HexDecimal / python-tdl

tcod/path.py

"""

Example::

    >>> import numpy as np
    >>> import tcod.path
    >>> dungeon = np.array(
    ...     [
    ...         [1, 0, 1, 1, 1],
    ...         [1, 0, 1, 0, 1],
    ...         [1, 1, 1, 0, 1],
    ...     ],
    ...     dtype=np.int8,
    ...     )
    ...
    >>> height, width = dungeon.shape

    # Create a pathfinder from a numpy array.
    # This is the recommended way to use the tcod.path module.
    >>> astar = tcod.path.AStar(dungeon)
    >>> print(astar.get_path(0, 0, 4, 2))
    [(0, 1), (1, 2), (2, 1), (3, 0), (4, 1), (4, 2)]
    >>> astar.cost[0, 1] = 1 # You can access the map array via this attribute.
    >>> print(astar.get_path(0, 0, 4, 2))
    [(1, 0), (2, 0), (3, 0), (4, 1), (4, 2)]

    # Create a pathfinder from an edge_cost function.
    # Calling Python functions from C is known to be very slow.
    >>> def edge_cost(my_x, my_y, dest_x, dest_y):
    ...     return dungeon[dest_y, dest_x]
    ...
    >>> dijkstra = tcod.path.Dijkstra(
    ...     tcod.path.EdgeCostCallback(edge_cost, width, height),
    ...     )
    ...
    >>> dijkstra.set_goal(0, 0)
    >>> print(dijkstra.get_path(4, 2))
    [(1, 0), (2, 0), (3, 0), (4, 1), (4, 2)]
"""

from __future__ import absolute_import

import numpy as np

from tcod.libtcod import lib, ffi


@ffi.def_extern()
def _pycall_path_old(x1, y1, x2, y2, handle):

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    """libtcodpy style callback, needs to preserve the old userData issue."""
    func, userData = ffi.from_handle(handle)

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    return func(x1, y1, x2, y2, userData)


@ffi.def_extern()
def _pycall_path_simple(x1, y1, x2, y2, handle):

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    """Does less and should run faster, just calls the handle function."""
    return ffi.from_handle(handle)(x1, y1, x2, y2)


@ffi.def_extern()
def _pycall_path_swap_src_dest(x1, y1, x2, y2, handle):

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    """A TDL function dest comes first to match up with a dest only call."""
    return ffi.from_handle(handle)(x2, y2, x1, y1)


@ffi.def_extern()
def _pycall_path_dest_only(x1, y1, x2, y2, handle):

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    """A TDL function which samples the dest coordinate only."""
    return ffi.from_handle(handle)(x2, y2)

def _get_pathcost_func(name):
    """Return a properly cast PathCostArray callback."""
    return ffi.cast('TCOD_path_func_t', ffi.addressof(lib, name))


class _EdgeCostFunc(object):
    """
    Args:
        userdata (Any): Custom userdata to send to the C call.
        width (int): The maximum width of this callback.
        height (int): The maximum height of this callback.
    """
    _CALLBACK_P = lib._pycall_path_old

    def __init__(self, userdata, width, height):
        self._userdata = userdata
        self.width = width
        self.height = height

    def get_tcod_path_ffi(self):
        """

        Returns:
            Tuple[CData, CData]: A cffi (callback, userdata) pair.


        """
        return (self._CALLBACK_P, ffi.new_handle(self._userdata),
                self.width, self.height)

    def __repr__(self):
        return '%s(%r, width=%r, height=%r)' % (
            self.__class__.__name__,
            self._userdata, self.width, self.height,
            )


class EdgeCostCallback(_EdgeCostFunc):
    """Calculate cost from an edge-cost callback.

    Args:
        callback (Callable[[int, int, int, int], float]):
            A callback which can handle the following parameters:
            `(source_x:int, source_y:int, dest_x:int, dest_y:int) -> float`
        width (int): The maximum width of this callback.
        height (int): The maximum height of this callback.
    """
    _CALLBACK_P = lib._pycall_path_simple

    def __init__(self, callback, width, height):
        self.callback = callback
        super(EdgeCostCallback, self).__init__(callback, width, height)

class NodeCostArray(np.ndarray):
    """Calculate cost from a numpy array of nodes.

