Note
Click here to download the full example code
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 -0.458120 4.056907 8.535537 15.745032 -3.709379
1 -0.912067 5.554084 9.716593 14.599073 -2.585703
2 0.266900 4.613901 8.135439 16.295019 -4.087893
3 -0.980402 3.158362 10.061375 13.394896 -5.312100
4 -1.673836 5.256106 10.119388 13.980514 -2.423078
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()

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"])

Total running time of the script: ( 0 minutes 1.507 seconds)
Download Python source code: plot_3_distributions.py