def __init__(self,ip,port):
# global flyermmap
print(posix_ipc.MESSAGE_QUEUES_SUPPORTED)
print(posix_ipc.QUEUE_MESSAGES_MAX_DEFAULT)
print(posix_ipc.QUEUE_MESSAGE_SIZE_MAX_DEFAULT)
self.model_pub = rospy.Publisher("/gazebo/set_model_state",ModelState,queue_size=1)
self.bridge = CvBridge()
#self.image_sub = rospy.Subscriber("/mycam/image_raw",Image,self.callback)
self.image_sub = rospy.Subscriber("/multisense/camera/left/image_raw",Image,self.callback)
self.imageflag = 0
self.imagecnt = 0
self.key = 0
firstFoot = Foot([0,-0.1,0],Foot.RIGHT)
standingFoot = Foot([0,0.1,0],Foot.LEFT)
self.list = Steplist(firstFoot,standingFoot)
self.height = 1.6
self.tilt = 0.0
memory = posix_ipc.SharedMemory("flyermmap", posix_ipc.O_CREAT, size=8*Mib)
self.sem = posix_ipc.Semaphore("flyersem", posix_ipc.O_CREAT)
self.memmap = mmap.mmap(memory.fd, memory.size)
# flyermmap = self.memmap
memory.close_fd()
self.queue = posix_ipc.MessageQueue("/flyerqueue", posix_ipc.O_CREAT)
#self.wsproc = prc.Popen('python flyerws.py', shell=True )
self.wsproc = prc.Popen('python -u flyerws.py --ip %s --port %s' % (ip,port), shell=True )
self.writecnt = 0
self.loc = (0,0,0)
self.walker = Walker()
self.walker.init()
class APlayer(threading.Thread):
mpd = None
mainloop = None
def __init__(self):
self.volume = 0
self.isPlaying = False
self.isRandom = False
self.walker = Walker()
self.currentlyPlaying = {}
self.nextId = []
self.currentPlaylist = ""
self.watchCurrentSubscriber = []
self.timer = None
try:
self.mpd = MPDClient()
self.mpd.timeout = 10;
self.mpd.idletimeout = None
self.walker.setMPD(self)
except Exception, e:
print "Problem with setting up the mpd stuff"
print "Error: {0}".format(str(e))
threading.Thread.__init__(self)
def __init__(self, model_dir, config, node_feat_dim, edge_feat_dim, out_dim, loss, node_feat_enc=None, edge_feat_enc=None):
"""
:param model_dir: Directory to store model in
:param config: Python Dict that specifies the configuration of the model
:param node_feat_dim: Dimension of the node features
:param edge_feat_dim: Dimension of the edge features
:param out_dim: Output dimension
:param loss: torch.nn Loss object used for training
:param node_feat_enc: Optional initial embedding of node features
:param edge_feat_enc: Optional initial embedding of edge features
"""
super(CRaWl, self).__init__()
self.model_dir = model_dir
self.config = config
self.out_dim = out_dim
self.node_feat_enc = node_feat_enc
self.edge_feat_enc = edge_feat_enc
self.layers = config['layers']
self.hidden = config['hidden_dim']
self.kernel_size = config['kernel_size']
self.dropout = config['dropout']
self.pool = config['pool'] if 'pool' in config.keys() else 'mean'
self.vn = config['vn'] if 'vn' in config.keys() else False
self.walker = Walker(config)
self.walk_dim = self.walker.struc_feat_dim
self.conv_dim = config['conv_dim'] if 'conv_dim' in config.keys() else self.hidden
modules = []
for i in range(self.layers):
modules.append(ConvModule(conv_dim=self.conv_dim,
node_dim_in=node_feat_dim if i == 0 else self.hidden,
edge_dim_in=edge_feat_dim,
w_feat_dim=self.walk_dim,
dim_out=self.hidden,
kernel_size=self.kernel_size))
if self.vn and i < self.layers - 1:
modules.append(VNUpdate(self.hidden, config))
self.convs = Sequential(*modules)
self.node_out = Sequential(BatchNorm1d(self.hidden), ReLU())
if config['graph_out'] == 'linear':
self.graph_out = Sequential(Dropout(self.dropout),
Linear(self.hidden, out_dim))
else:
self.graph_out = Sequential(Dropout(self.dropout),
Linear(self.hidden, self.hidden),
ReLU(),
Linear(self.hidden, out_dim))
self.criterion = loss
pytorch_total_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
print(f'Number of paramters: {pytorch_total_params}')
def main(argv):
# set up argument parsing
parser = argparse.ArgumentParser()
parser.add_argument('input_graph', help='Original graph input file, in\
edge list format')
parser.add_argument('seed', help='Seed file, to pull start nodes from')
