Reading and writing

Reading chain files from PolyChord, MultiNest, UltraNest, NestedFit, CosmoMC, or Cobaya

If you have finished nested sampling or MCMC runs from one of:

• PolyChord: https://github.com/PolyChord/PolyChordLite

• MultiNest: https://github.com/farhanferoz/MultiNest

• UltraNest: https://github.com/JohannesBuchner/UltraNest

• NestedFit: https://github.com/martinit18/nested_fit

• CosmoMC: https://github.com/cmbant/CosmoMC

• Cobaya: https://github.com/CobayaSampler/cobaya

then you should be able to read in the chain files directly, by passing the root to the anesthetic.read.chain.read_chains() function.

Feel free to use the testing data in anesthetic/tests/example_data to try out the examples listed here.

• PolyChord samples, which will be an instance of the anesthetic.samples.NestedSamples class:

  from anesthetic import read_chains
  samples = read_chains("anesthetic/tests/example_data/pc")
  

• UltraNest samples, which will be an instance of the anesthetic.samples.NestedSamples class:

  from anesthetic import read_chains
  samples = read_chains("anesthetic/tests/example_data/un")
  

• NestedFit samples, which will be an instance of the anesthetic.samples.NestedSamples class:

  from anesthetic import read_chains
  samples = read_chains("anesthetic/tests/example_data/nf")
  

• Cobaya samples, which will be an instance of the anesthetic.samples.MCMCSamples class:

  from anesthetic import read_chains
  samples = read_chains("anesthetic/tests/example_data/cb").remove_burn_in(burn_in=0.1)
  

Passing data as arguments

You can also pass your own (weighted) data directly to the main sample classes anesthetic.samples.Samples, anesthetic.samples.MCMCSamples, or anesthetic.samples.NestedSamples, e.g. here with randomly generated data:

import numpy as np
from scipy.stats import multivariate_normal as mvn
from anesthetic.samples import Samples

num_samples = 1000                    # number of samples
num_dim = 2                           # number of parameters/dimensions
params = ['a', 'b']
data = np.random.uniform(-5, 5, size=(num_samples, num_dim))
weights = mvn.pdf(data, mean=[0, 0], cov=np.diag([1, 1]))
samples = Samples(data, weights=weights, columns=params)

Saving NestedSamples or MCMCSamples

In principle you can use any of pandas options for saving your samples. However, since our weighted samples here make heavy use of pandas.MultiIndex feature, we recommended using either CSV files or parquet files for reading and writing.

• samples.to_csv("filename.csv"): CSV files are a useful option when you would like to have a human readable file. Check out pandas.DataFrame.to_csv() for the various options of saving the data (e.g. choosing the delimiter etc.).

• samples.to_hdf("filename.h5", "samples"): When reading and writing speed is an issue, we recommend using the hdf5 file format, which should be faster than to_csv while still capable of handling the pandas.MultiIndex format.

Loading NestedSamples or MCMCSamples

When loading in previously saved samples from csv, make sure to use the appropriate class: Samples, MCMCSamples, or NestedSamples.

• read_csv:

  from pandas import read_csv
  from anesthetic import Samples  # or MCMCSamples, or NestedSamples
  samples = Samples(read_csv("filename.csv"))
  

When loading in previously saved samples from hdf5, make sure to import the anesthetic.read_hdf function, and not the pandas.read_hdf version. If you forget to do this, the samples will be read in as a DataFrame, with a consequent loss of functionality

• read_hdf:

  from anesthetic import read_hdf
  samples = read_hdf("filename.h5", "samples")
  

Converting to GetDist

There are also tools for converting to alternative formats (e.g. GetDist), in case you have pipelines in other plotters:

from anesthetic.convert import to_getdist
getdist_samples = to_getdist(samples)