Commit c8b1159d authored by Alejandro Saez's avatar Alejandro Saez

Merge branch 'javier' into alejandro

parents 7f6a1be6 5eb61b12
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<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Home · juobs Documentation</title><link href="https://fonts.googleapis.com/css?family=Lato|Roboto+Mono" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.0/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.0/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.0/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.11.1/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL="."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="assets/documenter.js"></script><script src="siteinfo.js"></script><script src="../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="assets/themeswap.js"></script></head><body><div id="documenter"><nav class="docs-sidebar"><div class="docs-package-name"><span class="docs-autofit">juobs Documentation</span></div><form class="docs-search" action="search.html"><input class="docs-search-query" id="documenter-search-query" name="q" type="text" placeholder="Search docs"/></form><ul class="docs-menu"><li class="is-active"><a class="tocitem" href="index.html">Home</a><ul class="internal"><li><a class="tocitem" href="#Contents"><span>Contents</span></a></li></ul></li><li><a class="tocitem" href="reader.html">Reader</a></li><li><a class="tocitem" href="tools.html">Tools</a></li><li><a class="tocitem" href="obs.html">Observables</a></li><li><a class="tocitem" href="linalg.html">Linear Algebra</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><nav class="breadcrumb"><ul class="is-hidden-mobile"><li class="is-active"><a href="index.html">Home</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href="index.html">Home</a></li></ul></nav><div class="docs-right"><a class="docs-edit-link" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs" title="Edit on GitLab"><span class="docs-icon fab"></span><span class="docs-label is-hidden-touch">Edit on GitLab</span></a><a class="docs-settings-button fas fa-cog" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-sidebar-button fa fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a></div></header><article class="content" id="documenter-page"><h1 id="DOCUMENTATION"><a class="docs-heading-anchor" href="#DOCUMENTATION">DOCUMENTATION</a><a id="DOCUMENTATION-1"></a><a class="docs-heading-anchor-permalink" href="#DOCUMENTATION" title="Permalink"></a></h1><h2 id="Contents"><a class="docs-heading-anchor" href="#Contents">Contents</a><a id="Contents-1"></a><a class="docs-heading-anchor-permalink" href="#Contents" title="Permalink"></a></h2><ul><li><a href="reader.html#Reader">Reader</a></li><li><a href="tools.html#Tools">Tools</a></li><li><a href="obs.html#Observables">Observables</a></li><li><a href="linalg.html#Linear-Algebra">Linear Algebra</a></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="reader.html">Reader »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Tuesday 23 March 2021 11:06">Tuesday 23 March 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Home · juobs Documentation</title><link href="https://fonts.googleapis.com/css?family=Lato|Roboto+Mono" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.0/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.0/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.0/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.11.1/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL="."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="assets/documenter.js"></script><script src="siteinfo.js"></script><script src="../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="assets/themeswap.js"></script></head><body><div id="documenter"><nav class="docs-sidebar"><div class="docs-package-name"><span class="docs-autofit">juobs Documentation</span></div><form class="docs-search" action="search.html"><input class="docs-search-query" id="documenter-search-query" name="q" type="text" placeholder="Search docs"/></form><ul class="docs-menu"><li class="is-active"><a class="tocitem" href="index.html">Home</a><ul class="internal"><li><a class="tocitem" href="#Contents"><span>Contents</span></a></li></ul></li><li><a class="tocitem" href="reader.html">Reader</a></li><li><a class="tocitem" href="tools.html">Tools</a></li><li><a class="tocitem" href="obs.html">Observables</a></li><li><a class="tocitem" href="linalg.html">Linear Algebra</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><nav class="breadcrumb"><ul class="is-hidden-mobile"><li class="is-active"><a href="index.html">Home</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href="index.html">Home</a></li></ul></nav><div class="docs-right"><a class="docs-edit-link" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs" title="Edit