From: Neil Smith Date: Sat, 10 Jun 2017 19:06:57 +0000 (+0100) Subject: Done some work on tour shapes X-Git-Url: https://git.njae.me.uk/?a=commitdiff_plain;h=fda89d02980df6c79d05dfdc8db9bea3d7856254;p=ou-summer-of-code-2017.git Done some work on tour shapes --- diff --git a/06-tour-shapes/cross.txt b/06-tour-shapes/cross.txt new file mode 100644 index 0000000..cf0cb09 --- /dev/null +++ b/06-tour-shapes/cross.txt @@ -0,0 +1,21 @@ 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diff --git a/06-tour-shapes/small-squares.txt b/06-tour-shapes/small-squares.txt new file mode 100644 index 0000000..4118de8 --- /dev/null +++ b/06-tour-shapes/small-squares.txt @@ -0,0 +1,21 @@ +FFLFLFRFLFLFFFLF +FFRLFFFRFLLFFFFFFRLRFLFFFFLLFRFFFFRFLFFFFLRRLFLFLRRLFFLFFFLRFRFRFFFFFLLFRLRL +LFFRFRFFFLFFLRLFRLRFFFFFLLFFRRFFLFFFFFFFFLFLFFFFFRFFFFFFFFRFLFFFFFLFFFLFFRFFFFFFLL +FFFRRLFLFLFRFLRLRFLFRFFFLFFLFFFFFRFLFFRLFFFFFLFFFFFFRRLLFFFLRFLFLFFR +RFFLLFFRFFLRFFFFFFFFLFLLRFFFFRFFFFFRFLRLLFFFFLFRFRFLLFFRFFFFFLFLFRFFFLRL +LRFFRLFFLLRRFFLFFFFRLFFFRFLFLFRLFLLRFRFFRFFLFFLFRLFFLRFRFRLLRLFFFFFFLF +FFLFRFRFLFFLLRRFRLRFLFFFLFFLLRRFFFFLRFLFRRFFLLFFFFFFFFFRLFLFFFFFFRLFFFLF +FRRFFFFRRLFFFLRRFFFRFFRLLR +RFFLFLFFRFRLFFLFFFFRFFLFLFFRFRFLFLFFRFLFFFLFFFRFFFFFLFLFRFFFFFLF +FLFFRFFRFFLFRLFFFFLFLFRFFFFFFFFFFFLFFFRLFFFFFFLLFRFFRFLRRLLFFFFLFRFFFFLF +FRFLFRRFFFFRFFFFFRFRFF +FFFFFLFRFRLFFFFFLFFLFFFRFFFFLFRRFLFLFFFFRRLLFFFLFFLFRFFRFFFFFRLLFLFRFRFLFLFRLFRF +RFFLLFFRFRLFFFRLLFFRLFRFFFLLFRFFLLRFFRFLFRFFLFFFFFFFLLRFFFFLRFFFLR +LFRFRFRLRFRR +LRLRFFRFFLFLFFFRRFFLFLRFFRLLFLRLFFRFFLFRFFLFLRFFRLRLLRRFLFFLFFLFFFRRFFFLFFFLFRFLRL +RLLRFFLRRLFRFFLFFLFFFFLFFRFFFFFRFRLRFLRLFLFFFLFRFLFRLFFFFRFFLLFRFRFLFFFFLLRFFFFRLLFFLRFRFRFLLFRFFRFFLLRL +FFFRFLRFFRFFFRFFFF +LFRRLRFFFLFLFRFLFFFRFFFLFFFFFFFFFLFLRLFFFRFFRFFLFFFRFLLFFRRLFLFFLFLRFFFRRLRLFFRFLFFFLF +FLRFFRFLFFFFRLLFFFFFFLFFRFFFFLFFFFRLFFLFRLRLFFLFFFFRRLFRFRFLLFFRLLFF +RFRLLFFFLRRLFLRLFFFFFRFFFFFLFLFFRFFFFFFFLFFLFRFRFLLRFFLLRFFFFFRFFRFLFFLLFRFFFFFFLF +RRFFFRFFFFFRRFFFFLLFFRRFFL diff --git a/06-tour-shapes/tour-creation-for-background.ipynb b/06-tour-shapes/tour-creation-for-background.ipynb new file mode 100644 index 0000000..7fe0db2 --- /dev/null +++ b/06-tour-shapes/tour-creation-for-background.ipynb @@ -0,0 +1,710 @@ +{ + "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 +} diff --git a/06-tour-shapes/tour-shapes-problem-creation.ipynb b/06-tour-shapes/tour-shapes-problem-creation.ipynb index e5c4e21..2a6841a 100644 --- a/06-tour-shapes/tour-shapes-problem-creation.ipynb +++ b/06-tour-shapes/tour-shapes-problem-creation.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 1301, "metadata": { "collapsed": true }, @@ -19,12 +19,16 @@ "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 }, @@ -51,18 +55,19 @@ }, { "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 }, @@ -81,14 +86,14 @@ }, { "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", @@ -103,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 490, "metadata": { "collapsed": true }, @@ -114,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 491, "metadata": {}, "outputs": [ { @@ -123,7 +128,7 @@ "Step(x=1, y=2, dir=3)" ] }, - "execution_count": 10, + "execution_count": 491, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -143,7 +148,7 @@ "__main__.Step" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -154,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -163,7 +168,7 @@ "" ] }, - "execution_count": 22, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -184,7 +189,7 @@ "" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -195,19 +200,19 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{: ,\n", - " : ,\n", + "{: ,\n", " : ,\n", - " : }" + " : ,\n", + " : }" ] }, - "execution_count": 21, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -218,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 494, "metadata": {}, "outputs": [ { @@ -231,7 +236,7 @@ " Step(x=2, y=2, dir=)]" ] }, - "execution_count": 31, + "execution_count": 494, "metadata": {}, "output_type": "execute_result" } @@ -242,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -261,7 +266,7 @@ " Step(x=0, y=0, dir=)]" ] }, - "execution_count": 33, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -272,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -301,7 +306,7 @@ " Step(x=0, y=0, dir=)]" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -347,7 +352,7 @@ " Step(x=0, y=0, dir=)]" ] }, - "execution_count": 35, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -358,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -370,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -405,7 +410,7 @@ " (0, 0)]" ] }, - "execution_count": 113, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -416,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -430,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -439,7 +444,7 @@ "True" ] }, - "execution_count": 115, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -450,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -459,7 +464,7 @@ "False" ] }, - "execution_count": 116, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -470,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -479,7 +484,7 @@ "False" ] }, - "execution_count": 117, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -490,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -499,7 +504,7 @@ "False" ] }, - "execution_count": 118, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -510,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -519,7 +524,7 @@ "True" ] }, - "execution_count": 119, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -530,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -542,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -570,7 +575,7 @@ " 'tuvwxyz']" ] }, - "execution_count": 121, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -589,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 26, "metadata": { "collapsed": true }, @@ -601,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -610,7 +615,7 @@ "4" ] }, - "execution_count": 123, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -621,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -630,7 +635,7 @@ "9" ] }, - "execution_count": 124, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -641,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -650,7 +655,7 @@ "15" ] }, - "execution_count": 125, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -661,8 +666,10 @@ }, { "cell_type": "code", - "execution_count": 329, - "metadata": {}, + "execution_count": 30, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def step(s, current):\n", @@ -678,8 +685,10 @@ }, { "cell_type": "code", - "execution_count": 507, - "metadata": {}, + "execution_count": 31, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def valid_prefix(tour):\n", @@ -702,29 +711,30 @@ }, { "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 }, @@ -737,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 492, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -748,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 493, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -757,7 +767,7 @@ "'FFLRLLFLRL'" ] }, - "execution_count": 493, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -768,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 494, + "execution_count": 36, "metadata": { "scrolled": true }, @@ -779,7 +789,7 @@ "True" ] }, - "execution_count": 494, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -790,16 +800,16 @@ }, { "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" } @@ -810,69 +820,54 @@ }, { "cell_type": "code", - "execution_count": 526, + "execution_count": 89, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9 Step(x=0, y=0, dir=)\n" - ] - }, { "data": { "text/plain": [ - "[(10, Step(x=0, y=0, dir=)),\n", - " (12, Step(x=0, y=-1, dir=))]" + "[Mistake(i=1, step=Step(x=0, y=0, dir=)),\n", + " Mistake(i=11, step=Step(x=0, y=0, dir=)),\n", + " (13, Step(x=0, y=-1, dir=))]" ] }, - "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=)\n", - "12 Step(x=1, y=0, dir=)\n", - "13 Step(x=0, y=0, dir=)\n", - "14 Step(x=0, y=-1, dir=)\n" - ] - }, { "data": { "text/plain": [ - "[(10, Step(x=0, y=0, dir=)),\n", - " (13, Step(x=1, y=0, dir=)),\n", - " (14, Step(x=0, y=0, dir=)),\n", - " (15, Step(x=0, y=-1, dir=)),\n", - " (16, Step(x=0, y=-1, dir=))]" + "[Mistake(i=1, step=Step(x=0, y=0, dir=)),\n", + " Mistake(i=11, step=Step(x=0, y=0, dir=)),\n", + " Mistake(i=14, step=Step(x=1, y=0, dir=)),\n", + " Mistake(i=15, step=Step(x=0, y=0, dir=)),\n", + " Mistake(i=16, step=Step(x=0, y=-1, dir=)),\n", + " (17, Step(x=0, y=-1, dir=))]" ] }, - "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": [ { @@ -881,7 +876,7 @@ "'FFLRLLFLRL'" ] }, - "execution_count": 528, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -892,19 +887,19 @@ }, { "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": [ { @@ -913,7 +908,7 @@ "''" ] }, - "execution_count": 548, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -924,28 +919,29 @@ }, { "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": [], @@ -958,25 +954,25 @@ }, { "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" } @@ -987,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": 350, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1000,7 +996,7 @@ " Step(x=0, y=0, dir=)]" ] }, - "execution_count": 350, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1011,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1020,7 +1016,7 @@ "(-1, 3)" ] }, - "execution_count": 147, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, @@ -1028,7 +1024,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1047,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 49, "metadata": { "collapsed": true }, @@ -1062,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1071,7 +1067,7 @@ "(0, 2, -13, -13)" ] }, - "execution_count": 136, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1082,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 51, "metadata": { "collapsed": true }, @@ -1094,33 +1090,106 @@ }, { "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": [ - "" + "" ] }, "metadata": {}, @@ -1133,14 +1202,34 