-
Notifications
You must be signed in to change notification settings - Fork 865
/
space.py
1067 lines (877 loc) · 37.4 KB
/
space.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
"""
Mesa Space Module
=================
Objects used to add a spatial component to a model.
Grid: base grid, which creates a rectangular grid.
SingleGrid: extension to Grid which strictly enforces one agent per cell.
MultiGrid: extension to Grid where each cell can contain a set of agents.
HexGrid: extension to Grid to handle hexagonal neighbors.
ContinuousSpace: a two-dimensional space where each agent has an arbitrary
position of `float`'s.
NetworkGrid: a network where each node contains zero or more agents.
"""
# Instruction for PyLint to suppress variable name errors, since we have a
# good reason to use one-character variable names for x and y.
# pylint: disable=invalid-name
# Mypy; for the `|` operator purpose
# Remove this __future__ import once the oldest supported Python is 3.10
from __future__ import annotations
import collections
import itertools
import math
from numbers import Real
from typing import (
Any,
Callable,
Iterable,
Iterator,
List,
Sequence,
Tuple,
TypeVar,
Union,
cast,
overload,
)
from warnings import warn
import networkx as nx
import numpy as np
import numpy.typing as npt
# For Mypy
from .agent import Agent
# for better performance, we calculate the tuple to use in the is_integer function
_types_integer = (int, np.integer)
Coordinate = Tuple[int, int]
# used in ContinuousSpace
FloatCoordinate = Union[Tuple[float, float], npt.NDArray[float]]
NetworkCoordinate = int
Position = Union[Coordinate, FloatCoordinate, NetworkCoordinate]
GridContent = Union[Agent, None]
MultiGridContent = List[Agent]
F = TypeVar("F", bound=Callable[..., Any])
def accept_tuple_argument(wrapped_function: F) -> F:
"""Decorator to allow grid methods that take a list of (x, y) coord tuples
to also handle a single position, by automatically wrapping tuple in
single-item list rather than forcing user to do it."""
def wrapper(grid_instance, positions) -> Any:
if isinstance(positions, tuple) and len(positions) == 2:
return wrapped_function(grid_instance, [positions])
else:
return wrapped_function(grid_instance, positions)
return cast(F, wrapper)
def is_integer(x: Real) -> bool:
# Check if x is either a CPython integer or Numpy integer.
return isinstance(x, _types_integer)
class _Grid:
"""Base class for a rectangular grid.
Grid cells are indexed by [x, y], where [0, 0] is assumed to be the
bottom-left and [width-1, height-1] is the top-right. If a grid is
toroidal, the top and bottom, and left and right, edges wrap to each other
Properties:
width, height: The grid's width and height.
torus: Boolean which determines whether to treat the grid as a torus.
"""
def __init__(self, width: int, height: int, torus: bool) -> None:
"""Create a new grid.
Args:
width, height: The width and height of the grid
torus: Boolean whether the grid wraps or not.
"""
self.height = height
self.width = width
self.torus = torus
self.num_cells = height * width
# Internal list-of-lists which holds the grid cells themselves
self._grid: list[list[GridContent]]
self._grid = [
[self.default_val() for _ in range(self.height)] for _ in range(self.width)
]
# Flag to check if the empties set has been created. Better than initializing
# _empties as set() because in this case it would become impossible to discern
# if the set hasn't still being built or if it has become empty after creation.
self._empties_built = False
# Neighborhood Cache
self._neighborhood_cache: dict[Any, Sequence[Coordinate]] = {}
# Cutoff used inside self.move_to_empty. The parameters are fitted on Python
# 3.11 and it was verified that they are roughly the same for 3.10. Refer to
# the code in PR#1565 to check for their stability when a new release gets out.
self.cutoff_empties = 7.953 * self.num_cells**0.384
@staticmethod
def default_val() -> None:
"""Default value for new cell elements."""
return None
@property
def empties(self) -> set:
if not self._empties_built:
self.build_empties()
return self._empties
def build_empties(self) -> None:
self._empties = set(
filter(
self.is_cell_empty,
itertools.product(range(self.width), range(self.height)),
)
)
self._empties_built = True
@overload
def __getitem__(self, index: int | Sequence[Coordinate]) -> list[GridContent]:
...