on GitLab"><span class="docs-icon fab"></span><span class="docs-label is-hidden-touch">Edit on GitLab</span></a><a class="docs-settings-button fas fa-cog" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-sidebar-button fa fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a></div></header><article class="content" id="documenter-page"><h1 id="DOCUMENTATION"><a class="docs-heading-anchor" href="#DOCUMENTATION">DOCUMENTATION</a><a id="DOCUMENTATION-1"></a><a class="docs-heading-anchor-permalink" href="#DOCUMENTATION" title="Permalink"></a></h1><h2 id="Contents"><a class="docs-heading-anchor" href="#Contents">Contents</a><a id="Contents-1"></a><a class="docs-heading-anchor-permalink" href="#Contents" title="Permalink"></a></h2><ul><li><a href="reader.html#Reader">Reader</a></li><li><a href="tools.html#Tools">Tools</a></li><li><a href="obs.html#Observables">Observables</a></li><li><a href="linalg.html#Linear-Algebra">Linear Algebra</a></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="reader.html">Reader »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Friday 17 September 2021 08:40">Friday 17 September 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
......@@ -78,4 +78,4 @@ evals = getall_eigvals(matrices, 5) #where t_0=5
Julia&gt;</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="juobs.getall_eigvecs" href="#juobs.getall_eigvecs"><code>juobs.getall_eigvecs</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">getall_eigvecs(a::Vector{Matrix}, delta_t; iter=30 )</code></pre><p>This function solves a GEVP problem, returning the eigenvectors, for a list of matrices.</p><p><span>$C(t_i)v_i = λ_i C(t_i-\delta_t) v_i$</span>, with i=1:lenght(a)</p><p>Here <code>delta_t</code> is the time shift within the two matrices of the problem, and is kept fixed. It takes as input:</p><ul><li><p><code>a::Vector{Matrix}</code> : a vector of matrices</p></li><li><p><code>delta_t::Int64</code> : the fixed time shift t-t_0</p></li><li><p><code>iter=30</code> : the number of iterations of the qr algorithm used to extract the eigenvalues </p></li></ul><p>It returns:</p><ul><li><code>res = Vector{Matrix{uwreal}}</code></li></ul><p>where each <code>res[i]</code> is a matrix with the eigenvectors as columns Examples:</p><pre><code class="language-">mat_array = get_matrix(diag, upper_diag)
evecs = getall_eigvecs(mat_array, 5)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="obs.html">« Observables</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Tuesday 23 March 2021 11:06">Tuesday 23 March 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
evecs = getall_eigvecs(mat_array, 5)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="obs.html">« Observables</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Friday 17 September 2021 08:40">Friday 17 September 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
......@@ -18,12 +18,19 @@ ca = -0.006033 * g2 *( 1 + exp(p0 + p1/g2))
m12 = mpcac(corr_a0p, corr_pp, [50, 60], pl=false, ca=ca)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="juobs.dec_const" href="#juobs.dec_const"><code>juobs.dec_const</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">dec_const(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, y0::Int64; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const(a0p::Corr, pp::Corr, plat::Vector{Int64}, m::uwreal; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)</code></pre><p>Computes the bare decay constant using <span>$A_0P$</span> and <span>$PP$</span> correlators . The decay constant is computed in the plateau <code>plat</code>. Correlator can be passed as an <code>Corr</code> struct or <code>Vector{uwreal}</code>. If it is passed as a uwreal vector, effective mass <code>m</code> and source position <code>y0</code> must be specified.</p><p>The flags <code>pl</code> and <code>data</code> allow to show the plots and return data as an extra result. The <code>ca</code> variable allows to compute <code>dec_const</code> using the improved axial current.</p><p><strong>The method assumes that the source is close to the boundary.</strong> It takes the following ratio to cancel boundary effects. <span>$R = \frac{f_A(x_0, y_0)}{\sqrt{f_P(T-y_0, y_0)}} * e^{m (x_0 - T/2)}$</span></p><pre><code class="language-">data_pp = read_mesons(path, &quot;G5&quot;, &quot;G5&quot;, legacy=true)