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 1003, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "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": [ - "" + "" ] }, "metadata": {}, @@ -1153,28 +1242,50 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 1005, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Step(x=0, y=0, dir=),\n", - " Step(x=1, y=0, dir=),\n", - " Step(x=1, y=-1, dir=),\n", - " Step(x=1, y=-2, dir=),\n", - " Step(x=0, y=-2, dir=),\n", - " Step(x=-1, y=-2, dir=),\n", - " Step(x=-1, y=-3, dir=),\n", - " Step(x=-2, y=-3, dir=),\n", - " Step(x=-2, y=-2, dir=),\n", - " Step(x=-2, y=-1, dir=),\n", - " Step(x=-1, y=-1, dir=),\n", - " Step(x=-1, y=0, dir=),\n", + " Step(x=0, y=-1, dir=),\n", + " Step(x=0, y=-2, dir=),\n", + " Step(x=1, y=-2, dir=),\n", + " Step(x=1, y=-1, dir=),\n", + " Step(x=2, y=-1, dir=),\n", + " Step(x=3, y=-1, dir=),\n", + " Step(x=3, y=0, dir=),\n", + " Step(x=3, y=1, dir=),\n", + " Step(x=3, y=2, dir=),\n", + " Step(x=3, y=3, dir=),\n", + " Step(x=3, y=4, dir=),\n", + " Step(x=2, y=4, dir=),\n", + " Step(x=2, y=5, dir=),\n", + " Step(x=2, y=6, dir=),\n", + " Step(x=2, y=7, dir=),\n", + " Step(x=1, y=7, dir=),\n", + " Step(x=0, y=7, dir=),\n", + " Step(x=0, y=6, dir=),\n", + " Step(x=0, y=5, dir=),\n", + " Step(x=1, y=5, dir=),\n", + " Step(x=1, y=4, dir=),\n", + " Step(x=1, y=3, dir=),\n", + " Step(x=0, y=3, dir=),\n", + " Step(x=-1, y=3, dir=),\n", + " Step(x=-1, y=2, dir=),\n", + " Step(x=-2, y=2, dir=),\n", + " Step(x=-2, y=3, dir=),\n", + " Step(x=-3, y=3, dir=),\n", + " Step(x=-3, y=2, dir=),\n", + " Step(x=-3, y=1, dir=),\n", + " Step(x=-2, y=1, dir=),\n", + " Step(x=-1, y=1, dir=),\n", + " Step(x=-1, y=0, dir=),\n", " Step(x=0, y=0, dir=)]" ] }, - "execution_count": 216, + "execution_count": 1005, "metadata": {}, "output_type": "execute_result" } @@ -1185,14 +1296,14 @@ }, { "cell_type": "code", - "execution_count": 405, + "execution_count": 1006, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1205,7 +1316,7 @@ }, { "cell_type": "code", - "execution_count": 442, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1214,7 +1325,7 @@ "10" ] }, - "execution_count": 442, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1226,14 +1337,14 @@ }, { "cell_type": "code", - "execution_count": 443, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1246,14 +1357,14 @@ }, { "cell_type": "code", - "execution_count": 444, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1266,14 +1377,14 @@ }, { "cell_type": "code", - "execution_count": 445, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1286,14 +1397,14 @@ }, { "cell_type": "code", - "execution_count": 446, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1306,14 +1417,14 @@ }, { "cell_type": "code", - "execution_count": 447, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1326,14 +1437,14 @@ }, { "cell_type": "code", - "execution_count": 448, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1346,14 +1457,14 @@ }, { "cell_type": "code", - "execution_count": 449, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1366,14 +1477,14 @@ }, { "cell_type": "code", - "execution_count": 450, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1386,14 +1497,14 @@ }, { "cell_type": "code", - "execution_count": 451, + "execution_count": 66, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1406,25 +1517,25 @@ }, { "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" } @@ -1435,14 +1546,14 @@ }, { "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": [ - "" + "" ] }, "metadata": {}, @@ -1455,16 +1566,16 @@ }, { "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" } @@ -1477,14 +1588,14 @@ }, { "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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4BuzPuLRMdHS0Ko21u+j1enndOp1OtUsMQEC/aDqdzqUt3PqaEhISwtJ+rkXS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\nRK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1497,81 +1608,232 @@ }, { "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": 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+ "text/plain": [ + "" + ] + }, + "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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QRFaq6s4Atm2MOY+k98SqekhVt8bunwJ2ABclu13jzebNmwFv3UTJ0iZLkjS9H7Zjx44B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\nYII=\n", + "text/plain": [ + "" + ] + }, + "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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kBnQDtgATW/l3X4t4TWJmzpyp+fn5vhZ3X3LJJUm1Mw0YMMDXnC+99NK0tF8BumDBAs/z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\n", + "text/plain": [ + "" + ] + }, + "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=)),\n", + " Mistake(i=105, step=Step(x=-5, y=6, dir=)),\n", + " Mistake(i=106, step=Step(x=-5, y=7, dir=)),\n", + " Mistake(i=107, step=Step(x=-5, y=8, dir=)),\n", + " Mistake(i=108, step=Step(x=-5, y=9, dir=)),\n", + " Mistake(i=115, step=Step(x=-5, y=12, dir=)),\n", + " Mistake(i=116, step=Step(x=-5, y=11, dir=)),\n", + " Mistake(i=127, step=Step(x=2, y=11, dir=)),\n", + " Mistake(i=130, step=Step(x=3, y=11, dir=)),\n", + " (132, Step(x=3, y=11, dir=))]" + ] + }, + "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=)\n" + ] + }, + { + "data": { + "image/png": 