Basic usage

Using UltraNest in a project is as simple as:

import ultranest

sampler = ultranest.ReactiveNestedSampler(param_names, my_likelihood, my_prior_transform,
    log_dir="myanalysis", resume=True)
result = sampler.run()
sampler.print_results()

Simple example

To demonstrate, we define a spectral line fitting problem.

Lets generate some data over a wavelength range with a noisy signal.

[1]:
import numpy as np
x = np.linspace(400, 800, 100)
yerr = 1.0
y = np.random.normal(20 * np.exp(-0.5 * ((x-500)/4.2)**2), yerr)
[2]:
%matplotlib inline
import matplotlib.pyplot as plt
plt.errorbar(x, y, yerr=yerr, ls=' ', marker='x')
plt.plot(x, 20 * np.exp(-0.5 * ((x-500)/4.2)**2), ls=':', alpha=0.5, color='k')
plt.xlabel('x')
plt.ylabel('y');
_images/usage-spectral-line_4_0.svg

This problem has 3 parameters: The location, amplitude and width of the gaussian line. We assume that the noise level is known.

[3]:
param_names = ['location', 'amplitude', 'width']

In UltraNest, the parameter space is defined through a transform from unit values (0…1) to the physical range.

The next function demonstrates:

  • a uniform prior for a location parameter

  • a log-uniform prior for a scale parameter

  • a log-normal prior

[4]:
import scipy.stats

def my_prior_transform(cube):
    params = cube.copy()

    # transform location parameter: uniform prior
    lo = 400
    hi = 800
    params[0] = cube[0] * (hi - lo) + lo

    # transform amplitude parameter: log-uniform prior
    lo = 0.1
    hi = 100
    params[1] = 10**(cube[1] * (np.log10(hi) - np.log10(lo)) + np.log10(lo))

    # More complex prior, you can use the ppf functions
    # from scipy.stats, such as scipy.stats.norm(mean, std).ppf

    # transform for width:
    # a log-normal centered at 1 +- 1dex
    params[2] = 10**scipy.stats.norm.ppf(cube[2], 0, 1)

    return params

Next, we need to specify the data likelihood given certain parameters:

[5]:
from numpy import log

def my_likelihood(params):
    location, amplitude, width = params
    # compute intensity at every x position according to the model
    y_model = amplitude * np.exp(-0.5 * ((x - location)/width)**2)
    # compare model and data with gaussian likelihood:
    like = -0.5 * (((y_model - y)/yerr)**2).sum()
    return like

We are now ready to explore this parameter space!

[6]:
import ultranest

sampler = ultranest.ReactiveNestedSampler(param_names, my_likelihood, my_prior_transform)

ReactiveNestedSampler has several options to specify what logging and file output it should produce.

[7]:
result = sampler.run()
sampler.print_results()
[ultranest] Sampling 400 live points from prior ...
[ultranest] Explored until L=-4e+01
[ultranest] Likelihood function evaluations: 28071
[ultranest]   logZ = -52.08 +- 0.1374
[ultranest] Effective samples strategy satisfied (ESS = 1879.4, need >400)
[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.48+-0.09 nat, need <0.50 nat)
[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.34, need <0.5)
[ultranest]   logZ error budget: single: 0.18 bs:0.14 tail:0.01 total:0.14 required:<0.50
[ultranest] done iterating.

logZ = -52.025 +- 0.244
  single instance: logZ = -52.025 +- 0.184
  bootstrapped   : logZ = -52.079 +- 0.244
  tail           : logZ = +- 0.010

    location            499.66 +- 0.22
    amplitude           19.68 +- 0.96
    width               3.91 +- 0.21
[8]:
from ultranest.plot import cornerplot
cornerplot(result)
_images/usage-spectral-line_15_0.svg
sampler = ultranest.ReactiveNestedSampler(
    param_names,
    loglike=my_likelihood,
    transform=my_prior_transform,
    ## additional parameter properties:
    # identify circular parameters
    wrapped_params=[False, False, False],
    # add derived calculations
    derived_param_names=[],
    # store outputs for resuming:
    log_dir='my_folder/,
    resume='resume' or 'overwrite' or 'subfolder',
    )

Both ReactiveNestedSampler and its .run() function have several options to specify what logging and file output they should produce, and how they should explore the parameter space.

You can create more diagnostic plots with:

sampler.plot_run()
sampler.plot_trace()
sampler.plot_corner()

Further topics

Check out the rest of the documentation and the tutorials.

They illustrate the features of UltraNest, including:

  • Model comparison

  • Visualisation

  • Saving & Resuming output

  • Controlling output

  • Parallelisation

  • Handling high dimensional problems