def _SolveForT0(A, B, t, tInterval):
"""There are four solutions to the equation (including complex ones). Here we use x instead of t0
for convenience.
"""
"""
SolveQuartic(2*A, -4*A*T + 2*B, 3*A*T**2 - 3*B*T, -A*T**3 + 3*B*T**2, -B*T**3)
"""
if (Abs(A) < epsilon):
def f(x):
return Mul(number('2'), B) * x * x * x - Prod(
[number('3'), B, t]) * x * x + Prod(
[number('3'), B, t, t]) * x - Mul(B, mp.power(t, 3))
sols = [mp.findroot(f, x0=0.5 * t)]
else:
sols = SolveQuartic(
Add(A, A), Add(Prod([number('-4'), A, t]), Mul(number('2'), B)),
Sub(Prod([number('3'), A, t, t]), Prod([number('3'), B, t])),
Sub(Prod([number('3'), B, t, t]), Mul(A, mp.power(t, 3))),
Neg(Mul(B, mp.power(t, 3))))
realSols = [
sol for sol in sols if type(sol) is mp.mpf and sol in tInterval
]
if len(realSols) > 1:
# I think this should not happen. We should either have one or no solution.
raise NotImplementedError
elif len(realSols) == 0:
return None
else:
return realSols[0]
def _SolveForT0(A, B, t, tInterval):
"""There are four solutions to the equation (including complex ones). Here we use x instead of t0
for convenience.
"""
"""
SolveQuartic(2*A, -4*A*T + 2*B, 3*A*T**2 - 3*B*T, -A*T**3 + 3*B*T**2, -B*T**3)
"""
if (Abs(A) < epsilon):
def f(x):
return Mul(number('2'), B)*x*x*x - Prod([number('3'), B, t])*x*x + Prod([number('3'), B, t, t])*x - Mul(B, mp.power(t, 3))
sols = [mp.findroot(f, x0=0.5*t)]
else:
sols = SolveQuartic(Add(A, A),
Add(Prod([number('-4'), A, t]), Mul(number('2'), B)),
Sub(Prod([number('3'), A, t, t]), Prod([number('3'), B, t])),
Sub(Prod([number('3'), B, t, t]), Mul(A, mp.power(t, 3))),
Neg(Mul(B, mp.power(t, 3))))
realSols = [sol for sol in sols if type(sol) is mp.mpf and sol in tInterval]
if len(realSols) > 1:
# I think this should not happen. We should either have one or no solution.
raise NotImplementedError
elif len(realSols) == 0:
return None
else:
return realSols[0]
def Interpolate1DFixedDuration(x0, x1, v0, v1, newDuration, vm, am):
x0 = ConvertFloatToMPF(x0)
x1 = ConvertFloatToMPF(x1)
v0 = ConvertFloatToMPF(v0)
v1 = ConvertFloatToMPF(v1)
vm = ConvertFloatToMPF(vm)
am = ConvertFloatToMPF(am)
newDuration = ConvertFloatToMPF(newDuration)
log.debug("\nx0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; newDuration = {6}".\
format(mp.nstr(x0, n=_prec), mp.nstr(x1, n=_prec), mp.nstr(v0, n=_prec), mp.nstr(v1, n=_prec),
mp.nstr(vm, n=_prec), mp.nstr(am, n=_prec), mp.nstr(newDuration, n=_prec)))
# Check inputs
assert (vm > zero)
assert (am > zero)
if (newDuration < -epsilon):
return ParabolicCurve()
if (newDuration <= epsilon):
# Check if this is a stationary trajectory
if (FuzzyEquals(x0, x1, epsilon) and FuzzyEquals(v0, v1, epsilon)):
ramp0 = Ramp(v0, 0, 0, x0)
newCurve = ParabolicCurve(ramp0)
return newCurve
else:
# newDuration is too short to any movement to be made
return ParabolicCurve()
d = Sub(x1, x0)
# First assume no velocity bound -> re-interpolated trajectory will have only two ramps.
# Solve for a0 and a1 (the acceleration of the first and the last ramps).
# a0 = A + B/t0
# a1 = A + B/(t - t0)
# where t is the (new) total duration, t0 is the (new) duration of the first ramp, and
# A = (v1 - v0)/t
# B = (2d/t) - (v0 + v1).
newDurInverse = mp.fdiv(one, newDuration)
A = Mul(Sub(v1, v0), newDurInverse)
B = Sub(Prod([mp.mpf('2'), d, newDurInverse]), Add(v0, v1))
interval0 = iv.mpf([zero, newDuration]) # initial interval for t0
# Now consider the interval(s) computed from a0's constraints
sum1 = Neg(Add(am, A))
sum2 = Sub(am, A)
C = mp.fdiv(B, sum1)
D = mp.fdiv(B, sum2)
log.debug("\nA = {0}; \nB = {1}; \nC = {2}; \nD = {3}; \nsum1 = {4}; \nsum2 = {5};".\
format(mp.nstr(A, n=_prec), mp.nstr(B, n=_prec), mp.nstr(C, n=_prec), mp.nstr(D, n=_prec),
mp.nstr(sum1, n=_prec), mp.nstr(sum2, n=_prec)))
if (sum1 > zero):
# This implied that the duration is too short
log.debug("the given duration ({0}) is too short.".format(newDuration))
return ParabolicCurve()
if (sum2 < zero):
# This implied that the duration is too short
log.debug("the given duration ({0}) is too short.".format(newDuration))
return ParabolicCurve()
if IsEqual(sum1, zero):
raise NotImplementedError # not yet considered
elif sum1 > epsilon:
log.debug("sum1 > 0. This implies that newDuration is too short.")
return ParabolicCurve()
else:
interval1 = iv.mpf([C, inf])
if IsEqual(sum2, zero):
raise NotImplementedError # not yet considered
elif sum2 > epsilon:
interval2 = iv.mpf([D, inf])
else:
log.debug("sum2 < 0. This implies that newDuration is too short.")
return ParabolicCurve()
if Sub(interval2.a, interval1.b) > epsilon or Sub(interval1.a,
interval2.b) > epsilon:
# interval1 and interval2 do not intersect each other
return ParabolicCurve()
# interval3 = interval1 \cap interval2 : valid interval for t0 computed from a0's constraints
interval3 = iv.mpf(
[max(interval1.a, interval2.a),
min(interval1.b, interval2.b)])
# Now consider the interval(s) computed from a1's constraints
if IsEqual(sum1, zero):
raise NotImplementedError # not yet considered
elif sum1 > epsilon:
log.debug("sum1 > 0. This implies that newDuration is too short.")
