ecdf

xarray_einstats.numba.ecdf(da, dims=None, *, npoints=None, **kwargs)

Compute the x and y values of ecdf plots in a vectorized way.

Parameters:

daxarray.DataArray

Input data containing the samples on which we want to compute the ecdf.

dimsstr or iterable of str, optional

Dimensions over which the ecdf should be computed. They are flattened and converted to a quantile dimension that contains the values to plot; the other dimensions should be used for facetting and aesthetics. The default is computing the ecdf over the flattened input.

npointsint, optional

Number of points on which to evaluate the ecdf. It defaults to the minimum between 200 and the total number of points in each block defined by dims.

**kwargsdict, optional

Keyword arguments passed as-is to xarray.apply_ufunc through searchsorted.

Returns:

xarray.Dataset

Dataset with two data variables: x and y with the values to plot.

Warning

New and experimental feature, its API might change.

Examples

Compute and plot the ecdf over all the data:

from xarray_einstats import tutorial, numba
import matplotlib.pyplot as plt

ds = tutorial.generate_mcmc_like_dataset(3)
out = numba.ecdf(ds["mu"], dims=("chain", "draw", "team"))
plt.plot(out["x"], out["y"], drawstyle="steps-post");

Compute vectorized ecdf values to plot multiple subplots and multiple lines in each with different hue:

out = numba.ecdf(ds["mu"], dims="draw")
out["y"].assign_coords(x=out["x"]).plot.line(
    x="x", hue="chain", col="team", col_wrap=3, drawstyle="steps-post"
);