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 -0.687355  3.601443   9.064553  14.633026      -3.246376
1 -0.824088  4.231175  10.127202  14.391801      -5.019803
2 -1.195150  6.151881   9.094098  15.333396      -3.719671
3 -0.704756  5.168052   8.848108  15.067243      -2.384184
4 -0.274928  4.565905   9.046535  14.146517      -4.259976

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()

$A = -0.75^{+1.02}_{-0.99}$, $B = 4.8\pm 1.0$, $C = 9.42^{+0.99}_{-1.03}$, $D = 14.56^{+0.98}_{-1.04}$

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"])