dec_const(a0p::Corr, pp::Corr, plat::Vector{Int64}, m::uwreal; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const(a0pL::Vector{uwreal}, a0pR::Vector{uwreal}, ppL::Vector{uwreal}, ppR::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, y0::Int64; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const(a0pL::Corr, a0pR::Corr, ppL::Corr, ppR::Corr, plat::Vector{Int64}, m::uwreal; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)</code></pre><p>Computes the bare decay constant using <span>$A_0P$</span> and <span>$PP$</span> correlators . The decay constant is computed in the plateau <code>plat</code>. Correlator can be passed as an <code>Corr</code> struct or <code>Vector{uwreal}</code>. If it is passed as a uwreal vector, effective mass <code>m</code> and source position <code>y0</code> must be specified.</p><p>The flags <code>pl</code> and <code>data</code> allow to show the plots and return data as an extra result. The <code>ca</code> variable allows to compute <code>dec_const</code> using the improved axial current.</p><p><strong>The method assumes that the source is close to the boundary.</strong> It takes the following ratio to cancel boundary effects. <span>$R = \frac{f_A(x_0, y_0)}{\sqrt{f_P(T-y_0, y_0)}} * e^{m (x_0 - T/2)}$</span></p><p><strong>If left and right correlators are included in the input. The result is computed with the following ratio</strong> <span>$R = \sqrt{f_A(x_0, y_0) * f_A(x_0, T - 1 - y_0) / f_P(T - 1 - y_0, y_0)}$</span></p><pre><code class="language-">data_pp = read_mesons(path, &quot;G5&quot;, &quot;G5&quot;, legacy=true)
data_a0p = read_mesons(path, &quot;G5&quot;, &quot;G0G5&quot;, legacy=true)
corr_pp = corr_obs.(data_pp, L=32)
corr_a0p = corr_obs.(data_a0p, L=32)
corr_a0pL, corr_a0pR = [corr_a0p[1], corr_a0p[2]]
corr_ppL, corr_ppR = [corr_pp[1], corr_pp[2]]
m = meff(corr_pp[1], [50, 60], pl=false)
beta = 3.46
......@@ -32,12 +39,21 @@ p1 = -13.9847
g2 = 6 / beta
ca = -0.006033 * g2 *( 1 + exp(p0 + p1/g2))
f = dec_const(corr_a0p[1], corr_pp[1], [50, 60], m, pl=true, ca=ca)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="juobs.dec_const_pcvc" href="#juobs.dec_const_pcvc"><code>juobs.dec_const_pcvc</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">dec_const_pcvc(corr::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
f = dec_const(corr_a0p[1], corr_pp[1], [50, 60], m, pl=true, ca=ca)
f_ratio = dec_const(corr_a0pL, corr_a0pR, corr_ppL, corr_ppR, [50, 60], m, pl=true, ca=ca)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="juobs.dec_const_pcvc" href="#juobs.dec_const_pcvc"><code>juobs.dec_const_pcvc</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">dec_const_pcvc(corr::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const_pcvc(corr::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)</code></pre><p>Computes decay constant using the PCVC relation for twisted mass fermions. The decay constant is computed in the plateau <code>plat</code>. Correlator can be passed as an <code>Corr</code> struct or <code>Vector{uwreal}</code>. If it is passed as a uwreal vector, vector of twisted masses <code>mu</code> and source position <code>y0</code> must be specified.</p><p>The flags <code>pl</code> and <code>data</code> allow to show the plots and return data as an extra result.</p><p><strong>The method assumes that the source is in the bulk.</strong></p><pre><code class="language-">data = read_mesons(path, &quot;G5&quot;, &quot;G5&quot;)
dec_const_pcvc(corr::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const_pcvc(ppL::Vector{uwreal}, ppR::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const_pcvc(corrL::Corr, corrR::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)</code></pre><p>Computes decay constant using the PCVC relation for twisted mass fermions. The decay constant is computed in the plateau <code>plat</code>. Correlator can be passed as an <code>Corr</code> struct or <code>Vector{uwreal}</code>. If it is passed as a uwreal vector, vector of twisted masses <code>mu</code> and source position <code>y0</code> must be specified.</p><p>The flags <code>pl</code> and <code>data</code> allow to show the plots and return data as an extra result.</p><p><strong>The method extract the matrix element assuming that the source is in the bulk. ** **If left and right correlators are included in the input. The result is computed with a ratio that cancels boundary effects:</strong> <span>$R = \sqrt{f_P(x_0, y_0) * f_P(x_0, T - 1 - y_0) / f_P(T - 1 - y_0, y_0)}$</span></p><pre><code class="language-">data = read_mesons(path, &quot;G5&quot;, &quot;G5&quot;)
corr_pp = corr_obs.(data, L=32)
m = meff(corr_pp[1], [50, 60], pl=false)
f = dec_const_pcvc(corr_pp[1], [50, 60], m, pl=false)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="juobs.comp_t0" href="#juobs.comp_t0"><code>juobs.comp_t0</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false, rw::Union{Matrix{Float64}, Nothing}=nothing, npol::Int64=2, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
f = dec_const_pcvc(corr_pp[1], [50, 60], m, pl=false)
#left and right correlators
