def ensure_np(amount):
# Withdraws from the bank to get up at least [amount] NP.
np = NeoPage()
np.get('/bank.phtml')
if np.contains('Collect Interest ('):
bank_interest.bank_interest()
nps = np.current_np()
if nps >= amount: return
need = amount - nps
denom = 10**max(len(str(need)), len(str(amount)))
need = (need // denom + 1) * denom
np.post('/process_bank.phtml', 'type=withdraw', f'amount={need}')
print(f'Withdrawing {need} NP')
def ensure_np(amount):
# Withdraws from the bank to get up at least [amount] NP.
np = NeoPage()
np.get('/bank.phtml')
if np.contains('Collect Interest ('):
bank_interest.bank_interest()
nps = np.current_np()
if nps >= amount: return
need = amount - nps
denom = 10**max(len(str(need)), len(str(amount)))
need = (need // denom + 1) * denom
np.post('/process_bank.phtml', 'type=withdraw', f'amount={need}')
print(f'Withdrawing {need} NP')
def shapeshifter(timeout=10 * 60):
np = NeoPage()
np.get(path_index)
starting_np = np.current_np()
if np.contains('Continue Game!'):
np.get(path_game)
elif np.contains('Start Game!'):
np.post(path_process, f'type=init')
# Just in case we won but left it in the completed state
if np.contains('You Won!') or np.contains('You Lost!'):
np.post(path_process + '?type=init')
tbl = np.search(r'''<table border=1 bordercolor='gray'>(.*?)</table>''')[1]
imgs = re.findall(
r'''<img src='(.*?)' border=0 name='i_'>(<br><b><small>GOAL</small></b></td>)?''',
tbl)
imgs = imgs[:-1]
N = len(imgs)
goal_idx = next(i for i, (_, goal) in enumerate(imgs) if goal)
to_idx = {img: i for i, (img, _) in enumerate(imgs)}
tbl = np.search(
r'''<table align=center cellpadding=0 cellspacing=0 border=0>(.*?)</table>'''
)[1]
goal_grid = []
for row in re.findall(r'''<tr>(.*?)</tr>''', tbl, flags=re.DOTALL):
imgs = re.findall(r'''<img src='(.*?)' .*?>''', row)
goal_row = [to_idx[img] for img in imgs]
goal_grid.append(goal_row)
tbl = np.search(
r'''<center><b><big>ACTIVE SHAPE</big></b><p>(.*?)</table>\n''')[1]
shape_grids = []
for shape_info in re.findall(r'''<table.*?>(.*?)</table>''', tbl):
grid = []
for row_info in re.findall(r'''<tr>(.*?)</tr>''', shape_info):
tiles = re.findall(r'''<td.*?>(.*?)</td>''', row_info)
grid.append(
[int('square.gif' in tile_info) for tile_info in tiles])
shape_grids.append(grid)
# Compute kvho difficulty.
R = len(goal_grid)
C = len(goal_grid[0])
min_shifts_needed = R * C * (N - 1) - sum(map(sum, goal_grid))
shifts_available = sum(
sum(sum(rows) for rows in grid) for grid in shape_grids)
num_overshifts = shifts_available - min_shifts_needed
num_flips = num_overshifts // N
print(f'Puzzle permits {num_flips} flips ({num_overshifts} overshifts)')
kvho_input = make_kvho_input(goal_grid, shape_grids, goal_idx)
print(f'Waiting for kvho.')
start_time = datetime.now()
positions = []
try:
proc = subprocess.run(['c/ss'],
input=kvho_input,
encoding='utf-8',
capture_output=True,
timeout=timeout)
for line in proc.stdout.splitlines():
if 'x' in line and '=' in line:
x = list(
map(
int,
line.replace('x', ' ').replace('=',
' ').strip().split()))
for c, r, _ in zip(x[::3], x[1::3], x[2::3]):
positions.append((r, c))
print(f'Solution found in {datetime.now() - start_time}: {positions}')
except subprocess.TimeoutExpired:
print(
f'Solution not found in time. Throwing this puzzle to get a new one.'
