sisl.mixing.PulayMixer

class sisl.mixing.PulayMixer(weight: TypeWeight = 0.1, history: TypeArgHistory = 2, metric: TypeMetric | None = None)

Methods

`coefficients`()	Calculate coefficients of the Lagrangian
`mix`(coefficients)	Calculate a new variable \(\mathbf f'\) using history and input coefficients
`set_history`(history)	Replace the current history in the mixer with a new one
`set_weight`(weight)	Set a new weight for this mixer
`solve_lagrange`()	Calculate the coefficients according to Pulay's method, return everything + Lagrange multiplier

Attributes

`history`	History object tracked by this mixer
`weight`	This mixers mixing weight, the weight is the fractional contribution of the derivative

__call__(f: T, df: T, delta: Any | None = None, append: bool = True) → T: Append data to the history (omitting None values)!

coefficients() → npt.NDArray[np.float64]: Calculate coefficients of the Lagrangian

mix(coefficients: numpy.typing.ArrayLike) → Any

Calculate a new variable \(\mathbf f'\) using history and input coefficients

Parameters:: coefficients (numpy.ndarray) – coefficients used for extrapolation

set_history(history: int | History) → None

Replace the current history in the mixer with a new one

Parameters:: history – if an int a new History object will be created with that number of history elements Otherwise the object will be directly attached to the mixer.

set_weight(weight: float | int)

Set a new weight for this mixer

Parameters:: weight (float) – the new weight for this mixer, it must be bigger than 0

solve_lagrange() → tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: Calculate the coefficients according to Pulay’s method, return everything + Lagrange multiplier

property weight: float | int: This mixers mixing weight, the weight is the fractional contribution of the derivative