thztools.NoiseResult#

class thztools.NoiseResult(noise_model, mu, a, eta, fval, hess_inv, err_sigma_alpha, err_sigma_beta, err_sigma_tau, err_mu, err_a, err_eta, diagnostic)[source]#

Dataclass for the output of noisefit.

Parameters:

noise_modelNoiseModel: Noise parameters, represented as a NoiseModel object.
mundarray, shape (n,): Signal vector.
andarray, shape (m,): Signal amplitude drift vector.
etandarray, shape (m,): Signal delay drift vector.
fvalfloat: Value of optimized NLL cost function.
hess_invndarray: Inverse Hessian matrix of optimized NLL cost function.
err_sigma_alpha, err_sigma_beta, err_sigma_taufloat: Estimated uncertainty in the noise model parameters. Set equal to 0.0 when the parameter is fixed.
err_mundarray: Estimated uncertainty in mu.
err_andarray: Estimated uncertainty in a.
err_etandarray: Estimated uncertainty in eta.
diagnosticscipy.optimize.OptimizeResult: Instance of scipy.optimize.OptimizeResult returned by scipy.optimize.minimize. Note that the attributes fun, jac, and hess_inv represent functions over the internally scaled parameters.

Attributes

noise_model	(NoiseModel) Noise parameters, represented as a `NoiseModel` object.
mu	(ndarray, shape (n,)) Signal vector.
a	(ndarray, shape (m,)) Signal amplitude drift vector.
eta	(ndarray, shape (m,)) Signal delay drift vector.
fval	(float) Value of optimized NLL cost function.
hess_inv	(ndarray) Inverse Hessian matrix of optimized NLL cost function.
err_sigma_alpha, err_sigma_beta, err_sigma_tau	(float) Estimated uncertainty in the noise model parameters. Set equal to 0.0 when the parameter is fixed.
err_mu	(ndarray) Estimated uncertainty in `mu`.
err_a	(ndarray) Estimated uncertainty in `a`.
err_eta	(ndarray) Estimated uncertainty in `eta`.
diagnostic	(scipy.optimize.OptimizeResult) Instance of `scipy.optimize.OptimizeResult` returned by `scipy.optimize.minimize`. Note that the attributes `fun`, `jac`, and `hess_inv` represent functions over the internally scaled parameters.