f_ratio = dec_const_pcvc(ppL, ppR, [50, 60], m)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="juobs.comp_t0" href="#juobs.comp_t0"><code>juobs.comp_t0</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false, rw::Union{Matrix{Float64}, Nothing}=nothing, npol::Int64=2, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
comp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false, rw::Union{Vector{Matrix{Float64}}, Nothing}=nothing, npol::Int64=2, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)</code></pre><p>Computes <code>t0</code> using the energy density of the action <code>Ysl</code>(Yang-Mills action). <code>t0</code> is computed in the plateau <code>plat</code>. A polynomial interpolation in <code>t</code> is performed to find <code>t0</code>, where <code>npol</code> is the degree of the polynomial (linear fit by default)</p><p>The flag <code>pl</code> allows to show the plot.</p><pre><code class="language-">#Single replica
Y = read_ms(path)
......@@ -54,4 +70,4 @@ rw2 = read_ms(path_rw2)
t0 = comp_t0([Y1, Y2], [38, 58], L=32, pl=true)
t0_r = comp_t0(Y, [38, 58], L=32, rw=[rw1, rw2], pl=true)
</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="tools.html">« Tools</a><a class="docs-footer-nextpage" href="linalg.html">Linear Algebra »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Tuesday 23 March 2021 11:06">Tuesday 23 March 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="tools.html">« Tools</a><a class="docs-footer-nextpage" href="linalg.html">Linear Algebra »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Friday 17 September 2021 08:40">Friday 17 September 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
......@@ -24,4 +24,4 @@ truncate_data!(Y, nc)
dat = read_mesons([path1, path2], &quot;G5&quot;, &quot;G5&quot;)
Y = read_ms.([path1_ms, path2_ms])
truncate_data!(dat, [nc1, nc2])
truncate_data!(Y, [nc1, nc2])</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="index.html">« Home</a><a class="docs-footer-nextpage" href="tools.html">Tools »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Tuesday 23 March 2021 11:06">Tuesday 23 March 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
truncate_data!(Y, [nc1, nc2])</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="index.html">« Home</a><a class="docs-footer-nextpage" href="tools.html">Tools »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Friday 17 September 2021 08:40">Friday 17 September 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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[{"location":"reader.html#Reader","page":"Reader","title":"Reader","text":"","category":"section"},{"location":"reader.html","page":"Reader","title":"Reader","text":"read_mesons\nread_ms\nread_ms1\nread_md\ntruncate_data!","category":"page"},{"location":"reader.html#juobs.read_mesons","page":"Reader","title":"juobs.read_mesons","text":"read_mesons(path::String, g1::Union{String, Nothing}=nothing, g2::Union{String, Nothing}=nothing; id::Union{String, Nothing}=nothing, legacy::Bool=false)\n\nread_mesons(path::Vector{String}, g1::Union{String, Nothing}=nothing, g2::Union{String, Nothing}=nothing; id::Union{String, Nothing}=nothing, legacy::Bool=false)\n\nThis function read a mesons dat file at a given path and returns a vector of CData structures for different masses and Dirac structures. Dirac structures g1 and/or g2 can be passed as string arguments in order to filter correaltors. ADerrors id can be specified as argument. If is not specified, the id is fixed according to the ensemble name (example: \"H400\"-> id = \"H400\")\n\n*For the old version (without smearing, distance preconditioning and theta) set legacy=true.\n\nExamples:\n\nread_mesons(path)\nread_mesons(path, \"G5\")\nread_mesons(path, nothing, \"G5\")\nread_mesons(path, \"G5\", \"G5\")\nread_mesons(path, \"G5\", \"G5\", id=\"H100\")\nread_mesons(path, \"G5_d2\", \"G5_d2\", legacy=true)\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.read_ms","page":"Reader","title":"juobs.read_ms","text":"read_ms(path::String; id::Union{String, Nothing}=nothing, dtr::Int64=1, obs::String=\"Y\")\n\nReads openQCD ms dat files at a given path. This method return YData: \n\nt(t): flow time values\nobs(icfg, x0, t): the time-slice sums of the densities of the observable (Wsl, Ysl or Qsl)\nvtr: vector that contains trajectory number\nid: ensmble id\n\ndtr = dtr_cnfg / dtr_ms, where dtr_cnfg is the number of trajectories computed before saving the configuration. dtr_ms is the same but applied to the ms.dat file.\n\nExamples:\n\nY = read_ms(path)\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.read_ms1","page":"Reader","title":"juobs.read_ms1","text":"read_ms1(path::String; v::String=\"1.2\")\n\nReads openQCD ms1 dat files at a given path. This method returns a matrix W[irw, icfg] that contains the reweighting factors, where irw is the rwf index and icfg the configuration number. The function is compatible with the output files of openQCD v=1.2, 1.4 and 1.6. Version can be specified as argument.