM71szm1P8Mv29mZyfd05GY2W1m9ncz+08z\n+72ZpZLu6WBm9rSZbTKz/zzguk5m9rqZfWBmr5nZsUeq0ypCEngdOMPd+wFrgXEJ9/MVZlYAPAFc\nBJwB/MDMTk+2qyOqBW539/8JfBcYkwc97/MTYFXSTTTQY8Ar7v5N4ExgdcL9BJlZD+BmMucgvk3m\nVTLXJtvVIT1D5vF2oLuAN9y9D7CQBmRFqwhJd3/D3evqL75N5kx7SzMAWOvuZe5eA8wCrki4pyB3\n3+ju79Z/vpPMg7dnsl0dWf27wC4G/j3pXo7EzI4GznP3ZwDcvdbdqxJuqyEKgaPMrAjoCHyacD9f\n4e5/BQ4+kXwFMKP+8xnA945Up1WE5EFGAX9KuolD6AlsOODyJ+RB4OxjZicD/YAlyXbSIPveBZYP\nT7ifAmwxs2fqnx74jZl1SLqpEHf/FHgE+CeZl/1tc/c3ku2qwbq4+ybILAKArx/pH+RNSJrZgvrn\nP/Zt79V/vOyA24wHatz9PxJs9XAOdeY/Hx7EmFkJMBf4Sf2KssUys0uATfUrYKPlv+KiCOgPPOnu\n/cm87viuZFsKM7OvkVmRnQT0AErM7Lpku8qdI77jpqVw938Nfd3MRpA5xLqgeTpqtE+AEw+43IsW\neIhysPrDqbnAs+4+L+l+GmAgcLmZXQx0AI42s5nu/n8T7utwPiHz1t6l9ZfnAi39pN6FwMfuvhXA\nzP4A/AvQEhcnB9tkZl3dfZOZdQO+8saXg+XNSjLEzIYCdwKXu3t10v0cxt+AU83spPozgdcCLf7M\nK/BbYJW7P5Z0Iw3h7j939xPd/RQy9/HCFhyQ1B/6bTCz0+qvGkzLP+H0T+AcM2tvZkam55Z6sung\no4mXgBvqPx8BHPEXf96sJI/gcSAFLMh8z3jb3f8t2Za+zN33mtlNZM7EFwBPu3tL/cECwMwGAsOB\n98xsBZmnB37u7q8m21mrcwvwezMrBj4GRibcT5C7v2Nmc4EVQE39x98k29VXmdl/AKVAZzP7JzAR\nmAzMMbNRZML+miPW0YvJRUQOr1UcbouI5IpCUkQkQCEpIhKgkBQRCVBIiogEKCRFRAIUkiIiAQpJ\nEZGA/wZbNepTD8ADbQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85eddf7b8>"
+ "<matplotlib.figure.Figure at 0x7f4027c0a8d0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 446,
+ "execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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HOHDgQOIsd+fVV19l+vTplJaWcvvtt0f5nJtKZ4x2yN05//zz2bFjBxdccEHivClTpkQY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\nFUMk4P8ANAvxgu/mStQAAAAASUVORK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ef41048>"
+ "<matplotlib.figure.Figure at 0x7f4027d86518>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 447,
+ "execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMQAAAEACAYAAADsqNl9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ef240f0>"
+ "<matplotlib.figure.Figure at 0x7f4027d88ac8>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 448,
+ "execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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twFH5y4OB7YF5UqKmTZsGwFVXXcXevXsLPE3xCS3adcD3zew1cke3kj5F+ZZbbuGUU07h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\nKppIAiqaSAIqmkgCKppIAiqaSAL/C/h8KF2f/R1oAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc87c3185c0>"
+ "<matplotlib.figure.Figure at 0x7f4027db4390>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 449,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAARkAAAEACAYAAACHyQJEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFlVJREFUeJzt3Xt01PWZx/H3k4QkJESFKjaGS1RKFQ4ocERFXakWxYJh\nV3dtaxXrBbvdunhhwQu0Ac9iXYv10NP2D63r0ZiKVK0FLd4VQa1bCWAAi2yzcjFIqUvQCJqEefaP\nBA5LkwCZ7zeTmXxe58xhJvmd5/v8mOTDb34z/B5zd0REYslKdQMiktkUMiISlUJGRKJSyIhIVAoZ\nEYlKISMiUQUJGTM70sx+Y2bvmdlaMzs9RF0RSX85gerMB37v7v9kZjlAQaC6IpLmLNkP45lZEbDK\n3U8M05KIZJIQL5dOAP5qZg+ZWZWZ3W9mPQPUFZEMECJkcoCRwC/cfSSwC7gtQF0RyQAhzslsATa7\n+zstj58Abj1wIzPTf5ISyWDubq19PekjGXffBmw2s8EtXzofWNfGtlFv5eXl0dfQPmg/0u3WGfvQ\nnlDvLk0FKs2sB1ADXB2oroikuSAh4+6rgdNC1BKRzJJRn/gdO3ZsqltIWibsA2g/upJU70PSn5M5\n5IXMvLPWEpHOZWZ4rBO/IiLtUciISFQKGRGJSiEjIlEpZEQkKoWMiESlkBGRqBQyIhKVQkZEolLI\niEhUChkRiUohIyJRKWREJCqFjIhEFeSiVWb2AbATSACN7j46RF0RSX+hLr+ZAMa6+45A9UQkQ4R6\nuWQBa0kEW7duZefOnaluI2nr168/6IWrk7Fnzx42bNgQrT5AfX09W7ZsibpGV3q+g1wZz8xqgP8F\nHLjf3R9oZRtdGe8Q1NTUsGNH+APC8ePHs2vXLqZPn87UqVPp06dP8DX2ampqYvXq1cHrrlq1iuuu\nu44hQ4Zw5513UlpaGnyNiooK5s+fz7hx45gxYwa9e/cOvsbNN9/Mm2++yeTJk7nmmmvo2TP8LMTO\nfL6h/SvjhQqZL7v7R2Z2DPAicIO7Lz9gGy8vL9/3eOzYsSm/9mhX8/zzzzN+/Pjo6xx//PHU1NRE\nqz927FiWLl0arX5WVhZmxp49e6KukZuby+effx5tjc4S4/l+7bXXeO211/Y9njNnTpshE2P+Sjlw\nSytfd2lbIpHwU045xbOzs/2uu+4KXv+MM87wIUOG+OjRoz3mc1FdXe35+fmen5/vtbW1QWuvXbvW\nCwsL/eqrr/ZNmzYFrb3XwoULvVevXj579mzfuXNnlDWmTp3qffv29QceeMAbGhqirNFZz/deLWu0\nngltfeNQb0AB0KvlfiHwBnBBK9tF39F0tmLFCqf55ab36dPHE4lE0PqNjY2eSCR84sSJUX/orrzy\nyn37MWvWrOD1Gxsbg9fs7DUSiUT0NTrr+d6rvZAJcbL2WGC5ma0E/gAsdvcXAtTtVoYPH861114L\nwIIFCzBr/cizo3JycoLXbM0dd9yx7/6UKVOC18/JCfWGaOrWMLPoa3TW830oQoyp/R93P9XdR7j7\nMHe/O0Rj3U1OTg6TJk0CYNy4cSnupuNOOukkAIqLixkwYECKu5GuQG87i0hUChkRiUohIyJRKWRE\nJCqFjIhEpZARkagUMiISlUJGRKJSyIhIVAoZEYlKISMiUSlkRCQqhYyIRKWQEZGoFDIiEpVCRkSi\nChYyZpZlZlVmtihUTRFJfyGPZG4E1gWsJ4G9+OKLPPPMMwDMmDEjxd1IbF3l+Q4SMmbWD/gG8KsQ\n9SSOrKwsevToQXZ2NvX19UFruztPPfUUmzdvDlq3O9i9ezeVlZXBn5OYz/fhCHU14/uA6cCRgep1\nSx55+N15553HySefTHV1NZ988gnXX399sNoNDQ08/PDD5OfnA5CbmxusdiabO3cu8+bNo66ujqFD\nhzJmzJhgtd2dI444grq6On74wx8Gq3u4kg4ZM5sAbHP3VWY2luaRta2aPXv2vvsa7va3amtrycrK\nYuvWrRQXFwevb2b85Cc/4cILL6SysjJ4fWj+wc7Ly+O+++6LUj+TLF26lFmzZpGXlwfA2rVrWbt2\nbfB1pk2bFvzn6cDhbu1qa1bKod6Au4BNQA2wFagHHmllu4hTX9JfY2Ojl5SUeHZ2tn//+99PdTuH\nrbGx0c8++2wvLy/3urq6VLeTFhYtWuSAP/744z5q1ChfvXp1qlvqMNqZuxRkTO1eZnYuMM3dy1r5\nnodcK9NUVVUxatQoAL70pS+xffv2LjM3R+JYvHgxZWVl0V8md4b2ZmHrczJdxIgRI/jpT38KNAeO\nAkYyRdAxdu6+FIg3aT2DmRmDBg0C0FA0ySg6khGRqBQyIhKVQkZEolLIiEhUChkRiUohIyJRKWRE\nJCqFjIhEpZARkagUMiISlUJGRKJSyIhIVAoZEYlKISMiUSlkRCQqhYyIRBXiQuJ5wOtAbku9J9x9\nTrJ1RSQzJH0k4+5fAF9z9xHAqcBFZjY66c5EMti6desoK2u+FHZpaWlGXOe3LUFeLrn7rpa7eTQf\nzWTu31iGWLZsGTfeeCO7du06+MYS3IABAygqKsLMGDZsWEZf0znUBMksM1sJfAS86O5/DFG3u2ls\nbIy+Rm1tLcOGDWP8+PH87Gc/o7CwEDMLeistLeWdd96Jvi/prFevXkyfPh135+677051O1EFuZC4\nuyeAEWZ2BPC0mQ1x97+Zi63hbu3bO9hr7dq1DB06NMoat9xyC2vWrKGgoCBKfYAtW7bw3e9+l+rq\n6oz+FzpZe5/jWM91TJ063O3AG/Aj4JZWvh5hpFTm+PTTT72oqMjNzCdOnBhtnYkTJzrgr7/+uk+d\nOtU/++yzoPUfeeQRLyws9IKCAn/55ZeD1s40e4e7ZQLaGe6W9MslMzvazI5sud8T+Drwp2Trdjeb\nNm3i008/xd2prq6OfiLwnHPOYf78+cGPaN566y0+++wzdu3axdtvvx20tqSnEOdkioFXzWwV8Dbw\nvLv/PkDdbmXIkCEsWrQIgA8++CBtX2b88pe/BKC4uJjbb789xd1IV5D0ORl3rwZGBuhFRDKQPvEr\nIlEpZEQkKoWMiESlkBGRqBQyIhKVQkZEolLIiEhUChkRiUohIyJRKWREJCqFjIhEpZARkagUMiIS\nlUJGRKJSyIhIVCGujNfPzF4xs3VmVm1mU0M0JiKZIcSRTBPN1/QdApwJ/MDMTgpQVwL74osv2LRp\nEwDbt29PcTfdm7uzYcMGgH3PSaYKMdztI3df1XK/HngPKEm2roT30EMP8e677wLwzW9+M8XddG8r\nV65k2rRpAIwcOVLD3Q6VmZXSPEVSV5DugiZNmkR+fj49e/bkmmuuibpWIpHgpptuYsKECdHWWL9+\nPZdeeim33nprtDWWLVvG2WefzcKFC4PWHT58OCUlJWRnZ3PZZZel7TWdD0lbYwwO9wb0At4BJrXx\n/XjzGDLEb3/7Wwc8kUhEW+Oyyy7z3r17e1NTU7Q1jj/+eDcz79Gjh9M8TTTKzczczKLVP+aYYzw7\nOzvqPmRlZXltbW2056Kz0M5IlCDD3cwsB3gCqHD337W1nYa7te+FF14A4NVXX+W8886Lssbu3bvZ\nsWMH2dnZUeoDLFmyhNtuu40lS5YAMGXKlOBrFBUVsXz5cqqqqmhqaoqyRp8+fViwYAGbNm0iNzeX\nyZMnB63v7lx11VUUFxcHrdsZDme4m3mA14Jm9gjwV3e/pZ1tPMRamWrr1q0MHDiQxsZGRowYwYoV\nK6IcQl988cU888wznXIO4P333+fPf/4zF110UbQ1li1bRkFBAaNGjYpSP5FI8PTTT3PaaafRv3//\nKGtkAjPD3Vv9gU36SMbMzgK+A1S3zMN24A53fy7Z2t1JU1MTBQUF7Ny5k5KSzDhvPnjwYAYPHhx1\njXPOOSdq/aysLC655JKoa2S6EHOX3gDiHXt3E/3796eiooKysjIWL16c6nZEgtEnfkUkKoWMiESl\nkBGRqBQyIhKVQkZEolLIiEhUChkRiUohIyJRKWREJCqFjIhEpZARkagUMiISlUJGRKJSyIhIVAoZ\nEYlKISMiUQUJGTN70My2mdm7IeqJSOYIdSTzEHBhoFoSyfbt23n99dcBqKqqSnE30l0ECRl3Xw7s\nCFGrK2pqaopa392jrwHw5JNP8sknnwAwffr04PU7Yx8k/eiczEEsXLiQ3r17M3v27H2/oKHddNNN\nlJSU7BuJEssVV1xBUVEReXl5zJw5M2jthoYGjj32WC644AJWrlwZtLakubYGMh3uDRgIvNvO9yON\nlXJPJBI+cuTI6EO48vPzo66x9xbTrFmzog9cA/zHP/5x1P2QroV2hrsFmbsEYGYDgcXuPryN73t5\nefm+xyGHu7388suMGzeOwYMH8+ijjwadV1RRUcH8+fMZN24cM2bMoHfv3sFq73XzzTfz5ptvMnny\nZH70ox9RWloafI39VVdX09DQELRmQ0MDY8aMoW/fvvzlL3+huLiY2traoGtI13HgcLc5c+a0OXcp\nZMiU0hwyw9r4voda60CjRo2iqqoKM+OVV14JOplyz5491NTU8JWvfCVYzQPV19dTV1dHv379oq3R\nGWpqaujfvz+5ubkKmW6mveFuod7C/jXwJjDYzDaZ2dUh6h6q6667DoCjjjqKr371q0FrZ2dnRw0Y\ngF69eqV9wACccMIJ9OjRI9VtSBcT7EjmoAtFHlNrZpSXl/+/eduSGmamI5luJvqRjIhIWxQyIhKV\nQkZEolLIiEhUChkRiUohIyJRKWREJCqFjIhEpZARkagUMiISlUJGRKJSyIhIVAoZEYlKISMiUSlk\nRCQqhYyIRBXqynjjzexPZva+md0aoqaIZIakQ8bMsoCf0zzcbSjwbTM7Kdm6kp6effZZALZu3cob\nb7yR4m6kKwhxJDMa2ODuG929EVgATApQV9LQkiVLMDOysrJYvnx5qtuRLiBEyJQAm/d7vKXla9IN\nzZw5k9zcXAoLC7nhhhtS3Y50ATkBarR28eBWrxi+/0W+Q85dampq4uijjyYvLy9IPem44uJiBg8e\nTElJCYWFhaluRyI5cO5Se5KeVmBmZwCz3X18y+PbaJ4m9x8HbBdtWkFFRQXXXnstffv2ZePGjWRn\nZ0dZRw6NphV0P7GnFfwRGGRmA80sF/gWsChA3UM2d+5cGhsb+fDDD3nuuec6c2kROYikQ8bd9wA3\nAC8Aa4EF7v5esnUPx4MPPgjA6aefHnR6pIgkL8Q5Gdz9OSDs6MbDcNZZZwEwfvx4nQcQ6WL0iV8R\niUohIyJRKWREJCqFjIhEpZARkagUMiISlUJGRKJSyIhIVAoZEYlKISMiUSlkRCQqhYyIRKWQEZGo\nFDIiEpVCRkSiUsiISFRJhYyZ/aOZrTGzPWY2MlRTIpI5kj2SqQb+AVgaoBfJAPPmzQOah7s9+uij\nKe5GuoKkQsbd17v7Blofi5ISy5YtY8WKFaluIym7d++msrKS+vr6aGts376dBQsWsGfPnqB1Gxoa\n6NGjB7m5ucSaTiHpJemRKABm9iowzd2r2tkm2kiUlvqUlJSwbds2mpqamDJlSvA1zj33XC6//HLM\n4mXq3LlzmTdvHnV1dQwdOpQxY8YEre/uJBIJHnvsMXbv3k1ZWRnHHntssPqNjY1UVlZyzDHHsHHj\nRnJyglxGWrq49kaiHDRkzOxFYP+fQqN5eNtMd1/css0hhUx5efm+xyGHuwFceeWVVFZWAkT9F/Sl\nl17i/PPPj1J76dKljB07lry8PL744osoawAUFBSQSCT4/PPPo61RUVHBFVdcEa2+pNaBw93mzJnT\nZsjg7knfgFeBkQfZxmNbv369X3rppT5jxozgtSdMmOBm5iNGjPBEIhG8vrv7okWLHPDHH3/cR40a\n5atXr46yzq5du/zee+/10aNH+44dO6KsId1Ly+93q7/7IV8u/Zu7t3kyJPbLpZh2797NUUcdRUND\nAwAffvghxx13XPB1Fi9eTFlZmc5lSNqJNkHSzP7ezDYDZwDPmNmSZOp1VT179mTdunVA80uaGAEj\nkqmSOivn7k8DTwfqpUs78cQTARg0aFCKOxFJL/rEr4hEpZARkagUMiISlUJGRKJSyIhIVAoZEYlK\nISMiUSlkRCQqhYyIRKWQEZGoFDIiEpVCRkSiUsiISFQKGRGJSiEjIlEpZEQkqmSvjHePmb1nZqvM\n7EkzOyJUYyKSGZI9knkBGOrupwIbgNuTb6l7WrduHWVlZQCUlpbqOr+SMZId7vaSuydaHv4B6Jd8\nS13bxx9/zPTp03nrrbeC1h0wYABFRUWYGcOGDYs620mkMwWZVgBgZouABe7+6za+n7bTCvYyM/Lz\n86PNK2q54jtr1qxh6NChUdYQiaG9aQUHvZD4IQ53mwk0thUwe82ePXvf/dDD3TrD9773PR5++OGo\na5SXlytgpMs7cLhbe5I+kjGzq4DrgfPcvc2xh5lwJAPNM6TvueceLrnkEs4888xUtyPSJSQ1pvYg\nhccD9wJ/5+4fH2TbjAgZEflbMUNmA5AL7A2YP7j7v7SxrUJGJENFC5nDbEIhI5Khoo2pFRE5GIWM\niESlkBGRqBQyIhKVQkZEolLIiEhUChkRiUohIyJRKWREJCqFjIhEpZARkagUMiISlUJGRKJSyIhI\nVAoZEYkq2blLd5rZajNbaWbPmdmXQzUmIpkh2SOZe9z9FHcfATwLlAfoqcMO9cLGXVkm7ANoP7qS\nVO9DsnOX6vd7WAgk2tq2M6T6LzOETNgH0H50Janeh4OORDkYM/t3YDJQB3wt6Y5EJKMc9EjGzF40\ns3f3u1W3/HkxgLvPcvcBQCXwr7EbFpH0EnKC5ADgWXcf1sb3dRVxkQzW4QmS7TGzQe7+3y0PJwHv\nHW4DIpLZkp279AQwmOYTvhuBf3b3rYF6E5EM0Glzl0Ske8qIT/ya2Xgz+5OZvW9mt6a6n44ws35m\n9oqZrWs5uT411T11lJllmVmVmS1KdS8dZWZHmtlvzOw9M1trZqenuqeOMLObzWxNy5s1lWaW29k9\npH3ImFkW8HPgQmAo8G0zOym1XXVIE3CLuw8BzgR+kKb7AXAjsC7VTSRpPvB7dz8ZOIV2zjd2VWZ2\nHM3v+I509+E0n4P9Vmf3kfYhA4wGNrj7RndvBBbQfBI6rbj7R+6+quV+Pc0/1CWp7erwmVk/4BvA\nr1LdS0eZWRFwjrs/BODuTe7+SYrb6qhsoNDMcoACoLazG8iEkCkBNu/3eAtp+Mu5PzMrBU4F3k5t\nJx1yHzAdSOeTfScAfzWzh1pe9t1vZj1T3dThcvda4F5gE/AhUOfuL3V2H5kQMq29NZ62P+Bm1gt4\nArjxgP+20eWZ2QRgW8sRmdH6c5MOcoCRwC/cfSSwC7gttS0dPjM7iuaj+oHAcUAvM7u8s/vIhJDZ\nAgzY73E/UnBIGELLIe0TQIW7/y7V/XTAWUCZmdUAjwFfM7NHUtxTR2wBNrv7Oy2Pn6A5dNLN14Ea\nd/9fd98DPAWM6ewmMiFk/ggMMrOBLWfOvwWk67sa/wmsc/f5qW6kI9z9Dncf4O4n0Pw8vOLuk1Pd\n1+Fy923AZjMb3PKl80nPE9mbgDPMLN/MjOb96PQT2En/B8lUc/c9ZnYD8ALNofmgu6fjOwFnAd8B\nqs1sJc0v+e5w9+dS21m3NRWoNLMeQA1wdYr7OWzu/l8tH5hdCTS2/Hl/Z/ehD+OJSFSZ8HJJRLow\nhYyIRKWQEZGoFDIiEpVCRkSiUsiISFQKGRGJSiEjIlH9H8jaA16UZfPjAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ec78198>"
+ "<matplotlib.figure.Figure at 0x7f4027d1fcf8>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 450,
+ "execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAN4AAAEACAYAAADcJMhcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nr2s6ANnE1QAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85f006a90>"
+ "<matplotlib.figure.Figure at 0x7f4027ca70b8>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 451,
+ "execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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OE9fnzp0bef1M1RvPd1xSemTu/vWoGunrrr76agCuvfZafaDZifHjxwNQWFhIUVFRzN1I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\nrkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85efd7208>"
+ "<matplotlib.figure.Figure at 0x7f402c0d3588>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 416,
+ "execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "['RFLRFFRFFFFRLRFRFFRLLFFFFFRRLRRL',\n",
- " 'FFFFRFRLRLFRLRLRFRRLLFFRFRLFLRRFLRRL',\n",
- " 'RLLRLFFFRFFLLRRLLFRLLRRFLLRRLLFFFFLRFLRF',\n",
- " 'FFLRRLFRLFLFFFFLLRFRLFFFFLLRLRRL',\n",
- " 'FLFRFLFRFFFLFRLFRLLFFLRRFLRLLFRLRLRRLLFFFRFLFLRLRL',\n",
- " 'LRFRLRFLFFFFFRRFLRLRFRLRLFRLFFRRLR',\n",
- " 'LRFLFLRLRRFLFFRRFFFLFRFFLRFRFLLRFFRRLLRFFRLRRFFLLRLR',\n",
- " 'LFRLFFFRLLRLLFFRRLLRLFFFLRRLLFFL',\n",
- " 'FLRFFLFFRLFRLLFRLLFFFRLFRRLFLFFL',\n",
- " 'LLRFRLRRLRLFRFFFFRLRFFLRFFRLRRLFRFFL']"
+ "['RFLFFLFFFFFFLRRLLRLFFFLFFFLRFRLLFFRR',\n",
+ " 'FLRLFLFFLFRLLRRFLFRFLLFFFLLRRFLF',\n",
+ " 'LRFLFRFRLLRLRLRRLRRFLFRLRLFRLRRFLLRRLFFRFRRL',\n",
+ " 'RFLLRFLFFFFLRFFLFLFLRFRFLRRLLFLFRL',\n",
+ " 'LFFRLFRLLRFFLLRFLFFLRFRFFLLRLRLF',\n",
+ " 'LRFRLFRFFLRRLRLFFRFFFRFLRFFFFRLR',\n",
+ " 'LRFLFFFRRLLRFFFRRLFRFFRLLFLRFRFLRRLR',\n",
+ " 'LLRRLFLFRLFFFFFFLLRLFRLRFLFFRFRLRFLLRFLFLRFLFFFF',\n",
+ " 'FLFRFFRFLRFLLRFRFRLFRRLLRRFLFLRRFFLR',\n",
+ " 'FRFFFRFFRRLLFRFLRFRLFLRRFRFLFFRFRL']"
]
},
- "execution_count": 416,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 452,
+ "execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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2WNK1JH91f1tmDqEfEWwFZpvZQUlfA54luWXzZJK/7PZ0qzHcGDbMMoXjPGNmp0g6GnhW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\n8v89qQ80wuYWhQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85eaed1d0>"
+ "<matplotlib.figure.Figure at 0x7f402c0babe0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 582,
+ "execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(408, [8, 16, 20])"
+ "(310, [])"
]
},
- "execution_count": 582,
+ "execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 583,
+ "execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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n5ua67FetPiVSQs+vmyAIgvAKGtgjFHeMn2qPv2rGH7V0wThhiHOu2TAcKBwN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\nizjnH3POHwfwuH398z4AS6CcKcKeJ6i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+ "image/png": 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4BuzPuLRMdHS0Ko21u+j1enndOp1OtUsMQEC/aDqdzqUt3PqaEhISwtJ+rkXS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\nRK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85ce54208>"
+ "<matplotlib.figure.Figure at 0x7f402c0b97f0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 488,
+ "execution_count": 1216,
"metadata": {},
"outputs": [],
"source": [
- "walks_m = {i: list() for i in range(4)}\n",
- "while all(len(w_m) < 10 for w_m in walks_m.values()):\n",
- " w = random_walk()\n",
- " ps = mistake_positions(w)\n",
- " if not ps or ps[0][0] > 30:\n",
- " walks_m[0] += [w[:ps[0][0]]]\n",
- " if len(ps) >= 2\n",
- " \n",
- " if len(w) > 30: \n",
- " walks_m[i] += [w]"
+ "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": 489,
+ "execution_count": 299,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{0: ['LRLRLRLFRFLFFLRFRRLRLFLLRFLRRLLFFFLRLRLLRLF',\n",
- " 'FFRLLFFFFRFLRFRLLRFFRFFFFFFLLRRLRRFFR',\n",
- " 'LFRRFLLRFFLFRLRFLRRLLFRRFLFLRRFFLFFLRFFLFFLFLFRRR',\n",
- " 'FLFLFFLRRFFFFLFRFFLFRFFLFFRLFRRR',\n",
- " 'LLFLLRFFRLLFRFLFRLRFFRFFLFFLRLFRLLFLFF',\n",
- " 'RFRRLRLLRRFLFFRRLRFFFLFRRLFLFLRRR',\n",
- " 'FFLFFRFLFLRLFFLLFRRFFFRLLFFFRFFRRR',\n",
- " 'LRRLFRFLRFFFFRLLRLRFLRFLLRRFRLLRFRLFLFLRRR',\n",
- " 'LRLFLRRFLFRRLRFRFLFLRLLFRRFFRLRFLLFL',\n",
- " 'RLRRLFLFRRLLFFRFRFLFFLRLFLRLRFLLFRFFFLRRFRFLFFRFLFRLFFRRR'],\n",
- " 1: ['RRFLRRLLFLRLRRLFRLFFFRLLFRFLFRRLLLL',\n",
- " 'LRLFFLRRFLLRLRRLLRLRFLRLFFLLLFLRRR',\n",
- " 'RFRRLFRLRLRRFLLRFLFFLRRFLRRFRRL',\n",
- " 'FFFRFLFRLFFLFLLLFLFFLRFFRFFFRRLRRLF',\n",
- " 'FLFRFFRLLRFRLLRFFRFLFLFLRFFFLRRLLLF',\n",
- " 'FRRLRLLFRFLRFRLRRFRFFFLRFFRLRFFLLFRFLRFFRLFRRLLL',\n",
- " 'FFLRLRFLFLRRFFLFFRRRFRLFLFRRLLFRFRLLFFLLF',\n",
- " 'FLRRFLRLFFFFRFLFLLLFFRFLFRLLFRRLLL',\n",
- " 'RFFFRFLLRFFRFFFRRRFLRLFRFFLRLLFLF',\n",
- " 'FFRFLFLLRRLRLFFRLFFLFRLFLRFFLLFFLF'],\n",
- " 2: ['FFLRFRFRLFRFFFFLRFRLRFRLFLLLRLFLRLFLLRLR',\n",
- " 'FFLFRLLRLRRFFRLFLRFFFFRLFLRFFLLLFRFLFRRLR',\n",
- " 'RRLFRRFFFLLRFFLFFLFFLFLRFRLLRFFLFFLFRLLFRF',\n",
- " 'FFRFLRLLRRFFRRLFFFFFFLLLFRFLFRFL',\n",
- " 'FFRFFFLFLFRLRFRRLRLLFFLLLFLRFLRFLLLL',\n",
- " 'FFFFFFFFFFFFFFLLFLFLFRLRFFLRFFRFLFRFLFRRLLRLRLRLFRFLFLLLL',\n",
- " 'LFFRFFRFFFFRFRLFFRLFRLFFLLRFRLLFLRFLRRLLFR',\n",
- " 'FRFRLRLLFFRFFLRFLRFRFLLFLRLFFLLL',\n",
- " 'LRLLLLRFLRRFFLFRFRFFLFLLRRLFRLFRLFLFFFFFFRFRFLFRLFFLRFFLRFRRLLRFFRRLRRL',\n",
- " 'FLRFLRLLRLRFRLRLLRFLFFLRLRLFRRRLLFLFFRFLLRLLRFLRLFRFLFRRR']}"
- ]
- },
- "execution_count": 489,
+ "'RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLRRLRRRRFFFFFFRRRLLFFFFFFLRRRLLLRLLLLRFRLRFRRRFRLRLLRFRLFLLRFRFLRRRRLRFLLRLRRLFFFRLRRFFRFLRFLLFLFRLRLRLRRRLFFRLRRFFRRFFLLRLLFLLLFRFRFLRLFFLFRRRRLRRLRRLRFRRLRRRFRRRRLLRFLLRRFFRLRFFLFLFRRLLLRLFFFRFRLRLLFRRFLLRLFFFLLRLLRRLRFRFFFFFRRFFLFRFLRFRFLFLFRLFLFLRFFLFRLRRRLRRRRFFFRFLLRLFRFRRRLRFFFFFLLFRRLFRRFRFRFLRLLFRFFFLLRRFRRRRLFFFLFLLLFFLRRLRFLFLFFRLLFFRRFFLRLLRFLRLLFLFRLLLRFFFFFFFFLFLRLLRLRLRFLLFFFLFLFFLFRRRRRFFRLLFFFRLFFLFRLFFRFFFFRLRRRLLFLRRFLRFFLRRFRRFLRFLRRLFRRLFFFFRLFLFFLRFFFFFRLFLFLRFFFLLLFLLLFFLRRLFLFRRFRRFFRRRFFRFLFLLFLFRFRFLLLRRRLFRRFFRLRFLFLFLRRLFFFLFRLRFRFFRFLRRFFFFFLFRLRRFRLLRLLFFLLLFFLFFFRLFRFRRRRLFLFLLLLRLFRFRLRLLFLFLLLRRRLLLFRRFRLRFFRRRLLLFRLRLLFFLLRFFFFRLLRLRLLLRLFFFRRLRFRFFRRRFFFRFFFFLRFLFRFLRFRRLLLRLLFRRLFFLRRFFFRLLFRLFLRRFLRLLRRFFLLFLLLFFFFRRFFRLFFLLFLLFRFRLRFLRFFRFRLRLLLFFFLFLRFRFRFLRRLLLLLRFRRLFLLLLRRFFFLLRFLFFRLLLLFLFRLRFFFFFLRLLFFLLLFFLLRRFLRRFLRLRRLLLRRRRRLLLFLRRLFFFRFLRRLF'"
+ ]
+ },
+ "execution_count": 299,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "walks_m"
