def _BrakeTime(x, v, xbound):
[result, t] = _SolveAXMB(v, Mul(number('2'), Sub(xbound, x)), epsilon, 0, inf)
if not result:
log.debug("Cannot solve for braking time from the equation {0}*t - {1} = 0".\
format(mp.nstr(v, n=_prec), mp.nstr(Mul(number('2'), Sub(xbound, x)), n=_prec)))
return 0
return t
def figs(x, n=20, exp=0, sign=False):
if (sign and x >= 0.0):
s = ' '
else:
s = ''
# mp always returns '0.0' for zero. Bypass it using standard formats
if (x == 0.0):
fmt = '{{0:.{0}f}}'.format(n)
# python uses two-digit exponent, but mp uses the least amount. Hard code it for zero
if (exp):
fmt += 'e+'+'0'*exp
return s+fmt.format(0.0)
# Convert significant figures to decimal places:
# With exponent, integer part should be 1 figure, so it's n+1
# Without exponent, floor(log10(abs(x)))+1 gives the number of integer figures (except for zero)
if (exp):
tmp = s+mp.nstr(mp.mpf(x), n+1, strip_zeros=False, min_fixed=mp.inf, max_fixed=-mp.inf, show_zero_exponent=True)
# Add zeros to the exponent if necessary
exppos = tmp.find('e')+2
diff = exp-(len(tmp)-exppos)
if (diff > 0):
tmp = tmp[:exppos] + '0'*diff + tmp[exppos:]
return tmp
else:
return s+mp.nstr(mp.mpf(x), int(mp.floor(mp.log10(abs(x))))+n+1, strip_zeros=False, min_fixed=-mp.inf, max_fixed=mp.inf)
def _BrakeTime(x, v, xbound):
[result, t] = _SolveAXMB(v, Mul(number('2'), Sub(xbound, x)), epsilon, 0,
inf)
if not result:
log.debug("Cannot solve for braking time from the equation {0}*t - {1} = 0".\
format(mp.nstr(v, n=_prec), mp.nstr(Mul(number('2'), Sub(xbound, x)), n=_prec)))
return 0
return t
def _BrakeAccel(x, v, xbound):
coeff0 = Mul(number('2'), Sub(xbound, x))
coeff1 = Sqr(v)
[result, a] = _SolveAXMB(coeff0, Neg(coeff1), epsilon, -inf, inf)
if not result:
log.debug("Cannot solve for braking acceleration from the equation {0}*a + {1} = 0".\
format(mp.nstr(coeff0, n=_prec), mp.nstr(coeff1, n=_prec)))
return 0
return a
def _BrakeAccel(x, v, xbound):
coeff0 = Mul(number('2'), Sub(xbound, x))
coeff1 = Sqr(v)
[result, a] = _SolveAXMB(coeff0, Neg(coeff1), epsilon, -inf, inf)
if not result:
log.debug("Cannot solve for braking acceleration from the equation {0}*a + {1} = 0".\
format(mp.nstr(coeff0, n=_prec), mp.nstr(coeff1, n=_prec)))
return 0
return a
def _CalculateLeastUpperBoundInoperativeInterval(x0, x1, v0, v1, vm, am):
# All input must already be of mp.mpf type
d = x1 - x0
temp1 = Prod([
number('2'),
Neg(Sqr(am)),
Sub(Prod([number('2'), am, d]), Add(Sqr(v0), Sqr(v1)))
])
if temp1 < zero:
T0 = number('-1')
T1 = number('-1')
else:
term1 = mp.fdiv(Add(v0, v1), am)
term2 = mp.fdiv(mp.sqrt(temp1), Sqr(am))
T0 = Add(term1, term2)
T1 = Sub(term1, term2)
temp2 = Prod([
number('2'),
Sqr(am),
Add(Prod([number('2'), am, d]), Add(Sqr(v0), Sqr(v1)))
])
if temp2 < zero:
T2 = number('-1')
T3 = number('-1')
else:
term1 = Neg(mp.fdiv(Add(v0, v1), am))
term2 = mp.fdiv(mp.sqrt(temp2), Sqr(am))
T2 = Add(term1, term2)
T3 = Sub(term1, term2)
newDuration = max(max(T0, T1), max(T2, T3))
if newDuration > zero:
dStraight = Prod([pointfive, Add(v0, v1), newDuration])
if Sub(d, dStraight) > 0:
amNew = am
vmNew = vm
else:
amNew = -am
vmNew = -vm
# import IPython; IPython.embed()
vp = Mul(pointfive, Sum([Mul(newDuration, amNew), v0,
v1])) # the peak velocity
if (Abs(vp) > vm):
dExcess = mp.fdiv(Sqr(Sub(vp, vmNew)), am)
assert (dExcess > 0)
deltaTime = mp.fdiv(dExcess, vm)
newDuration = Add(newDuration, deltaTime)
newDuration = Mul(newDuration, number('1.01')) # add 1% safety bound
return newDuration
else:
log.debug('Unable to calculate the least upper bound: T0 = {0}; T1 = {1}; T2 = {2}; T3 = {3}'.\
format(mp.nstr(T0, n=_prec), mp.nstr(T1, n=_prec), mp.nstr(T2, n=_prec), mp.nstr(T3, n=_prec)))
return number('-1')
def _CalculateLeastUpperBoundInoperativeInterval(x0, x1, v0, v1, vm, am):
# All input must already be of mp.mpf type
d = x1 - x0
temp1 = Prod([number('2'), Neg(Sqr(am)), Sub(Prod([number('2'), am, d]), Add(Sqr(v0), Sqr(v1)))])
if temp1 < zero:
T0 = number('-1')
T1 = number('-1')
else:
term1 = mp.fdiv(Add(v0, v1), am)
term2 = mp.fdiv(mp.sqrt(temp1), Sqr(am))
T0 = Add(term1, term2)
T1 = Sub(term1, term2)
temp2 = Prod([number('2'), Sqr(am), Add(Prod([number('2'), am, d]), Add(Sqr(v0), Sqr(v1)))])
if temp2 < zero:
T2 = number('-1')
T3 = number('-1')
else:
term1 = Neg(mp.fdiv(Add(v0, v1), am))
term2 = mp.fdiv(mp.sqrt(temp2), Sqr(am))
T2 = Add(term1, term2)
T3 = Sub(term1, term2)
newDuration = max(max(T0, T1), max(T2, T3))
if newDuration > zero:
dStraight = Prod([pointfive, Add(v0, v1), newDuration])
if Sub(d, dStraight) > 0:
amNew = am
vmNew = vm
else:
amNew = -am
vmNew = -vm
# import IPython; IPython.embed()
vp = Mul(pointfive, Sum([Mul(newDuration, amNew), v0, v1])) # the peak velocity
if (Abs(vp) > vm):
dExcess = mp.fdiv(Sqr(Sub(vp, vmNew)), am)
assert(dExcess > 0)
deltaTime = mp.fdiv(dExcess, vm)
newDuration = Add(newDuration, deltaTime)
log.debug('Calculation successful: T0 = {0}; T1 = {1}; T2 = {2}; T3 = {3}'.format(mp.nstr(T0, n=_prec), mp.nstr(T1, n=_prec), mp.nstr(T2, n=_prec), mp.nstr(T3, n=_prec)))
newDuration = Mul(newDuration, number('1.01')) # add 1% safety bound
return newDuration
else:
if (FuzzyEquals(x0, x1, epsilon) and FuzzyZero(v0, epsilon) and FuzzyZero(v1, epsilon)):
# t = 0 is actually a correct solution
newDuration = 0
return newDuration
log.debug('Unable to calculate the least upper bound: T0 = {0}; T1 = {1}; T2 = {2}; T3 = {3}'.\
format(mp.nstr(T0, n=_prec), mp.nstr(T1, n=_prec), mp.nstr(T2, n=_prec), mp.nstr(T3, n=_prec)))
return number('-1')
def z_Zolotarev_poly(N, m, interp_num=100, full=False):
r"""
Function to evaluate the polynomial coefficients of the Zolotarev polynomial.
