agepy.mag.hysteresis.faraday_correction

faraday_correction(*hysteresis, initial_guess, saturation_percentage=10, log_level=20)

Performs a correction of the Faraday-effect via analytic optimization method.

Note: It is recommended to use optimzed_verdet_correction instead as it removes the additional guess of the cos amplitude.

Performs optimization of a functional describing the difference between saturation region and analytic expression of the Faraday- effect. The correction might need to be combined with other correction methods (thermal drift, relative shifting, …) to obtain proper looking hysteresis curves. Note, that the optimized functional considers the linearly corrected data, but the function itself does not subtract the linear part. Hysteresis data can be supplied either as one tuple containing the magntic field and magnetization repectively or as multiple tuples containing the two hysteresis branches.

Parameters:

*hysteresis: ArrayLike

One or two 2-D arrays of shape (2, n) containing the magnetic field and magnetization of length n in the first axis.

initial_guess: Sequence[float, float, float]

Initial guess for verdet constant of the objective lens, amplitude and left shift of cos^2 function.

saturation_percentage: float, default 10

Percentage value of points in at the end of the hysteresis branches belonging to the saturation region.

log_level: int, default 10

log behavior of python.logging module for the optimization results.

Returns:

NDArray

The corrected magnetization values. If hysteresis is supplied as whole sequence , then a 1-D array with the same length as the magnetization is returned. If hysteresis is two branches, a 2-D array is returned containing the two magnetization branches in the first axis.

Return type:

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

Examples

Viable example inputs.

>>> faraday_correction((H, M), initial_guess=(0.01, 5, 100),
                        saturation_percentage=10)
array([<M-values>])

>>> faraday_correction((H1, M1), (H2, M2), initial_guess=(0.01, 5, 100),
                        saturation_percentage=10)
array([[<M1-values>], [<M2-values>]])

>>> hyst = array([H, M])
>>> faraday_correction(hyst, initial_guess=(0.01, 5, 100),
                        saturation_percentage=10)
array([<M-values>])

>>> hyst = array([[H1, M1], [H2, M2]])
>>> faraday_correction(*hyst, initial_guess=(0.01, 5, 100),
                        saturation_percentage=10)
array([[<M1-values>], [<M2-values>]])