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X0NjYiKioKNX23+rqaum6KMoJCQnS/moymdDc3OyzLIGApjwRTEpKivR/9Cfdu3e3CSoe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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "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=)\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1579,7 +1841,1073 @@ } ], "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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+ "text/plain": [ + "" + ] + }, + "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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3QD61kj8AsmTJkrAi4sSJE0PW5+fnRxVjw4mIgVNH2rVrZ9ONRkSsS2nKDPba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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "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": [ + "" + ] + }, + "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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+ "text/plain": [ + "" + ] + }, + "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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+ "text/plain": [ + "" + ] + }, + "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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ZZ57hwIED7Nu3r84N3FRGpzCGuczMTFJSUjx/7Pv27aN79+6WU6lgVv6cyBEj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\nVBW0UCqlVBW0UCqlVBX+P0VFnoIKajtRAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "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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gmSDHZxrub6Xly5cbHIkQ/ktNdX5cgzEiwG63c9lll/H+++8D3w6fapvYBg0a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\nktiEEFFHEpsQIur8f/yi651PnLLMAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "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=) going to Step(x=0, y=0, dir=), currently left, by step L to Step(x=-1, y=0, dir=)\n", + "At Step(x=-1, y=0, dir=) going to Step(x=0, y=0, dir=), currently left, by step F to Step(x=0, y=0, dir=)\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": [ + "" + ] + }, + "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": [ + "" + ] + }, + "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=) going to Step(x=0, y=0, dir=), currently above, by step F to Step(x=2, y=2, dir=)\n", + "At Step(x=2, y=2, dir=) going to Step(x=0, y=0, dir=), currently above, by step F to Step(x=2, y=1, dir=)\n", + "At Step(x=2, y=1, dir=) going to Step(x=0, y=0, dir=), currently right, by step R to Step(x=1, y=1, dir=)\n", + "At Step(x=1, y=1, dir=) going to Step(x=0, y=0, dir=), currently above, by step L to Step(x=1, y=0, dir=)\n", + "At Step(x=1, y=0, dir=) going to Step(x=0, y=0, dir=), currently right, by step R to Step(x=0, y=0, dir=)\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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\noC4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "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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+/v5IT08X1el0Ojz33HP46aefHChdzVNYWGiV011D+x7aYodRf8wBGxBEhF69ekGv1/N0\nAjk5OUhISEC7du0AOD7KVk2zd+9e+Pn5AQAePHiAnJwcXlasWAEAojoJ65A6jDpGSUkJTp48iSNH\njgAAQkNDAQD5+fn4+uuved6PHj16OExGRzB27FhoNBpMmDAB3t7e8PLy4kVIGG1cN3PmTMk+wwok\nb9U6xuDBg/Hbb78BAMLCwhAZGYkbN24gLCwMABAcHIzU1FSefrGhEB4ejkaNGmHTpk3ljplaVl2z\nZg0AICYmptplq09II4w6wC+//ALGGJydnXln0bhxY6SkpCA2NhZDhw7lSqn79+8DaHhTkqSkJFy/\nfh2PPfYYdzxr1KgRGGO4ffu2yWu+++67emngVp1II4w6wKxZswAYLBtVKhXCwsIQGBiI27dvQ6VS\n4ZdffkFsbCx0Oh2Pnt3QQtfp9XpotVrEx8cjJiYGRISgoCAkJyebzAwHSCbjVmHt8oqtBQ1wOcta\nli5danZJcd68eSbrz54962ixa5TJkyfzpVhjvL29udOaMYDpTGsNAUjLqvUfIoJMJkNhYSFPpixR\nOULYPiESuXH9tm3bMHLkSAdK5xikZdUGQEObYtiTe/cM7kxbt24V1dsrG11DQuow6gjSaMw6hg0b\nxjuMUaNGYfXq1fyYpMOoOlKHUceQOo6q8fPPP2PDhg18v6CggG+PHDkSr7zyiiPEqrNIHUYdID09\nHdOmTQMgTU2swThuhhAb4/Dhw2jSpAm+/vprR4lVJ5GWVesAo0aNwoEDBxAeHg5nZ2dHi1PnMDbc\nEpade/fujejoaNy8edNRYtVJpBFGHUCj0SAoKAh37tyR5t1WIPiYAIagTYJh186dO6UOuIpIHUYd\n4P79+0hLS3O0GHWWF154gfvePP/889i+fTt27NgBNze3epUhriaQpiR1AH9/f8nD0kYEZ7w2bdpg\n+PDhAABXV1eRElSicqQRRi1HoVDg5MmTCA4OdrQodZK2bduKkhoJ7v8A4OLiIimRq4hk6VnLYYxh\n+/bteOaZZyT9RRVQq9UYNWoUfv75Z16nUqng4uICZ2dnKJVK3LlzB0DDW6q2xdJTmpLUAUJDQ012\nFjqdDjqdjtv5y2Qy6HQ6yGQyKJVKB0jqOIiIJ6TW6/U4ceIEfv75Zzg7O3M9hVqtllIO2Ig0JakD\nmAuT7+7uDqVSyX85lUolXF1doVKpsHTp0hqW0rEMHToUcrkcSqUSLi4u6NOnD2QyGTQaDcaOHQsA\nUCqVPG4IYNANSVOSqiF1GHWAjh078jyigMEHomPHjvzXMjQ0FB4eHqJr/vjjjxqV0dGcPXuWRx8D\ngA4dOvDscDqdDoBheTolJYWfo1arG9x0xFaszd7uwxjbxxhLYIztZYx5GR1bW5q9/SxjrH11Cd5Q\nWLhwIRQKBcaNGwfA8KWPjY3FmTNn0KRJEwQHB6Nx48Y8tqdA165dHSCtY0hLS0NWVhays7N5naDo\nVKvVoo7EOKu91FlYQWX+7wB6AmgPo2TMAJYDeKt0ez6AZaXbTwHYWbrdFcAfFbRrXyf/eky/fv14\nnpEnnnjCZNwHZ2dn6tatG4+H0a9fP0eI6hBgIh5IaGgoERG5u7ubjSUSHBxMpcr3BgWqOy8JymRv\nB3AVQFDpdjCAK6XbGwC8aHTeFeE8E21W82uxD8bZsjw9Penxxx8v98Xz9vYmAJSRkWHTve7cuUMA\nyN/fn7dtnJtDyKNhSYmOjrbTG6j9WPpOjIuzs7PFiYwePnxISqWS5HJ5pe0af3atW7eugaevOrZ0\nGNbqMAKJKK30vz