+ "w = 'RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLRRLRRRRFFFFFFRRRLLFFFFFFLRRRLLLRLLLLRFRLRFRRRFRLRLLRFRLFLLRFRFLRRRRLRFLLRLRRLFFFRLRRFFRFLRFLLFLFRLRLRLRRRLFFRLRRFFRRFFLLRLLFLLLFRFRFLRLFFLFRRRRLRRLRRLRFRRLRRRFRRRRLLRFLLRRFFRLRFFLFLFRRLLLRLFFFRFRLRLLFRRFLLRLFFFLLRLLRRLRFRFFFFFRRFFLFRFLRFRFLFLFRLFLFLRFFLFRLRRRLRRRRFFFRFLLRLFRFRRRLRFFFFFLLFRRLFRRFRFRFLRLLFRFFFLLRRFRRRRLFFFLFLLLFFLRRLRFLFLFFRLLFFRRFFLRLLRFLRLLFLFRLLLRFFFFFFFFLFLRLLRLRLRFLLFFFLFLFFLFRRRRRFFRLLFFFRLFFLFRLFFRFFFFRLRRRLLFLRRFLRFFLRRFRRFLRFLRRLFRRLFFFFRLFLFFLRFFFFFRLFLFLRFFFLLLFLLLFFLRRLFLFRRFRRFFRRRFFRFLFLLFLFRFRFLLLRRRLFRRFFRLRFLFLFLRRLFFFLFRLRFRFFRFLRRFFFFFLFRLRRFRLLRLLFFLLLFFLFFFRLFRFRRRRLFLFLLLLRLFRFRLRLLFLFLLLRRRLLLFRRFRLRFFRRRLLLFRLRLLFFLLRFFFFRLLRLRLLLRLFFFRRLRFRFFRRRFFFRFFFFLRFLFRFLRFRRLLLRLLFRRLFFLRRFFFRLLFRLFLRRFLRLLRRFFLLFLLLFFFFRRFFRLFFLLFLLFRFRLRFLRFFRFRLRLLLFFFLFLRFRFRFLRRLLLLLRFRRLFLLLLRRFFFLLRFLFFRLLLLFLFRLRFFFFFLRLLFFLLLFFLLRRFLRRFLRLRRLLLRRRRRLLLFLRRLFFFRFLRRLF'\n",
+ "w"
]
},
{
"cell_type": "code",
- "execution_count": 490,
+ "execution_count": 238,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPEAAAD7CAYAAAC7UHJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrC2Hf85MaAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4027310390>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "w_short = w[:100]\n",
+ "plot_trace(trace_tour(w_short))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 214,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "turning from Direction.UP to Direction.UP with F\n",
+ "RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n",
+ "RFFRRFFFRFFRFLLRFLFFFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40274ccbe0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "trimmed = trim_loop(w_short)\n",
+ "print('{}\\n{}'.format(w_short, trimmed))\n",
+ "plot_trace(trace_tour(trimmed))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 215,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "turning from Direction.UP to Direction.LEFT with L\n",
+ "RFFRRFFFRFFRFLLRFLFFFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n",
+ "RFFRRFFFRFFRFLLRFLFFLLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40271e3240>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "trimmed2 = trim_loop(trimmed)\n",
+ "print('{}\\n{}'.format(trimmed, trimmed2))\n",
+ "plot_trace(trace_tour(trimmed2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 250,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Mistake(i=104, step=Step(x=-4, y=6, dir=<Direction.DOWN: 3>)),\n",
+ " Mistake(i=105, step=Step(x=-5, y=6, dir=<Direction.LEFT: 4>)),\n",
+ " Mistake(i=106, step=Step(x=-5, y=7, dir=<Direction.UP: 1>)),\n",
+ " Mistake(i=107, step=Step(x=-5, y=8, dir=<Direction.UP: 1>)),\n",
+ " Mistake(i=108, step=Step(x=-5, y=9, dir=<Direction.UP: 1>)),\n",
+ " Mistake(i=115, step=Step(x=-5, y=12, dir=<Direction.LEFT: 4>)),\n",
+ " Mistake(i=116, step=Step(x=-5, y=11, dir=<Direction.DOWN: 3>)),\n",
+ " Mistake(i=127, step=Step(x=2, y=11, dir=<Direction.LEFT: 4>)),\n",
+ " Mistake(i=130, step=Step(x=3, y=11, dir=<Direction.UP: 1>)),\n",
+ " (132, Step(x=3, y=11, dir=<Direction.UP: 1>))]"
+ ]
+ },
+ "execution_count": 250,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mistake_positions(trace_tour(w[:130]))[-10:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 288,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Step(x=40, y=36, dir=<Direction.UP: 1>)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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4BuzPuLRMdHS0Ko21u+j1enndOp1OtUsMQEC/aDqdzqUt3PqaEhISwtJ+rkXS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\nRK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4027beb4a8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "w_tour_start = w #[:250]\n",
+ "print(trace_tour(w_tour_start)[-1])\n",
+ "plot_trace(trace_tour(w_tour_start))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 289,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLRRLRRRRFFFFFFRRRLLFFFFFFLRRRLLLRLLLLRFRLRFRRRFRLRLLRFRLFLLRFRFLRRRRLRFLLRLRRLFFFRLRRFFRFLRFLLFLFRLRLRLRRRLFFRLRRFFRRFFLLRLLFLLLFRFRFLRLFFLFRRRRLRRLRRLRFRRLRRRFRRRRLLRFLLRRFFRLRFFLFLFRRLLLRLFFFRFRLRLLFRRFLLRLFFFLLRLLRRLRFRFFFFFRRFFLFRFLRFRFLFLFRLFLFLRFFLFRLRRRLRRRRFFFRFLLRLFRFRRRLRFFFFFLLFRRLFRRFRFRFLRLLFRFFFLLRRFRRRRLFFFLFLLLFFLRRLRFLFLFFRLLFFRRFFLRLLRFLRLLFLFRLLLRFFFFFFFFLFLRLLRLRLRFLLFFFLFLFFLFRRRRRFFRLLFFFRLFFLFRLFFRFFFFRLRRRLLFLRRFLRFFLRRFRRFLRFLRRLFRRLFFFFRLFLFFLRFFFFFRLFLFLRFFFLLLFLLLFFLRRLFLFRRFRRFFRRRFFRFLFLLFLFRFRFLLLRRRLFRRFFRLRFLFLFLRRLFFFLFRLRFRFFRFLRRFFFFFLFRLRRFRLLRLLFFLLLFFLFFFRLFRFRRRRLFLFLLLLRLFRFRLRLLFLFLLLRRRLLLFRRFRLRFFRRRLLLFRLRLLFFLLRFFFFRLLRLRLLLRLFFFRRLRFRFFRRRFFFRFFFFLRFLFRFLRFRRLLLRLLFRRLFFLRRFFFRLLFRLFLRRFLRLLRRFFLLFLLLFFFFRRFFRLFFLLFLLFRFRLRFLRFFRFRLRLLLFFFLFLRFRFRFLRRLLLLLRFRRLFLLLLRRFFFLLRFLFFRLLLLFLFRLRFFFFFLRLLFFLLLFFLLRRFLRRFLRLRRLLLRRRRRLLLFLRRLFFFRFLRRLF\n",
+ "RFFRRFFFRFLRFRFLLRFLLRLRRLFRLRRFFLLRLLRRFRFLRLFFLFLRRLRRLRLFLLRRFFLRLLFFFFFRRFFLFRLFFRLLRRFLFFFLRFLRRLRFLFLFFRLLFFRRFFLRLFRFFRLFFLRRFLRFLRRLFRRLFFFFRLFLFFLRFFLFRFLRRFFFFFLFRLRRFRLLRLLFRFLFFFRLFRFLFLRLRFFFFFLFFRFFFFLRFLFRFLRLRLFFLRRFFFRLLFRLFLRRFLRLLRLFFRFLRRLFLLRFLFFRFLFRLRFFFFFLRLLFRFLLRRFLRRFLRLRFLRRLFFFRFLRRLF\n",
+ "Step(x=40, y=36, dir=<Direction.UP: 1>)\n"
+ ]
+ },
{
"data": {
- "image/png": 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nAaEhk3w+Hz169ABg/fr1LF68OKkEc+LECSorK3n11VetRNfa2sqcOXMoLPyk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\nnqDJTinlCZrslFKeoMlOKeUJ/x9O8a6mC0FDEQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fc85eae9c88>"
+ "<matplotlib.figure.Figure at 0x7f4026fcb9b0>"
]
},
"metadata": {},
}
],
"source": [
- "plot_trace(trace_tour(walks_m[0][0]))"
+ "w_tour = w_tour_start\n",
+ "w_trace = trace_tour(w_tour)\n",
+ "mks = mistake_positions(w_trace)\n",
+ "while len(mks) > 1:\n",
+ "# print(len(mks))\n",
+ " w_tour = trim_loop(w_tour)\n",
+ " w_trace = trace_tour(w_tour)\n",
+ " mks = mistake_positions(w_trace)\n",
+ "print('{}\\n{}\\n{}'.format(w_tour_start, w_tour, trace_tour(w_tour)[-1]))\n",
+ "plot_trace(trace_tour(w_tour))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 950,
+ "metadata": {},
+ "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": 306,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'RFFRRFFFRFLRFRFLLRFLLRLRRLFRLRRFFLLRLLRRFRFLRLFFLFLRRLRRLRLFLLRRFFLRLLFFFFFRRFFLFRLFFRLLRRFLFFFLRFLRRLRFLFLFFRLLFFRRFFLRLFRFFRLFFLRRFLRFLRRLFRRLFFFFRLFLFFLRFFLFRFLRRFFFFFLFRLRRFRLLRLLFRFLFFFRLFRFLFLRLRFFFFFLFFRFFFFLRFLFRFLRLRLFFLRRFFFRLLFRLFLRRFLRLLRLFFRFLRRLFLLRFLFFRFLFRLRFFFFFLRLLFRFLLRRFLRRFLRLRFLRRLFFFRFLRRLF'"
+ ]
+ },
+ "execution_count": 306,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w_trimmed = trim_all_loops(w)\n",
+ "w_trimmed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 304,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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nAaEhk3w+Hz169ABg/fr1LF68OKkEc+LECSorK3n11VetRNfa2sqcOXMoLPyk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\nnqDJTinlCZrslFKeoMlOKeUJ/x9O8a6mC0FDEQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40271d91d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(trim_all_loops(w)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 309,
+ "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": 308,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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nAaEhk3w+Hz169ABg/fr1LF68OKkEc+LECSorK3n11VetRNfa2sqcOXMoLPyk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\nnqDJTinlCZrslFKeoMlOKeUJ/x9O8a6mC0FDEQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4027562b38>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(w_trimmed))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 310,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+P07WWIriKKEW4snJycEYw7Zt2xKlVrvotBElKTh69ChdunQJec1aLtzj8dCr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\ngjJMbcJZvAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40275594e0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(reverse_tour(w_trimmed)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 