def predict_CarSim(L0, V0, dt, ept, g, cf=CAR_FRICTION):
st = CarState(Vector3(*L0), Vector3(*V0))
pt = []
for i in range(int(dt / ept)):
st = PhyLib.carStep(st, ept, g, cf) # calling the step function
pt.append([a3(st.Location), a3(st.Velocity)])
# if dt%ept>0:
st = PhyLib.carStep(st, dt % ept, g, cf)
pt.append([a3(st.Location), a3(st.Velocity)])
return pt
def bot_composite(s):
GeneralInfo(s)
safe_haven = set_dist(
s.bL, s.ogoal,
min(dist3d(s.bL, s.ogoal) - 200,
dist3d(s.pV) + 1200, 2000))
safe_haven[2] = 0
# obehind = dist3d(s.oL, s.ogoal) < dist3d(s.bL, s.ogoal)
behind = dist3d(s.pL, s.ogoal) > dist3d(s.bL, s.ogoal)
infront = s.bL[1] * s.color > s.pL[1] * s.color + 99
# blockable = min(abs(s.oglinex), abs(s.obglinex)) < 999 and s.bV[1] * s.color < 0 and infront
if s.kickoff:
bot_default(s)
else:
if s.bL[2] > 500 and s.pL[2] > 60 and dist3d(
s.bL, s.goal) < 4500 and s.poG and not infront:
go_to(s, (safe_haven + s.bL) * a3([1, 1, 0]))
elif dist3d(s.ogoal, s.bL) < 3000 or (s.bV[1] * s.color < 0
and s.pV[1] * s.color < 0
and s.bd < 999):
bot_fastbot(s)
else:
if not behind and dist3d(s.oL, s.bL) > 2000:
bot_shooter(s)
else:
bot_default(s)
def predict_CarLoc(L0, V0, dt, ept, g, cf=CAR_FRICTION):
st = CarState(Vector3(*L0), Vector3(*V0))
for i in range(int(dt / ept)):
st = PhyLib.carStep(st, ept, g, cf) # calling the step function
# if dt%ept>0:
st = PhyLib.carStep(st, dt % ept, g, cf)
return a3(st.Location)
def state_ca3(st): # nested numpy array
return [a3(st.Location), a3(st.Velocity)]
def state_ba3(st): # nested numpy array
return [a3(st.Location), a3(st.Velocity), a3(st.AngularVelocity)]