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  0.472820  6.329963   9.336536  14.615040      -8.565616
1 -0.087954  3.956986   8.406439  15.388204      -2.554302
2 -2.370081  5.537084  11.435061  12.901383      -4.578809
3 -1.067292  4.509693   9.548522  14.501728      -2.359490
4 -0.754020  5.365414   9.974251  14.614890      -3.909660

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()

$A = -0.7\pm 1.0$, $B = 4.82^{+1.00}_{-1.03}$, $C = 9.4\pm 1.0$, $D = 14.54^{+0.99}_{-1.01}$

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"])