--- /dev/null
+
+# coding: utf-8
+
+# 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":
+# 1. which tours are closed?
+# 2. what is the area enclosed by the tour?
+
+# In[1]:
+
+import collections
+import enum
+import random
+import os
+
+import matplotlib.pyplot as plt
+
+# Use PIL to save some image metadata
+from PIL import Image
+from PIL import PngImagePlugin
+
+# In[2]:
+
+class Direction(enum.Enum):
+ UP = 1
+ RIGHT = 2
+ DOWN = 3
+ LEFT = 4
+
+turn_lefts = {Direction.UP: Direction.LEFT, Direction.LEFT: Direction.DOWN,
+ Direction.DOWN: Direction.RIGHT, Direction.RIGHT: Direction.UP}
+
+turn_rights = {Direction.UP: Direction.RIGHT, Direction.RIGHT: Direction.DOWN,
+ Direction.DOWN: Direction.LEFT, Direction.LEFT: Direction.UP}
+
+def turn_left(d):
+ return turn_lefts[d]
+
+def turn_right(d):
+ return turn_rights[d]
+
+
+# In[3]:
+
+Step = collections.namedtuple('Step', ['x', 'y', 'dir'])
+Mistake = collections.namedtuple('Mistake', ['i', 'step'])
+
+
+# In[4]:
+
+def advance(step, d):
+ if d == Direction.UP:
+ return Step(step.x, step.y+1, d)
+ elif d == Direction.DOWN:
+ return Step(step.x, step.y-1, d)
+ elif d == Direction.LEFT:
+ return Step(step.x-1, step.y, d)
+ elif d == Direction.RIGHT:
+ return Step(step.x+1, step.y, d)
+
+
+# In[5]:
+
+def trace_tour(tour, startx=0, starty=0, startdir=Direction.RIGHT):
+ current = Step(startx, starty, startdir)
+ trace = [current]
+ for s in tour:
+ if s == 'F':
+ current = advance(current, current.dir)
+ elif s == 'L':
+ current = advance(current, turn_left(current.dir))
+ elif s == 'R':
+ current = advance(current, turn_right(current.dir))
+ trace += [current]
+ return trace
+
+
+# In[6]:
+
+def positions(trace):
+ return [(s.x, s.y) for s in trace]
+
+
+# In[7]:
+
+def valid(trace):
+ return (trace[-1].x == 0
+ and trace[-1].y == 0
+ and len(set(positions(trace))) + 1 == len(trace))
+
+
+# In[8]:
+
+def chunks(items, n=2):
+ return [items[i:i+n] for i in range(len(items) - n + 1)]
+
+
+# Using the [Shoelace formula](https://en.wikipedia.org/wiki/Shoelace_formula)
+
+# In[9]:
+
+def shoelace(trace):
+ return abs(sum(s.x * t.y - t.x * s.y for s, t in chunks(trace, 2))) // 2
+
+
+# In[10]:
+
+def step(s, current):
+ if s == 'F':
+ return advance(current, current.dir)
+ elif s == 'L':
+ return advance(current, turn_left(current.dir))
+ elif s == 'R':
+ return advance(current, turn_right(current.dir))
+ else:
+ raise ValueError
+
+
+# In[11]:
+
+def valid_prefix(tour):
+ current = Step(0, 0, Direction.RIGHT)
+ prefix = []
+ posns = []
+ for s in tour:
+ current = step(s, current)
+ prefix += [s]
+ if (current.x, current.y) in posns:
+ return ''
+ elif current.x == 0 and current.y == 0:
+ return ''.join(prefix)
+ posns += [(current.x, current.y)]
+ if current.x == 0 and current.y == 0:
+ return ''.join(prefix)
+ else:
+ return ''
+
+
+# In[12]:
+
+def mistake_positions(trace, debug=False):
+ mistakes = []
+ current = trace[0]
+ posns = [(0, 0)]
+ for i, current in enumerate(trace[1:]):
+ if (current.x, current.y) in posns:
+ if debug: print(i, current)
+ mistakes += [Mistake(i+1, current)]
+ posns += [(current.x, current.y)]
+ if (current.x, current.y) == (0, 0):
+ return mistakes[:-1]
+ else:
+ return mistakes + [Mistake(len(trace)+1, current)]
+
+
+# In[13]:
+
+def returns_to_origin(mistake_positions):
+ return [i for i, m in mistake_positions
+ if (m.x, m.y) == (0, 0)]
+
+
+# In[14]:
+
+def random_walk(steps=1000):
+ return ''.join(random.choice('FFLR') for _ in range(steps))
+
+
+# In[15]:
+
+def bounds(trace):
+ return (max(s.x for s in trace),
+ max(s.y for s in trace),
+ min(s.x for s in trace),
+ min(s.y for s in trace))
+
+
+# In[16]:
+
+
+plot_wh = {Direction.UP: (0, 1), Direction.LEFT: (-1, 0),
+ Direction.DOWN: (0, -1), Direction.RIGHT: (1, 0)}
+
+def plot_trace(trace, colour='k', xybounds=None, fig=None, subplot_details=None, filename=None):
+ # plt.axis('on')
+ plt.axis('off')
+ plt.axes().set_aspect('equal')
+ for s, t in chunks(trace, 2):
+ w, h = plot_wh[t.dir]
+ plt.arrow(s.x, s.y, w, h, head_width=0.1, head_length=0.1, fc=colour, ec=colour, length_includes_head=True)
+ xh, yh, xl, yl = bounds(trace)
+ if xybounds is not None:
+ bxh, byh, bxl, byl = xybounds
+ plt.xlim([min(xl, bxl)-1, max(xh, bxh)+1])
+ plt.ylim([min(yl, byl)-1, max(yh, byh)+1])
+ else:
+ plt.xlim([xl-1, xh+1])
+ plt.ylim([yl-1, yh+1])
+ if filename:
+ plt.savefig(filename)
+ plt.close()
+
+
+# In[17]:
+
+def trim_loop(tour):
+ trace = trace_tour(tour)
+ mistakes = mistake_positions(trace)
+ end_mistake_index = 0
+# 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))
+ # while this mistake extends to the next step in the trace...
+ while (mistakes[end_mistake_index].i + 1 < len(trace) and
+ end_mistake_index + 1 < len(mistakes) and
+ mistakes[end_mistake_index].i + 1 ==
+ mistakes[end_mistake_index + 1].i):
+# 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))
+ # push this mistake finish point later
+ end_mistake_index += 1
+ mistake = mistakes[end_mistake_index]
+
+ # find the first location that mentions where this mistake ends (which the point where the loop starts)
+ mistake_loop_start = max(i for i, loc in enumerate(trace[:mistake.i])
+ if (loc.x, loc.y) == (mistake.step.x, mistake.step.y))
+# print('Dealing with mistake from', mistake_loop_start, 'to', mistake.i, ', trace has len', len(trace))
+
+ # direction before entering the loop
+ direction_before = trace[mistake_loop_start].dir
+
+ # find the new instruction to turn from heading before the loop to heading after the loop
+ new_instruction = 'F'
+ if (mistake.i + 1) < len(trace):
+ if turn_left(direction_before) == trace[mistake.i + 1].dir:
+ new_instruction = 'L'
+ if turn_right(direction_before) == trace[mistake.i + 1].dir:
+ new_instruction = 'R'
+# if (mistake.i + 1) < len(trace):
+# print('turning from', direction_before, 'to', trace[mistake.i + 1].dir, 'with', new_instruction )
+# else:
+# print('turning from', direction_before, 'to BEYOND END', 'with', new_instruction )
+ return tour[:mistake_loop_start] + new_instruction + tour[mistake.i+1:]
+# return mistake, mistake_loop_start, trace[mistake_loop_start-2:mistake_loop_start+8]
+
+
+# In[18]:
+
+def trim_all_loops(tour, mistake_reduction_attempt_limit=10):
+ trace = trace_tour(tour)
+ mistake_limit = 1
+ if trace[-1].x == 0 and trace[-1].y == 0:
+ mistake_limit = 0
+ mistakes = mistake_positions(trace)
+
+ old_mistake_count = len(mistakes)
+ mistake_reduction_tries = 0
+
+ while len(mistakes) > mistake_limit and mistake_reduction_tries < mistake_reduction_attempt_limit:
+ tour = trim_loop(tour)
+ trace = trace_tour(tour)
+ mistakes = mistake_positions(trace)
+ if len(mistakes) < old_mistake_count:
+ old_mistake_count = len(mistakes)
+ mistake_reduction_tries = 0
+ else:
+ mistake_reduction_tries += 1
+ if mistake_reduction_tries >= mistake_reduction_attempt_limit:
+ return ''
+ else:
+ return tour
+
+
+# In[19]:
+
+def reverse_tour(tour):
+ def swap(tour_step):
+ if tour_step == 'R':
+ return 'L'
+ elif tour_step == 'L':
+ return 'R'
+ else:
+ return tour_step
+
+ return ''.join(swap(s) for s in reversed(tour))
+
+
+# In[20]:
+
+def wander_near(locus, current, limit=10):
+ valid_proposal = False
+ while not valid_proposal:
+ s = random.choice('FFFRL')
+ if s == 'F':
+ proposed = advance(current, current.dir)
+ elif s == 'L':
+ proposed = advance(current, turn_left(current.dir))
+ elif s == 'R':
+ proposed = advance(current, turn_right(current.dir))
+ if abs(proposed.x - locus.x) < limit and abs(proposed.y - locus.y) < limit:
+ valid_proposal = True
+# print('At {} going to {} by step {} to {}'.format(current, locus, s, proposed))
+ return s, proposed
+
+
+# In[21]:
+
+def seek(goal, current):
+ dx = current.x - goal.x
+ dy = current.y - goal.y
+
+ if dx < 0 and abs(dx) > abs(dy): # to the left
+ side = 'left'
+ if current.dir == Direction.RIGHT:
+ s = 'F'
+ elif current.dir == Direction.UP:
+ s = 'R'
+ else:
+ s = 'L'
+ elif dx > 0 and abs(dx) > abs(dy): # to the right
+ side = 'right'
+ if current.dir == Direction.LEFT:
+ s = 'F'
+ elif current.dir == Direction.UP:
+ s = 'L'
+ else:
+ s = 'R'
+ elif dy > 0 and abs(dx) <= abs(dy): # above
+ side = 'above'
+ if current.dir == Direction.DOWN:
+ s = 'F'
+ elif current.dir == Direction.RIGHT:
+ s = 'R'
+ else:
+ s = 'L'
+ else: # below
+ side = 'below'
+ if current.dir == Direction.UP:
+ s = 'F'
+ elif current.dir == Direction.LEFT:
+ s = 'R'
+ else:
+ s = 'L'
+ if s == 'F':
+ proposed = advance(current, current.dir)
+ elif s == 'L':
+ proposed = advance(current, turn_left(current.dir))
+ elif s == 'R':
+ proposed = advance(current, turn_right(current.dir))
+
