def sendCommand(self, totalX, totalY, tank):
othertanks = self.bzrc.get_othertanks()
me = Point(tank.x, tank.y)
cur = 100000
mins = 100000
enemy = Point(0, 0)
for other in othertanks:
cur = Point(other.x, other.y).distance(me)
if (cur < mins):
mins = cur
enemy = other
if mins <= 200:
self.doKalman(enemy.x, enemy.y)
else:
theta = math.atan2(totalY, totalX)
theta = self.normalize_angle(theta - tank.angle)
speed = math.sqrt(totalY**2 + totalX**2)
self.commands = []
command = Command(tank.index, .15 * speed, .85 * theta, False)
self.commands.append(command)
self.bzrc.do_commands(self.commands)
self.commands = []
def question_22_17(query):
VertexB = Point(Fraction(1), Fraction(-2), "B")
VertexC = Point(Fraction(-2), Fraction(-1), "C")
EdgeA = Line.get_line_contains_points(VertexB, VertexC)
p1 = Point(Fraction(2), Fraction(0), "p1")
p2 = Point(Fraction(2), Fraction(1), "p2")
EdgeC = Line.get_line_contains_points(VertexB, p1)
EdgeB = Line.get_line_contains_points(VertexC, p2)
del p1, p2
BisectorAngleA = Line.get_bisectors_for_2lines(EdgeB, EdgeC)[0]
VertexOfTargetRhombusInsideEdgeA = EdgeA.get_cross_point(BisectorAngleA)
EageOfTargetRhombusParallelsToEdgeC = Line.get_line_parallel_to(EdgeC, VertexOfTargetRhombusInsideEdgeA)
EageOfTargetRhombusParallelsToEdgeB = Line.get_line_parallel_to(EdgeB, VertexOfTargetRhombusInsideEdgeA)
VertexOfTargetRhombusInsideEdgeB = Line.get_cross_point(EageOfTargetRhombusParallelsToEdgeC, EdgeB)
VertexOfTargetRhombusInsideEdgeC = Line.get_cross_point(EageOfTargetRhombusParallelsToEdgeB, EdgeC)
TriangleEdges = {(EdgeA.a, EdgeA.b, EdgeA.c), (EdgeB.a, EdgeB.b, EdgeB.c), (EdgeC.a, EdgeC.b, EdgeC.c)}
init_figure, grid_size = common.INIT_FIGURE(), common.GRID_SIZE()
init_figure.extend(TriangleEdges)
resolution_vertex_in_edge_a = query.query_point_by_symmetry(VertexOfTargetRhombusInsideEdgeA, init_figure)
resolution_vertex_in_edge_b = query.query_point_by_symmetry(VertexOfTargetRhombusInsideEdgeB, init_figure)
resolution_vertex_in_edge_c = query.query_point_by_symmetry(VertexOfTargetRhombusInsideEdgeC, init_figure)
resolution_product = itertools.product(resolution_vertex_in_edge_a,
resolution_vertex_in_edge_b,
resolution_vertex_in_edge_c)
solution_for_all = [frozenset((set(r[0]) | set(r[1]) | set(r[2]))) for r in resolution_product]
solution_for_all.sort()
targets = (VertexOfTargetRhombusInsideEdgeA, VertexOfTargetRhombusInsideEdgeB, VertexOfTargetRhombusInsideEdgeC)
for r in solution_for_all:
draw_resolution(r, targets, init_figure, grid_size)
def __init__(self, bzrc, tank, job):
self.updatecounts = 0
self.tank = tank
self.bzrc = bzrc
self.fields = fields.Calculations()
self.throttle = .15
self.commands = []
self.constants = self.bzrc.get_constants()
self.job = job
self.base = bzrc.get_bases()[self.getFlagIndex()]
c1 = Point(self.base.corner1_x, self.base.corner1_y)
c3 = Point(self.base.corner3_x, self.base.corner3_y)
line = Line(c1, c3)
self.base = line.getMidpoint()
flags = bzrc.get_flags()
for flag in flags:
if flag.color == self.constants['team']:
self.flag = flag
self.obstacles = []
#the .07125 comes from the 4 Ls world where 7.125% of the world was occupied
self.time = time.time()
self.locationList = []
self.oldlocation = []
self.startTime = time.time()
self.mytanks = self.bzrc.get_mytanks()
def doKalman(self, X, Y):
self.Kfilter = Kalman(X, Y)
Mu, Sigma = self.Kfilter.runKalman(self.bzrc)
enemyX = Mu.item((0, 0))
