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  0.669882  3.819161   8.467454  15.254021      -2.901461
1 -0.122759  5.017488   8.669994  15.114327      -2.725776
2  0.245389  4.476860   9.198622  15.034753      -3.287342
3 -2.490597  6.430734  10.883342  13.031819      -4.017329
4 -1.774488  5.114378  10.198350  13.224094      -3.925202

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()

$A = -0.71^{+0.99}_{-1.03}$, $B = 4.82^{+0.99}_{-1.02}$, $C = 9.42^{+0.99}_{-1.02}$, $D = 14.55^{+0.98}_{-1.03}$

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"])