:param N: Order of the Zolotarev polynomial
:param m: m is the elliptic parameter (not the modulus k and not the nome q)
:param interp_num: Number of points for interpolation
:param full: Switch determining nature of return value. When it is False (the default)
just the coefficients are returned, when True diagnostic information from
the singular value decomposition is also returned.
:rtype: Returns [polynomial_coef, polynomial_roots]
"""
m = mpf(m)
# Evaluating the polynomial at some discrete points (for polynomial fitting)
x = np.linspace(-1.04, 1.04, interp_num)
y = []
for i in range(len(x)):
tmp = mp.nstr(z_Zolotarev(N, x[i], m), n=6)
tmp = z_str2num(tmp)
y.append(tmp)
# Interpolating the data to fit to a Nth order polynomial
coef = np.polyfit(x, y, N, full)
roots = np.sort(np.roots(coef))
return coef, roots
def entropy_pf(Lph,Tph,meas,G,prec=15,q=2,dps=200):
# setting digit precision
mp.dps = dps
# =============================================================================
# Evaluation of the entropy using partition function
# Lph,Tph -- physical size (number of qubits) and time (circuit depth)
# G -- (large) parameter controling the gap between relevant and irrelevant states
# prec -- number of digits in the answer
# q -- qudit dimension
# =============================================================================
# -- define effective inverse temperature
beta = mp.log((q**2+1)/q)
# -- dimension of the effective lattice
L,T = int(Lph/2)+(Lph+1)%2,Tph+1
# -- corresponding couplings for the numerator and denominator
J_matrix1,J_matrix2 = generate_lattice_couplings(L,T,meas,G,beta)
# -- including the temperature
J_matrix1 = -beta*mp.matrix(J_matrix1.tolist())
J_matrix2 = -beta*mp.matrix(J_matrix2.tolist())
# -- evaluation of the entropy
entropy = -log_partition_function(J_matrix1,L,T).real\
+log_partition_function(J_matrix2,L,T).real
return mp.nstr(entropy,prec)
def z_Zolotarev_poly(N, m, interp_num=100, full=False):
r"""
Function to evaluate the polynomial coefficients of the Zolotarev polynomial.
:param N: Order of the Zolotarev polynomial
:param m: m is the elliptic parameter (not the modulus k and not the nome q)
:param interp_num: Number of points for interpolation
:param full: Switch determining nature of return value. When it is False (the default)
just the coefficients are returned, when True diagnostic information from
the singular value decomposition is also returned.
:rtype: Returns [polynomial_coef, polynomial_roots]
"""
m = mpf(m)
# Evaluating the polynomial at some discrete points (for polynomial fitting)
x = np.linspace(-1.04, 1.04, interp_num)
y = []
for i in range(len(x)):
tmp = mp.nstr(z_Zolotarev(N, x[i], m), n=6)
tmp = z_str2num(tmp)
y.append(tmp)
# Interpolating the data to fit to a Nth order polynomial
coef = np.polyfit(x, y, N, full)
roots = np.sort(np.roots(coef))
return coef, roots
def Log10ProbPerBin(self, lessX, eqX):
log10probX = _zeros_like(self.WaveDec_data)
for l, level in enumerate(self.WaveDec_data):
for j, coeff in enumerate(level):
grteqX = 1 - lessX[l][j]
log10pX = float(mp.nstr(mp.log10(grteqX), g_logdigits))
log10probX[l][j] = log10pX
return log10probX
def NSigmaPerBin(self, lessX):
nsigma = _zeros_like(self.WaveDec_data)
for l, level in enumerate(self.WaveDec_data):
for j, data_coeff in enumerate(level):
hypo_coeff = self.WaveDec_hypo[l][j]
sign = -1 if data_coeff < hypo_coeff else 1
nsig = float(mp.nstr(sign * _nsigma(lessX[l][j]), g_logdigits))
nsigma[l][j] = nsig
return nsigma
def compare_mp(nb: mp.mpf, ref: mp.mpf, precision: int = 25) -> None:
nb = mp.nstr(nb, precision)
nb_len = len(nb)
ref = mp.nstr(ref, precision)
ref_len = len(ref)
cursor = 0
while cursor < nb_len and cursor < ref_len and nb[cursor] == ref[cursor]:
cursor += 1
print(nb, end='')
if ref_len > nb_len:
print('-' * (ref_len - nb_len), end='')
print()
print('*' * cursor, end='')
if cursor < ref_len:
print('#' * (ref_len - cursor), end='')
if nb_len > ref_len:
print('+' * (nb_len - ref_len), end='')
print()
def VectToString(A):
A_ = ConvertFloatArrayToMPF(A)
separator = ""
s = "["
for a in A_:
s += separator
s += mp.nstr(a, n=_prec)
separator = ", "
return s
def VectToString(A):
A_ = ConvertFloatArrayToMPF(A)
separator = ""
s = "["
for a in A_:
s += separator
s += mp.nstr(a, n=_prec)
separator = ", "
return s
def dim_str(dim, units):
"""Format a dimension to a given unit."""
dim = to_mm(dim, units, True)
prefix = ""
if units == "ft":
if dim >= mp.mpf(1):
prefix = str(int(mp.floor(dim))) + "'"
units = "in"
dim = mp.frac(dim) * 12
dim = prefix + ("{:." + str(PRECS[units]) + "f}").format(float(mp.nstr(dim)))
if units == "in":
return dim + '"'
return dim + " " + units
def z_Zolotarev_poly(N, m, interp_num=100, full=False):
"""
Function to evaluate the polynomial coefficients of the Zolotarev
polynomial.
