pyobs 1.5.2

A Python3 library to analyse data generated from (Monte Carlo) Markov chains.

The software is hosted on GitHub and is distributed under the GNU GPLv2 license.

Features

  • object-oriented for easy manipulation of data

  • FFT acceleration for calculation of autocorrelations

  • minimal memory footprint for large data sets and memory monitoring system

  • support for master-field setup, ie autocorrelations in more than one dimension

  • support for systematic errors and external input data

  • flexible interface for fits

  • support for linear algebra operations (e.g. matrix inverse, eigenvalue problem, etc..)

Citation

If you use this library in your publications cite it following the instructions here Zenodo webpage.

Please also consider citing:

Authors

Copyright (C) 2020-2025, Mattia Bruno

Installation

pyobs can be installed in three different ways. After installation it can be imported like any other package

>>> import pyobs
>>> help(pyobs.observable)

  • pip install from remote repository

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

  • pip install from local repository

$ git clone git+https://github.com/mbruno46/pyobs.git
$ pip install ./pyobs

  • developer mode

$ pip install -e ./pyobs

The library can alternatively be used without installation by adding the appropriate path to sys. In this case it is recommended to recompile the C++ extensions manually with python setup.py build_ext.

>>> import sys
>>> sys.path.append('/path/to/pyobs/directory/')
>>> import pyobs

Note that recompiling the C++ extensions might be necessary also after pulling the latest commits, in the developer mode.

Python Environments

Virtual environments can be used to create a copy of the python distribution in the home directory where the user can freely install additional packages, including pyobs.

$ cd $HOME
$ python3.6 -m venv mypy

The environment can be activated, meaning that pip and python commands will be automatically identified with those in the environment

$ source mypy/bin/activate
$ pip install numpy jupyter notebook

Alternatively, just use the full path

$ cd mypy/bin
$ ./pip install numpy
$ ./pip install git+https://github.com/mbruno46/pyobs.git@master#egg=pyobs

Changelog

All notable changes to this project will be documented in this file. The format is based on Keep a changelog.

[Unreleased]

Added

  • jackknife error support

  • exponential tail

Fixed

  • BDIO format has bug for python>=3.10

[1.5.2] - XX.01.2025

Added

  • added full support for complex observables

  • added conj method to observable class

  • temporary class complex_observable migrated to tests

Fixed

  • minor bugs

[1.5.1] - 15.11.2024

Added

  • temporary support for complex observables via complex_observable class (binary operations @, *, unary operations inv, eig, T)

  • extended output for peek function

Fixed

  • fixed bug in chiexp for combined fits

  • faster index search in transform.py for large observables

[1.5.0] - 10.10.2023

Added

  • added support for expected chi square

Fixed

  • improved error estimation and plotting in presence of holes

  • minor bugs fixed (I/O xml)

[1.4.1] - 18.01.2023

Fixed

  • fixed bug in transform (roll, sort..)

  • improved behavior under roundoff errors

  • fixed compatibility issues with numpy>=1.24.0

[1.4.0] - 20.08.2022

Added

  • finite volume 2to2 quantization condition

  • write support for xml.gz format

  • documentation updated

Fixed - several minor bugs fixed

version 1.3.0, 06.02.2022:

  • added utility functions (tensor_remove, import_string)

  • minor bugs fixed

  • added support xml and bdio format (dobs-tools, ALPHA Collab.)

  • interface for synthetic autocorrelated data changed

  • several bugs fixed

version 1.2.0, 21.07.2021:

  • added support for binning of Monte Carlo data, and blocking for master-field data

  • added support for linting of code

  • performance improvements for slicing/indexing/extending observables

version 1.1.0, 08.05.2021:

  • added support for root finder, basic interpolation, cumsum

  • fixed documentation bugs

  • minor improvements to mfit

  • added support for variance and cov. matrix (data.py rewritten)

version 1.0.1, 10.11.2020:

  • added IO with binary file format (default), based on bison

  • performance improvements in create and derobs

  • additional gradient class for optimization of element-wise operations

  • minor bug fixes

version 1.0.0-alpha, 11.10.2020:

  • IO with json file format supported

  • pip install supported

  • error of the error and 4th moment bias supported

  • support for code coverage and automatic unit tests

  • flexible fit modules (Levenberg-Marquardt minimizer)

  • documentation and tutorials

  • symbolic differentiation for fit functions

  • support for eigenvalue/eigenvectors (including non-symmetric case)

  • support for exp, log, cosh, arccosh, besselk

  • fast C++ extensions for master-field case

  • overloaded basic math operations (+,-,*,/)

  • tensor operations: unary support (sum, trace), matmul via @ operand

  • basic tensor manipulation (reshape, concatenate) and getitem

  • FFT for autocorrelations

  • support for external data with known covariances and systematic errors

  • memory management system, utility functions

  • random module for generation of autocorrelated data sets