Figures

pathomx.figures.category_bar(data, figure=None, styles=None)

pathomx.figures.difference(data1, data2, figure=None, ax=None, styles=None)

pathomx.figures.extend_limits(a, b)

pathomx.figures.find_linear_scale(data)

pathomx.figures.get_text_bbox_data_coords(fig, ax, t)

pathomx.figures.get_text_bbox_screen_coords(fig, t)

pathomx.figures.heatmap(data, figure=None, ax=None, styles=None)

pathomx.figures.histogram(data, bins=100, figure=None, ax=None, styles=None, regions=None)

pathomx.figures.plot_cov_ellipse(cov, pos, nstd=2, **kwargs)

Plots an nstd sigma error ellipse based on the specified covariance matrix (cov). Additional keyword arguments are passed on to the ellipse patch artist.

cov : The 2x2 covariance matrix to base the ellipse on pos : The location of the center of the ellipse. Expects a 2-element

sequence of [x0, y0].

nstd : The radius of the ellipse in numbers of standard deviations.

Defaults to 2 standard deviations.

Additional keyword arguments are pass on to the ellipse patch.

A matplotlib ellipse artist

pathomx.figures.plot_point_cov(points, nstd=2, **kwargs)

Plots an nstd sigma ellipse based on the mean and covariance of a point “cloud” (points, an Nx2 array).

points : An Nx2 array of the data points. nstd : The radius of the ellipse in numbers of standard deviations.

Defaults to 2 standard deviations.

Additional keyword arguments are pass on to the ellipse patch.

A matplotlib ellipse artist

pathomx.figures.scatterplot(data, figure=None, ax=None, styles=None, lines=[], label_index=None)

pathomx.figures.spectra(data, figure=None, ax=None, styles=None, regions=None)