return ParabolicCurve()
else:
interval4 = iv.mpf([Neg(inf), Add(C, newDuration)])
if IsEqual(sum2, zero):
raise NotImplementedError # not yet considered
elif sum2 > epsilon:
interval5 = iv.mpf([Neg(inf), Add(D, newDuration)])
else:
log.debug("sum2 < 0. This implies that newDuration is too short.")
return ParabolicCurve()
if Sub(interval5.a, interval4.b) > epsilon or Sub(interval4.a,
interval5.b) > epsilon:
log.debug("interval4 and interval5 do not intersect each other")
return ParabolicCurve()
# interval6 = interval4 \cap interval5 : valid interval for t0 computed from a1's constraints
interval6 = iv.mpf(
[max(interval4.a, interval5.a),
min(interval4.b, interval5.b)])
# import IPython; IPython.embed()
if Sub(interval3.a, interval6.b) > epsilon or Sub(interval6.a,
interval3.b) > epsilon:
log.debug("interval3 and interval6 do not intersect each other")
return ParabolicCurve()
# interval7 = interval3 \cap interval6
interval7 = iv.mpf(
[max(interval3.a, interval6.a),
min(interval3.b, interval6.b)])
if Sub(interval0.a, interval7.b) > epsilon or Sub(interval7.a,
interval0.b) > epsilon:
log.debug("interval0 and interval7 do not intersect each other")
return ParabolicCurve()
# interval8 = interval0 \cap interval7 : valid interval of t0 when considering all constraints (from a0 and a1)
interval8 = iv.mpf(
[max(interval0.a, interval7.a),
min(interval0.b, interval7.b)])
# import IPython; IPython.embed()
# We choose the value t0 (the duration of the first ramp) by selecting the mid point of the
# valid interval of t0.
t0 = _SolveForT0(A, B, newDuration, interval8)
if t0 is None:
# The fancy procedure fails. Now consider no optimization whatsoever.
# TODO: Figure out why solving fails.
t0 = mp.convert(interval8.mid) # select the midpoint
# return ParabolicCurve()
t1 = Sub(newDuration, t0)
a0 = Add(A, Mul(mp.fdiv(one, t0), B))
if (Abs(t1) < epsilon):
a1 = zero
else:
a1 = Add(A, Mul(mp.fdiv(one, Neg(t1)), B))
assert (Sub(Abs(a0), am) < epsilon
) # check if a0 is really below the bound
assert (Sub(Abs(a1), am) < epsilon
) # check if a1 is really below the bound
# import IPython; IPython.embed()
# Check if the velocity bound is violated
vp = Add(v0, Mul(a0, t0))
if Abs(vp) > vm:
vmnew = Mul(mp.sign(vp), vm)
D2 = Prod([
pointfive,
Sqr(Sub(vp, vmnew)),
Sub(mp.fdiv(one, a0), mp.fdiv(one, a1))
])
# print "D2",
# mp.nprint(D2, n=_prec)
# print "vmnew",
# mp.nprint(vmnew, n=_prec)
A2 = Sqr(Sub(vmnew, v0))
B2 = Neg(Sqr(Sub(vmnew, v1)))
t0trimmed = mp.fdiv(Sub(vmnew, v0), a0)
t1trimmed = mp.fdiv(Sub(v1, vmnew), a1)
C2 = Sum([
Mul(t0trimmed, Sub(vmnew, v0)),
Mul(t1trimmed, Sub(vmnew, v1)),
Mul(mp.mpf('-2'), D2)
])
log.debug("\nA2 = {0}; \nB2 = {1}; \nC2 = {2}; \nD2 = {3};".format(
mp.nstr(A2, n=_prec), mp.nstr(B2, n=_prec), mp.nstr(C2, n=_prec),
mp.nstr(D2, n=_prec)))
temp = Prod([A2, B2, B2])
initguess = mp.sign(temp) * (Abs(temp)**(1. / 3.))
root = mp.findroot(lambda x: Sub(Prod([x, x, x]), temp), x0=initguess)
# import IPython; IPython.embed()
log.debug("root = {0}".format(mp.nstr(root, n=_prec)))
a0new = mp.fdiv(Add(A2, root), C2)
if (Abs(a0new) > Add(am, epsilon)):
if FuzzyZero(Sub(Mul(C2, a0new), A2), epsilon):
# The computed a0new is exceeding the bound and its corresponding a1new is
# zero. Therefore, there is no other way to fix this. This is probably because the
# given newDuration is less than the minimum duration (x0, x1, v0, v1, vm, am) can
# get.
log.debug(
"abs(a0new) > am and a1new = 0; Cannot fix this case. This happens probably because the given newDuration is too short."
)
return ParabolicCurve()
a0new = Mul(mp.sign(a0new), am)
if (Abs(a0new) < epsilon):
a1new = mp.fdiv(B2, C2)
if (Abs(a1new) > Add(am, epsilon)):
# Similar to the case above
log.debug(
"a0new = 0 and abs(a1new) > am; Cannot fix this case. This happens probably because the given newDuration is too short."
)
return ParabolicCurve()
else:
if FuzzyZero(Sub(Mul(C2, a0new), A2), epsilon):
# import IPython; IPython.embed()
a1new = 0
else:
a1new = Mul(mp.fdiv(B2, C2),
Add(one, mp.fdiv(A2, Sub(Mul(C2, a0new), A2))))
if (Abs(a1new) > Add(am, epsilon)):
a1new = Mul(mp.sign(a1new), am)
a0new = Mul(mp.fdiv(A2, C2),
Add(one, mp.fdiv(B2, Sub(Mul(C2, a1new), B2))))
if (Abs(a0new) > Add(am, epsilon)) or (Abs(a1new) > Add(am, epsilon)):
log.warn("Cannot fix acceleration bounds violation")
return ParabolicCurve()
log.debug(
"\na0 = {0}; \na0new = {1}; \na1 = {2}; \na1new = {3};".format(
mp.nstr(a0, n=_prec), mp.nstr(a0new, n=_prec),
mp.nstr(a1, n=_prec), mp.nstr(a1new, n=_prec)))
if (Abs(a0new) < epsilon) and (Abs(a1new) < epsilon):
log.warn("Both accelerations are zero. Should we allow this case?")
