- 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] #
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.
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.
Whether to plot data points and the estimated line. Default is
Whether to return the estimated tilt in radians (
False, default) or degrees (
Whether to return the plotted figure. Default is
Whether to return a mask with
Truefor PC values considered outliers. Default is
detect_outliersis assumed to be
Trueand the value passed is not considered.
Keyword arguments passed to
Estimated tilt about detector \(X_d\) in radians (
degrees=False) or degrees (
return_outliers=True, in the shape of
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.