enemyY = Mu.item((3, 0))
distance = Point(self.tank.x,
self.tank.y).distance(Point(enemyX, enemyY))
if distance > 450:
return
pred = (distance / float(self.constants['shotspeed'])) * (1.0 / t)
pred = int(pred + 1)
MuPred, garbage = self.Kfilter.predictiveKalman(Mu, Sigma, pred)
targetX, targetY = MuPred.item((0, 0)), MuPred.item((3, 0))
deltaX, deltaY = self.getDeltaXY(self.tank, Point(targetX, targetY))
theta = math.atan2(deltaY, deltaX)
theta = self.normalize_angle(theta - self.tank.angle)
#distance = Point(tank.x, tank.y).distance(Point(targetX,targetY))
if distance <= 390 and (theta < 0.2 and theta > -0.2):
shoot = True
else:
shoot = False
self.kalmanCommand(deltaX, deltaY, theta, shoot, self.tank)
def shoot_em(self, tank):
my_position = Point(tank.x, tank.y)
for enemy in self.enemies:
enemy_position = Point(enemy.x, enemy.y)
if my_position.distance(enemy_position) <= 50:
theta = self.fields.angle(tank, enemy)
if theta < 1.5 and theta > -1.5:
line_to_enemy = Line(my_position, enemy_position)
safe = True
for teamMate in self.mytanks:
if teamMate.index == tank.index:
continue
teamMate_position = Point(teamMate.x, teamMate.y)
cp2 = teamMate_position.closestPointOnLine(
line_to_enemy)
teamMate_enemy_line = Line(teamMate_position, cp2)
if teamMate_position.distance(cp2) < 10:
safe = False
break
if safe == True:
return True
return False
def getDeltaXY(self, tank, enemy):
theta = self.angle(tank, enemy)
distance = Point(tank.x, tank.y).distance(Point(enemy.x, enemy.y))
deltaX = distance * math.cos(theta)
deltaY = distance * math.sin(theta)
return deltaX, deltaY
def tick(self):
mytanks, othertanks, flags, shots = self.bzrc.get_lots_o_stuff()
''' list of teammates with velocities and position and flag posession etc. '''
self.mytanks = self.bzrc.get_mytanks()
''' positions and angle of othertanks '''
self.othertanks = othertanks
self.enemies = [tank for tank in othertanks if tank.color !=
self.constants['team']]
self.commands = []
tank = self.bzrc.get_mytanks()[0]
if(self.enemies[0].status == 'dead'):
return
'''do a predictive filter here maybe, before they shoot'''
Mu,Sigma = self.Kfilter.runKalman(self.bzrc)
enemyX = Mu.item((0,0))
enemyY = Mu.item((3,0))
distance = Point(tank.x, tank.y).distance(Point(enemyX,enemyY))
if distance > 450:
return
pred = (distance/float(self.constants['shotspeed']))*(1.0/t)
pred = int(pred+1)
MuPred,SigmaPred = self.Kfilter.predictiveKalman(Mu,Sigma,pred)
targetX,targetY = MuPred.item((0,0)),MuPred.item((3,0))
deltaX, deltaY = self.getDeltaXY(tank,Point(targetX,targetY))
theta = math.atan2(deltaY,deltaX)
theta = self.normalize_angle(theta-tank.angle)
#distance = Point(tank.x, tank.y).distance(Point(targetX,targetY))
if distance <= 390 and (theta < 0.1 and theta > -0.1):
shoot = True
else:
shoot = False
self.sendCommand(deltaX, deltaY, theta, shoot, tank)
'''call gnuplot here'''
sigmaX,sigmaY,rho = self.Kfilter.covarianceMatrix(self.bzrc,Mu,Sigma)
#print "sigmaX and Y", sigmaX,sigmaY
#print "rho" , rho
plotter.plot(sigmaX,sigmaY,0,enemyX,enemyY)
return True
def question_16_15(query):
line1 = Line.get_line_contains_points(Point(-2, 0), Point(0, 1))
line2 = Line.get_line_contains_points(Point(0, -1), Point(1, 1))
line = Line.get_bisectors_for_2lines(line1, line2)[0]
A = line1.get_cross_point(line2)
# query and show it(them)
init_figure, grid_size = common.INIT_FIGURE(), common.GRID_SIZE()
init_figure.extend(((line1.a, line1.b, line1.c), (line2.a, line2.b, line2.c)))
