cortex.quickflat.composite.add_hatch

cortex.quickflat.composite.add_hatch(fig: Axes, hatch_data: Dataview, extents: tuple[float, float, float, float] | None = None, height: int | None = None, hatch_space: int = 4, hatch_color: tuple[int, int, int] = (0, 0, 0), sampler: str = 'nearest', recache: bool = False) → AxesImage

Add hatching to figure at locations specified in hatch_data

Parameters:

figmatplotlib figure

Figure into which to plot the hatches. Should have pycortex flatmap image in it already.

hatch_datacortex.Volume

cortex.Volume object created from data scaled from 0-1; locations with values of 1 will have hatching overlaid on them in the resulting image.

extentsarray-like

4 values for [Left, Right, Top, Bottom] extents of image plotted. If None, defaults to extents of images already present in figure.

heightscalar

Height of image. if None, defaults to height of images already present in figure.

hatch_spacescalar

Spacing between hatch lines, in pixels

hatch_color3-tuple

(R, G, B) tuple for color of hatching. Values for R,G,B should be 0-1

samplerstr

Name of sampling function used to sample underlying volume data. Options include ‘trilinear’,’nearest’,’lanczos’; see functions in cortex.mapper.samplers.py for all options

recacheboolean

Whether or not to recache intermediate files. Takes longer to plot this way, potentially resolves some errors.

Returns:

imgmatplotlib.image.AxesImage

matplotlib axes image object for plotted hatch image

Notes

Possibly to add: add hatch_width, hatch_offset arguments.