parser.add_argument('-e', '--restart_prob', type=float, default=0.7,
help='Restart probability for random walk')
parser.add_argument('-l', '--low_list', nargs='?', default=None,
help='<Optional> List of genes expressed and\
unexpressed in the current tissue, if applicable')
parser.add_argument('-n', '--node_list', nargs='?', default=None,
help='<Optional> Order of output probs')
parser.add_argument('-o', '--original_graph_prob', type=float, default=0.1,
help='Probability of walking on the original (non-\
tissue specific) graph, if applicable')
parser.add_argument('-r', '--remove', nargs='+',
help='<Optional> Nodes to remove from the graph, if any')
opts = parser.parse_args()
seed_list = generate_seed_list(opts.seed)
node_list = get_node_list(opts.node_list) if opts.node_list else []
# filter nodes we want to remove out of the starting seed, if any
remove_list = opts.remove if opts.remove else []
if remove_list:
seed_list = [s for s in seed_list if s not in remove_list]
# run the experiments, and write a rank list to stdout
wk = Walker(opts.input_graph, opts.low_list, remove_list)
wk.run_exp(seed_list, opts.restart_prob,
opts.original_graph_prob, node_list)
def makeMeasurements(self):
w = Walker(r.randint(350,450),2)
path = w.startWalking()
ts = datetime(2015,02,13,6)
for x in path:
ts = ts - timedelta(days=1)
self.measurements.append(Measurement(ts,x,r.random()*5))
def translate(ast, output_header, namespace=None):
"""
Walk concord message format(CMF) AST and generate C++ code.
Return C++ code as a string.
"""
with open(os.path.join(os.path.dirname(__file__), "serialize.hpp")) as f:
cmf_base_header = f.read()
with open(os.path.join(os.path.dirname(__file__), "serialize.cpp")) as f:
cmf_base_serialization = f.read()
visitor = CppVisitor()
walker = Walker(ast, visitor)
walker.walk()
impl = copyright() \
+ include_header(output_header) + '\n' \
+ cmf_base_serialization + '\n' \
+ file_namespace(namespace) + '\n' \
+ visitor.output \
+ file_trailer(namespace)
header = copyright() \
+ header_guard(output_header) \
+ header_includes() + '\n' \
+ cmf_base_header + '\n' \
+ file_namespace(namespace) \
+ visitor.output_declaration + '\n' \
+ file_trailer(namespace) \
+ header_guard_trailer(output_header)
return header, impl
def __init__ (self, start_dir, sqlitefile):
if 0 and os.path.exists (sqlitefile):
raise AlreadyExistsError, '\nERROR: sqlite_file already exists at {}\n'.format(sqlitefile)
Walker.__init__(self, start_dir)
self.db_table = CommsDBTable(sqlitefile)
def __init__(self, objectLogicPath, object):
Live.__init__(self,objectLogicPath, object)
Walker.__init__(self, objectLogicPath, object)
logicFileName = object.GetLogicFileName()
configFile=ini.IniFile(objectLogicPath)
configFile.ReadString("npc", "group")
self.SetHealth(configFile.ReadInt("npc", "health"))
self._clock = sf.Clock()
self._alarm = False
def test_walker():
"""Test that Walker class can be used as required."""
start, home = 10, 20
w = Walker(start, home)
assert not w.is_at_home()
move()
assert w.get_position() != start
move()
assert w.get_steps() == 2
def generate_walks(network_data, num_walks, walk_length, schema, file_name):
if schema is not None:
node_type = load_node_type(file_name + '/node_type.txt')
else:
node_type = None
from walker import Walker
walker = Walker(network_data, num_walks, walk_length, schema, node_type)
all_walks = walker.walk()
print('Finish generating the walks')
return all_walks
class RandomWalker(Sketch):
width = 800
height = 200
def setup(self):
self.walker = Walker(self)
self.background(255)
def draw(self):
self.walker.step()
self.walker.render()
def __init__(self):
''' connect to midi device and set up ros
'''
self.sp = SmartPAD('SmartPAD')
self.axis = Axis()
self.axis.init()
self.walker = Walker()
self.walker.init()
self.web = pages.Pages()
rospy.loginfo("Using input device %s" % self.sp.device)
os.environ['PORT'] = '8081'
def translate(ast, namespace=None):
"""
Walk concord message format(CMF) AST and generate Python code.