@overload
def __getitem__(
self, index: tuple[int | slice, int | slice]
) -> GridContent | list[GridContent]:
...
def __getitem__(self, index):
"""Access contents from the grid."""
if isinstance(index, int):
# grid[x]
return self._grid[index]
elif isinstance(index[0], tuple):
# grid[(x1, y1), (x2, y2), ...]
index = cast(Sequence[Coordinate], index)
return [self._grid[x][y] for x, y in map(self.torus_adj, index)]
x, y = index
x_int, y_int = is_integer(x), is_integer(y)
if x_int and y_int:
# grid[x, y]
index = cast(Coordinate, index)
x, y = self.torus_adj(index)
return self._grid[x][y]
elif x_int:
# grid[x, :]
x, _ = self.torus_adj((x, 0))
y = cast(slice, y)
return self._grid[x][y]
elif y_int:
# grid[:, y]
_, y = self.torus_adj((0, y))
x = cast(slice, x)
return [rows[y] for rows in self._grid[x]]
else:
# grid[:, :]
x, y = (cast(slice, x), cast(slice, y))
return [cell for rows in self._grid[x] for cell in rows[y]]
def __iter__(self) -> Iterator[GridContent]:
"""Create an iterator that chains the rows of the grid together
as if it is one list:"""
return itertools.chain(*self._grid)
def coord_iter(self) -> Iterator[tuple[GridContent, Coordinate]]:
"""An iterator that returns positions as well as cell contents."""
for row in range(self.width):
for col in range(self.height):
yield self._grid[row][col], (row, col) # agent, position
def iter_neighborhood(
self,
pos: Coordinate,
moore: bool,
include_center: bool = False,
radius: int = 1,
) -> Iterator[Coordinate]:
"""Return an iterator over cell coordinates that are in the
neighborhood of a certain point.
Args:
pos: Coordinate tuple for the neighborhood to get.
moore: If True, return Moore neighborhood
(including diagonals)
If False, return Von Neumann neighborhood
(exclude diagonals)
include_center: If True, return the (x, y) cell as well.
Otherwise, return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
An iterator of coordinate tuples representing the neighborhood. For
example with radius 1, it will return list with number of elements
equals at most 9 (8) if Moore, 5 (4) if Von Neumann (if not
including the center).
"""
yield from self.get_neighborhood(pos, moore, include_center, radius)
def get_neighborhood(
self,
pos: Coordinate,
moore: bool,
include_center: bool = False,
radius: int = 1,
) -> Sequence[Coordinate]:
"""Return a list of cells that are in the neighborhood of a
certain point.
Args:
pos: Coordinate tuple for the neighborhood to get.
moore: If True, return Moore neighborhood
(including diagonals)
If False, return Von Neumann neighborhood
(exclude diagonals)
include_center: If True, return the (x, y) cell as well.
Otherwise, return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
A list of coordinate tuples representing the neighborhood;
With radius 1, at most 9 if Moore, 5 if Von Neumann (8 and 4
if not including the center).
"""
cache_key = (pos, moore, include_center, radius)
neighborhood = self._neighborhood_cache.get(cache_key, None)
if neighborhood is not None:
return neighborhood
if self.out_of_bounds(pos):
raise Exception("The `pos` tuple passed is out of bounds.")
# we use a dict to keep insertion order
neighborhood = {}
x, y = pos
# First we check if the neighborhood is inside the grid
if (
x >= radius
and self.width - x > radius
and y >= radius
and self.height - y > radius
):
# If the radius is smaller than the distance from the borders, we
# can skip boundary checks.
x_range = range(x - radius, x + radius + 1)
y_range = range(y - radius, y + radius + 1)
for new_x in x_range:
for new_y in y_range:
if not moore and abs(new_x - x) + abs(new_y - y) > radius:
continue
neighborhood[(new_x, new_y)] = True
else:
# If the radius is larger than the distance from the borders, we
# must use a slower method, that takes into account the borders
# and the torus property.
for dx in range(-radius, radius + 1):
for dy in range(-radius, radius + 1):
if not moore and abs(dx) + abs(dy) > radius:
continue
new_x = x + dx
new_y = y + dy
if self.torus:
new_x %= self.width
new_y %= self.height
if not self.out_of_bounds((new_x, new_y)):
neighborhood[(new_x, new_y)] = True
if not include_center:
neighborhood.pop(pos, None)
self._neighborhood_cache[cache_key] = tuple(neighborhood.keys())
return tuple(neighborhood.keys())
def iter_neighbors(
self,
pos: Coordinate,
moore: bool,
include_center: bool = False,
radius: int = 1,
) -> Iterator[Agent]:
"""Return an iterator over neighbors to a certain point.
Args:
pos: Coordinates for the neighborhood to get.
moore: If True, return Moore neighborhood
(including diagonals)
If False, return Von Neumann neighborhood
(exclude diagonals)
include_center: If True, return the (x, y) cell as well.
Otherwise,
return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
An iterator of non-None objects in the given neighborhood;
at most 9 if Moore, 5 if Von-Neumann
(8 and 4 if not including the center).
"""
neighborhood = self.get_neighborhood(pos, moore, include_center, radius)
return self.iter_cell_list_contents(neighborhood)
def get_neighbors(
self,
pos: Coordinate,
moore: bool,
include_center: bool = False,
radius: int = 1,
) -> list[Agent]:
"""Return a list of neighbors to a certain point.
Args:
pos: Coordinate tuple for the neighborhood to get.
moore: If True, return Moore neighborhood
(including diagonals)
If False, return Von Neumann neighborhood
(exclude diagonals)
include_center: If True, return the (x, y) cell as well.
Otherwise,
return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
A list of non-None objects in the given neighborhood;
at most 9 if Moore, 5 if Von-Neumann
(8 and 4 if not including the center).
"""
return list(self.iter_neighbors(pos, moore, include_center, radius))
def torus_adj(self, pos: Coordinate) -> Coordinate:
"""Convert coordinate, handling torus looping."""
if not self.out_of_bounds(pos):
return pos
elif not self.torus:
raise Exception("Point out of bounds, and space non-toroidal.")
else:
return pos[0] % self.width, pos[1] % self.height
def out_of_bounds(self, pos: Coordinate) -> bool:
"""Determines whether position is off the grid, returns the out of
bounds coordinate."""
x, y = pos
return x < 0 or x >= self.width or y < 0 or y >= self.height
@accept_tuple_argument
def iter_cell_list_contents(
self, cell_list: Iterable[Coordinate]
) -> Iterator[Agent]:
"""Returns an iterator of the agents contained in the cells identified
in `cell_list`; cells with empty content are excluded.
Args:
cell_list: Array-like of (x, y) tuples, or single tuple.
Returns:
An iterator of the agents contained in the cells identified in `cell_list`.
"""
# iter_cell_list_contents returns only non-empty contents.
return (
cell
for x, y in cell_list
if (cell := self._grid[x][y]) != self.default_val()
)
@accept_tuple_argument
def get_cell_list_contents(self, cell_list: Iterable[Coordinate]) -> list[Agent]:
"""Returns an iterator of the agents contained in the cells identified
in `cell_list`; cells with empty content are excluded.
Args:
cell_list: Array-like of (x, y) tuples, or single tuple.
Returns:
A list of the agents contained in the cells identified in `cell_list`.
"""
return list(self.iter_cell_list_contents(cell_list))
def place_agent(self, agent: Agent, pos: Coordinate) -> None:
...
def remove_agent(self, agent: Agent) -> None:
...
def move_agent(self, agent: Agent, pos: Coordinate) -> None:
"""Move an agent from its current position to a new position.
Args:
agent: Agent object to move. Assumed to have its current location
stored in a 'pos' tuple.
pos: Tuple of new position to move the agent to.