+# print('At {} going to {}, currently {}, by step {} to {}'.format(current, goal, side, s, proposed))
+
+ return s, proposed
+
+
+# In[22]:
+
+def guided_walk(loci, locus_limit=5, wander_limit=10, seek_step_limit=20):
+ trail = ''
+ current = Step(0, 0, Direction.RIGHT)
+ l = 0
+ finished = False
+ while not finished:
+ if abs(current.x - loci[l].x) < locus_limit and abs(current.y - loci[l].y) < locus_limit:
+ l += 1
+ if l == len(loci) - 1:
+ finished = True
+ s, proposed = wander_near(loci[l], current, limit=wander_limit)
+ trail += s
+ current = proposed
+# print('!! Finished loci')
+ seek_steps = 0
+ while not (current.x == loci[l].x and current.y == loci[l].y) and seek_steps < seek_step_limit:
+# error = max(abs(current.x - loci[l].x), abs(current.y - loci[l].y))
+# s, proposed = wander_near(loci[l], current, limit=error+1)
+ s, proposed = seek(loci[l], current)
+ trail += s
+ current = proposed
+ seek_steps += 1
+ if seek_steps >= seek_step_limit:
+ return ''
+ else:
+ return trail
+
+
+# In[24]:
+
+def square_tour(a=80):
+ "a is width of square"
+ return ('F' * a + 'L') * 4
+
+
+# In[25]:
+
+def cross_tour(a=50, b=40):
+ "a is width of cross arm, b is length of cross arm"
+ return ('F' * a + 'L' + 'F' * b + 'R' + 'F' * b + 'L') * 4
+
+
+# In[26]:
+
+def quincunx_tour(a=60, b=30, c=50):
+ "a is length of indent, b is indent/outdent distance, c is outdent outer length"
+ return ('F' * a + 'R' + 'F' * b + 'L' + 'F' * c + 'L' + 'F' * c + 'L' + 'F' * b + 'R') * 4
+
+
+# In[27]:
+
+heart_points = [Step(60, 50, Direction.UP), Step(50, 90, Direction.UP),
+ Step(20, 70, Direction.UP),
+ Step(-40, 90, Direction.UP), Step(-60, 80, Direction.UP),
+ Step(0, 0, Direction.RIGHT)]
+
+heart_tour = ''
+current = Step(0, 0, Direction.RIGHT)
+
+for hp in heart_points:
+ while not (current.x == hp.x and current.y == hp.y):
+ s, proposed = seek(hp, current)
+ heart_tour += s
+ current = proposed
+
+def heart_tour_func(): return heart_tour
+
+
+# In[28]:
+
+# success_count = 0
+# while success_count <= 20:
+# lc = trace_tour(square_tour(a=10))
+# rw = guided_walk(lc, wander_limit=4, locus_limit=2)
+# if rw:
+# rw_trimmed = trim_all_loops(rw)
+# if len(rw_trimmed) > 10:
+# with open('small-squares.txt', 'a') as f:
+# f.write(rw_trimmed + '\n')
+# success_count += 1
+
+
+# In[29]:
+
+# success_count = 0
+# while success_count <= 20:
+# lc = trace_tour(square_tour())
+# rw = guided_walk(lc)
+# if rw:
+# rw_trimmed = trim_all_loops(rw)
+# if len(rw_trimmed) > 10:
+# with open('large-squares.txt', 'a') as f:
+# f.write(rw_trimmed + '\n')
+# success_count += 1
+
+
+# In[30]:
+
+# success_count = 0
+# while success_count <= 20:
+# lc = trace_tour(cross_tour())
+# rw = guided_walk(lc)
+# if rw:
+# rw_trimmed = trim_all_loops(rw)
+# if len(rw_trimmed) > 10:
+# with open('cross.txt', 'a') as f:
+# f.write(rw_trimmed + '\n')
+# success_count += 1
+
+
+# In[31]:
+
+# success_count = 0
+# while success_count <= 20:
+# lc = trace_tour(quincunx_tour())
+# rw = guided_walk(lc)
+# if rw:
+# rw_trimmed = trim_all_loops(rw)
+# if len(rw_trimmed) > 10:
+# with open('quincunx.txt', 'a') as f:
+# f.write(rw_trimmed + '\n')
+# success_count += 1
+
+
+# In[32]:
+
+# with open('tours.txt') as f:
+# for i, tour_s in enumerate(f.readlines()):
+# tour = tour_s.strip()
+# filename = 'tour-{:03}-s{:04}-m{:03}.png'.format(i, len(tour), len(mistake_positions(trace_tour(tour))))
+# plot_trace(trace_tour(tour), filename=filename)
+# im = Image.open(filename)
+# meta = PngImagePlugin.PngInfo()
+# meta.add_text('Description', tour)
+# im.save(filename, 'PNG', pnginfo=meta)
+
+# with open('tours-with-mistakes.txt') as f:
+# for i, tour_s in enumerate(f.readlines()):
+# tour = tour_s.strip()
+# filename = 'tour-{:03}-s{:04}-m{:03}.png'.format(i, len(tour), len(mistake_positions(trace_tour(tour))))
+# plot_trace(trace_tour(tour), filename=filename)
+# im = Image.open(filename)
+# meta = PngImagePlugin.PngInfo()
+# meta.add_text('Description', tour)
+# im.save(filename, 'PNG', pnginfo=meta)
+
+with open('tours-open.txt') as f:
+ for i, tour_s in enumerate(f.readlines()):
+ tour = tour_s.strip()
+ filename = 'tour-{:03}-s{:04}-m{:03}-open.png'.format(i, len(tour), len(mistake_positions(trace_tour(tour))))
+ plot_trace(trace_tour(tour), filename=filename)
+ im = Image.open(filename)
+ meta = PngImagePlugin.PngInfo()
+ meta.add_text('Description', tour)
+ im.save(filename, 'PNG', pnginfo=meta)
\ No newline at end of file
},
{
"cell_type": "code",
- "execution_count": 1301,
+ "execution_count": 2,
"metadata": {
"collapsed": true
},
},
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+ "execution_count": 3,
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+ "execution_count": 4,
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+ "execution_count": 5,
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+ "execution_count": 6,
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+ "execution_count": 7,
"metadata": {
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},
},
{
"cell_type": "code",
- "execution_count": 491,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"Step(x=1, y=2, dir=3)"
]
},
- "execution_count": 491,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"__main__.Step"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"<Direction.UP: 1>"
]
},
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"<Direction.RIGHT: 2>"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{<Direction.RIGHT: 2>: <Direction.DOWN: 3>,\n",
+ " <Direction.UP: 1>: <Direction.RIGHT: 2>,\n",
" <Direction.DOWN: 3>: <Direction.LEFT: 4>,\n",
- " <Direction.LEFT: 4>: <Direction.UP: 1>,\n",
- " <Direction.UP: 1>: <Direction.RIGHT: 2>}"
+ " <Direction.LEFT: 4>: <Direction.UP: 1>}"
]
},
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 494,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
" Step(x=2, y=2, dir=<Direction.UP: 1>)]"
]
},
- "execution_count": 494,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
" Step(x=0, y=0, dir=<Direction.DOWN: 3>)]"
]
},
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
" Step(x=0, y=0, dir=<Direction.RIGHT: 2>)]"
]
},
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
" Step(x=0, y=0, dir=<Direction.UP: 1>)]"
]
},
- "execution_count": 15,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
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"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
" (0, 0)]"
]
},
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+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
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- "execution_count": 19,
+ "execution_count": 20,
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"outputs": [
{
"True"
]
},
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+ "execution_count": 20,
"metadata": {},
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}
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{
"False"
]
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{
"False"
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{
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{
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{
"True"
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"outputs": [
{
" 'tuvwxyz']"
]
},
- "execution_count": 25,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 27,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"4"
]
},
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
"9"
]
},
- "execution_count": 28,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
"15"
]
},
- "execution_count": 29,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 31,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 32,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 267,
+ "execution_count": 33,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": 34,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 35,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
"'FFLRLLFLRL'"
]
},
- "execution_count": 35,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 37,
"metadata": {
"scrolled": true
},
"True"
]
},
- "execution_count": 36,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
"True"
]
},
- "execution_count": 37,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[Mistake(i=1, step=Step(x=0, y=0, dir=<Direction.RIGHT: 2>)),\n",
- " Mistake(i=11, step=Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
- " (13, Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
+ "[Mistake(i=10, step=Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
+ " Mistake(i=13, step=Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
]
},
- "execution_count": 89,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[Mistake(i=1, step=Step(x=0, y=0, dir=<Direction.RIGHT: 2>)),\n",