\n\nExamples:\n\nread_ms1(path)\nread_ms1(path, v=\"1.4\")\nread_ms1(path, v=\"1.6\")\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.read_md","page":"Reader","title":"juobs.read_md","text":"read_md(path::String)\n\nReads openQCD pbp.dat files at a given path. This method returns a matrix md[irw, icfg] that contains the derivatives dSdm, where mdirw=1 = dSdm_l and mdirw=2 = dSdm_s\n\nSeff = -tr(log(D+m))\n\ndSeff dm = -tr((D+m)^-1)\n\nExamples:\n\nmd = read_md(path)\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.truncate_data!","page":"Reader","title":"juobs.truncate_data!","text":"truncate_data!(data::YData, nc::Int64)\n\ntruncate_data!(data::Vector{YData}, nc::Vector{Int64})\n\ntruncate_data!(data::Vector{CData}, nc::Int64)\n\ntruncate_data!(data::Vector{Vector{CData}}, nc::Vector{Int64})\n\nTruncates the output of read_mesons and read_ms taking the first nc configurations.\n\nExamples:\n\n#Single replica\ndat = read_mesons(path, \"G5\", \"G5\")\nY = read_ms(path)\ntruncate_data!(dat, nc)\ntruncate_data!(Y, nc)\n\n#Two replicas\ndat = read_mesons([path1, path2], \"G5\", \"G5\")\nY = read_ms.([path1_ms, path2_ms])\ntruncate_data!(dat, [nc1, nc2])\ntruncate_data!(Y, [nc1, nc2])\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#Linear-Algebra","page":"Linear Algebra","title":"Linear Algebra","text":"","category":"section"},{"location":"linalg.html","page":"Linear Algebra","title":"Linear Algebra","text":"uweigvals\nuweigvecs\nuweigen\nget_matrix\nenergies\ngetall_eigvals\ngetall_eigvecs","category":"page"},{"location":"linalg.html#juobs.uweigvals","page":"Linear Algebra","title":"juobs.uweigvals","text":"uweigvals(a::Matrix{uwreal}; iter = 30)\n\nuweigvals(a::Matrix{uwreal}, b::Matrix{uwreal}; iter = 30)\n\nThis function computes the eigenvalues of a matrix of uwreal objects. If a second matrix b is given as input, it returns the generalised eigenvalues instead. It takes as input:\n\na::Matrix{uwreal} : a matrix of uwreal\nb::Matrix{uwreal} : a matrix of uwreal, optional\niter=30: optional flag to set the iterations of the qr algorithm used to solve the eigenvalue problem\n\nIt returns:\n\nres = Vector{uwreal}: a vector where each elements is an eigenvalue \n\na = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\nb = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\n\nres = uweigvals(a) ##eigenvalues\nres1 = uweigvals(a,b) ## generalised eigenvalues\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.uweigvecs","page":"Linear Algebra","title":"juobs.uweigvecs","text":"uweigvecs(a::Matrix{uwreal}; iter = 30)\n\nuweigvecs(a::Matrix{uwreal}, b::Matrix{uwreal}; iter = 30)\n\nThis function computes the eigenvectors of a matrix of uwreal objects. If a second matrix b is given as input, it returns the generalised eigenvectors instead. It takes as input:\n\na::Matrix{uwreal} : a matrix of uwreal\nb::Matrix{uwreal} : a matrix of uwreal, optional\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nres = Matrix{uwreal}: a matrix where each column is an eigenvector \n\nExamples:\n\na = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\nb = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\n\nres = uweigvecs(a) ##eigenvectors in column \nres1 = uweigvecs(a,b) ## generalised eigenvectors in column \n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.uweigen","page":"Linear Algebra","title":"juobs.uweigen","text":"uweigen(a::Matrix{uwreal}; iter = 30)\n\nuweigen(a::Matrix{uwreal}, b::Matrix{uwreal}; iter = 30)\n\nThis function computes the eigenvalues and the eigenvectors of a matrix of uwreal objects. If a second matrix b is given as input, it returns the generalised eigenvalues and eigenvectors instead. It takes as input:\n\na::Matrix{uwreal} : a matrix of uwreal\nb::Matrix{uwreal} : a matrix of uwreal, optional\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nevals = Vector{uwreal}: a vector where each elements is an eigenvalue \nevecs = Matrix{uwreal}: a matrix where the i-th column is the eigenvector of the i-th eigenvalue\n\nExamples:\n\na = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\nb = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\n\neval, evec = uweigen(a) \neval1, evec1 = uweigvecs(a,b) \n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.get_matrix","page":"Linear Algebra","title":"juobs.get_matrix","text":"get_matrix(diag::Vector{Array}, upper::Vector{Array} )\n\nThis function allows the user to build an array of matrices, where each matrix is a symmetric matrix of correlators at a given timeslice. \n\nIt takes as input:\n\ndiag::Vector{Array}: vector of correlators. Each correlator will constitute a diagonal element of the matrices A[i] where i runs over the timeslices, i.e. A[i][1,1] = diag[1], .... A[i][n,n] = diag[n] Given n=length(diag), the matrices will have dimension n*n \nupper::Vector{Array}: vector of correlators liying on the upper diagonal position. A[i][1,2] = upper[1], .. , A[i][1,n] = upper[n-1], A[i][2,3] = upper[n], .. , A[i][n-1,n] = upper[n(n-1)/2] Given n, length(upper)=n(n-1)/2\n\nThe method returns an array of symmetric matrices of dimension n for each timeslice \n\nExamples:\n\n## load data \npp_data = read_mesons(path, \"G5\", \"G5\")\npa_data = read_mesons(path, \"G5\", \"G0G5\")\naa_data = read_mesons(path, \"G0G5\", \"G0G5\")\n\n## create Corr struct\ncorr_pp = corr_obs.