Graphical User Interface (GUI)

For an interactive view of a nested sampling run, you can use the anesthetic GUI by running the follwing in your console:

$ anesthetic <nested/samples/file/root>

Replace the <nested/samples/file/root> with the correct path and file root of your nested sampling run (e.g. try it out with the anesthetic example data in anesthetic/tests/example_data/pc). This will launch a new window looking somewhat like this:

Warning

Note that the GUI only works out-of-the-box if your files follow a specific structure, matching either the one from PolyChord, MultiNest, or from UltraNest.

Note

However, you can also manually feed in your data and launch the GUI as demonstrated in the following example:

import numpy as np
import matplotlib.pyplot as plt
from anesthetic import NestedSamples

# Set up `data`, `logL`, and `logL_birth`
file_path = "../../tests/example_data/pc_dead-birth.txt"
file_data = np.loadtxt(file_path)
data, logL, logL_birth = np.split(file_data, [-2, -1], axis=1)

samples = NestedSamples(data=data, logL=logL, logL_birth=logL_birth)
samples.gui()
plt.show()

To make this example work with the anesthetic example data, place the above code snippet into a python script (let’s call it my_anesthetic_gui_script.py), make sure the file path correctly points to the example data in the anesthetic/tests/ folder, and launch it from the command line:

$ python my_anesthetic_gui_script.py

To modify the above example for your own case, you only need to change the middle block to read in your data file(s) instead of the anesthetic example data and pass on the data, logL, and logL_birth arrays, which contain the parameter samples, their corresponding log-likelihood values, and the log-likelihood values of their birth contours, respectively.

Note

If you are using a jupyter notebook, you should use %matplotlib interactive magic command (or equivalent) to use the interactive GUI.