A Python library to analyse data generated from (Monte Carlo) Markov chains.

• Website: https://mbruno46.github.io/pyobs/
• Documentation: https://mbruno46.github.io/pyobs/
• Examples: tests, tutorials
• Source code: https://github.com/mbruno46/pyobs/
• Bug reports: https://github.com/mbruno46/pyobs/issues

If you use this library in your publications please consider citing:

• U. Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153.
• S. Schaefer, R. Sommer, F. Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
• M. Bruno, R. Sommer, In preparation.

Copyright (C) 2020, Mattia Bruno

To install the library directly in your local python distribution, simply run

pip install git+https://github.com/mbruno46/pyobs.git@master#egg=pyobs
# or for upgrading
pip install -U git+https://github.com/mbruno46/pyobs.git@master#egg=pyobs

After installation, pyobs can be imported like any other package

import pyobs
help(pyobs.observable)

The library can also be installed from a local clone of the repository in developer mode, as described in the documentation. Recompilation of the C++ extensions might be necessary in this case.

import numpy
import pyobs

data = numpy.loadtxt('plaquette.dat')

plaq = pyobs.observable(description='the plaquette')
plaq.create('ensembleA',data)

# perform arbitrary operations
print(plaq, plaq**2)

logplaq = pyobs.log(plaq)