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 -0.883045  4.974523  10.998559  13.157865      -4.447133
1 -2.316235  5.627659   9.021673  14.564376      -6.763575
2  0.029162  3.957694   9.268998  14.759980      -2.753904
3 -2.199486  6.957857   9.797532  14.500153      -4.724817
4  1.651175  1.817449   7.405531  16.073365      -6.567789

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()

$A = -0.7\pm 1.0$, $B = 4.83^{+0.98}_{-1.03}$, $C = 9.4\pm 1.0$, $D = 14.52^{+1.01}_{-1.00}$

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"])