Return Python code as a string.
"""
with open(os.path.join(os.path.dirname(__file__), "serialize.py")) as f:
base_serializers = f.read() + '\n'
visitor = PyVisitor()
walker = Walker(ast, visitor)
walker.walk()
return header + base_serializers + visitor.output
def translate(ast, namespace=None):
"""
Walk concord message format(CMF) AST and generate C++ code.
Return C++ code as a string.
"""
with open(os.path.join(os.path.dirname(__file__), "serialize.h")) as f:
cmf_base_serialization = f.read()
s = cmf_base_serialization + '\n' + file_header(namespace)
visitor = CppVisitor()
walker = Walker(ast, visitor)
walker.walk()
return s + visitor.output + file_trailer(namespace)
def run(self, niter=50000):
self.p0 = np.identity(26, dtype=bool)
self.chain = np.zeros((niter+1, 26, 26), dtype=bool)
self.probchain = np.zeros((niter+1))
self.chain[0] = self.p0
self.probchain[0] = self.lnprob(self.p0)
w1 = Walker(p0=self.p0, k=30, lnprobfunc=self.lnprob)
for i in range(niter):
w1.iterate()
self.chain[i+1] = w1.p
self.probchain[i+1] = w1.lnprob
return
def __init__(self):
self.n_features = 366
self.n_actions = 8
self.max_epoch = 1000
self.max_steps = 40
# define sound source information
self.src_pos_x = -2.0
self.src_pos_y = 1.6
self.src_pos_z = -4.0
# sample as a grid map with 0.5m unit
# change step length to 1m
self.unit = 1.0
self.room_grids_x = [i for i in np.arange(-3.0, 3.0 + self.unit, self.unit)]
self.room_grids_z = [i for i in np.arange(-4.0, 4.0 + self.unit, self.unit)]
# define wall and obstacles
self.wall_axis_z = {-4: [i for i in np.arange(-5.0, 6.0, 1.0)],
4: [i for i in np.arange(-5.0, 6.0, 1.0)],
0: [i for i in np.arange(-5.0, 6.0, 1.0) if i != 0]}
self.wall_axis_x = {5: [i for i in np.arange(-4.0, 5.0, 1.0)],
1: [i for i in np.arange(-4.0, 5.0, 1.0) if i != -2 and i != 2],
-1: [i for i in np.arange(-4.0, 5.0, 1.0) if i != -2 and i != 2],
-5: [i for i in np.arange(-4.0, 5.0, 1.0)]}
# define checkpoints: room gates, hall center
self.room_gates = [[-2.0, 1, -1.0], [2.0, 1, -1.0], [-2.0, 1, 1.0], [2.0, 1, 1.0]]
self.hall_center = [[0, 0, 0]]
# define room zone
self.room1_x = [i for i in np.arange(-3.5, 0, 0.5)]
self.room1_z = [i for i in np.arange(-4.5, -1, 0.5)]
self.room2_x = [i for i in np.arange(0.5, 4.0, 0.5)]
self.room2_z = [i for i in np.arange(-4.5, -1, 0.5)]
self.room3_x = [i for i in np.arange(-3.5, 0, 0.5)]
self.room3_z = [i for i in np.arange(1.5, 5.0, 0.5)]
self.room4_x = [i for i in np.arange(0.5, 4.0, 0.5)]
self.room4_z = [i for i in np.arange(1.5, 5.0, 0.5)]
self.hall_x = [i for i in np.arange(-3.5, 4.0, 0.5)]
self.hall_z = [i for i in np.arange(-0.5, 1.0, 0.5)]
self.walker = Walker(self.n_features, self.n_actions)
self.BayeProb = {1: 0.25, 2: 0.25, 3: 0.25, 4: 0.25}
self.Pzx = {1: 1, 2: 1, 3: 1, 4: 1}
def main(argv):
# set up argument parsing
parser = argparse.ArgumentParser()
parser.add_argument('input_graph',
help='Original graph input file, in\
edge list format')
parser.add_argument('seed', help='Seed file, to pull start nodes from')
parser.add_argument('-e',
'--restart_prob',
type=float,
default=0.7,
help='Restart probability for random walk')
parser.add_argument('-l',
'--low_list',
nargs='?',
default=None,
help='<Optional> List of genes expressed and\
unexpressed in the current tissue, if applicable'
)
parser.add_argument('-n',
'--node_list',
nargs='?',
default=None,
help='<Optional> Order of output probs')
parser.add_argument('-o',
'--original_graph_prob',
type=float,
default=0.1,
help='Probability of walking on the original (non-\
