def _get_size(self):
"""Return the (width, height) for this Image.
Returns:
Tuple[int, int]: The (width, height) of this Image
"""
w = ffi.new("int *")
h = ffi.new("int *")
lib.TCOD_image_get_size(self.image_c, w, h)
return w[0], h[0]
def __init__(
self,
dimensions: int,
algorithm: int = 2,
implementation: int = SIMPLE,
hurst: float = 0.5,
lacunarity: float = 2.0,
octaves: float = 4,
seed: Optional[tcod.random.Random] = None,
):
if not 0 < dimensions <= 4:
raise ValueError("dimensions must be in range 0 < n <= 4, got %r" %
(dimensions, ))
self._random = seed
_random_c = seed.random_c if seed else ffi.NULL
self._algorithm = algorithm
self.noise_c = ffi.gc(
ffi.cast(
"struct TCOD_Noise*",
lib.TCOD_noise_new(dimensions, hurst, lacunarity, _random_c),
),
lib.TCOD_noise_delete,
)
self._tdl_noise_c = ffi.new("TDLNoise*",
(self.noise_c, dimensions, 0, octaves))
self.implementation = implementation # sanity check
def __init__(self, width, height, order="C"):
self._key_color = None
self._ch = np.full((height, width), 0x20, dtype=np.intc)
self._fg = np.zeros((height, width), dtype="(3,)u1")
self._bg = np.zeros((height, width), dtype="(3,)u1")
self._order = tcod._internal.verify_order(order)
# libtcod uses the root console for defaults.
bkgnd_flag = alignment = 0
if lib.TCOD_ctx.root != ffi.NULL:
bkgnd_flag = lib.TCOD_ctx.root.bkgnd_flag
alignment = lib.TCOD_ctx.root.alignment
self._console_data = self.console_c = ffi.new(
"struct TCOD_Console*",
{
"w": width,
"h": height,
"ch_array": ffi.cast("int*", self._ch.ctypes.data),
"fg_array": ffi.cast("TCOD_color_t*", self._fg.ctypes.data),
"bg_array": ffi.cast("TCOD_color_t*", self._bg.ctypes.data),
"bkgnd_flag": bkgnd_flag,
"alignment": alignment,
"fore": (255, 255, 255),
"back": (0, 0, 0),
},
)
def _setstate_old(self, state):
self._random = state[0]
self.noise_c = ffi.new('struct TCOD_Noise*')
self.noise_c.ndim = state[3]
ffi.buffer(self.noise_c.map)[:] = state[4]
ffi.buffer(self.noise_c.buffer)[:] = state[5]
self.noise_c.H = state[6]
self.noise_c.lacunarity = state[7]
ffi.buffer(self.noise_c.exponent)[:] = state[8]
if state[9]:
# high change of this being prematurely garbage collected!
self.__waveletTileData = ffi.new('float[]', 32 * 32 * 32)
ffi.buffer(self.__waveletTileData)[:] = state[9]
self.noise_c.noise_type = state[10]
self._tdl_noise_c = ffi.new(
'TDLNoise*', (self.noise_c, self.noise_c.ndim, state[1], state[2]))
def __init__(self, width, height, order='C'):
self._key_color = None
self._ch = np.zeros((height, width), dtype=np.intc)
self._fg = np.zeros((height, width), dtype='(3,)u1')
self._bg = np.zeros((height, width), dtype='(3,)u1')
self._order = tcod._internal.verify_order(order)