"""
pos = self.torus_adj(pos)
self.remove_agent(agent)
self.place_agent(agent, pos)
def swap_pos(self, agent_a: Agent, agent_b: Agent) -> None:
"""Swap agents positions"""
agents_no_pos = []
if (pos_a := agent_a.pos) is None:
agents_no_pos.append(agent_a)
if (pos_b := agent_b.pos) is None:
agents_no_pos.append(agent_b)
if agents_no_pos:
agents_no_pos = [f"<Agent id: {a.unique_id}>" for a in agents_no_pos]
raise Exception(f"{', '.join(agents_no_pos)} - not on the grid")
if pos_a == pos_b:
return
self.remove_agent(agent_a)
self.remove_agent(agent_b)
self.place_agent(agent_a, pos_b)
self.place_agent(agent_b, pos_a)
def is_cell_empty(self, pos: Coordinate) -> bool:
"""Returns a bool of the contents of a cell."""
x, y = pos
return self._grid[x][y] == self.default_val()
def move_to_empty(self, agent: Agent) -> None:
"""Moves agent to a random empty cell, vacating agent's old cell."""
num_empty_cells = len(self.empties)
if num_empty_cells == 0:
raise Exception("ERROR: No empty cells")
# This method is based on Agents.jl's random_empty() implementation. See
# https://github.com/JuliaDynamics/Agents.jl/pull/541. For the discussion, see
# https://github.com/projectmesa/mesa/issues/1052 and
# https://github.com/projectmesa/mesa/pull/1565. The cutoff value provided
# is the break-even comparison with the time taken in the else branching point.
if num_empty_cells > self.cutoff_empties:
while True:
new_pos = (
agent.random.randrange(self.width),
agent.random.randrange(self.height),
)
if self.is_cell_empty(new_pos):
break
else:
new_pos = agent.random.choice(sorted(self.empties))
self.remove_agent(agent)
self.place_agent(agent, new_pos)
def exists_empty_cells(self) -> bool:
"""Return True if any cells empty else False."""
return len(self.empties) > 0
class SingleGrid(_Grid):
"""Rectangular grid where each cell contains exactly at most one agent.
Grid cells are indexed by [x, y], where [0, 0] is assumed to be the
bottom-left and [width-1, height-1] is the top-right. If a grid is
toroidal, the top and bottom, and left and right, edges wrap to each other.
Properties:
width, height: The grid's width and height.
torus: Boolean which determines whether to treat the grid as a torus.
"""
def place_agent(self, agent: Agent, pos: Coordinate) -> None:
"""Place the agent at the specified location, and set its pos variable."""
if self.is_cell_empty(pos):
x, y = pos
self._grid[x][y] = agent
if self._empties_built:
self._empties.discard(pos)
agent.pos = pos
else:
raise Exception("Cell not empty")
def remove_agent(self, agent: Agent) -> None:
"""Remove the agent from the grid and set its pos attribute to None."""
if (pos := agent.pos) is None:
return
x, y = pos
self._grid[x][y] = self.default_val()
if self._empties_built:
self._empties.add(pos)
agent.pos = None
class MultiGrid(_Grid):
"""Rectangular grid where each cell can contain more than one agent.
Grid cells are indexed by [x, y], where [0, 0] is assumed to be at
bottom-left and [width-1, height-1] is the top-right. If a grid is
toroidal, the top and bottom, and left and right, edges wrap to each other.
Properties:
width, height: The grid's width and height.
torus: Boolean which determines whether to treat the grid as a torus.
"""
grid: list[list[MultiGridContent]]
@staticmethod
def default_val() -> MultiGridContent:
"""Default value for new cell elements."""
return []
def place_agent(self, agent: Agent, pos: Coordinate) -> None:
"""Place the agent at the specified location, and set its pos variable."""
x, y = pos
if agent.pos is None or agent not in self._grid[x][y]:
self._grid[x][y].append(agent)
agent.pos = pos
if self._empties_built:
self._empties.discard(pos)
def remove_agent(self, agent: Agent) -> None:
"""Remove the agent from the given location and set its pos attribute to None."""
pos = agent.pos
x, y = pos
self._grid[x][y].remove(agent)
if self._empties_built and self.is_cell_empty(pos):
self._empties.add(pos)
agent.pos = None
@accept_tuple_argument
def iter_cell_list_contents(
self, cell_list: Iterable[Coordinate]
) -> Iterator[Agent]:
"""Returns an iterator of the agents contained in the cells identified
in `cell_list`; cells with empty content are excluded.
