effective_variance¶
- nbi_stat.effective_variance(x, ex, f, p, ey, df=None, df_step=None)[source]¶
Calculates the effective variance
That is, the function calculates the square uncertainty on
d = y - f(x)
as
ed**2 = ey**2 + diff(f(x,p),x)**2 * ex**2
where diff is the derivative of f wrt to x. The derivate can be given as a callable, or be calculated numerically
- Parameters:
x (array) – Independent variable
ex (array) – Uncertainty on x
f (callable) –
A callable representing f, with the signature
f(x,*p)
ey (array) – Uncertainty on y
df (callable) –
The differential of f wrt x. A function of the form
df(x,*p)
df_step (None, float, array) – The step size to use when evaluating the differential numerically
- Returns:
ed2 – The squared effective variance
- Return type:
array
See also