Introduction to Distributions¶

When you have a few chains and want to contrast them all with each other, you probably want a summary plot.

To show you how they work, let's make some sample data that all has the same average.

from chainconsumer import Chain, ChainConsumer, Truth, make_sample

# Here's what you might start with
df_1 = make_sample(num_dimensions=4, seed=1, randomise_mean=True)
df_2 = make_sample(num_dimensions=5, seed=2, randomise_mean=True)
print(df_1.head())

Out:

          A         B         C          D  log_posterior
0  1.165687  4.567259  9.776522  14.295710      -7.959083
1 -1.672359  6.250147  9.198599  15.252295      -4.100925
2 -1.842862  5.645696  9.244599  14.286349      -4.885728
3  0.399578  3.775206  8.951355  14.615673      -2.912755
4 -0.555741  4.439733  8.699755  15.788146      -4.023341

Using distributions¶

# And now we give this to chainconsumer
c = ChainConsumer()
c.add_chain(Chain(samples=df_1, name="An Example Contour"))
fig = c.plotter.plot_distributions()

$A = -0.7\pm 1.0$, $B = 4.81^{+0.99}_{-1.01}$, $C = 9.4\pm 1.0$, $D = 14.51^{+1.02}_{-0.99}$

If you want the summary stats you'll need to keep it just one chain. And if you don't want them, you can pass summarise=False to the PlotConfig.

When you add a second chain, you'll see the summaries disappear.

c.add_chain(Chain(samples=df_2, name="Another contour!"))
c.add_truth(Truth(location={"A": 0, "B": 0}))
fig = c.plotter.plot_distributions(col_wrap=3, columns=["A", "B"])