def check(theta, a0, a1, b0, b1):
P = T.find_P(theta, a0, a1, b0, b1)
Qs = St.satoshiTestComment(P)
tlm = T.TLM(T.find_P(np.pi / 2, a0, a1, b0, b1))
wholeSp, thetaNew = St.SPlusCondition(P)
Qw = newTest(theta, a0, a1, b0, b1)
# flag = optimalTheta(a0,a1,b0,b1)
Qn = T.is_exposed(theta, a0, a1, b0, b1, 0.0001)
print(theta, a0, a1, b0, b1)
print(Qs, Qw, Qn, "extremal")
print(tlm, wholeSp)
def newTest(theta, a0, a1, b0, b1):
Pmax = T.find_P(np.pi / 2, a0, a1, b0, b1)
tlm = T.TLM(Pmax)
if tlm:
P = T.find_P(theta, a0, a1, b0, b1)
wholeSp, thetaNew = St.SPlusCondition(P)
if np.abs(thetaNew - theta) > acc:
print("something is wrong with theta")
if wholeSp:
return 1
return 0
def testWagnerPoints(N):
for theta in np.linspace(0, np.pi / 4, N):
P = Pp.WagnerPoints(theta)
b = Pp.getWagnerB(theta)
_, a0, a1, b0, b1 = Pp.WagnerRealisation(theta)
wholeSp, theta = St.SPlusCondition(P)
stlm = St.STLM(P, theta)
if wholeSp and stlm:
Qs = 1
else:
Qs = 0
accuracy = 0.001
Qn = T.is_exposed(theta, a0, a1, b0, b1, accuracy, limit=1)
print(Qn, Qs)
def SpvsThreshold(N):
for i in range(N):
if i % 100 == 0:
print(i)
a0 = np.random.rand() * 2 * np.pi
a1 = np.random.rand() * 2 * np.pi
b0 = np.random.rand() * 2 * np.pi
b1 = np.random.rand() * 2 * np.pi
theta = np.random.rand() * np.pi / 2
thetaB = np.arcsin(T.hypoTreshold(a0, a1, b0, b1))
# print("graniczna", thetaB)
P = T.find_P(theta, a0, a1, b0, b1)
c1, thetan = St.SPlusCondition(P)
if theta >= thetaB:
c2 = 1
else:
c2 = 0
if c1 != c2:
print(c1, c2, "aaaaaa")
def tlmVSstlm(N):
for i in range(N):
if i % 100 == 0:
print(f'{i+1}/{N}')
a0 = np.random.rand() * 2 * np.pi
a1 = np.random.rand() * 2 * np.pi
b0 = np.random.rand() * 2 * np.pi
b1 = np.random.rand() * 2 * np.pi
theta = np.random.rand() * np.pi / 2
P1 = T.find_P(np.pi / 2, a0, a1, b0, b1)
P2 = T.find_P(theta, a0, a1, b0, b1)
tlm = T.TLM(P1)
stlm = St.STLM(P2, theta)
wholeSp, theta = St.SPlusCondition(P2)
# print(tlm, stlm)
if stlm and (not tlm):
print(tlm, stlm)
if stlm and (not tlm) and wholeSp:
print(tlm, stlm, wholeSp)
print("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa")
def functionalBehaviour(N):
for i in range(N):
print(f'{i+1}/{N}')
a0 = np.random.rand() * 2 * np.pi
a1 = np.random.rand() * 2 * np.pi
b0 = np.random.rand() * 2 * np.pi
b1 = np.random.rand() * 2 * np.pi
F1 = []
Tet = []
MAXI = []
tlm = T.TLM(T.find_P(np.pi / 2, a0, a1, b0, b1))
print(tlm)
if tlm:
thetag = T.getThetaFromSinSquared(
T.hypoTreshold(a0, a1, b0, b1)**2)
n = 500
Theta = np.linspace(thetag, np.pi / 2, n)
for theta in Theta:
# a0 = 3/4*np.pi
# a1 = np.pi/4
# b0 = np.pi/2
# b1 = np.pi
P = T.find_P(theta, a0, a1, b0, b1)
wholeSp, thetaNew = St.SPlusCondition(P)
# if wholeSp:
# f1,m1 = T.Best_func(theta, a0, a1, b0, b1)
f2, m2 = T.Best_func_restricted(theta, a0, a1, b0, b1)
# print(m1-m2,"hmm")
# print(f1)
# print(f2)
MAXI.append(m2)
Tet.append(theta)
F1.append(f2)
# plt.plot(Tet,F1)
plt.plot(Tet, MAXI)
plt.show()
def nonnegativitySingularity(N):
for i in range(1):
a0 = 0
a1 = np.random.rand() * 2 * np.pi
b0 = 0
b1 = np.random.rand() * 2 * np.pi
P = T.find_P(np.pi / 2, a0, a1, b0, b1)
# print(P)
tlm = T.TLM(P)
# Sp, Sm = St.SPlusTemp(P)
# print(Sp)
# print(Sm)
# theta = np.random.rand()*np.pi/2
# thetaB = np.arcsin(T.hypoTreshold(a0,a1,b0,b1))
# print(thetaB)
thetaGr = np.arcsin(T.hypoTresholdImproved(a0, a1, b0, b1))
print(thetaGr)
Theta = np.linspace(0, np.pi / 2, N)
for theta in Theta:
# print(theta)
# if i%100 == 0:
# print(i)
P = T.find_P(theta, a0, a1, b0, b1)
stlm = St.STLM(P, theta)
c1, thetan = St.SPlusCondition(P)
if theta >= thetaGr:
c2 = 1
else:
c2 = 0
# sat = St.satoshiTest(P)
# ex = T.is_exposed(theta,a0,a1,b0,b1, 0.001)
if c1 and (not stlm):
stlm2 = St.STLMComment(P, theta)
print(tlm, stlm, c1, c2)