Args:
cell_list: Array-like of (x, y) tuples, or single tuple.
Returns:
An iterator of the agents contained in the cells identified in `cell_list`.
"""
return itertools.chain.from_iterable(
cell
for x, y in cell_list
if (cell := self._grid[x][y]) != self.default_val()
)
class _HexGrid:
"""Hexagonal Grid which handles hexagonal neighbors.
Functions according to odd-q rules.
See http://www.redblobgames.com/grids/hexagons/#coordinates for more.
Properties:
width, height: The grid's width and height.
torus: Boolean which determines whether to treat the grid as a torus.
Methods:
get_neighbors: Returns the objects surrounding a given cell.
get_neighborhood: Returns the cells surrounding a given cell.
iter_neighbors: Iterates over position neighbors.
iter_neighborhood: Returns an iterator over cell coordinates that are
in the neighborhood of a certain point.
"""
def torus_adj_2d(self, pos: Coordinate) -> Coordinate:
return pos[0] % self.width, pos[1] % self.height
def get_neighborhood(
self, pos: Coordinate, include_center: bool = False, radius: int = 1
) -> list[Coordinate]:
"""Return a list of coordinates that are in the
neighborhood of a certain point. To calculate the neighborhood
for a HexGrid the parity of the x coordinate of the point is
important, the neighborhood can be sketched as:
Always: (0,-), (0,+)
When x is even: (-,+), (-,0), (+,+), (+,0)
When x is odd: (-,0), (-,-), (+,0), (+,-)
Args:
pos: Coordinate tuple for the neighborhood to get.
include_center: If True, return the (x, y) cell as well.
Otherwise, return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
A list of coordinate tuples representing the neighborhood. For
example with radius 1, it will return list with number of elements
equals at most 9 (8) if Moore, 5 (4) if Von Neumann (if not
including the center).
"""
cache_key = (pos, include_center, radius)
neighborhood = self._neighborhood_cache.get(cache_key, None)
if neighborhood is not None:
return neighborhood
queue = collections.deque()
queue.append(pos)
coordinates = set()
while radius > 0:
level_size = len(queue)
radius -= 1
for _i in range(level_size):
x, y = queue.pop()
if x % 2 == 0:
adjacent = [
(x, y - 1),
(x, y + 1),
(x - 1, y + 1),
(x - 1, y),
(x + 1, y + 1),
(x + 1, y),
]
else:
adjacent = [
(x, y - 1),
(x, y + 1),
(x - 1, y),
(x - 1, y - 1),
(x + 1, y),
(x + 1, y - 1),
]
if self.torus:
adjacent = [
coord
for coord in map(self.torus_adj_2d, adjacent)
if coord not in coordinates
]
else:
adjacent = [
coord
for coord in adjacent
if not self.out_of_bounds(coord) and coord not in coordinates
]
coordinates.update(adjacent)
if radius > 0:
queue.extendleft(adjacent)
if include_center:
coordinates.add(pos)
else:
coordinates.discard(pos)
neighborhood = tuple(sorted(coordinates))
self._neighborhood_cache[cache_key] = neighborhood
return neighborhood
def iter_neighborhood(
self, pos: Coordinate, include_center: bool = False, radius: int = 1
) -> Iterator[Coordinate]:
"""Return an iterator over cell coordinates that are in the
neighborhood of a certain point.
Args:
pos: Coordinate tuple for the neighborhood to get.
include_center: If True, return the (x, y) cell as well.
Otherwise, return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
An iterator of coordinate tuples representing the neighborhood.
"""
yield from self.get_neighborhood(pos, include_center, radius)
def iter_neighbors(
self, pos: Coordinate, include_center: bool = False, radius: int = 1
) -> Iterator[Agent]:
"""Return an iterator over neighbors to a certain point.
Args:
pos: Coordinates for the neighborhood to get.
include_center: If True, return the (x, y) cell as well.
Otherwise,
return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
An iterator of non-None objects in the given neighborhood
"""
neighborhood = self.get_neighborhood(pos, include_center, radius)
return self.iter_cell_list_contents(neighborhood)
def get_neighbors(
self, pos: Coordinate, include_center: bool = False, radius: int = 1
) -> list[Agent]:
"""Return a list of neighbors to a certain point.