4VQGBpfb3L3i7kLQUM+UoPHz4sOu7i4oIBAwYAAL755hub7rV27VoAQL9+/bg5\ns16vR2RkJPz9/flcHBAPrY3rmjRpAgAi5V59Z/Xq1ejWrRt8fX1F9RW9g+LiYri6uvJgwBWxb98+\naDQarhMxRaNGjRAZGYnGjRtj4MCB6N+/Py5evGj5Q9QR7L2sWqXs7bU9GTOVznGjoqLAGMOVK1fK\nnRMVFYX/+7//A2PM5qS/SqUSCoUCW7duxZgxY7B7927odDokJSUhJCQEKpWKKzpNBX/R6/Xcmaoh\nWYbOnDkTM2fOREZGBgIDA8EYg4+PD5KTk8utgri7uyM/Px8AcOXKFTRq1KjS9k197mURbDqSkpKQ\nmJiIwMBAeHt7W/E09scRyZjLTkn4VAMVT0n41MVEm3YZXu3evbvcsNDd3Z2Ki4ur3Na+ffsIMOTz\nLNumJWXDhg1ERNSuXTuRLMK2i4sLAaBNmzbxe16/fp3nETVViIh69epFYWFhFBgYSACoadOmRESk\nVCpF506bNo2IDEPOVq1a2fpqaz3btm0r946F4urqSkTmpytVYcaMGVZ9H4xL+/btq+MVWAVsmJJY\nOsJgEI8efgEwDgbl5zgAO4zqXwewjTEWAyCHSqcu1cWXX34JAHjrrbeQk5ODc+fO4cSJE8jJyUFA\nQECV2hJsF6ZPn45169ahsLAQzz77LPz8/HDkyBFcvXoVPXr0QH5+Ps6dOwcAiI6OxlNPPYWwsDBM\nmjQJAHDu3Dk888wziIyMhKurK/Lz88EYg5ubG5YtW4Y333wTqamp0Gq1+PPPP3m8BplMxkcOrVq1\nwq+//goAuH37Nl8OnDlzJj788EMAhhUT42vy8/N5Xg7jGBD1lQ8++ACAYdm5qKgIRUVFfBpQWFgo\n8lK1haSkJL6tUqmg1+tFTmullpN8v0mTJvD29kZ8fDxCQ0Oh0Whw9uxZfPDBB9DpdFAqlfDz88Pk\nyZPrnh1IZT0KTGdv9wEQCyABwG8AvI3O/ycM2dvPAehYQbt26S3/9re/8V+M4cOHcyWkNSOMESNG\nkJeXFxERtWnThm8TERUUFPBfi+bNm/Pt6dOnl2sHAMXGxpq8Byr4FWrVqlW50QIR0RdffEEAyM/P\nT5R0GGV+XYXo2EDDWCUR3oslysiypSpMmzbN5hGGqbJkyZJqejMVg+pUehLRy0QUSkRKImpERF8R\n0QMi6k9ELYloABHlGJ0/nYiaEVE7IoqvrH1bUSqVPBVeTk4O3Nzc8ODBA6v0CSUlJcjNzQUAeHt7\ni0Yorq6uICIEBwfj2rVrAAwWmObu079/f4wfPx4A8O6774p0HL6+vvwD+OqrrwAYOm5jJZ1KpeLb\nEydOBBEhMzNTZCIuk8n4fBwAd+EGgEceqdO6ZosQ3otWqxV9qcPDwzFo0CD+q9+7d2+b7uPlZTAz\nGjhwoEXnZ2ZmWvJDbJNr/eDBg7mtiVAGDRpkdXuWUqd9SX777Tfs3r0b+fn5mDhxIuLj421yVxY6\nC8AwDRAUWcZs3rwZU6dOxc2bN6HVakX/sAKXLl3Ck08+if/85z/46quvsG7dOjDG0L17d8TFxUGt\nVuO1116DRqPhQ+gJEybgxo0bvI1NmzYhISEBISEhyMvLg5OTEzw8PJCTkwPGGLy8vKDX60W+El26\ndMGjjz6KwMBALFu2zOr3UNdJTk6Gj48P37958yb69++PvLw8JCUlISMjo0rtCdO7ffv2wcPDgxvJ\nPfvssxg4cCCcnJzw4YcfIiUlBYcPH7Z4KlRUVFQlOYzZvXs3unbtivHjx4Mxhn//+9/Yt28fACA7\nOxvTp09H586dMWvWLPtOe6wdmthaYIcpCYyGd4Li0BZDnGeffZYUCgUREUVFRZFSqazwngBo6tSp\nJtsaMGAAeXp6EhHRl19+yadKwnVlFZbGdgHGRaVS8W0fHx++7evry7cFY66JEyda/ez1CQDUuXNn\nIiJ64403yk3dJkyYUKX2FixYwK8NDw/n22vXruXnGH/elsq4cOHCKslR9vopU6bw/YkTJ5abmgOg\n5ORkk9eStf+31l5oa7FHh8EYI+Avq0c/Pz+bOgwhe1bZMn78eJP1xh2AqRIcHFzuHiEhIdS3b1++\n/9VXX5nsMB599FEKCAioUF4fHx8aOnQoERF5enrSzJkzrX72+sSjjz5KAMjDw8Mu7S1btkz0mZr7\nvIUfG0twdXXlequKMDYcLFs++OADft6MGTN4hzV27Fh+zoULF8q12WA7jNTUVL7UGBYWJvrgGGP0\n4YcfVrnN48ePEwAKCgqilStX8jb9/PwoLi6OAFBERAQBoOzsbDp48CDFxcVRXFwcHThwgOLi4ujw\n4cN06NAhUqvV5doHxFaYn3/+Ob+Hk5MTtWjRggDwpdmKAEDdunXj2+PHj6/y89ZH8vLy6NNPP62y\nctMcP/30k9nRX4cOHeixxx7jdePGjbOozczMTHrhhRe4jOnp6dSlSxcaMmQIdezYkR577DEaMmSI\n6H4HDx6kw4cPU1xcHP3555+i9oQfNSIivV5Px44dIwAUFRVFHTp0oCFDhlDPnj0pMTGx4XYYREQ7\nd+6s8FfeGgYMGCBqw8/Pjx599FEig+DUunVrq9t2c3Ojrl278v2NGzeWk7lZs2ZchooAQP379+fb\nr776qlUy1Ud+//33au8whO9GQECAqO7evXsWtTt37lwu44svvkgAKDIykrdjvN2nT58K23r11VfL\nPa+xTMKPXHR0tE0dRp13bx88eLDQAZkMY2eNwmffvn3cOxQAsrKyuKIsNDSUa82toWnTpjhx4gTX\nbE+ePJkfmzdvHogI165dQ0hICAIDAytoCWjWrBliY2P5M3br1s1queobwnfCHoSHh5us79OnDzIz\nM7myXEhhYGkKRldXV7i5uQEAN+k3tvkw3q7MUlP47I1XTYC/VmySkpLQokUL21fPrO1pbC2oYu+f\nmZlJbm5uJJfLSaFQUNu2balp06bk7OxM69atI8BgAQk7jTJ0Oh1lZmaK2hEsNW1tNz09nTIyMggA\neXl58fYYY9S6dWtq0aIFdzqriJKSEkpPT6f09HTKzs4mvV5vlUz1hZKSEho2bBj5+vpyJae16PV6\n6t27NzVt2pRUKhXXMQm/0IBBdxYUFESAWBlq6Qhj9uzZIhkzMzP5dyMtLY3S09MpMzOTT5MBg/K2\nY8eOFBISQmlpaSJ5s7OzKT09ndLS0igjI4MePnwoup+TkxO1adOmYYwwlixZgoKCApSUlECn00Gr\n1UKn06G4uBhvvPEGgL8cskaMGGHz/WQyGXdmmj9/PgDDL4cQ/m7evHlWtxsQEAB/f38AQFBQEK83\nHg0pFApRij9TODk5ISAgAAEBAfDx8al7VoN2JicnBzt2GIyO5XI5t7ytCgUFBUhLS8Mff/yBuLg4\nAODfM0A8ejB2AExPTwdgiPxlqYVxWQdCPz8/uLu7g4jg7++PgIAA+Pr6is4TlmLv37+Pv//977xe\n8J8JCAhAYGAg/P39uX2SgE6ng0ajsUg2s1jb09haUMXef9WqVRXqKoQil8uJiKhTp04WKQ4rQq/X\nEwCKiYmxaVRhjvbt2/ORi4TtpKen2/wZlf0+ERHt2LGDGGN8VU4on332Gb/G3d2dnJycqnSv9957\nr9zSvdB2x44diYhE1rthYWH0/PPPExGRl5cXzZo1q0r3a9q0KfXt27dhjDBM6Q2ICE899ZSorqSk\nBCqVCjdu3OD+HrZy/fp1NGvWzC5tGXPmzBkQEQoLC+3edkNg7969UKlUaNy4MRhjlep8KoNK9R7G\nlqPAX74iRITu3bvz819//XUsWrQIgMFq0zj8QGXMnj0bH330ETQaDdzc3ER6BwCIj4+HSqXCkSNH\nEBISAiJCSkoKT2iVm5uLvLy8Kj1fRkaGyDjRGhzaYaSnp+Pw4cMWPfipU6dM1hvHHBBMrzUaDb78\n8ktER0djxIgROHr0qE3OWJmZmXjw4IHV10vYn9TUVEybNg0ajQatW7dGhw4d0KVLF8yePdvu9xo4\ncCD+9re/oVu3bujXrx+vVygU3BFQ+H6tX7/eojZXr17Nv6/mfjCE6cP9+/dx8OBBABB1SkKHZil5\neXm2hz2wdmhia4HR0K5JkyaVDqeEdXWULh+idLjYq1cvXv/4449zuwxBuSMUa2wUhClJZGQktW3b\n1u5TEgnrET5XYYhuD4TPW6vVmj3nzz//5Pc2jq5lXM6cOVPpvSoyyKqoREZGEpF1hnphYWE8YhzV\n1SlJ27ZtRRm09Xo9WrRoAcYYtm7dCsCw3DhnzhwAhg7OOOiJ4LBFZMjLIfT0Fy5cQHR0NB+mCgos\na0hKSqooUhvvAAAgAElEQVRyby5hf5555hkwxjBjxgxe16ZNG6vbi4uLA2MMfn5+YIxx5WJFy6Iu\nLi5wcXEBYBh5msISX5WrV6/irbfeAmD47hoHKq6IpKQk9O3bF3l5eSb9mCpCq9XankvW2p7G1gKI\nfSrmzp1L//jHP2jEiBG8N1WpVDRmzBi+hDVkyBD6+OOP+dLWxo0bueKwefPmIv+MYcOGiZajTPmF\nWMLGjRvpxRdfpNjYWGmE4WBgtLQ9a9Ysbpq/a9cuq9rr168fAaDXXntNtGS+YcMGs+ERPvzwQ9Ey\nuLBtHHTpxIkTFt1fWFb9/PPP+YgjKiqKt2PsL2SqvPLKK5XeQ6PR0MaNG7lFcaNGjWwaYTi8w6iu\nkpaWRjdu3OD7QpQqa0lISJA6DAdT9jP29PTk2zk5OVVu78UXXyQ/Pz8iMkxtmzVrxp0YO3XqZPIa\nwa+kQ4cOIln69etHoaGhBKCc2bY5zpw5U+6ZjIM+mysRERGkUqno9ddfr/Qewg+qEEBasPuhujol\ncXV1Fe0L9glCPERTVnavvPIKf4DWrVubbDcoKAirV6/m+1WNr7hmzRowxqBSqcAYQ8uWLat0vUT1\nYLwSYqwsr2h4fufOHZEF5LBhwwAYhvdZWVlQqVT4