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 -0.987396  4.916078   8.926030  15.202678      -2.534576
1 -0.250944  3.985085   9.096198  14.948847      -2.544312
2 -0.515510  4.849797   9.212124  14.870106      -2.040490
3 -3.148086  6.719520  11.727416  12.363656      -5.312591
4 -1.610575  5.181521  10.463630  13.487482      -2.517751

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()

$A = -0.7\pm 1.0$, $B = 4.78^{+1.04}_{-0.99}$, $C = 9.42^{+0.99}_{-1.01}$, $D = 14.50^{+1.04}_{-0.98}$

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"])