systole.plots.plot_poincare

systole.plots.plot_poincare(rr: Union[ndarray, list], input_type: str = 'peaks', figsize: Optional[Union[int, List[int], Tuple[int, int]]] = None, backend: str = 'matplotlib', ax: Optional[Axes] = None) → Union[figure, Axes]

Poincare plot.

Parameters

rr

Boolean vector of peaks detection or RR intervals.

input_type

The type of input vector. Default is “peaks” (a boolean vector where 1 represents the occurrence of R waves or systolic peaks). Can also be “rr_s” or “rr_ms” for vectors of RR intervals, or interbeat intervals (IBI), expressed in seconds or milliseconds (respectively).

figsize

Figure size. Default is (13, 5).

backend

Select plotting backend {“matplotlib”, “bokeh”}. Defaults to “matplotlib”.

ax

Where to draw the plot. Default is None (create a new figure).

Returns

plot

The matplotlib axes, or the boken figure containing the plot.

See also

plot_frequency

Examples

Visualizing poincare plot from RR time series using Matplotlib as plotting backend.

from systole import import_rr
from systole.plots import plot_poincare

# Import PPG recording as numpy array
rr = import_rr().rr.to_numpy()

plot_poincare(rr, input_type="rr_ms")

<Axes: title={'center': 'Poincare plot'}, xlabel='RR (n)', ylabel='RR (n+1)'>

Using Bokeh backend

from bokeh.io import output_notebook
from bokeh.plotting import show
output_notebook()

from systole import import_rr
from systole.plots import plot_poincare

show(
 plot_poincare(rr, input_type="rr_ms", backend="bokeh")
)

Loading BokehJS ...