cortex.quickflat.composite.add_custom¶
- cortex.quickflat.composite.add_custom(fig: Axes, dataview: Dataview, svgfile: str, layer: str, extents: tuple[float, float, float, float] | None = None, height: int | None = None, with_labels: bool = False, shape_list: Sequence[str] | None = None, shadow: float | None = None, **kwargs) AxesImage[source]¶
Add a custom data layer
- Parameters:
- figmatplotlib figure
Figure into which to plot the hatches. Should have pycortex flatmap image in it already.
- dataviewcortex.Volume
cortex.Volume object containing
- svgfilestring
Filepath for custom svg file to use. Must be formatted identically to overlays.svg file for subject in dataview
- layerstring
Layer name within custom svg file to display
- extentsarray-like
4 values for [Left, Right, Bottom, Top] 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.
- with_labelsbool
Whether to display text labels on ROIs
- shape_listlist
list of paths/shapes within svg layer to render, if only a subset of the paths/shapes within the layer are desired.
- shadowfloat, optional
Standard deviation of the gaussian shadow. Set to 0 if you want no shadow. None (default) leaves the svg file’s own shadow setting untouched.
- Returns:
- imgmatplotlib.image.AxesImage
matplotlib axes image object for plotted data
- Other Parameters:
- kwargsdict
maps to svg keyword arguments for e.g. line width, color, etc