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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: merged combined fit and prior fit without breaking the routine. Fitting with priors does not work yet. * refactor: correlated fits without priors work now. * refactor: prior error propagation and dof fixed. * refactor: old prior fit implementation moved to tests. * refactor: moved _extract_val_and_dval out of least_squares. * refactor: comment removed. * tests: additional tests and exceptions added. * tests: test for constrained prior fit added. * docs: least_squares docstring extended. * fix: linting errors fixed. * feat: additional if cause for fits without priors added to achieve original speed. * tests: test_constrained_and_prior_fit fixed. * fix: fix array cast of least_squares dict mode. * tests: test for lists in dict fit added. * fix: additional asarray added in resplot. Co-authored-by: Simon Kuberski <simon.kuberski@uni-muenster.de> |
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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