solution = query.query_line_by_symmetry(line, (A,), init_figure)
points = set([s[0] for s in solution])
for r in solution[:4]:
p, fig = r
draw_resolution(fig, points, init_figure, grid_size)
def __init__ (self,enemyx,enemyy):
self.enemy = Point(enemyx,enemyy)
self.F = np.matrix([[1,t,(.5*(t**2)),0,0,0],
[0,1,t,0,0,0],
[0,0,1,0,0,0],
[0,0,0,1,t,(.5*(t**2))],
[0,0,0,0,1,t],
[0,0,0,0,0,1]])
self.SigmaS = np.matrix([[.1,0,0,0,0,0],
[0,.1,0,0,0,0,],
[0,0,50,0,0,0],
[0,0,0,.1,0,0],
[0,0,0,0,.1,0],
[0,0,0,0,0,50]])
self.H = np.matrix([[1,0,0,0,0,0],
[0,0,0,1,0,0]])
self.SigmaE = np.matrix([[25,0],[0,25]])
self.FTrans = self.F.transpose()
self.HTrans = self.H.transpose()
self.MuKnot = np.matrix([[enemyx],[0],[0],[enemyy],[0],[0]])
self.SigmaKnot = np.matrix([[100,0,0,0,0,0],
[0,.1,0,0,0,0],
[0,0,.1,0,0,0],
[0,0,0,100,0,0],
[0,0,0,0,.1,0],
[0,0,0,0,0,.1]])
self.SigmaCurrent = self.SigmaKnot
self.SigmaPrev = self.SigmaKnot
self.MuCurrent = self.MuKnot
self.MuPrev = self.MuKnot
def parse_xml(xml):
cities = []
for node in xml:
if node.tag == "node":
lon = float(node.attrib["lon"])
lat = float(node.attrib["lat"])
id_ = node.attrib["id"]
city = {
"name": id_,
"pos": Point(lon, lat),
"population": 0,
"zipcode": "UNKNOWN"
}
ctable = {
"name": "name",
"population": "population",
"ref:INSEE": "zipcode"
}
for data in node:
if data.tag == "tag":
key, value = data.attrib["k"], data.attrib["v"]
if key in ctable:
new_key = ctable[key]
city[new_key] = value
city["population"] = int(city["population"])
cities.append(city)
else:
# Not a 'node' tag
pass
return cities
def __init__(self, vid, length, width, height, weight, cat, axles, doublemount, studs, fuel, cc, maxspeed, maxdecel, maxaccel,
position = Point(0.0,0.0,0.0), direction = Direction(0.0,0.0), speed = 0.0, acceleration = 0.0):
object.__init__(self)
# unique vehicle ID
self._vid = vid
# size properties
self._length = length # in m
self._width = width # in m
self._height = height # in m
self._weight = weight # in kg
# emission related properties
self._cat = cat # emission category (usually an integer), to be interpreted by the emission models
self._axles = axles # integer, number of axles of vehicle
self._doublemount = doublemount # boolean, true for double-mounted tires (for trucks)
self._studs = studs # boolean, true if the vehicle has winter tires
self._fuel = fuel # the type of fuel the vehicle uses (string, e.g. 'petrol', 'diesel', 'electric')
self._cc = cc # the cilinder capacity (cc)
# kinematic properties
self._maxspeed = maxspeed # in km/h
self._maxdecel = maxdecel # in m/s^2 (negative value)
self._maxaccel = maxaccel # in m/s^2
# dynamic properties
self._position = position # position Point(x,y,z) - center of bottom plane - in m
self._direction = direction # Direction(bearing,gradient) of the vehicle, in degrees (0-360)
self._speed = speed # velocity (km/h)
self._acceleration = acceleration # acceleration (m/s^2)
def tick(self):
self.commands = []
curtime = time.time()
self.mytanks = self.bzrc.get_mytanks()
for tank in self.mytanks:
curLocation = Point(tank.x, tank.y)
target = self.locationList[tank.index]
if curLocation.distance(target) < 10:
target = self.getRandomCoordinate(tank.index)
while self.grid.get(target.x, target.y) > .95:
target = self.getRandomCoordinate(tank.index)
self.locationList[tank.index] = target
self.oldlocation[tank.index] = curLocation
self.goToPoint(tank, target)
