def make(self):
"""Generates path from start pin to end pin."""
p = self.parse_options()
anchors = p.anchors
# Set the CPW pins and add the points/directions to the lead-in/out arrays
self.set_pin("start")
self.set_pin("end")
# Align the lead-in/out to the input options set from the user
start_point = self.set_lead("start")
end_point = self.set_lead("end")
self.intermediate_pts = OrderedDict()
for arc_num, coord in anchors.items():
arc_pts = self.connect_astar_or_simple(self.get_tip(),
QRoutePoint(coord))
if arc_pts is None:
self.intermediate_pts[arc_num] = [coord]
else:
self.intermediate_pts[arc_num] = np.concatenate(
[arc_pts, [coord]], axis=0)
arc_pts = self.connect_astar_or_simple(self.get_tip(), end_point)
if arc_pts is not None:
self.intermediate_pts[len(anchors)] = np.array(arc_pts)
# concatenate all points, transforming the dictionary into a single numpy array
self.trim_pts()
self.intermediate_pts = np.concatenate(list(
self.intermediate_pts.values()),
axis=0)
# Make points into elements
self.make_elements(self.get_points())
def make(self):
"""Generates path from start pin to end pin."""
p = self.parse_options()
anchors = p.anchors
between_anchors = p.between_anchors
# Set the CPW pins and add the points/directions to the lead-in/out arrays
self.set_pin("start")
self.set_pin("end")
# Align the lead-in/out to the input options set from the user
start_point = self.set_lead("start")
end_point = self.set_lead("end")
# approximate length needed for individual meanders
# the meander algorithm directly reads from self._length_segment
count_meanders_list = [
1 if x == "M" else 0 for x in list(between_anchors.values())
]
self._length_segment = None
if any(count_meanders_list):
self._length_segment = ((self.p.total_length - (self.head.length + self.tail.length) \
- self.free_manhattan_length_anchors()) / sum(count_meanders_list)) \
+ (self.free_manhattan_length_anchors() / len(count_meanders_list))
# find the points to connect between each pair of anchors, or between anchors and leads
# at first, store points "per segment" in a dictionary, so it is easier to apply length requirements
self.intermediate_pts = OrderedDict()
meanders = set()
for arc_num, coord in anchors.items():
# determine what is the connection strategy for this pair, based on user inputs
connect_method = self.select_connect_method(arc_num)
if connect_method == self.connect_meandered:
meanders.add(arc_num)
# compute points connecting the anchors, all but the last
arc_pts = connect_method(self.get_tip(), QRoutePoint(coord))
if arc_pts is None:
self.intermediate_pts[arc_num] = [coord]
else:
self.intermediate_pts[arc_num] = np.concatenate(
[arc_pts, [coord]], axis=0)
# compute last connection point to the output QRouteLead
connect_method = self.select_connect_method(len(anchors))
if connect_method == self.connect_meandered:
meanders.add(len(anchors))
arc_pts = connect_method(self.get_tip(), end_point)
if arc_pts is not None:
self.intermediate_pts[len(anchors)] = np.array(arc_pts)
# concatenate all points, transforming the dictionary into a single numpy array
self.trim_pts()
dictionary_intermediate_pts = self.intermediate_pts
self.intermediate_pts = np.concatenate(list(
self.intermediate_pts.values()),
axis=0)
if any(count_meanders_list):
# refine length of meanders
total_delta_length = self.p.total_length - self.length
individual_delta_length = total_delta_length / len(meanders)
for m in meanders:
arc_pts = dictionary_intermediate_pts[m][:-1]
if m == 0:
meander_start_point = start_point
else:
meander_start_point = QRoutePoint(anchors[m - 1])
if m == len(anchors):
meander_end_point = end_point
else:
meander_end_point = QRoutePoint(anchors[m])
dictionary_intermediate_pts[m] = self.adjust_length(
individual_delta_length, arc_pts, meander_start_point,
meander_end_point)
dictionary_intermediate_pts[m] = np.concatenate(
[dictionary_intermediate_pts[m], [anchors[m]]], axis=0)
self.intermediate_pts = np.concatenate(list(
dictionary_intermediate_pts.values()),
axis=0)
# Make points into elements
self.make_elements(self.get_points())
def connect_astar_or_simple(self, start_pt: QRoutePoint,
end_pt: QRoutePoint) -> list:
"""Connect start and end via A* algo if connect_simple doesn't work.