"""
m = mpf(m)
# Evaluating the polynomial at some discrete points
x = np.linspace(-1.04, 1.04, interp_num)
y = []
for i in range(len(x)):
tmp = mp.nstr(z_Zolotarev(N, x[i], m), n=6)
tmp = z_str2num(tmp)
y.append(tmp)
# Interpolating the data to fit to a Nth order polynomial
coef = np.polyfit(x, y, N, full)
roots = np.sort(np.roots(coef))
return coef, roots
def z_Zolotarev_poly(N, m, interp_num=100, full=False):
"""
Function to evaluate the polynomial coefficients of the Zolotarev
polynomial.
"""
m = mpf(m)
# Evaluating the polynomial at some discrete points
x = np.linspace(-1.04, 1.04, interp_num)
y = []
for i in range(len(x)):
tmp = mp.nstr(z_Zolotarev(N, x[i], m), n=6)
tmp = z_str2num(tmp)
y.append(tmp)
# Interpolating the data to fit to a Nth order polynomial
coef = np.polyfit(x, y, N, full)
roots = np.sort(np.roots(coef))
return coef, roots
def calculate(self, expr, q):
def safe_eval(expr, symbols={}):
if expr.find("_") != -1:
return None
try:
return eval(expr, dict(__builtins__=None), symbols) # :(
except:
e = sys.exc_info()[0]
return "Error de sintaxis o algo por el estilo: " + str(e)
expr = expr.replace('^', '**')
resp = safe_eval(expr, self.vrs)
try:
resp = mp.nstr(resp, mp.dps, min_fixed=-mp.inf)
except:
pass
q.put(str(resp))
def calculate(self, expr, q, bot, ev):
def safe_eval(expr, symbols={}):
if expr.find("_") != -1 or expr.find("=") != -1 or expr.find("'") != -1 or expr.find("\"") != -1:
return None
if expr.find("sys.") != -1 or expr.find("lambda") != -1 or expr.find("os.") != -1 or expr.find("import") != -1:
return "u h4x0r"
try:
return eval(expr, dict(__builtins__=None), symbols) # :(
except:
e = sys.exc_info()[0]
return bot._(ev, self, "syntaxerror").format(str(e))
expr = expr.replace('^', '**')
resp = safe_eval(expr, self.vrs)
try:
resp = mp.nstr(resp, mp.dps, min_fixed=-mp.inf)
except:
pass
q.put(str(resp))
def calculate(self, expr, q, bot, ev):
def safe_eval(expr, symbols={}):
if expr.find("_") != -1 or expr.find("=") != -1 or expr.find(
"'") != -1 or expr.find("\"") != -1:
return None
if expr.find("sys.") != -1 or expr.find(
"lambda") != -1 or expr.find("os.") != -1 or expr.find(
"import") != -1:
return "u h4x0r"
try:
return eval(expr, dict(__builtins__=None), symbols) # :(
except:
e = sys.exc_info()[0]
return bot._(ev, self, "syntaxerror").format(str(e))
expr = expr.replace('^', '**')
resp = safe_eval(expr, self.vrs)
try:
resp = mp.nstr(resp, mp.dps, min_fixed=-mp.inf)
except:
pass
q.put(str(resp))
def perform_ss_rate_analysis(prefix,
ENs,
Evib=0.,
T=473,
p0=1,
N2_frac=0.25,
X=0.01,
site='step',
plot=True,
alkali_promotion=False,
use_alpha=False,
model='effective',
nlevels=1,
dist='Treanor',
Tv=3500,
metals=[],
Agg=False):
if Agg:
import matplotlib
matplotlib.use('Agg')
from mkm import NH3mkm
Rs = []
Rss = []
thetas = []
theta0 = np.zeros(4) # [0, 0, 0, 0]
for i, EN in enumerate(ENs):
# print type(EN), EN
mod = NH3mkm(T,
EN,
Evib,
p0,
N2_frac,
X=X,
site=site,
alkali_promotion=alkali_promotion,
model=model,
dist=dist,
Tv=Tv,
nlevels=nlevels,
use_alpha=use_alpha)
Rds, rsab = mod.get_sabatier_rate()
Rs.append(rsab)
if i == 0:
theta = mod.integrate_odes(theta0=theta0,
h0=1e-24,
rtol=1e-12,
atol=1e-15,
timespan=[0, 1e9],
mxstep=200000)
try:
theta_ss = mod.find_steady_state_roots(theta)
except:
# If there is an issue re-solve ode
theta = mod.integrate_odes(theta0=theta,
h0=1e-24,
rtol=1e-12,
atol=1e-15,
timespan=[0, 1e9],
mxstep=200000)
# Try one more time with reduced tolerance
try:
theta_ss = mod.find_steady_state_roots(theta, tol=1e-15)
except:
# set it to the ode theta
theta_ss = theta
# Now check if theta_ss has unphysical values
if (np.array(theta) <= 1).all() and (np.array(theta) >= 0.).all():
theta = theta_ss
theta_and_empty = list(theta) + [1 - sum(theta)]
theta_and_empty = [mp.nstr(t, 25) for t in theta_and_empty]
r = mod.get_rates(theta)
thetas.append(theta_and_empty)
Rss.append(r[0])
# This needs to be updated
store_variables(prefix,
ENs,
Rs, [],
Rss, [],
thetas,
T,
p0,
N2_frac,
X,
site,
Evib,
metals=metals)
if plot:
plot_rates(ENs, Rs, [], Rss, prefix)
plot_coverages(ENs, [], thetas, prefix)
R = 10 # 'R' is the value of the peak within [-1,+1]
# Getting the value of the parameter 'm' from a given 'R' (remember!, m=k^2)
m = z_m_frm_R(N, R)
print('m =', m)
print(type(m))
# Testing the side-lobe ratio (i.e., SLR in linear scale)
R = z_Zolotarev_x2(N, m)
print('R =', R)
print('SLR =', 10 * mp.log10(R**2),
'(dB)') # SLR depends only on the magnitude of R here
# x1, x2, x3 values ... just for the plotting purpose
x1, x2, x3 = z_x123_frm_m(N, m)
x11 = z_str2num(mp.nstr(x1, n=6))
x22 = z_str2num(mp.nstr(x2, n=6))
x33 = z_str2num(mp.nstr(x3, n=6))
# Evaluating the polynomial at some discrete points
x = np.linspace(-1.04, 1.04, num=500)