Args:
pos: Coordinate tuple for the neighborhood to get.
include_center: If True, return the (x, y) cell as well.
Otherwise,
return surrounding cells only.
radius: radius, in cells, of neighborhood to get.
Returns:
A list of non-None objects in the given neighborhood
"""
return list(self.iter_neighbors(pos, include_center, radius))
class HexSingleGrid(_HexGrid, SingleGrid):
"""Hexagonal SingleGrid: a SingleGrid where neighbors are computed
according to a hexagonal tiling of the grid.
Functions according to odd-q rules.
See http://www.redblobgames.com/grids/hexagons/#coordinates for more.
Properties:
width, height: The grid's width and height.
torus: Boolean which determines whether to treat the grid as a torus.
"""
class HexMultiGrid(_HexGrid, MultiGrid):
"""Hexagonal MultiGrid: a MultiGrid where neighbors are computed
according to a hexagonal tiling of the grid.
Functions according to odd-q rules.
See http://www.redblobgames.com/grids/hexagons/#coordinates for more.
Properties:
width, height: The grid's width and height.
torus: Boolean which determines whether to treat the grid as a torus.
"""
class HexGrid(HexSingleGrid):
"""Hexagonal Grid: a Grid where neighbors are computed
according to a hexagonal tiling of the grid.
Functions according to odd-q rules.
See http://www.redblobgames.com/grids/hexagons/#coordinates for more.
Properties:
width, height: The grid's width and height.
torus: Boolean which determines whether to treat the grid as a torus.
"""
def __init__(self, width: int, height: int, torus: bool) -> None:
super().__init__(width, height, torus)
warn(
(
"HexGrid is being deprecated; use instead HexSingleGrid or HexMultiGrid "
"depending on your use case."
),
DeprecationWarning,
stacklevel=2,
)
class ContinuousSpace:
"""Continuous space where each agent can have an arbitrary position.
Assumes that all agents have a pos property storing their position as
an (x, y) tuple.
This class uses a numpy array internally to store agents in order to speed
up neighborhood lookups. This array is calculated on the first neighborhood
lookup, and is updated if agents are added or removed.
"""
def __init__(
self,
x_max: float,
y_max: float,
torus: bool,
x_min: float = 0,
y_min: float = 0,
) -> None:
"""Create a new continuous space.
Args:
x_max, y_max: Maximum x and y coordinates for the space.
torus: Boolean for whether the edges loop around.
x_min, y_min: (default 0) If provided, set the minimum x and y
coordinates for the space. Below them, values loop to
the other edge (if torus=True) or raise an exception.
"""
self.x_min = x_min
self.x_max = x_max
self.width = x_max - x_min
self.y_min = y_min
self.y_max = y_max
self.height = y_max - y_min
self.center = np.array(((x_max + x_min) / 2, (y_max + y_min) / 2))
self.size = np.array((self.width, self.height))
self.torus = torus
self._agent_points: npt.NDArray[FloatCoordinate] | None = None
self._index_to_agent: dict[int, Agent] = {}
self._agent_to_index: dict[Agent, int | None] = {}
def _build_agent_cache(self):
"""Cache agents positions to speed up neighbors calculations."""
self._index_to_agent = {}
for idx, agent in enumerate(self._agent_to_index):
self._agent_to_index[agent] = idx
self._index_to_agent[idx] = agent
# Since dicts are ordered by insertion, we can iterate through agents keys
self._agent_points = np.array([agent.pos for agent in self._agent_to_index])
def _invalidate_agent_cache(self):
"""Clear cached data of agents and positions in the space."""
self._agent_points = None
self._index_to_agent = {}
def place_agent(self, agent: Agent, pos: FloatCoordinate) -> None:
"""Place a new agent in the space.
Args:
agent: Agent object to place.
pos: Coordinate tuple for where to place the agent.
"""
self._invalidate_agent_cache()
self._agent_to_index[agent] = None
pos = self.torus_adj(pos)
agent.pos = pos
def move_agent(self, agent: Agent, pos: FloatCoordinate) -> None:
"""Move an agent from its current position to a new position.
