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  0.570273  4.543291   8.357645  15.335895      -3.320930
1  0.083139  3.900860   8.862940  14.569845      -2.892365
2 -0.716809  4.700727   9.481979  15.031434      -3.566021
3 -0.375732  3.763941   9.422466  14.292632      -2.639621
4 -1.650441  3.973439  10.426896  13.473854      -4.498279

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()

$A = -0.7\pm 1.0$, $B = 4.83^{+0.98}_{-1.03}$, $C = 9.39^{+1.02}_{-1.00}$, $D = 14.56^{+0.98}_{-1.05}$

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"])