tissue specific) graph, if applicable')
parser.add_argument(
'-r',
'--remove',
nargs='+',
help='<Optional> Nodes to remove from the graph, if any')
opts = parser.parse_args()
seed_list = generate_seed_list(opts.seed)
node_list = get_node_list(opts.node_list) if opts.node_list else []
# filter nodes we want to remove out of the starting seed, if any
remove_list = opts.remove if opts.remove else []
if remove_list:
seed_list = [s for s in seed_list if s not in remove_list]
# run the experiments, and write a rank list to stdout
wk = Walker(opts.input_graph, opts.low_list, remove_list)
wk.run_exp(seed_list, opts.restart_prob, opts.original_graph_prob,
node_list)
def __init__(self):
events.AddListener(self)
Walker.__init__(self)
self.health = 10
self.energy = 1
self.xMax = 4
self.xMin = -4
self.yMax = 2
self.yMin = -2
self.state = State.fastWalking
self.knownAvatars = []
self.xFightingReach = 10
self.yFightingReach = 4
self.attack = attacks.Hug()
self.stunCounter = 0
def __init__(self):
events.AddListener(self)
Walker.__init__(self)
self.health = 10
self.energy = 1
self.xMax = 4
self.xMin = -4
self.yMax = 2
self.yMin = -2
self.state = State.fastWalking
self.knownAvatars = []
self.xFightingReach = 10
self.yFightingReach = 4
self.attack = attacks.Hug()
self.stunCounter = 0
def __init__(self, ip, port):
self.bridge = CvBridge()
self.imageflags = [0, 0, 0, 0,
0] #I 1gray 2small 3fc 4seg (zero not used)
self.imagecnt = 0
self.key = 0
self.height = 1.6
self.tilt = 0.0
self.torso = 0.0
self.headtilt = 0.0
self.headrot = 0.0
self.stepspending = 0
self.totalsteps = 0
self.nextposetime = 0
self.nextfsstime = 0
memory = posix_ipc.SharedMemory("flyermmap",
posix_ipc.O_CREAT,
size=8 * IPComm.Mib)
self.sem = posix_ipc.Semaphore("flyersem", posix_ipc.O_CREAT)
self.memmap = mmap.mmap(memory.fd, memory.size)
memory.close_fd()
self.image_sub = rospy.Subscriber("/olrun/image/gray", Image,
self.callback)
self.fcimage_sub = rospy.Subscriber("/olrun/image/fc", Image,
self.callbackfc)
self.segimage_sub = rospy.Subscriber("/olrun/image/seg", Image,
self.callbackseg)
self.smallimage_sub = rospy.Subscriber("/olrun/image/small", Image,
self.callbacksmall)
ROBOT_NAME = rospy.get_param('/ihmc_ros/robot_name')
fss_name = "/ihmc_ros/{0}/output/footstep_status".format(ROBOT_NAME)
pose_name = "/ihmc_ros/{0}/output/robot_pose".format(ROBOT_NAME)
self.fssSubscriber = rospy.Subscriber(fss_name,
FootstepStatusRosMessage,
self.rcvdfss)
self.poseSubscriber = rospy.Subscriber(pose_name, Odometry,
self.rcvdpose)
self.queue = posix_ipc.MessageQueue("/flyerqueue", posix_ipc.O_CREAT)
self.wsproc = prc.Popen('python -u flyerws.py --ip %s --port %s' %
(ip, port),
shell=True)
self.writecnt = 0
self.loc = [0.0, 0.0, 0.0]
self.walker = Walker()
self.walker.init()
self.neck_joint = [0.0, 0.0, 0.0]
self.torso_joint = 0.0
def __init__(self, path):
self.path = path
self.queue = Queue.PriorityQueue()
self.watcher = Watcher(path, self.queue)
self.walker = Walker(path, self.queue, Settings.is_rescan_forced())
self.reader = Reader(self.queue)
self.validator = Validator(self.queue)
def Setup(self):
#Create the ground
self.world.CreateStaticBody(position=(0, -10),
shapes=b2PolygonShape(box=(10000, 10)))
#Make some walkers
self.walkerList = []
for i in range(learningSettings.groupSize):
self.walkerList.append(
Walker(self.world, (i * 10, 0),
learningSettings.useSimpleWalkers))
#Make a population of agents
if (learningSettings.loadPopulation):
self.population = Population.loadFromFile(
learningSettings.populationFile)
