import matplotlib.pyplot as plt
import numpy as np
import uncertainties.unumpy as unp
from scipy.optimize import curve_fit
from uncertainties import correlated_values, correlation_matrix
from uncertainties import ufloat
from uncertainties.unumpy import (nominal_values as noms, std_devs as stds)

C = ufloat(2.098, 0.006)*1e-9
L = ufloat(10.11 , 0.03)*1e-3
Ra =(unp.sqrt((4*L)/C))
R = 3.3*1e3
print(((Ra-R)/Ra))

06775788f8565cae46a8c43a6fb14b8bf036e79b
<mask token>

<mask token>
print((Ra - R) / Ra)

<mask token>
C = ufloat(2.098, 0.006) * 1e-09
L = ufloat(10.11, 0.03) * 0.001
Ra = unp.sqrt(4 * L / C)
R = 3.3 * 1000.0
print((Ra - R) / Ra)

import matplotlib.pyplot as plt
import numpy as np
import uncertainties.unumpy as unp
from scipy.optimize import curve_fit
from uncertainties import correlated_values, correlation_matrix
from uncertainties import ufloat
from uncertainties.unumpy import nominal_values as noms, std_devs as stds
C = ufloat(2.098, 0.006) * 1e-09
L = ufloat(10.11, 0.03) * 0.001
Ra = unp.sqrt(4 * L / C)
R = 3.3 * 1000.0
print((Ra - R) / Ra)

import matplotlib.pyplot as plt
import numpy as np
import uncertainties.unumpy as unp
from scipy.optimize import curve_fit
from uncertainties import correlated_values, correlation_matrix
from uncertainties import ufloat
from uncertainties.unumpy import (nominal_values as noms, std_devs as stds)

C = ufloat(2.098, 0.006)*1e-9
L = ufloat(10.11 , 0.03)*1e-3
Ra =(unp.sqrt((4*L)/C))
R = 3.3*1e3
print(((Ra-R)/Ra))
