def wavgstd(values, asufloat=False):
"""
Returns the weighted average and standard deviation.
values, weights -- Numpy ndarrays with the same shape.
"""
values, weights = checkufloat(values)
if weights and asufloat is True:
weights = numpy.array(weights)
values = numpy.array(values)
average = numpy.average(values, weights=weights)
variance = numpy.dot(weights, (values - average) ** 2.0) / weights.sum() # Fast and numerically precise
if asufloat:
ufstdev = uncertainties.sqrt(variance)
ufvalue = ufloat((average, ufstdev))
else:
ufvalue = (average, uncertainties.sqrt(variance))
else:
if asufloat:
if isinstance(values[0], uncertainties.UFloat):
ufvalue = numpy.mean(values)
else:
ufvalue = ufloat(str(numpy.mean(values))) # assign minimal error if no std deviation
elif asufloat is False:
ufvalue = (numpy.mean(values), numpy.std(values, ddof=1))
else:
raise NameError('ILL DEFINED VARIABLES')
return ufvalue
import numpy as np
from uncertainties import ufloat
from uncertainties import sqrt
L = ufloat(32.351 * 10**(-3), 0 )
C = ufloat(0.851 * 10**(-9), 0)
Csp = ufloat(0.037 * 10**(-9), 0)
fp = 1 / (2 * np.pi * sqrt(L * C))
print("fp ohne Csp:")
print(fp)
Ck = ufloat(9.99 * 10**(-9),(9.99 * 10**(-9)) * 0.03)
fp = 1 / (2 * np.pi * sqrt(L * (C + Csp)))
fn = 1 / (2 * np.pi * sqrt((L/((1/C)+(2/Ck)) + L * Csp))
#print("fp:")
#print(fp)
#print("fn:")
#print(fn)
#nth = (fn + fp) / (2 * (fn - fp))
#print("nth:")
#print(nth)
#nexp = 12
#abw = (nexp/nth * 100) - 100
#print("abw:")
#print(abw)
Ck = ufloat(8.00 * 10**(-9),(8.00 * 10**(-9)) * 0.03)
#fp = 1 / (2 * np.pi * sqrt(L * (C + Csp)))
#fn = 1 / (2 * np.pi * sqrt((L/((1/C)+(2/Ck)) + L * Csp))
#print("fp:")