575,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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3QD61kj8AsmTJkrAi4sSJE0PW5+fnRxVjw4mIgVNH2rVrZ9ONRkSsS2nKDPba\na6+F/MfOzs6WhQsX1mgPyMSJE0VE5LLLLrPbZ2RkBNy/devWpMy3Xbt2gQyWllazkREbg8oPLnoD\nyBENJDrGU37961/X+TOLxmCesW8Sccstt5Cens4JJ5zA4sWLyczMZODAgSxevJjbb7/dzvHlhrHu\nePPNN+nRowetW7emqKiIvXv3UlVVxXHHHUenTp2SMt/CwkJ+MMn3WrbUEXiD8f772oGxsBAWL2a/\nUmzKy+OHtDQ67N/PgbQ0Bvn9nAXMys/n4MGDZGVl2ca4ubm5iIidHcbv91NRUWGylAD6nM9oEN10\nXl4ePp+PrKws/H4/Pp/PPnvz+/30798fv99vGzPnW+MburKykuzsbPx+P4cOHbI/66QiEvfVR6EJ\nr2C4NuS4RERwtIjB7Y2ICMirr75af5OVGEVEN0DkhhtC1r8yYoRFRhYR3Slk3SJi165dbXrcuHFR\nxb/A4fVZl6Gzs7NDtqkPEdFbwZII8+9rLAlKSkpsesGCBSHdVNxnPWL9o9cXatg4Vlc7idLDYfJk\nXYIwet48UEoffg7V6RsqgczjjwfgAIBS9AU2oJ/VJEcP/lzitR/s0aMHLYxBMdgrZDBitemsCzwG\nSyK2bt3K3XffzcGDB5kxYwYtW7Zk5MiRzJgxg1atWjFixAiqqqpskeftt9/mk08+se+vjx+AG3lW\nSABA5wm7+24dr95K/0pxMWzZoqNC/c//6FzO69bpnRdoZhRh7datbGjVioIWLVizdStd2rYlPzub\ng+npZPt8pIuwsrSUfsXFZOzbx+B9+0Apxo4dywsvvEBZWRmlpaX2H8zeOKNCuY2DDYqLi9myZQtK\nKS6++GK6devGQw89VKvPKS5EWt7qo9CERUQ3AOlmBfUkgojoPoCubxHRfaAbNDFdDH322RH7AeT1\n11+3aSMiGrhFxI8mTXL6diEZIiI4hr+JAp4WMTVAkAbLODxGamNKbm6u7NixI+lz7NKlS2QGM2Xg\nwIj9hHuOUKWv1Wc898RSTjnlFBGRGmds4UqsHg2hnlUi/L49h8t6wp49e5gwYQLjxo3j17/+NX/8\n4x9DthsyZAjjxo0D4Oyzz2bcuHGUl5fbvk/JREQr/awsMFbtUfzXli1bxrhx47jjjjsAaNOmDR2s\nYKMDBw60k/YNHjyYrl27Avq5TWySIUOG2HaF48aN49hjj7XpsWPH2p9jQUGB3d7YZ44bN45bb72V\nd955B9ApOCxxAAAgAElEQVQLSG5urv2ZZmRk2Nb5xxxzjF0/ffr0WD6i+BGJ++qj0ExWsFhAGC0i\nEPLcLNFo3759+BWsRw+HNgfNMYAgLWJpaWlYEdGtRTzxxBOjioiDBw+26wsLC0O2gUBbxFAHzYA8\n/vjjMT9TcP/iiYiNAwSJLU899ZRdv2DBgqSPf/TRR0t6enqoiQWWX/0q5j6Dnym4xCMiiohMnz49\noK5z584iUjOfWbjSpUsXERFJT08P2yae+BzRGMzTIqYQtmzZwqxZswAYM2aMnQUT6icv8cGDB0On\npT1wAF57zY4GxXXXxdVvnz59GDBgADt27GD//v1UV1ezbNkyLrvsMqqXL4dVqzjxxBNZv349ZWVl\njBo1itWrV7Ns2TJGjRrF3/72NzskwLRp0wCYNGkS1dXVXHDBBQAsX76cadOmUVFRYae1nTRpErt2\n7WLZsmVkZGTQpk0bWzRfuXIl//znP8nMzGTMmDFccMEFnHPOOYwZM4avv/46cTE6InFffRS8FSwk\nwMkqST2JiAGmUgkCIOPGjQuoKysrs8d5/d57bRHxiiuusOsnTZpk0yZ0gEh4d5XgMeN5DlwiIiBv\nv/12XPeKJyI2PhAktvz9739P+pgnn3xywJh33XVXnft091dD1IyjrI9RjAxVnnvuuRrzOv7448O2\n/3//7/8FtPX5fDW0ke7nkwi/b0+LmMK4/fbbmWOlVd28eXPSx/vkk0+YN2+ePeaTTz5Z5z5ffPFF\nrrjiCq6wcn/98xe/4LN772Uk8PoVVzD9f/8XLPqu/v0ZCTx+xhk8fsYZjB84kLv69+cdpegBTJw4\nkd69e4NFG5vMOXPmMGvWLObOnWvP3dQDPP/88zXmtWTJEn70ox/ZbXr37s3EiRPta26kpaUhIrzx\nxhu8/fbb8X0AkbivPgreChYSEKhFfPbZZ0VE+4TV1/i1/m7CzRFk+bx5AVrEdbNn2yJiDXcVn09E\nRP6Yn2+3cYuIAwcOtGm/329/Nu65A9K7d2+7jfv5xowZY9Pnn3++Tf/kJz8J2X7nzp2yb9++uFYw\nj8FSCMHpZD/77DMRqSkuuktVVVVS5tKjRw/pY/l6xY3c3EARr6REqioqRECWzJ4tlZWV9vxProW4\n+GUdxMVElYqKChGJzmCeFjGFcODAAW666SZuvfVWcnNz6d69u31t2rRpDBo0CBEdVNPn83HcccfF\nHbQ0VmzYsKH2fZeXw+OP6+Tj/fpBaSkZVk6yjOxsMjMzKS0tZc+ePeQvWABjxrBg0iTarFhB/0mT\nYLYVS6m6GjIy2HTxxXStqOC72bPZddVVDNm9m1WW5rG8vJzZs2fb2s/09HR+/OMfA7Bq1Sr69etH\nr169mDVrFv369WPgwIE8+OCDHDx4kN27dwPaBtMcPvfr14+nn36aESNG2AnpTf0LL7xAYWEhl156\naUCC+IiIxH31UfBWMBsQ2qMZamoRTfoi9/tQdCww7f1+v03jFhF9Pltck+pqRwR0j+OmQcSyRRQQ\nMbaIlogY0N4lIsqcOQ4tItXWKrHcJSIu6tTJpnNycgJExFBzB6RXr142bYLehPuMwNEiutsA8u23\n38YtInpKjhRDuFVDf5cOzLmYUsoO2hmKjnZ+9thjj9nt09LSbBpwwuedfTakp2tr+YwMHWfe0ErV\npIHTLr/c7mdLaSnV1jnWsSNGBLa3Vhv8fjBW8FY/6dnZoBTHWk6TKMVJpaX4rOc2DpOh5m5eu3fv\nzrp16+z3c+bMsT+j999/P+RncvHFF9f4HM13Y0KQhzwvDAGPwVIM4dLoBDNKWloahw4dory8nPLy\ncvbv31+DnuRKwxoO7777LqD90A4dOhTQz4cffqgbffQR9OypRb/9+/VreTns2+fQrvos4PpXX7V9\n28p27SIjK4sMYNmiRYH3PvOMeSAnS2V5OZOfeYZ84HBZGS9YbjQbly1jTlYW6ehsmjmWx3Xw3MH5\nQ1q/fr39PABnnnmmTc+dO7fG51FdXW2337dvX8C9BQUF9h9grLEp65r84THgx0AF8B1wtYjss655\nyR9qge+//56lS5eSlpZGu3btIraNFgba7BO2bdsG6H/d9PR0RLSLfXp6up0fOicnR68iVvYSs/+x\nUVlZM4RAqJAC6NzOBw4coKysjI7A96tWseKDD2gP7NmwgR3Z2eTl5NCyVSvtb6YnqS1GgG1lZew6\neJBDQFl5ORUWs5SsWkWWZU2yfPlym4mMIXRBQUHAZ7J06VL8fj/t2rWzGSMnJ8dmzFCrUHp6us08\nOUHPt3379tj3XgaR5MdoBTgbSLPoR4FHLNrERfSSP8QBImitpk2bFnd/y5Yti0kj1rNnT33D9deH\n195df32tniNeDaGEmN/pQddL66D9e/HFF0VEJCsrS37zm9/E9Dw+n69GP+5nlWTtwURkvogYt9tF\noHNYAz/BS/5QK9x33332P/Orr75q0/EmSged/Nzn89lfdjh63bp1+gazJxHRq5nflRovRFiASBhr\n5QerrnByqqQB20pLOVxebucEw0r+gAiLrYNeEbHFWxHh2w4d7PZdiovpZNUPGDDAbmOSspvPy6zU\n4OQH8/l8XGfZUVZWVsYc9MYcNLs/u1iRSB3vNYBxqukMfOa6ttWq8xCEXbt2sXbtWvvHsGXLFj77\nTH90H330ERUVFXXq3+3jFY62YZQJED0WRxRs27aNzz77jKqqKjumhltNI8Bnn31GbkkJAy162+bN\nDLZow/SfffYZlZWVCDB58uSA8AFucS04Pr47qaG5FvzMVVVVtXq2WLPbQAwMFkvyB6XUBKBKRKa7\n2gRDQtQBzS/5gxtHHXWUfR4DMGXKFDshwdSpU5k6dSoAJ510UvInc+yxOuZGAjBz5kxmzpxZo75V\nq1b2D37o0KH0BVZZ9DnARRZt4KZvvPFGwNG0tm7d2mayAQMG8P333wPQqVMn2xmzY8eOtnmVG9nZ\n2XabeFDvyR+Aq4BPgGxXnZf8IUaAE8IZkPvuu6/hJuM6Y6oLIDAmh0n+YOC2ppdVq+wxv5g40abd\n1vRDhgyRUL8TY7ArEhjZF7BDgIPjcBk8x5QP26aUGgmMA04XEbcsMwv4q1LqKbRo2Av4oi5jNWWs\nWbPGdlmvD7+vsNi/P2Fd/ec//7Hp6dOns3z5cjp16kS3bt3sYKABmD6dNl9/HbIvc3Qxffp0fD4f\naWlpOuWttS+dPn16AA1ajDO00ZQGI+UDj6KVFxuBr6wyyXXNS/4QA4Kzm6xcubLhJnPaaSJR4ufH\nAuNXFq1UVFSI7Nqlk0xYqpAy6/fw/PPP2ytSt27daq01BOTBBx+sMcfMzEy544476vysJHMFE5Ga\nwq1z7RHgkbr03xywdOnShp6Cg/XrdTzEOsLshUCvyJ9++imnnHIKSilKS0spLCwkNzdX75/atLE9\npefOnct5552HELiSmxVMJHAbP2TIEBYvXoyIUFhYyJ49exDRtpp5eXmhV0oLVVVVSUsc6IZn7OvB\nQVmZfn3kEX2wPGYMRDnsjgXTpk3jo48+AmDUqFG2kuLhhx9GKcXw4cMZNGhQyHvPOeccdloWHo88\nov+vr732Wo444ghbOXTOOefYzGLamPcbNmxg6tSpZGdn29Yv5gA5EgMmDJGWt/ooNHMRMaXwyCO2\nqCYgMnRonbvs3LlzTGKc3++X999/3xYLp0yZErG9iBbzwl2/9957RUSkuLg4bJt5xvC4DiCZB80e\nmhjuusthL4Av6q6X2rJlS8Q/2FXWQXOwYbI5/xMR25VERLj44ovtNubAN1S/DzzwgD2+OWgOLsOH\nD6/z80WDJyJ6CI/qarjxRjh8GHJzNeNVVcFzz4E7jn0wZs6EWbO0reJJJ8HVV4c9uJagfRXADTfc\nYEfUuvHGG+1D5759+7LFOqe78cYbY47d7z5nrHdE+nepj4InIqYmHnwwUFx0FyvaVVgEt1+8OGzT\nFStW2CLinj17aqUljISioiI566yzavMJxAQ8EdFDrXDPPaHZC2Djxuj3v/660z6CSZIxO1JK0apV\nK/uH6bZFNOGtRYTi4mKbzs/PjzqNXbt22d4EDQFPRPQQGqtXwzXX6EyXIrB4sXa6BPjXv6CoSL8/\nfBgyM3UpL3dcXB56CF56KebhzjzzTDIyMsjNzeXAgQNs3brVvmaYcPjw4fYRQFFRUcxawDKjHW0I\nRFre6qPgiYipiV69wouI8Zby8rDDVFRUSFZWVkjR78ILLxQRkR07doQVD00WlXBo2bJlyFRRiQJR\nREQlITaZ9QmllDT0HDyEgHHrr6rSdK9esHatpocPh3kN4z976aWXMmPGjJDKkVBQStGvXz9WrlyZ\nlPlYuaXD2rc1XhHxgw/gN7/RYgnAV19pccaNhx6CZ5+FggI47TR44QWdhufmmx0tV5cuOktjdjZ0\n7Kj3F1lZcNNNuv+miP37wUohRF4e7Nmjn7mgQHs0G69gK/4EACUlYKzS48w4mUgY15fevXuTl5dH\nWVkZ2dnZtGzZkh07dpCXl0dhYSEbN260reXjzZCZUERa3uqjUFsRMVgM+eUvo7f5179q1nfs6NBF\nRYHXmip++cvoYl16ushbb+n2zz7r1BcWinzxRYNNfcOGDVG1ihkZGTadn58v/zLfexJAk9YiDh3q\naKrC/Uu99JLTxi1WmNzCpaXOz2rnTv26cGFy593QMJ9VJBarroZLLtHtbrnFqd+9GwYPbrCpd+/e\nPeqfdlVVlU0fOHCAM888s8Hm27gZzH3Y+frrWpPVqVNA+DCuu07Xgw4/Zujgg8+qKvjZz/R1K156\nk4Vx38jM1J/Dq6826HSaMho3g5kISAbV1To6UTDce4nqahg5EoqLA9ts3w5/+5vT1vx7N0U89RT0\n6eM869VXN+x8mjAaN4O5lRrdujki4IQJDn3VVQ49f76m587Vm/pQMKLQW28lb94NjSOP1OdcIlrM\n9pA0NG4Gc8dU2LjREfseesih3VYEZ5+t641opJTWHioFVjLuZodwh7Vvvqm1rOZz+uyz0O08RETj\nZrDgAC3uaD+Gdis2CgqgbVstGiml21RW6n1bYSFceWXy55xqaNs2dP2oUVBRoT8bgLFj629OTQiN\nm8HcK1i3bmDi1U2Y4NBuUfCee+CHHzS9dq3TZtMmrR2zIjg1K5hzxFDIyABjsrRvX/3Mp6khmsoz\n2YV4zpuefDL6+U2iyjnn6DH37w+sv/LK2OfbGDBoUPjPwOQHA5GuXRt2nikKmpSplNkznXgifPqp\nXp0GDdJ0WppW21dWOuY9fr82Su3ZUzsPZmfr/deBA3p/kZGh6fx8/ZM6dEif8ZSXw4oVum7qVBg9\nGk4+WYukW7YEip2NHT/8oH2+duzQq1RxsX49cEDvw/r00Z9l//6wbFlDzzblEM1Uqq6rzwPAMnQc\n+n8AHVzXnkVHnVoKDIzQRzx/Fzp2n6GNSzuIXH556PZXXeXQ8+c79Lp1Dl1dLbJpk9jWGwsXOvSr\nrzr0/fc7dHMCaONfDzVAlBWsrgzWwkXfAvzJos8D3rfok4BFEfqI52m0aZOISEZGoDhjQnCdf37y\nxcemijffDP/MHoOFRDQGq2vYNndEx3zA+HD/BJhmtflcKdVKKdVeRHYE9xE33IaoSsHPf65FwQcf\n1PXvv6+NUgcP1tYdxcVw+uma/p//0ca9r78O550HrVtr+pJLtMZs1iy47DLYtUufmY0apZPCff65\nHuef/9TKkKaKu+7Sr5dfrsXv3buhRQto1Qp+//uGnVtjRSTui6UAvwM2AV8DRVbdbGCoq8184IQw\n98fzd+GsYCDSsmXoNhMmOHQiRcRhw5r2CmYMnz3EDKKsYFHV9EqpD5RSX7vKcuv1xxZ33CMiXYG/\nosVEiDP5Q1zYts05RN6/3zkIddsfug+a3d6sRuAB7d9k2mRkOAfNSjm2iEppBYeBsWNsqog3uZyH\nqIgqIopIrLGtpgPvAfcDW4AurmvFQNiQsTFnV9m3D558UmsJd+zQmsBWrfQPf+9erUU0zNWihWbA\nVq2c+91MOGqU1i4+9BDceafWIP7xjzB+PGzeDH/5i6aV0iKTGd9Ds0a9ZlcBernoW4C3pKaS42QS\npeSIbc0WOfVUhw4nIrp9wyKJiG40dRGxffum/XxJAHUVEaPgUUtcXIpOJ3ubxTFzgA1KqXXAC8CY\nOo4TGWedFbg6ffKJQ7vzGIvYcdCprnasyauqHKsOd5tgtLfSpJmxhjSipJ3/+legKH3WWbp+xgyn\nbseOpi8G1zPqmkL2YhHpLyIDReQCEdnmujZWRHqJyAAR+aruU42ADz/UP3YTxahnT4c+/ninnbE/\nBL3vMhGQMjOdiEnuNsHo2VO/vvSS1lIuXpzY50gm/vEP/Wrm/uGH+v1TTzn1L7/sicEJRuO2RXSj\nZ0+49lpNt2/v0IZxEgGjBLj2WjjmmMT1Wx8wKVWvvdb5owBn5b72Wu0XZiVG8JAYNN6gN8Fw5eQN\n8Fa+7jpdQLurGBgRCZyVLPjeYLhXuvz8QPEz0WjTJlADmii4n68hk/01EzQdBrNySNXA8OFaS3j2\n2fDYYzqa0urVYLIpvvii/vfevFmHIps/X9d3716zr0OHnH/83bsjW6LXFWVlMG4cjBih537TTdpR\nUiQ0Yyilr61dq59p/nx96P7UU/q5QR+yt2sHDz+sRUTzrKeemrznaO6IpAGpj0IitFYgctllDu22\nUQylRfT7A++trhZ5993oGjS3LeLo0cnVuIE+5Db0woWads89FD1/vjOvl14KPcfLLvO0hQkCyTSV\nSim8+aYuoK3rzb/81KmOn5dbRHTDLSL6fOH3bfUpIkLgShVvIJ5QoqA5G2zRou5z8xATmgaDHT7s\neDf36qX3V+eeC7/9rRYJ33pL10+bpmNQuJmoVy945x1YvhzuvTeyUuTwYUdE3LMnuSIiOFYnu3Y5\n+zG/39FyhqO//96ZW6tW2uby8cd14FVI/rw92GgaDJadHagZGzJER+X97W8Dr3XqpOnq6sBVa9iw\nmvua4DYQyHz1cV5kGKxNG11ihfuzMHCHBjB/Eh6SjqbBYMF45BFdILQ4FMwcrVs7tJvRzA/cwChS\n6kv7Fu48rjY444xAG81ICfQ8JAxN5xzMjaFDHSsNE7QlGGvWOG127dIxEUHXmUPYYJgVraJCu68k\nG8EMXhecdZYWcY0Fy4ED0e/xUGek/gpWVaWDghrarD5VVVr88/s1nZXl/CDLy3VIbNDh3GbN0vTq\n1c6K9vXXzr7myy91KG3QYmDwPmznTt3n/v36fVaWc+62ebNjpZ+WpudjTI+qqjRTGtrMMT09POMH\nj7t5sx7riCPi+9xCwX1W6KF+EEnFWB+FaOriiy4yP9/6KT5foLHvBx8EXm/fXte/807dxrnhhmj6\n38Bi/Nc8pBRIsrFv8mFWH7GMcP3+0LT5KYKTFMJdX10d2MYkfwB9bdMmTQfve0zYMnOvCc19wQWB\n/YeiDYLnC7BqVeTndrOXex4eGhVSX0R0x5UPZ+YTrHTYuxc+/lifVZ14oq4LFvu++CL8jzbS3ieW\nOQTP5+OPdd1JJzn2jAcP6nqfT4uRhgkNgxcVNT57Rw81kPoMNmBAxCTaIbFypXMwu3ChjslhYJjH\nOFGCVloYRpYwpki1QX6+ZiQzl1/8Qp/FnXWWdh+Jdnj8/vs6doiHRovUFxFXrowuTgVj/HiHkdau\nDbxmbPbc4pfbdSWRKngT992M8+9/61eThCJSAScKsYdGi9RfwdwiYqxwM0ksh6o33aQNeQGuv97R\nQNYV6emaWYypltFCxgrDkB4aLVKfwQYMCG8pHw7uPVQkiwtzFvTKK06dcdQM1Ve8uPtu+N3vnMA5\nY+Jw7O7YEaZM0QVCW2d4SHk0DhFx9er47nGvYOHc/8E5E6uocLSIIoEpZOsiMj74YKDY98ADsd/r\nTm0rAp07134eHhoMqb+C1UZEjPf+s892VsmJE2H9eof+8svQ94jA5Mn6EPzwYW165PdrZr3hhtD+\nZB6aHVKfwfr3j1+L6N53RYr1d/iwfnXvdVwh5Gy6V6+a9954o3ZsDIU33nCY1EOzRuoz2KpV8a9i\n7jOvSCKiiT9RURHIiB9/rFXokfZfhumD2/ToocNte/BAghhMKfVb4DGgrYjstuqeBc4FDgKjRWRp\nrTqvjYg4c6ZziBzL/UcfrZny0ku1O30s+y6zSvburV/79dMuJYnSQHpoEqgzgymlitExETe66s4F\neopIb6XUScCf0QFI48cxx8SnRRw5Uoco++YbvUKF82J2o6REvz7yiNb6xcKURkFijITNK8Add8Q+\nXw9NGolYwZ4C7gBmueouIFHZVdxuJbFg7tz4+jci3uef6yR7aWmxhXozXsEi2tdqwYLEupd4aBKo\nE4NZCSA2i8hyFShWdQY2u95vterCM1hZmT4n2r5du+N36aIPZuuqRYyGP/wB3ntPu4YAnHaaY4Fx\n+uma2bKzNUOZg2ORwKODYM/nUFi5UntYHz6s93z5+Y5vVna2k45JKS1+tmmjfdS80GqNGlF/GUqp\nD4D27ipAgHuA8UCo5BBxZVe5//774dVXYeNGhgHDAJa6tmyJDB7qRnW1/tG78cknDh3NkuLGG/Wr\nYchIOPbY+OYGmsF+9rP47/OQNNRb8gfgWGA7sB7YAFQBJcAR6D3XZa623wDtw/SjHWuGD3d8sAId\nbkLXJwqgw7YtWuSMEy75QzjEkhQCRLp3j29eJmybh5QFUfzBai0iisgKoIN5r5TagE6yV6aUmgXc\nDLyplDoZ2CPR9l/G27ZHD2jZUhu61lcY5+LiQMNfo9rPzNSrXNeuem9WVgYDB2ov6bIyfUa3ebMu\n7rl//70O6damjT4rO+EEfb1ly/jmFemIwUOjQCLPwQRLNBSROUqp86zsKgeBq6Pe/fjjei9kNHpu\nxBNRKV706xdore8+pDb7P2NGBVpbaI4Avv1WZyQxCJ57SYn2RzPMuyNOHY+nNGn0SJgtoogcKdYZ\nmPU+vuwqffqEdtvIyEhuXuSVK/U4oWwRRfTe0NAiOv6iobdv168mYWAolxM34o3klKy9p4d6Q+pb\nciRbi2hgLDOCPZNjCZ0Wzg2lUyd98Gz6LCnRdK9ezrlZ376ORvKYYzTDG3giYqNH6lvTd+mSmIhK\n0RDOuVEpuOeeyPe2bx+6ftq02s/n9NPhwgtrf7+HlEDqr2DbttXfKgZatIvFFtEN46wZjLPOcvpQ\nSlvYe3aKzQqpz2D1yVwQOnhNenrdw03n5zsiYmGhE5OxfXtH+dGjhw566rm6NBmkPoN16KAtH+pj\nHND7JhOerXNnbWVfUKAVLdnZek92+LBWw5t55eXBE09E7v/dd+GSS3Qyhg4dtK1k69ZaBD58WDPd\nhg3ax+zhh5P7rB7qDanPYDt31s8qZpQZW7c6IqLJ2JIInHVWdG2oUl703SaG1FdyGBg7vVjKrl3x\n9280diZIaUMhkQkfPDQ4Un8FW7cObrlFq8IPHtQBOQ8f1qVlS61er6rSVh/V1foM65ln4ot/Ac5+\nS6nIXtDJhpe7q0kh9RmsWzcnfHYsUKp250exBspJNjwRsUmhacoj8cbwAEc0Uyr+dK11wWWXOaIt\nOMoWD00Cqb+C1Qax+GcFo1MnePllreCorIThobxwkoC33tJq+WHDtHHxDTfUz7ge6gVNk8Fq46So\nFFx9tS71jWHDAoOfemgyaJoiokmVqlTqB6FJT9eH0B6aJJoeg23YoOMZmpiG8cboqG/4fPG7sXho\nNGh6DNa9O9x3ny6NBaEStXtoEmh6DBYMY2zbpk3gYXSqJAHPy9M+Z2Ze8cR78JDyaPoMZhQeZWUw\nbpzjQJkqYtmKFVp7aeb1wQcNOh0PiUXTZzA3RoyAq65q6FkEokcPrbk082pIKxIPCUfTZ7DrrnNW\nsY4dHZExFb2F09Mj5zPz0OjQNM/BDHbudFzwW7XSAW4MUtGo1ufzbBGbGOr0K1NK3aeU2qKU+soq\nI13X7lZKrVVKrVZKjaj7VGuBoiLten/66TrEWmNAbaxQPKQsEvFtPikiT7orlFJ9gUuBvkAxMF8p\n1dsK1NgwMGGvDVLxcDc93WOwJoZEyEmh7JIuAN4QkWoRKQHWAkMSMFbtIQKvv67Ds+3enZpGtT5f\n/PmoPaQ0EsFgNyulliqlXlJKtbLqwiV/aFgccYR20S8srP+xRTQD+Xzal83QFRUODam5N/RQa9Ql\n+cMEYBLwgIiIUup3wB+A66hN8gcLw4YNY5gJ5NmU0K+fjsMRDfXpKuMhbsSb/EElaluklOoGzBaR\n/kqpu9BB8X9vXfsHcJ+IfB7ivvrZmikF8+fr2BgNAaX0MUFpqfa8NnutcLSHRgGlFCIS1n2jrvnB\nOojIduvtRcAKi54F/FUp9RRaNOwFfFGXsRKC0lL47jstjpmw1H6/I5YVFelIT8mC8VZ2M1E42kOT\nQF2/0ceUUgMBPzp10Y0AIrJKKfUWsAqd1mhMg2oQDa68Mnobvz85Se/attVOnR6aFRImItZ6AvUp\nIj72mM6fLOIwkaE3b9ZpipI1F6U0g5nMLB6aBKKJiM1LZbV2LfzrX/D1105dfaZo9ewMmx2a1wrm\nxvbtgUkbSkq04W2yRMQWLbQm8YuG34p6SBy8FcyN+fMdETDYH8woPZK1oh08mPrhCzwkHM1LbTV3\nrpOF8q23oF077QF99tn1M74X87DZofmKiG5UV2slRzJFxLQ0OO44WLYs8X17aDB4IqIbjz3miIjr\n1gVqDJMtIoo4KYs8NBs0LxHxuefgnXc0/eST2kcMtDtLovywqqp03/v3a5vDnBznmqdFbHZoPiJi\nRkZsSfTqOpc+fWDNmtDXpk2DX/yibv17SCkk1VSqUSFcSiKltEX7jh36oLmuOHxYv6aA4YqHhkfz\nYbBQMExw5ZWxhXETgWuu0T5bSunXzEyHSbOynH3W//2fvrZnj3buLCiA3/9ev3poNmg+ImIomFSw\nbgDSeswAAAYrSURBVETSIv75z/DLX9Z+vG7dnGMCD00CnhYxEowCoroaFi3SdCQtoomlKBK+mATm\nwfXR+vbQJNG8RUQDEUdNX1CgNYGtW2vri8pK6N1b17uVF/v3w403wsaNWgzs0UOLh9u3hx4DdBRf\nD80KzZvBjOKjuhqOPFLvofbv13U7djgrz4oVNe/95S9h+nTn/bffOv09/njo8fbsScy8PTQaNG8R\n0Tg4Zmbq2PUVFY5I5/frVxPSWkTn8TIwAUJN+6oqh/7tb0OP552DNTs0bwYzK1QkJYv7mlHBQ+2C\n03gaxGaH5s1g5uA50gG0O8S22yrj4MH4x4u0P/PQJNG8GcyIiJFiYRjlRzCC1fuxwFNyNDs0bwYz\nyMjQKvSePZ08XV266NfRo3Wb4NxdbqZcuTLw3uJih27f3lHPFxXV1xN5SBF4DGZ8wTIyHH+tzMzQ\nEaDcmU/27nXoq66C9eudttnZoemXXkr8/D2kNOrMYEqpW5RS3yilliulHnXVN3zyh1jwwQeOFnDV\nKk1XVjruLIYpRGDCBOc+d2z7li31q9EkfvedQ2/a5NADB9bfc3lICdQ1LuIw4MfAsSJSrZRqa9Wn\nXvKH2iKUFUawRYZhMA8eglDXFeyXwKMiUg0gIjut+tRL/lBbXHYZjBoFvXo5io1evfR+aupU/X7z\n5vD3e2jWqCuDHQWcrpRapJT6SCl1olWfmskfaoPcXG2xsXYt3Hmnrlu7Vif3M4FMkxkN2EOjRlQG\nU0p9oJT62lWWW68/QYuYrUXkZGAcMMPcFqKrBhEP4wnUHxXmTMxoCLOzHe2iS52f0DFjREOM2VDj\nNqZnjboHE5Hh4a4ppW4C/ma1W6yU8imlioAtgNt7sRgIG7MsmdlVFixYkLj+xo+HXbu03aHbwHfP\nHh3vIxljxoiGGLOhxm3IZ3W/xoK6Gvu+A5wFfKyUOgrIEpFdSimT/OFJYkj+4GawlEZ2to7r4aHZ\nIngBmDhxYsT2dWWwV4CXlVLLgQrgSkjh5A8ePNQzUsKjuUEn4MFDHRHJo7nBGcyDh6YMz1TKg4ck\nwmMwDx6SiCbJYEqpB5RSy5RSS5RS/1BKdXBde9aykVxqZedM5LiPWbaXS5VSM5VSBa5rSbHNVEpd\nrJRaYR2RnBB0LWn2oEqpkZYN6rdKqTsT2XfQOFOUUjuUUl+76gqVUvOUUmuUUv9USrVK8JjFSqkP\nlVKrrHPfW2s9rog0uQK0cNG3AH+y6POA9y36JGBRgsc9G0iz6EeBRyy6H7AErbXtDqzD2v8mYMyj\ngd7Ah8AJrvq+SRwzzeqvG5AJLAX6JOm7PA0YCHztqvs9MM6i70Sb6yVyzA7AQPNbAtYAfWozbpNc\nwUTEHUU0H51DGuAnwDSrzedAK6VUexIEEZkvImasRegDdjNuUmwzRWSNiKylpvVMMu1BhwBrRWSj\niFQBb1jjJRwi8h8gOGvGBYBlCMpU4KcJHnO7iCy16APAavR3Gfe4TZLBAJRSv1NKbQIuB/6fVV2f\nNpLXAHMaYFyDZI4Z3PeWBPYdC44QkR2gmQFol6yBlFLd0SvoIqB9vOM22rBtSqkPAPfqo9D2jhNE\nZLaI3APcY+0PbgHuJwE2ktHGtdpMAKpEZLqrTa3HjWXMULfVZcxoU0pi3ykDpVQL4G3gNhE5UJsz\n20bLYBLBRjII04H30Ay2BejiuhbRRrI24yqlrkLv9c50Vddp3Die1Y06P2uUvmO2NU0Cdiil2ovI\nDkuB9X2iB1BKZaCZ6y8i8m5tx22SIqJSqpfr7QXANxY9C8ucSyl1MrDHLPkJGnck2qvgJyJS4bo0\nCxillMpSSvUgim1mXaZQT2MuBnoppboppbKAUdZ4yYKi5rONtuirgHeDb0gAXgZWicgzdRo3GZqf\nhi7of56v0dqtd4GOrmvPoTVgy3Bp3RI07lpgI/CVVSa5rt1tjbsaGJHAMX+K3g+VA9uAucke0+p7\nJFq7tha4K4nf5evo1bEC2ARcDRQC863xP0C7TCVyzFMBn/X7WWJ9lyOBNvGO65lKefCQRDRJEdGD\nh1SBx2AePCQRHoN58JBEeAzmwUMS4TGYBw9JhMdgHjwkER6DefCQRHgM5sFDEvH/AagmSS/hKsjg\nAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4026efc4e0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "w1 = random_walk()\n",
+ "w2 = random_walk()\n",
+ "plot_trace(trace_tour(w1))\n",
+ "w1bounds = bounds(trace_tour(w1))\n",
+ "plot_trace(trace_tour(w2), colour='r', xybounds=w1bounds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 562,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "w1 = 'FFRFFRRFFFRRFRFRFRLFRRLLFFLRFFLLRFLLRRLFFLLLFLRRFFLLLFLFLRFRRLRRFLLLFFRRFRFRRRRFRRRFLRLLRRRFRFFFLFLRLFFLRFLRLFLRRFFRRFFLFLRFLLFRFLLRRRLLFFRLLRRLFRRFLLLRLRLLLLRRRRRRFFLRRRFLLLLRFRRFRFLRLFRFLFLLLLLFFFLFRLLLRLRFRRFRRRRLRFFRRFRLRFFRLRFLLFLLLRLRFFRLRFFRFLRRLLRRRFFRLRLFLRFLLRLFFRFFLFFFFFLFFLFLLLFLFRFLLFRLFRFRFLFFRRLLFRLRFRFFLRFRFFLRFFLFFLFRLRFRLLRFRRRRFRFRRFRRRRFLFRFLFFLFLFFRFLFFFFLLRRFFLLRFRRFLFRFLLRLRLRRFFLFFLFFRLFLLRFFLRRFLFLRRRRFLFFLFRLFFFRRLFRLFLLFRRLFLFFRRLRLRFFFFFFRFRRRRFLLLFRFRFRFLRFFRFRRRRFFFFRLFRRLLFLLLRRLFLFRLRLLRRFFLRRFRLFLLFRLLRRLLRLFLRRFLLLRFLRFRFFFFRFLLLRRFRFFFFFRFRLRFFLFFLRLLRFRLRFLRLRLRFRFRLLLLFFLFRLRLLRFRRLRLFLLRFLLRRLRRFRLLFRFRLFLLRFRLFLRLLLLLFRFFRLLFFLLFRLFLRFRFLFFRRRFRFRRFRFFRLFFRRLRRRLLLFLFRFRRRFFFRFFFFRRFLRLLLRFFFFFRLRLRLLLLFRRRLFRLFRRLRFFFLFFLRFRFRFFFFFRLFLLRFRRRFFLFLFFFLRRFLLLLFLFFLRRLLLRLRRFFRLRLLRFRLRRLRRRFRFLLRLFFLLFFLLLLRRLRFLLRFRRRRRRRLRFFRFLFLRLLFFRFRRRLRLRLLFLRLFLFRLRLRRRLLLLFFFRFFRFFFFLLFLLLLRRLFFRLLFFFFRRRLFRFLRFLLFRLLRLRFLRRFFRRRRLRLFFFFLLFLFFFLRFFLRLRFRFLL\n",
+ "w2 = 'FFFLRFFLLFFRFRFLFLRLLLRRRLRRLFFFLFRFLRRFFRLFRRLFRRFRRLLFLLFFFFRFLLFRFFFFLFRFFRFLFRRLFRRLFRRRRLLFRRLFRRRLFFFFFLFLRLFFLRLLRFFLFLRLFLRLLRLLLRLLRFLFFRFRFLRRLLLRFLFFFLFRFFRLFRFLLLRLFFLFFLFRFRFFFFFRLLRLFLLRRFLFFFLRFLFFLLLFFRRRRFLLRFFRFLRRFLLFFFFRFLLRRLRFRLRLRLRLRRLRFLRLFLLRLFLFRLFRFRLLRFFLRLRRLFRLRFFRRFLLLRFFLLFRRLRRRFRLRLRRLFFRLFRRRRRFLFRFLLRLFFRFLFRLFRLRFRFRFLLRLRLFLRLFLLFRFRRFRFLLLFFFRLLLLFFFRFLLLFRLRFRLRRLLFLLLRFLLFRFFRRRRLRFRLFLRLRLLRFLRLRFRLRFRLLRFLFRRLRRLFRFLRLRRFLRRLRFLRRRRLFLFLFLFLRRFFFLLFRFRLLFLFRRLLLRRRLLLRFRFFRFRFFLLFFLFRFLRFRRLLLLLRFRLLRRRFRRRLRLRLFRRRRLRFRFRLLRLRFLLFLLFFLFLFRRLLLLLFFFRFRFRFRRLRFLFLLRRFLLLFFRLRRLLFRRLLRLFLLLFFLRFFLLFRLLRRLFFFRLRFLRLFRRLRRLRRFRFFRLLLFRLLFLLRFFRLFLLFFFRLFRFFLRRLFLFRLLRLRFFLRLFFRFRFRRFFRFFLFFFFFFFRFRFFRLLLRFLFRLRLLLLRFFRLLFFFLLLLRFRFFLFRFFRRFFFRFRRLFLLFFRRLFLFFRFRFRLFFRLRFFLLFFFLLRRFFFFFFLRFLLRRRFFLFFFRFLFFFRLFFRLRRFFRRRRLFFLRFLLFRFFRLFFRRLFRRRFFFRFLFRFFRFFFRFRRRRRLRLLLLFFFRLRLLLFRLRRLFRFRFLFLRRLRFRFFRFRLLRLRFLLLRLRFLFFRLRLLRFLLLFFRRFFFLLLFRRRLFFRL\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"w1 = '{}\".format(w1))\n",
+ "print(\"w2 = '{}\".format(w2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 725,
+ "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": 1192,
+ "metadata": {},
+ "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": 1145,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def guided_walk(loci, locus_limit=5, wander_limit=10, seek_step_limit=20):\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:\n",
+ " return ''\n",
+ " else:\n",