- " Mistake(i=11, step=Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
- " Mistake(i=14, step=Step(x=1, y=0, dir=<Direction.UP: 1>)),\n",
- " Mistake(i=15, step=Step(x=0, y=0, dir=<Direction.LEFT: 4>)),\n",
- " Mistake(i=16, step=Step(x=0, y=-1, dir=<Direction.DOWN: 3>)),\n",
- " (17, Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
+ "[Mistake(i=10, step=Step(x=0, y=0, dir=<Direction.DOWN: 3>)),\n",
+ " Mistake(i=13, step=Step(x=1, y=0, dir=<Direction.UP: 1>)),\n",
+ " Mistake(i=14, step=Step(x=0, y=0, dir=<Direction.LEFT: 4>)),\n",
+ " Mistake(i=15, step=Step(x=0, y=-1, dir=<Direction.DOWN: 3>)),\n",
+ " Mistake(i=17, step=Step(x=0, y=-1, dir=<Direction.DOWN: 3>))]"
]
},
- "execution_count": 90,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
"'FFLRLLFLRL'"
]
},
- "execution_count": 91,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 574,
+ "execution_count": 42,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
"''"
]
},
- "execution_count": 42,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
{
"data": {
"text/plain": [
- "['RFLFFLFFFFFFLRRLLRLFFFLFFFLRFRLLFFRR',\n",
- " 'FLRLFLFFLFRLLRRFLFRFLLFFFLLRRFLF',\n",
- " 'LRFLFRFRLLRLRLRRLRRFLFRLRLFRLRRFLLRRLFFRFRRL',\n",
- " 'RFLLRFLFFFFLRFFLFLFLRFRFLRRLLFLFRL',\n",
- " 'LFFRLFRLLRFFLLRFLFFLRFRFFLLRLRLF',\n",
- " 'LRFRLFRFFLRRLRLFFRFFFRFLRFFFFRLR',\n",
- " 'LRFLFFFRRLLRFFFRRLFRFFRLLFLRFRFLRRLR',\n",
- " 'LLRRLFLFRLFFFFFFLLRLFRLRFLFFRFRLRFLLRFLFLRFLFFFF',\n",
- " 'FLFRFFRFLRFLLRFRFRLFRRLLRRFLFLRRFFLR',\n",
- " 'FRFFFRFFRRLLFRFLRFRLFLRRFRFLFFRFRL']"
+ "['FFFFRFFLRLRRFFFFFFFLRFFFFRFFFRFFFFFLFR',\n",
+ " 'RFLFFFLFFFFLFRFRFLRFLLFRLFFFFFFLFFFFFLFRFL',\n",
+ " 'FFRFLFFRFFFLRFRFFFFFRFFFLFRFRFLF',\n",
+ " 'FFRLFLFFFLRRLLFLRFFFFFLFLRFFLFFL',\n",
+ " 'FFFFFRFFFFRFLFRFRFFFLFFFFRFFRFFF',\n",
+ " 'RRLFFLRRLFLFRRFLFLFRFLLFFFFFFLRFFFFL',\n",
+ " 'LFFFRRLLRFLFLLRFFFRLFFFLFFRFFLFLFFFLRFRLRFLFLL',\n",
+ " 'FRFLFLRFLRFLFLLRFRFFFFLLFRFFLFFF',\n",
+ " 'RFFFRLRFFRFFFFLLFFFRFRFFFFFFRFRFLFFRLF',\n",
+ " 'LLFRFRLFFFFLFLFFFRFFLFFFLLRRFLFLRF']"
]
},
"execution_count": 46,
"data": {
"image/png": 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aZghIUsMMAUlqmCEgSQ3rFAJJPpzkn5McTXLeceo2J9mb5IdJru3SpiSpP11nAnuA/wz8\n/VwFSdYANwHvB34PuDzJOzq2uyINh8NJd+EV5fhWttU8vtU8tq46hUBV/aCq9gHH+5b7C4B9VfWj\nqjoC3AFs6dLuSrXaH4iOb2VbzeNbzWPraimuCZwB7J9x+yej+yRJE7b2RAVJvgWcPvMuoID/UVX3\nzKONcbOEml/3JEmvpFR1fz5O8m3gz6vqsTH7NgFTVbV5dPs6oKrqhjG1hoMkLUJVHW9Zfk4nnAks\nwFwdeAR4a5INwM+Ay4DLxxUudhCSpMXp+hbRDyXZD2wCtie5b3T/m5NsB6iqo8CfAfcD/xe4o6qe\n6NZtSVIfelkOkiStTBP9xPBq/7BZktcnuT/JD5L8XZJT56g7muSxJDuT3LXU/VyoE52PJCcluSPJ\nviQPJTlrEv1crHmM74okPx+ds8eS/Okk+rkYSW5JciDJ7uPUfGl07nYl2biU/evqRONLclGSQzPO\n3eeWuo+LleTMJA8meTzJniRXz1G3sPNXVRP7Ad4OnAM8CJw3R80a4F+ADcA6YBfwjkn2ewHjuwH4\n76Pta4G/nKPu2Un3dQFjOuH5AD4NfHm0/VGmlwAn3vcex3cF8KVJ93WR4/v3wEZg9xz7LwXuHW1f\nCPzjpPvc8/guAu6edD8XObY3ARtH268BfjDmsbng8zfRmUCt/g+bbQFuHW3fCnxojrqVdEF8Pudj\n5ri3Ae9dwv51Nd/H20o6Z8dU1XeBXx2nZAtw26h2B3BqktOPU7+szGN8sHLP3TNVtWu0/WvgCV7+\nmasFn7+V8B/IreQPm72xqg7A9AkEfnuOuvVJHk7yvSTLPeDmcz6O1dT0GwMOJTltabrX2Xwfb380\nmm7fmeTMpenakpg9/p+ycv7e5mvTaOn13iTvmnRnFiPJ2UzPeHbM2rXg89fnW0THWu0fNjvO+Bay\n1nhWVT2T5HeAB5Psrqon++xnj+ZzPmbXZEzNcjWf8d0N3F5VR5JcxfSsZyXNdo5nWf+99eBRYENV\nPZ/kUuAu4G0T7tOCJHkN0zPsa0YzgpfsHvMrxz1/r3gIVNUfdjzET4CZFxbPBJ7ueMzeHG98owtU\np1fVgSRvAn4+xzGeGf37ZJIhcC6wXENgPudjP/AW4OkkrwJeW1UnmqIvFycc36yxfI3paz+rxU+Y\nPncvWlZ/b13NfNKsqvuSfDnJaVV1cJL9mq8ka5kOgK9X1TfHlCz4/C2n5aATftgsyUlMf9js7qXr\nVid3Ax8fbV8BvOykJXndaFwkeQPw74DHl6qDizCf83EP0+MF+AjTF/5XihOObxToL9rC8j5f44S5\n/97uBj4Gxz7tf+jFJc0VZM7xzVwfT3IB02+TXxEBMPLXwONVdeMc+xd+/iZ8tftDTL9qfIHpTxPf\nN7r/zcD2GXWbmb4Svg+4btJX6RcwvtOA/zPq+7eA143uPx+4ebT9B8BuYCfwT8DHJ93veYzrZecD\nuB74T6Pt9cCdo/3/CJw96T73PL4vAP88OmcPAG+bdJ8XMLbbmX5leBj4MfAJ4CrgUzNqbmL6HVL/\nxBzv2luuPycaH/DfZpy77wEXTrrPCxjbu4GjTL9jbSfw2Oix2un8+WExSWrYcloOkiQtMUNAkhpm\nCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSG/X+XpHhUiY9RnAAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40411df278>"
+ "<matplotlib.figure.Figure at 0x7f1034b579e8>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1198,
+ "execution_count": 52,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 995,
+ "execution_count": 53,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 967,
+ "execution_count": 54,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 968,
+ "execution_count": 55,
"metadata": {},
"outputs": [
{
"'1:2;3'"
]
},
- "execution_count": 968,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 1002,
+ "execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4026af4b00>"
+ "<matplotlib.figure.Figure at 0x7f10348a6358>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1003,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4026adc278>"
+ "<matplotlib.figure.Figure at 0x7f10347d76a0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1004,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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+ACSCwAeARJQU+LbPsv2Y7c22f2t7QAftmmyvsf2s7V+U0icA4NQ4Ik79w/Y3JL0WEXNt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\nRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027cd51d0>"
+ "<matplotlib.figure.Figure at 0x7f1034735828>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1005,
+ "execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Step(x=0, y=0, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=0, y=-1, dir=<Direction.DOWN: 3>),\n",
- " Step(x=0, y=-2, dir=<Direction.DOWN: 3>),\n",
- " Step(x=1, y=-2, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=1, y=-1, dir=<Direction.UP: 1>),\n",
- " Step(x=2, y=-1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=1, y=0, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=2, y=0, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=2, y=-1, dir=<Direction.DOWN: 3>),\n",
" Step(x=3, y=-1, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=3, y=0, dir=<Direction.UP: 1>),\n",
- " Step(x=3, y=1, dir=<Direction.UP: 1>),\n",
- " Step(x=3, y=2, dir=<Direction.UP: 1>),\n",
- " Step(x=3, y=3, dir=<Direction.UP: 1>),\n",
+ " Step(x=4, y=-1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=4, y=0, dir=<Direction.UP: 1>),\n",
+ " Step(x=4, y=1, dir=<Direction.UP: 1>),\n",
+ " Step(x=4, y=2, dir=<Direction.UP: 1>),\n",
+ " Step(x=4, y=3, dir=<Direction.UP: 1>),\n",
+ " Step(x=3, y=3, dir=<Direction.LEFT: 4>),\n",
" Step(x=3, y=4, dir=<Direction.UP: 1>),\n",
- " Step(x=2, y=4, dir=<Direction.LEFT: 4>),\n",
- " Step(x=2, y=5, dir=<Direction.UP: 1>),\n",
- " Step(x=2, y=6, dir=<Direction.UP: 1>),\n",
- " Step(x=2, y=7, dir=<Direction.UP: 1>),\n",
- " Step(x=1, y=7, dir=<Direction.LEFT: 4>),\n",
- " Step(x=0, y=7, dir=<Direction.LEFT: 4>),\n",
- " Step(x=0, y=6, dir=<Direction.DOWN: 3>),\n",
- " Step(x=0, y=5, dir=<Direction.DOWN: 3>),\n",
- " Step(x=1, y=5, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=1, y=4, dir=<Direction.DOWN: 3>),\n",
- " Step(x=1, y=3, dir=<Direction.DOWN: 3>),\n",
- " Step(x=0, y=3, dir=<Direction.LEFT: 4>),\n",
- " Step(x=-1, y=3, dir=<Direction.LEFT: 4>),\n",
- " Step(x=-1, y=2, dir=<Direction.DOWN: 3>),\n",
- " Step(x=-2, y=2, dir=<Direction.LEFT: 4>),\n",
- " Step(x=-2, y=3, dir=<Direction.UP: 1>),\n",
- " Step(x=-3, y=3, dir=<Direction.LEFT: 4>),\n",
- " Step(x=-3, y=2, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=4, y=4, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=4, y=5, dir=<Direction.UP: 1>),\n",
+ " Step(x=3, y=5, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=2, y=5, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=2, y=4, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=1, y=4, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=0, y=4, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-1, y=4, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-2, y=4, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-3, y=4, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-4, y=4, dir=<Direction.LEFT: 4>),\n",
+ " Step(x=-4, y=3, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=-4, y=2, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=-3, y=2, dir=<Direction.RIGHT: 2>),\n",
" Step(x=-3, y=1, dir=<Direction.DOWN: 3>),\n",
- " Step(x=-2, y=1, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=-1, y=1, dir=<Direction.RIGHT: 2>),\n",
- " Step(x=-1, y=0, dir=<Direction.DOWN: 3>),\n",
- " Step(x=0, y=0, dir=<Direction.RIGHT: 2>)]"
+ " Step(x=-3, y=0, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=-3, y=-1, dir=<Direction.DOWN: 3>),\n",
+ " Step(x=-2, y=-1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=-1, y=-1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=0, y=-1, dir=<Direction.RIGHT: 2>),\n",