return ParabolicCurve()
if (Abs(a0new) < epsilon):
# This is likely because v0 is at the velocity bound
t1new = mp.fdiv(Sub(v1, vmnew), a1new)
assert (t1new > 0)
ramp2 = Ramp(v0, a1new, t1new)
t0new = Sub(newDuration, t1new)
assert (t0new > 0)
ramp1 = Ramp(v0, zero, t0new, x0)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
elif (Abs(a1new) < epsilon):
t0new = mp.fdiv(Sub(vmnew, v0), a0new)
assert (t0new > 0)
ramp1 = Ramp(v0, a0new, t0new, x0)
t1new = Sub(newDuration, t0new)
assert (t1new > 0)
ramp2 = Ramp(ramp1.v1, zero, t1new)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
else:
# No problem with those new accelerations
# import IPython; IPython.embed()
t0new = mp.fdiv(Sub(vmnew, v0), a0new)
if (t0new < 0):
log.debug(
"t0new < 0. The given newDuration not achievable with the given bounds"
)
return ParabolicCurve()
t1new = mp.fdiv(Sub(v1, vmnew), a1new)
if (t1new < 0):
log.debug(
"t1new < 0. The given newDuration not achievable with the given bounds"
)
return ParabolicCurve()
if (Add(t0new, t1new) > newDuration):
# Final fix. Since we give more weight to acceleration bounds, we make the velocity
# bound saturated. Therefore, we set vp to vmnew.
# import IPython; IPython.embed()
if FuzzyZero(A, epsilon):
log.warn(
"(final fix) A is zero. Don't know how to fix this case"
)
return ParabolicCurve()
t0new = mp.fdiv(Sub(Sub(vmnew, v0), B), A)
if (t0new < 0):
log.debug("(final fix) t0new is negative")
return ParabolicCurve()
t1new = Sub(newDuration, t0new)
a0new = Add(A, Mul(mp.fdiv(one, t0new), B))
a1new = Add(A, Mul(mp.fdiv(one, Neg(t1new)), B))
ramp1 = Ramp(v0, a0new, t0new, x0)
ramp2 = Ramp(ramp1.v1, a1new, t1new)
newCurve = ParabolicCurve([ramp1, ramp2])
else:
ramp1 = Ramp(v0, a0new, t0new, x0)
ramp3 = Ramp(ramp1.v1, a1new, t1new)
ramp2 = Ramp(ramp1.v1, zero, Sub(newDuration,
Add(t0new, t1new)))
newCurve = ParabolicCurve([ramp1, ramp2, ramp3])
# import IPython; IPython.embed()
return newCurve
else:
ramp1 = Ramp(v0, a0, t0, x0)
ramp2 = Ramp(ramp1.v1, a1, t1)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
def Ht_complex_root_finder(complex_guess, t):
result = mp.findroot(lambda z: Ht_complex(z, t), mp.mpc(complex_guess))
return result
def Interpolate1DFixedDuration(x0, x1, v0, v1, newDuration, vm, am):
x0 = ConvertFloatToMPF(x0)
x1 = ConvertFloatToMPF(x1)
v0 = ConvertFloatToMPF(v0)
v1 = ConvertFloatToMPF(v1)
vm = ConvertFloatToMPF(vm)
am = ConvertFloatToMPF(am)
newDuration = ConvertFloatToMPF(newDuration)
log.debug("\nx0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; newDuration = {6}".\
format(mp.nstr(x0, n=_prec), mp.nstr(x1, n=_prec), mp.nstr(v0, n=_prec), mp.nstr(v1, n=_prec),
mp.nstr(vm, n=_prec), mp.nstr(am, n=_prec), mp.nstr(newDuration, n=_prec)))
# Check inputs
assert(vm > zero)
assert(am > zero)
if (newDuration < -epsilon):
log.info("duration = {0} is negative".format(newDuration))
return ParabolicCurve()
if (newDuration <= epsilon):
# Check if this is a stationary trajectory
if (FuzzyEquals(x0, x1, epsilon) and FuzzyEquals(v0, v1, epsilon)):
log.info("stationary trajectory")
ramp0 = Ramp(v0, 0, 0, x0)
newCurve = ParabolicCurve(ramp0)
return newCurve
else:
log.info("newDuration is too short for any movement to be made")
return ParabolicCurve()
# Correct small discrepancies if any
if (v0 > vm):
if FuzzyEquals(v0, vm, epsilon):
v0 = vm
else:
log.info("v0 > vm: {0} > {1}".format(v0, vm))
return ParabolicCurve()
elif (v0 < -vm):
if FuzzyEquals(v0, -vm, epsilon):
v0 = -vm
else:
log.info("v0 < -vm: {0} < {1}".format(v0, -vm))
return ParabolicCurve()
if (v1 > vm):
if FuzzyEquals(v1, vm, epsilon):
v1 = vm
else:
log.info("v1 > vm: {0} > {1}".format(v1, vm))
return ParabolicCurve()
elif (v1 < -vm):
if FuzzyEquals(v1, -vm, epsilon):
v1 = -vm
else:
log.info("v1 < -vm: {0} < {1}".format(v1, -vm))
return ParabolicCurve()
d = Sub(x1, x0)
# First assume no velocity bound -> re-interpolated trajectory will have only two ramps.
# Solve for a0 and a1 (the acceleration of the first and the last ramps).
# a0 = A + B/t0
# a1 = A + B/(t - t0)
# where t is the (new) total duration, t0 is the (new) duration of the first ramp, and
# A = (v1 - v0)/t
# B = (2d/t) - (v0 + v1).
newDurInverse = mp.fdiv(one, newDuration)
A = Mul(Sub(v1, v0), newDurInverse)
B = Sub(Prod([mp.mpf('2'), d, newDurInverse]), Add(v0, v1))
interval0 = iv.mpf([zero, newDuration]) # initial interval for t0
# Now consider the interval(s) computed from a0's constraints
sum1 = Neg(Add(am, A))
sum2 = Sub(am, A)
C = mp.fdiv(B, sum1)
D = mp.fdiv(B, sum2)
log.debug("\nA = {0}; \nB = {1}; \nC = {2}; \nD = {3}; \nsum1 = {4}; \nsum2 = {5};".\
format(mp.nstr(A, n=_prec), mp.nstr(B, n=_prec), mp.nstr(C, n=_prec), mp.nstr(D, n=_prec),
mp.nstr(sum1, n=_prec), mp.nstr(sum2, n=_prec)))
if (sum1 > zero):
# This implied that the duration is too short
log.debug("the given duration ({0}) is too short.".format(newDuration))
return ParabolicCurve()
if (sum2 < zero):
# This implied that the duration is too short
log.debug("the given duration ({0}) is too short.".format(newDuration))
return ParabolicCurve()
if IsEqual(sum1, zero):
raise NotImplementedError # not yet considered
elif sum1 > epsilon:
log.debug("sum1 > 0. This implies that newDuration is too short.")