+ " return trail"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 896,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4027b86898>"
+ ]
+ },
+ "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": 898,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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D2cZDEJHngaUjXgq5D/4/Aw+Ty4JXvW2Zaes+LbJKDF9R1Z8483wFSKvqD0oR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\nybOEYIzJ+/+3mLJIMXCXRQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4027da7048>"
+ ]
+ },
+ "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": 1181,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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Ukpa9WWhgqFF+bEHHGFQ509PTzM7Out0MR2hgqJGOMahqqqVkbyYaGJRySDgcrno7c7PQ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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f402c0cc5c0>"
+ ]
+ },
+ "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(quinqux_tour()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 792,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4026e9cc18>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(('F' * 10 + 'R' + 'F' * 5 + 'L' + 'F' * 10 + 'L' + 'F' * 10 + 'L' + 'F' * 5 + 'R') * 4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1205,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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ZZ57hwIED7Nu3r84N3FRGpzCGuczMTFJSUjx/7Pv27aN79+6WU6lgVv6cyBEj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\nVBW0UCqlVBW0UCqlVBX+P0VFnoIKajtRAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f401d61d860>"
+ ]
+ },
+ "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": 882,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'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'"
+ ]
+ },
+ "execution_count": 882,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# lc = trace_tour(('F' * 50 + 'L') * 4)\n",
+ "# lc = trace_tour(('F' * 50 + 'L' + 'F' * 20 + 'R' + 'F' * 20 + 'L') * 4)\n",
+ "# lc = trace_tour(('F' * 60 + 'R' + 'F' * 30 + 'L' + 'F' * 60 + 'L' + 'F' * 60 + 'L' + 'F' * 30 + 'R') * 4)\n",
+ "lc = trace_tour(heart_tour)\n",
+ "rw = guided_walk(lc, wander_limit=8)\n",
+ "rw"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 883,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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0acLX11cEBQUpltDvv/++U8L6NGWioqLEjTfeaLb/l19+MbH6T0lJsVrHqFGj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\n0sqKCeMgjitXruTnn38mMDCQwsJCvLy8KC8v58s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fqeomAPv3eIIk16mfhm4y8Gvbtd4h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\nRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f402c1a68d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(rw))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 884,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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gmSDHZxrub6Xly5cbHIkQ/ktNdX5cgzEiwG63c9lll/H+++8D3w6fapvYBg0a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\nktiEEFFHEpsQIur8f/yi651PnLLMAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40271d9320>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rw_trimmed = trim_all_loops(rw)\n",
+ "plot_trace(trace_tour(rw_trimmed))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 885,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "760"
+ ]
+ },
+ "execution_count": 885,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(rw_trimmed)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1180,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "At Step(x=-2, y=0, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently left, by step L to Step(x=-1, y=0, dir=<Direction.RIGHT: 2>)\n",
+ "At Step(x=-1, y=0, dir=<Direction.RIGHT: 2>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently left, by step F to Step(x=0, y=0, dir=<Direction.RIGHT: 2>)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(204,\n",
+ " 80,\n",
+ " 'RLLRFRFLFFFLFRFFRFLFFLFFFFFLLFRFFRFFFLFRFLFFFFRFRFFFLFLFFFFFFLFFFFRFLRLLRFLRFFLF')"
+ ]
+ },
+ "execution_count": 1180,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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UIuI9BZWIeK/fBdW+ffuA8POTtNW2SDPnHKWlpQBUVVXF3E0zb7d076n0nb/5+fnMmDEj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\nRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4027412588>"
+ ]
+ },
+ "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": 1194,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "success_count = 0\n",
+ "while success_count <= 20:\n",
+ " lc = trace_tour(square_tour(a=10))\n",
+ " rw = guided_walk(lc, wander_limit=4, locus_limit=2)\n",
+ " if rw:\n",
+ " rw_trimmed = trim_all_loops(rw)\n",
+ " if len(rw_trimmed) > 10:\n",
+ " with open('small-squares.txt', 'a') as f:\n",
+ " f.write(rw_trimmed + '\\n')\n",
+ " success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1195,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "success_count = 0\n",
+ "while success_count <= 20:\n",
+ " lc = trace_tour(square_tour())\n",
+ " rw = guided_walk(lc)\n",
+ " if rw:\n",
+ " rw_trimmed = trim_all_loops(rw)\n",
+ " if len(rw_trimmed) > 10:\n",
+ " with open('large-squares.txt', 'a') as f:\n",
+ " f.write(rw_trimmed + '\\n')\n",
+ " success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1196,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "success_count = 0\n",
+ "while success_count <= 20:\n",
+ " lc = trace_tour(cross_tour())\n",
+ " rw = guided_walk(lc)\n",
+ " if rw:\n",
+ " rw_trimmed = trim_all_loops(rw)\n",
+ " if len(rw_trimmed) > 10:\n",
+ " with open('cross.txt', 'a') as f:\n",
+ " f.write(rw_trimmed + '\\n')\n",
+ " success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1197,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "success_count = 0\n",
+ "while success_count <= 20:\n",
+ " lc = trace_tour(quincunx_tour())\n",
+ " rw = guided_walk(lc)\n",
+ " if rw:\n",
+ " rw_trimmed = trim_all_loops(rw)\n",
+ " if len(rw_trimmed) > 10:\n",
+ " with open('quincunx.txt', 'a') as f:\n",
+ " f.write(rw_trimmed + '\\n')\n",
+ " success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1203,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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MmTOnQXUOGDDA6fvs2bOVzxERETzxxBMNlFbD3xFCNPglE6io+mqsnDEpMTGR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\nNDQATRloaGhUoCkDDQ0NAP4/oI3jTPrjMJgAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40276510b8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(rw_trimmed), filename='test.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1206,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "patterns = [square_tour, cross_tour, quincunx_tour, heart_tour_func]\n",
+ "tours_filename = 'tours.txt'\n",
+ "\n",
+ "try:\n",
+ " os.remove(tours_filename)\n",
+ "except OSError:\n",
+ " pass\n",
+ "\n",
+ "success_count = 0\n",
+ "while success_count < 100:\n",
+ " lc = trace_tour(random.choice(patterns)())\n",
+ " rw = guided_walk(lc)\n",
+ " if rw:\n",
+ " rw_trimmed = trim_all_loops(rw)\n",
+ " if len(rw_trimmed) > 10:\n",
+ " with open(tours_filename, 'a') as f:\n",
+ " f.write(rw_trimmed + '\\n')\n",
+ " success_count += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1190,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "At Step(x=2, y=3, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently above, by step F to Step(x=2, y=2, dir=<Direction.DOWN: 3>)\n",
+ "At Step(x=2, y=2, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently above, by step F to Step(x=2, y=1, dir=<Direction.DOWN: 3>)\n",
+ "At Step(x=2, y=1, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently right, by step R to Step(x=1, y=1, dir=<Direction.LEFT: 4>)\n",
+ "At Step(x=1, y=1, dir=<Direction.LEFT: 4>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently above, by step L to Step(x=1, y=0, dir=<Direction.DOWN: 3>)\n",
+ "At Step(x=1, y=0, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently right, by step R to Step(x=0, y=0, dir=<Direction.LEFT: 4>)\n",
+ "Found mistakes: 2052\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(5348,\n",
+ " 1210,\n",
+ " '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')"
+ ]
+ },