+ " Step(x=0, y=0, dir=<Direction.UP: 1>)]"
]
},
- "execution_count": 1005,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 1006,
+ "execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEACAYAAAAnVmqqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCZYCTkSC9X9HKanR5sBgvwAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027bb3e80>"
+ "<matplotlib.figure.Figure at 0x7f10347d5630>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 61,
"metadata": {},
"outputs": [
{
"10"
]
},
- "execution_count": 57,
+ "execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
"len(long_walks)"
]
},
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027d30588>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[0]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027d86518>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[1]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027c0a8d0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[2]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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HOHDgQOIsd+fVV19l+vTplJaWcvvtt0f5nJtKZ4x2yN05//zz2bFjBxdccEHivClTpkQY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\nFUMk4P8ANAvxgu/mStQAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027d86518>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[3]))"
- ]
- },
{
"cell_type": "code",
"execution_count": 62,
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAADTCAYAAABOQ5KuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027d88ac8>"
+ "<matplotlib.figure.Figure at 0x7f1034735710>"
]
},
"metadata": {},
}
],
"source": [
- "plot_trace(trace_tour(long_walks[4]))"
+ "plot_trace(trace_tour(long_walks[0]))"
]
},
{
"outputs": [
{
"data": {
- "image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027db4390>"
+ "['FFFFRFFLRLRRFFFFFFFLRFFFFRFFFRFFFFFLFR',\n",
+ " 'RFLFFFLFFFFLFRFRFLRFLLFRLFFFFFFLFFFFFLFRFL',\n",
+ " 'FFRFLFFRFFFLRFRFFFFFRFFFLFRFRFLF',\n",
+ " 'FFRLFLFFFLRRLLFLRFFFFFLFLRFFLFFL',\n",
+ " 'FFFFFRFFFFRFLFRFRFFFLFFFFRFFRFFF',\n",
+ " 'RRLFFLRRLFLFRRFLFLFRFLLFFFFFFLRFFFFL',\n",
+ " 'LFFFRRLLRFLFLLRFFFRLFFFLFFRFFLFLFFFLRFRLRFLFLL',\n",
+ " 'FRFLFLRFLRFLFLLRFRFFFFLLFRFFLFFF',\n",
+ " 'RFFFRLRFFRFFFFLLFFFRFRFFFFFFRFRFLFFRLF',\n",
+ " 'LLFRFRLFFFFLFLFFFRFFLFFFLLRRFLFLRF']"
]
},
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[5]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027d1fcf8>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[6]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027ca70b8>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[7]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c0d3588>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_trace(trace_tour(long_walks[8]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 67,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['RFLFFLFFFFFFLRRLLRLFFFLFFFLRFRLLFFRR',\n",
- " 'FLRLFLFFLFRLLRRFLFRFLLFFFLLRRFLF',\n",
- " 'LRFLFRFRLLRLRLRRLRRFLFRLRLFRLRRFLLRRLFFRFRRL',\n",
- " 'RFLLRFLFFFFLRFFLFLFLRFRFLRRLLFLFRL',\n",
- " 'LFFRLFRLLRFFLLRFLFFLRFRFFLLRLRLF',\n",
- " 'LRFRLFRFFLRRLRLFFRFFFRFLRFFFFRLR',\n",
- " 'LRFLFFFRRLLRFFFRRLFRFFRLLFLRFRFLRRLR',\n",
- " 'LLRRLFLFRLFFFFFFLLRLFRLRFLFFRFRLRFLLRFLFLRFLFFFF',\n",
- " 'FLFRFFRFLRFLLRFRFRLFRRLLRRFLFLRRFFLR',\n",
- " 'FRFFFRFFRRLLFRFLRFRLFLRRFRFLFFRFRL']"
- ]
- },
- "execution_count": 67,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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2WNK1JH91f1tmDqEfEWwFZpvZQUlfA54luWXzZJK/7PZ0qzHcGDbMMoXjPGNmp0g6GnhW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\n8v89qQ80wuYWhQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c0babe0>"
+ "<matplotlib.figure.Figure at 0x7f10345119b0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(310, [])"
+ "(383, [])"
]
},
- "execution_count": 69,
+ "execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w = random_walk()\n",
- "ms = mistake_positions(w)\n",
+ "ms = mistake_positions(trace_tour(w))\n",
"len(ms), returns_to_origin(ms)"
]
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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4BuzPuLRMdHS0Ko21u+j1enndOp1OtUsMQEC/aDqdzqUt3PqaEhISwtJ+rkXS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\ngmjlBCR6F2MsCsAiAOMAFAL4hTG2hnN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YpX2bzYaLLmJ9dH/xi1/g2LFj2Lt3ryhrb2/HiBEj4HA4/Aqp4Q8lJSWmGLOV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\n+wAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c0b97f0>"
+ "<matplotlib.figure.Figure at 0x7f10345996a0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1216,
- "metadata": {},
+ "execution_count": 68,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"def trim_loop(tour, random_mistake=False):\n",
},
{
"cell_type": "code",
- "execution_count": 299,
+ "execution_count": 69,
"metadata": {},
"outputs": [
{
"'RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLRRLRRRRFFFFFFRRRLLFFFFFFLRRRLLLRLLLLRFRLRFRRRFRLRLLRFRLFLLRFRFLRRRRLRFLLRLRRLFFFRLRRFFRFLRFLLFLFRLRLRLRRRLFFRLRRFFRRFFLLRLLFLLLFRFRFLRLFFLFRRRRLRRLRRLRFRRLRRRFRRRRLLRFLLRRFFRLRFFLFLFRRLLLRLFFFRFRLRLLFRRFLLRLFFFLLRLLRRLRFRFFFFFRRFFLFRFLRFRFLFLFRLFLFLRFFLFRLRRRLRRRRFFFRFLLRLFRFRRRLRFFFFFLLFRRLFRRFRFRFLRLLFRFFFLLRRFRRRRLFFFLFLLLFFLRRLRFLFLFFRLLFFRRFFLRLLRFLRLLFLFRLLLRFFFFFFFFLFLRLLRLRLRFLLFFFLFLFFLFRRRRRFFRLLFFFRLFFLFRLFFRFFFFRLRRRLLFLRRFLRFFLRRFRRFLRFLRRLFRRLFFFFRLFLFFLRFFFFFRLFLFLRFFFLLLFLLLFFLRRLFLFRRFRRFFRRRFFRFLFLLFLFRFRFLLLRRRLFRRFFRLRFLFLFLRRLFFFLFRLRFRFFRFLRRFFFFFLFRLRRFRLLRLLFFLLLFFLFFFRLFRFRRRRLFLFLLLLRLFRFRLRLLFLFLLLRRRLLLFRRFRLRFFRRRLLLFRLRLLFFLLRFFFFRLLRLRLLLRLFFFRRLRFRFFRRRFFFRFFFFLRFLFRFLRFRRLLLRLLFRRLFFLRRFFFRLLFRLFLRRFLRLLRRFFLLFLLLFFFFRRFFRLFFLLFLLFRFRLRFLRFFRFRLRLLLFFFLFLRFRFRFLRRLLLLLRFRRLFLLLLRRFFFLLRFLFFRLLLLFLFRLRFFFFFLRLLFFLLLFFLLRRFLRRFLRLRRLLLRRRRRLLLFLRRLFFFRFLRRLF'"
]
},
- "execution_count": 299,
+ "execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 238,
+ "execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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iIm2AD0RkharuCGDbxpizSHpPrKr7VXVL7PpxYDtwXrLbNd5s2rQJ8NZNlCxtsCRJw+th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\nrC2Hf85MaAAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027310390>"
+ "<matplotlib.figure.Figure at 0x7f1033e45b38>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 214,
+ "execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "turning from Direction.UP to Direction.UP with F\n",
"RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n",
"RFFRRFFFRFFRFLLRFLFFFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n"
]
"data": {
"image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40274ccbe0>"
+ "<matplotlib.figure.Figure at 0x7f1033e457f0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 215,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "turning from Direction.UP to Direction.LEFT with L\n",
"RFFRRFFFRFFRFLLRFLFFFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n",
"RFFRRFFFRFFRFLLRFLFFLLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLR\n"
]
"data": {
"image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40271e3240>"
+ "<matplotlib.figure.Figure at 0x7f1033c33940>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 250,
+ "execution_count": 73,
"metadata": {},
"outputs": [
{
" Mistake(i=116, step=Step(x=-5, y=11, dir=<Direction.DOWN: 3>)),\n",
" Mistake(i=127, step=Step(x=2, y=11, dir=<Direction.LEFT: 4>)),\n",
" Mistake(i=130, step=Step(x=3, y=11, dir=<Direction.UP: 1>)),\n",
- " (132, Step(x=3, y=11, dir=<Direction.UP: 1>))]"
+ " Mistake(i=132, step=Step(x=3, y=11, dir=<Direction.UP: 1>))]"
]
},
- "execution_count": 250,
+ "execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 288,
+ "execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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4BuzPuLRMdHS0Ko21u+j1enndOp1OtUsMQEC/aDqdzqUt3PqaEhISwtJ+rkXS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\nRK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027beb4a8>"
+ "<matplotlib.figure.Figure at 0x7f1033a7d6a0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 289,
+ "execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nAaEhk3w+Hz169ABg/fr1LF68OKkEc+LECSorK3n11VetRNfa2sqcOXMoLPyk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\nnqDJTinlCZrslFKeoMlOKeUJ/x9O8a6mC0FDEQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4026fcb9b0>"
+ "<matplotlib.figure.Figure at 0x7f1033a7dfd0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 950,
- "metadata": {},
+ "execution_count": 76,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"def trim_all_loops(tour, mistake_reduction_attempt_limit=10):\n",