return ParabolicCurve()
else:
interval1 = iv.mpf([C, inf])
if IsEqual(sum2, zero):
raise NotImplementedError # not yet considered
elif sum2 > epsilon:
interval2 = iv.mpf([D, inf])
else:
log.debug("sum2 < 0. This implies that newDuration is too short.")
return ParabolicCurve()
if Sub(interval2.a, interval1.b) > epsilon or Sub(interval1.a, interval2.b) > epsilon:
# interval1 and interval2 do not intersect each other
return ParabolicCurve()
# interval3 = interval1 \cap interval2 : valid interval for t0 computed from a0's constraints
interval3 = iv.mpf([max(interval1.a, interval2.a), min(interval1.b, interval2.b)])
# Now consider the interval(s) computed from a1's constraints
if IsEqual(sum1, zero):
raise NotImplementedError # not yet considered
elif sum1 > epsilon:
log.debug("sum1 > 0. This implies that newDuration is too short.")
return ParabolicCurve()
else:
interval4 = iv.mpf([Neg(inf), Add(C, newDuration)])
if IsEqual(sum2, zero):
raise NotImplementedError # not yet considered
elif sum2 > epsilon:
interval5 = iv.mpf([Neg(inf), Add(D, newDuration)])
else:
log.debug("sum2 < 0. This implies that newDuration is too short.")
return ParabolicCurve()
if Sub(interval5.a, interval4.b) > epsilon or Sub(interval4.a, interval5.b) > epsilon:
log.debug("interval4 and interval5 do not intersect each other")
return ParabolicCurve()
# interval6 = interval4 \cap interval5 : valid interval for t0 computed from a1's constraints
interval6 = iv.mpf([max(interval4.a, interval5.a), min(interval4.b, interval5.b)])
# import IPython; IPython.embed()
if Sub(interval3.a, interval6.b) > epsilon or Sub(interval6.a, interval3.b) > epsilon:
log.debug("interval3 and interval6 do not intersect each other")
return ParabolicCurve()
# interval7 = interval3 \cap interval6
interval7 = iv.mpf([max(interval3.a, interval6.a), min(interval3.b, interval6.b)])
if Sub(interval0.a, interval7.b) > epsilon or Sub(interval7.a, interval0.b) > epsilon:
log.debug("interval0 and interval7 do not intersect each other")
return ParabolicCurve()
# interval8 = interval0 \cap interval7 : valid interval of t0 when considering all constraints (from a0 and a1)
interval8 = iv.mpf([max(interval0.a, interval7.a), min(interval0.b, interval7.b)])
# import IPython; IPython.embed()
# We choose the value t0 (the duration of the first ramp) by selecting the mid point of the
# valid interval of t0.
t0 = _SolveForT0(A, B, newDuration, interval8)
if t0 is None:
# The fancy procedure fails. Now consider no optimization whatsoever.
# TODO: Figure out why solving fails.
t0 = mp.convert(interval8.mid) # select the midpoint
# return ParabolicCurve()
t1 = Sub(newDuration, t0)
a0 = Add(A, Mul(mp.fdiv(one, t0), B))
if (Abs(t1) < epsilon):
a1 = zero
else:
a1 = Add(A, Mul(mp.fdiv(one, Neg(t1)), B))
assert(Sub(Abs(a0), am) < epsilon) # check if a0 is really below the bound
assert(Sub(Abs(a1), am) < epsilon) # check if a1 is really below the bound
# import IPython; IPython.embed()
# Check if the velocity bound is violated
vp = Add(v0, Mul(a0, t0))
if Abs(vp) > vm:
vmnew = Mul(mp.sign(vp), vm)
D2 = Prod([pointfive, Sqr(Sub(vp, vmnew)), Sub(mp.fdiv(one, a0), mp.fdiv(one, a1))])
# print("D2", end=' ')
# mp.nprint(D2, n=_prec)
# print("vmnew", end=' ')
# mp.nprint(vmnew, n=_prec)
A2 = Sqr(Sub(vmnew, v0))
B2 = Neg(Sqr(Sub(vmnew, v1)))
t0trimmed = mp.fdiv(Sub(vmnew, v0), a0)
t1trimmed = mp.fdiv(Sub(v1, vmnew), a1)
C2 = Sum([Mul(t0trimmed, Sub(vmnew, v0)), Mul(t1trimmed, Sub(vmnew, v1)), Mul(mp.mpf('-2'), D2)])
log.debug("\nA2 = {0}; \nB2 = {1}; \nC2 = {2}; \nD2 = {3};".format(mp.nstr(A2, n=_prec), mp.nstr(B2, n=_prec), mp.nstr(C2, n=_prec), mp.nstr(D2, n=_prec)))
temp = Prod([A2, B2, B2])
initguess = mp.sign(temp)*(Abs(temp)**(1./3.))
root = mp.findroot(lambda x: Sub(Prod([x, x, x]), temp), x0=initguess)
# import IPython; IPython.embed()
log.debug("root = {0}".format(mp.nstr(root, n=_prec)))
a0new = mp.fdiv(Add(A2, root), C2)
if (Abs(a0new) > Add(am, epsilon)):
if FuzzyZero(Sub(Mul(C2, a0new), A2), epsilon):
# The computed a0new is exceeding the bound and its corresponding a1new is
# zero. Therefore, there is no other way to fix this. This is probably because the
# given newDuration is less than the minimum duration (x0, x1, v0, v1, vm, am) can
# get.
log.debug("abs(a0new) > am and a1new = 0; Cannot fix this case. This happens probably because the given newDuration is too short.")
return ParabolicCurve()
a0new = Mul(mp.sign(a0new), am)
if (Abs(a0new) < epsilon):
a1new = mp.fdiv(B2, C2)
if (Abs(a1new) > Add(am, epsilon)):
# Similar to the case above
log.debug("a0new = 0 and abs(a1new) > am; Cannot fix this case. This happens probably because the given newDuration is too short.")
return ParabolicCurve()
else:
if FuzzyZero(Sub(Mul(C2, a0new), A2), epsilon):
# import IPython; IPython.embed()
a1new = 0
else:
a1new = Mul(mp.fdiv(B2, C2), Add(one, mp.fdiv(A2, Sub(Mul(C2, a0new), A2))))
if (Abs(a1new) > Add(am, epsilon)):
a1new = Mul(mp.sign(a1new), am)
a0new = Mul(mp.fdiv(A2, C2), Add(one, mp.fdiv(B2, Sub(Mul(C2, a1new), B2))))
if (Abs(a0new) > Add(am, epsilon)) or (Abs(a1new) > Add(am, epsilon)):
log.warn("Cannot fix acceleration bounds violation")
return ParabolicCurve()
log.debug("\na0 = {0}; \na0new = {1}; \na1 = {2}; \na1new = {3};".format(mp.nstr(a0, n=_prec), mp.nstr(a0new, n=_prec), mp.nstr(a1, n=_prec), mp.nstr(a1new, n=_prec)))
if (Abs(a0new) < epsilon) and (Abs(a1new) < epsilon):
log.warn("Both accelerations are zero. Should we allow this case?")