#Reduce the number of agents to the best performing ones
self.population.pruneToTopAgents(learningSettings.groupSize)
else:
jointCount = len(self.walkerList[0].jointList)
sampleNetwork = Network(jointCount + 2, jointCount, [jointCount])
self.population = Population(learningSettings.walkerCount,
sampleNetwork)
#If we need it, setup an environment for speciation
if (learningSettings.selectionCriteria == selection.SPECIATION):
self.environment = speciation.Environment()
self.environment.generateAllFood(self.walkerList[0],
learningSettings.foodCount,
learningSettings.foodUses,
learningSettings.foodEnergy)
def __init__(self):
self.n_features = 366
self.n_actions = 8
self.max_epoch = 100
self.max_steps = 100
# define sound source information
self.src_pos_x = -3.0
self.src_pos_y = 1.6
self.src_pos_z = -3.0
# sample as a grid map with 0.5m unit
self.unit = 0.5
self.room_grids = [i for i in np.arange(-3.5, 3.5 + self.unit, self.unit)]
self.walker = Walker(self.n_features, self.n_actions)
def main():
# Initialize pygame
pygame.init()
clock = pygame.time.Clock()
globals.window = pygame.display.set_mode((globals.WIDTH, globals.HEIGHT), False)
globals.window.fill(BACKGROUND_COLOR)
loop = True
# Create the walkers
walkers = []
for i in range(NUM_WALKERS):
if RANDOM_COLORS:
color = (random.randint(0, 255), random.randint(0, 150), random.randint(100, 255))
else:
color = (0, 0, 0) # Default to black if no random colors
walkers.append(Walker(globals.WIDTH / 2, globals.HEIGHT / 2, color))
while(loop):
clock.tick(60)
for i in range(len(walkers)):
walkers[i].update()
walkers[i].draw()
# Check if the escape key has been pressed
for event in pygame.event.get():
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_ESCAPE:
pygame.quit()
quit()
pygame.display.flip()
def make_graph(self):
for h_line in self.horizon_lines:
coeff_x, coeff_y, bias = h_line.eq
for v_line in self.vertical_lines:
cx, cy, b = v_line.eq
A = np.array([[coeff_x, coeff_y], [cx, cy]])
B = np.array([bias, b])
root = Corner(np.linalg.solve(A, B).astype(int))
h_line.corners.append(root)
v_line.corners.append(root)
self.corners.append(root)
for line in self.lines:
line.line_link()
self.walker = Walker(self.corners)
def AddSpike(self):
# Release/spawn some number of ions at Post Synaptic Density
amp = random.randint(
1, 15) #should be drawn from Gaussian and have some meaning
print "Spike of size ", amp
for i in xrange(amp):
#draw a random position in proximity of PSD
self.Ions.append(
Walker([random.random(), 1 - 0.1 * random.random()]))
def generate_code_and_tests(ast):
""" Walk concord message format(CMF) AST and generate C++ code and C++ tests"""
namespace = "cmf::test"
print("Generating C++ Message structs and serialization code")
code = cppgen.translate(ast, namespace)
test_code = file_header(namespace)
print("Generating C++ message instances and serialization tests")
visitor = InstanceVisitor()
# We generate `max_size` msg instances for tests
for i in range(0, MAX_SIZE):
visitor.size = i
walker = Walker(ast, visitor)
walker.walk()
for msg_name, instances in visitor.existing_instances.items():
for instance in instances:
test_code += instance + "\n\n"
test_code += testSerializationStr(msg_name)
return code, test_code + file_trailer(namespace, ast)
def initialize_walkers_(self, population):
if self.memory_safe:
self.__dict__['population'] = population
for i in range(self.chain.no_of_nodes):
trans_prob = self.chain.get_node_transition_probability(self.chain.node_ids[i])
next_step_processor = Markov(trans_prob)
