From 78c3405fb76a9b9713b2470252a05fa6a8f0c751 Mon Sep 17 00:00:00 2001 From: Fabian Joswig Date: Mon, 15 Nov 2021 15:16:26 +0000 Subject: [PATCH] docs: typos fixed --- pyerrors/__init__.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/pyerrors/__init__.py b/pyerrors/__init__.py index b7f8f1ed..2f194514 100644 --- a/pyerrors/__init__.py +++ b/pyerrors/__init__.py @@ -6,7 +6,7 @@ It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/ab - **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...) +- **real and complex matrix operations** and their error propagation based on automatic differentiation (Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...) ## Getting started @@ -66,17 +66,19 @@ my_m_eff = np.log(my_obs1 / my_obs2) ## Error estimation The error estimation within `pyerrors` is based on the gamma method introduced in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). - After having arrived at the derived quantity of interest the `gamma_method` can be called as detailed in the following example. Example: ```python my_sum.gamma_method() +print(my_sum) +> 1.70(57) my_sum.details() -> Result 1.70000000e+00 +/- 3.89934513e+00 +/- 5.84901770e-01 (229.373%) -> t_int 3.72133617e+00 +/- 9.81032454e-01 S = 2.00 +> Result 1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%) +> t_int 2.71422900e+00 +/- 6.40320983e-01 S = 2.00 > 1000 samples in 1 ensemble: > · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000) + ``` The standard value for the automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the `gamma_method` as parameter. @@ -85,8 +87,8 @@ Example: ```python my_sum.gamma_method(S=3.0) my_sum.details() -> Result 1.70000000e+00 +/- 3.77151850e+00 +/- 6.47779576e-01 (221.854%) -> t_int 3.48135280e+00 +/- 1.06547679e+00 S = 3.00 +> Result 1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%) +> t_int 3.29909703e+00 +/- 9.77310102e-01 S = 3.00 > 1000 samples in 1 ensemble: > · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000) @@ -108,8 +110,8 @@ Example: ```python my_sum.gamma_method(tau_exp=7.2) my_sum.details() -> Result 1.70000000e+00 +/- 3.77806247e+00 +/- 3.48320149e-01 (222.239%) -> t_int 3.49344429e+00 +/- 7.62747210e-01 tau_exp = 7.20, N_sigma = 1 +> Result 1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%) +> t_int 3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20, N_sigma = 1 > 1000 samples in 1 ensemble: > · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000) ```