if(curtime - self.time > 4.25 ):
for tank in self.mytanks:
command = Command(tank.index,0,0,False)
self.commands = []
self.commands.append(command)
self.bzrc.do_commands(self.commands)
self.commands = []
self.getObservation(tank)
self.time = time.time()
def tick(self):
if(self.ticks < 10):
pass
if self.tank.status == "dead":
return
self.commands = []
curtime = time.time()
curLocation = Point(self.tank.x, self.tank.y)
target = self.locationList[self.tank.index]
if curLocation.distance(target) < 10:
target = self.getRandomCoordinate(self.tank.index)
while self.grid.get(target.x, target.y) > .95:
target = self.getRandomCoordinate(self.tank.index)
self.locationList[self.tank.index] = target
self.oldlocation[self.tank.index] = curLocation
self.goToPoint(self.tank, target)
if(curtime - self.time > 2.25 ):
command = Command(self.tank.index,0,0,False)
self.commands = []
self.commands.append(command)
self.bzrc.do_commands(self.commands)
self.commands = []
self.getObservation(self.tank)
self.time = time.time()
def query_point_by_symmetry(self, point, init_fig=None):
# init_fig: will be subtracted from each result item
if init_fig is None:
init_fig = set()
init_fig = set(init_fig)
point_vector = (point.x.numerator, point.x.denominator,
point.y.numerator, point.y.denominator)
mat_table = common.get_symmetry_matrix_table()
result = []
for mat in mat_table:
p = common.apply_mat_on_point(point_vector, mat)
p0 = Point(Fraction(p[0], p[1]), Fraction(p[2], p[3]))
fig_list = self.query_point(p0)
for fig in fig_list:
fig0 = common.apply_mat_on_figure(
fig, common.inv_for_symmetry_mat(mat))
result.append(tuple(set(fig0) - init_fig))
shortest = min(map(len, result))
result = [r for r in result if len(r) == shortest]
result = list(set(result))
result.sort()
return result
def build_list(self, difficulty=1):
if not self.ranking_available():
difficulty = 1.1
min_rank = max(10, difficulty * max(self.ranks))
return {((clean_city(city), hint), Point(lon, lat))
for city, hint, lon, lat, rank in zip(
self.places, self.hints, self.lons, self.lats, self.ranks)
if rank <= min_rank}
def getRandomCoordinate(self, tankIndex):
#1 and 2: quadrant 1 (x,y)
if tankIndex == 1 or tankIndex == 2:
rval = Point(random.randrange(0,400), random.randrange(0,400))
#3 and 4: quadrant 2 (x,-y)
elif tankIndex == 3 or tankIndex == 4:
rval = Point(random.randrange(0,400), random.randrange(-400,0))
#5 and 6: quadrant 3 (-x,-y)
elif tankIndex == 5 or tankIndex == 6:
rval = Point(random.randrange(-400,0), random.randrange(-400,0))
#7 and 8: quadrant 4 (-x, y)
elif tankIndex == 7 or tankIndex == 8:
rval = Point(random.randrange(-400,0), random.randrange(0,400))
#9 and 10: middle ([-200,200],[-200,200])
else:
rval = Point(random.randrange(-200,200), random.randrange(-200,200))
return rval
def __init__(self, bzrc, enemy):
self.bzrc = bzrc
self.constants = self.bzrc.get_constants()
self.commands = []
self.throttle = .15
self.obstacles = []
self.fields = Calculations()
self.flagIndex = self.getIndex()
self.base = bzrc.get_bases()[self.flagIndex]
self.enemyFlag = int(enemy)
c1 = Point(self.base.corner1_x, self.base.corner1_y)
c3 = Point(self.base.corner3_x, self.base.corner3_y)
line = Line(c1, c3)
self.base = line.getMidpoint()
for obs in bzrc.get_obstacles():
self.obstacles.append(Obstacle(obs))
def add(self, source):
""" add the effect of a single source to the noise map """
for i in range(len(self.recx)):
