def test_edge_costs(self) -> None:
graph = AngleGraph(self.example_inst, self.working_expl_corr)
self.cfg.angle_weight = 0
self.cfg.edge_weight = 0.5
path = graph.single_sp(**vars(self.cfg))
path, _, cost_sum = graph.transform_path(path)
dest_ind = graph.pos2node[tuple(self.dest_inds)]
dest_costs = np.min(graph.dists[dest_ind])
a = []
path = np.array(path)
weighted_inst = self.example_inst
for p in range(len(path) - 1):
line = bresenham_line(path[p, 0], path[p, 1], path[p + 1, 0],
path[p + 1, 1])[1:-1]
line_costs = [weighted_inst[i, j] for (i, j) in line]
line_costs = [li for li in line_costs if li < np.inf]
bresenham_edge_dist = np.mean(line_costs) * self.cfg.edge_weight
shift_costs = np.linalg.norm(path[p] - path[p + 1])
# append edge cost
a.append((0.5 * (weighted_inst[tuple(path[p])] +
weighted_inst[tuple(path[p + 1])]) +
shift_costs * bresenham_edge_dist))
dest_costs_gt = np.sum(a)
self.assertTrue(np.isclose(cost_sum, dest_costs_gt))
self.assertTrue(np.isclose(dest_costs, dest_costs_gt))
out_compute_cost_method = compute_geometric_costs(
path, weighted_inst, edge_weight=self.cfg.edge_weight)
self.assertEqual(np.sum(out_compute_cost_method), cost_sum)
self.cfg.angle_weight = 0.25
self.cfg.edge_weight = 0
def test_correct_shortest_path(self) -> None:
""" Test the implicit line graph construction """
graph = AngleGraph(self.example_inst, self.working_expl_corr)
path = graph.single_sp(**vars(self.cfg))
path, path_costs, cost_sum = graph.transform_path(path)
self.assertTupleEqual(graph.instance.shape, self.expl_shape)
self.assertEqual(graph.angle_weight + graph.resistance_weight, 1)
self.assertNotEqual(len(graph.shifts), 0)
# all initialized to infinity
# self.assertTrue(not np.any([graph.dists[graph.dists < np.inf]]))
# start point was set to normalized value
start_ind = graph.pos2node[tuple(self.start_inds)]
self.assertEqual(0, graph.dists[start_ind, 0])
# all start dists have same value
self.assertEqual(len(np.unique(graph.dists[start_ind])), 1)
# not all values still inf
self.assertLess(np.min(graph.dists[start_ind]), np.inf)
# get actual best path
# path, path_costs, cost_sum = graph.get_shortest_path(
# self.start_inds, self.dest_inds
# )
self.assertNotEqual(len(path), 0)
self.assertEqual(len(path), len(path_costs))
self.assertGreaterEqual(cost_sum, 5)
for (i, j) in path:
self.assertEqual(self.example_inst[i, j], 1)
def test_empty_path(self) -> None:
self.cfg.max_angle = np.pi
graph = AngleGraph(self.example_inst, self.no_connection_corr)
_ = graph.single_sp(**vars(self.cfg))
# assert that destination can NOT be reached
dest_ind = graph.pos2node[tuple(self.dest_inds)]
self.assertFalse(np.min(graph.dists[dest_ind]) < np.inf)
def test_angle_sp(self) -> None:
graph = AngleGraph(self.example_inst, self.high_angle_corr)
_ = graph.single_sp(**vars(self.cfg))
# assert that destination can NOT be reached
dest_ind = graph.pos2node[tuple(self.dest_inds)]
self.assertFalse(np.min(graph.dists[dest_ind]) < np.inf)
# Note: this test does not change the stack array because the path is
# only empty because of an inf in angles_all
# NEXT TRY: more angles allowed
self.cfg.max_angle = np.pi
graph = AngleGraph(self.example_inst, self.high_angle_corr)
path = graph.single_sp(**vars(self.cfg))
path, _, cost_sum = graph.transform_path(path)
# assert that dest CAN be reached
dest_ind = graph.pos2node[tuple(self.dest_inds)]
self.assertTrue(np.min(graph.dists[dest_ind]) < np.inf)
def _initialize_graph(instance, cfg):
"""
Auxiliary method to preprocess the instance and initialize the graph
@params:
instance: 2D numpy array of resistances values (float or int)
If cells are forbidden, set them to NaN or a value x and
specify x in cfg["forbidden_val"].
