estimate_xtilt#

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

Estimate the tilt about the detector \(X_d\) axis.

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

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 radians (False, default) or degrees (True).
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 assumed to be True and the value passed is not considered.
figure_kwargs: Keyword arguments passed to matplotlib.pyplot.Figure() if plot=True.

Returns:

x_tilt: Estimated tilt about detector \(X_d\) in radians (degrees=False) or degrees (degrees=True).
outliers: Returned if return_outliers=True, in the shape of navigation_shape.
fig: Returned if plot=True and return_figure=True.

Examples using `EBSDDetector.estimate_xtilt`#

Estimate tilt about the detector x axis

estimate_xtilt#

Examples using EBSDDetector.estimate_xtilt#

Examples using `EBSDDetector.estimate_xtilt`#