Args:
start_direction (np.array): Vector indicating direction of starting point
start (np.array): 2-D coordinates of first anchor
end (np.array): 2-D coordinates of second anchor
Returns:
List of vertices of a CPW going from start to end
Raises:
QiskitMetalDesignError: If the connect_simple() has failed.
"""
start_direction = start_pt.direction
start = start_pt.position
end_direction = end_pt.direction
end = end_pt.position
step_size = self.parse_options().step_size
starting_dist = sum(
abs(end - start)) # Manhattan distance between start and end
key_starting_point = (starting_dist, start[0], start[1])
pathmapper = {key_starting_point: [starting_dist, [start]]}
# pathmapper maps tuple(total length of the path from self.start + Manhattan distance to destination, coordx, coordy) to [total length of
# path from self.start, path]
visited = set(
) # maintain record of points we've already visited to avoid self-intersections
visited.add(tuple(start))
# TODO: add to visited all of the current points in the route, to prevent self intersecting
priority_queue = list() # A* priority queue. Implemented as heap
priority_queue.append(key_starting_point)
# Elements in the heap are ordered by the following:
# 1. The total length of the path from self.start + Manhattan distance to destination
# 2. The x coordinate of the latest point
# 3. The y coordinate of the latest point
while priority_queue:
tot_dist, x, y = heapq.heappop(
priority_queue
) # tot_dist is the total length of the path from self.start + Manhattan distance to destination
length_travelled, current_path = pathmapper[(tot_dist, x, y)]
# Look in forward, left, and right directions a fixed distance away.
# If the line segment connecting the current point and this next one does
# not collide with any bounding boxes in design.components, add it to the
# list of neighbors.
neighbors = list()
if len(current_path) == 1:
# At starting point -> initial direction is start direction
direction = start_direction
else:
# Beyond starting point -> look at vector difference of last 2 points along path
direction = current_path[-1] - current_path[-2]
# The dot product between direction and the vector connecting the current
# point and a potential neighbor must be non-negative to avoid retracing.
# Check if connect_simple works at each iteration of A*
try:
simple_path = self.connect_simple(
QRoutePoint(np.array([x, y]), direction),
QRoutePoint(end, end_direction))
except QiskitMetalDesignError:
simple_path = None
# try the pathfinder algorithm
pass
if simple_path is not None:
current_path.extend(simple_path)
return current_path
for disp in [
np.array([0, 1]),
np.array([0, -1]),
np.array([1, 0]),
np.array([-1, 0])
]:
# Unit displacement in 4 cardinal directions
if mao.dot(disp, direction) >= 0:
# Ignore backward direction
curpt = current_path[-1]
nextpt = curpt + step_size * disp
if self.unobstructed([curpt, nextpt]):
neighbors.append(nextpt)
for neighbor in neighbors:
if tuple(neighbor) not in visited:
new_remaining_dist = sum(abs(end - neighbor))
new_length_travelled = length_travelled + step_size
new_path = current_path + [neighbor]
if new_remaining_dist < 10**-8:
# Destination has been reached within acceptable error tolerance (errors due to rounding in Python)
return new_path[:-1] + [
end
] # Replace last element of new_path with end since they're basically the same
heapq.heappush(priority_queue,
(new_length_travelled + new_remaining_dist,
neighbor[0], neighbor[1]))
pathmapper[(new_length_travelled + new_remaining_dist,
neighbor[0], neighbor[1])] = [
new_length_travelled, new_path
]
visited.add(tuple(neighbor))
return [
] # Shouldn't actually reach here - if it fails, there's a convergence issue