/fffcf36dT4tEewqyiKsZMTHx6N9e0PaHYVC\ngf379+PevXsAxLFMKqJ9+/b8ezxlyhQAlVuJPvXUU0hKSoJarbZIkS+Xy9GyZUs4OzsDsM2lHoDj\nRxjGw7rRo0dz68rs7GyaOnUqP2Yc99Lb25tmz55N8+bNE9nbt2jRgrp06WKyV27WrBmtXLmSZs+e\nTdOnT6e5c+fSm2++SVOnTqVZs2bRW2+9RW+88QYlJSUREfFfi8cff5w6d+7Mo2xJOI6yn6nxd2fc\nuHH04MEDIiK6ePEivf7663Tw4EEiIvrnP/9JAGjx4sXcUezo0aP8O7V06VJuGWrsGl6WK1euUOfO\nnfkxYfrcu3dv0ZTGlEt5ZajVaj7diY6OJg8PD5E39JNPPknLly+nkpISPsoZO3ZshW1+8803ZsMm\nUH2Ykuzdu5e0Wi0BIL1eTxcuXCAA9Prrr5t86Krk6ahsqGes/yAi2rRpEwHlAwJLOI6yn5mxhzIA\neuSRR4iIuLm28Hlt2LCBb8+aNcvUPw+tX79eVGfKVb3sNSdPniSgvMvAgQMHbHpGwWNZcIE3vqfg\nVAeA9u/fX6X3BfwVzpDq6pQkMjKSbwvDf8BgNitowD/77DPRNYsXLwYRoaSkBBMnToSnp2el9zG8\nP/MdpFqtxosvvsjNeidMmAAiQn5+PogIiYmJdnleCdsRTPaN86QCwN27d8EYQ1pamijcobFBlLOz\ns8nM9lOmTOHTAYVCYXJa4eLigoiICL5//fp1AIZhvnHKjKoaVJVFkKOsfQZjDL169QJg+B737du3\n0rZ69uzJt+VyeTlz8ari8BB9OTk56NSpE06dOoUFCxbAx8cHAwYMQGpqKp5++ml8/PHHaNeuHbKz\ns5GcnIy9e/eid+/emDhxIrKzs3HkyBH+AQUFBSEsLAynT58udx+hw6gIwSv15ZdfxoMHD6DX6+Hv\n74+cnBybLeQk7IOTk5PJJE5lEToTlUrFlxJDQ0ORnZ0NjUaDwYMHY8+ePdDr9Rg1ahQyMzN5R6LV\nak0mLSoqKuJ6ChcXF54jBhDn2LH1u3Lr1i0AEIUDBIDvvvsOMpnMZN7c1NRUboXapEkT7gWr0+nQ\noUMHhIaGYufOnVVeii2HtUOT0n/A2QAuAjgP4FsAzgAiAPwBQ0Tx7wHIzVxrdnogDCk7duwo8kAd\nMmQIEYmXtqpSBgwYUOEQTliGNY6qZLzds2fPCq+XqF7KTkFsKcYxLIx1BcKQ3ZSfhhDur7Ii6FKs\nQQi3CKCc70pFGC/HmirC91h4h1TTOgwAoQBuAnAu3d8G4JXSvyNK69YDmGzmepMP9vLLL1f5wzeX\ncFdQUFnywiUk6jJ9+vQRfdcFi05BiU9E1KJFC+rfv79DdRhOANwYY3IALjDkL3kCwE+lxzcDeLYq\nDX733Xd8Ozg4GBcuXABgmFc++uijJq8xN8z6888/q3JrCYk6i7FupWnTpjwHsLH+JjExEXfv3rXp\nPlZ3GER0D8A/YEhudBdALoB4GLKdCYvJKTCMRExiLq+lm5sb3N3d8Z///AetW7fG2rVrodVqodVq\neVSjqmKJgkhCoi4ixGkREGwugPIZ8MgCXV5FMGsbYIx5wzCSGAFDZ/EDgP8C+DsRtSg9JwzATiJq\nZ+L6Cm+s1+sbfEAYCYmK+N///odnnnnG5DG5XI6SkhIkJSXx0UeLFi3QuHFjxMbGgois+ueyZUrS\nH8BNIsomIh2A7QC6A/BmjAnthsEwTamU2NhYaLVarnkWHIJGjx5tg4gSEvWXy5cvl6sTRu3CKk9U\nVBS3VL558yaf4luLLR3GHQAxjDEVMwwF+gG4BOAgDKMOwKAE3WHmegCGZa/vvvsO/fr1g1wuh1Kp\nxEsvvYSgoCAEBATgu+++w9WrV5GQkIBr167h9u3bNogsIVF/MJ6GqFQqREZGIigoSDRt9/T0RFBQ\nEBITE6HT6eDh4WHbTa3VlpZOZRYBuALDsupmAAoAkQBOAEiEYcVEYeZaAirO/iQEyi1b1qxZY6Uu\nWUKi/rB69WoCDFapq1ev5vXmki1FRETwZVtyxCoJEX1ARNFE1JaIXiEiLRElEVFXImpBRC8SUYUe\nMkuWLOGxCATnIBcXFxAZAqGWFRgQh1+XkGioCAZpwoKAgLFBmYBcLsetW7dM5guuCg639BTw9PTk\nFnJqtRq5ubk4dOgQZDIZnJ2dMWjQIJw4cQJAec2vhERDRPgBBcT/E02aNEF8fDyAv+KRCjoN42BV\n1lBrOoyy5rR9+/bFmTNn+P6gQYOwd+9eAMCoUaNqVDYJidqIEBMUAI8CBhhM4BMSElBQUFBuGbXs\nflVxeIfh7OzMh1Zt27bFw4cPkZSUxDsLwZ5e6CxsfWAJifqC8f+CEBy4R48eOHbsGLfF8PHxsWsA\na4d7qwo8/fTTuH79OpKSkkRBcTp27AjAYPX5008/mbtcQqLBYRyf87PPPkOrVq1w7Ngx0TFbPWfL\nYrXhls03rsRwS0AYgfz66694+umnq1ssCYk6w40bN6qcL8fX1xfZ2dkOMdyyG0FBQSAiUc4Hf39/\ndOjQARqNBkQkdRYSEjCEDhRWE5s1awY3NzcQEaZPnw6lUlluRfHmzZt8PzIy0qYo60At6DCaNGmC\nzz77DNu2bYOvry86deoEwBDCXcjyJCEhAZw8eRIjR44U1RUUFAAwTD0qypv6xRdfICkpCcnJyTbJ\n4FCl5xNPPIGDBw9i7NixouhCQkp6m1PTS0jUI/r37y8KqGOMqShiAmq1Gq+99hoAYOnSpXjppZes\nlsGhIwwhzaFgypqYmAgiQw5JmUxmtWeqhER9RCaTYejQoeVWChlj+OKLL/g2YwzDhw8HYBjBC0uu\nRIQXX3zRNhlsutpGMjIy8Pe//x2pqakAIAqLlpycjNOnT2PYsGHo3r07OnfujE8//dRRokpIOJzc\n3FwcOHAAy5cvBwCu8Pztt9/w5JNPAgC6desGpVKJHTt2YMCAAWjVqhXWrVuHK1eu2EcIa23KbS0o\ntW//8ccfiQwVlJiYyO3hhSjOQqYmoUhINFRWrFgh+l/o1KkT/5+4fv06AaDw8HB+vHHjxgQYoo8b\nAwdG3LKZstMOnU4HxhimTp2K2bNn4/bt24iJiXGQdBIStYd58+Zxk28AOHXqFA/227RpUxAR7ty5\ng6lTpwIA9+zu0aOH3WRwuB2Gi4sLwsPDkZiYiKtXr6JZs2bl3HPz8vLg5OSE9u3bIz09HdnZ2WjR\nogUKCwuRkpKCiIgIeHl54ffff7coorSERF0mOzsbfn5+GDFiBLZs2WJS4Xn//n0QERhjCAgIEP1P\nlfqX1D07jFmzZqG4uBgajQbdu3dH8+bNIZPJMGjQIH5OXl4eGGM8gpBGo0FhYSG/rrCwEBqNBseO\nHcPRo0cd+DQSEjWDkJfFw8PD7OpISEgIQkNDERISYtfFA4cuQ6xatQqrVq0qV79nzx70798f+/fv\nB1B5vkngL+2whERDwN3d3aIEXvam1q5bxsbGVvkaqcOQaCjk5+ebtcmoTurVhD8+Pt72zE4SEnUE\nS0be9qbedBhubm6YMWMGunXr5mhRJCSqHScnJ9vjc1pBvekw8vPz0blzZ249KiFRn9HpdBX6jlQX\n9abDAOCQFygh4ShMxe6sbmzqMBhjXoyxHxhjVxhjlxhjXRljPoyxfYyxBMbYXsaYl72ErYyQkBCE\nh4fX1O0kJByKYLRVk9g6wlgDYBcRRQNoB+AqgAUAYomoJYADAN628R4WERUVhb1799qcO1JCoi7g\nKDMCqzsMxpgHgF5E9BUAEFEJEeUCGAZDjhKU/h1us5RGJCYmYvbs2YiLixPVJyQkYPTo0bh//749\nbychUSshIoesCNpih9EEQCZj7CsYRhenAMwCEEREaQBARKmMsQDbxfyLli1bAgBWr15dzs33tdde\nQ2BgoD1vJyFRazFOulxT2DIlkQPoCOAzIuoIoACG6Ui1OqcYm8IyxkTOOBISDQVXV9cKg+ZUF7aM\nMFIAJBPRqdL9n2DoMNIYY0FElMYYCwaQbq6B999/n2/36dMHffr0qfSmV69excyZM/HLL78AAI4e\nPcojizvKkU5CoqYpLCzEzz//jNTUVDg7OyM7OxtKpRJRUVFYvHixyH/k0KFDOHTokH1ubK1ffOk/\n52EALeivPKvLS8v80rr5AJaZudbquACLFi3iPv/r16/nPv6HDh2yuk0JibrEG2+8wf8HPD09RXEy\n9u3bV+G1cGA8jBkAvmWMnYVBj7G0tMMYwBhLANAfwDIb71EO47nb1KlTuba4T58+eOqpp+x9OwmJ\nWsfatWv5P3Fubq4oUrhx5Dp7Y5PzGRGdA9DZxKH+trRbGaYMtFq1aoXr169jz5491XlrCYlaT3Ua\nMNZJS89x48ahSZMmfN/d3R15eXkoLi7Ghg0bJF2GRINGpVJVW9t1ssOIjIzEjRs3eLTkhw8fIjk5\nGUSEyZMnQyaTgTGG5557ztGiSkjUONX5g+nQEH223lvQXcjlcshkMjx8+BDOzs5gjCEwMBCMMbRv\n3x5xcXGIi4vjSZIkJOorQnS6jRs3YsKECWbPoboYos9Wli5dCoVCgZKSEhQXF2Px4sU88Gl+fj7y\n8/Oxd+9eFBUVYdq0aQ6WVkKi+pkzZw4YY3j11Verpf06PcIADNadW7duLRd9qE2bNiAiXLx4EZGR\nkWjVqhX+97//2Xw/CYnaznPPPYfjx4+bdZOwZYRRa0P0WUJkZCRu3bpl8tiFCxf4dlJSkhRNXKJB\n8Nxzz2H79u1wcnKqlvbrdIdhrrMo7UFFdSkpKTUgkYREzXLixAlkZWVBr9eDMYbt27cjKioKa9as\nqZb71ekOwxxCZzFp0iSec9J4GVZCoj6wZcsWjB07tlx9bGxstSUyr1fj9ObNm8Pf359bvS1ZsoQf\nc0T8QwmJ6qS4uBiAOFq+u7s7wsLC8NZbb1XLPeu00nPHjh344YcfcP78eVy4cAFBQUFIS0vjI4yM\njAzu7t6mTRucP3/eZrklJCZMmMAziykUCsjlchQUFCA5ORl+fn7Q6/U4d+4cZDIZiAjOzs7w8/OD\nTqdDXl4eNwNwd3eHQqHAgwcPkJubi6ioKDRu3Bh5eXkAgMGDB2POnDlm5SAivPnmm/j0008hk8mg\n1+vRq1cvxMfHo6CgwKw9hi1KT4cmY7YnAKhJkyakUql4XUZGBgGgRx55hAYNGmTX+0k0TEaPHi1y\n9KrukpubW6lM3bt3L3fd+PHjzZ6PupyM2Z7cvHkTQUFBfJ9Ke9isrCwUFhY6SiyJekRNh8WzxJHs\n6NGjeP3116FQKBAVFQUA+Oqrr3gYP8YYN2jcsGGDTfLUqw5j8uTJOHz4MN8XOgy1Wo2srCxHiSVR\nj9iyZQvGjx8PwOCz0aZNGwCAj48PAgJMB5czTmmoUCgAAM8//zyWLVuGTz75BIC4I7JmSTQ/Px9a\nrRZXr17FuHHjsGLFCgDAlClTsGzZMqxcuRIA8PHHH1e5bRHWDk1sLaiGKcnhw4dFdcKUxN/fn/r2\n7WvX+0k0bGDhlCIoKIjat29PAMjJyYm6du1KZb/7ACgwMNDk9Xfv3rVInkmTJvFrdu7cyduNi4vj\n54SGhlKPHj0a9pRkx44dvHf29vYWHRPiZmRmZjokca1E/Wbo0KGVnpOWloazZ88CMCQfOnHiBACI\npgsAkJ6ejpCQEAAQpcqwNPO6MBUBgCFDhvB2jT1XicjmKVWdt8P45ptvAABnzpxB27ZtRcc8PT1x\n5swZZGRkoH//ag3RYREzZsxAYmIiNm7ciMaNGztanAbHjz/+iOXLl3M9V2ZmJry9vREWFoYvv/zS\nojZ0Oh2GDRsGADh9+jSio6OhVCpx//595Ofno6CggJ/bpk0bFBQUQKfT4fbt2/DyMqToyc3NxYcf\nfoji4mIoFAosWrQIc+bMwTvvvAN/f3+R3sLd3R16vR7z58/HiRMnoNfr8eyzz2Lu3LkoKirC5MmT\nkZOTg+3bt6Ndu3bo378/wsLC4OXlhUuXLuH111+HUqlEYGAg7t+/DxcXF9teorVDE1sL7DQlEbTW\ntZ3c3Fw+ZGzXrp2jxWmQCO/fx8eHXF1dCQA5OzsTALp8+bJFbaxatarctMHLy4tvy2Qyvv3II4/w\nbcYY33755ZfLybV582a+3apVK95mYWEh3blzp9w9iYj+8Y9/8P1//vOfREQ0ZMgQk1MbQZbt27c3\n7CmJq6trnTDKolIFbNOmTREcHMzrjx8/LhqeCmkUJOyPSqXC0qVLkZ2dze0UhOhUOp3OojZMpbHI\nzc3l28YZ1Y2Tagmf//Lly/Htt99CLpeLpiSvvPIK3/by8oKPjw+/TpCNiLBr1y7eplwu59Pu6dOn\ngzGGnTt3imTLzMwEESElJQVEhOHDbUsTVOenJLm5ueU8VWsT06ZNw3//+1++nHXjxg1RB3fu3DkA\nBge5xYsXY9OmTQ6RsyGgVquRmpqKmJgY3L59GyUlJVi3bh0AWOyc+PLLL+OJJ55AaGgoRo8ejfnz\n58PDwwN6vR737t0DYDC4Ki4uxtWrV8EYQ0lJCWQyGRQKBTfZ1ul0iI6OxsaNG6FSqeDv7w/AMAXp\n0aMH95MSri9LZGQk0tPTUVxcjFatWuGnn34S6Sv0ej08PT3h5+dn9fsyRZ3vMGraC1Wv15uMmSiT\nyUzmiVi/fj0AiOITFBcXo6SkRPQLERERwee4EvZH+Mz++OMPnDx5ktePGjVKdLwihM9eUK536dKF\nL6sCf/kreXt7Q61WIyIiosL2vLy80KtXr3L1X375JUaMGIHo6GgQEe8wsrKyEB0djU6dOiExMZHb\nFl2+fBl79uzBrFmzKn0GW7G5w2CMyWDIepZCREMZYxEAtgLwARAPYAwRVVsYY09PT/j6+lZX8+WI\niorCtWvXTB47fPgwevfuXa6+Q4cO0Gq1yM7ORt++fXHgwAEoFArDnNCow3N1dYWrq2u1yd5QOXr0\nKHr27AlAHHG+bdu2SEtLQ1paGrePqIiAgABkZ2fz/c6dTcW/BkJDQy3qgMwpIHv37o3U1FTIZDJR\nwmVhFFIWhUJRcwZl1io/hAJgNoBvAPxSur8NwIjS7fUAJpu5ziIlU2W8+OKLVVJ6fvzxxwSAIiMj\nyd3dnbKysiq9ZsuWLQSAWrRoQQBoyJAhpNfrSa/XExGRXq8nALR27dpy1wIgPz+/StfrGzVqJFJo\nSdiHJUuWlHvXSqWSAFDnzp153ZUrVyptCwBNmTJF9NmbwsPDgwDQ448/XmFbMTExREQ0btw4rhjt\n1KkTFRYW8u9UQUEB6fV60ul0pNPpiIj49urVq7n8n3zyicXvBDYoPW0aYTDGwgAMBrAEgOAl0xfA\nqNLtzQDeB7DRlvtUBJUqk9LT05GUlASlUgkiQnFxMZRKJYKDg0VKxrfffhvu7u5o3LgxkpKSsGDB\nAkyZMoXH0KDStWqZTIbi4mLI5XKMGTMGcrkcjRo1glwux7/+9S9Rjy5s37x5E6dPn+ZOR4KyasuW\nLXjzzTdx+fJlREREcIejgoICFBcXIzQ0FBEREWCMibLBSdjOwoUL4ebmhtmzZ2Px4sVwcnJCeHg4\nGjVqhDfeeAMLFy7EY489hqZNm1rUnvD9qAhBp2ZsdWyOq1ev4j//+Q/8/Pzg6emJU6dO4erVq+jQ\noQOA8vYawF/T8GnTpmHbtm3w8fHBpEmTLJLfZqztaUr/UX8A0B7A4wB+AeAHINHoeBiA82autbhH\nrIhJkyaRr69vhb/exqhUKpo4cSIRETVu3Nhii7233nqrQjmCg4PNXpufn08ajYbvjxs3zi7PLlE5\nvr6+NHDgQLOfjZ+fn8VtyWQymjt3bqXnGY8ozQGAmjVrZlKm//3vf3yEUVhYaLF8lgJHLKsyxoYA\nSCOiswCE7o8ZbfM+ydp7WMLDhw/5vDIuLk70cOnp5dO6qtVqPr+8deuWxS9q+fLlFcohuDubKm5u\nbnB2duajoTNnztj5LUiYIzs7W+RH9P777/PPZfjw4SKdRGXo9Xqo1epKzzMe0VbE9evXRfvz588H\nADz99NP4xz/+YbFcNYktU5IeAIYyxgYDcAHgAWA1AC/GmIyI9DCMMO6Za8DSZMz37t3D/v37IZfL\nodfrUVJSAicnJ6SmpuKPP/7g5+3cuRM3b96Es7MzdDqd2S9DUVFRFR/Vvmi1Wofev6GRkZHBt0+f\nPo1vv/0WPj4+ePjwIe/ELcWSz+7HH3/EzJkzsW/fPnz77bcoLi6GSqWCTqdDSUkJX02LiIjAqlWr\ncOfOHSxcuJD/KMlkMsybN69KclVErUnGLBSUTknoL6Xni/SX0nOKmWssHkJVNNyvrBjHxxCGYxXF\nCqhuIiIiqH///g67f0NjwIABBICio6OpY8eOou9G7969SSaTWdwWAJo1a5ZF5xYXF3MrUnMlPj6e\nt2uulJSUWPXclT0H1fSUpAIWAJjDGEsE4AvAZkuk1NRUAKh06evQoUOihysuLoaHhwcYY9i6dStv\nIzc3F05OTlyZNGLECFtFrBS1Wg3GGG7duoXY2FiRMqus01xFLFmyBIwxvpRmXPbv31+NT1A32bdv\nH4gIly9fxunTp41/sFBcXCxatrQnCoUCGo2mwn8+QbEJGHyiBLkA4J133gERVVv0b2uxi+EWER0G\ncLh0OwlAV3u0K3Dnzh2sXbsWSqUSxcXF/IO+f/8+zp49i1mzZuGVV14pN7zMycnhw9E33ngDiYmJ\n0Gq12L17N/R6Pbp3747ExET8+OOP9hTXJKb0KQK5ubno1asXNBoNpkyZYjZjFQC8++67UCqVmDdv\nHtRqNfR6PVxcXLBkyRJ8/fXX6NevX3WIX2/Q6/VYvHgxACAvL69SK+H79+/jn//8J7ffsKdVMRFx\nWb7//ntcuXIFI0eOxI4dO7B06VJRTNpag7VDE1sL7GxvAJiPhzFr1izR+nvZ8uyzz9pVFlNkZmZa\nPI2qiJYtW1KnTp3K1QOg1157rbrErzcsW7aMAJBcLqe+fftW+r6Fz0QulxMAOnPmjN1k+fTTT81+\nB+bPn2+3+5QFtWxK4jDKro9T6Yhj1apVICKo1WqTL+G///1vtcsmyAKII5ibitJUdqohFFdXVxQU\nFJjU1Lu4uIisGCVMk5mZCcCgvAwICDBrPSkQGBiI559/HlqtFkSE9u3b200Wc9agDx48wLJly+x2\nH3tS531JjBk2bBjc3NygUqlQUFAg8hx0NMZxEry9vfnQNiYmBlFRUcjOzoZarYazszNkMhnUajXk\ncjnkcjm2bduG/Px8FBUVISUlRdSWQFFREXbs2IFnnnkGAwcOrLHnqo1cunQJixYtgqenJx4+fIji\n4mL4+PhArVbzADZjx47FgQMHeAdSFipdSk9PTxetstiT2bNnIy4uDrt378b48ePRvXt3TJo0CTdu\n3MBjjz1WLfe0GWuHJrYWVMOUxFypDdy+fZvLY2ywUzY2giliYmIoICCAX7dp06Zy50ybNq1WPa8j\nEd6DsQGVp6dnlYb+N2/e5OccPHiwWuScP38+v0d2djaX/dSpU9VyPwFIUxIDlhhuOQpjpzJjgx0h\nLFtFBAQEICMjg1/36quvlpuu/Otf/7K/0HWUiIgIDB48mMeCICLk5uaCiLhxlFBvbugveIgSkcg+\n6LfffuPvXAhNYC2PPvoo3/b19eVTaiEWRm2kXk1JXnnlFXh4eCAkJAQZGRl86N6jRw9s3rwZzZo1\nc5hs/v7+iIuLQ+/evREcHIxNmzYhJCREtLRmjh07duDPP/8EkcGPQa1Wc+MfrVbLPV/nzJmD48eP\nY/jw4diyZUudCCxUHdy6dcusB7PwnXj88ceRlpYGf39/qFQq3Lt3D25ubvDz88Pdu3d5VrEOHTrg\nhx9+wHvvvYfjx4/zJEOAYRm/Xbt2Vss5ZswYtGnTBmq1GiUlJVAoFPD396/daT2tHZrYWmDnofPw\n4cP58K5p06blhp