def solver(G, source): # type: (nx.Graph, typing.Any) -> typing.Tuple[typing.List[typing.Any], int]
"""
Produces the optimal TSP tour using a naive solution - O(n!)
:param G: A fully connected networkx graph.
:param source: A source node in G.
:return: A list of nodes to visit, forming a TSP tour, and the cost of that tour.
"""
utils.check_arguments(G, source)
n = G.number_of_nodes()
best_node_permutation = None
best_cost = INFINITY
distance = utils.get_adjacency_dicts(G)
for node_permutation in itertools.permutations(G.nodes):
if node_permutation[0] != source:
continue
cost_of_node_permutation = 0
for i in range(0, n):
current_node = node_permutation[i]
next_node = node_permutation[(i + 1) % n]
cost_of_node_permutation += distance[current_node][next_node]
if cost_of_node_permutation < best_cost:
best_node_permutation = tuple(node_permutation + (source,))
best_cost = cost_of_node_permutation
return best_node_permutation, best_cost
def main():
#
# ---> Check for 'restart' argument
#
arguments = utils.check_arguments(sys.argv)
if ("restart" in arguments):
restart_clean(db)
#
# ---> Catch the exit signal to commit the database with last checkpoint
#
signal.signal(signal.SIGINT, exit_handler)
#
# --> Marking files for deletion
#
nb, size = find_for_deletion(db)
print(FMT_STR_MARKED_FILES.format(nb, utils.humanbytes(size)))
#
# --> Deleting/Trashing files
#
nb_trash, nb_fail, size = move_files(db)
print(FMT_STR_TRASHED_FILES.format(nb_trash, trash,
utils.humanbytes(size)))
return
def handle_dig_circular(req):
try:
interface = MoveGroupPythonInteface()
print "Starting full traj planning session"
dig_circular_args = activity_full_digging_traj.arg_parsing_circ(req)
if utils.check_arguments(dig_circular_args[1], dig_circular_args[2],
dig_circular_args[3]) != True:
print "[ERROR] Invalid trench input arguments. Exiting path_planning_commander..."
return
currentDT = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
location = "full_traj_"
bagname = location + currentDT
utils.start_traj_recording(dig_circular_args[6], bagname)
result = activity_full_digging_traj.dig_circular(
interface.move_arm, interface.move_limbs, dig_circular_args)
utils.stop_traj_recording(result, bagname)
except rospy.ROSInterruptException:
return
except KeyboardInterrupt:
return
print "Finished planning session succesfully..."
return True, "Done"
def handle_deliver_sample(req):
try:
interface = MoveGroupPythonInteface()
print "Starting sample delivery session"
deliver_sample_args = activity_deliver_sample.arg_parsing(req)
if utils.check_arguments(deliver_sample_args[1],
deliver_sample_args[2],
deliver_sample_args[3]) != True:
print "[ERROR] Invalid sample delivery input arguments. Exiting path_planning_commander..."
return
currentDT = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
location = "full_traj_"
bagname = location + currentDT
utils.start_traj_recording(deliver_sample_args[4], bagname)
result = activity_deliver_sample.deliver_sample(
interface.move_arm, deliver_sample_args)
utils.stop_traj_recording(result, bagname)
except rospy.ROSInterruptException:
return
except KeyboardInterrupt:
return
print "Finished planning session for linear trenching succesfully..."
return True, "Done"
def handle_grind(req):
try:
interface = MoveGroupPythonInteface()
print "Starting grinder planning session"
grind_args = activity_grind.arg_parsing(req)
if utils.check_arguments(grind_args[1], grind_args[2],
grind_args[3]) != True:
print "[ERROR] Invalid grinder trajectory input arguments. Exiting path_planning_commander..."
return
currentDT = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
location = "full_traj_"
bagname = location + currentDT
utils.start_traj_recording(grind_args[6], bagname)
result = activity_grind.grind(interface.move_arm, interface.move_limbs,
interface.move_grinder, grind_args)
utils.stop_traj_recording(result, bagname)
except rospy.ROSInterruptException:
return
except KeyboardInterrupt:
return
print "Grinder planning succesfully finished..."
