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 -1.465718  5.174256  10.635668  13.410997      -2.720730
1 -0.263183  4.116389   9.158892  14.286064      -2.753526
2  1.964528  3.259570   7.403567  16.099272      -5.779732
3  1.391785  3.090585   7.078695  16.545356      -4.882716
4 -0.903425  3.219918  10.635006  13.477061      -7.056437

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()

$A = -0.7\pm 1.0$, $B = 4.8\pm 1.0$, $C = 9.42^{+1.00}_{-1.01}$, $D = 14.51^{+1.02}_{-0.98}$

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"])