# libtcod uses the root console for defaults.
bkgnd_flag = alignment = 0
if lib.TCOD_ctx.root != ffi.NULL:
bkgnd_flag = lib.TCOD_ctx.root.bkgnd_flag
alignment = lib.TCOD_ctx.root.alignment
self._console_data = self.console_c = ffi.new(
'struct TCOD_Console*',
{
'w': width,
'h': height,
'ch_array': ffi.cast('int*', self._ch.ctypes.data),
'fg_array': ffi.cast('TCOD_color_t*', self._fg.ctypes.data),
'bg_array': ffi.cast('TCOD_color_t*', self._bg.ctypes.data),
'bkgnd_flag': bkgnd_flag,
'alignment': alignment,
'fore': (255, 255, 255),
'back': (0, 0, 0),
},
)
def __as_cdata(self):
return ffi.new('struct TCOD_Map*', (
self.width,
self.height,
self.width * self.height,
ffi.cast('struct TCOD_MapCell*', self.__buffer.ctypes.data),
))
def __as_cdata(self):
return ffi.new('map_t *', (
self.width,
self.height,
self.width * self.height,
ffi.cast('cell_t*', self.__buffer.ctypes.data),
))
def __setstate__(self, state: Any) -> None:
"""Create a new cdata object with the stored paramaters."""
try:
cdata = state["random_c"]
except KeyError: # old/deprecated format
cdata = state["cdata"]
del state["cdata"]
state["random_c"] = ffi.new("mersenne_data_t*", cdata)
self.__dict__.update(state)
def __setstate__(self, state):
"""Create a new cdata object with the stored paramaters."""
try:
cdata = state['random_c']
except KeyError: # old/deprecated format
cdata = state['cdata']
del state['cdata']
state['random_c'] = ffi.new('mersenne_data_t*', cdata)
self.__dict__.update(state)
def blit(self,
dest,
dest_x=0,
dest_y=0,
src_x=0,
src_y=0,
width=0,
height=0,
fg_alpha=1.0,
bg_alpha=1.0,
key_color=None):
"""Blit from this console onto the ``dest`` console.
Args:
dest (Console): The destintaion console to blit onto.
dest_x (int): Leftmost coordinate of the destintaion console.
dest_y (int): Topmost coordinate of the destintaion console.
src_x (int): X coordinate from this console to blit, from the left.
src_y (int): Y coordinate from this console to blit, from the top.
width (int): The width of the region to blit.
If this is 0 the maximum possible width will be used.
height (int): The height of the region to blit.
If this is 0 the maximum possible height will be used.
fg_alpha (float): Foreground color alpha vaule.
bg_alpha (float): Background color alpha vaule.
key_color (Optional[Tuple[int, int, int]]):
None, or a (red, green, blue) tuple with values of 0-255.
.. versionchanged:: 4.0
Parameters were rearraged and made optional.
Previously they were:
`(x, y, width, height, dest, dest_x, dest_y, *)`
"""
# The old syntax is easy to detect and correct.
if hasattr(src_y, 'console_c'):
src_x, src_y, width, height, dest, dest_x, dest_y = \
dest, dest_x, dest_y, src_x, src_y, width, height
warnings.warn(
"Parameter names have been moved around, see documentation.",
DeprecationWarning,
stacklevel=2,
)
if key_color or self._key_color:
key_color = ffi.new('TCOD_color_t*', key_color)
lib.TCOD_console_blit_key_color(self.console_c, src_x, src_y,
width, height, dest.console_c,
dest_x, dest_y, fg_alpha, bg_alpha,
key_color)
else:
lib.TCOD_console_blit(self.console_c, src_x, src_y, width, height,
dest.console_c, dest_x, dest_y, fg_alpha,
bg_alpha)
def __setstate__(self, state: Any) -> None:
if isinstance(state, tuple): # deprecated format
return self._setstate_old(state)
# unpack wavelet tile data if it exists
if "_waveletTileData" in state:
state["_waveletTileData"] = ffi.new("float[]",
state["_waveletTileData"])
state["noise_c"]["waveletTileData"] = state["_waveletTileData"]
else:
state["noise_c"]["waveletTileData"] = ffi.NULL
# unpack TCOD_Noise and link to Random instance
state["noise_c"]["rand"] = state["_random"].random_c
state["noise_c"] = ffi.new("struct TCOD_Noise*", state["noise_c"])
# unpack TDLNoise and link to libtcod noise
state["_tdl_noise_c"]["noise"] = state["noise_c"]
