sisl.mixing.PulayMixer

class sisl.mixing.PulayMixer(weight: float | int = 0.1, history: int | History = 2, metric: Callable[[Any, Any], Any] | None = None)

Bases: DIISMixer

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

Parameters:

• weight (TypeWeight)

• history (TypeArgHistory)

• metric (Optional[TypeMetric])

__call__(f: T, df: T, delta: Any | None = None, append: bool = True) → T

Append data to the history (omitting None values)!

Parameters:

• f (T)

• df (T)

• delta (Any | None)

• append (bool)

Return type:

T

__init__(weight: float | int = 0.1, history: int | History = 2, metric: Callable[[Any, Any], Any] | None = None)

Parameters:

• weight (float | int)

• history (int | History)

• metric (Callable[[Any, Any], Any] | None)

coefficients() → ndarray[Any, dtype[_ScalarType_co]]

Calculate coefficients of the Lagrangian

Return type:

ndarray[Any, dtype[_ScalarType_co]]

property history: History

History object tracked by this mixer

mix(coefficients: ndarray[Any, dtype[_ScalarType_co]]) → Any

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

Parameters:

coefficients (ndarray) – coefficients used for extrapolation

Return type:

Any

set_history(history: int | History) → None

Replace the current history in the mixer with a new one

Parameters:

history (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: 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[ndarray[Any, dtype[_ScalarType_co]], ndarray[Any, dtype[_ScalarType_co]]]

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

Return type:

Tuple[ndarray[Any, dtype[_ScalarType_co]], ndarray[Any, dtype[_ScalarType_co]]]

property weight: float | int

This mixers mixing weight, the weight is the fractional contribution of the derivative