sisl.mixing.PulayMixer

class sisl.mixing.PulayMixer(weight=0.1, history=2, metric=None)

Methods

`coefficients`()	Calculate coefficients of the Lagrangian
`mix`(coefficients)	Calculate a new variable \(f'\) 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
`history`	History object tracked by this mixer
`weight`	This mixers mixing weight, the weight is the fractional contribution of the derivative

__call__(f, df, delta=None, append=True)

Append data to the history (omitting None values)!

coefficients()

Calculate coefficients of the Lagrangian

Return type: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

mix(coefficients)

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

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

set_history(history)

Replace the current history in the mixer with a new one

Parameters: history (Union[int, 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.
Return type: None

set_weight(weight)

Set a new weight for this mixer

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

solve_lagrange()

Calculate the coefficients according to Pulay’s method, return everything + Lagrange multiplier

Return type: Tuple[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]], ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]

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