},
{
"cell_type": "code",
- "execution_count": 306,
+ "execution_count": 77,
"metadata": {},
"outputs": [
{
"'RFFRRFFFRFLRFRFLLRFLLRLRRLFRLRRFFLLRLLRRFRFLRLFFLFLRRLRRLRLFLLRRFFLRLLFFFFFRRFFLFRLFFRLLRRFLFFFLRFLRRLRFLFLFFRLLFFRRFFLRLFRFFRLFFLRRFLRFLRRLFRRLFFFFRLFLFFLRFFLFRFLRRFFFFFLFRLRRFRLLRLLFRFLFFFRLFRFLFLRLRFFFFFLFFRFFFFLRFLFRFLRLRLFFLRRFFFRLLFRLFLRRFLRLLRLFFRFLRRLFLLRFLFFRFLFRLRFFFFFLRLLFRFLLRRFLRRFLRLRFLRRLFFFRFLRRLF'"
]
},
- "execution_count": 306,
+ "execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 304,
+ "execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nAaEhk3w+Hz169ABg/fr1LF68OKkEc+LECSorK3n11VetRNfa2sqcOXMoLPyk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\nnqDJTinlCZrslFKeoMlOKeUJ/x9O8a6mC0FDEQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40271d91d0>"
+ "<matplotlib.figure.Figure at 0x7f1033a3d908>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 309,
+ "execution_count": 79,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 308,
+ "execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nAaEhk3w+Hz169ABg/fr1LF68OKkEc+LECSorK3n11VetRNfa2sqcOXMoLPyk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\nnqDJTinlCZrslFKeoMlOKeUJ/x9O8a6mC0FDEQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027562b38>"
+ "<matplotlib.figure.Figure at 0x7f1033accef0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 310,
+ "execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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+P07WWIriKKEW4snJycEYw7Zt2xKlVrvotBElKTh69ChdunQJec1aLtzj8dCr\nVy9XuOK1xlJcjdW3Gj16NMXFxQDcdtttgC/lLPgWASouLqakpIQFCxYkRM+WaB9LSSparkQciK55\noSidJFkWPdWmoOJ6SktLw67X2KVLl6C1G+vr6xOsrQ9tCiquR0QoLS2lR48eFBQUsGfPHjsdUI8e\nPaisrCQ3N5cBAwbw4osv2n2xeOil6woqSUsyRl4kR4NVSXveeustevbsicfj4dprr+1wbuZDhw6x\nZs0awOdpnDhxoqOparXGUlyNx+MhLy8vKKvLlVdeyYoVKzpUTksvYs+ePe3xr86ikRdK0pKZmcmp\nU6fsdD4AH330UafKmj9/PsYYJk+e7Pj6g2pYStIRbQK/eLSQ1LCUpMPr9dou9qysrCD3+9q1awH4\n4osvWqVRspwfVuZOJ1HnhZJU1NfX86tf/YqGhgYaGxvJz8+3+19z585lzpw5vPvuu5SXlwPw0EMP\nYYyhS5cu9hST3Nxcx/VUw1KSiry8PB599NGQ1+bOnWvvW8bz9NNPt7ovHk1BNSwlpfj000+ZOXMm\nn332Wdh7qqurAZg5cybNzc0MHDiQxx9/PLaKBCZQTsTmU0FRoufmm282+BYtMoA5//zzQ9733nvv\nBd0HmJ07d3ZYnv+3G/J3reNYStojImzatIlzzz23w88ZHcdSlPihhqWkBdYyaaE2gK5du8ZUnjYF\nlbRARLjjjjuYMmUKXq8XYwyZmZmAz+hGjBjRqTLDNQXVK6ikDcOHD49bwgRtCippQ0NDQ9xkqWEp\naUNOTk7cZKlhKWlDRkb8fu5qWEra0NzcjDEGj8fjeCBuTLyCIvIA8AxQbIw54j8XUapU9Qoq8aDl\nRMczzzyTHTt2RF2mYwPEItIfX0KEfQHnrgbOMMYMA/4V+F/RylGUaAgMNyorK2Pnzp2OyotFU/A3\nwM9bnCsjIFUqUCgiJTGQpShRE48lqKMyLH9Wx/3GmM9bXAqXKlVREs6JEyeCji+88MKwURnFxcX2\nfo8ePVi3bl1EMtodIBaR94HA2kbwRQT/AvifQKiMjx1KlaoZHZV40rNnz6Dj9evXU1RURFFRET16\n9ODrr7+27+nTpw979uyha9eufP3111xzzTXcc8897QsJF/be3gacDVQAXwN7gCZgL9AbX5/qvwfc\nuwMoCVNOh8P1FSUaJk+ebAJ/d4DZtGlTu8/RYioKbUwb6XRIkzFmK9DHOhaRPcA4Y8xREXkLuBN4\nzZ8q9ZiGqYF3AAADgklEQVQxprKzshQlltTW1gLw/vvv2+fWrVtHVVUVxhiam5vJzs62jSRw/MtE\n6sEOZ3Ed3fDVXD0Djn8HfAVsxmdwHZ7ouGrVqnbfIvHALXoY4x5dklmP5557rtVEx0i3JUuW2OXQ\nRo0VswFiY8xQ4x/D8h/fZYwpNcaca4zZ0JkyI81Q7jRu0QPco0sy6zFr1qxOVyDTp0+PSIZGXiiK\nA6hhKYoDuGKiY0IVUJQoMG5N46MoqYg2BRXFAdSwFMUBXGlYInK9iGwVEY+IjGtxbbaI7BKR7SJy\nVRx0mSQiO0TkSxF50Gl5LWQvFpFKEdkScK5IRFaIyE4ReU9ECuOgR38R+VBEtonI5yLy00ToIiK5\nIrJeRDb69XjUf36wiHzi12OpiCR+LZfO+vOd3IAzgWHAhwQMLgNnARvxxTgOxjcALQ7qkeGXMQjI\nBjYBI+L4PVwMjAG2BJybB/ybf/9BYG4c9OgDjPHv5wM7gREJ0iXP/zcT+AS4AHgNuMF//gXgX+P1\nPwq3ubLGMsbsNMbsonUwbxmwzBjTbIzZC+wCzndQlfOBXcaYfcaYJmCZX4e4YIxZCxxtcboM+IN/\n/w/A5DjoUWH8E1WNMXXAdqB/gnQ56d/NxfeCNcDlwF8C9LjOaT3aw5WG1Qbxno7SUt4Bh+VFQm/j\nj7s0xlQAveIpXEQG46tFP8EXWB1XXUQkQ0Q24gsAfx/YjS8W1eu/5QDQ12k92iNhbdE2pqM8bIx5\nO9xjIc45OV4Qb3muRkTygdeBe4wxdYkYg/Qb0FgRKQD+iq970Oq2+GrVmoQZljEm1Dyu9jgABKY6\n7w8cjI1GYeUNjKO8SKgUkRJjTKWI9AGq4iHU7xB4HfijMebNROoCYIw5ISIfARcCPUQkw290bvgf\nJUVTMLDWeAuYKiI5IjIEKAX+y0HZ/w8oFZFBIpIDTPXrEE+E1t/Bbf79W4E3Wz7gEEuAbcaYBYnS\nRUSKLc+jiHTFt9bKNmAVcEO89IiIRHtPwnh+JuPr23wLHAL+T8C12fg8dduBq+KgyyR8XrBdwENx\n/h5exff2PQV8A0wHioCVfp3eB3rEQY9/Ajz4vKIbgQ3+76VnPHUBRvtlbwK24Os2AAwB1gNf4vMQ\nZsfz/xRq05AmRXGAZGgKKkrSoYalKA6ghqUoDqCGpSgOoIalKA6ghqUoDqCGpSgOoIalKA7w/wFK\ngjJMbcJZvAAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40275594e0>"
+ "<matplotlib.figure.Figure at 0x7f1033cf2eb8>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 575,
+ "execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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3QD61kj8AsmTJkrAi4sSJE0PW5+fnRxVjw4mIgVNH2rVrZ9ONRkSsS2nKDPba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\nAAAAAElFTkSuQmCC\n",
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Mst8dQoCICPk8MNB0zC8khA1R2NOfmzpVToO7/V+1ijWxAwOBxYvluOYWW7XWPYmTEH1I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\nkAKBF/H/E7cUOhxnMSgAAAAASUVORK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4026efc4e0>"
+ "<matplotlib.figure.Figure at 0x7f103452c198>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 562,
+ "execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "w1 = 'FFRFFRRFFFRRFRFRFRLFRRLLFFLRFFLLRFLLRRLFFLLLFLRRFFLLLFLFLRFRRLRRFLLLFFRRFRFRRRRFRRRFLRLLRRRFRFFFLFLRLFFLRFLRLFLRRFFRRFFLFLRFLLFRFLLRRRLLFFRLLRRLFRRFLLLRLRLLLLRRRRRRFFLRRRFLLLLRFRRFRFLRLFRFLFLLLLLFFFLFRLLLRLRFRRFRRRRLRFFRRFRLRFFRLRFLLFLLLRLRFFRLRFFRFLRRLLRRRFFRLRLFLRFLLRLFFRFFLFFFFFLFFLFLLLFLFRFLLFRLFRFRFLFFRRLLFRLRFRFFLRFRFFLRFFLFFLFRLRFRLLRFRRRRFRFRRFRRRRFLFRFLFFLFLFFRFLFFFFLLRRFFLLRFRRFLFRFLLRLRLRRFFLFFLFFRLFLLRFFLRRFLFLRRRRFLFFLFRLFFFRRLFRLFLLFRRLFLFFRRLRLRFFFFFFRFRRRRFLLLFRFRFRFLRFFRFRRRRFFFFRLFRRLLFLLLRRLFLFRLRLLRRFFLRRFRLFLLFRLLRRLLRLFLRRFLLLRFLRFRFFFFRFLLLRRFRFFFFFRFRLRFFLFFLRLLRFRLRFLRLRLRFRFRLLLLFFLFRLRLLRFRRLRLFLLRFLLRRLRRFRLLFRFRLFLLRFRLFLRLLLLLFRFFRLLFFLLFRLFLRFRFLFFRRRFRFRRFRFFRLFFRRLRRRLLLFLFRFRRRFFFRFFFFRRFLRLLLRFFFFFRLRLRLLLLFRRRLFRLFRRLRFFFLFFLRFRFRFFFFFRLFLLRFRRRFFLFLFFFLRRFLLLLFLFFLRRLLLRLRRFFRLRLLRFRLRRLRRRFRFLLRLFFLLFFLLLLRRLRFLLRFRRRRRRRLRFFRFLFLRLLFFRFRRRLRLRLLFLRLFLFRLRLRRRLLLLFFFRFFRFFFFLLFLLLLRRLFFRLLFFFFRRRLFRFLRFLLFRLLRLRFLRRFFRRRRLRLFFFFLLFLFFFLRFFLRLRFRFLL\n",
- "w2 = 'FFFLRFFLLFFRFRFLFLRLLLRRRLRRLFFFLFRFLRRFFRLFRRLFRRFRRLLFLLFFFFRFLLFRFFFFLFRFFRFLFRRLFRRLFRRRRLLFRRLFRRRLFFFFFLFLRLFFLRLLRFFLFLRLFLRLLRLLLRLLRFLFFRFRFLRRLLLRFLFFFLFRFFRLFRFLLLRLFFLFFLFRFRFFFFFRLLRLFLLRRFLFFFLRFLFFLLLFFRRRRFLLRFFRFLRRFLLFFFFRFLLRRLRFRLRLRLRLRRLRFLRLFLLRLFLFRLFRFRLLRFFLRLRRLFRLRFFRRFLLLRFFLLFRRLRRRFRLRLRRLFFRLFRRRRRFLFRFLLRLFFRFLFRLFRLRFRFRFLLRLRLFLRLFLLFRFRRFRFLLLFFFRLLLLFFFRFLLLFRLRFRLRRLLFLLLRFLLFRFFRRRRLRFRLFLRLRLLRFLRLRFRLRFRLLRFLFRRLRRLFRFLRLRRFLRRLRFLRRRRLFLFLFLFLRRFFFLLFRFRLLFLFRRLLLRRRLLLRFRFFRFRFFLLFFLFRFLRFRRLLLLLRFRLLRRRFRRRLRLRLFRRRRLRFRFRLLRLRFLLFLLFFLFLFRRLLLLLFFFRFRFRFRRLRFLFLLRRFLLLFFRLRRLLFRRLLRLFLLLFFLRFFLLFRLLRRLFFFRLRFLRLFRRLRRLRRFRFFRLLLFRLLFLLRFFRLFLLFFFRLFRFFLRRLFLFRLLRLRFFLRLFFRFRFRRFFRFFLFFFFFFFRFRFFRLLLRFLFRLRLLLLRFFRLLFFFLLLLRFRFFLFRFFRRFFFRFRRLFLLFFRRLFLFFRFRFRLFFRLRFFLLFFFLLRRFFFFFFLRFLLRRRFFLFFFRFLFFFRLFFRLRRFFRRRRLFFLRFLLFRFFRLFFRRLFRRRFFFRFLFRFFRFFFRFRRRRRLRLLLLFFFRLRLLLFRLRRLFRFRFLFLRRLRFRFFRFRLLRLRFLLLRLRFLFFRLRLLRFLLLFFRRFFFLLLFRRRLFFRL\n"
+ "w1 = 'FRRFRRFFLFRRRLFFFLRFFFRLRFRRFLFLFFFFLRLFFFFFFLRRFLRLLFLLRFLFLLFFFLRLLFRFFFLFLFFRRLFRFRFFFRLFFFFRFFFRFFLRFRRRRFLLFFFRFFLLFLFLRFLFFLFFFRFLRLFRFLFFLLFFFLFFFFLFFFFFLRFFRRRFFLFRFFRFRRRRLLRFFFRFFRFRRFRLLRLLRFRFFLFLFRFRRLFLFLFLFFFFFLLLRRRFRFFRLLRRLRFLFLLLFFFLLFLLFLRFRLRLFFFRLLFLFRFRFRRLFRLFFLFFFFLFFFFRRRLFFFRLLLLFFRFRLRFFFFRFFRFFLLLFFFLRFRLFFFRFFFFRLLFFLRRFFLFRFFFFFFFFRLRLFFLFFLLFFLRFLRFFFFFFFFFLFFFLFRRRRFRFLLLRRFFRFRLFRFLFFFLLRFLLFLFRFLFFRFFFFFRLFRLLRRFFLRRFFFFFFRRFFFFFFRFFLRFFFLFLRRFLLLFFRFLFFFFFLFLLLRFRLFFFLFRFLRLLFRFRFFLFLFLRLFLFFFFFFFFFRFRLFRRRLFLRFLFFFFLLLLFFLFFFFFFFLLFLLLRRRLFLLLRLLFFFFFLFFFFRFFFLLRLRRLFFRFRLLRRFFFFFLFRFFRFLLFFFLRRRFFLLRLLFFFLLRFLFFLFRFFFRLRFFLRFLFFRFFRFFLFRRFFRFRLFLFRRRFRFFRFFFFFRFFFLRRFRFFRRFFFRFRFLFRLRLFLRLFFRFFFFFRRFRFLFFRLFRRFLFFLFRFLFLLLFLRFLLFLFRRRRFFRFFRFFFRLFFFLFRFRLRRFFFLRFRFFLFRRRFFRFFFFRFLRFFRFRFRRFFLFLFLRLFLFLFLLFLFFRFFFFFFFLFRFFLRLFRRLFFRRRFFRFRFRRLRLFFRFFRRFFFRFRFRLLRLFFLLLRFFRRFRFRFRFLLFFFFFLFFLFFFLLFFFFFLRFLFFFFLFRRFFFLFLFLLFFLFFFFFFFRFFLLFFLLLLFFRFFLL\n",
+ "w2 = 'FLFFFRLRLFFFLRLRRFFFFFFLFFFFLRFFFFRLFLFFLLRFLRLLFLFRLRRFFLFFRRLLRFRLRRLRRLRFFFFFRLFLRLFFFFLFFFFFRRLFFLFRFRFLFFRRLFFLRLLFRFFRFRFFLLFLRFRRFLFFRRFFFLRLFFRLLFFFFFFFRFFRFFFFRFRFLRFFLFLFLFLFRRRRFFFRFRRFFFFRFFLFFLRLFLFFFRLFFFRRLFFFFFLFRFRFFFFRRFRRFRLFRFFLRRRFFFLFLFFRRRFFFFRFFFFFLRLLLFRFLFLRFRLLFFFRFRLRLFRFFLLFFLFFRLFLLLFRRFRLFRFLLRRRFFLFLLFRFFFFFLLLRRLFLLRLLFFFFRRFLFFFLFFLLFFFRFRFFFRFFRFRLFFFLFLRRRLRFLLLRFLLFFRRFRFRFRFFFFLFLFLFFFFFRFFLLLRRFLLLRFRFFFFFFFLLFLFLFLFFLRFLFRLLFFFFRLFRFFLLFFLFLLRFRRLFFRFFRFFRFFLLLFFFFLLLFRRLFRFLLRLRFLLFLRFRFFFFLFLFLRFFLLLLLRRRLFRFFFLFRFRLLFRFFLRFFLFFFFFLRFRFFFFRLFLFRLRRRFLRFFFFLFRRRRFRFFLLRRFRFFRFFFLLRFFFLFLLLFRFLRLRFLRFFRFRFLRRFRFLLRFFLLRLRFFFRLFFFRFLRRRLRRFFLRFLLLLFFFLFRLRLRLLLRFFLFRRFRRLRFRFRLLLFRFFLFLFFLFLFLFFRLRFRFFLLLLRFFLFRFLFLRRRRRFFFFFLFRFRFLLRFRFLFFFFFLRLFLFLFFRLFFFLRRFLRFLFFFFRLFRFLLRFFRFFRFLRFFLLFFRLLRFFLLRRRLFLLFFFLLFFFRRLLRFLFFLLLLFFFFRFFRFRFRLFFFRLRFRLFFFRLLRFFFFFFFFFRLLFFFRRFFRFRFRRFRLFRLLRFRRLRFRFRFFFLFFFFRRFFFLRFRLFRFFFRFRFFLFFFRLLFRRRRFRLFFFFFRFFL\n"
