Note
Click here to download the full example code
KDE¶
I don't recommend using KDEs in general, as its very easy to have them inflate your contours if the bandpass isn't tuned well, and its hard to see when it's too large.
Always run more samples if you can, instead of covering up a lack of data with some extra smoothing.
But, if there's no other way, here's how you can do it.
Notice how the KDE, unless its perfectly matched to your distribution, increses the width of the marginal distributions.
Out:
/home/runner/work/ChainConsumer/ChainConsumer/src/chainconsumer/analysis.py:299: DeprecationWarning: 'scipy.integrate.simps' is deprecated in favour of 'scipy.integrate.simpson' and will be removed in SciPy 1.14.0
area = simps(ys, x=kde_xs)
from chainconsumer import Chain, ChainConsumer, PlotConfig, make_sample
df = make_sample(num_dimensions=2, seed=3, num_points=1000)
c = ChainConsumer()
c.add_chain(Chain(samples=df, name="No KDE"))
c.add_chain(Chain(samples=df + 1, name="KDE", kde=1.0))
c.add_chain(Chain(samples=df + 2, name="KDE that's too large", kde=2.0))
c.set_plot_config(PlotConfig(flip=True))
fig = c.plotter.plot()
Total running time of the script: ( 0 minutes 12.423 seconds)
Download Python source code: plot_2_kde.py