md = 552.3e-3
dmd = 0.1e-3
rd = 104.9e-3 / 2.0
drd = 0.5e-3 / 2.0
T1 = [24.10, 23.98, 24.18, 24.02, 24.07]
T2 = [33.15, 33.50, 33.23, 33.38, 33.09]
mbp = 679.00e-3
dmbp = 0.20e-3
T = [[46.56, 46.64, 46.62], [46.75, 46.73, 46.64], [47.54, 47.39, 47.39],
[48.39, 48.20, 48.32], [49.96, 50.17, 50.09], [52.50, 52.48, 52.46]]
a = [0.0e-3, 5.7e-3, 11.8e-3, 18.6e-3, 29.9e-3, 36.8e-3]
da = [0.6e-3, 0.6e-3, 0.6e-3, 0.6e-3, 0.6e-3, 0.6e-3]
# Measurement series reduction
dT1 = ms.std_dev_m(T1) / 20
T1 = ms.mean_value(T1) / 20
dT2 = ms.std_dev_m(T2) / 20
T2 = ms.mean_value(T2) / 20
dT = [ms.std_dev_m(T[i]) / 20 for i in range(len(T))]
T = [ms.mean_value(T[i]) / 20 for i in range(len(T))]
# 1.1 Determination of the deflecting force by torque-deflection angle-dependency measurement
M = []
dM = []
for i in range(len(m)):
M.append(m[i] * g * r)
dM.append(
ms.sqrt((m[i] * g * dr)**2 + (m[i] * r * dg)**2 + (g * r * dm[i])**2))
Dt = ms.reg_grad(phi, M, dPhi, dM) * 180.0 / ms.pi
pl = 100980.0
dpl = 60.0
t = 982.89
n = 500
s = [8e-3, 16e-3, 21e-3, 26e-3, 34e-3, 38e-3, 42e-3, 48e-3, 50e-3, 54e-3, 58e-3, 60e-3, 64e-3, 67e-3, 70e-3, 73e-3, 76e-3, 78e-3, 81e-3, 84e-3, 85e-3]
ds = [3e-3 for i in range(len(s))]
sn = 119e-3
dsn = 1e-3
sa = 852e-3
dsa = 3e-3
# Preparation
lp = [lpo[i] - lpu[i] for i in range(len(lpo))]
dlp = ms.std_dev_m(lp) # "+" ms.sqrt(2) * dlp # If one uses the addition of the systematic and statistic uncertainty, it must be quadratic
lp = ms.mean_value(lp)
l = lp - rk
dl = ms.sqrt(dlp**2 + drk**2)
dt = ms.std_dev_e(t0)
T0 = ms.mean_value(t0) / n0
n1 = 2 * l * dt / (0.3 * T0 * dl)
print()
print(ms.val("n1", int(n1 + 0.5)))
print()
print(ms.val("gl", gl, dgl))
# Simple calculation
T = t / n
dT = dt / n
gs = 4 * ms.pi**2 * l / T**2
fl1 = 1213
dfl1 = 13
fr1 = 1293
dfr1 = 13
fl2 = 1187
dfl2 = 13
fr2 = 1307
dfr2 = 13
bmax1 = 8.3
dbmax1 = 0.3
bmax2 = 4.9
dbmax2 = 0.1
# Measure series reduction
dT = ms.std_dev_m(T) / 20
T = ms.mean_value(T) / 20
dT1 = ms.std_dev_m(T1) / 15
T1 = ms.mean_value(T1) / 15
dT2 = ms.std_dev_m(T2) / 10
T2 = ms.mean_value(T2) / 10
print()
print(ms.val("T0", T, dT))
print(ms.val("T1", T1, dT1))
print(ms.val("T2", T2, dT2))
# Damping constant calculated with time of half amplitude
t1 = n1 * T1