cfg: configuration as specified on top of this file
@returns:
graph: AngleGraph object initialized with instance
cfg: updated configuration file
"""
forbidden_val = cfg.get("forbidden_val", np.nan)
logger.info(f"forbidden val: {forbidden_val}")
# make forbidden region array
project_region = np.ones(instance.shape)
project_region[np.isnan(instance)] = 0
project_region[instance == forbidden_val] = 0
# instance must not contain zeros
non_zero_add = 1e-8
instance += non_zero_add
# compute maximum values without forbidden weight
normal_vals = instance[
np.logical_and(instance != forbidden_val, ~np.isnan(instance))]
assert np.all(
normal_vals < np.inf
), "check forbidden_val parameter in cfg, it is\
not inf but there are inf values in array"
# fill values in instance
instance[project_region == 0] = np.max(normal_vals)
# normalize instance by maximum value (excluding forbidden areas)
# normalization is necessary to balance angle- and resistance-costs
instance = instance / np.max(normal_vals)
assert np.min(instance) > 0 and np.isclose(
np.max(instance), 1
), "Minimum must be greater than zero and maximum ~1 after normalizing"
# initialize graph
graph = AngleGraph(instance, project_region)
return graph, cfg
def test_discrete(self) -> None:
""" DISCRETE """
self.cfg["angle_cost_function"] = "discrete"
self.cfg["max_angle"] = np.pi / 4
self.cfg["angle_weight"] = .3
graph = AngleGraph(self.instance, self.corridor)
_ = graph.single_sp(**self.cfg)
gt_angle_cost_arr = graph.angle_cost_array.copy()
gt_dists = graph.dists.copy()
graph.dists = np.zeros(
(len(graph.stack_array), len(graph.shifts))
) + np.inf
graph.dists[0, :] = 0
graph.preds = np.zeros(graph.dists.shape) - 1
graph.angle_cost_function = "some_non_existant"
graph.build_source_sp_tree(**self.cfg)
self.assertTrue(np.all(gt_angle_cost_arr == graph.angle_cost_array))
self.assertTrue(np.all(np.isclose(gt_dists, graph.dists)))
def test_linear(self) -> None:
""" LINEAR """
self.cfg["angle_cost_function"] = "linear"
self.cfg["max_angle"] = np.pi
self.cfg["angle_weight"] = .3
graph = AngleGraph(self.instance, self.corridor)
_ = graph.single_sp(**self.cfg)
gt_angle_cost_arr = graph.angle_cost_array.copy()
gt_dists = graph.dists.copy()
graph.dists = np.zeros(
(len(graph.stack_array), len(graph.shifts))
) + np.inf
graph.dists[0, :] = 0
graph.preds = np.zeros(graph.dists.shape) - 1
graph.angle_cost_function = "some_non_existant"
graph.build_source_sp_tree(**self.cfg)
self.assertTrue(
np.all(np.isclose(gt_angle_cost_arr, graph.angle_cost_array))
)
# Errors can occur randomly, but seem to be only precision errors
# errors only appear if angle_weight > 0, try 1 to reproduce
self.assertTrue(np.all(np.isclose(gt_dists, graph.dists, atol=1e-04)))
def optimal_point_spotting(
instance, cfg, corridor=None, k=1, algorithm=KSP.ksp
):
"""
Compute the (angle-) optimal point spotting
@params:
instance: 2D np array of resistances (see details top of file)
cfg: config dict, must contain start and dest (see details top of file)
corridor: relevant region --> either
- a path, e.g. output of optimal_route, or
- a 2D array with 0: do not consider, 1: consider
@returns:
a single optimal path of points (list of X Y coordinates)
or empty list if no path exists
"""
# initialize graph
graph, cfg = _initialize_graph(instance, cfg)
# initial project region
original_inst = graph.edge_inst.copy()
original_corr = (graph.instance < np.inf).astype(int)
# initialize corridor
if corridor is None:
corridor = np.ones(original_corr.shape)
# estimate how many edges the graph will have:
graph.set_shift(cfg["start_inds"], cfg["dest_inds"], **cfg)
orig_shifts = len(graph.shift_tuples)
instance_vertices = np.sum(original_corr * corridor > 0)
mem_limit = cfg.get("memory_limit", 5e7)
# define pipeline
pipeline = cfg.get(
"pipeline",
ut_general.get_pipeline(instance_vertices, orig_shifts, mem_limit)
)
assert all(
pipeline[i] > pipeline[i + 1] for i in range(len(pipeline) - 1)
), "pipeline must consist of decreasing downsampling factors"
assert pipeline[
-1] == 1, "last factor in pipeline must be 1 (= no downsampling)"
# execute pipeline
logger.info(f"Pipeline set to: {pipeline}")
# execute iterative shortest path computation
for pipe_step, factor in enumerate(pipeline):
assert isinstance(factor, int) or float(factor).is_integer(
), "downsampling factors in pipeline must be integers"
logger.info(
f"---------- Start {pipe_step+1}th step {factor} ---------------"
)
# rescale and set parameters accordingly
corridor = (corridor * original_corr > 0).astype(int)
current_instance, current_corridor, current_cfg = ut_general.rescale(
original_inst, corridor, cfg, factor
)
# run shortest path computation
graph = AngleGraph(current_instance, current_corridor)
if k > 1:
paths = _run_ksp(graph, current_cfg, k, algorithm=algorithm)
paths = [np.array(path) * factor for path in paths]
else:
path = graph.single_sp(**current_cfg)
paths = [np.asarray(path) * factor]
logger.debug(f"got {len(paths)} paths in this step")
# compute next corridor
if pipe_step < len(pipeline) - 1 and len(paths[0]) > 0:
corridor = ut_general.pipeline_corridor(
paths, instance.shape, orig_shifts, mem_limit,
pipeline[pipe_step + 1]
)
if k == 1:
return path
else:
return paths