)
for _ in shape_grids:
positions.append((0, 0))
for i, (shape, (r, c)) in enumerate(zip(shape_grids, positions)):
print(f'\rPlacing piece {i+1}/{len(positions)}', end='')
np.set_referer_path(path_game)
np.get(path_process, 'type=action', f'posx={c}', f'posy={r}')
time.sleep(0.5)
print()
if np.contains('You Won!'):
np.set_referer_path(path_game)
np.post(path_process + '?type=init')
ending_np = np.current_np()
print(f'Done level, earned {ending_np - starting_np} NP')
return 1
elif np.contains('reached your max neopoints'):
print('Done for today.')
return 2
else:
print('Did not solve level??')
return 0
def shapeshifter(timeout=10*60):
np = NeoPage()
np.get(path_index)
starting_np = np.current_np()
if np.contains('Continue Game!'):
np.get(path_game)
elif np.contains('Start Game!'):
np.post(path_process, f'type=init')
# Just in case we won but left it in the completed state
if np.contains('You Won!') or np.contains('You Lost!'):
np.post(path_process + '?type=init')
tbl = np.search(r'''<table border=1 bordercolor='gray'>(.*?)</table>''')[1]
imgs = re.findall(r'''<img src='(.*?)' border=0 name='i_'>(<br><b><small>GOAL</small></b></td>)?''', tbl)
imgs = imgs[:-1]
N = len(imgs)
goal_idx = next(i for i, (_, goal) in enumerate(imgs) if goal)
to_idx = {img: i for i, (img, _) in enumerate(imgs)}
tbl = np.search(r'''<table align=center cellpadding=0 cellspacing=0 border=0>(.*?)</table>''')[1]
goal_grid = []
for row in re.findall(r'''<tr>(.*?)</tr>''', tbl, flags=re.DOTALL):
imgs = re.findall(r'''<img src='(.*?)' .*?>''', row)
goal_row = [to_idx[img] for img in imgs]
goal_grid.append(goal_row)
tbl = np.search(r'''<center><b><big>ACTIVE SHAPE</big></b><p>(.*?)</table>\n''')[1]
shape_grids = []
for shape_info in re.findall(r'''<table.*?>(.*?)</table>''', tbl):
grid = []
for row_info in re.findall(r'''<tr>(.*?)</tr>''', shape_info):
tiles = re.findall(r'''<td.*?>(.*?)</td>''', row_info)
grid.append([int('square.gif' in tile_info) for tile_info in tiles])
shape_grids.append(grid)
# Compute kvho difficulty.
R = len(goal_grid)
C = len(goal_grid[0])
min_shifts_needed = R * C * (N - 1) - sum(map(sum, goal_grid))
shifts_available = sum(sum(sum(rows) for rows in grid) for grid in shape_grids)
num_overshifts = shifts_available - min_shifts_needed
num_flips = num_overshifts // N
print(f'Puzzle permits {num_flips} flips ({num_overshifts} overshifts)')
kvho_input = make_kvho_input(goal_grid, shape_grids, goal_idx)
print(f'Waiting for kvho.')
start_time = datetime.now()
positions = []
try:
proc = subprocess.run(['c/ss'], input=kvho_input, encoding='utf-8', capture_output=True, timeout=timeout)
for line in proc.stdout.splitlines():
if 'x' in line and '=' in line:
x = list(map(int, line.replace('x', ' ').replace('=', ' ').strip().split()))
for c, r, _ in zip(x[::3], x[1::3], x[2::3]):
positions.append((r, c))
print(f'Solution found in {datetime.now() - start_time}: {positions}')
except subprocess.TimeoutExpired:
print(f'Solution not found in time. Throwing this puzzle to get a new one.')
for _ in shape_grids:
positions.append((0, 0))
for i, (shape, (r, c)) in enumerate(zip(shape_grids, positions)):
print(f'\rPlacing piece {i+1}/{len(positions)}', end='')
np.set_referer_path(path_game)
np.get(path_process, 'type=action', f'posx={c}', f'posy={r}')
time.sleep(0.5)
print()
if np.contains('You Won!'):
np.set_referer_path(path_game)
np.post(path_process + '?type=init')
ending_np = np.current_np()
print(f'Done level, earned {ending_np - starting_np} NP')
return 1
elif np.contains('reached your max neopoints'):
print('Done for today.')
return 2
else:
print('Did not solve level??')
return 0