for j in range(population[i]):
wlk = Walker(next_step_processor = next_step_processor,\
initial_id = self.chain.node_ids[i])
self.walkers.append(wlk)
def simulate(self):
s = Simulation()
robot = Walker(s.space, [self.chromosome[0], self.chromosome[1]], self.chromosome[2], self.chromosome[3],\
self.chromosome[4], self.chromosome[5], self.chromosome[6], self.chromosome[7], self.chromosome[8])
print "Simulating best Individual with score: ", self.score
currentPosition = s.step(0.02)
energy = s.get_ke()
while (currentPosition > 0 and energy > 1):
energy = s.get_ke()
#print "currentPosition: ", currentPosition
currentPosition = s.step(0.02)
def load_map(file, move_keys, player_number):
# Load map
# Load background color
f = open(path.join("levels", file + ".txt"), "r")
data = f.read()
f.close()
data = data.split("\n")
data[0] = data[0].split(",")
color = data[0]
tile_chars = {}
data.pop(0)
# Load tiles
while "=" in data[0]:
tile_chars[data[0][0]] = data[0].split("=")[1].split(",")
data.pop(0)
# Initialize objects
tiles = []
net = []
walkers = []
climbers = []
stones = []
players = []
# Load objects
y = 1
for row in range(len(data)):
x = -1
for tile in range(len(data[row])):
if data[row][tile] in tile_chars:
tiles.append(
Tile(x, y, TILE_WIDTH, TILE_HEIGHT,
tile_chars[data[row][tile]]))
elif data[row][tile] == "p" and len(players) < player_number:
players.append(
Player(x, y, CHAR_WIDTH, CHAR_HEIGHT, 0,
move_keys[len(players)]))
elif data[row][tile] == "w":
walkers.append(Walker(x, y, CHAR_WIDTH, CHAR_HEIGHT, 0))
elif data[row][tile] == "n":
net.append(Net(x, y, TILE_WIDTH, TILE_HEIGHT, 6))
elif data[row][tile] == "c":
net.append(Net(x, y, TILE_WIDTH, TILE_HEIGHT, 6))
climbers.append(Climber(x, y, CHAR_WIDTH, CHAR_HEIGHT, 0))
elif data[row][tile] == "s":
stones.append(Stone(x, y, TILE_WIDTH, TILE_HEIGHT, 7))
x += TILE_WIDTH
y -= TILE_HEIGHT
# Return objects
return tiles, net, stones, players, walkers, climbers, color
def __init__(self):
Walker.__init__(self)
events.AddListener(self)
self.health = 3
self.state = State.normal
self.upPressed = False
self.rightPressed = False
self.downPressed = False
self.leftPressed = False
self.xFriction = 1
self.yFriction = 2
self.xAccel = 1
self.yAccel = 1
self.xMax = 6
self.xMin = -6
self.yMax = 2
self.yMin = -2
self.stunCounter = 0
self.yoyo = LogicalYoyo()
self.attacks = attacks.makeYoyoAttacks(self.yoyo)
self.strings = []
self.selectedAttack = None
def send():
send.butt.setEnabled(False)
robot = Robot("192.168.0.14", "5469")
walk = Walker(robot)
robot.locomotion.gait(stance, step, zmp, hack, sensor, stiff, odo, arm)
walk.linear_go_to(300.0, 0.0, 100.0)
time.sleep(1.0)
while not walk.is_done():
time.sleep(1.0)
walk.stop()
send.butt.setEnabled(True)
def main(argv):
# set up argument parsing
# parser = argparse.ArgumentParser()
# parser.add_argument('input_graph', help='Original graph input file, in\
# edge list format')
# parser.add_argument('seed', help='Seed file, to pull start nodes from')
# parser.add_argument('-e', '--restart_prob', type=float, default=0.7,
# help='Restart probability for random walk')
# parser.add_argument('-l', '--low_list', nargs='?', default=None,
# help='<Optional> List of genes expressed and\
# unexpressed in the current tissue, if applicable')
# parser.add_argument('-n', '--node_list', nargs='?', default=None,
# help='<Optional> Order of output probs')
# parser.add_argument('-o', '--original_graph_prob', type=float, default=0.1,
# help='Probability of walking on the original (non-\
# tissue specific) graph, if applicable')
# parser.add_argument('-r', '--remove', nargs='+',
# help='<Optional> Nodes to remove from the graph, if any')
# opts = parser.parse_args()
#*************************************************************************