for j in range(len(self.recy)):
receiver = Receiver(
position=Point(self.recx[i], self.recy[j], self.recz))
level = pmodel.immission(
source, receiver).laeq() # A-weighted SPL at receiver
self.energy[i, j] += fromdB(level)
def get_latlng_from_tile_at(self, bounds, x, y):
scale = self.size / float(self.zoom_scale)
ne = bounds.getNorthEast()
sw = bounds.getSouthWest()
top_right = projection.fromLatLngToPoint(ne)
bottom_left = projection.fromLatLngToPoint(sw)
point = Point(float(x) * scale + bottom_left.x, float(y) * scale + top_right.y)
return projection.fromPointToLatLng(point)
def receivers(self):
""" construct a list of receivers based on the stored parameters """
if not hasattr(self, '_receivers'):
locs = self.get('receivers-locations').strip()
if len(locs) == 0:
self._receivers = []
else:
points = [Point(x) for x in locs.split(';')]
self._receivers = [Receiver(p) for p in points]
return self._receivers
def read_track(fname, max_points=None):
k = read_kml(fname)
track = extract_track(k)
name = track.name
points = []
for line in track.geometry.geoms:
for lon, lat, alt in line.coords:
points.append(Point(lon, lat))
if max_points is not None and len(points) == max_points:
return points, name
return points, name
def clear(self):
""" clear the simulation """
self._t = 0.0
self._passbys = [] # list with vehicle passbys (at the origin)
self._passbytimes = {1: [], 3: []}
self._history = [] # history of vehicles at each timestep
self._factory = VehicleFactory(fleet=self._fleet,
position=Point(self._xmin, 0.0, 0.0),
direction=Direction(bearing=0.0),
speed=self._vlimit,
acceleration=0.0)
def question_14_9(query):
# get Point B
line1 = Line.get_line_contains_points(Point(-3, 1), Point(3, 3))
line2 = Line(1, 0, -2)
B = line1.get_cross_point(line2)
# get Point A
line1 = Line.get_line_contains_points(Point(2, -3), Point(3, 0))
line2 = Line(0, 1, -1)
A = line1.get_cross_point(line2)
del line1, line2
# get the midpoint
target_point_x = (A.x + B.x) / 2
target_point_y = (A.y + B.y) / 2
point_target = Point(target_point_x, target_point_y, "Target")
# query and show it(them)
solution = query.query_point_by_symmetry(point_target)
init_figure, grid_size = common.INIT_FIGURE(), common.GRID_SIZE()
for r in solution:
draw_resolution(r, [point_target], init_figure, grid_size)
def question_27_14(query):
total = []
total.append(centroid_of_2x2_grid(-2, +2, (True, True, True, False)))
total.append(centroid_of_2x2_grid(+2, +2, (True, True, False, True)))
total.append(centroid_of_2x2_grid(-2, -2, (True, False, True, True)))
total.append(centroid_of_2x2_grid(+2, -2, (True, True, True, False)))
x, y, w = centroid_of_iter(total)
point_target = Point(x, y, "Target")
solution = query.query_point_by_symmetry(point_target)
init_figure, grid_size = common.INIT_FIGURE(), common.GRID_SIZE()
for r in solution:
draw_resolution(r, [point_target], init_figure, grid_size)
def question_24_6(query):
A = Point(1, -2)
B = Point(0, 1)
line1 = Line.get_line_contains_points(A, B)
line2 = Line.get_line_by_point_slope(A, Fraction(4))
tan_alpha = Fraction(1, 4)
tan_beta = Fraction(1, 3)
tan_theta = tan_of_add(tan_of_double(tan_beta), tan_alpha)
slope = tan_of_neg(tan_of_complementary(tan_theta))
line = Line.get_line_by_point_slope(A, slope)
# query and show it(them)
init_figure, grid_size = common.INIT_FIGURE(), common.GRID_SIZE()
init_figure.extend(((line1.a, line1.b, line1.c), (line2.a, line2.b, line2.c)))
