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 -0.469914  3.256283   9.867501  13.207609      -5.562992
1 -0.848919  5.498176   9.603388  13.661533      -5.115437
2 -2.153234  5.322640  10.829578  13.099793      -3.269516
3 -1.240623  4.946374  10.445236  13.958658      -3.537487
4 -0.479193  3.951049   8.646900  14.973988      -2.815894

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()

$A = -0.76^{+1.04}_{-0.99}$, $B = 4.8\pm 1.0$, $C = 9.4\pm 1.0$, $D = 14.54^{+0.99}_{-1.02}$

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"])