NumPy Integration

Using Jax to provide auto-differentiation capabilities (either jax.grad or jax.jacfwd), linear uncertainty propagation is enabled for most numpy operations.

>>> from auto_uncertainties import Uncertainty
>>> import numpy as np
>>> value = np.linspace(start=0, stop=10, num=5)
>>> error = np.ones_like(value)*0.1
>>> u = Uncertainty(value, error)
>>> u
[0 +/- 0.1, 2.5 +/- 0.1, 5 +/- 0.1, 7.5 +/- 0.1, 10 +/- 0.1]

>>> from auto_uncertainties import Uncertainty
>>> import numpy as np
>>> value = np.linspace(start=0, stop=10, num=5)
>>> error = np.ones_like(value)*0.1
>>> u = Uncertainty(value, error)
>>> np.exp(u)
[1 +/- 0.1, 12.1825 +/- 1.21825, 148.413 +/- 14.8413, 1808.04 +/- 180.804, 22026.5 +/- 2202.65]
>>> np.sum(u)
25 +/- 0.223607
>>> u.sum()
25 +/- 0.223607
>>> np.sqrt(np.sum(error**2))
0.223606797749979