return ParabolicCurve()
if (Abs(a0new) < epsilon):
# This is likely because v0 is at the velocity bound
t1new = mp.fdiv(Sub(v1, vmnew), a1new)
assert(t1new > 0)
ramp2 = Ramp(v0, a1new, t1new)
t0new = Sub(newDuration, t1new)
assert(t0new > 0)
ramp1 = Ramp(v0, zero, t0new, x0)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
elif (Abs(a1new) < epsilon):
t0new = mp.fdiv(Sub(vmnew, v0), a0new)
assert(t0new > 0)
ramp1 = Ramp(v0, a0new, t0new, x0)
t1new = Sub(newDuration, t0new)
assert(t1new > 0)
ramp2 = Ramp(ramp1.v1, zero, t1new)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
else:
# No problem with those new accelerations
# import IPython; IPython.embed()
t0new = mp.fdiv(Sub(vmnew, v0), a0new)
if (t0new < 0):
log.debug("t0new < 0. The given newDuration not achievable with the given bounds")
return ParabolicCurve()
t1new = mp.fdiv(Sub(v1, vmnew), a1new)
if (t1new < 0):
log.debug("t1new < 0. The given newDuration not achievable with the given bounds")
return ParabolicCurve()
if (Add(t0new, t1new) > newDuration):
# Final fix. Since we give more weight to acceleration bounds, we make the velocity
# bound saturated. Therefore, we set vp to vmnew.
# import IPython; IPython.embed()
if FuzzyZero(A, epsilon):
log.warn("(final fix) A is zero. Don't know how to fix this case")
return ParabolicCurve()
t0new = mp.fdiv(Sub(Sub(vmnew, v0), B), A)
if (t0new < 0):
log.debug("(final fix) t0new is negative")
return ParabolicCurve()
t1new = Sub(newDuration, t0new)
a0new = Add(A, Mul(mp.fdiv(one, t0new), B))
a1new = Add(A, Mul(mp.fdiv(one, Neg(t1new)), B))
ramp1 = Ramp(v0, a0new, t0new, x0)
ramp2 = Ramp(ramp1.v1, a1new, t1new)
newCurve = ParabolicCurve([ramp1, ramp2])
else:
ramp1 = Ramp(v0, a0new, t0new, x0)
ramp3 = Ramp(ramp1.v1, a1new, t1new)
ramp2 = Ramp(ramp1.v1, zero, Sub(newDuration, Add(t0new , t1new)))
newCurve = ParabolicCurve([ramp1, ramp2, ramp3])
# import IPython; IPython.embed()
return newCurve
else:
ramp1 = Ramp(v0, a0, t0, x0)
ramp2 = Ramp(ramp1.v1, a1, t1)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
def _getcusps(self):
"""Get cusps coordinates"""
# precision parameters
maxsteps = 300 # max iterations of mp.findroot
tol = 1e-20 # tolerance in mp.findroot
# compute (Ox) roots
witt = [
1., 2. * self.s, self.s**2 - 1., -2. * self.s / (1. + self.q),
-(self.s**2 / (1. + self.q))
]
zc = np.roots(witt)
# topology: close
if self.topo == 'close':
self._yl = []
method = 'illinois'
dico_interp = self._enveloppes()
# (Ox)
arg = np.argsort(np.imag(zc))
arg = arg[[1, 2]]
zc = zc[arg]
arg = np.argsort(np.real(zc))
sort = zc[arg]
## central caustic
# A, b3, [1, 1+q] -> Witt
ccA = complex(np.real(sort[1]))
# C, b4, [1, 1+1/q] -> Witt
ccC = complex(np.real(sort[0]))
# B, b3, [0, 1] -> poly
loglbdmin, loglbdmax = dico_interp[
'cb3B'] # Use interpolation on the envelopes to find the optimize search interval of lambda
loglbdmax = np.log10(1.)
self._br = 2
loglbd = mp.findroot(
self._fpoly, (mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method) # Use mpmath to compute lbd near 1
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccB = complex(bn[2]) * self.s
self._yl.append((3, lbd, ccB))
# D -> conj(B)
ccD = np.conj(ccB)
## secondary caustics
bn, sn = self._sortbnccu(0.)