solution = query.query_line_by_symmetry(line, (A,), init_figure)
points = set([s[0] for s in solution])
for r in solution[:4]:
p, fig = r
draw_resolution(fig, points, init_figure, grid_size)
def sources(self, vehicle, road = ReferenceRoadsurface()):
# calculate position of sources
pos = vehicle.position()
h = {1: (0.01, 0.30), 2: (0.01, 0.75), 3: (0.01, 0.75), 4: (0.30, 0.30), 5: (0.30, 0.30)}[vehicle.cat()]
lopos = Point(pos[0], pos[1], pos[2] + h[0])
hipos = Point(pos[0], pos[1], pos[2] + h[1])
# calculate rolling and propulsion noise
r = fromdB(self.rollingNoise(vehicle, road))
p = fromdB(self.propulsionNoise(vehicle, road))
# calculate low and high noise
lonoise = 10.0*numpy.log10(0.8*r + 0.2*p)
hinoise = 10.0*numpy.log10(0.2*r + 0.8*p)
# construct sources
return [Source(position = lopos,
direction = asDirection(vehicle.direction()),
emission = TertsBandSpectrum(lonoise),
directivity = ImagineDirectivity(cat = vehicle.cat(), h = h[0])),
Source(position = hipos,
direction = asDirection(vehicle.direction()),
emission = TertsBandSpectrum(hinoise),
directivity = ImagineDirectivity(cat = vehicle.cat(), h = h[1]))]
def question_14_8(query):
# get Point B
line1 = Line.get_line_contains_points(Point(-3, -1), Point(3, -3))
line_tmp = Line(1, 0, -2)
B = line1.get_cross_point(line_tmp)
# get Point A
line2 = Line.get_line_contains_points(Point(-3, 1), Point(2, 2))
line_tmp = Line(1, 0, 1)
A = line2.get_cross_point(line_tmp)
line = Line.get_line_contains_points(A, B)
point_target = A.middle(B)
# query and show it(them)
init_figure, grid_size = common.INIT_FIGURE(), common.GRID_SIZE()
for ll in (line, line1, line2):
init_figure.append((ll.a, ll.b, ll.c))
solution = query.query_point_by_symmetry(point_target, init_figure)
for r in solution[:4]:
draw_resolution(r, [point_target], init_figure, grid_size)
def createVehicle(self, tokens):
""" create a Vehicle object based on the supplied logger line tokens """
# fetch vehicle parameters
vID = int(tokens[0])
vLength = float(tokens[1])
vPosition = Point(float(tokens[2]), float(tokens[3]))
vDirection = Direction(bearing = float(tokens[4]))
vSpeed = float(tokens[5])
vAcceleration = float(tokens[6])
# simple check between light and heavy vehicles
vClass = {False: LightVehicle, True: HeavyVehicle}[(vLength>10.0)]
return vClass(vid = vID, position = vPosition, direction = vDirection, speed = vSpeed, acceleration = vAcceleration)
def __init__(self, db_file_name='new_graph.db'):
try:
self.db_file_name = db_file_name
self.connect = sqlite3.connect("file:" + self.db_file_name +
"?mode=ro",
uri=True)
self.query_point(Point(Fraction(0), Fraction(0)))
except Exception:
try:
self.connect.close()
except:
pass
raise FileNotFoundError
def question_24_7(query):
A = Point(2, -1)
B = Point(-2, 0)
line1 = Line.get_line_contains_points(A, B)
line2 = Line.get_line_by_point_slope(A, Fraction(-1))
tan_alpha = Fraction(1, 4)
tan_beta = tan_of_sub(Fraction(1), tan_alpha)
tan_theta = tan_of_sub(tan_beta, tan_alpha)
slope = tan_theta
line = Line.get_line_by_point_slope(A, slope)
# query and show it(them)
init_figure, grid_size = common.INIT_FIGURE(), common.GRID_SIZE()
init_figure.extend(((line1.a, line1.b, line1.c), (line2.a, line2.b, line2.c)))
solution = query.query_line_by_symmetry(line, (A,), init_figure)
points = set([s[0] for s in solution])
for r in solution[:4]:
p, fig = r
draw_resolution(fig, points, init_figure, grid_size)