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#!/usr/bin/env python3
# Copyright 2020 vg
# SPDX-License-Identifier: MIT
'''
Usage: solver.py -h|--help
solver.py [-p] YAML_GRID
solver.py --print YAML_GRID
Options:
-h, --help Display this help message
-p Display solution with parents [DEFAULT is without parents]
--print Display only the original (unresolved) grid
'''
import copy
import os
import pickle
import time
import zlib
import docopt
import yaml
class Kana:
types = ('void', 'norm', 'froz', 'rock', 'myst', 'slim',
'ar_u', 'ar_r', 'ar_d', 'ar_l')
slime_merge_types = ('norm', 'froz', 'ar_u', 'ar_r', 'ar_d', 'ar_l')
cache = {}
def __init__(self, type_name, kana=None):
self.type_name = type_name
self.kana = kana
#print(type_name)
#print(kana)
assert type_name in self.types
# missing yet: 'n' and 'y...' and 'w...' are not we all vowels
if kana:
if type_name == 'slim':
assert kana[0] in 'aiueo'
else:
assert kana[0] in 'kstnhmryw'
assert kana[1] in 'aiueo'
def create(type_name, kana=None):
key = (type_name, kana)
if key in Kana.cache:
return Kana.cache[key]
new_kana = Kana.cache[key] = Kana(type_name, kana)
return new_kana
def dump(self):
'return a list representing the members of this Kana'
return [self.type_name, self.kana]
def __repr__(self):
return "%s(%s)" % (self.type_name, self.kana)
def __eq__(self, other):
return self.type_name == other.type_name and self.kana == other.kana
kana_void = Kana.create('void')
class KanaGrid:
actions = ('reveal', 'up', 'right', 'down', 'left')
def __init__(self, size, grid,
action_count=0, score=0, myst_count=0, parent=None):
self.width = size[0]
self.height = size[1]
self.grid = grid
self.action_count = action_count
self.score = score
self.myst_count = myst_count
self.parent = parent
def copy(self):
return KanaGrid(
(self.width, self.height),
copy.copy(self.grid),
action_count=self.action_count,
score=self.score,
myst_count=self.myst_count,
parent=self.parent,
)
def is_swappable(self, pos1, pos2):
kana1 = self.get_kana(pos1)
kana2 = self.get_kana(pos2)
table_ok = {
'norm': ('norm', 'froz', 'ar_u', 'ar_r', 'ar_d', 'ar_l'),
'froz': ('norm', 'ar_u', 'ar_r', 'ar_d', 'ar_l'),
'ar_u': ('norm', 'froz', 'ar_d' ),
'ar_r': ('norm', 'froz', 'ar_l'),
'ar_d': ('norm', 'froz', 'ar_u' ),
'ar_l': ('norm', 'froz', 'ar_r' ),
}
if kana1.type_name in table_ok:
if kana2.type_name in table_ok[kana1.type_name]:
# early return for empty norm to empty norm
if kana1.type_name == kana2.type_name == 'norm' \
and kana1.kana is kana2.kana is None:
return False
ar_vect_ok = {
'ar_u': ( 0, -1),
'ar_l': (-1, 0),
'ar_d': ( 0, 1),
'ar_r': ( 1, 0),
}
vect1_target = ar_vect_ok.get(kana1.type_name, None)
vect2_target = ar_vect_ok.get(kana2.type_name, None)
if vect1_target or vect2_target:
vect1 = (pos2[0] - pos1[0], pos2[1] - pos1[1])
vect2 = (pos1[0] - pos2[0], pos1[1] - pos2[1])
if vect1 != vect1_target and vect2 != vect2_target:
return False
return True
return False
def action(self, pos, action_type):
kana = self.get_kana(pos)
if action_type == 'reveal':
if kana.type_name == 'myst':
new_grid = self.copy()
new_grid.action_count += 1
new_grid.set_kana(pos, Kana.create('norm', kana.kana))
return new_grid
elif action_type in ('up', 'right', 'down', 'left'):
pos_map = {
'up': (pos[0], pos[1]-1),
'right': (pos[0]+1, pos[1]),
'down': (pos[0], pos[1]+1),
'left': (pos[0]-1, pos[1]),
}
pos_dest = pos_map[action_type]
if kana.type_name == 'slim':
kana2 = self.get_kana(pos_dest)
if kana2.type_name in Kana.slime_merge_types and kana2.kana:
# early return for slime merging for same vowel
if kana2.kana[1] == kana.kana[0]:
return
new_grid = self.copy()
new_grid.action_count += 1
new_kana1 = kana_void
new_kana2 = Kana.create(
kana2.type_name,
kana2.kana[0] + kana.kana[0],
)
new_grid.set_kana(pos, new_kana1)
new_grid.set_kana(pos_dest, new_kana2)
return new_grid
elif self.is_swappable(pos, pos_dest):
new_grid = self.copy()
new_grid.action_count += 1
new_grid.swap_kana(pos, pos_dest)
return new_grid
def generate_valid_pos_for_chain(self):
for y in range(self.height):