zsadd = complex(bn[0])
# E, b1, [0, ∞] -> poly
loglbdmin, loglbdmax = dico_interp[
'cb1'] # Use interpolation on the envelopes to find the optimize search interval of lambda
self._br = 0
loglbd = mp.findroot(self._fpoly,
(mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method)
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccE = complex(bn[0])
self._yl.append((1, lbd, ccE))
ccE = ccE * self.s
# H -> conj(E)
ccH = np.conj(ccE)
# F, b3, [1+q, ∞] -> poly
loglbdmin, loglbdmax = dico_interp[
'cb3F'] # Use interpolation on the envelopes to find the optimize search interval of lambda
self._br = 2
loglbd = mp.findroot(self._fpoly,
(mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method)
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccF = complex(bn[2])
self._yl.append((3, lbd, ccF))
ccF = ccF * self.s
# I -> conj(F)
ccI = np.conj(ccF)
# G, b5, [1+1/q, ∞] -> poly
loglbdmin, loglbdmax = dico_interp[
'cb5'] # Use interpolation on the envelopes to find the optimize search interval of lambda
self._br = 4
loglbd = mp.findroot(self._fpoly,
(mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method)
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccG = complex(bn[4])
self._yl.append((5, lbd, ccG))
ccG = ccG * self.s
# J -> conj(G)
ccJ = np.conj(ccG)
## store cusps
zcu = np.array([ccA, ccB, ccC, ccD])
self.cusps['central'] = zcu - 1. / (
1. + self.q) * (1. / np.conj(zcu) + self.q /
(np.conj(zcu) + self.s)) + self._AC2CM
zcu = np.array([ccE, ccF, ccG])
self.cusps['secondary_up'] = zcu - 1. / (
1. + self.q) * (1. / np.conj(zcu) + self.q /
(np.conj(zcu) + self.s)) + self._AC2CM
zcu = np.array([ccH, ccI, ccJ])
self.cusps['secondary_down'] = zcu - 1. / (
1. + self.q) * (1. / np.conj(zcu) + self.q /
(np.conj(zcu) + self.s)) + self._AC2CM
# for cusp curves only
self._zcu = np.array([
ccA, ccB, ccC, ccD, ccE, ccF, ccG, ccH, ccI, ccJ
]) + self._AC2CM
# topology: intermediate
if self.topo == 'interm':
self._yl = []
method = 'illinois'
dico_interp = self._enveloppes()
# (Ox)
# zc = copy(self.zc[0] - self._AC2CM)
arg = np.argsort(np.imag(zc))
arg = arg[[1, 2]]
zc = zc[arg]
arg = np.argsort(np.real(zc))
sort = zc[arg]
## resonant caustic
# A, b3, [1, 1+q] -> Witt
ccA = complex(np.real(sort[1]))
# D, b4, [1, 1+1/q] -> Witt
ccD = complex(np.real(sort[0]))
# B, b3, [1+q, ∞]
loglbdmin, loglbdmax = dico_interp[
'cb3'] # Use interpolation on the envelopes to find the optimize search interval of lambda
loglbdmin = np.log10(1. + self.q)
self._br = 2
loglbd = mp.findroot(self._fpoly,
(mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method)
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccB = complex(bn[2]) * self.s
self._yl.append((3, lbd, ccB))
# F -> conj(B)
ccF = np.conj(ccB)
# C, b5, [1+1/q, ∞]
loglbdmin, loglbdmax = dico_interp[
'cb5'] # Use interpolation on the envelopes to find the optimize search interval of lambda
loglbdmin = np.log10(1. + 1. / self.q)
self._br = 4
loglbd = mp.findroot(self._fpoly,
(mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method)
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccC = complex(bn[4])
self._yl.append((5, lbd, ccC))
ccC = ccC * self.s
# E -> conj(C)
ccE = np.conj(ccC)
## store cusps
zcu = np.array([ccA, ccB, ccC, ccD, ccE, ccF])
self.cusps['resonant'] = zcu - 1. / (
1. + self.q) * (1. / np.conj(zcu) + self.q /
(np.conj(zcu) + self.s)) + self._AC2CM
# for cusp curves only
self._zcu = zcu + self._AC2CM
# topology: wide
if self.topo == 'wide':
self._yl = []
method = 'illinois'
dico_interp = self._enveloppes()
# (Ox)
# zc = copy(self.zc[0] - self._AC2CM)
arg = np.argsort(np.real(zc))
sort = zc[arg]
## central caustic
# A, b3, [1, 1+q] -> Witt
ccA = complex(np.real(sort[3]))
# C, b6, [0, 1+q] -> Witt
ccC = complex(np.real(sort[2]))
# B, b3, [1+q, ∞] -> poly
loglbdmin, loglbdmax = dico_interp[
'cb3'] # Use interpolation on the envelopes to find the optimize search interval of lambda
loglbdmin = np.log10(1. + self.q)
self._br = 2
loglbd = mp.findroot(self._fpoly,
(mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method)
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccB = complex(bn[2]) * self.s
self._yl.append((3, lbd, ccB))
# D -> conj(B)
ccD = np.conj(ccB)
## secondary caustic
# E, b5, [0, 1+1/q] -> Witt
ccE = complex(np.real(sort[1]))
# G, b4, [1, 1+1/q] -> Witt
ccG = complex(np.real(sort[0]))
# F, b5, [1+1/q, ∞] -> poly
loglbdmin, loglbdmax = dico_interp[
'cb5'] # Use interpolation on the envelopes to find the optimize search interval of lambda
loglbdmin = np.log10(1. + 1. / self.q)
self._br = 4
loglbd = mp.findroot(self._fpoly,
(mp.mpf(loglbdmin), mp.mpf(loglbdmax)),
tol=tol,
maxsteps=maxsteps,
solver=method)
lbd = mp.power(10, loglbd)
bn, sn = self._sortbnccu(lbd)
ccF = complex(bn[4])
self._yl.append((5, lbd, ccF))
ccF = ccF * self.s
# H -> conj(F)
ccH = np.conj(ccF)
## store cusps
zcu = np.array([ccA, ccB, ccC, ccD])
self.cusps['central'] = zcu - 1. / (
1. + self.q) * (1. / np.conj(zcu) + self.q /
(np.conj(zcu) + self.s)) + self._AC2CM
zcu = np.array([ccE, ccF, ccG, ccH])
self.cusps['secondary'] = zcu - 1. / (
1. + self.q) * (1. / np.conj(zcu) + self.q /
(np.conj(zcu) + self.s)) + self._AC2CM
# for cusp curves only
self._zcu = np.array([ccA, ccB, ccC, ccD, ccE, ccF, ccG, ccH
]) + self._AC2CM
def _Stretch1D(curve, newDuration, vm, am):
log.debug("\nx0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; prevDuration = {6}; newDuration = {7}".\
format(mp.nstr(curve.x0, n=_prec), mp.nstr(curve.EvalPos(curve.duration), n=_prec),
mp.nstr(curve.v0, n=_prec), mp.nstr(curve.EvalVel(curve.duration), n=_prec),
mp.nstr(vm, n=_prec), mp.nstr(am, n=_prec), mp.nstr(curve.duration, n=_prec),
mp.nstr(newDuration, n=_prec)))
# Check types
if type(newDuration) is not mp.mpf:
newDuration = mp.mpf("{:.15e}".format(newDuration))
if type(vm) is not mp.mpf:
vm = mp.mpf("{:.15e}".format(vm))
if type(am) is not mp.mpf:
am = mp.mpf("{:.15e}".format(am))
# Check inputs
# assert(newDuration > curve.duration)
assert(vm > zero)
assert(am > zero)
if (newDuration < -epsilon):
return ParabolicCurve()
if (newDuration <= epsilon):
# Check if this is a stationary trajectory
if (FuzzyEquals(curve.x0, curve.EvalPos(x1), epsilon) and FuzzyEquals(curve.v0, curve.v1, epsilon)):
ramp0 = Ramp(curve.v0, 0, 0, curve.x0)
newCurve = ParabolicCurve(ramp0)
return newCurve
else:
# newDuration is too short to any movement to be made
return ParabolicCurve()