state["_tdl_noise_c"] = ffi.new("TDLNoise*", state["_tdl_noise_c"])
self.__dict__.update(state)
def __as_cdata(self) -> Any:
return ffi.new(
"struct TCOD_Map*",
(
self.width,
self.height,
self.width * self.height,
ffi.cast("struct TCOD_MapCell*", self.__buffer.ctypes.data),
),
)
def __setstate__(self, state):
if isinstance(state, tuple): # deprecated format
return self._setstate_old(state)
# unpack wavelet tile data if it exists
if '_waveletTileData' in state:
state['_waveletTileData'] = ffi.new('float[]',
state['_waveletTileData'])
state['noise_c']['waveletTileData'] = state['_waveletTileData']
else:
state['noise_c']['waveletTileData'] = ffi.NULL
# unpack TCOD_Noise and link to Random instance
state['noise_c']['rand'] = state['_random'].random_c
state['noise_c'] = ffi.new('struct TCOD_Noise*', state['noise_c'])
# unpack TDLNoise and link to libtcod noise
state['_tdl_noise_c']['noise'] = state['noise_c']
state['_tdl_noise_c'] = ffi.new('TDLNoise*', state['_tdl_noise_c'])
self.__dict__.update(state)
def get_path(self, x: int, y: int) -> List[Tuple[int, int]]:
"""Return a list of (x, y) steps to reach the goal point, if possible.
"""
lib.TCOD_dijkstra_path_set(self._path_c, x, y)
path = []
pointer_x = ffi.new("int[2]")
pointer_y = pointer_x + 1
while lib.TCOD_dijkstra_path_walk(self._path_c, pointer_x, pointer_y):
path.append((pointer_x[0], pointer_y[0]))
return path
def __setstate__(self, state):
self._key_color = None
self.__dict__.update(state)
self._console_data.update(
{
'ch_array': ffi.cast('int*', self._ch.ctypes.data),
'fg_array': ffi.cast('TCOD_color_t*', self._fg.ctypes.data),
'bg_array': ffi.cast('TCOD_color_t*', self._bg.ctypes.data),
}
)
self._console_data = self.console_c = ffi.new(
'struct TCOD_Console*', self._console_data)
def __setstate__(self, state):
self._key_color = None
self.__dict__.update(state)
self._console_data.update(
{
"ch_array": ffi.cast("int*", self._ch.ctypes.data),
"fg_array": ffi.cast("TCOD_color_t*", self._fg.ctypes.data),
"bg_array": ffi.cast("TCOD_color_t*", self._bg.ctypes.data),
}
)
self._console_data = self.console_c = ffi.new(
"struct TCOD_Console*", self._console_data
)
def get_tcod_path_ffi(self) -> Tuple[Any, Any, Tuple[int, int]]:
if len(self.shape) != 2:
raise ValueError("Array must have a 2d shape, shape is %r" %
(self.shape, ))
if self.dtype.type not in self._C_ARRAY_CALLBACKS:
raise ValueError("dtype must be one of %r, dtype is %r" %
(self._C_ARRAY_CALLBACKS.keys(), self.dtype.type))
array_type, callback = self._C_ARRAY_CALLBACKS[self.dtype.type]
userdata = ffi.new(
"struct PathCostArray*",
(ffi.cast("char*", self.ctypes.data), self.strides),
)
return callback, userdata, self.shape
def get_tcod_path_ffi(self):
if len(self.shape) != 2:
raise ValueError('Array must have a 2d shape, shape is %r' %
(self.shape, ))
if self.dtype.type not in self._C_ARRAY_CALLBACKS:
raise ValueError('dtype must be one of %r, dtype is %r' %
(self._C_ARRAY_CALLBACKS.keys(), self.dtype.type))
array_type, callback = \
self._C_ARRAY_CALLBACKS[self.dtype.type]
userdata = ffi.new(
'PathCostArray*',
(self.width, ffi.cast(array_type, self.ctypes.data)),
)
return callback, userdata, self.width, self.height
def __init__(
self,
width: int,
height: int,
order: str = "C",
buffer: Optional[np.array] = None,
):
self._key_color = None # type: Optional[Tuple[int, int, int]]
self._order = tcod._internal.verify_order(order)
if buffer is not None:
if self._order == "F":
buffer = buffer.transpose()
self._ch = np.ascontiguousarray(buffer["ch"], np.intc)
self._fg = np.ascontiguousarray(buffer["fg"], "u1")
self._bg = np.ascontiguousarray(buffer["bg"], "u1")
else:
self._ch = np.ndarray((height, width), dtype=np.intc)
self._fg = np.ndarray((height, width), dtype=self._DTYPE_RGB)
self._bg = np.ndarray((height, width), dtype=self._DTYPE_RGB)
