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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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pyerrors/roots.py
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@ -27,15 +27,17 @@ def find_root(d, func, guess=1.0, **kwargs):
Obs Obs
`Obs` valued root of the function. `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 # Error propagation as detailed in arXiv:1809.01289
dx = jacobian(func)(root[0], d.value) dx = jacobian(func)(root[0], d_val)
try: 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: except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within root functions, see the documentation for details.") from None 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 deriv = - da / dx
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],
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]) np.array(d).reshape(-1), man_grad=np.array(deriv).reshape(-1))
return res return res