v0 = curve[0].v0
v1 = curve[-1].v1
d = curve.d
# First assume no velocity bound -> re-interpolated trajectory will have only two ramps.
# Solve for a0 and a1 (the acceleration of the first and the last ramps).
# a0 = A + B/t0
# a1 = A + B/(t - t0)
# where t is the (new) total duration, t0 is the (new) duration of the first ramp, and
# A = (v1 - v0)/t
# B = (2d/t) - (v0 + v1).
newDurInverse = mp.fdiv(one, newDuration)
A = Mul(Sub(v1, v0), newDurInverse)
B = Sub(Prod([mp.mpf('2'), d, newDurInverse]), Add(v0, v1))
interval0 = iv.mpf([zero, newDuration]) # initial interval for t0
# Now consider the interval(s) computed from a0's constraints
sum1 = Neg(Add(am, A))
sum2 = Sub(am, A)
C = mp.fdiv(B, sum1)
D = mp.fdiv(B, sum2)
log.debug("\nA = {0}; \nB = {1}; \nC = {2}; \nD = {3}; \nsum1 = {4}; \nsum2 = {5};".\
format(mp.nstr(A, n=_prec), mp.nstr(B, n=_prec), mp.nstr(C, n=_prec), mp.nstr(D, n=_prec),
mp.nstr(sum1, n=_prec), mp.nstr(sum2, n=_prec)))
assert(not (sum1 > zero))
assert(not (sum2 < zero))
if IsEqual(sum1, zero):
raise NotImplementedError # not yet considered
elif sum1 > epsilon:
log.debug("sum1 > 0. This implies that newDuration is too short.")
return ParabolicCurve()
else:
interval1 = iv.mpf([C, inf])
if IsEqual(sum2, zero):
raise NotImplementedError # not yet considered
elif sum2 > epsilon:
interval2 = iv.mpf([D, inf])
else:
log.debug("sum2 < 0. This implies that newDuration is too short.")
return ParabolicCurve()
if Sub(interval2.a, interval1.b) > epsilon or Sub(interval1.a, interval2.b) > epsilon:
# interval1 and interval2 do not intersect each other
return ParabolicCurve()
# interval3 = interval1 \cap interval2 : valid interval for t0 computed from a0's constraints
interval3 = iv.mpf([max(interval1.a, interval2.a), min(interval1.b, interval2.b)])
# Now consider the interval(s) computed from a1's constraints
if IsEqual(sum1, zero):
raise NotImplementedError # not yet considered
elif sum1 > epsilon:
log.debug("sum1 > 0. This implies that newDuration is too short.")
return ParabolicCurve()
else:
interval4 = iv.mpf([Neg(inf), Add(C, newDuration)])
if IsEqual(sum2, zero):
raise NotImplementedError # not yet considered
elif sum2 > epsilon:
interval5 = iv.mpf([Neg(inf), Add(D, newDuration)])
else:
log.debug("sum2 < 0. This implies that newDuration is too short.")
return ParabolicCurve()
if Sub(interval5.a, interval4.b) > epsilon or Sub(interval4.a, interval5.b) > epsilon:
# interval4 and interval5 do not intersect each other
return ParabolicCurve()
# interval6 = interval4 \cap interval5 : valid interval for t0 computed from a1's constraints
interval6 = iv.mpf([max(interval4.a, interval5.a), min(interval4.b, interval5.b)])
# import IPython; IPython.embed()
if Sub(interval3.a, interval6.b) > epsilon or Sub(interval6.a, interval3.b) > epsilon:
# interval3 and interval6 do not intersect each other
return ParabolicCurve()
# interval7 = interval3 \cap interval6
interval7 = iv.mpf([max(interval3.a, interval6.a), min(interval3.b, interval6.b)])
if Sub(interval0.a, interval7.b) > epsilon or Sub(interval7.a, interval0.b) > epsilon:
# interval0 and interval7 do not intersect each other
return ParabolicCurve()
# interval8 = interval0 \cap interval7 : valid interval of t0 when considering all constraints (from a0 and a1)
interval8 = iv.mpf([max(interval0.a, interval7.a), min(interval0.b, interval7.b)])
# import IPython; IPython.embed()
# We choose the value t0 (the duration of the first ramp) by selecting the mid point of the
# valid interval of t0.
t0 = _SolveForT0(A, B, newDuration, interval8)
if t0 is None:
# The fancy procedure fails. Now consider no optimization whatsoever.
# TODO: Figure out why solving fails.
t0 = mp.convert(interval8.mid) # select the midpoint
# return ParabolicCurve()
t1 = Sub(newDuration, t0)
a0 = Add(A, Mul(mp.fdiv(one, t0), B))
if (Abs(t1) < epsilon):
a1 = zero
else:
a1 = Add(A, Mul(mp.fdiv(one, Neg(t1)), B))
assert(Sub(Abs(a0), am) < epsilon) # check if a0 is really below the bound
assert(Sub(Abs(a1), am) < epsilon) # check if a1 is really below the bound
# import IPython; IPython.embed()
# Check if the velocity bound is violated
vp = Add(v0, Mul(a0, t0))
if Abs(vp) > vm:
vmnew = Mul(mp.sign(vp), vm)
D2 = Prod([pointfive, Sqr(Sub(vp, vmnew)), Sub(mp.fdiv(one, a0), mp.fdiv(one, a1))])
# print "D2",
# mp.nprint(D2, n=_prec)
# print "vmnew",
# mp.nprint(vmnew, n=_prec)
A2 = Sqr(Sub(vmnew, v0))
B2 = Neg(Sqr(Sub(vmnew, v1)))
t0trimmed = mp.fdiv(Sub(vmnew, v0), a0)
t1trimmed = mp.fdiv(Sub(v1, vmnew), a1)
C2 = Sum([Mul(t0trimmed, Sub(vmnew, v0)), Mul(t1trimmed, Sub(vmnew, v1)), Mul(mp.mpf('-2'), D2)])
log.debug("\nA2 = {0}; \nB2 = {1}; \nC2 = {2}; \nD2 = {3};".format(mp.nstr(A2, n=_prec), mp.nstr(B2, n=_prec), mp.nstr(C2, n=_prec), mp.nstr(D2, n=_prec)))
temp = Prod([A2, B2, B2])
initguess = mp.sign(temp)*(Abs(temp)**(1./3.))