(pp_data)\ncorr_pa = corr_obs.(pa_data)\ncorr_aa = corr_obs.(aa_data) # array of correlators for different \\mu_q combinations\n\n## set up matrices\ncorr_diag = [corr_pp[1], corr_aa[1]] \ncorr_upper = [corr_pa[1]]\n\nmatrices = [corr_diag, corr_upper]\n\nJulia> matrices[i]\n pp[i] ap[i]\n pa[i] aa[i]\n\n## where i runs over the timeslices\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.energies","page":"Linear Algebra","title":"juobs.energies","text":"energies(evals::Vector{Array}; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nThis method computes the energy level from the eigenvalues according to:\n\nE_i(t) = log(λ(t) λ(t+1))\n\nwhere i=1,..,n with n=length(evals[1]) and t=1,..,T total time slices. It returns a vector array en where each entry en[i][t] contains the i-th states energy at time t \n\nExamples:\n\n## load data\npp_data = read_mesons(path, \"G5\", \"G5\")\npa_data = read_mesons(path, \"G5\", \"G0G5\")\naa_data = read_mesons(path, \"G0G5\", \"G0G5\")\n\n## create Corr struct\ncorr_pp = corr_obs.(pp_data)\ncorr_pa = corr_obs.(pa_data)\ncorr_aa = corr_obs.(aa_data) # array of correlators for different \\mu_q combinations\n\n## set up matrices \ncorr_diag = [corr_pp[1], corr_aa[1]] \ncorr_upper = [corr_pa[1]]\n\nmatrices = [corr_diag, corr_upper]\n\n## solve the GEVP\nevals = getall_eigvals(matrices, 5) #where t_0=5\nen = energies(evals)\n\nJulia> en[i] # i-th state energy at each timeslice\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.getall_eigvals","page":"Linear Algebra","title":"juobs.getall_eigvals","text":"getall_eigvals(a::Vector{Matrix}, t0; iter=30 )\n\nThis function solves a GEVP problem, returning the eigenvalues, for a list of matrices, taking as generalised matrix the one at index t0, i.e:\n\nC(t_i)v_i = λ_i C(t_0) v_i, with i=1:lenght(a)\n\nIt takes as input:\n\na::Vector{Matrix} : a vector of matrices\nt0::Int64 : idex value at which the fixed matrix is taken\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nres = Vector{Vector{uwreal}}\n\nwhere res[i] are the generalised eigenvalues of the i-th matrix of the input array. \n\nExamples:\n\n## load data\npp_data = read_mesons(path, \"G5\", \"G5\")\npa_data = read_mesons(path, \"G5\", \"G0G5\")\naa_data = read_mesons(path, \"G0G5\", \"G0G5\")\n\n## create Corr struct\ncorr_pp = corr_obs.(pp_data)\ncorr_pa = corr_obs.(pa_data)\ncorr_aa = corr_obs.(aa_data) # array of correlators for different \\mu_q combinations\n\n## set up matrices \ncorr_diag = [corr_pp[1], corr_aa[1]] \ncorr_upper = [corr_pa[1]]\n\nmatrices = [corr_diag, corr_upper]\n\n## solve the GEVP\nevals = getall_eigvals(matrices, 5) #where t_0=5\n\n\nJulia>\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.getall_eigvecs","page":"Linear Algebra","title":"juobs.getall_eigvecs","text":"getall_eigvecs(a::Vector{Matrix}, delta_t; iter=30 )\n\nThis function solves a GEVP problem, returning the eigenvectors, for a list of matrices.\n\nC(t_i)v_i = λ_i C(t_i-delta_t) v_i, with i=1:lenght(a)\n\nHere delta_t is the time shift within the two matrices of the problem, and is kept fixed. It takes as input:\n\na::Vector{Matrix} : a vector of matrices\ndelta_t::Int64 : the fixed time shift t-t_0\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nres = Vector{Matrix{uwreal}}\n\nwhere each res[i] is a matrix with the eigenvectors as columns Examples:\n\nmat_array = get_matrix(diag, upper_diag)\nevecs = getall_eigvecs(mat_array, 5)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#Observables","page":"Observables","title":"Observables","text":"","category":"section"},{"location":"obs.html","page":"Observables","title":"Observables","text":"meff\nmpcac\ndec_const\ndec_const_pcvc\ncomp_t0","category":"page"},{"location":"obs.html#juobs.meff","page":"Observables","title":"juobs.meff","text":"meff(corr::Vector{uwreal}, plat::Vector{Int64}; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing) \n\nmeff(corr::Corr, plat::Vector{Int64}; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes effective mass for a given correlator corr at a given plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}.\n\nThe flags pl and data allow to show the plots and return data as an extra result.