Args:
agent: The agent object to move.
pos: Coordinate tuple to move the agent to.
"""
pos = self.torus_adj(pos)
agent.pos = pos
if self._agent_points is not None:
# instead of invalidating the full cache,
# apply the move to the cached values
idx = self._agent_to_index[agent]
self._agent_points[idx] = pos
def remove_agent(self, agent: Agent) -> None:
"""Remove an agent from the space.
Args:
agent: The agent object to remove
"""
if agent not in self._agent_to_index:
raise Exception("Agent does not exist in the space")
del self._agent_to_index[agent]
self._invalidate_agent_cache()
agent.pos = None
def get_neighbors(
self, pos: FloatCoordinate, radius: float, include_center: bool = True
) -> list[Agent]:
"""Get all agents within a certain radius.
Args:
pos: (x,y) coordinate tuple to center the search at.
radius: Get all the objects within this distance of the center.
include_center: If True, include an object at the *exact* provided
coordinates. i.e. if you are searching for the
neighbors of a given agent, True will include that
agent in the results.
"""
if self._agent_points is None:
self._build_agent_cache()
deltas = np.abs(self._agent_points - np.array(pos))
if self.torus:
deltas = np.minimum(deltas, self.size - deltas)
dists = deltas[:, 0] ** 2 + deltas[:, 1] ** 2
(idxs,) = np.where(dists <= radius**2)
neighbors = [
self._index_to_agent[x] for x in idxs if include_center or dists[x] > 0
]
return neighbors
def get_heading(
self, pos_1: FloatCoordinate, pos_2: FloatCoordinate
) -> FloatCoordinate:
"""Get the heading vector between two points, accounting for toroidal space.
It is possible to calculate the heading angle by applying the atan2 function to the
result.
Args:
pos_1, pos_2: Coordinate tuples for both points.
"""
one = np.array(pos_1)
two = np.array(pos_2)
heading = two - one
if self.torus:
inverse_heading = heading - np.sign(heading) * self.size
def get_min_abs(x, y):
return x if abs(x) < abs(y) else y
# Choose the smaller heading based on their absolute value for
# each dimension independently.
heading = tuple(
get_min_abs(heading[i], inverse_heading[i]) for i in range(2)
)
if isinstance(pos_1, np.ndarray):
heading = np.asarray(heading)
else:
heading = tuple(heading)
return heading
def get_distance(self, pos_1: FloatCoordinate, pos_2: FloatCoordinate) -> float:
"""Get the distance between two point, accounting for toroidal space.
Args:
pos_1, pos_2: Coordinate tuples for both points.
"""
x1, y1 = pos_1
x2, y2 = pos_2
dx = abs(x1 - x2)
dy = abs(y1 - y2)
if self.torus:
dx = min(dx, self.width - dx)
dy = min(dy, self.height - dy)
return math.sqrt(dx * dx + dy * dy)
def torus_adj(self, pos: FloatCoordinate) -> FloatCoordinate:
"""Adjust coordinates to handle torus looping.
If the coordinate is out-of-bounds and the space is toroidal, return
the corresponding point within the space. If the space is not toroidal,
raise an exception.
Args:
pos: Coordinate tuple to convert.
"""
if not self.out_of_bounds(pos):
return pos
elif not self.torus:
raise Exception("Point out of bounds, and space non-toroidal.")
else:
x = self.x_min + ((pos[0] - self.x_min) % self.width)
y = self.y_min + ((pos[1] - self.y_min) % self.height)
if isinstance(pos, tuple):
return (x, y)
else:
return np.array((x, y))
def out_of_bounds(self, pos: FloatCoordinate) -> bool:
"""Check if a point is out of bounds."""
x, y = pos
return x < self.x_min or x >= self.x_max or y < self.y_min or y >= self.y_max
class NetworkGrid:
"""Network Grid where each node contains zero or more agents."""
def __init__(self, g: Any) -> None:
"""Create a new network.
Args:
G: a NetworkX graph instance.
"""
self.G = g
for node_id in self.G.nodes: