free_angles(shadows);
return 0;
}
+
+static uint16_t * score_map = NULL;
+static uint16_t neighbor_scores[6];
+
+/* Init AI score map. Return 1 on failure, else 0. */
+extern uint8_t init_score_map()
+{
+ uint32_t map_size = maplength * maplength;
+ score_map = malloc(map_size * sizeof(uint16_t));
+ if (!score_map)
+ {
+ return 1;
+ }
+ uint32_t i = 0;
+ for (; i < map_size; i++)
+ {
+ score_map[i] = UINT16_MAX;
+ }
+ return 0;
+}
+
+/* Set score_map[pos] to score. Return 1 on failure, else 0. */
+extern uint8_t set_map_score(uint16_t pos, uint16_t score)
+{
+ if (!score_map)
+ {
+ return 1;
+ }
+ score_map[pos] = score;
+/*
+ uint32_t mup_size = maplength * maplength;
+ uint32_t pus;
+ FILE * file = fopen("test_set", "w");
+ for (pus = 0; pus < mup_size; pus++)
+ {
+ fprintf(file, "%d ", score_map[pus]);
+ if (0 == pus % maplength)
+ {
+ fprintf(file, "\n");
+ }
+ }
+ fclose(file);
+*/
+ return 0;
+}
+
+/* Get score_map[pos]. Return uint16_t value on success, -1 on failure. */
+extern int32_t get_map_score(uint16_t pos)
+{
+ if (!score_map)
+ {
+ return -1;
+ }
+ return score_map[pos];
+}
+
+/* Free score_map. */
+extern void free_score_map()
+{
+ free(score_map);
+ score_map = NULL;
+}
+
+/* Write into "neighbors" scores of the immediate neighbors of the score_map
+ * cell at pos_i (array index), as found in the directions north-east, east,
+ * south-east etc. (clockwise order). Use kill_score for illegal neighborhoods
+ * (i.e. if direction would lead beyond the map's border).
+ */
+static void get_neighbor_scores(uint16_t pos_i, uint16_t kill_score,
+ uint16_t * neighbors)
+{
+ uint32_t map_size = maplength * maplength;
+ uint8_t open_north = pos_i >= maplength;
+ uint8_t open_east = pos_i + 1 % maplength;
+ uint8_t open_south = pos_i + maplength < map_size;
+ uint8_t open_west = pos_i % maplength;
+ uint8_t is_indented = (pos_i / maplength) % 2;
+ uint8_t open_diag_west = is_indented || open_west;
+ uint8_t open_diag_east = !is_indented || open_east;
+ neighbors[0] = !(open_north && open_diag_east) ? kill_score :
+ score_map[pos_i - maplength + is_indented];
+ neighbors[1] = !(open_east) ? kill_score : score_map[pos_i + 1];
+ neighbors[2] = !(open_south && open_diag_east) ? kill_score :
+ score_map[pos_i + maplength + is_indented];
+ neighbors[3] = !(open_south && open_diag_west) ? kill_score :
+ score_map[pos_i + maplength - !is_indented];
+ neighbors[4] = !(open_west) ? kill_score : score_map[pos_i - 1];
+ neighbors[5] = !(open_north && open_diag_west) ? kill_score :
+ score_map[pos_i - maplength - !is_indented];
+}
+
+/* Call get_neighbor_scores() on neighbor_scores buffer. Return 1 on error. */
+extern uint8_t ready_neighbor_scores(uint16_t pos)
+{
+ if (!score_map)
+ {
+ return 1;
+ }
+ get_neighbor_scores(pos, UINT16_MAX, neighbor_scores);
+ return 0;
+}
+
+/* Return i-th position from neighbor_scores buffer.*/
+extern uint16_t get_neighbor_score(uint8_t i)
+{
+ return neighbor_scores[i];
+}
+
+/* Iterate over scored cells in score_map geometry. Compare each cell's score
+ * against the score of its immediate neighbors in 6 directions. If any
+ * neighbor's score is at least two points lower than the current cell's score,
+ * re-set it to 1 point higher than its lowest-scored neighbor. Repeat this
+ * whole process until all cells have settled on their final score. Ignore cells
+ * whose score is greater than UINT16_MAX - 1 (treat those as unreachable).
+ * Return 1 on error, else 0.
+ */
+extern uint8_t dijkstra_map()
+{
+ if (!score_map)
+ {
+ return 1;
+ }
+ uint16_t max_score = UINT16_MAX - 1;
+ uint32_t map_size = maplength * maplength;
+ uint32_t pos;
+ uint16_t i_scans, neighbors[6], min_neighbor;
+ uint8_t scores_still_changing = 1;
+ uint8_t i_dirs;
+ for (i_scans = 0; scores_still_changing; i_scans++)
+ {
+ scores_still_changing = 0;
+ for (pos = 0; pos < map_size; pos++)
+ {
+ if (score_map[pos] <= max_score)
+ {
+ get_neighbor_scores(pos, max_score, neighbors);
+ min_neighbor = max_score;
+ for (i_dirs = 0; i_dirs < 6; i_dirs++)
+ {
+ if (min_neighbor > neighbors[i_dirs])
+ {
+ min_neighbor = neighbors[i_dirs];
+ }
+ }
+ if (score_map[pos] > min_neighbor + 1)
+ {
+ score_map[pos] = min_neighbor + 1;
+ scores_still_changing = 1;
+ }
+ }
+ }
+ }
+ return 0;
+}
libpr.build_fov_map.argtypes = [ctypes.c_uint8, ctypes.c_uint8,
ctypes.c_char_p, ctypes.c_char_p]
libpr.build_fov_map.restype = ctypes.c_uint8
+ libpr.init_score_map.restype = ctypes.c_uint8
+ libpr.set_map_score.argtypes = [ctypes.c_uint16, ctypes.c_uint16]
+ libpr.set_map_score.restype = ctypes.c_uint8
+ libpr.get_map_score.argtypes = [ctypes.c_uint16]
+ libpr.get_map_score.restype = ctypes.c_int32
+ libpr.get_neighbor_score.argtypes = [ctypes.c_uint8]
+ libpr.get_neighbor_score.restype = ctypes.c_uint16
+ libpr.ready_neighbor_scores.argtpes = [ctypes.c_uint16]
+ libpr.ready_neighbor_scores.restype = ctypes.c_uint8
+ libpr.dijkstra_map.restype = ctypes.c_uint8
return libpr
decrement_lifepoints(t)
-def get_dir_to_nearest_target(t, c):
- # Dummy
- return False
+def get_dir_to_target(t, filter):
+ """Try to set T_COMMAND/T_ARGUMENT for move to "filter"-determined target.
+
+ The path-wise nearest target is chosen, via the shortest available path.
+ Target must not be t. On succcess, return positive value, else False.
+ Filters:
+ "a": Thing in FOV is below a certain distance, animate, but of ThingType
+ that is not t's, and starts out weaker than t is; build path as
+ avoiding things of t's ThingType
+ "f": neighbor cell (not inhabited by any animate Thing) further away from
+ animate Thing not further than x steps away and in FOV and of a
+ ThingType that is not t's, and starts out stronger or as strong as t
+ is currently; or (cornered), if no such flight cell, but Thing of
+ above criteria is too near,1 a cell closer to it, or, if less near,
+ just wait
+ "c": Thing in memorized map is consumable
+ "s": memory map cell with greatest-reachable degree of unexploredness
+ """
+
+ def set_map_score(pos, score):
+ test = libpr.set_map_score(pos, score)
+ if test:
+ raise RuntimeError("No score map allocated for set_map_score().")
+
+ def get_map_score(pos):
+ result = libpr.get_map_score(pos)
+ if result < 0:
+ raise RuntimeError("No score map allocated for get_map_score().")
+ return result
+
+ def seeing_thing():
+ if t["fovmap"] and ("a" == filter or "f" == filter):
+ for id in world_db["Things"]:
+ Thing = world_db["Things"][id]
+ if Thing != t and Thing["T_LIFEPOINTS"] and \
+ t["T_TYPE"] != Thing["T_TYPE"] and \
+ 'v' == chr(t["fovmap"][(Thing["T_POSY"]
+ * world_db["MAP_LENGTH"])
+ + Thing["T_POSX"]]):
+ ThingType = world_db["ThingTypes"][Thing["T_TYPE"]]
+ if ("f" == filter and ThingType["TT_LIFEPOINTS"] >=
+ t["T_LIFEPOINTS"]) \
+ or ("a" == filter and ThingType["TT_LIFEPOINTS"] <
+ t["T_LIFEPOINTS"]):
+ return True
+ elif t["T_MEMMAP"] and "c" == filter:
+ for mt in t["T_MEMTHING"]:
+ if ' ' != chr(t["T_MEMMAP"][(mt[1] * world_db["MAP_LENGTH"])
+ + mt[2]]) \
+ and world_db["ThingTypes"][mt[0]]["TT_CONSUMABLE"]:
+ return True
+ return False
+
+ def init_score_map():
+ test = libpr.init_score_map()
+ if test:
+ raise RuntimeError("Malloc error in init_score_map().")
+ for i in [i for i in range(world_db["MAP_LENGTH"] ** 2)
+ if '.' == chr(t["T_MEMMAP"][i])]:
+ set_map_score(i, 65535 - 1)
+ if "a" == filter:
+ for id in world_db["Things"]:
+ Thing = world_db["Things"][id]
+ pos = Thing["T_POSY"] * world_db["MAP_LENGTH"] + Thing["T_POSX"]
+ if t != Thing and Thing["T_LIFEPOINTS"] and \
+ t["T_TYPE"] != Thing["T_TYPE"] and \
+ 'v' == chr(t["fovmap"][pos]) and \
+ t["T_LIFEPOINTS"] > \
+ world_db["ThingTypes"][Thing["T_TYPE"]]["TT_LIFEPOINTS"]:
+ set_map_score(pos, 0)
+ elif t["T_TYPE"] == Thing["T_TYPE"]:
+ set_map_score(pos, 65535)
+ elif "f" == filter:
+ for id in [id for id in world_db["Things"]
+ if world_db["Things"][id]["T_LIFEPOINTS"]]:
+ Thing = world_db["Things"][id]
+ pos = Thing["T_POSY"] * world_db["MAP_LENGTH"] + Thing["T_POSX"]
+ if t["T_TYPE"] != Thing["T_TYPE"] and \
+ 'v' == chr(t["fovmap"][pos]) and \
+ t["T_LIFEPOINTS"] <= \
+ world_db["ThingTypes"][Thing["T_TYPE"]]["TT_LIFEPOINTS"]:
+ set_map_score(pos, 0)
+ elif "c" == filter:
+ for mt in [mt for mt in t["T_MEMTHING"]
+ if ' ' != chr(t["T_MEMMAP"][mt[1]
+ * world_db["MAP_LENGTH"]
+ + mt[2]])
+ if world_db["ThingTypes"][mt[0]]["TT_CONSUMABLE"]]:
+ set_map_score(mt[1] * world_db["MAP_LENGTH"] + mt[2], 0)
+ elif "s" == filter:
+ for i in [i for i in range(world_db["MAP_LENGTH"] ** 2)
+ if t["T_MEMDEPTHMAP"][i] == mem_depth_c[0]]:
+ set_map_score(i, 0)
+
+ def rand_target_dir(neighbors, cmp, dirs):
+ candidates = []
+ n_candidates = 0
+ for i in range(len(dirs)):
+ if cmp == neighbors[i]:
+ candidates.append(dirs[i])
+ n_candidates += 1
+ return candidates[rand.next() % n_candidates] if n_candidates else 0
+
+ def get_neighbor_scores(dirs, eye_pos):
+ scores = []
+ if libpr.ready_neighbor_scores(eye_pos):
+ raise RuntimeError("No score map allocated for " +
+ "ready_neighbor_scores.()")
+ for i in range(len(dirs)):
+ scores.append(libpr.get_neighbor_score(i))
+ return scores
+
+ def get_dir_from_neighbors():
+ dir_to_target = False
+ dirs = "edcxsw"
+ eye_pos = t["T_POSY"] * world_db["MAP_LENGTH"] + t["T_POSX"]
+ neighbors = get_neighbor_scores(dirs, eye_pos)
+ if "f" == filter:
+ inhabited = [world_db["Things"][id]["T_POSY"]
+ * world_db["MAP_LENGTH"]
+ + world_db["Things"][id]["T_POSX"]
+ for id in world_db["Things"]
+ if world_db["Things"][id]["T_LIFEPOINTS"]]
+ for i in range(len(dirs)):
+ mv_yx_in_dir_legal(dirs[i], t["T_POSY"], t["T_POSX"])
+ pos_cmp = libpr.result_y() * world_db["MAP_LENGTH"] \
+ + libpr.result_x()
+ for pos in [pos for pos in inhabited if pos == pos_cmp]:
+ neighbors[i] = 65535
+ break
+ minmax_start = 0 if "f" == filter else 65535 - 1
+ minmax_neighbor = minmax_start
+ for i in range(len(dirs)):
+ if ("f" == filter and get_map_score(eye_pos) < neighbors[i] and
+ minmax_neighbor < neighbors[i] and 65535 != neighbors[i]) \
+ or ("f" != filter and minmax_neighbor > neighbors[i]):
+ minmax_neighbor = neighbors[i]
+ if minmax_neighbor != minmax_start:
+ dir_to_target = rand_target_dir(neighbors, minmax_neighbor, dirs)
+ if "f" == filter:
+ if not dir_to_target:
+ if 1 == get_map_score(eye_pos):
+ dir_to_target = rand_target_dir(neighbors, 0, dirs)
+ elif 3 >= get_map_score(eye_pos):
+ t["T_COMMAND"] = [id for id in world_db["ThingActions"]
+ if world_db["ThingActions"][id]["TA_NAME"]
+ == "wait"][0]
+ return 1;
+ elif dir_to_target and 3 < get_map_score(eye_pos):
+ dir_to_target = 0
+ elif "a" == filter and 10 <= get_map_score(eye_pos):
+ dir_to_target = 0
+ return dir_to_target
+
+ dir_to_target = False
+ mem_depth_c = b' '
+ run_i = 9 + 1 if "s" == filter else 1
+ while run_i and not dir_to_target and ("s" == filter or seeing_thing()):
+ run_i -= 1
+ init_score_map()
+ mem_depth_c = b'9' if b' ' == mem_depth_c \
+ else bytes([mem_depth_c[0] - 1])
+ if libpr.dijkstra_map():
+ raise RuntimeError("No score map allocated for dijkstra_map().")
+ dir_to_target = get_dir_from_neighbors()
+ libpr.free_score_map()
+ if dir_to_target and str == type(dir_to_target):
+ t["T_COMMAND"] = [id for id in world_db["ThingActions"]
+ if world_db["ThingActions"][id]["TA_NAME"]
+ == "move"][0]
+ t["T_ARGUMENT"] = ord(dir_to_target)
+ return dir_to_target
def standing_on_consumable(t):
"""
t["T_COMMAND"] = [id for id in world_db["ThingActions"]
if world_db["ThingActions"][id]["TA_NAME"] == "wait"][0]
- if not get_dir_to_nearest_target(t, "f"):
+ if not get_dir_to_target(t, "f"):
sel = get_inventory_slot_to_consume(t)
if -1 != sel:
t["T_COMMAND"] = [id for id in world_db["ThingActions"]
t["T_COMMAND"] = [id for id in world_db["ThingActions"]
if world_db["ThingActions"][id]["TA_NAME"]
== "pick_up"][0]
- elif (not get_dir_to_nearest_target(t, "c")) and \
- (not get_dir_to_nearest_target(t, "a")):
- get_dir_to_nearest_target(t, "s")
+ elif (not get_dir_to_target(t, "c")) and \
+ (not get_dir_to_target(t, "a")):
+ get_dir_to_target(t, "s")
def turn_over():
whilebreaker = True
break
ai(Thing)
- Thing["T_COMMAND"] = 1
try_healing(Thing)
Thing["T_PROGRESS"] += 1
taid = [a for a in world_db["ThingActions"]
projects / plomrogue / commitdiff