y = []
for i in range(len(x)):
tmp = mp.nstr(z_Zolotarev(N, x[i], m), n=6)
tmp = z_str2num(tmp)
y.append(tmp)
# Plotting the data obtained at those discrete points
import matplotlib.pyplot as plt
f1 = plt.figure(1)
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 mparr_to_npreal(mparr):
arr = np.zeros(len(mparr))
for i in range(len(mparr)):
arr[i] = np.float(mp.nstr(mparr[i],17))
return arr
def ReinterpolateNDFixedDuration(curves,
vmVect,
amVect,
maxIndex,
delta=zero,
tryHarder=False):
ndof = len(curves)
assert (ndof == len(vmVect))
assert (ndof == len(amVect))
assert (maxIndex < ndof)
# Convert all vector elements into mp.mpf (if necessary)
vmVect_ = ConvertFloatArrayToMPF(vmVect)
amVect_ = ConvertFloatArrayToMPF(amVect)
newCurves = []
if not tryHarder:
newDuration = curves[maxIndex].duration
for (idof, curve) in enumerate(curves):
if idof == maxIndex:
newCurves.append(curve)
continue
stretchedCurve = _Stretch1D(curve, newDuration, vmVect[idof],
amVect[idof])
if stretchedCurve.isEmpty:
log.debug(
'ReinterpolateNDFixedDuration: dof {0} failed'.format(
idof))
return ParabolicCurvesND()
else:
newCurves.append(stretchedCurve)
assert (len(newCurves) == ndof)
return ParabolicCurvesND(newCurves)
else:
# Try harder to re-interpolate this trajectory. This is guaranteed to have 100% success rate
isPrevDurationSafe = False
newDuration = zero
for (idof, curve) in enumerate(curves):
t = _CalculateLeastUpperBoundInoperativeInterval(
curve.x0, curve.EvalPos(curve.duration), curve.v0,
curve.EvalVel(curve.duration), vmVect[idof], amVect[idof])
assert (t > zero)
if t > newDuration:
newDuration = t
assert (not (newDuration == zero))
if curves[maxIndex].duration > newDuration:
# The duration of the slowest DOF is already safe
newDuration = curves[maxIndex].duration
isPrevDurationSafe = True
for (idof, curve) in enumerate(curves):
if (isPrevDurationSafe) and (idof == maxIndex):
newCurves.append(curve)
continue
stretchedCurve = _Stretch1D(curve, newDuration, vmVect[idof],
amVect[idof])
if stretchedCurve.isEmpty:
log.debug(
'ReinterpolateNDFixedDuration: dof {0} failed even when trying harder'
.format(idof))
log.debug('x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; newDuration = {6}'.\
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(vmVect[idof], n=_prec), mp.nstr(amVect[idof], n=_prec),
mp.nstr(newDuration, n=_prec)))
raise Exception(
'Something is wrong when calculating the least upper bound of inoperative intervals'
)
newCurves.append(stretchedCurve)
assert (len(newCurves) == ndof)
return ParabolicCurvesND(newCurves)
# N = 10
R = 10 # 'R' is the value of the peak within [-1,+1]
# Getting the value of the parameter 'm' from a given 'R' (remember!, m=k^2)
m = z_m_frm_R(N, R)
print 'm =', m
print type(m)
# Testing the side-lobe ratio (i.e., SLR in linear scale)
R = z_Zolotarev_x2(N, m)
print 'R =', R
print 'SLR =', 10 * mp.log10(R ** 2), '(dB)' # SLR depends only on the magnitude of R here
# x1, x2, x3 values ... just for the plotting purpose
x1, x2, x3 = z_x123_frm_m(N, m)
x11 = z_str2num(mp.nstr(x1, n=6))
x22 = z_str2num(mp.nstr(x2, n=6))
x33 = z_str2num(mp.nstr(x3, n=6))
# Evaluating the polynomial at some discrete points
x = np.linspace(-1.04, 1.04, num=500)
y = []
for i in range(len(x)):
tmp = mp.nstr(z_Zolotarev(N, x[i], m), n=6)
tmp = z_str2num(tmp)
y.append(tmp)
# Plotting the data obtained at those discrete points
import matplotlib.pyplot as plt
f1 = plt.figure(1)
p1 = plt.subplot(111)
def ReinterpolateNDFixedDuration(curves, vmVect, amVect, maxIndex, delta=zero, tryHarder=False):
ndof = len(curves)
assert(ndof == len(vmVect))
assert(ndof == len(amVect))
assert(maxIndex < ndof)
# Convert all vector elements into mp.mpf (if necessary)
vmVect_ = ConvertFloatArrayToMPF(vmVect)
amVect_ = ConvertFloatArrayToMPF(amVect)
newCurves = []
if not tryHarder:
newDuration = curves[maxIndex].duration
for (idof, curve) in enumerate(curves):
if idof == maxIndex:
newCurves.append(curve)
continue
stretchedCurve = _Stretch1D(curve, newDuration, vmVect[idof], amVect[idof])
if stretchedCurve.isEmpty:
log.debug('ReinterpolateNDFixedDuration: dof {0} failed'.format(idof))
return ParabolicCurvesND()
else:
newCurves.append(stretchedCurve)
assert(len(newCurves) == ndof)
return ParabolicCurvesND(newCurves)
else:
# Try harder to re-interpolate this trajectory. This is guaranteed to have 100% success rate
isPrevDurationSafe = False
newDuration = zero
for (idof, curve) in enumerate(curves):
t = _CalculateLeastUpperBoundInoperativeInterval(curve.x0, curve.EvalPos(curve.duration), curve.v0, curve.EvalVel(curve.duration), vmVect[idof], amVect[idof])
assert(t > zero)
if t > newDuration:
newDuration = t