dt1 = ms.sqrt((n1 * dT1)**2 + (T1 * dn1)**2)
t2 = n2 * T2
s2 = 160.0e-3 d_s2 = 3.0e-3 s3 = 360.0e-3 d_s3 = 3.0e-3 s4 = 640.0e-3 d_s4 = 3.0e-3 s5 = 60.0e-3 d_s5 = 1.0e-3 s6 = 101e-3 d_s6 = 1.0e-3 s56 = s6 - s5 d_s56 = m.sqrt(d_s6**2 + d_s5**2) # Zeiten tmp = [0.360, 0.365, 0.369, 0.357, 0.361] t1_vz = ms.mean_value(tmp) d_t1_vz = ms.std_dev_m(tmp) tmp = [0.725, 0.730, 0.734, 0.722, 0.725] t2_vz = ms.mean_value(tmp) d_t2_vz = ms.std_dev_m(tmp) tmp = [1.085, 1.091, 1.093, 1.081, 1.085] t3_vz = ms.mean_value(tmp) d_t3_vz = ms.std_dev_m(tmp) tmp = [1.440, 1.445, 1.447, 1.435, 1.438] t4_vz = ms.mean_value(tmp) d_t4_vz = ms.std_dev_m(tmp) tmp = [1.744, 1.746, 1.741, 1.744, 1.740] t5_vz = ms.mean_value(tmp) d_t5_vz = ms.std_dev_m(tmp) tmp = [1.780, 1.782, 1.777, 1.781, 1.777] t6_vz = ms.mean_value(tmp) d_t6_vz = ms.std_dev_m(tmp)
g = 9.81
d = 6.00e-3
U = 501
b = 7.78e-3
p = 9.98e4
sf = [10, 10, 10, 10, 10]
tf = [11.64, 13.26, 12.90, 12.33, 12.19]
ss = [10, 10, 10, 10, 10]
ts = [13.92, 16.16, 15.58, 16.59, 15.88]
r = [0.0, 0.0, 0.0, 0.0, 0.0]
q = [0.0, 0.0, 0.0, 0.0, 0.0]
eta = [0.0, 0.0, 0.0, 0.0, 0.0]
for i in range(5):
sf[i] *= 5e-5
ss[i] *= 5e-5
r[i] = 3 * m.sqrt((eta0 * sf[i]) / (2 * rho * g * tf[i]))
eta[i] = eta0 / (1.0 + b / (r[i] * p))
q[i] = (sf[i] / tf[i] + ss[i] / ts[i]) * 18 * m.pi * d / U * m.sqrt(
sf[i] * eta[i]**3 / (2 * tf[i] * rho * g))
print()
print(r)
print(eta)
print(q)
print(ms.mean_value(q))
print(ms.std_dev_m(q))
print(ms.std_dev_e(q))
#print(f_mv)
#print(d_f_mv)
# chromatische Aberration
L = 0.65
d_L = 1.0e-3
s1 = [[0.360, 0.363, 0.358], [0.353, 0.359, 0.35]]
d_s1 = [0.0, 0.0]
s2 = [[0.691, 0.69, 0.69], [0.693, 0.688, 0.693]]
d_s2 = [0.0, 0.0]
d = [0.0, 0.0]
d_d = [0.0, 0.0]
f = [0.0, 0.0]
d_f = [0.0, 0.0]
for i in range(2):
d_s1[i] = ms.std_dev_m(s1[i])
s1[i] = ms.mean_value(s1[i])
d_s2[i] = ms.std_dev_m(s2[i])
s2[i] = ms.mean_value(s2[i])
d[i] = s2[i] - s1[i]
d_d[i] = m.sqrt(d_s2[i]**2 + d_s1[i]**2)
f[i] = (L**2 - d[i]**2) / (4 * L)
d_f[i] = m.sqrt(((L**2 + d[i]**2) * d_L / (2 * L))**2 +
(d[i] * d_d[i])**2) / (2 * L)
sigma = [abs(f[0] - f[1]) / d_f[0], abs(f[0] - f[1]) / d_f[1]]
#print()
#print(d)
#print(d_d)
#print()
#print(f)