attitude.error package

Submodules

attitude.error.axes module

Functions for converting fit parameters into covariance and hyperbolic axes. These all operate in axis-aligned space—rotation into global coordinate systems should occur after these transformations are applied.

attitude.error.axes.angular_errors(hyp_axes)

Minimum and maximum angular errors corresponding to 1st and 2nd axes of PCA distribution.

Ordered as [minimum, maximum] angular error.

attitude.error.axes.apply_error_level(cov, level)

Adds sigma level to covariance matrix

attitude.error.axes.apply_error_scaling(nominal, errors, variance_on_all_axes=True)

attitude.error.axes.apply_error_scaling_old(nominal, errors, **kw)

This method does not account for errors on the in-plane axes

attitude.error.axes.axis_angular_error(hyp_axes, axis_length)

The angular error for an in-plane axis of given length (either a PCA major axis or an intermediate direction).

attitude.error.axes.babamoradi_axes(fit, confidence_level=0.95, **kw)

attitude.error.axes.eigenvalue_axes(fit, **kw)

attitude.error.axes.fisher_statistic(n, confidence_level, dof=2)

attitude.error.axes.francq_axes(fit, confidence_level=0.95, **kw)

attitude.error.axes.hyperbolic_axes(fit, **kwargs)

attitude.error.axes.jolliffe_axes(fit, confidence_level=0.95, dof=2, **kw)

attitude.error.axes.mean_estimator(data_variance, n)

Get the variance of the mean from a data variance term (e.g. an eigenvalue) and return an estimator of the precision of the mean

attitude.error.axes.noise_axes(fit, **kw)

attitude.error.axes.noise_covariance(fit, dof=2, **kw)

Covariance taking into account the ‘noise covariance’ of the data. This is technically more realistic for continuously sampled data. From Faber, 1993

attitude.error.axes.regression_axes(fit, confidence_level=0.95, **kw)

attitude.error.axes.sampling_axes(fit, **kw)

Hyperbolic axis lengths based on sample-size normal statistics

attitude.error.axes.sampling_covariance(fit, **kw)

attitude.error.axes.statistical_axes(fit, **kw)

Hyperbolic error using a statistical process (either sampling or noise errors)

Integrates covariance with error level and degrees of freedom for plotting confidence intervals.

Degrees of freedom is set to 2, which is the relevant number of independent dimensions to planar fitting of a priori centered data.

attitude.error.axes.variance_axes(fit)

attitude.error.axes.weingarten_axes(fit, confidence_level=0.95)

This is basically meaningless in its current form

attitude.error.bootstrap module

attitude.error.bootstrap.bootstrap(array)

Provides a bootstrap resampling of an array. Provides another statistical method to estimate the variance of a dataset.

For a PCA object in this library, it should be applied to Orientation.array method.

attitude.error.ellipse module

attitude.error.ellipse.ellipse(center, covariance_matrix, level=1, n=1000)

Returns error ellipse in slope-azimuth space

Module contents

attitude.error.asymptotes(hyp, n=1000)

Gets a cone of asymptotes for hyperbola

attitude.error.average_orientation(orientations)

Find the average orientation of a set of fitted or reconstructed orientations, taking into account uncertainty.

attitude.error.error_bounds(hyp_errors)

attitude.error.from_normal_errors(ax1)

Hyperbolic error axis lengths for planes from the equivalent representation for normal vector endpoints

attitude.error.pca_to_mapping(pca, **extra_props)

A helper to return a mapping of a PCA result set suitable for reconstructing a planar error surface in other software packages

kwargs: method (defaults to sampling axes)

attitude.error.to_normal_errors(axes)

A temporary method that gets normal vector errors corresponding with a fitted orientation