]
}
],
},
{
"cell_type": "code",
- "execution_count": 725,
+ "execution_count": 84,
"metadata": {
"collapsed": true
},
},
{
"cell_type": "code",
- "execution_count": 1192,
- "metadata": {},
+ "execution_count": 85,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"def seek(goal, current):\n",
},
{
"cell_type": "code",
- "execution_count": 1145,
+ "execution_count": 124,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
- "def guided_walk(loci, locus_limit=5, wander_limit=10, seek_step_limit=20):\n",
+ "def guided_walk(loci, locus_limit=5, wander_limit=10, seek_step_limit=20, return_anyway=False):\n",
" trail = ''\n",
" current = Step(0, 0, Direction.RIGHT) \n",
" l = 0\n",
" trail += s\n",
" current = proposed\n",
" seek_steps += 1\n",
- " if seek_steps >= seek_step_limit:\n",
+ " if seek_steps >= seek_step_limit and not return_anyway:\n",
" return ''\n",
" else:\n",
" return trail"
},
{
"cell_type": "code",
- "execution_count": 896,
+ "execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
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FIBNJl6SFQEV+muMwZFu3buWGG25QGDKRAEKHIVsFPOHufxk9LykEWfRehSETqYDQYcgs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\nEKlyKgREqpwKAZEqp0JApMr9P8nYgJi0MFyGAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027b86898>"
+ "<matplotlib.figure.Figure at 0x7f10342c3b00>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 898,
+ "execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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k6yMEKDzGmzHGPSUtKopIo4g8KSJxEXlbRG4WkWYReU5E+kXkuIg0rvT+6urqghdbGGPc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\nybOEYIzJ+/+3mLJIMXCXRQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027da7048>"
+ "<matplotlib.figure.Figure at 0x7f10345c7fd0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1181,
+ "execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Ukpa9WWhgqFF+bEHHGFQ509PTzM7Out0MR2hgqJGOMahqqqVkbyYaGJRySDgcrno7c7PQ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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c0cc5c0>"
+ "<matplotlib.figure.Figure at 0x7f10346c3cf8>"
]
},
"metadata": {},
"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()))"
+ "plot_trace(trace_tour(quincunx_tour()))"
]
},
{
"cell_type": "code",
- "execution_count": 792,
+ "execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4026e9cc18>"
+ "<matplotlib.figure.Figure at 0x7f10346ee208>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1205,
+ "execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ZZ57hwIED7Nu3r84N3FRGpzCGuczMTFJSUjx/7Pv27aN79+6WU6lgVv6cyBEj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\nVBW0UCqlVBW0UCqlVBX+P0VFnoIKajtRAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f401d61d860>"
+ "<matplotlib.figure.Figure at 0x7f10341de518>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 882,
+ "execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'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'"
+ "'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'"
]
},
- "execution_count": 882,
+ "execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 883,
+ "execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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cPHmSs2fPsnfvXr744osGnJVKY+CqME0NwR1iP1qj1ummECJVkqTlQCJQAuwCjgJ5QgjZ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\n0sqKCeMgjitXruTnn38mMDCQwsJCvLy8KC8v58s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fqeomAPv3eIIk16mfhm4y8Gvbtd4h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\nRU5ErkJggg==\n",
+ "image/png": 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A0caxrWrln376qZnmb8/UHZPJ1KxszZGYM6NirZUxY8aM9r1anEBrsircR1M3E1s0vv/a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\n0guCoKWIpBcEQUvxN5buseIr1WPNAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c1a68d0>"
+ "<matplotlib.figure.Figure at 0x7f10339959b0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 884,
+ "execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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gmSDHZxrub6Xly5cbHIkQ/ktNdX5cgzEiwG63c9lll/H+++8D3w6fapvYBg0a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\nAxGwtLQ0T2lFKcXJkydJTU3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Yq8MIqGAurxlufLl8HTNmDNHR0Z76wClTpgQ6rG65J2Vxx/Kd73wH0MtwKw36JOleLGsw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\nUimleqBJUimleqBJUimlevD/A80VXGG6+r+KAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40271d9320>"
+ "<matplotlib.figure.Figure at 0x7f103350d470>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 885,
+ "execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "760"
+ "764"
]
},
- "execution_count": 885,
+ "execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 1180,
+ "execution_count": 97,
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "At Step(x=-2, y=0, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently left, by step L to Step(x=-1, y=0, dir=<Direction.RIGHT: 2>)\n",
- "At Step(x=-1, y=0, dir=<Direction.RIGHT: 2>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently left, by step F to Step(x=0, y=0, dir=<Direction.RIGHT: 2>)\n"
- ]
- },
{
"data": {
"text/plain": [
- "(204,\n",
- " 80,\n",
- " 'RLLRFRFLFFFLFRFFRFLFFLFFFFFLLFRFFRFFFLFRFLFFFFRFRFFFLFLFFFFFFLFFFFRFLRLLRFLRFFLF')"
+ "(284, 22, 'FFRRLLRRFFFFFRFFFRFFRL')"
]
},
- "execution_count": 1180,
+ "execution_count": 97,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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UIuI9BZWIeK/fBdW+ffuA8POTtNW2SDPnHKWlpQBUVVXF3E0zb7d076n0nb/5+fnMmDEj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\nRU5ErkJggg==\n",
+ "image/png": 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HP/xh7CidMpmMnn76ad18882xo3zMSy+9pGuvvVYfffRR7CidkrCf33///aqoqJAklZeX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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027412588>"
+ "<matplotlib.figure.Figure at 0x7f10346f29b0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1194,
- "metadata": {},
+ "execution_count": 98,
+ "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"
+ "# 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,
+ "execution_count": 99,
"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"
+ "# 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,
+ "execution_count": 100,
"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"
+ "# 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,
+ "execution_count": 101,
"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"
+ "# 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,
+ "execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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MmTOnQXUOGDDA6fvs2bOVzxERETzxxBMNlFbD3xFCNPglE6io+mqsnDEpMTGR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\nNDQATRloaGhUoCkDDQ0NAP4/oI3jTPrjMJgAAAAASUVORK5CYII=\n",
+ "image/png": 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HP/xh7CidMpmMnn76ad18882xo3zMSy+9pGuvvVYfffRR7CidkrCf33///aqoqJAklZeX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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40276510b8>"
+ "<matplotlib.figure.Figure at 0x7f10342e2390>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1206,
- "metadata": {},
+ "execution_count": 103,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
- "patterns = [square_tour, cross_tour, quincunx_tour, heart_tour_func]\n",
- "tours_filename = 'tours.txt'\n",
+ "# 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",
+ "# 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"
+ "# 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,
+ "execution_count": 104,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "At Step(x=2, y=3, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently above, by step F to Step(x=2, y=2, dir=<Direction.DOWN: 3>)\n",
- "At Step(x=2, y=2, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently above, by step F to Step(x=2, y=1, dir=<Direction.DOWN: 3>)\n",
- "At Step(x=2, y=1, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently right, by step R to Step(x=1, y=1, dir=<Direction.LEFT: 4>)\n",
- "At Step(x=1, y=1, dir=<Direction.LEFT: 4>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently above, by step L to Step(x=1, y=0, dir=<Direction.DOWN: 3>)\n",