assert(not (newDuration == zero))
if curves[maxIndex].duration > newDuration:
# The duration of the slowest DOF is already safe
newDuration = curves[maxIndex].duration
isPrevDurationSafe = True
for (idof, curve) in enumerate(curves):
if (isPrevDurationSafe) and (idof == maxIndex):
newCurves.append(curve)
continue
stretchedCurve = _Stretch1D(curve, newDuration, vmVect[idof], amVect[idof])
if stretchedCurve.isEmpty:
log.debug('ReinterpolateNDFixedDuration: dof {0} failed even when trying harder'.format(idof))
log.debug('x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; newDuration = {6}'.\
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(vmVect[idof], n=_prec), mp.nstr(amVect[idof], n=_prec),
mp.nstr(newDuration, n=_prec)))
raise Exception('Something is wrong when calculating the least upper bound of inoperative intervals')
newCurves.append(stretchedCurve)
assert(len(newCurves) == ndof)
return ParabolicCurvesND(newCurves)
f.write("\n")
f.write("------------------------------------\n")
f.write("Options for Boys function Fn(x):\n")
f.write(" Max n: {}\n".format(maxn))
f.write(" Max x: {}\n".format(maxx))
f.write(" Spacing: {}\n".format(inc))
f.write(" npoints: {}\n".format(npoints))
f.write(" DPS: {}\n".format(args.dps))
f.write("------------------------------------\n")
f.write("*/\n\n")
f.write("const double boys_shortgrid[{}][{}] = \n".format(npoints, maxn+1))
f.write("{\n")
for p,x in zip(F,pts):
f.write("/* x = {:12}*/ {{".format(mp.nstr(x, 4)))
for n in p:
f.write("{:32}, ".format(mp.nstr(n, 18)))
f.write("},\n")
f.write("};\n")
with open(args.filename + ".h", 'w') as f:
f.write("#pragma once\n")
f.write("\n")
f.write("#define BOYS_SHORTGRID_MAXN {}\n".format(maxn))
f.write("#define BOYS_SHORTGRID_MAXX {}\n".format(maxx))
f.write("#define BOYS_SHORTGRID_SPACE {}\n".format(inc))
f.write("#define BOYS_SHORTGRID_NPOINT {}\n".format(npoints))
f.write("#define BOYS_SHORTGRID_LOOKUPFAC {}\n".format(1.0/inc))
f.write("#define BOYS_SHORTGRID_LOOKUPFAC2 {}\n".format(0.5*inc))
f.write("\n")
# Compute asymptotic (x -> inf) Rys roots and weights
# from the second half of the Hermite solution for degree 2n
# r_rys = r_h^2 / x
# w_rys = w_h / sqrt(x)
y = one
if (x != 0):
y = one / x
roots_asymp = [r * r * y for r in roots_herm[n:]]
weights_asymp = [w * mp.sqrt(y) for w in weights_herm[n:]]
# Compute maximum error in roots and weights, and stop when below accuracy
error_roots = max(
[abs(r1 - r2) / r2 for r1, r2 in zip(roots_asymp, roots)])
error_weights = max(
[abs(w1 - w2) / w2 for w1, w2 in zip(weights_asymp, weights)])
error_tot = max(error_roots, error_weights)
print('x = {0}: error = {1}'.format(figs(x, 2),
figs(error_tot, 4, exp=True)))
if (error_tot < acc):
asymp.append(x)
break
# Take a step back for the next pass
init = [a - s for a in asymp]
print()
print('Step: {0}'.format(mp.nstr(s)))
for n, a in zip(order, asymp):
print('n = {0}: x = {1}'.format(n, figs(a, 2)))
tin = 0.1
dt = 0.1
tfin = 2
Win = 0 #first amplitude of disorder
Wfin = 6 #last amplitude of disorder
dW = .2 #resolution of disorder amplitude
L = 64
t_vec = [mp.mpf(0.001)] + list(mp.arange(tin, tfin, dt))
W_vec = mp.arange(Win, Wfin, dW)
outputfile = "./BUMP_SD_length=" + str(L) + ".dat"
f1 = open(outputfile, "w")
for t in t_vec:
for s in W_vec:
print(t, s)
inputfile = "./BUMP_SD_length=" + str(L) + "_t=" + mp.nstr(
t, 3) + "_s=" + mp.nstr(s, 3) + ".dat"
ND = np.genfromtxt(inputfile) #,dtype=float, max_rows=250)
ND = ND[~np.isnan(ND)]
value = np.mean(ND)
st = np.std(ND)
f1.write("%.10f,%.10f,%.10f,%.10f,%i\n" % (t, s, value, st, len(ND)))
f1.close()
def strF(dig, chop_=True, signifi_=9):
if dig: dig = chop(dig)
return mp.nstr(dig, signifi_)
def __repr__(self):
bmin, bmax = self.GetPeaks()
return "x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; bmin = {6}; bmax = {7}".\
format(mp.nstr(self.x0, n=_prec), mp.nstr(self.x1, n=_prec),
mp.nstr(self.v0, n=_prec), mp.nstr(self.v1, n=_prec), mp.nstr(self.a, n=_prec),
mp.nstr(self.duration, n=_prec), mp.nstr(bmin, n=_prec), mp.nstr(bmax, n=_prec))
def mp_to_complex(mpcplx):
mpreal = mp.re(mpcplx)
mpimag = mp.im(mpcplx)
flreal = np.float(mp.nstr(mpreal, mp.dps))
flimag = np.float(mp.nstr(mpimag, mp.dps))
return flreal + 1j * flimag
while abs(f / g) > 10.0**(2 - prec):
x0 = x0 - f / g
f, g = poly(x0)
xk[k] = x0
wk[k] = gamma[n - 2] / (g * pa[n - 2])
# Capture data for derivative matrix
for kp in range(n - 1):
z[k, kp] = pa[kp]
zp[k, kp] = qa[kp]
zpp[k, kp] = ra[kp]
zp = zp * mp.powm(z, -1) * b
zpp = zpp * mp.powm(z, -1) * b * b
# Write to datafile
fout = open('out.cgyro.egrid', 'w')
for k in range(n - 1):
fout.write(
mp.nstr(xk[k]**2, 17) + ' ' +
mp.nstr(wk[k] * 4 / mp.sqrt(mp.pi) * xk[k]**2, 17) + '\n')