# libtcod uses the root console for defaults.
default_bg_blend = 0
default_alignment = 0
if lib.TCOD_ctx.root != ffi.NULL:
default_bg_blend = lib.TCOD_ctx.root.bkgnd_flag
default_alignment = lib.TCOD_ctx.root.alignment
self._console_data = self.console_c = ffi.new(
"struct TCOD_Console*",
{
"w": width,
"h": height,
"ch_array": ffi.cast("int*", self._ch.ctypes.data),
"fg_array": ffi.cast("TCOD_color_t*", self._fg.ctypes.data),
"bg_array": ffi.cast("TCOD_color_t*", self._bg.ctypes.data),
"bkgnd_flag": default_bg_blend,
"alignment": default_alignment,
"fore": (255, 255, 255),
"back": (0, 0, 0),
},
)
if buffer is None:
self.clear()
def __setstate__(self, state: Any) -> None:
self._key_color = None
if "_tiles" not in state:
tiles = np.ndarray((self.height, self.width), dtype=self.DTYPE)
tiles["ch"] = state["_ch"]
tiles["fg"][..., :3] = state["_fg"]
tiles["fg"][..., 3] = 255
tiles["bg"][..., :3] = state["_bg"]
tiles["bg"][..., 3] = 255
state["_tiles"] = tiles
del state["_ch"]
del state["_fg"]
del state["_bg"]
self.__dict__.update(state)
self._console_data["tiles"] = ffi.cast("struct TCOD_ConsoleTile*",
self._tiles.ctypes.data)
self._console_data = self.console_c = ffi.new("struct TCOD_Console*",
self._console_data)
def get_path(self, start_x, start_y, goal_x, goal_y):
"""Return a list of (x, y) steps to reach the goal point, if possible.
Args:
start_x (int): Starting X position.
start_y (int): Starting Y position.
goal_x (int): Destination X position.
goal_y (int): Destination Y position.
Returns:
List[Tuple[int, int]]:
A list of points, or an empty list if there is no valid path.
"""
lib.TCOD_path_compute(self._path_c, start_x, start_y, goal_x, goal_y)
path = []
x = ffi.new('int[2]')
y = x + 1
while lib.TCOD_path_walk(self._path_c, x, y, False):
path.append((x[0], y[0]))
return path
def __init__(self, dimensions, algorithm=2, implementation=SIMPLE,
hurst=0.5, lacunarity=2.0, octaves=4, seed=None):
if not 0 < dimensions <= 4:
raise ValueError('dimensions must be in range 0 < n <= 4, got %r' %
(dimensions,))
self._random = seed
_random_c = seed.random_c if seed else ffi.NULL
self._algorithm = algorithm
self.noise_c = ffi.gc(
ffi.cast(
'perlin_data_t*',
lib.TCOD_noise_new(dimensions, hurst, lacunarity,
_random_c),
),
lib.TCOD_noise_delete)
self._tdl_noise_c = ffi.new('TDLNoise*', (self.noise_c,
dimensions,
0,
octaves))
self.implementation = implementation # sanity check