- Vector3d.scatter(projection: str = 'stereographic', figure: Optional[Figure] = None, axes_labels: Optional[List[str]] = None, vector_labels: Optional[List[str]] = None, hemisphere: Optional[str] = None, reproject: bool = False, show_hemisphere_label: Optional[bool] = None, grid: Optional[bool] = None, grid_resolution: Optional[Tuple[float, float]] = None, figure_kwargs: Optional[Dict] = None, reproject_scatter_kwargs: Optional[Dict] = None, text_kwargs: Optional[Dict] = None, return_figure: bool = False, **kwargs: Any) Optional[Figure] #
Plot vectors in the stereographic projection.
Which projection to use. The default is
"stereographic", the only current option.
Which figure to plot onto. Default is
None, which creates a new figure.
Reference frame axes labels, defaults to
[None, None, None].
Vector text labels, which by default are not added.
Which hemisphere(s) to plot the vectors in, defaults to
None, which means
"upper"if a new figure is created, otherwise adds to the current figure’s hemispheres. Options are
"both", which plots two projections side by side.
Whether to reproject vectors onto the chosen hemisphere. Reprojection is achieved by reflection of the vectors located on the opposite hemisphere in the projection plane. Ignored if
"both". Default is
Whether to show hemisphere labels
"lower". Default is
Whether to show the azimuth and polar grid. Default is whatever
axes.gridis set to in
Azimuth and polar grid resolution in degrees, as a tuple. Default is whatever is default in
Dictionary of keyword arguments passed to
Dictionary of keyword arguments for the reprojected scatter points which is passed to
scatter(), which passes these on to
matplotlib.axes.Axes.scatter(). The default marker style for reprojected vectors is
"+". Values used for vector(s) on the visible hemisphere are used unless another value is passed here.
Whether to return the figure (default is
The created figure, returned if
This is a somewhat customizable convenience method which creates a figure with axes using
StereographicPlot, however, it is meant for quick plotting and prototyping. This figure and the axes can also be created using Matplotlib directly, which is more customizable.