tbplas.ParamFit

class tbplas.ParamFit(k_points: ndarray, weights: ndarray = None)

Class for fitting on-site energies and hopping parameters to reference band data.

_k_points

FRACTIONAL coordinates of the k-points corresponding to the band data

_bands_ref

copy of reference band data

_weights

weights of each band during fitting process

__init__(k_points: ndarray, weights: ndarray = None) → None

Parameters:

k_points – (num_kpt, 3) float64 array FRACTIONAL coordinates of the k-points
weights – (num_orb,) float64 array weights of each band for fitting

Methods

`__init__`(k_points[, weights])
`calc_bands_fit`(params)	Method for calculating fitted band data from given parameters. :param params: parameters to fit :return: (num_kpt, num_orb) float64 array band data of the model built from parameters.
`calc_bands_ref`()	Method for calculating reference band data. :return: (num_kpt, num_orb) float64 array reference band data.
`estimate_error`(params)	Object function for minimizing the error between reference and fitted band data.
`fit`(params0, **kwargs)	Fit the parameters to reference band data.

__init__(k_points: ndarray, weights: ndarray = None) → None

Parameters:

k_points – (num_kpt, 3) float64 array FRACTIONAL coordinates of the k-points
weights – (num_orb,) float64 array weights of each band for fitting

abstractmethod calc_bands_fit(params: ndarray) → ndarray: Method for calculating fitted band data from given parameters. :param params: parameters to fit :return: (num_kpt, num_orb) float64 array

band data of the model built from parameters

abstractmethod calc_bands_ref() → ndarray: Method for calculating reference band data. :return: (num_kpt, num_orb) float64 array

reference band data

estimate_error(params: ndarray) → ndarray: Object function for minimizing the error between reference and fitted band data. :param params: parameters to fit :return: flattened difference between band data

fit(params0: ndarray, **kwargs) → ndarray: Fit the parameters to reference band data. :param params0: initial value of parameters to fit :param kwargs: keyword parameters for the ‘leastsq’ function of scipy :return: the optimal parameters after fitting :raises RuntimeError: if the fitting fails