def find_placement(pieces, resistors_as_components, cost_type):
"""
Given a list of |pieces|, returns a placement of the pieces that requires
comparatively small wiring. Finding the absolute best placement is too
expensive. If the |pieces| cannot be placed so as to fit on a protoboard,
returns None with a cost of float('inf').
"""
assert isinstance(pieces, list), 'pieces must be a list'
assert all(isinstance(piece, Circuit_Piece)
for piece in pieces), ('all '
'items in pieces must be Circuit_Pieces')
pieces = deepcopy(pieces)
if len(pieces) == 0:
return [], 0
# order pieces in decreasing number of nodes
pieces.sort(key=lambda piece: -len(piece.nodes))
queue = Queue()
def add_to_queue(piece):
if piece in pieces:
queue.push(piece)
pieces.remove(piece)
placement = []
placement_cost = float('inf')
while pieces:
add_to_queue(pieces[0])
while queue:
current_piece = queue.pop()
# try inserting the current piece at all possible indicies in the current
# placement, consider both regular and inverted piece
best_placement = None
best_placement_cost = float('inf')
# all indicies in which the piece can be inserted
for i in xrange(len(placement) + 1):
# both regular and inverted piece
for piece in set([current_piece, current_piece.inverted()]):
for top_left_row in piece.possible_top_left_rows:
piece.top_left_row = top_left_row
new_placement = deepcopy(placement)
new_placement.insert(i, piece)
new_placement_cost = cost(new_placement,
resistors_as_components,
cost_type)
if new_placement_cost < best_placement_cost:
best_placement = deepcopy(new_placement)
best_placement_cost = new_placement_cost
if best_placement is None:
return None, float('inf')
placement = best_placement
placement_cost = best_placement_cost
# add pieces connected to this piece to the queue
for piece in reduce(
list.__add__,
[[piece for piece in pieces if node in piece.nodes]
for node in current_piece.nodes], []):
add_to_queue(piece)
return placement, placement_cost
def find_placement(pieces, resistors_as_components, cost_type):
"""
Given a list of |pieces|, returns a placement of the pieces that requires
comparatively small wiring. Finding the absolute best placement is too
expensive. If the |pieces| cannot be placed so as to fit on a protoboard,
returns None with a cost of float('inf').
"""
assert isinstance(pieces, list), 'pieces must be a list'
assert all(isinstance(piece, Circuit_Piece) for piece in pieces), ('all '
'items in pieces must be Circuit_Pieces')
pieces = deepcopy(pieces)
if len(pieces) == 0:
return [], 0
# order pieces in decreasing number of nodes
pieces.sort(key=lambda piece: -len(piece.nodes))
queue = Queue()
def add_to_queue(piece):
if piece in pieces:
queue.push(piece)
pieces.remove(piece)
placement = []
placement_cost = float('inf')
while pieces:
add_to_queue(pieces[0])
while queue:
current_piece = queue.pop()
# try inserting the current piece at all possible indicies in the current
# placement, consider both regular and inverted piece
best_placement = None
best_placement_cost = float('inf')
# all indicies in which the piece can be inserted
for i in xrange(len(placement) + 1):
# both regular and inverted piece
for piece in set([current_piece, current_piece.inverted()]):
for top_left_row in piece.possible_top_left_rows:
piece.top_left_row = top_left_row
new_placement = deepcopy(placement)
new_placement.insert(i, piece)
new_placement_cost = cost(new_placement, resistors_as_components,
cost_type)
if new_placement_cost < best_placement_cost:
best_placement = deepcopy(new_placement)
best_placement_cost = new_placement_cost
if best_placement is None:
return None, float('inf')
placement = best_placement
placement_cost = best_placement_cost
# add pieces connected to this piece to the queue
for piece in reduce(list.__add__, [[piece for piece in pieces if node
in piece.nodes] for node in current_piece.nodes], []):
add_to_queue(piece)
return placement, placement_cost
def closest_vertex(self, frm, target_list):
""" closest_vertex(frm, target_list) -> id Uses bfs-like
algorithm to find closest vertex to frm in target_list
"""
if frm in target_list:
return frm
target_list = set(target_list)
visited = set([frm])
parent = {}
q = Queue()
q.push(frm)
while 1:
try:
current = q.pop()
except q.EmptyQueue:
raise GraphException("no vertices reachable: %s %s" %
(frm, list(target_list)))
efrom = self.edges_from(current)
for (to, eid) in efrom:
if to in target_list:
return to
if to not in visited:
parent[to] = current
q.push(to)
visited.add(to)
def closest_vertex(self, frm, target_list):
""" closest_vertex(frm, target_list) -> id Uses bfs-like
algorithm to find closest vertex to frm in target_list
"""
if frm in target_list:
return frm
target_list = set(target_list)
visited = set([frm])
parent = {}
q = Queue()
q.push(frm)
while 1:
try:
current = q.pop()
except q.EmptyQueue:
raise GraphException("no vertices reachable: %s %s" % (frm, list(target_list)))
efrom = self.edges_from(current)
for (to, eid) in efrom:
if to in target_list:
return to
if to not in visited:
parent[to] = current
q.push(to)
visited.add(to)
def bfs(self, frm):
""" bfs(frm:id type) -> dict(id type)
Perform Breadth-First-Search and return a dict of parent id
Keyword arguments:
frm -- 'immutable' vertex id
"""
visited = set([frm])
parent = {}
q = Queue()
q.push(frm)
while 1:
try:
current = q.pop()
except q.EmptyQueue:
break
efrom = self.edges_from(current)
for (to, eid) in efrom:
if to not in visited:
parent[to] = current
q.push(to)
visited.add(to)
return parent
def bfs(self, frm):
""" bfs(frm:id type) -> dict(id type)
Perform Breadth-First-Search and return a dict of parent id
Keyword arguments:
frm -- 'immutable' vertex id
"""
visited = set([frm])
parent = {}
q = Queue()
q.push(frm)
while 1:
try:
current = q.pop()
except q.EmptyQueue:
break
efrom = self.edges_from(current)
for (to, eid) in efrom:
if to not in visited:
parent[to] = current
q.push(to)
visited.add(to)
return parent