for k in range(n - 1):
for kp in range(n - 1):
fout.write(mp.nstr(zp[k, kp], 17) + ' ')
fout.write('\n')
for k in range(n - 1):
for kp in range(n - 1):
fout.write(mp.nstr(zpp[k, kp], 17) + ' ')
fout.write('\n')
fout.close()
def _ImposeJointLimitFixedDuration(curve, xmin, xmax, vm, am):
bmin, bmax = curve.GetPeaks()
if (bmin >= Sub(xmin, epsilon)) and (bmax <= Add(xmax, epsilon)):
# Joint limits are not violated
return curve
duration = curve.duration
x0 = curve.x0
x1 = curve.EvalPos(duration)
v0 = curve.v0
v1 = curve.v1
bt0 = inf
bt1 = inf
ba0 = inf
ba1 = inf
bx0 = inf
bx1 = inf
if (v0 > zero):
bt0 = _BrakeTime(x0, v0, xmax)
bx0 = xmax
ba0 = _BrakeAccel(x0, v0, xmax)
elif (v0 < zero):
bt0 = _BrakeTime(x0, v0, xmin)
bx0 = xmin
ba0 = _BrakeAccel(x0, v0, xmin)
if (v1 < zero):
bt1 = _BrakeTime(x1, -v1, xmax)
bx1 = xmax
ba1 = _BrakeAccel(x1, -v1, xmax)
elif (v1 > zero):
bt1 = _BrakeTime(x1, -v1, xmin)
bx1 = xmin
ba1 = _BrakeAccel(x1, -v1, xmin)
# import IPython; IPython.embed()
newCurve = ParabolicCurve()
if ((bt0 < duration) and (Abs(ba0) < Add(am, epsilon))):
# Case IIa
log.debug("Case IIa")
firstRamp = Ramp(v0, ba0, bt0, x0)
if (Abs(Sub(x1, bx0)) < Mul(Sub(duration, bt0), vm)):
tempCurve1 = Interpolate1D(bx0, x1, zero, v1, vm, am)
if not tempCurve1.isEmpty:
if (Sub(duration, bt0) >= tempCurve1.duration):
tempCurve2 = _Stretch1D(tempCurve1, Sub(duration, bt0), vm, am)
if not tempCurve2.isEmpty:
tempbmin, tempbmax = tempCurve2.GetPeaks()
if not ((tempbmin < Sub(xmin, epsilon)) or (tempbmax > Add(xmax, epsilon))):
log.debug("Case IIa successful")
newCurve = ParabolicCurve([firstRamp] + tempCurve2.ramps)
if ((bt1 < duration) and (Abs(ba1) < Add(am, epsilon))):
# Case IIb
log.debug("Case IIb")
lastRamp = Ramp(0, ba1, bt1, bx1)
if (Abs(Sub(x0, bx1)) < Mul(Sub(duration, bt1), vm)):
tempCurve1 = Interpolate1D(x0, bx1, v0, zero, vm, am)
if not tempCurve1.isEmpty:
if (Sub(duration, bt1) >= tempCurve1.duration):
tempCurve2 = _Stretch1D(tempCurve1, Sub(duration, bt1), vm, am)
if not tempCurve2.isEmpty:
tempbmin, tempbmax = tempCurve2.GetPeaks()
if not ((tempbmin < Sub(xmin, epsilon)) or (tempbmax > Add(xmax, epsilon))):
log.debug("Case IIb successful")
newCurve = ParabolicCurve(tempCurve2.ramps + [lastRamp])
if (bx0 == bx1):
# Case III
if (Add(bt0, bt1) < duration) and (max(Abs(ba0), Abs(ba1)) < Add(am, epsilon)):
log.debug("Case III")
ramp0 = Ramp(v0, ba0, bt0, x0)
ramp1 = Ramp(zero, zero, Sub(duration, Add(bt0, bt1)))
ramp2 = Ramp(zero, ba1, bt1)
newCurve = ParabolicCurve([ramp0, ramp1, ramp2])
else:
# Case IV
if (Add(bt0, bt1) < duration) and (max(Abs(ba0), Abs(ba1)) < Add(am, epsilon)):
log.debug("Case IV")
firstRamp = Ramp(v0, ba0, bt0, x0)
lastRamp = Ramp(zero, ba1, bt1)
if (Abs(Sub(bx0, bx1)) < Mul(Sub(duration, Add(bt0, bt1)), vm)):
tempCurve1 = Interpolate1D(bx0, bx1, zero, zero, vm, am)
if not tempCurve1.isEmpty:
if (Sub(duration, Add(bt0, bt1)) >= tempCurve1.duration):
tempCurve2 = _Stretch1D(tempCurve1, Sub(duration, Add(bt0, bt1)), vm, am)
if not tempCurve2.isEmpty:
tempbmin, tempbmax = tempCurve2.GetPeaks()
if not ((tempbmin < Sub(xmin, epsilon)) or (tempbmax > Add(xmax, epsilon))):
log.debug("Case IV successful")
newCurve = ParabolicCurve([firstRamp] + tempCurve2.ramps + [lastRamp])
if (newCurve.isEmpty):
log.warn("Cannot solve for a bounded trajectory")
log.warn("x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; xmin = {4}; xmax = {5}; vm = {6}; am = {7}; duration = {8}".\
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(xmin, n=_prec), mp.nstr(xmax, n=_prec),
mp.nstr(vm, n=_prec), mp.nstr(am, n=_prec), mp.nstr(duration, n=_prec)))
return newCurve
newbmin, newbmax = newCurve.GetPeaks()
if (newbmin < Sub(xmin, epsilon)) or (newbmax > Add(xmax, epsilon)):
log.warn("Solving finished but the trajectory still violates the bounds")
# import IPython; IPython.embed()
log.warn("x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; xmin = {4}; xmax = {5}; vm = {6}; am = {7}; duration = {8}".\
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(xmin, n=_prec), mp.nstr(xmax, n=_prec),
mp.nstr(vm, n=_prec), mp.nstr(am, n=_prec), mp.nstr(duration, n=_prec)))
return ParabolicCurve()
log.debug("Successfully fixed x-bound violation")
return newCurve
f.write("Options for asymptotic Boys function factor sqrt(pi)*(2n-1)!!/(2**(n+1)):\n")
f.write(" Max n: {}\n".format(maxn))
f.write(" DPS: {}\n".format(args.dps))
f.write("------------------------------------\n")
f.write("*/\n\n")
f.write("namespace psr_modules {\n")
f.write("namespace integrals {\n")
f.write("namespace lut {\n")
f.write("\n")
f.write("extern const double boys_longfac[{}] = \n".format(maxn+1))
f.write("{\n")
for i,n in enumerate(longfac):