for x in range(self.width):
kana = self.get_kana((x, y))
if kana.kana and kana.type_name != 'slim':
yield (x, y)
def populate_chain(self, pos1, chain_positions):
myst_count = 0
if pos1 in chain_positions:
return myst_count
kana1 = self.get_kana(pos1)
if kana1.type_name == 'myst':
myst_count += 1
chain_positions.add(pos1)
pos2_list = [
(pos1[0], pos1[1]-1), # up
(pos1[0]+1, pos1[1]), # right
(pos1[0], pos1[1]+1), # down
(pos1[0]-1, pos1[1]), # left
]
for pos2 in pos2_list:
if pos2 in chain_positions:
continue
kana2 = self.get_kana(pos2)
if kana2.kana and kana2.type_name != 'slim':
if is_kana_compatible(kana1, kana2):
myst_count += self.populate_chain(pos2, chain_positions)
return myst_count
def longest_chain(self):
already_evaluated_pos = set()
highest_length = 0
highest_length_chain = 0
highest_myst_count = 0
for pos in self.generate_valid_pos_for_chain():
if pos in already_evaluated_pos:
continue
chain = set()
myst_count = self.populate_chain(pos, chain)
already_evaluated_pos = already_evaluated_pos.union(chain)
if myst_count > highest_myst_count:
highest_myst_count = myst_count
if highest_length < len(chain):
highest_length = len(chain)
highest_length_chain = chain
return highest_length, highest_myst_count
#, highest_length_chain # easy to add if needed
def update_score(self):
if not self.score:
# if self.score == 0 calculate the score as the score is always at
# the bare minimum equal to 1.
self.score, self.myst_count = self.longest_chain()
def get_hash(self):
data = ''.join((
str(self.width),
str(self.height),
str(self.grid),
str(self.action_count),
))
return zlib.crc32(data.encode('utf8'))
def get_kana(self, pos):
if pos[0] < 0 or pos[0] >= self.width:
return kana_void
elif pos[1] < 0 or pos[1] >= self.height:
return kana_void
return self.grid[pos[0]+pos[1]*self.width]
def set_kana(self, pos, kana):
if pos[0] < 0 or pos[0] >= self.width:
return
elif pos[1] < 0 or pos[1] >= self.height:
return
self.grid[pos[0]+pos[1]*self.width] = kana
self.score = 0
def swap_kana(self, pos1, pos2):
kana_dst = self.get_kana(pos2)
if kana_dst.type_name in ('froz', 'fblk'):
pos_tmp = pos1
pos1 = pos2
pos2 = pos_tmp
kana_dst = self.get_kana(pos2)
kana_src = self.get_kana(pos1)
pos_src = pos1
pos_dst = pos2
vect = (pos2[0] - pos1[0], pos2[1] - pos1[1])
while self.is_swappable(pos_src, pos_dst):
#print("swap between src %s (%s) dst %s (%s)"
# % (kana_src, pos_src, kana_dst, pos_dst))
self.set_kana(pos_src, kana_dst)
self.set_kana(pos_dst, kana_src)
if kana_src.type_name not in ('froz', 'fblk') :
break
pos_src = pos_dst
pos_dst = (pos_dst[0] + vect[0], pos_dst[1] + vect[1])
kana_dst = self.get_kana(pos_dst)
def load(input_dict):
grid = [
Kana.create(serialized_kana[0], serialized_kana[1])
for serialized_kana in input_dict['grid']
]
params = {
'size': input_dict['size'],
'grid': grid,
'action_count': input_dict.get('action_count', 0),
'score': input_dict.get('score', 0),
'myst_count': input_dict.get('myst_count', 0),
}
return KanaGrid(**params)
def load_reversed_parents(input_dict_list):
last_kanagrid = None
for input_dict in input_dict_list:
kanagrid = KanaGrid.load(input_dict)
kanagrid.parent = last_kanagrid
last_kanagrid = kanagrid
return last_kanagrid
def dump(self):
'returns a dict representation of the kanagrid'
output_dict = {
'size': [self.width, self.height],
'action_count': self.action_count,
'score': self.score,
'myst_count': self.myst_count,
'grid': [kana.dump() for kana in self.grid],
}
return output_dict
def dump_reversed_parents(self):
kana_grids = []
grid = self
while grid:
kana_grids.append(grid.dump())
grid = grid.parent
return reversed(kana_grids)
def __repr__(self):
self.update_score()
return (
('KanaGrid (cnt: %d, score: %d): \n ' % (self.action_count, self.score))
+ '\n '.join(repr_grid(self.grid, (self.width, self.height)).splitlines())
)
def __eq__(self, other):
return (
self.width == other.width
and self.height == other.height
and self.grid == other.grid
and self.action_count == other.action_count
)
def repr_grid(grid, grid_size):
indicator_map = {