+ "execution_count": 1190,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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8FxpNdyTYVgFx29AopeKA8SLyGoCINIrIUWAqtlUrsf+d5rGWGo3GoE+fPnz2\n2WeYTCZjCkJ8fHxAR9Z74qPpD5QopV7D1prZDNwB9BGRIgARKVRKtdmZPH7ZlJycHH744QcaGhqI\nioqisbGRKVOm0KdPHw9U1WhCh88++4xly5YRFRVFTU0NSinmzp1LSUkJqamp/lbPJW4H7CmlsoCN\nwBkislkptRCoAP4oIr2crjsiIoku7heA8847j/Hjx5OdnU1qamqziEd7gBAnnXQSO3bscEtPjaY7\noJQiPz+flJQUr9WZk5NDTk6OcTxv3ryuXwVBKdUH+FJE+tuPz8LmnxkAZItIkVIqGVhv9+Ecf78h\nuGfPns0SITvrNGzYMGPlvOrq6mZ1OJf16tWL0tJS1q5dq9N6arodSiny8vJ82qLxJDLY7a6T3ZAc\nVEoNFpHvgfOBnfbtWuAJYCawsrU61q5dy3PPPUfv3r2prKykqamJ888/v9k1OTk5zJo1C4vFQmlp\nKbGxsSilOHr0KImJiVRXV9PQ0EBCQgKvv/46DzzwgDY0Gk2A4dFcJ6XUKcArgAXYB/wOMANvAxnA\nAeAKESl3ca/bOYNbIyMjg6ysLN5//32v1qvRBDqB3qLxaHhbRP4rImNF5FQRuVREjopIqYhcICJD\nRORCV0amo4wfPx6z2YxSiqFDh6KUwmKxGKsf9OjRg759+xqJfw4dOqTzB2u6LVar1d8qtIrfI4Pb\n4vPPP2fIkCE0NjaSkpJCfX094eHh9OnTh5qaGnr16kVsbCwNDQ1kZmaSnp7O0qVL/a22RuMXAjmf\nU8Bq5uhW7dq1K+jmdWg0/iCQ06sElKERES6++GIOHjxojEJpI6PRdAznFQwCjYAyNNu3b2f16tXE\nxcVRUVHBhRdeiIhoY6PRdADddeokjtUQNBpNx9Fdpw7i8JpPmzYNk8lEfHw8paWlWCwWoqOjXcbR\n1NXV8b//+79YLBY/a6/R+Bc96tRBhg4dSlpaGitX2mL8EhMTOXLkCECbkcH9+/fnscce63J9NZpA\nwmQKqGXamhFQhiYmJoZDhw516h6lVAsDpNF0RwK56xRQS+K6WQ+jRo1i+vTp1NfX09jYSHR0NHV1\nddx000161remW6CU4o477iAhIYHw8HBqamoICwsjLCyM2tparrjiCkaMGOGxjC6fVOkp3jI006ZN\nY+XKlZjNZmN4LyIigrq6OsaMGcPXX3/tsQyNJtBJSkqipKQEoNl3wRlPv29Bu9yKN3j//fcRERob\nG41FsmoPgTYUAAAdUUlEQVRra0lOTu50N8yBiJCZmWkkFXJOMHR82c6dO738RJpQ5qabbkIpRVxc\nnPEunXDCCR7XW1xcbLz/zt8FEeHll1/2guaeEVA+Gm9SVlbm9r1KKQ4cOMANN9yAyWQiKiqKiooK\nIiIiMJvNVFVVERsby/PPP8/zzz/P3//+d5f17N27l127dvGLX/yCXr16ubxG073429/+RmZmJpdf\nfjlVVVWICH/729/48MMPaWxsJDU1ldNOO82rMo9PLgdw9OhRPv/8c5RSWK1WJkyY4Nu0uu0toemr\nzSbad4wePVpGjx7t1r1Wq1UAsVqtbV4XHR0ts2fPbvU8IIAMGDDALT00oYdSSn7zm98Yx1arVcLC\nwox3BZC9e/d6VeaSJUvk+O/bqFGjmsnMyMhotx57HW5934O+69Qaubm5/PTTT27d64hENplM/PWv\nf231uurqap599lnS09ONZrBjNrmjjrFjx7J3716jqZyQkGCcnzFjhlv6aYKPQ4cO0bdv3xaR7kop\nGhoanH+AvR6w2tDQ0KJs27ZtDBo0CBFh9OjRPh+5DdmuU1NTEyJCQUEBYHOWdSZEe9asWbz44ov8\n8Y9/5NZbb3V5zcqVK3nkkUdIT08nMzMTEaFPnz6kpaVhsVg46aSTePDBB7n++utRSlFQUEBKSgq1\ntbXs27eP5cuXs2zZMq88ryawWb16NQcPHuSss87i+eefb/NaXyUZz8/Pb3bsCPArLi424tV8hrtN\nIU83fNx1mj59erOm4cknn9zpOs4++2wZOHCgD7SzNWfNZrNP6tYEHq+++mqL7osrANmyZYtXZW/d\nurXZd8Gx3XjjjSLS8fec7tJ12r9/PyeccAKjRo3CYrHQt29fTjrpJJRSDBs2jAEDBhAREcHo0aPZ\nsGEDcXFxZGVlYTab+eabb9qsu7a2lokTJ5KQkEDv3r2ZMmUKJSUlbo9ctUdTU1OLIciVK1fSu3dv\nxowZQ3R0NO+++65PZGu6ns6sxmo2m70qe9SoUS6//C+99BKAT99zB0FlaO655x6Ki4s7fV9MTEy7\nXZQ9e/bw8ccfG8cffPABtbW1Pluu19VIwLRp0ygvtyUkrKur0z6cEKIz3fauXiLal++5g6Dy0Qwa\nNAiwpZMAOHjwIAD/+te/uOSSSzyq29G6KCsrszX1TCb27dvH+PHjPaq3NSIjI1uUDRkyhNTUVNat\nW8c555wTdMuealrHbDa32VKZPXs2CxcuBCA5OdlneuTn55OWltai3FfvuYOgMjR//vOf+dOf/oTV\najW896mpqaxZs6ZVQ7NgwQJWrlxJjx49CA8PJz8/n9TUVGprazl27BipqamUlpY2W+5FKcWxY8eo\nqqqid+/ePnkWh8PvrLPOoqGhgZSUFPbs2WO0dH766SfDkGoCmx07dnDrrbcyfPhwFi9e7PKaurq6\nNhNTvfPOO4Dthy4+Pt4negJGF6mgoIDGxkYjJ7ev3nMDd507nm54yRkMyC233NLmeUAsFovExcUJ\nIPHx8UZ5SkqKsX/eeed5RaeOcPDgQcnIyDBkp6enCyAffvihiNjigBITE7tMH437JCYmGp9jQUGB\ny2tefvnlNp3BXfV5b9u2rUNOaVfggTM4qFo0reEqovHWW2/lxRdfBKC0tJSEhISuVqtN0tPTOXDg\ngMtzjjiLjIyMrlRJ00lqamqIjY3FarVy7rnnsn79+lZzwrSXwiE8PNy3kbl2/JWzxmNDo5QyYVt3\n+5CITFFKnQi8CSQAW4FrRKSl59OLfPTRR5hMJiOk+7TTTmPJkiVYLBY2btwYcEbGFU888QS5ubnG\ni/DBBx8wYcIEP2ulaYvS0lKsViurV6/m7LPPJi4ujrvvvpv4+HgsFguVlZXExMTQ1NTEf//73zbr\nys3NpbCwkNmzZxvTXXr27MmCBQs80nHr1q0sXryYuLg46uvr+fWvf+1RfW7jblPIsQGzgP8FVtmP\n38K2aBzAYuDGVu5zq/l2PFdffbUAEh0d3SxGIDk5WZKTk70iw9d89913zXTv27dvu9MfNP4nLy+v\nWTdk8ODBLuNVHNu5557bal1///vfBRD7UtHGtmLFCo90dNQTHh4ugFx66aV+6Tp5amTSgU+AbCdD\nUwyY7PvjgI9audeth22P/Px8I0DPnSA9f7Blyxa3P3yN/3C8a77CYSTOP/98j+p49NFHm9XnD0Pj\naddpIXA30BNAKZUIlImIoyN4CPDdGp0usP0/aDdALxBYsWIFZWVlbs/J0vgXx7vmK7799ltuvfVW\n/v3vf3tUjyNGJi8vj5dfftnrs8M7gtuGRik1GSgSke1KqWxHsX1zptVPY+7cucZ+dnY22dnZrV3a\naQYMGOC1unzB6tWrmT59unHsy9gJjW/w9TJAI0aMYOLEiWzYsMGjeiIiIgBITU1lzpw5Hb4vJyeH\nnJwcj2Q78KRFcyYwRSk1CYgC4oDngJ5KKZO9VZMO5LdWgbOh8RaOCMy9e/cydOhQr9fvLYqKioz9\nr776irFjx/pRG407+LpFA65nXneETZs2GUF4MTExbtVx/I//vHnz3KoHPJiCICL3i0hfEekPXAWs\nE5HfAOuBK+yXzQRWuq2dGyQlJfHkk08ydepU3nvvva4U3SmuueYa7rzzTgDuvvtutm3bxn//+182\nbdrEtm3b2L59u066HsDU19d3Sffc1VSVjvDKK6/Q0NDANddcw5/+9Ccva+UG7jp3nDfgHH52BvcD\nNgHfYxuBsrRyj1sOqVBj7NixrY5SnH766f5WT9MKqampHjlWO8pjjz0m4eHhnb5v1qxZYs/L7TXw\n9+xtEfk/EZli3/9JRE4XkcEicqWIuNf26yZ89dVXLT6Us846C7A1f73Bddddh1LKJ13V7kp+fj7Z\n2dk+7z7V1dVRX1/PvHnzUEoxZMgQI3Ha0KFDUUoRFRXFwIEDjakEqampLFy4sEu6dh0lJCKDQ43P\nP/+cIUOGGJHNnvLaa68RERHBvHnztLHxIjU1NT6X4QjgnDt3LrGxsaSmpiIiWK1WUlNTaWhoIDIy\nkqSkJOrr60lKSiI8PBwR4ZFHHvG5fh1FG5oAw/ErtGvXLq+OakyaNIlPPvnEa/V1d9LT00lJSfG5\nnOjoaGN6wo033sjTTz/tc5m+QBuaAMNhXPr160fPnj3p0aMH+/btIy0tDavVSl5eHvfeey+33357\np+qtrKyksrLSFyp3Sxw5gH1NTU0N9fX11NfXB/XnF1SJr7oDIsKFF17I/v37jVQR+fn5HDhwwJgP\nc8cdd3S6XovF4gNtuzdd7QPxddyOL9GGJsBQSrF27VpEhGPHjpGbm4uIUFhYSElJCUuWLDGuc94c\nSZNaIzw8PKhf1K5m0aJFLf7HzhtAVlaWz/VwbjWNGjXK5/J8he46BRnXXXcdZ555JlVVVVitVkwm\nE1lZWcyfP59vv/2W0tJSHn/8cYYOHUpZWZnRxaqurg6oUYhAxxH2P3nyZMrKyvjNb37D6aefbvzP\nw8LCGD58uM/1cHzejY2NXSLPZ7g7Lu7pho6j8RoPPfSQANK7d28BZOjQoSIiMn78eAHEZDLJDTfc\noCdudoKvvvpKAOnZs2eXxMsEA/g7jkbjXx555BFEhOLiYgYMGGCErcfHx5OSkkJTU5PLHMWa1hk7\ndiwiQnl5OY8++qi/1Ql6dNcpxNi7dy9gS6S1efNmYwG9qqoqf6oV1NTV1flbhaBHt2hCDEeQ1n33\n3UdBQQE33HADALGxsdoZ7CadWSpF4xr9HwwxHnroIR566KFmZWPHjmXz5s1+0kij0S2absHmzZuZ\nMGECO3bs8LcqQUdhYSHffvutv9UIenSLpptw2223cdJJJ/lbjaDDMc1g5MiRftYkuNEtmgBlzZo1\nKKXIyMhAKcXDDz/c5vVNTU0MGjTICCjr27dvs+Cy9pb70DTnP//5D7GxsQC88cYbQZEaNpBR4qcg\nLnuuDL/IDkTKy8upqakxfkEHDhzI3r17mTp1Khs2bKCsrMxYItdhPBz/PxHh8OHDjB49mvHjx9PQ\n0EBycjJFRUUopRgxYgSLFi3qknWDQoWpU6eyatUqJk6cyOrVq7WhxvbeiYh7IwruBuB4uqEDoAwO\nHDhgBIVdccUVIiJy7rnnypAhQ0Tk56U4OrJpvIPz//yjjz7ytzoBAf5absWTTX8pfmb79u0CSERE\nhJhMJpk+fbr069dPGw4/ccstt4jZbJYXX3xRADGbzcaSJd0ZTwyN7joFAJs2bWLcuHG88847XHPN\nNdTU1DB8+HD27dvXJcmVNM1x+LZEhLvvvptnnnkGq9VKd39fPek66Y5nAOBI4XDZZZcZCcm/++67\n4J5EF8CMGDGi3ZnZl19+OfHx8Tz55JM8+uijxpIlGvfQw9t+wmq1cskll1BbW8vBgwebnTt27BgV\nFRVurfW0Y8cObr31VuLj4ykoKKBHjx4kJiby5ptv6shgOzt37qRnz56MHTuW2tpaHn/8cfr3749S\nisbGRmJjY5k5cybl5eWAbfqGnobgIe72uTzd6Ob+hzfeeEMAiYmJEUAmTpzolXoTExMFkJSUFAHE\nYrEIIGvXrvVK/aHA8bPdBw0a1OKa6dOnGz6yefPmaX+ZeOaj0S0aP1FfXw/g9fSMmZmZZGZmsmXL\nFqNMKRWyv8g1NTXExsYaSbw7Q0lJCQAnn3xyi3NJSUnAz6EEer6TZ3iyJG468AaQDDQBL4vIC0qp\nBGzrOWUCucB0ETnqBV1Dis6sQNjQ0MDDDz9MdXU1VVVVXHnllVx44YXG+dzcXJ588kmio6PZvn27\ny7Sd7nwRg4HS0lKsViurV6+mtraWyMhIrFarkaDKarViNpsRsa0cEBYWhojQ0NBgZB10tRTzokWL\nuPTSS6mpqaGxsVGvJOohnpjpRmC22NbejgW2KKXWAr8DPhWRJ5VS/wPcB9zrBV1Dis78Ql5zzTW8\n9dZbxvGSJUuajYD069cPgLi4OKxWK0888USLOsxmswfaBi6OFsekSZO8Xu95553n1Tq7M54siVso\nItvt+5XALmxrbU8FXrdf9jowzVMlQ5Ho6Gjg56HUiIgIY98R+u4gPDyc6OhoRIT58+e3qCspKYnJ\nkydz7NgxRMTlCgnOhimU0A7u4MArHU+l1InAqcBGoI+IFIHNGCmlkrwhI9S4+uqraWxsJDc3l8bG\nRmJiYqitrcVqtTZb+Gvnzp1s27bNGPZ2rMX88ssv09TURHh4OMXFxcb0hNZYvXo1Q4cOZeDAgb57\nKD8QqgY01PA4YM/ebcoB5ovISqVUqYj0cjp/REQSXdwnc+bMMY6zs7Nd9pW7GyJi+Bac4zqmTJnC\nypUr+eyzzzj77LNb3Ld06VJmzpzpss5+/fqRm5tLREQEtbW1PtW/qykoKDBWb9R4l5ycHHJycozj\nefPmuR2w55GhUUqFAR8C/09EnreX7QKyRaRIKZUMrBeRYS7uFf1ytMRhaJz59ttvGTFihEf13nLL\nLSxevDgkvpDFxcWMHDmSoqIioywUnivQ8SQy2NOu06vAdw4jY2cVcC3wBDATWOmhjG6FUornn3+e\ndevWYbFYyMzM9NjIwM9drlBg48aNFBUVMW3aNESEs846y98qadrB7RaNUupMYAPwLT/PHr4f+Ap4\nG8gADgBXiEi5i/t1i6YLueuuu/jb3/5GRUWFv1XxmNWrV3PRRRfpVkwX45cWjYj8B2htzPQCd+vV\neJ8ZM2awfPlyf6vhNToTg6QJDPSkym7A8uXLSUpK4vXXX2//4iBAR+kGH/oT6yYsXbrU60Ft/kJn\nuws+9CfWTQglf4ZjOkVWVhZ9+/blnHPOYcSIEWzatMnPmmlaQ7dougmh5NeYMGECGRkZ7N+/nyNH\njhAREcGPP/7IxIkTKSsr87d6GhfoFk03wdVEy2AlPDycAwcOUFJSgojwww8/8Mtf/pLy8nKUUkYU\ntSZw0Iamm9DU1ORvFbxGU1MTDz74IJdeeinFxcUAvP/++2zfvh1At2oCEN110gQdn376KQsWLABs\nXcIPPviAyMhITjnlFEA7iwMR/YmECLW1tc3y3oaFhTWbK5WQkOBnDb2Ho3UWHh7uMqFXKDm+QwXd\nogkRSktLAXjxxRexWCyYTCbq6uqMaQzjx4/3s4bewzGdor6+3siSpwlstKEJERwtl5tvvtnPmvge\nR9coKSmJ1NTUFud1jprAQ3edQoTu1F1wGJLi4mKOHTvW4nxqaipKKWJiYozuo8lkarGsinNZXFyc\nsZ+YmBgSc8ICCd2iCREchmbNmjWcffbZLbL0hRLOI2jHL7C3ZMkSNm7cSH5+PhaLherqasLCwggL\nC6O6upqoqCisViv19fXExsZSW1trLLFSU1ODiPDxxx+zZ88exowZ09WPFrJoQxMi9OrVC5PJxOTJ\nkzn33HNZt26dv1XyGY6uU1paGmlpac3OXXfddXz88cesXr3aIxmOPMwa76ANTYgQFRVFU1MTiYmJ\nRjxJqOKYgpCXl0deXh5nnHEGX375pXH+yJEjWCwWY0kbjf/RhiYEaGxsNALXGhsbu8wZWlZWZuQ5\n7tmzZ5d11xwtmg0bNjBr1iw2btxIYWEhDQ0NhIWFceTIkZCachEKaEMTAmRlZfHNN98Yx9OnT/e5\nzK1bt5KVldWsrKsc0g5DM378eHJycoiLiyMlJaXZNa7yKmv8hzY0IcA333zDuHHjmnUffE1ubi5g\nMy6FhYUtvui+xDnBemxsbLcacQtW9PB2iNDVkyYjIyON/a7upjjL1gQH2tCEACkpKX6dYtAVGe9W\nrVplxLlMnjxZz2cKMvSnFQIUFBRQUFDQpTIdsSy/+tWvukT25s2bAdtI06FDh6isrPS5TI330D6a\nEMBisdCjR48ulXnBBRcQHx/PRx991CXzjRytJldTDjSBj27RhAARERGEh4d3qcyoqCjKyspIS0sj\nMbHFQqReJzw8HLO5tUU3NIGOzwyNUmqiUmq3Uup7pdT/+EqOBiorK7u86+QgLy/Pa7Lfeustbrzx\nRnbv3t3iXE1NTUgl7+pu+KTrpJQyAX8Bzgfyga+VUitFpOUbpPGY+Pj4Lh1ePp6oqCiP6xARrrrq\nKgA+/vhjY/jcgV5iJbjxVYvmNOAHEdkvIg3Am8BUH8nqtpSUlKCUory83Pi1nz9/PkopzGazy6RQ\n3iYpKYm4uDiANmdKt7ZFREQY9wCMHDmSI0eOtJCjDU1w46tPLw046HR8CJvx0XiJvXv38vTTTwPw\n1FNPMWvWLAAefvhhwDYf6MiRIz53nhYXF3PkyBE+/PBDNm/ejIgQFRVFTU0NSinCw8OpqakxfEiN\njY1ERETQ0NBAY2MjMTExxjSGESNGMGvWLJcjSs5Beprgw1eGxtVkmxbhm3PnzjX2s7Ozyc7O9pE6\nocfw4cOpr6/nxBNP5K677jLKBwwYANgMUVcQHh5O7969mTx5MpMnT/a4vr/85S8uu2IREREe163p\nHDk5OeTk5HinMhHx+gaMAz5yOr4X+J/jrhGN+wBy8803tygfPHiwDB48WADJz8/3mrwpU6YIIDNm\nzGhWHhMTIxMmTPCanKFDhwog0dHRAsisWbMM2fqd8S/2/79bNsFXLZqvgYFKqUygALgK+LWPZHVb\nCgoK2Lp1K2FhYQwfPpywsLBmLRlHOgVvsGrVKk444QSWLVvG7NmzsVqtmEwmqqqqvBpHk5OTw6xZ\ns7BYLKxdu5aFCxcCMGLEiGYtYE2Q4a6Fam8DJgJ7gB+Ae12c95nl7Q44fvkd2x//+EcRERk5cqSM\nHDnS6y0aQG6//fZmMh3bs88+6zU5zrzwwguGjB07dvhEhqbj4EGLRomfZr4qpcRfskMNR/6ZIUOG\nsGfPHjIyMjh48CAFBQUkJyd7TcbixYu56aabvFKfJvhQSiEibiU70pHBIcCHH37IgAEDSE1NpX//\n/rz55pvAz8uSuIvjh8DxV0fmatxFt2hCFKUU+fn5bgXyzZw5kzfeeKNF+aZNmzjtNB2l0F3RLRqN\nwRdffGEMcbub0nPTpk2YTCasVisigtVqxWq1aiOjcRsdbhliXHXVVRw8eJDx48fTu3dvt+pobGzE\narUahkovyKbxFG1oQoz4+Hji4+PZsGGD23VkZGTokH+NV9E+mhAjLCwMq9XKzJkzKS0t5Z577uHM\nM89s9Xqr1cqCBQvYsmULJpOJ+Ph43nrrLaqrq3UuXk0zPPHRaEMTYixevJhbbrmF+Ph4ysvLiYmJ\naTMb3bfffsvJJ59sHCcmJnLkyBGefvpp7rzzzq5QWRMkdHtnsNfmY4SA7JtvvhkRoaysjClTplBV\nVYVSisLCQpd1OIbAHYFVJSUliEi7RibQnlvLDmzZIdERz8nJ8duEzECW/d5777Fs2TJ++9vfcvvt\ntzNy5Eig+QzqAwcO+ES2L9Gy/SPbE0LC0GhcYzKZuOaaa3jqqad4++23efvtt11el5GR0cWaabob\n2tB0A5xXsdRo/IFfncF+EazRaNwm6Ea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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f402c03b908>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lc = trace_tour(cross_tour())\n",
+ "rw = guided_walk(lc)\n",
+ "print('Found mistakes:', len(mistake_positions(trace_tour(rw))))\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": 1187,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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q0aIFdzqriJKSEkpPT6f09HTKzs4mvV5vlUz1hZKSEho2bBj5+vpyJae16PV6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\nGAwR8//9Y6WTlqlWbwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4026f76c18>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(rw))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1037,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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Tn/wk3d3dpNNpFi1axMqVK4PmOV91dTWtra20tbUFzfHwww+zceNGKisrqampYfXq1Uyb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\nf+gzCYz9KNTkAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f402c0e22e8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_trace(trace_tour(trim_all_loops(rw)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1013,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Mistake(i=20, step=Step(x=8, y=0, dir=<Direction.UP: 1>)), Mistake(i=38, step=Step(x=10, y=2, dir=<Direction.UP: 1>)), Mistake(i=42, step=Step(x=8, y=4, dir=<Direction.LEFT: 4>))]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'FFFFFLLRRFRFLFLLRFFRRFLFFFFRRFFRFFLFFRFFFFRRRRRRFLRRFLLFFFLRFRRFFFFFRRFRRFRFFRFLLFLLLFLLFFLFLFFFFFLFFFFRRFFFFRRFFFFRRFFFFFRFRFFFFLFLRFRRRLLRFFRRFFRFRFLRLFFFFRRRFLFFRFRLFRRFRFLFFLLFLRFFFRFRFLRFLLFFFFRLLLLFLFFFLFLFRRFFLR'"
+ ]
+ },
+ "execution_count": 1013,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w_tour = rw\n",
+ "w_trace = trace_tour(w_tour)\n",
+ "mks = mistake_positions(w_trace)\n",
+ "print(mks[:3])\n",
+ "trim_loop(w_tour)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1014,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "74\n",
+ "73\n",
+ "72\n",
+ "70\n",
+ "66\n",
+ "54\n",
+ "47\n",
+ "45\n",
+ "41\n",
+ "37\n",
+ "25\n",
+ "22\n",
+ "15\n",
+ "14\n",
+ "13\n",
+ "RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLRRLRRRRFFFFFFRRRLLFFFFFFLRRRLLLRLLLLRFRLRFRRRFRLRLLRFRLFLLRFRFLRRRRLRFLLRLRRLFFFRLRRFFRFLRFLLFLFRLRLRLRRRLFFRLRRFFRRFFLLRLLFLLLFRFRFLRLFFLFRRRRLRRLRRLRFRRLRRRFRRRRLLRFLLRRFFRLRFFLFLFRRLLLRLFFFRFRLRLLFRRFLLRLFFFLLRLLRRLRFRFFFFFRRFFLFRFLRFRFLFLFRLFLFLRFFLFRLRRRLRRRRFFFRFLLRLFRFRRRLRFFFFFLLFRRLFRRFRFRFLRLLFRFFFLLRRFRRRRLFFFLFLLLFFLRRLRFLFLFFRLLFFRRFFLRLLRFLRLLFLFRLLLRFFFFFFFFLFLRLLRLRLRFLLFFFLFLFFLFRRRRRFFRLLFFFRLFFLFRLFFRFFFFRLRRRLLFLRRFLRFFLRRFRRFLRFLRRLFRRLFFFFRLFLFFLRFFFFFRLFLFLRFFFLLLFLLLFFLRRLFLFRRFRRFFRRRFFRFLFLLFLFRFRFLLLRRRLFRRFFRLRFLFLFLRRLFFFLFRLRFRFFRFLRRFFFFFLFRLRRFRLLRLLFFLLLFFLFFFRLFRFRRRRLFLFLLLLRLFRFRLRLLFLFLLLRRRLLLFRRFRLRFFRRRLLLFRLRLLFFLLRFFFFRLLRLRLLLRLFFFRRLRFRFFRRRFFFRFFFFLRFLFRFLRFRRLLLRLLFRRLFFLRRFFFRLLFRLFLRRFLRLLRRFFLLFLLLFFFFRRFFRLFFLLFLLFRFRLRFLRFFRFRLRLLLFFFLFLRFRFRFLRRLLLLLRFRRLFLLLLRRFFFLLRFLFFRLLLLFLFRLRFFFFFLRLLFFLLLFFLLRRFLRRFLRLRRLLLRRRRRLLLFLRRLFFFRFLRRLF\n",
+ "FFFFFLLRRFRFLFLLRFFFFFRFLFFFFLFFLFRFRFLRLFFFLLFRLRFFFRFRFLRFLLFFFFRLLLLFLFFFLFLFRRFFLR\n",
+ "Step(x=0, y=0, dir=<Direction.DOWN: 3>)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40276002e8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "w_tour = rw\n",
+ "w_trace = trace_tour(w_tour)\n",
+ "mks = mistake_positions(w_trace)\n",
+ "while len(mks) > 3:\n",
+ " print(len(mks))\n",
+ " w_tour = trim_loop(w_tour)\n",
+ " w_trace = trace_tour(w_tour)\n",
+ " mks = mistake_positions(w_trace)\n",
+ "print('{}\\n{}\\n{}'.format(w_tour_start, w_tour, trace_tour(w_tour)[-1]))\n",
+ "plot_trace(trace_tour(w_tour))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1015,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Mistake(i=70, step=Step(x=-2, y=6, dir=<Direction.UP: 1>)),\n",
+ " Mistake(i=71, step=Step(x=-3, y=6, dir=<Direction.LEFT: 4>))]"
+ ]
+ },
+ "execution_count": 1015,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1016,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'FFFFFLLRRFRFLFLLRFFFFFRFLFFFFLFFLFRFRFLRLFFFLLFRLRFFFRFRFLRFLLFFFFRFLFFFLFLFRRFFLR'"
+ ]
+ },
+ "execution_count": 1016,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trim_loop(w_tour)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1212,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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ROL4KX77jh4E+ffoEO4VGC+fcWzIpfCEcSApfCAeSwhfCgaTwhXAgKXwhHEgK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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4027ba30b8>"
+ ]
+ },
+ "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",
+ "plot_trace(trace_tour(rw))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1247,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(254,\n",
+ " 20,\n",
+ " 'RLLFLRRFLLFFFFLFLFRL',\n",
+ " [Step(x=-1, y=0, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=0, y=0, dir=<Direction.RIGHT: 2>)])"
+ ]
+ },
+ "execution_count": 1247,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f402c1a5cf8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rw_trimmed = trim_all_loops(rw)\n",
+ "plot_trace(trace_tour(rw_trimmed))\n",
+ "len(rw), len(rw_trimmed), rw_trimmed, trace_tour(rw_trimmed)[-2:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1281,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1, Step(x=0, y=0, dir=<Direction.RIGHT: 2>))"
+ ]
+ },
+ "execution_count": 1281,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40275e2908>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rw_trimmed = rw\n",
+ "mks = len(mistake_positions(trace_tour(rw_trimmed)))\n",
+ "while mks >= 4:\n",
+ " rw_trimmed = trim_loop(rw_trimmed, random_mistake=True)\n",
+ " mks = len(mistake_positions(trace_tour(rw_trimmed)))\n",
+ " \n",
+ "# rw_trimmed = trim_all_loops(rw)\n",
+ "plot_trace(trace_tour(rw_trimmed))\n",
+ "mks, trace_tour(rw_trimmed)[-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1282,
+ "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": 1299,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1, Step(x=0, y=0, dir=<Direction.RIGHT: 2>))"
+ ]
+ },
+ "execution_count": 1299,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f402c0c5f98>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rw_trimmed = trim_some_mistakes(rw, 4)\n",
+ "\n",
+ "plot_trace(trace_tour(rw_trimmed))\n",
+ "len(mistake_positions(trace_tour(rw_trimmed))), trace_tour(rw_trimmed)[-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1300,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "patterns = [square_tour, cross_tour, quincunx_tour, heart_tour_func]\n",
+ "tours_filename = 'tours-with-mistakes.txt'\n",
+ "\n",
+ "try:\n",
+ " os.remove(tours_filename)\n",
+ "except OSError:\n",
+ " pass\n",
+ "\n",
+ "success_count = 0\n",
+ "while success_count < 100:\n",
+ " lc = trace_tour(random.choice(patterns)())\n",
+ " rw = guided_walk(lc)\n",
+ " if rw:\n",
+ " rw_trimmed = trim_some_mistakes(rw, random.randint(0, 15) + random.randint(1, 3))\n",
+ " if len(rw_trimmed) > 10:\n",
+ " with open(tours_filename, 'a') as f:\n",
+ " f.write(rw_trimmed + '\\n')\n",
+ " success_count += 1"
]
},
{
projects / ou-summer-of-code-2017.git / commitdiff