- "At Step(x=1, y=0, dir=<Direction.DOWN: 3>) going to Step(x=0, y=0, dir=<Direction.RIGHT: 2>), currently right, by step R to Step(x=0, y=0, dir=<Direction.LEFT: 4>)\n",
- "Found mistakes: 2052\n"
+ "Found mistakes: 1916\n"
]
},
{
"data": {
"text/plain": [
- "(5348,\n",
- " 1210,\n",
- " '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')"
+ "(5540, 14, 'LFFFFLLRLFFLRL')"
]
},
- "execution_count": 1190,
+ "execution_count": 104,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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8FxpNdyTYVgFx29AopeKA8SLyGoCINIrIUWAqtlUrsf+d5rGWGo3GoE+fPnz2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\noC4AAAAASUVORK5CYII=\n",
+ "image/png": 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23wCPFrOWvH0uBe7p4L44FRgFrCpw+9nAb5PtU4ClneWox46g7t6Yd/EbQLad3c4CFrn7\ndnf/jFwb8QnJ+GfdvXXMUmBYgVJGO88ERY4v+B1PXfgQolD9YsZ39B1T5wO/TrZ/DVzQQf3O5mqV\nP+dj5L5XoJi1tFdvL+7+MvBpoduT+X6T7PsaMNDMajvYv2ffqDezfzazDcAPgZvb2WUo8GHe5Y3J\ndW39GCh0AubAM2a2zMyuLLBPofFt639UoH5HiqlfSEf1D3b3LQDuvhk4qMAc1clLi3/n63/P9n6X\nPfXcfTfwmZkNLmIt+S5Mnp7nm1mhg0Yhxf699yjpbabO3sR395uAm5LXM9cAt7SZYjLwTTP7QXL5\nYCBrZu958iGAmf0c+NLd5xao3wxUARXAPUmtfyxmPLkHzbDkq3Qg95Kjv5kt9iI+hCimficK1X+2\niLGtvuXum83sJ8AsMxvu7u8nt7U9A2579LO8fYr5vqsngbnu/qWZTSZ3ND5z72EFFf2dWq1KCqi7\njy9y13nAb9k7oDcDY939HwDM7H7g+bxwXQqcA3ynmPpmNgPYUex4cg+Q9r7jqahPyDqrX4RC9Z9M\nTjZq3X1LcoL5xwJr2JxsLgc+B04A3if3kmZTm90/BA4DNlnu64MGuHvrU/JHQP5J017j8/YFeIjc\n6+Su+CipX7DGXjp7kdrdH+DIvO1rgPnt7JN/ktS6PSi5bQLwB+DADmrUkJyMkXud+3vge10YX8FX\nJwZV5E4MRrTZ53lgdFfrFzm+YH1yf/zp3sFJErkTy6pk+2Dg/8g9GAv9Lj/hq5OkH/D1k6Ri7otD\n8ra/D7zSzpqOAFYX+H3P4auTpHqKOEnqyYA+BqxKftEngEOT60cDD+bt9/fAe8Ba4Ed5178HrCd3\nZFied8ceCixItocn868g90//ru/K+Lwgv5vsnz/+AnJHnCZyX1D2dBfrdzq+k/qDgWeT2xbz1QN3\nz/0HjEnu4xXASuDutnMBM4G/TLargfnJ7UuBI9r8zfZaS5vxvwDeSur9N3BUm/FzyR0RW4ANwGXk\nniUm5e0zm9wDYSUdvDvS+qM36iVq+ud2EjUFVKKmgErUFFCJmgIqUVNAJWoKqERNAZWo/T9ydJit\nx0SoEAAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c03b908>"
+ "<matplotlib.figure.Figure at 0x7f1033d81390>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1187,
+ "execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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q0aIFdzqriJKSEkpPT6f09HTKzs4mvV5vlUz1hZKSEho2bBj5+vpyJae16PV6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\nIil4qKdS6jKwEvhvrPujFvgSuOahnm56h+jW2y6P+t7ttoF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+ "image/png": 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AgTh37hwuX74MoGI3gOJ5npkzZ+LcuXMoKSlBeno6Pvnkk2pfV6OkurOl9iY04lWSAwcO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\nQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4026f76c18>"
+ "<matplotlib.figure.Figure at 0x7f103402bef0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1037,
+ "execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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23wCPFrOWvH0uBe7p4L44FRgFrCpw+9nAb5PtU4ClneWox46g7t6Yd/EbQLad3c4CFrn7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\nx0SoEAAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c0e22e8>"
+ "<matplotlib.figure.Figure at 0x7f1034757358>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1013,
- "metadata": {},
+ "execution_count": 108,
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[Mistake(i=20, step=Step(x=8, y=0, dir=<Direction.UP: 1>)), Mistake(i=38, step=Step(x=10, y=2, dir=<Direction.UP: 1>)), Mistake(i=42, step=Step(x=8, y=4, dir=<Direction.LEFT: 4>))]\n"
+ "[Mistake(i=9, step=Step(x=0, y=5, dir=<Direction.RIGHT: 2>)), Mistake(i=29, step=Step(x=5, y=6, dir=<Direction.DOWN: 3>)), Mistake(i=33, step=Step(x=5, y=2, dir=<Direction.DOWN: 3>))]\n"
]
},
{
"data": {
"text/plain": [
- "'FFFFFLLRRFRFLFLLRFFRRFLFFFFRRFFRFFLFFRFFFFRRRRRRFLRRFLLFFFLRFRRFFFFFRRFRRFRFFRFLLFLLLFLLFFLFLFFFFFLFFFFRRFFFFRRFFFFRRFFFFFRFRFFFFLFLRFRRRLLRFFRRFFRFRFLRLFFFFRRRFLFFRFRLFRRFRFLFFLLFLRFFFRFRFLRFLLFFFFRLLLLFLFFFLFLFRRFFLR'"
+ "'LFFFFRFFRFFLFFLFFFLFFRRFRFFFFFFFFFFFRFRLRRLLRFFFFRFLFFLLFLRFFFFFLFFFFFLFFLFRLFRFRLFFFFLLFFFFLFFFRLFRRFFFLFFFFFFFFFLFLRFFFRLRLLFRRFLLFFFLFFFFFRLFFFRRLFFFLLFFLRLFLFLRFRFFFFLFFFRRFFLLLFLLLRLFFFFFLLFFFFFFLFFLRLLFFFRRLFLLLFRRFLFLLFFRFFLFFRFFFFFFFFFFFFFRFRRRRRFFRRRFFFLRRFLRFFFLLFFLRFLFLFFFLFFFLFLFRFFFFFRLFFFFFLFFLFFRRRRFLFFFFFFFFFFFFLFFFFLFRFLLLFFRRFFLLFFLFFLFFFFFFRRFFRLFLLFFFFFFLFFLLFFRFRRLLFRFFRRFLRLFLLFFFRFRFFFFFFFFFFFFFFFFRFFFFRFLFFFFFFFRFFLFFFFRRFFFLLFRFFFRRFFFFRRFFFFFFLLFLFFFFLRRLLFRFFLRFRFLFFFFRRRFFFRRFFRRRRRRFLRRFRFRFRFLFFFFLLLLFFLRRRFFFFFFRFFRFFLLFFFRFFRRFFFLFFFFFLFLFFFFLFLLFFFFFRFFLLFFFLFRFFLLRLFFFFFFLFFLRRLFFRFFFLLFLFFFRRFRRFRFFRFFRFRRRFFRFFFRFRRLRFFFFLFLFFFRFFFFFFFFRFFRFFFRLFRFRFRRRRFRFLRFFFLRLRFRFLFFFFFFLRRFFLLFFFLFLFFFFRFRFRFFLFFRRFFFFFRLRFFFFFRFFFFRFFFFFRFFLLFFRFFLRRLFLFLFFFFFFFFFFFLFFFFFRFFLRLFLLFFFRFRFFFRFFRFFRFLFFLRFFRRLRRFFLLRRLFFFLFFFFFFFRRRLRFFLFRFFRLFRFFRFFFFFLFFFFLFFFFLFFFRRFRFRFLFLFRFFLFFLRFRFFFFRFFFFLFLFFFRRRLFLFFLFRFRFFLFLFFLFLFLLFFFFFFLFFFRFFFLFFRFFFFRFFFLLFLFFRFRFFRFRFFFFFFFFLLLFFLFFFLFFFFFLFRRFFFFLFFFFRRRFRFRFFLLFRFLFRLFLLLRFFLFFLFFFFFRRLFFLFFFFRRFFFLFRFRRRRFRLFLFRFFFLFLRFFFFLFLFLRFFLFRFRFRFLFLFLFRFLRFFRFRLFFFRFLLFFRRFFFFFFFRLFLLFLFFFLFRLRRFFFFLFFLLRFFLFRRRFLFLLFFFFFFLFFLLLFFFFFFLLRRFRFFFRRFLLFFFLLFFFFFRFRRLFFFFLRRLRRFFLRRFFFLFLFFFFLRLFLLRFFRFRFRFRFFLLRRLRLRFRFFFFFFFFFFFFFFFRFFLFRRFFRFFFLRLLLRRFFLFRFRRLFRFRFFRFFFRLFFFRRLFRFFFFRFFFRFFFFFRFFFRLLFFRLFRFFFRFRRRRFFFRRLLFFLFFFFFFFFRFFFRFFFLFFLFFFFFFFFLFLFFRFFFFFFRFFRFFFFLLLLFFFFLLLFRFLFRFRFFFRFFFLRFFRRLFFLFLLRFFRRRLLRFRFRFLFFFFLRRLRRLLFFFFFFRFFRRRFFLFFFFFLFFFFLRFFRLFFFLFFLFLFFLFFFLFFFLLLFFFFLFFFFFLRFFFRLFFFFLFLRRRRFFRFFFLRRLFFFRFLFFLFFFFFLRRFRLFLFLLFRLFRLFLFRFRFRLLFFRFLRFFRFFLRRLRFFFLRFRFFFFFLFFRLRFFFRFRFLRFRFLFFFRRRRRLFFFFFLFFFRFLFFFLFRFFFFFFFRFLFLFLLLRRFFFFFFFFRLFLRFRFFLLLFFFLFLFFRFLFFFRRFFRLFFRLFRFRFFLRFRFFFFFFFFRFFFRLFFRFFFLFLRFFFFLFFLFFFRFFFRFFFLFLLLLFLFFFLFLFFRFLLFLFFLFRFFFFLFFFRFFFFFFFFFFFFLLFFFFFFRLLFRRRFFFFLRFFFFRFFFFRFLFRLFFRFFFLFLFRFRLFFFRRLFRFLRFFFFFFFFRLFRFFFFFRRFRFRRFLFRFRFFFLFFFFFLRRLFLRFRRFRFFRFRRFFFFFFFLFRFFFRFRRLRRFFFFFFRLLLLFRFLLFFLFFLFFFFFFFFFFFFFFFFFLFFLRFFLRFFLFRFLRRLFLFFFLFFRFLRFFFFLRFFRFFFFFFFFRFRFFFRRFFFLLFFFFFFLRFFRRFRFLFFLFLFFLFLFRFLFFFLRFFRFLLFLFRFRFFFFLLFFFFFFFFLFRRRRFFFFFFFFRFFLFFRRFRRFLFFRRFFRRFFFRRFRFRFFFFFRFFFFFRRFFRRFFLFFLFRFLRRRFFFLLFFFFFFFFFLLLFRFFFFRRFLFLFFLRRFFFFRFFLRRFLFRFFFFRLLFLLFFFFRFRFFRFLFLRFLRFLFRFLFFRRRLFFRLFLRLFRFFRFFLFLFRFFFFRFFFFFFFRFFLFRLFLLRLFFFFFFRFFFRFFFFRFFRFFFRFFRLRFFFRLFRRRRRFFRRRRRFFRLRFFRFFLRLFFLRFRRFFRFFFFRFFFFFRFFFFRFFFFFRFRFLLFRFFFFFRFLLFRFRFRFRLFFRRRRRRFFFRRFFFLLFRFRFFRRLFFLLFFFFLFFLRRRFFFFRLLFRFFRFLRRRFRLFFLLFRFFFRFFFFFRFFFFRFFRFRFFFFFFRFLFLFFFFFRRFLLFFFFFLFFLFFFLFFFLRLFFLFFFFFFFLFRFLLFLFFLFFRFFFLFFLLRLFFRFFRFRLFRFFRFFRLRFFLLFRLFFFFRFFLFFLFRRFRFFFLFFLRFRLFFLFLRFFLFLFFFFLFFFLFFFFFFFFFLFFFFFLFFFFRLRFFFFFFFFFFRRFFFFFRRRRFFLLFFLRFFFFRFFFFRFFFFLFFFRFFFFFFRLFFLFFRFFRFRFFLLRFLFLFLFFLFLFFLFLRFFFFRRRFFFRFFLFLRFLFFRRFLFLFLLFFFFRLLLLLRLRRFFRRFFLRLFFRLLFRRLFFRFLLFFFLRLFFFFFFFFFLFRLFRLFLFFFFFRLRLRFFFRFRFFFRFFFFFFRFRFFFLFLFLLRRFRFLRFRFLFRRFRLFFFFFLFLFFFFFFFFLFRFFFFFFRRFFRLFFRFLLFLRFFFFLFRFFFLFFLFLFLFFFRRFRFFLRFLFRLFFFRFFFLFFFFFFRFLFFLFFRFRFFRFFFFFFFFFFFFRFFFFFFRRRFFFRFFFFRFFFLLRFFRLRFRFFLFFFRFFLFLFFFFLLRFFRRRFRLLRRLFFFLFLLFRFFFFRFFFFRFLFFLLFFFLFFFFLFFFFLFLLFRFLFFLRLFFFFLRFRLFRFFRFFFRLFFFRFLFFLLRFRRFFFFFFFFFFFFFFFFRRFLLFFLLRLFFFFFFFFFFFFFFLRFRRFFFRRFLLRFFFRFFLFLFRLRFFFFFRFFRFFFRLRRFFFFFRRRRRLFLLFFLLFFRRFRFFFFFLFFFFLFLFFRFFLFFFFLRLLFFFLFLFFFLLFLFLFFLRRRFFFFFRLFFFRLFFFFRFRFFFFRFFLFLLFLFRRFFRRRFFFLFRFFFRFFFFFFFFRRLFFFFFFRFLLFFFFRLFFFLFFLFFRLFFLFRFFFLFFFFLLFFFRFRLRFRFFRFLFFFLLLRFFFRFFFFFFFFLRLLFRRFLRFRRRLLRFRFFLLRFFLFLLRFFFFFLLFRRRRRRFFFFFRFFRRFLFFFFFRLFFFLFLFFLFLLFFLFFRFFRRRFFLRRRFLFFRFFFFFLFFLLFFRFRRRFFRFRRFRFRFFLRFRFFRRFRFFFRFFLFRFLRFFLFFRLRFFFFFFLRFFFFRLLLFLFLFLFLLFFLRFFRFFRFFLFFFRFFRRRFRLFLFFFLFFLFFFLFFFFFLLRLFFLFLFFFFFFFFLLFFFRRFFLRLFFFLLFLLFFFFLFFFRFFFFLFRFFRFRFRFRLLFFFLLFFFLRRFFRFRFLFLFFFLLRRRRFRRLLLRLFLFFLFFFLFFFFFFFFFLRRRFFFFRFFFRRLFFFFFFFLRFLRLFLFFFFFLFFLFFRFRRFFRRLFFFLFFFFFRLRFFLFRFFFRFFRRRRRLFFFRFFRFRRRLFFFFLFFLLFFRRRRLFLFRRFRRLFFFRFFFLLFRLLLRLFFFRFFRFFFLRRFFFFFFFFFRRRLFFFFLRFFFRRFFFFFRLFFFFRRRLFFFFRRFRLFFFFFFRLLLRLLFFFFFRFLLFFRFFFFRFRFFFLFRFFFFFFFRRRLFFLRFRLFFFFFFFFLFFFFLFRFLRLRFLFFFFFFFFFLFFLLFFFFLFLFFFFFLFFFFLFLFFFLLFFRRFFFRFFFRFFFFRLRFFLFFFFFLRFLLLFRRLLRFFFFFFFFFRFLLLFRLFFFLFRFFRFRFFFFFFFRFLLLFFFFFFLFFFFFRFRLFFFRFLRLFLFLLRFFLRFFFRFFRFFFLRFFLFFLFLFFFFRFFFFFFFFFRFLRRFFFFRRLFRFFFFFRRFRLLFFRRRLLFFRLFFFLLFFFFRFFFFFFRRFRFFLRFFRFFFFFFFFFFFFFFFFRRFLFFFRRFRFFLLFFLRLFLFFFRFFFFRFFRLFFFFFFLFFLFFRLRFFFFRFFFRRLFFFFLLFLRFFLFFFFFFFFFFFRRFRFLFLFRRFFLLFFRFRFFFFLRLRFLLRLLFRFLFRRFLFFRFRFFFRFFFFLFFFRFFRRFFFLFFFFLFFRFLLLFRRLFLRLLLFLFFLLFFFFFFRRFFLLFLLRFRFRFFRFFFFFRFFRRFLFLRFLFFRLFFFLRRRRRFFFRRFFRLFFFRFRLFRFRFLRFRLRFRRFRLFFFLFRFFLRLRFFFRFFFRFFFFFFFFRFLFFFFRFFFFRFFFFLFRRFLFFFFRLLFFFFFLFLFFFRFRFLRLRRFRFLLFLFFFRRLFRFRLFFFLFRFRFLFFFFFFRRFLRRLRLFLLFFFFFFRRFRFFRLFFRFLFFLFFRFFFLFFLFFLFRFRFFFFLLRRRRFFFFFFFFLFFFFFFFFFLFLFRRFFFFRFRFFFFRFFRRFRFFFFLLLRLFFFRFFFFFFRFRLFFFLFFFFFFFFFFLRFFFRRRFRRFRFRFFFFLFLFFLRFLFFLFLFFFLFLFFFLFFFFFLFFFFFRFFFFLLRFFRFFFRFRFLLLFFFLFFFFLFFFFLRRFRFFLRLLFFFRFFFFLLLLRFFFRFFFFFFFFFFFRFRLLFFLFLFRLFFFFFFFFFFFLFFFFFFFFFFFFLRFFFFLFLFFLFLFFRRFRLRFFFFFFFLFLFFRFLRFFFFFRFRFFLLFRLFRFFRRFRLLFFRFLRFFFFRRFRFLFFLFFLFLRFFRFFRRFLLLLFLRFFFFLFFRRFRLFFFFLRFFLFFLFFLFRLFFLFRFFLFFFFLFFFFRRFLFRFFFFRFFFLFLLLRFFFFFFRFFFLLFRFFRRFRFLFFLFFRFLFFFFRFFLRFRFFRFFFLRRFLFFFLRFFLRRFLFRLFFRFFFFRLRLLLRLFFFFFFFFFLFLFFFFRFFFLLFFFFFRFRFFFFFFFLFFLRFFRFRRRLFFLLLRFRFFFLFFFLFRLRFLLFLFFFLFFLRFLFFFLFFLLFLLFRFRFLRLFFLFFFRFFFLLLFRFFFFFFFRLFFLFFLFLFFRLFFFFFRRLRLFFLRL'"
