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feat: multi parameter root feature added.

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Fabian Joswig 2023-01-10 10:28:12 +00:00
parent 43cf9c29d4
commit a45e20b51c
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1 changed files with 7 additions and 5 deletions
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12
pyerrors/roots.py
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@ -27,15 +27,17 @@ def find_root(d, func, guess=1.0, **kwargs):
Obs
`Obs` valued root of the function.
'''
root = scipy.optimize.fsolve(func, guess, d.value)
d_val = np.vectorize(lambda x: x.value)(np.array(d))
root = scipy.optimize.fsolve(func, guess, d_val)
# Error propagation as detailed in arXiv:1809.01289
dx = jacobian(func)(root[0], d.value)
dx = jacobian(func)(root[0], d_val)
try:
da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
da = jacobian(lambda u, v: func(v, u))(d_val, root[0])
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within root functions, see the documentation for details.") from None
deriv = - da / dx
res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (d.value + np.finfo(np.float64).eps) * root[0], [d], man_grad=[deriv])
res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (np.array(d).reshape(-1)[0].value + np.finfo(np.float64).eps) * root[0],
np.array(d).reshape(-1), man_grad=np.array(deriv).reshape(-1))
return res