estimate_xtilt

EBSDDetector.estimate_xtilt(detect_outliers: bool = False, plot: bool = True, degrees: bool = False, return_figure: Literal[False] = False, return_outliers: Literal[False] = False, figure_kwargs: dict | None = None) → float

EBSDDetector.estimate_xtilt(detect_outliers: bool = False, plot: bool = True, degrees: bool = False, return_figure: Literal[False] = False, return_outliers: Literal[True] = False, figure_kwargs: dict | None = None) → tuple[float, ndarray]

EBSDDetector.estimate_xtilt(detect_outliers: bool = False, plot: Literal[True] = True, degrees: bool = False, return_figure: Literal[True] = False, return_outliers: Literal[False] = False, figure_kwargs: dict | None = None) → tuple[float, mfigure.Figure]

EBSDDetector.estimate_xtilt(detect_outliers: bool = False, plot: Literal[True] = True, degrees: bool = False, return_figure: Literal[True] = False, return_outliers: Literal[True] = False, figure_kwargs: dict | None = None) → tuple[float, np.ndarray, mfigure.Figure]

EBSDDetector.estimate_xtilt(detect_outliers: bool = False, plot: Literal[False] = True, degrees: bool = False, return_figure: Literal[True] = False, return_outliers: Literal[False] = False, figure_kwargs: dict | None = None) → float

EBSDDetector.estimate_xtilt(detect_outliers: bool = False, plot: Literal[False] = True, degrees: bool = False, return_figure: Literal[True] = False, return_outliers: Literal[True] = False, figure_kwargs: dict | None = None) → tuple[float, ndarray]

Return an estimate of the tilt about the detector \(X_d\) axis.

The tilt is assumed to bring the sample plane normal into coincidence with the detector plane normal (but in the opposite direction). See [Winkelmann et al., 2020] for further details.

See the reference frame tutorial for details on the detector-sample geometry.

An estimate is found by linear regression of pcz vs. pcy.

Parameters:

detect_outliers

Whether to attempt to detect outliers. If False (default), a linear fit to all points is performed. If True, a robust fit using the RANSAC algorithm is performed instead, which also detects outliers.

plot

Whether to plot data points and the estimated line. Default is True.

degrees

Whether to return the estimated tilt in degrees, radians otherwise.

return_figure

Whether to return the plotted figure. Default is False.

return_outliers

Whether to return a mask with True for PC values considered outliers. Default is False. If True, detect_outliers is set to True.

figure_kwargs

Keyword arguments passed to matplotlib.pyplot.Figure() if plot is True.

Returns:

x_tilt

Estimated tilt about detector \(X_d\) in radians, unless degrees is True.

outliers

Returned if return_outliers is True, in the shape of navigation_shape.

fig

Returned if plot is True and return_figure is True.

See also

sklearn.linear_model.LinearRegression

sklearn.linear_model.RANSACRegressor

estimate_xtilt_ztilt()

fit_pc()

Notes

This method is adapted from Aimo Winkelmann’s function fit_xtilt() in the xcdskd Python package. See [Winkelmann et al., 2020] for their use of related functions.

Examples using EBSDDetector.estimate_xtilt

Estimate tilt about the detector x axis

Estimate tilt about the detector x axis