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Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd
autocorrelationautogradautomatic-differentiationcondensed-mattercorrelationdata-analysiserror-propagationlattice-field-theorylattice-qcdmarkov-chainmonte-carloparticle-physicsphysicspythonqcdstatistical-analysisstatistical-mechanics
* refactor: _standard_fit method made redundant. * fix: xs and yz in Corr.fit promoted to arrays. * fix: x promoted to array in _combined_fit if input is just a list. * feat: residual_plot and qqplot now work with combined fits with dictionary inputs. * tests: test for combined fit resplot and qqplot added. * docs: docstring of fits.residual_plot extended. |
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examples | ||
pyerrors | ||
tests | ||
.gitignore | ||
CHANGELOG.md | ||
CITATION.cff | ||
conftest.py | ||
CONTRIBUTING.md | ||
LICENSE | ||
pyproject.toml | ||
README.md | ||
setup.py |
pyerrors
pyerrors
is a python package for error computation and propagation of Markov chain Monte Carlo data.
- Documentation: https://fjosw.github.io/pyerrors/pyerrors.html
- Examples: https://github.com/fjosw/pyerrors/tree/develop/examples
- Contributing: https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md
- Bug reports: https://github.com/fjosw/pyerrors/issues
Installation
To install the most recent release (via pypi) run
pip install pyerrors # Fresh install
pip install -U pyerrors # Upgrade
to install the current develop
version run
pip install git+https://github.com/fjosw/pyerrors.git@develop