return True, "Done"
def main():
# Colorama init
init()
#
# ---> Check for 'restart' argument
#
arguments = utils.check_arguments(sys.argv)
if ("restart" in arguments):
restart = True
else:
restart = False
#
# ---> Catch the exit signal to commit the database with last checkpoint
#
signal.signal(signal.SIGINT, exit_handler)
#
# ---> Read the directory files list
#
with open(filelist, "r") as f:
basepath = f.readlines()
#print(basepath)
print("Default blocksize for this system is {} bytes.".format(
io.DEFAULT_BUFFER_SIZE))
#
# ---> DB connection
#
cnx = db_connect(db, restart)
last_step, last_id = get_status(cnx)
print("Last step: {}, last ID: {}".format(last_step, last_id))
next_step = False
# Looking for files
# ---
if (last_step == None) | ((last_step == "directory_lookup") &
(last_id == "in progress")):
t, nb = directories_lookup(cnx, basepath)
print("Files lookup duration: {:.2f} sec for {} files.".format(t, nb))
next_step = True
else:
print("Files lookup already done.")
# Calculating pre hash (quick hash on first bytes)
# ---
if (next_step | ((last_step == "directory_lookup") & (last_id == "all")) |
((last_step == "filelist_pre_hash") & (last_id != "all"))):
t = filelist_pre_hash(cnx, 'md5')
print("Pre-hash calculation duration: {:.2f} sec. ".
format(t))
next_step = True
else:
print("Pre-hash calculation already done.")
# Calculate size of all files
# ---
res = cnx.execute("select sum(size) FROM filelist")
size = res.fetchone()[0]
print("Size of all files: {}".format(utils.humanbytes(size)))
# Recomputing hashes for duplicates candidates
# ---
if (next_step | ((last_step == "filelist_pre_hash") & (last_id == "all")) |
((last_step == "pre_duplicates_rehash") & (last_id != "all"))):
t, nb = pre_duplicates_rehash(cnx)
print("Pre-duplicates rehashing duration: {:.2f} sec. for {} records.".
format(t, nb))
next_step = True
else:
print("Pre-duplicates rehashing already done.")
# Dealing with duplicates
# ---
if (next_step | (last_step == "pre_duplicates_rehash")):
t, nb_dup, size_dup = duplicates_update(cnx)
else:
nb_dup, size_dup = duplicates_select(cnx)
# Result summary
# ---
print("{} files have duplicates, total size of duplicate files is {}.".
format(nb_dup, utils.humanbytes(size_dup)))
# Closing database
# ---
cnx.close()
return
def solver(G, source): # type: (nx.Graph, typing.Any) -> typing.Tuple[typing.List[typing.Any], int]
"""
Produces the optimal TSP tour using the Held-Karp, dynamic programming approach - O(n^2 * 2^n)
:param G: A fully connected networkx MultiDiGraph.
:param source: A source node in G.
:return: A list of nodes to visit, forming a TSP tour, and the cost of that tour.
"""
utils.check_arguments(G, source)
n = G.number_of_nodes()
distance = utils.get_adjacency_dicts(G)
min_cost_dp = {} # type: typing.Dict[Index, int]
parent = {} # type: typing.Dict[Index, typing.Any]
nodes_except_source = list(G.nodes)
nodes_except_source.remove(source)
for _set in _power_set(nodes_except_source): # type: set
_set = set(_set)
for current_vertex in nodes_except_source:
if current_vertex in _set:
continue
index = Index(current_vertex, _set)
min_cost = INFINITY
min_prev_vertex = source
set_copy = set(copy.deepcopy(_set))
for prev_vertex in _set:
cost = distance[prev_vertex][current_vertex] + _get_cost(set_copy, prev_vertex, min_cost_dp)
if cost < min_cost:
min_cost = cost
min_prev_vertex = prev_vertex
if len(_set) == 0:
min_cost = distance[source][current_vertex]
min_cost_dp[index] = min_cost
parent[index] = min_prev_vertex
_set = set(nodes_except_source)
min = INFINITY
prev_vertex = None
set_copy = copy.deepcopy(_set)
for vertex in _set:
cost = distance[vertex][source] + _get_cost(set_copy, vertex, min_cost_dp)
if cost < min:
min = cost
prev_vertex = vertex
parent[Index(source, _set)] = prev_vertex
tour = _get_tour(source, parent, G.nodes)
return tour, min