root = mp.findroot(lambda x: Sub(Prod([x, x, x]), temp), x0=initguess)
# import IPython; IPython.embed()
log.debug("root = {0}".format(mp.nstr(root, n=_prec)))
a0new = mp.fdiv(Add(A2, root), C2)
if (Abs(a0new) > Add(am, epsilon)):
a0new = Mul(mp.sign(a0new), am)
if (Abs(a0new) < epsilon):
a1new = mp.fdiv(B2, C2)
if (Abs(a1new) > Add(am, epsilon)):
log.warn("abs(a1new) > am; cannot fix this case")
# Cannot fix this case
return ParabolicCurve()
else:
if FuzzyZero(Sub(Mul(C2, a0new), A2), epsilon):
# import IPython; IPython.embed()
a1new = 0
else:
a1new = Mul(mp.fdiv(B2, C2), Add(one, mp.fdiv(A2, Sub(Mul(C2, a0new), A2))))
if (Abs(a1new) > Add(am, epsilon)):
a1new = Mul(mp.sign(a1new), am)
a0new = Mul(mp.fdiv(A2, C2), Add(one, mp.fdiv(B2, Sub(Mul(C2, a1new), B2))))
if (Abs(a0new) > Add(am, epsilon)) or (Abs(a1new) > Add(am, epsilon)):
log.warn("Cannot fix acceleration bounds violation")
return ParabolicCurve()
log.debug("\na0 = {0}; \na0new = {1}; \na1 = {2}; \na1new = {3};".format(mp.nstr(a0, n=_prec), mp.nstr(a0new, n=_prec), mp.nstr(a1, n=_prec), mp.nstr(a1new, n=_prec)))
if (Abs(a0new) < epsilon) and (Abs(a1new) < epsilon):
log.warn("Both accelerations are zero. Should we allow this case?")
return ParabolicCurve()
if (Abs(a0new) < epsilon):
# This is likely because v0 is at the velocity bound
t1new = mp.fdiv(Sub(v1, vmnew), a1new)
assert(t1new > 0)
ramp2 = Ramp(v0, a1new, t1new)
t0new = Sub(newDuration, t1new)
assert(t0new > 0)
ramp1 = Ramp(v0, zero, t0new, curve.x0)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
elif (Abs(a1new) < epsilon):
t0new = mp.fdiv(Sub(vmnew, v0), a0new)
assert(t0new > 0)
ramp1 = Ramp(v0, a0new, t0new, curve.x0)
t1new = Sub(newDuration, t0new)
assert(t1new > 0)
ramp2 = Ramp(ramp1.v1, zero, t1new)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
else:
# No problem with those new accelerations
# import IPython; IPython.embed()
t0new = mp.fdiv(Sub(vmnew, v0), a0new)
assert(t0new > 0)
t1new = mp.fdiv(Sub(v1, vmnew), a1new)
assert(t1new > 0)
if (Add(t0new, t1new) > newDuration):
# Final fix. Since we give more weight to acceleration bounds, we make the velocity
# bound saturated. Therefore, we set vp to vmnew.
# import IPython; IPython.embed()
t0new = mp.fdiv(Sub(Sub(vmnew, v0), B), A)
t1new = Sub(newDuration, t0new)
assert(t1new > zero)
a0new = Add(A, Mul(mp.fdiv(one, t0new), B))
a1new = Add(A, Mul(mp.fdiv(one, Neg(t1new)), B))
ramp1 = Ramp(v0, a0new, t0new, curve.x0)
ramp2 = Ramp(ramp1.v1, a1new, t1new)
newCurve = ParabolicCurve([ramp1, ramp2])
else:
# t0new = mp.fdiv(Sub(vmnew, v0), a0new)
# assert(t0new > 0)
ramp1 = Ramp(v0, a0new, t0new, curve.x0)
# t1new = mp.fdiv(Sub(v1, vmnew), a1new)
# assert(t1new > 0)
ramp3 = Ramp(ramp1.v1, a1new, t1new)
# print "a1",
# mp.nprint(a1, n=_prec)
# print "a1new",
# mp.nprint(a1new, n=_prec)
# print "t0trimmed",
# mp.nprint(t0trimmed, n=_prec)
# print "t0new",
# mp.nprint(t0new, n=_prec)
# print "t1trimmed",
# mp.nprint(t1trimmed, n=_prec)
# print "t1new",
# mp.nprint(t1new, n=_prec)
# print "T",
# mp.nprint(newDuration, n=_prec)
ramp2 = Ramp(ramp1.v1, zero, Sub(newDuration, Add(t0new , t1new)))
newCurve = ParabolicCurve([ramp1, ramp2, ramp3])
# import IPython; IPython.embed()
return newCurve
else:
ramp1 = Ramp(v0, a0, t0, curve.x0)
ramp2 = Ramp(ramp1.v1, a1, t1)
newCurve = ParabolicCurve([ramp1, ramp2])
return newCurve
t = 0.0
evalfilename = get_file_path("Ht_eval_vlargez_"+str(t)+".csv")
Htrootfilename = get_file_path("Ht_roots_vlargez_"+str(t)+".csv")
evalrows = []
htroots = []
step_size = 0.01
prev_eval = 0.0
rootcount = 0
z = 25.0
for i in range(1, 5000000001):
z += step_size
curr_eval = (Ht_AFE_ABC(z, t).real)*mp.exp(mp.pi()*z/8)
root_check = sign_change(curr_eval, prev_eval)
if root_check == 1:
try: approx_root = mp.findroot(lambda y: Ht_AFE_ABC(y,t).real,[z,z-step_size],solver="bisect")
except: approx_root = z - step_size/2
print(approx_root)
rootcount += 1
htroots.append([t, rootcount, approx_root])
evalrows.append([t, z, curr_eval, root_check])
prev_eval = curr_eval
if i % 10000 == 0:
append_data(evalfilename, evalrows)
evalrows = []
if rootcount % 100 == 0:
append_data(Htrootfilename, htroots)
htroots = []
append_data(evalfilename, evalrows)
t = 0.4
Htrootfilename = "Htroots_" + str(t) + ".csv"
htroots = []
rootcount = 0
known_root = 684130
midpoint_estimate_flag = 0
for i in range(1, 5000001):
avggap = expected_zero_gap(t, known_root)
interval_min, interval_max = known_root + avggap / 2, known_root + 3 * avggap / 2
interval_min_eval = Ht_AFE_ADJ_AB(interval_min, t).real
interval_max_eval = Ht_AFE_ADJ_AB(interval_max, t).real
root_check = sign_change(interval_min_eval, interval_max_eval)
if root_check == 1:
try:
approx_root = mp.findroot(lambda y: Ht_AFE_ADJ_AB(y, t).real,
[interval_min, interval_max],
solver="ridder")
midpoint_estimate_flag = 0
except:
approx_root = (interval_min + interval_max) / 2
midpoint_estimate_flag = 1
print(approx_root)
rootcount += 1
htroots.append([
t, rootcount, approx_root, interval_min, interval_max,
midpoint_estimate_flag
])
known_root = approx_root
else:
known_root += avggap
if rootcount % 100 == 0: