sas.qtgui.Utilities.MuMag package

Subpackages

Submodules

sas.qtgui.Utilities.MuMag.MuMag module

class sas.qtgui.Utilities.MuMag.MuMag.MuMag(parent=None)

Bases: QMainWindow, Ui_MuMagTool

Main widget for the MuMag tool

get_fit_parameters() → FitParameters

Get an object containing all the parameters needed for doing the fitting

hide_everything()

Hide all plots, disable tabs

importData()

Callback for the import data button

onFit()
onHelp()
onSave()

Save button pressed

show_fit_results()

Show the results of the fit in the widget

show_input_data()

Plot Experimental Data: Generate Figure

staticMetaObject = PySide6.QtCore.QMetaObject("MuMag" inherits "QMainWindow": )
sas.qtgui.Utilities.MuMag.MuMag.main()

Show a demo of the slider

sas.qtgui.Utilities.MuMag.MuMagLib module

class sas.qtgui.Utilities.MuMag.MuMagLib.MuMagLib

Bases: object

Library for methods supporting MuMag

static directory_popup()
static import_data(directory)

Import experimental data and get information from filenames

static least_squares_parallel(data: list[ExperimentalData], A)

Least squares fitting for a given exchange stiffness, A, parallel case

We are fitting the equation:

I_sim = I_res + response_H * S_H

= (I_res, S_H) . (1, response_H) = (I_res, S_H) . least_squares_x

finding I_res and S_H for each q value

static least_squares_perpendicular(data: list[ExperimentalData], A) → LeastSquaresOutputPerpendicular

Least squares fitting for a given exchange stiffness, A, perpendicular case

We are fitting the equation:

I_sim = I_res + response_H * S_H + response_M * S_M

= (I_res, S_H, S_M) . (1, response_H, response_M) = (I_res, S_H, S_M) . least_squares_x

finding I_res, S_H, and S_M for each q value

logger = <Logger MuMag (WARNING)>
mu_0 = 1.2566370614359173e-06
static nice_log_plot_bounds(data: list[ndarray])

Get nice bounds for the loglog plots

Returns:

(lower, upper) bounds appropriate to pass to plt.xlim/ylim

static refine_exchange_A(data: list[ExperimentalData], exchange_A_initial: float, geometry: ExperimentGeometry, epsilon: float = 0.0001) → LeastSquaresOutputPerpendicular | LeastSquaresOutputParallel

Refines the A parameter using Jarratt’s method of successive parabolic interpolation

static save_data(data: FitResults, directory: str)

Save the data

static simple_fit(data: list[ExperimentalData], parameters: FitParameters)

Main fitting (“simple fit”)

static sweep_exchange_A(parameters: FitParameters, data: list[ExperimentalData]) → SweepOutput

Sweep over Exchange Stiffness A for perpendicular SANS geometry to get an initial estimate which can then be refined

static uncertainty(data: list[ExperimentalData], A_opt: float, geometry: ExperimentGeometry) → float

Calculate the uncertainty for the optimal exchange stiffness A

sas.qtgui.Utilities.MuMag.datastructures module

class sas.qtgui.Utilities.MuMag.datastructures.ExperimentGeometry(*values)

Bases: Enum

Type of experiment

PARALLEL = 1
PERPENDICULAR = 2
class sas.qtgui.Utilities.MuMag.datastructures.ExperimentalData(scattering_curve: Data1D, applied_field: float, saturation_magnetisation: float, demagnetising_field: float)

Bases: object

Datapoint used as input for the MuMag tool

applied_field: float
demagnetising_field: float
restrict_by_index(max_index: int)

Remove all points from data up to given index

saturation_magnetisation: float
scattering_curve: Data1D
exception sas.qtgui.Utilities.MuMag.datastructures.FitFailure

Bases: Exception

Fit failed

class sas.qtgui.Utilities.MuMag.datastructures.FitParameters(q_max: float, min_applied_field: float, exchange_A_min: float, exchange_A_max: float, exchange_A_n: int, experiment_geometry: ExperimentGeometry)

Bases: object

Input parameters for the fit

exchange_A_max: float
exchange_A_min: float
exchange_A_n: int
experiment_geometry: ExperimentGeometry
min_applied_field: float
q_max: float
class sas.qtgui.Utilities.MuMag.datastructures.FitResults(parameters: FitParameters, input_data: list[ExperimentalData], sweep_data: SweepOutput, refined_fit_data: LeastSquaresOutputParallel | LeastSquaresOutputPerpendicular, optimal_exchange_A_uncertainty: float)

Bases: object

Output the MuMag fit

input_data: list[ExperimentalData]
optimal_exchange_A_uncertainty: float
parameters: FitParameters
refined_fit_data: LeastSquaresOutputParallel | LeastSquaresOutputPerpendicular
sweep_data: SweepOutput
class sas.qtgui.Utilities.MuMag.datastructures.LeastSquaresOutput(exchange_A: float, exchange_A_chi_sq: float, q: ndarray, I_simulated: ndarray, I_residual: ndarray, S_H: ndarray, I_residual_stdev: ndarray, S_H_stdev: ndarray)

Bases: object

Output from least squares method

I_residual: ndarray
I_residual_stdev: ndarray
I_simulated: ndarray
S_H: ndarray
S_H_stdev: ndarray
exchange_A: float
exchange_A_chi_sq: float
q: ndarray
class sas.qtgui.Utilities.MuMag.datastructures.LeastSquaresOutputParallel(exchange_A: float, exchange_A_chi_sq: float, q: ndarray, I_simulated: ndarray, I_residual: ndarray, S_H: ndarray, I_residual_stdev: ndarray, S_H_stdev: ndarray)

Bases: LeastSquaresOutput

Output from least squares method for parallel case

class sas.qtgui.Utilities.MuMag.datastructures.LeastSquaresOutputPerpendicular(exchange_A: float, exchange_A_chi_sq: float, q: ndarray, I_simulated: ndarray, I_residual: ndarray, S_H: ndarray, I_residual_stdev: ndarray, S_H_stdev: ndarray, S_M: ndarray, S_M_stdev: ndarray)

Bases: LeastSquaresOutput

Output from least squares method for perpendicular case

S_M: ndarray
S_M_stdev: ndarray
exception sas.qtgui.Utilities.MuMag.datastructures.LoadFailure

Bases: Exception

File loading failed

class sas.qtgui.Utilities.MuMag.datastructures.SweepOutput(exchange_A_checked: ndarray, exchange_A_chi_sq: ndarray, optimal: T)

Bases: Generic[T]

Results from brute force optimisiation of the chi squared for the exchange A parameter

exchange_A_checked: ndarray
exchange_A_chi_sq: ndarray
optimal: T

sas.qtgui.Utilities.MuMag.models module

sas.qtgui.Utilities.MuMag.models.LorentzianModelPAR(q, A, M_s, H_0, H_dem, a_H, l_c)

Lorentzian Model for the generation of clean synthetic test data for parallel SANS geometry

sas.qtgui.Utilities.MuMag.models.LorentzianModelPERP(q, A, M_s, H_0, H_dem, a_H, a_M, l_c)

Lorentzian Model for the generation of clean synthetic test data for perpendicular SANS geometry

sas.qtgui.Utilities.MuMag.models.LorentzianNoisyModelPERP(q, A, M_s, H_0, H_dem, a_H, a_M, l_c, beta)

Lorentzian Model for the generation of noisy synthetic test data for perpendicular SANS geometry

sas.qtgui.Utilities.MuMag.models.SANS_Model_PAR(q, S_H, I_res, M_s, H_0, H_dem, A)

1D-Cross-Section of the parallel model

sas.qtgui.Utilities.MuMag.models.SANS_Model_PERP(q, S_H, S_M, I_res, M_s, H_0, H_dem, A)

1D-Cross-Section of the perendicular model

Module contents