seed = 'testdata/seeds.tsv'
node_list = None
seed_list = generate_seed_list(seed)
node_list = get_node_list(node_list) if node_list else []
remove = None
# filter nodes we want to remove out of the starting seed, if any
remove_list = remove if remove else []
if remove_list:
seed_list = [s for s in seed_list if s not in remove_list]
# run the experiments, and write a rank list to stdout
input_graph = 'testdata/observed_network.txt'
restart_prob = 0.7
original_graph_prob = 0.1
low_list = None
wk = Walker(input_graph, low_list, remove_list)
wk.run_exp(seed_list, restart_prob, original_graph_prob, node_list)
def interactive(self):
# Interactive mode
running = True
robot = None
while running:
# Deal with clicks and other events
for event in pygame.event.get():
if event.type == QUIT:
running = False
if event.type == MOUSEBUTTONDOWN:
robot = Walker(self.space, \
self._invy(event.pos), \
80, 60, 10, pi/16, 0, 0, 0)
self.step(0.02)
class Scanner:
def __init__(self, path):
self.path = path
self.queue = Queue.PriorityQueue()
self.watcher = Watcher(path, self.queue)
self.walker = Walker(path, self.queue, Settings.is_rescan_forced())
self.reader = Reader(self.queue)
self.validator = Validator(self.queue)
def start(self):
self.validator.start()
self.watcher.start()
self.walker.start()
self.reader.start()
def stop(self):
self.watcher.stop()
self.reader.stop()
self.validator.join()
self.walker.join()
self.watcher.join()
self.reader.join()
def main(file_path=None, all_sync=False):
inifile = cp.SafeConfigParser()
inifile.read(os.getcwd() + "/confing.ini")
""" load config file """
host = inifile.get("receiver", "host")
port = inifile.get("receiver", "port")
user = inifile.get("receiver", "user")
passwd = inifile.get("receiver", "passwd")
header_path = inifile.get("file", "header_path")
transfer = Transfer("ftp")
transfer.inst.connect(host, port, user, passwd)
if all_sync:
syncdir = inifile.get("all_sync", "syncdir")
walker = Walker(syncdir)
w = walker.start()
while True:
try:
file_path = w.next()
remote_path = file_path.replace(header_path, "")
dirname = os.path.dirname(remote_path)
filename = os.path.basename(remote_path)
send(transfer.inst, dirname, filename, file_path)
except StopIteration:
return
if file_path:
remote_path = file_path.replace(header_path, "")
if remote_path[0] != "/":
remote_path = "/" + remote_path
dirname = os.path.dirname(remote_path)
filename = os.path.basename(remote_path)
""" Connection with remote server """
send(transfer.inst, dirname, filename, file_path)
class Smartie(object):
''' Class to connect to and publish the Korg NanoKontrol midi device
as a ros joystick
'''
def __init__(self):
''' connect to midi device and set up ros
'''
self.sp = SmartPAD('SmartPAD')
self.axis = Axis()
self.axis.init()
self.walker = Walker()
self.walker.init()
self.web = pages.Pages()
rospy.loginfo("Using input device %s" % self.sp.device)
os.environ['PORT'] = '8081'
def finish(self):
self.sp.finish()
self.axis.fini()
self.web.finish()
#del self.sp
#del self.axis
def run(self):
try:
thread.start_new_thread(self.web.run, ())
while not rospy.is_shutdown():
done, data, steps = self.sp.readin()
if done:
rospy.signal_shutdown("Smartie Done")
self.axis.process_keys(data)
self.walker.process_keys(steps)
finally:
rospy.loginfo("Smartie Died")
self.finish()
def send():
send.butt.setEnabled(False)
robot = Robot("192.168.0.14", "5469")
walk = Walker(robot)
robot.locomotion.gait(stance, step, zmp, hack, sensor, stiff, odo, arm)
walk.linear_go_to(300.0, 0.0, 100.0)
time.sleep(1.0)
while not walk.is_done():
time.sleep(1.0)
walk.stop()
send.butt.setEnabled(True)
def make_graph(self):
for h_line in self.horizon_lines:
coeff_x, coeff_y, bias = h_line.eq
for v_line in self.vertical_lines:
cx, cy, b = v_line.eq
A = np.array([[coeff_x, coeff_y], [cx, cy]])
B = np.array([bias, b])
root = Corner(np.linalg.solve(A, B).astype(int))
h_line.corners.append(root)
v_line.corners.append(root)
self.corners.append(root)
for line in self.lines:
line.line_link()
self.walker = Walker(self.corners)
def run_memory_safe_(self, time, display_progress = False):
population_changes = {key: np.zeros(self.times.shape[0]) for key in self.chain.node_ids}
prg = np.arange(0, np.sum(self.population), 1)
prg_count = 0
for p in range(len(self.population)):
trans_prob = self.chain.get_node_transition_probability(self.chain.node_ids[p])
next_step_processor = Markov(trans_prob)
for i in range(self.population[p]):
iteration = 0
walker = Walker(next_step_processor = next_step_processor,\
initial_id = self.chain.node_ids[p])
for tt in self.times[1:]:
curr = walker.current_position()
walker.next_step_processor.transition_probability = \
self.chain.nodes[curr].transition_probability
next_pos = self.chain.next(curr)
if len(next_pos) == 0:
walker.empty_step()
continue
pmf = self.chain.get_node_probability_mass_function(curr)
pmf = [pmf[pos] for pos in next_pos]
walker.walk(possible_states = next_pos, \
current_state = curr, \
probability_mass_function = pmf)
nxt = walker.current_position()
population_changes[curr][iteration] -= 1
population_changes[nxt][iteration] += 1
iteration += 1
if display_progress:
self._display_progress(prg_count, prg)
prg_count += 1
return population_changes
class Graph():
def __init__(self, lines):
self.lines = lines
self.corners = []
self.horizon_lines = []
self.vertical_lines = []
self.walker = None
self.set_directed_lines()
self.make_graph()
def set_directed_lines(self):
for line in self.lines:
if line.direction is 'h':
self.horizon_lines.append(line)
else:
self.vertical_lines.append(line)
def make_graph(self):
for h_line in self.horizon_lines:
coeff_x, coeff_y, bias = h_line.eq
for v_line in self.vertical_lines:
cx, cy, b = v_line.eq
A = np.array([[coeff_x, coeff_y], [cx, cy]])
B = np.array([bias, b])
root = Corner(np.linalg.solve(A, B).astype(int))
h_line.corners.append(root)
v_line.corners.append(root)
self.corners.append(root)
for line in self.lines:
line.line_link()
self.walker = Walker(self.corners)
def get_quad_list(self):
return self.walker.get_quad_list()
def setup(self):
self.walker = Walker(self)
self.background(255)
def visitFunction(self, node): print "[fn]", node.name Walker.visitFunction(self, node)
WIDTH = 400
HEIGHT = 400
BACKGROUND = (0, 170, 0)
screen = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Random walk with coloured trail")
screen.fill(BACKGROUND)
pygame.display.update()
clock = pygame.time.Clock()
def move():
return (random.choice([-2,0,2]),
random.choice([-2,0,2]))
pos = [WIDTH//2, HEIGHT//2]
walker = Walker(screen, pos, get_move=move, size=2)
it = 0
while True:
clock.tick(120)
for ev in pygame.event.get():
if ev.type == QUIT:
pygame.quit()
sys.exit()
walker.walk()
walker.c = (55,0,it)
walker.pos[0] %= WIDTH
walker.pos[1] %= HEIGHT
walker.draw()