6PPfaYXe9nLS4uLtS1a9dqaXvRokX8eavivVjfAEAdO3Y0eezy5cv0yCOP8OmK\nt7c3hYeHE2AI12fqu9OpU6dydU2bNqXk5OQafjL7ABumJPVmhLF9+/ZydQUFBXB3dwcAk4FKHEFw\ncHC1Bcox/rWzZORSX4mMjDQ71YuOjkZKSkqlbRin2Tx16pTomJeXVzk/kIZCnc6taglFRUXQarXc\n4tPRCPPfMWPGYPPmzXZvXxhyV5cFY12AMYauXbuK/IysoaioCEVFRcjPz4eLiwvPierm5mZx+P/a\niC25Vet9h1GbyM3NFZmBN7TnrykYY+jQoQPi4+MdLUqtxJYOo16tktRmioqKeGdhycqIhPWEh4dL\n75m8/2gAABeWSURBVLiakDqMGiI/P59v379/34GS1H8yMjJM+nwIofjnzp3rAKnqB9KUpIa4ePGi\nKMI0ABw5cgQ9evRwkET1F8YY2rZtW85OgjEGpVIJjUaD2NhY6HQ69OzZs8EFXpamJHUAU51jz549\nayQ0fEMjICCAp0M0pmXLltx/o3///hg0aJDJ8yTMI3UYNYSpFRrGmFlfBgnr0ev1JjOZXb16lXfQ\nRITnnntOin5WRaQOo4YoO8Lo0aMHiAjx8fHl1vklbCMrKwtpaWkmjwkdxNWrV3H+/HmL8odI/EXd\nXUyuY5SdJx89ehQAcOXKFXTu3FlaYrUjrq6uZlMEurq6QiaTITo6uoalqh9II4wawjjvpSlMxb/4\n29/+VkPS1Q+Ki4vh5eWFwsJCkx1Gv379sHLlyloV9qCuIa2S1BCC0dZHH33EtfXFxcXQ6XS4fPky\nHjx4AI1GA6VSib1798LFxQVFRUXlRh7Xr1/HJ598And3d27F+tJLL6Fv374OerLaQ3p6OoKCgrB0\n6VJMnz69nPMdYwy9evWCv78/GGM4c+YMkpKSGtzozpZVknrjfFbbycnJsThWBUodnKZNm1buWMuW\nLU2GG5QgSk9Pr/BdAKD333+f7z///PPk6upaE6LVKuCIeBiMsTDG2AHG2GXG2AXG2IzSeh/G2D7G\nWAJjbC9jTEpJbgXTpk3DZ599Vq6+uLgY7dq14+EGd+3a5QDpaieV+Qo5OTmJIs8zxnjcCwnLsEXp\nWQJgDhGdZYy5AzjNGNsHYDyAWCJawRibD+BtGFIPSFQBIR4DYIiaPm3aNLi5uSEpKQlyuRwjR45E\nVlZWrQoS5GiodGrx1FNPobCwEAEBASguLsaDBw/g4+MDnU6H9evX49ChQ/Dy8sL+/ftF71micqzu\nMIgoFUBq6XY+Y+wKDJnOhsGQ2AgwJGM+BKnDqPI82TgfxeDBg3Hp0iXuH1FSUoJjx47h7t27AIAn\nn3zSfoLWYXx9fREZGYk9e/YAMLihazQaqNVqnsA4JSUFKSkpCAoKwoMHD7Bo0SJHilznsMuyKmMs\nAoakzH8ACCKiNMDQqTDGyofFboCYGi536NABZ8+ehZ+fHzIzM3HgwAGeV8Q4mnVwcDC0Wi0SEhLQ\nqlUreHp62uy6XR9xcnLCzZs3ARiibkVGRvJjwsrI9u3bMXz4cIfIVx+wucMonY78CGBm6UijYamc\nLUToMCIiIgAYPCrPnj2LRx99FJcuXUJUVBRu377Nz1+6dClWrFgBlUrFHal69+6NK1eumExNICFG\nCHCTkJAAvV4PmUwGlUqFRo0aOViyuo1NHQZjTA5DZ7GFiHaUVqcxxoKIKI0xFgzA7CTb0mTM9QE3\nNzcMGDAABw8eBGMMbm5ucHV1xb///W9MnDgRt2/f5vlGhCVVY7NlmUyG7OxseHp6VkvgnfqGYPfS\nokULB0vieOyZjNkmOwzG2NcAMolojlHdcgDZRLS8VOnpQ0TldBgNzQ7DHH/++Se6dOnC9319fZGV\nleVAieoHv//+O3r37t3gbCwswSHeqoyxHgBGA+jLGDvDGItnjD0JYDmAAYyxBAD9AdTOFE41DBEh\nJiYGPj4+IivO8+fPAzDMsYlI6iyqwJo1ayCTyaBUKsEYw44dO/gxYQoYHh5uczoACSOsNeCwtaCB\nGRsJhltt27al4OBgbmC0fv16yfDKSlQqlch4zc3Njc6fP09ERHq9nnr37k0AaOTIkQ6WtHYBKZFR\n3eHcuXP4+eef+b6Liwtf8pOoGmUVmAUFBWjbti0A4Nq1a4iLiwMAJCYm1rhs9RXpm1pDCEPkwYMH\nIyYmhtfrdDrJGcpKwsPDTdaXnZ7k5ubWlEj1Hsn5rIbIyckplwJv27Zt2LdvHzZt2iQp56zg7t27\nmDhxIjfUAoCgoCCkpaXx9JKAIfXg4MGDUVJSgqFDh+LcuXPIzs5GcnIyFi1ahG7dujnqERyClGag\nDiB4qwoxJY1p0qQJbty44SDJ6j5ljeLkcnmVfEQa0vcQkGJ61il8fX15VjJBkSR1FrYhvMfs7GwA\nkBzKqhGpw6hh7t+/bzZ8nIRt+Pj4cA9f48xkjDF4eXnBz88PXl5ecHNzg7u7O/z8/PDll186Stw6\nidRh1BDGeUkmTJjgQEnqN9OmTYNcLkdUVBSvGz16NHJycpCZmYmcnBzk5+fj4cOHyMzMxKuvvupA\naeseUodRQxjPs/Py8vj2pUuXeEg+wYhLwjZCQkJEcS/MraZIVB0pCHANYRwEWFjyGzVqFK5du8br\nv/vuO25HIGE9ycnJfIXkiy++wLhx4xwrUD1CGmHUEMZTksWLFyM4OBhbt27F6dOn8dRTTwFAnc4I\nXpv44IMPuCPfxIkTpfdqR6Rl1Rrizp07aNy4sWg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+ "text/plain": [ + "" + ] + }, + "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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+ "text/plain": [ + "" + ] + }, + "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=)), Mistake(i=38, step=Step(x=10, y=2, dir=)), Mistake(i=42, step=Step(x=8, y=4, dir=))]\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=)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "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=)),\n", + " Mistake(i=71, step=Step(x=-3, y=6, dir=))]" + ] + }, + "execution_count": 1015, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mks" + ] + }, + { + "cell_type": "code", + "execution_count": 1016, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + 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ROL4KX77jh4E+ffoEO4VGC+fcWzIpfCEcSApfCAeSwhfCgaTwhXAgKXwhHEgK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\n", + "text/plain": [ + "" + ] + }, + "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=),\n", + " Step(x=0, y=0, dir=)])" + ] + }, + "execution_count": 1247, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ubBEp0ujRo7F+/XoAQGVlJZ566ikkk8nAqc4PKigRcUsFJSJuqaBExC0VlIi4pYISEbdU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\npss0Ar0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "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=))" + ] + }, + "execution_count": 1281, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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lImGpoEQkLBWUiIRVVEGZ2TQzW2dmrWZ2elahRAaSt99+m+nTpwPw5ptvcvnll/P+++8n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\nL6Qd6rXaC/YAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "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=))" + ] + }, + "execution_count": 1299, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "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" ] }, { diff --git a/06-tour-shapes/tours-with-mistakes.txt b/06-tour-shapes/tours-with-mistakes.txt new file mode 100644 index 0000000..0d34629 --- /dev/null +++ b/06-tour-shapes/tours-with-mistakes.txt @@ -0,0 +1,100 @@ +FFLFFFLFFLFFFFFRLFRRFRLRLR 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