f.write("/* n = {:4} */ {:32},\n".format(i, mp.nstr(n, 18)))
f.write("};\n")
f.write("\n")
f.write("} // closing namespace lut\n")
f.write("} // closing namespace integrals\n")
f.write("} // closing namespace psr_modules\n")
f.write("\n")
with open(args.filename + ".hpp", 'w') as f:
f.write("#pragma once\n")
f.write("\n")
f.write("#define PSR_MODULES_BOYS_LONGFAC_MAXN {}\n".format(maxn))
f.write("\n")
f.write("namespace psr_modules {\n")
f.write("namespace integrals {\n")
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
# Set the dps option
mp.dps = 256
# Convert stuff to mpmath
inc = mp.mpf(args.spacing)
eps = mp.power(10, -args.eps)
cutoffs = [None] * (args.max_n+1)
cutoff_diffs = [None] * (args.max_n+1)
x = mp.mpf(0)
maxdiff = eps * 2.0 # force loop to run
while maxdiff > eps:
x = x + inc
F_real = BoysValue(args.max_n, x)
F_long = BoysValue_Asymptotic(args.max_n, x)
diff = [ mp.fabs(F_real[i] - F_long[i]) for i in range(0, len(F_real)) ]
maxdiff = max(diff)
for idx,val in enumerate(diff):
if not cutoffs[idx] and diff[idx] < eps:
cutoffs[idx] = x
cutoff_diffs[idx] = val
for idx,val in enumerate(cutoffs):
print("{:4} {}".format(idx, mp.nstr(val)))
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
def _ImposeJointLimitFixedDuration(curve, xmin, xmax, vm, am):
bmin, bmax = curve.GetPeaks()
if (bmin >= Sub(xmin, epsilon)) and (bmax <= Add(xmax, epsilon)):
# Joint limits are not violated
return curve
duration = curve.duration
x0 = curve.x0
x1 = curve.EvalPos(duration)
v0 = curve.v0
v1 = curve.v1
bt0 = inf
bt1 = inf
ba0 = inf
ba1 = inf
bx0 = inf
bx1 = inf
if (v0 > zero):
bt0 = _BrakeTime(x0, v0, xmax)
bx0 = xmax
ba0 = _BrakeAccel(x0, v0, xmax)
elif (v0 < zero):
bt0 = _BrakeTime(x0, v0, xmin)
bx0 = xmin
ba0 = _BrakeAccel(x0, v0, xmin)
if (v1 < zero):
bt1 = _BrakeTime(x1, -v1, xmax)
bx1 = xmax
ba1 = _BrakeAccel(x1, -v1, xmax)
elif (v1 > zero):
bt1 = _BrakeTime(x1, -v1, xmin)
bx1 = xmin
ba1 = _BrakeAccel(x1, -v1, xmin)
# import IPython; IPython.embed()
newCurve = ParabolicCurve()
if ((bt0 < duration) and (Abs(ba0) < Add(am, epsilon))):
# Case IIa
log.debug("Case IIa")
firstRamp = Ramp(v0, ba0, bt0, x0)
if (Abs(Sub(x1, bx0)) < Mul(Sub(duration, bt0), vm)):
tempCurve1 = Interpolate1D(bx0, x1, zero, v1, vm, am)
if not tempCurve1.isEmpty:
if (Sub(duration, bt0) >= tempCurve1.duration):
tempCurve2 = _Stretch1D(tempCurve1, Sub(duration, bt0), vm,
am)
if not tempCurve2.isEmpty:
tempbmin, tempbmax = tempCurve2.GetPeaks()
if not ((tempbmin < Sub(xmin, epsilon)) or
(tempbmax > Add(xmax, epsilon))):
log.debug("Case IIa successful")
newCurve = ParabolicCurve([firstRamp] +
tempCurve2.ramps)
if ((bt1 < duration) and (Abs(ba1) < Add(am, epsilon))):
# Case IIb
log.debug("Case IIb")
lastRamp = Ramp(0, ba1, bt1, bx1)
if (Abs(Sub(x0, bx1)) < Mul(Sub(duration, bt1), vm)):
tempCurve1 = Interpolate1D(x0, bx1, v0, zero, vm, am)
if not tempCurve1.isEmpty:
if (Sub(duration, bt1) >= tempCurve1.duration):
tempCurve2 = _Stretch1D(tempCurve1, Sub(duration, bt1), vm,
am)
if not tempCurve2.isEmpty:
tempbmin, tempbmax = tempCurve2.GetPeaks()
if not ((tempbmin < Sub(xmin, epsilon)) or
(tempbmax > Add(xmax, epsilon))):
log.debug("Case IIb successful")
newCurve = ParabolicCurve(tempCurve2.ramps +
[lastRamp])
if (bx0 == bx1):
# Case III
if (Add(bt0, bt1) < duration) and (max(Abs(ba0), Abs(ba1)) < Add(
am, epsilon)):
log.debug("Case III")
ramp0 = Ramp(v0, ba0, bt0, x0)
ramp1 = Ramp(zero, zero, Sub(duration, Add(bt0, bt1)))
ramp2 = Ramp(zero, ba1, bt1)
newCurve = ParabolicCurve([ramp0, ramp1, ramp2])
else:
# Case IV
if (Add(bt0, bt1) < duration) and (max(Abs(ba0), Abs(ba1)) < Add(
am, epsilon)):
log.debug("Case IV")
firstRamp = Ramp(v0, ba0, bt0, x0)
lastRamp = Ramp(zero, ba1, bt1)
if (Abs(Sub(bx0, bx1)) < Mul(Sub(duration, Add(bt0, bt1)), vm)):
tempCurve1 = Interpolate1D(bx0, bx1, zero, zero, vm, am)
if not tempCurve1.isEmpty:
if (Sub(duration, Add(bt0, bt1)) >= tempCurve1.duration):
tempCurve2 = _Stretch1D(tempCurve1,
Sub(duration, Add(bt0, bt1)),
vm, am)
if not tempCurve2.isEmpty:
tempbmin, tempbmax = tempCurve2.GetPeaks()
if not ((tempbmin < Sub(xmin, epsilon)) or
(tempbmax > Add(xmax, epsilon))):
log.debug("Case IV successful")
newCurve = ParabolicCurve([firstRamp] +
tempCurve2.ramps +
[lastRamp])
if (newCurve.isEmpty):
log.warn("Cannot solve for a bounded trajectory")
log.warn("x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; xmin = {4}; xmax = {5}; vm = {6}; am = {7}; duration = {8}".\
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(xmin, n=_prec), mp.nstr(xmax, n=_prec),
mp.nstr(vm, n=_prec), mp.nstr(am, n=_prec), mp.nstr(duration, n=_prec)))
return newCurve
newbmin, newbmax = newCurve.GetPeaks()
if (newbmin < Sub(xmin, epsilon)) or (newbmax > Add(xmax, epsilon)):
log.warn(
"Solving finished but the trajectory still violates the bounds")
# import IPython; IPython.embed()
log.warn("x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; xmin = {4}; xmax = {5}; vm = {6}; am = {7}; duration = {8}".\
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(xmin, n=_prec), mp.nstr(xmax, n=_prec),
mp.nstr(vm, n=_prec), mp.nstr(am, n=_prec), mp.nstr(duration, n=_prec)))
return ParabolicCurve()
log.debug("Successfully fixed x-bound violation")
return newCurve
def __repr__(self):
bmin, bmax = self.GetPeaks()
return "x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; bmin = {6}; bmax = {7}".\
format(mp.nstr(self.x0, n=_prec), mp.nstr(self.EvalPos(self.duration), n=_prec),
mp.nstr(self.v0, n=_prec), mp.nstr(self.v1, n=_prec), mp.nstr(self.a, n=_prec),
mp.nstr(self.duration, n=_prec), mp.nstr(bmin, n=_prec), mp.nstr(bmax, n=_prec))
longfac[n] = longfac[n - 1] * 0.5 * (2.0 * n - 1)
# Output to file
with open(args.filename + ".h", 'w') as f:
f.write("/*\n")
f.write(" Generated with:\n")
f.write(" " + " ".join(sys.argv[:]))
f.write("\n")
f.write("------------------------------------\n")
f.write(
"Options for long Boys function factor sqrt(pi)*(2n-1)!!/(2**(n+1)):\n"
)
f.write(" Max n: {}\n".format(maxn))
f.write(" DPS: {}\n".format(args.dps))
f.write("------------------------------------\n")
f.write("*/\n\n")
f.write("#pragma once\n")
f.write("\n")
f.write("#define BOYS_LONGFAC_MAXN {}\n\n".format(maxn))
f.write("static const double boys_longfac[BOYS_LONGFAC_MAXN + 1] = \n")
f.write("{\n")
for i, n in enumerate(longfac):
f.write("/* n = {:4} */ {:32},\n".format(i, mp.nstr(n, 18)))
f.write("};\n")
f.write("\n")
mp.log(mp.mpf(1.0) + mp.exp(-x)) + mp.fdiv(x,
mp.exp(x) + mp.mpf(1.0))
for x in EH_eigval
]))
return (S)
minLen = 8
maxLen = 67
pace = 4
r_vec = [1.5]
L_vec = range(minLen, maxLen, pace)
ClosedLoop = False
for r in r_vec:
outputfile = "./SD_of_L_r=" + mp.nstr(r, 2) + ".dat"
f1 = open(outputfile, "w")
Sq = np.zeros(len(L_vec))
iteration = 0
R = r
for L in L_vec: # L is chain length
print(R, L)
mp.dps = L * 4.0 # sets the digits of the decimal numbers
N = int(L / 2)
Np = N
A = list(range(int(L / 4)))
B = list(range(int(L / 4), int(L / 2)))
D = list(range(int(L / 2), int(3 * L / 4)))
C = list(range(int(3 * L / 4), L))
def __init__(self, n, l):
self.number = n
self.log10 = l[0:2] + "\,".join([l[i:i+3] for i in range(2, len(l), 3)])
if __name__ == "__main__":
input_dps = 5
output_dps = 60
rows_per_page = 50
mp.dps = output_dps + 10
table_raw = []
for number in mp.arange(1.0, 10.0, mp.power(10, -(input_dps-1))):
table_raw.append([number, mp.log10(number)])
table_str = []
table_str = [str_item(mp.nstr(row[0], input_dps), mp.nstr(row[1], output_dps)) for row in table_raw]
pages = [table_str[i:i+rows_per_page] for i in range(0, len(table_str), rows_per_page)]
latex_renderer = Environment(
block_start_string = "%{",
block_end_string = "%}",
line_statement_prefix = "%#",
variable_start_string = "%{{",
variable_end_string = "%}}",
loader = FileSystemLoader("."))
template = latex_renderer.get_template("template.tex")
with open("log_table.tex", mode="w") as f:
f.write(template.render(pages=pages))
def perform_rate_analysis(prefix,
ENs,
Evib=0.,
T=473,
p0=1,
N2_frac=0.25,
X=0.01,
site='step',
plot=True,
alkali_promotion=False,
use_alpha=False,
model='effective',
nlevels=1,
dist='Treanor',
Tv=3500,
metals=[],
Agg=False):
if Agg:
import matplotlib
matplotlib.use('Agg')
from mkm import NH3mkm
Rs = []
Ro = []
Rss = []
thetao = []
thetass = []
theta0 = np.zeros(4)
for i, EN in enumerate(ENs):
mod = NH3mkm(T,
EN,
Evib,
p0,
N2_frac,
X=X,
site=site,
alkali_promotion=alkali_promotion,
model=model,
dist=dist,
Tv=Tv,
nlevels=nlevels,
use_alpha=use_alpha)
Rds, rsab = mod.get_sabatier_rate()
Rs.append(rsab)
theta = mod.integrate_odes(theta0=theta0,
h0=1e-24,
rtol=1e-12,
atol=1e-15,
timespan=[0, 1e9],
mxstep=200000)
if not len(metals) == 0:
theta0 = theta
r = mod.get_rates(theta)
theta_and_empty = list(theta) + [1 - sum(theta)]
thetao.append(theta_and_empty)
Ro.append(r[0])
try:
theta = mod.find_steady_state_roots(theta0=theta)
theta_and_empty = list(theta) + [1 - sum(theta)]
theta_and_empty = [mp.nstr(t, 25) for t in theta_and_empty]
if (np.array(theta) > 1).any() or (np.array(theta) < 0.).any():
theta_and_empty = [np.nan] * 5
except ValueError:
# In case there is a tolerance issue
theta_and_empty = [np.nan] * 5
thetass.append(theta_and_empty)
r = mod.get_rates(theta)
Rss.append(r[0])
store_variables(prefix,
ENs,
Rs,
Ro,
Rss,
thetao,
thetass,
T,
p0,
N2_frac,
X,
site,
Evib,
metals=metals)
if not len(metals) and plot:
plot_rates(ENs, Rs, Ro, Rss, prefix)
plot_coverages(ENs, thetao, thetass, prefix)