]
},
- "execution_count": 1013,
+ "execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 1014,
- "metadata": {},
+ "execution_count": 109,
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"RFFRRFFFRFFRFLLRFLFFLLLLFRRRFLFFFFRRFFFRLLLRRFLLFRLLLFRLLRFLRRLFLFFRLLRFLLFRRLLRLFRFFFLLFFLFLRRLFRLRRLRRRRFFFFFFRRRLLFFFFFFLRRRLLLRLLLLRFRLRFRRRFRLRLLRFRLFLLRFRFLRRRRLRFLLRLRRLFFFRLRRFFRFLRFLLFLFRLRLRLRRRLFFRLRRFFRRFFLLRLLFLLLFRFRFLRLFFLFRRRRLRRLRRLRFRRLRRRFRRRRLLRFLLRRFFRLRFFLFLFRRLLLRLFFFRFRLRLLFRRFLLRLFFFLLRLLRRLRFRFFFFFRRFFLFRFLRFRFLFLFRLFLFLRFFLFRLRRRLRRRRFFFRFLLRLFRFRRRLRFFFFFLLFRRLFRRFRFRFLRLLFRFFFLLRRFRRRRLFFFLFLLLFFLRRLRFLFLFFRLLFFRRFFLRLLRFLRLLFLFRLLLRFFFFFFFFLFLRLLRLRLRFLLFFFLFLFFLFRRRRRFFRLLFFFRLFFLFRLFFRFFFFRLRRRLLFLRRFLRFFLRRFRRFLRFLRRLFRRLFFFFRLFLFFLRFFFFFRLFLFLRFFFLLLFLLLFFLRRLFLFRRFRRFFRRRFFRFLFLLFLFRFRFLLLRRRLFRRFFRLRFLFLFLRRLFFFLFRLRFRFFRFLRRFFFFFLFRLRRFRLLRLLFFLLLFFLFFFRLFRFRRRRLFLFLLLLRLFRFRLRLLFLFLLLRRRLLLFRRFRLRFFRRRLLLFRLRLLFFLLRFFFFRLLRLRLLLRLFFFRRLRFRFFRRRFFFRFFFFLRFLFRFLRFRRLLLRLLFRRLFFLRRFFFRLLFRLFLRRFLRLLRRFFLLFLLLFFFFRRFFRLFFLLFLLFRFRLRFLRFFRFRLRLLLFFFLFLRFRFRFLRRLLLLLRFRRLFLLLLRRFFFLLRFLFFRLLLLFLFRLRFFFFFLRLLFFLLLFFLLRRFLRRFLRLRRLLLRRRRRLLLFLRRLFFFRFLRRLF\n",
- "FFFFFLLRRFRFLFLLRFFFFFRFLFFFFLFFLFRFRFLRLFFFLLFRLRFFFRFRFLRFLLFFFFRLLLLFLFFFLFLFRRFFLR\n",
- "Step(x=0, y=0, dir=<Direction.DOWN: 3>)\n"
+ "LFFFFRFFRFLRFFFFLFFFRLFFLFLRFRFFFFLFFFRRFLLFFRFFLFFRFFFFFFFFFFFFFRFRFLFFRFFFLFFFLFLFRFFFFFRLFRFFFFFFFFFLFFFFFFLFRLFFFLRLRFFRFLFFRFFFLFFLRFFFRLFLFFFFFRFLRFFFLRLRFFRLRRFFLLRRLFFFLFFFFFLFFLFRFFRLFRFFRFFFFFLFFRLFFLRFRFFFFRFFFFLFLRFFLFRFRFLLFRFFFLRFRFFLLFRFLFRRFLFFLFFFFFRRLFLRFFFFFFFLFRRLFLLRFFFFFFFRLLFFRLFRFFFRFFFFRRLLFFLFFFFFFFFRFFFRFFFLFFLFFFFFFFFFRFFFLFFFFFLFFFFFFFFRLFLRFRFLRFFFFRLLFFRFFFFFFFFFFFFFFFFLFFLRFFLRFFLFRFLRFLRFFFFFRFFFFFLFFLRFFLFRFFRFLFLRFLRFLFRFRRLFLRLFRFFRFFLFLFRFFFFRFFFLFRFLRFFFFLRLFLFRFLLFRFFRFFFFRRLLFLRFFFFFRFFLFFFRFLFLFFFFFRRFLLFRLRLFFLFFFFFFFFRRLFFFFRFFFFFLRFRLFFFLFFFFFFFFFLFFFFFLFFFFRLRFFFFFFFFFFRFFFFFRFFFFLFFFRFFFFFFRLFFRFLFFLFLRFFFLFFRFFLFLRFLFFRRFLRFFRFFLFRLFRLFLFFFFFRLRLFRFFFFFFFLFRFFLFFFFFFRFFFFFFFFLRFLLRFLFFFFFRRLFFFFFFRFFLFLFRFFFFFLRFFLRFRLFFRFFLFRRFFFLFFFRFFFRFLFFFLFFFFFLFFFRRLFFFFFFLFRRLFFFLFFFFFRLRFFLFRFFLRFFFFRFLLFFRFFFFRFRFFFLFLFFFFFFLFFFFLFRLRLLRFFFFFFFFFRRFFFFFLFFFRFFLLFFFFRFFFFLFLRFFRFFFFFFFFFFFFFFFFRRFLRLRLFLFFFRFFFFRLRFLLRRFLFLRFLFFRLFFFFFFLFLFFFFRFFFFRFFFFLFRRFLFFFFRLLFFFFFLFLFFFRFRFRLFLFFFRRLFRFRLFFFLFRFRFLRLFFFFLFRLFFRFLFFFFFFFFFFLLFRLFRFFFRFFFFFFRFRLFFFLFFFFFFFFFFFFFFFFRRFFLFFFLFFFRRLFFRFLFFFLFFFLRFFLRRFLFRLRRFFFLLFFFFFRFRFFFFFFFLFRFFFLFRRFLFFLRFLFFFLFFRLFFFFFFRRLRLFFLRL\n",
+ "Step(x=0, y=0, dir=<Direction.RIGHT: 2>)\n"
]
},
{
"data": {
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d+BSY4WMdPnPkyBHOnDkTbDE0Gp+Ij4+v02OWq+1FZGSkz34v6sLrHoaIJACX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\nq91u6gL46quvEBGSk5ONz6l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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40276002e8>"
+ "<matplotlib.figure.Figure at 0x7f10540d85c0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1015,
+ "execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[Mistake(i=70, step=Step(x=-2, y=6, dir=<Direction.UP: 1>)),\n",
- " Mistake(i=71, step=Step(x=-3, y=6, dir=<Direction.LEFT: 4>))]"
+ "[Mistake(i=1219, step=Step(x=0, y=5, dir=<Direction.DOWN: 3>))]"
]
},
- "execution_count": 1015,
+ "execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 1016,
+ "execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'FFFFFLLRRFRFLFLLRFFFFFRFLFFFFLFFLFRFRFLRLFFFLLFRLRFFFRFRFLRFLLFFFFRFLFFFLFLFRRFFLR'"
+ "'LFFFFLLRLFFLRL'"
]
},
- "execution_count": 1016,
+ "execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 1212,
+ "execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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WL0S5ubl8/PHHAcnRQzr3AsTfHvo+ffpw5MgRy1ZjgOuvv55vvvmG6upqo+W+xaXt2bOH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\nhX/bbbexc+dO43g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pfCFcSApfCBeSwg8ik5Vk26LlfeMjTVtyD7T9uaamJuB9uJ0UfhBkZmYCsGjR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\nRK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f4027ba30b8>"
+ "<matplotlib.figure.Figure at 0x7f1033a4ae10>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1247,
+ "execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(254,\n",
- " 20,\n",
- " 'RLLFLRRFLLFFFFLFLFRL',\n",
- " [Step(x=-1, y=0, dir=<Direction.DOWN: 3>),\n",
+ "(326,\n",
+ " 80,\n",
+ " 'RFLLRFFLFRFFLFFFRFLFRFFLRLLFLRRFFFFFLFFFFFLFRFRLFLRFFRLLFFLFFFRFFFRFFLFFRLFLRLFF',\n",
+ " [Step(x=-1, y=0, dir=<Direction.RIGHT: 2>),\n",
" Step(x=0, y=0, dir=<Direction.RIGHT: 2>)])"
]
},
- "execution_count": 1247,
+ "execution_count": 113,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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+ "image/png": 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QVFTkbSHka4GALwcUpzRfBUVBQQGAXxf2IeqvoaEBAPD+++97XEn68VVQcCEfOpfYL5Kc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\nRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c1a5cf8>"
+ "<matplotlib.figure.Figure at 0x7f10345d82e8>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1281,
+ "execution_count": 114,
"metadata": {},
"outputs": [
{
"(1, Step(x=0, y=0, dir=<Direction.RIGHT: 2>))"
]
},
- "execution_count": 1281,
+ "execution_count": 114,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEACAYAAACTecuMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f40275e2908>"
+ "<matplotlib.figure.Figure at 0x7f1033d95908>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1282,
+ "execution_count": 132,
"metadata": {
"collapsed": true
},
"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",
+ " 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,
+ "execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(1, Step(x=0, y=0, dir=<Direction.RIGHT: 2>))"
+ "(3, Step(x=0, y=0, dir=<Direction.RIGHT: 2>))"
]
},
- "execution_count": 1299,
+ "execution_count": 116,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f402c0c5f98>"
+ "<matplotlib.figure.Figure at 0x7f1034745a20>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 1300,
+ "execution_count": 117,
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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pbQWEhISQn5/PokWLuP322xk2bBiHDx+mQ4cOHDhwAIAlS5ZgNpspKSmRdvzX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\nA8MAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f10348f8470>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lc = trace_tour(square_tour())\n",
+ "rw = guided_walk(lc, seek_step_limit=0, return_anyway=True)\n",
+ "plot_trace(trace_tour(rw))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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3Dzu2nsOsWbMoLy8HYO7cuV201mjsixOrl9laOZSXl5Ofn8+BAwesFkWjCQvD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\nDhqNxi9aOWg0Gr/8f+oSxfuPC6HEAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f10341ba780>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rw_trimmed = trim_some_mistakes(rw, 1)\n",
+ "plot_trace(trace_tour(rw_trimmed))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Mistake(i=702, step=Step(x=-1, y=1, dir=<Direction.RIGHT: 2>))]"
+ ]
+ },
+ "execution_count": 140,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mistake_positions(trace_tour(rw_trimmed))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"patterns = [square_tour, cross_tour, quincunx_tour, heart_tour_func]\n",
- "tours_filename = 'tours-with-mistakes.txt'\n",
+ "tours_filename = 'tours-open.txt'\n",
"\n",
"try:\n",
" os.remove(tours_filename)\n",
"success_count = 0\n",
"while success_count < 100:\n",
" lc = trace_tour(random.choice(patterns)())\n",
- " rw = guided_walk(lc)\n",
+ " rw = guided_walk(lc, seek_step_limit=0, return_anyway=True)\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",
projects / ou-summer-of-code-2017.git / commitdiff