    Args:
        array (numpy.ndarray): A numpy array with the cost of each node.
    """

    _C_ARRAY_CALLBACKS = {
        np.float32: ('float*', _get_pathcost_func('PathCostArrayFloat32')),
        np.bool_: ('int8_t*', _get_pathcost_func('PathCostArrayInt8')),
        np.int8: ('int8_t*', _get_pathcost_func('PathCostArrayInt8')),
        np.uint8: ('uint8_t*', _get_pathcost_func('PathCostArrayUInt8')),
        np.int16: ('int16_t*', _get_pathcost_func('PathCostArrayInt16')),
        np.uint16: ('uint16_t*', _get_pathcost_func('PathCostArrayUInt16')),
        np.int32: ('int32_t*', _get_pathcost_func('PathCostArrayInt32')),
        np.uint32: ('uint32_t*', _get_pathcost_func('PathCostArrayUInt32')),
        }

    def __new__(cls, array):
        """Validate a numpy array and setup a C callback."""
        self = np.ascontiguousarray(array).view(cls)
        return self

    @property
    def width(self):

This method should have a docstring.

        return self.shape[1]

    @property
    def height(self):

This method should have a docstring.

        return self.shape[0]

    def __repr__(self):
        return '%s(%r)' % (self.__class__.__name__,
                           repr(self.view(np.ndarray)))

    def get_tcod_path_ffi(self):

This method should have a docstring.

        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


class _PathFinder(object):
    """A class sharing methods used by AStar and Dijkstra."""

    def __init__(self, cost, diagonal=1.41):
        self.cost = cost
        self.diagonal = diagonal
        self._path_c = None
        self._callback = self._userdata = None

        if hasattr(self.cost, 'map_c'):
            self.width, self.height = self.cost.width, self.cost.height
            self._path_c = ffi.gc(
                self._path_new_using_map(self.cost.map_c, diagonal),
                self._path_delete,
                )
            return

        if not hasattr(self.cost, 'get_tcod_path_ffi'):
            assert not callable(self.cost), \
                "Any callback alone is missing width & height information. " \
                "Wrap your callback in tcod.path.EdgeCostCallback"
            self.cost = NodeCostArray(self.cost)

        self._callback, self._userdata, self.width, self.height = \
            self.cost.get_tcod_path_ffi()
        self._path_c = ffi.gc(
            self._path_new_using_function(
                self.width,
                self.height,
                self._callback,
                self._userdata,
                diagonal
                ),
            self._path_delete,
            )

    def __repr__(self):
        return '%s(cost=%r, diagonal=%r)' % (self.__class__.__name__,
                                             self.cost, self.diagonal)

    def __getstate__(self):
        state = self.__dict__.copy()
        del state['_path_c']
        del state['width']
        del state['height']
        del state['_callback']
        del state['_userdata']
        return state

    def __setstate__(self, state):
        self.__dict__.update(state)
        self.__init__(self.cost, self.diagonal)

    _path_new_using_map = lib.TCOD_path_new_using_map
    _path_new_using_function = lib.TCOD_path_new_using_function
    _path_delete = lib.TCOD_path_delete


class AStar(_PathFinder):
    """
    Args:
        cost (Union[tcod.map.Map, numpy.ndarray, Any]):
        diagonal (float): Multiplier for diagonal movement.
            A value of 0 will disable diagonal movement entirely.
    """

    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]')

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        y = x + 1

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        while lib.TCOD_path_walk(self._path_c, x, y, False):
            path.append((x[0], y[0]))
        return path


class Dijkstra(_PathFinder):
    """
    Args:
        cost (Union[tcod.map.Map, numpy.ndarray, Any]):
        diagonal (float): Multiplier for diagonal movement.
            A value of 0 will disable diagonal movement entirely.
    """

    _path_new_using_map = lib.TCOD_dijkstra_new
    _path_new_using_function = lib.TCOD_dijkstra_new_using_function
    _path_delete = lib.TCOD_dijkstra_delete

    def set_goal(self, x, y):

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        """Set the goal point and recompute the Dijkstra path-finder.
        """
        lib.TCOD_dijkstra_compute(self._path_c, x, y)

    def get_path(self, x, y):

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        """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