'norm': (' ', ' '),
'froz': ('\x1b[36m[', ']\x1b[0m'),
'rock': (' \x1b[1;40m', '\x1b[0m '),
'myst': ('\x1b[33m?', '?\x1b[0m'),
'slim': ('\x1b[32m~', '~\x1b[0m'),
'ar_u': ('\x1b[31m∧\x1b[0m', '\x1b[31m∧\x1b[0m'),
'ar_r': ('\x1b[31m>\x1b[0m', '\x1b[31m>\x1b[0m'),
'ar_d': ('\x1b[31m∨\x1b[0m', '\x1b[31m∨\x1b[0m'),
'ar_l': ('\x1b[31m<\x1b[0m', '\x1b[31m<\x1b[0m'),
}
lines = []
kana_iter = iter(grid)
for y in range(grid_size[1]):
line = ''
for x in range(grid_size[0]):
kana = next(kana_iter)
tname = kana.type_name
kkana = kana.kana
if not kkana:
kkana = ' '
if tname == 'void':
line += ' '
elif tname in indicator_map:
line += '|%s%2s%s|' % (
indicator_map[tname][0],
kkana,
indicator_map[tname][1],
)
lines.append(line)
return '\n'.join(lines)
def display_grid(grid, grid_size):
print(repr_grid(grid, grid_size))
def is_kana_compatible(kana1, kana2):
if kana1.type_name == 'slim' or kana2.type_name == 'slim':
return False
if kana1.kana[0] == kana2.kana[0] or kana1.kana[1] == kana2.kana[1]:
return True
return False
def generate_possible_grids(kanagrid):
for y in range(kanagrid.height):
for x in range(kanagrid.width):
for action_type in KanaGrid.actions:
new_grid = kanagrid.action((x, y), action_type)
if new_grid and new_grid.grid != kanagrid.grid:
yield (x, y), action_type, new_grid
def generate_all_possible_grids(grid, grids, max_actions):
for pos, action_type, new_grid in generate_possible_grids(grid):
grid_hash = new_grid.get_hash()
if grid_hash in grids or new_grid.action_count > max_actions:
continue
grids[grid_hash] = new_grid
new_grid.parent = grid
generate_all_possible_grids(new_grid, grids, max_actions)
def repr_grid_with_parents(grid):
items = []
while grid:
items.append(str(grid))
grid = grid.parent
return '\n'.join(reversed(items))
def print_score_over(node, target_score):
node.grid.update_score()
if node.grid.score >= target_score:
print("="*80)
print(node_repr_with_parents(node))
return
for child in node.children:
print_score_over(child, target_score)
def search_all_solution(kanagrid, target_score, max_actions):
grids = {}
generate_all_possible_grids(kanagrid, grids=grids, max_actions=max_actions)
for grid in grids.values():
grid.update_score()
if grid.score >= target_score and grid.myst_count == 0:
yield grid
def get_solutions_from_cache(cache):
for solution in cache['solutions']:
kanagrid = KanaGrid.load_reversed_parents(solution)
yield kanagrid
def main():
args = docopt.docopt(__doc__)
grid_fn = args['YAML_GRID']
with open(grid_fn, encoding='utf8') as stream:
input_dict = yaml.safe_load(stream)
kanagrid = KanaGrid.load(input_dict)
target_score = input_dict['target_score']
max_actions = input_dict['max_actions']
print('Size: %dx%d' % (kanagrid.width, kanagrid.height))
print('Target score: %d' % target_score)
print('Max actions: %d' % max_actions)
print('Initial grid:')
print(kanagrid)
if args['--print']:
return
solver_start_time = int(time.time())
mtime = os.path.getmtime
cache_file = '%s.cache' % grid_fn
if os.path.exists(cache_file) and mtime(cache_file) >= mtime(grid_fn):
print(f'Using cached version from {cache_file}')
with open(cache_file, 'rb') as stream:
cached_version = pickle.load(stream)
generator = get_solutions_from_cache(cached_version)
cache_generation = None
else:
generator = search_all_solution(kanagrid, target_score, max_actions)
cache_generation = {
'max_actions': 1,
'target_score': 1,
'solutions': [],
}
for grid in generator:
if cache_generation:
cache_generation['solutions'].append(grid.dump_reversed_parents())
print("="*80)
if args['-p']:
print(repr_grid_with_parents(grid))
else:
print(grid)
if cache_generation:
with open(cache_file, 'wb') as stream:
pickle.dump(cache_generation, stream)
solver_stop_time = int(time.time())
solver_delta_time = solver_stop_time - solver_start_time
hours = solver_delta_time // 3600
minutes = (solver_delta_time // 60) % 60
seconds = solver_delta_time % 60
print(f'time taken to calculate: {hours:02d}:{minutes:02d}:{seconds:02d}')
if __name__ == '__main__':
main()
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