From 20edefad43d8bcdda74e5a213a5db809a3725190 Mon Sep 17 00:00:00 2001 From: Fabian Joswig Date: Sat, 11 Dec 2021 22:59:01 +0000 Subject: [PATCH] docs: Introduction restructured --- pyerrors/__init__.py | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/pyerrors/__init__.py b/pyerrors/__init__.py index 5ff2c925..9142ddea 100644 --- a/pyerrors/__init__.py +++ b/pyerrors/__init__.py @@ -1,17 +1,14 @@ r''' # What is pyerrors? `pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data. -It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are: -- **automatic differentiation** as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package) -- **treatment of slow modes** in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228) -- coherent **error propagation** for data from **different Markov chains** -- **non-linear fits with x- and y-errors** and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) -- **real and complex matrix operations** and their error propagation based on automatic differentiation (Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...) +It is based on the gamma method [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are: +- automatic differentiation for exact liner error propagation as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package). +- treatment of slow modes in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228). +- coherent error propagation for data from different Markov chains. +- non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289). +- real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...). -There exist similar publicly available implementations of gamma method error analysis suites in -- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors) -- [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl) -- [Python](https://github.com/mbruno46/pyobs) +There exist similar publicly available implementations of gamma method error analysis suites in [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors), [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl) and [Python](https://github.com/mbruno46/pyobs). ## Basic example