\n\ndata = read_mesons(path, \"G5\", \"G5\")\ncorr_pp = corr_obs.(data)\nm = meff(corr_pp[1], [50, 60], pl=false)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.mpcac","page":"Observables","title":"juobs.mpcac","text":"mpcac(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64}; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nmpcac(a0p::Corr, pp::Corr, plat::Vector{Int64}; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes the bare PCAC mass for a given correlator a0p and pp at a given plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}.\n\nThe flags pl and data allow to show the plots and return data as an extra result. The ca variable allows to compute mpcac using the improved axial current.\n\ndata_pp = read_mesons(path, \"G5\", \"G5\")\ndata_a0p = read_mesons(path, \"G5\", \"G0G5\")\ncorr_pp = corr_obs.(data_pp)\ncorr_a0p = corr_obs.(data_a0p)\nm12 = mpcac(corr_a0p, corr_pp, [50, 60], pl=false)\n\np0 = 9.2056\np1 = -13.9847\ng2 = 1.73410\nca = -0.006033 * g2 *( 1 + exp(p0 + p1/g2))\n\nm12 = mpcac(corr_a0p, corr_pp, [50, 60], pl=false, ca=ca)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.dec_const","page":"Observables","title":"juobs.dec_const","text":"dec_const(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, y0::Int64; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const(a0p::Corr, pp::Corr, plat::Vector{Int64}, m::uwreal; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes the bare decay constant using A_0P and PP correlators . The decay constant is computed in the plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}. If it is passed as a uwreal vector, effective mass m and source position y0 must be specified.\n\nThe flags pl and data allow to show the plots and return data as an extra result. The ca variable allows to compute dec_const using the improved axial current.\n\nThe method assumes that the source is close to the boundary. It takes the following ratio to cancel boundary effects. R = fracf_A(x_0 y_0)sqrtf_P(T-y_0 y_0) * e^m (x_0 - T2)\n\ndata_pp = read_mesons(path, \"G5\", \"G5\", legacy=true)\ndata_a0p = read_mesons(path, \"G5\", \"G0G5\", legacy=true)\n\ncorr_pp = corr_obs.(data_pp, L=32)\ncorr_a0p = corr_obs.(data_a0p, L=32)\n\nm = meff(corr_pp[1], [50, 60], pl=false)\n\nbeta = 3.46\np0 = 9.2056\np1 = -13.9847\ng2 = 6 / beta\nca = -0.006033 * g2 *( 1 + exp(p0 + p1/g2))\n\nf = dec_const(corr_a0p[1], corr_pp[1], [50, 60], m, pl=true, ca=ca)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.dec_const_pcvc","page":"Observables","title":"juobs.dec_const_pcvc","text":"dec_const_pcvc(corr::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const_pcvc(corr::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes decay constant using the PCVC relation for twisted mass fermions. The decay constant is computed in the plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}. If it is passed as a uwreal vector, vector of twisted masses mu and source position y0 must be specified.\n\nThe flags pl and data allow to show the plots and return data as an extra result.\n\nThe method assumes that the source is in the bulk.\n\ndata = read_mesons(path, \"G5\", \"G5\")\ncorr_pp = corr_obs.(data, L=32)\nm = meff(corr_pp[1], [50, 60], pl=false)\nf = dec_const_pcvc(corr_pp[1], [50, 60], m, pl=false)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.comp_t0","page":"Observables","title":"juobs.comp_t0","text":"comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false, rw::Union{Matrix{Float64}, Nothing}=nothing, npol::Int64=2, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ncomp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false, rw::Union{Vector{Matrix{Float64}}, Nothing}=nothing, npol::Int64=2, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes t0 using the energy density of the action Ysl(Yang-Mills action). t0 is computed in the plateau plat. A polynomial interpolation in t is performed to find t0, where npol is the degree of the polynomial (linear fit by default)\n\nThe flag pl allows to show the plot.\n\n#Single replica\nY = read_ms(path)\nrw = read_ms(path_rw)\n\nt0 = comp_t0(Y, [38, 58], L=32)\nt0_r = comp_t0(Y, [38, 58], L=32, rw=rw)\n\n#Two replicas\nY1 = read_ms(path1)\nY2 = read_ms(path2)\nrw1 = read_ms(path_rw1)\nrw2 = read_ms(path_rw2)\n\nt0 = comp_t0([Y1, Y2], [38, 58], L=32, pl=true)\nt0_r = comp_t0(Y, [38, 58], L=32, rw=[rw1, rw2], pl=true)\n\n\n\n\n\n\n","category":"function"},{"location":"index.html#DOCUMENTATION","page":"Home","title":"DOCUMENTATION","text":"","category":"section"},{"location":"index.html#Contents","page":"Home","title":"Contents","text":"","category":"section"},{"location":"index.html","page":"Home","title":"Home","text":"Pages = [\"reader.md\", \"tools.md\", \"obs.md\", \"linalg.md\"]\nDepth = 3","category":"page"},{"location":"tools.html#Tools","page":"Tools","title":"Tools","text":"","category":"section"},{"location":"tools.html","page":"Tools","title":"Tools","text":"corr_obs\nmd_sea\nmd_val\nlin_fit\nfit_routine","category":"page"},{"location":"tools.html#juobs.corr_obs","page":"Tools","title":"juobs.corr_obs","text":"corr_obs(cdata::CData; real::Bool=true, rw::Union{Array{Float64, 2}, Nothing}=nothing, L::Int64=1)\n\ncorr_obs(cdata::Array{CData, 1}; real::Bool=true, rw::Union{Array{Array{Float64, 2}, 1}, Nothing}=nothing, L::Int64=1)\n\nCreates a Corr struct with the given CData struct cdata (read_mesons) for a single replica. An array of CData can be passed as argument for multiple replicas.\n\nThe flag real select the real or imaginary part of the correlator. If rw is specified, the method applies reweighting. rw is passed as a matrix of Float64 (read_ms1) The correlator can be normalized with the volume factor if L is fixed.\n\n#Single replica\ndata = read_mesons(path, \"G5\", \"G5\")\nrw = read_ms1(path_rw)\ncorr_pp = corr_obs.(data)\ncorr_pp_r = corr_obs.(data, rw=rw)\n\n#Two replicas\ndata = read_mesons([path_r1, path_r2], \"G5\", \"G5\")\nrw1 = read_ms1(path_rw1)\nrw2 = read_ms1(path_rw2)\n\ncorr_pp = corr_obs.(data)\ncorr_pp_r = corr_obs.(data, rw=[rw1, rw2])\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.md_sea","page":"Tools","title":"juobs.md_sea","text":"md_sea(a::uwreal, md::Vector{Matrix{Float64}}, ws::ADerrors.wspace=ADerrors.wsg)\n\nComputes the derivative of an observable A with respect to the sea quark masses.\n\nfracd Adm(sea) = sum_i fracpartial Apartial O_i fracd O_id m(sea)\n\nfracd O_idm(sea) = O_i fracpartial Spartial m - O_i fracpartial Spartial m = - (O_i - O_i) (fracpartial Spartial m - fracpartial Spartial m)\n\nwhere O_i are primary observables \n\nmd is a vector that contains the derivative of the action S with respect to the sea quark masses for each replica. md[irep][irw, icfg]\n\nmd_sea returns a tuple of uwreal observables (dAdm_l dAdm_s)_sea, where m_l and m_s are the light and strange quark masses.\n\n#Single replica\ndata = read_mesons(path, \"G5\", \"G5\")\nmd = read_md(path_md)\nrw = read_ms1(path_rw)\n\ncorr_pp = corr_obs.(data, rw=rw)\nm = meff(corr_pp[1], plat)\nm_mdl, m_mds = md_sea(m, [md], ADerrors.wsg)\nm_shifted = m + 2 * dml * m_mdl + dms * m_mds\n\n#Two replicas\ndata = read_mesons([path_r1, path_r2], \"G5\", \"G5\")\nmd1 = read_md(path_md1)\nmd2 = read_md(path_md2)\n\ncorr_pp = corr_obs.(data)\nm = meff(corr_pp[1], plat)\nm_mdl, m_mds = md_sea(m, [md1, md2], ADerrors.wsg)\nm_shifted = m + 2 * dml * m_mdl + dms * m_mds\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.md_val","page":"Tools","title":"juobs.md_val","text":"md_val(a::uwreal, obs::Corr, derm::Vector{Corr})\n\nComputes the derivative of an observable A with respect to the valence quark masses.\n\nfracd Adm(val) = sum_i fracpartial Apartial O_i fracd O_id m(val)\n\nfracd O_idm(val) = fracpartial O_ipartial m(val)\n\nwhere O_i are primary observables \n\nmd is a vector that contains the derivative of the action S with respect to the sea quark masses for each replica. md[irep][irw, icfg]\n\nmd_val returns a tuple of uwreal observables (dAdm_1 dAdm_2)_val, where m_1 and m_2 are the correlator masses.\n\ndata = read_mesons(path, \"G5\", \"G5\", legacy=true)\ndata_d1 = read_mesons(path, \"G5_d1\", \"G5_d1\", legacy=true)\ndata_d2 = read_mesons(path, \"G5_d2\", \"G5_d2\", legacy=true)\n\nrw = read_ms1(path_rw)\n\ncorr_pp = corr_obs.(data, rw=rw)\ncorr_pp_d1 = corr_obs.(data_d1, rw=rw)\ncorr_pp_d2 = corr_obs.(data_d2, rw=rw)\nderm = [[corr_pp_d1[k], corr_pp_d2[k]] for k = 1:length(pp_d1)]\n\nm = meff(corr_pp[1], plat)\nm_md1, m_md2 = md_val(m, corr_pp[1], derm[1])\nm_shifted = m + 2 * dm1 * m_md1 + dm2 * m_md2\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.lin_fit","page":"Tools","title":"juobs.lin_fit","text":"lin_fit(x::Vector{<:Real}, y::Vector{uwreal})\n\nComputes a linear fit of uwreal data points y. This method return uwreal fit parameters and chisqexpected.\n\nfitp, csqexp = lin_fit(phi2, m2)\nm2_phys = fitp[1] + fitp[2] * phi2_phys\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.fit_routine","page":"Tools","title":"juobs.fit_routine","text":"fit_routine(model::Function, xdata::Array{<:Real}, ydata::Array{uwreal}, param::Int64=3; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nfit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}, param::Int64=3; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing, covar::Bool=false)\n\nGiven a model function with a number param of parameters and an array of uwreal, this function fit ydata with the given model and print fit information The method return an array upar with the best fit parameters with their errors. The flag wpm is an optional array of Float64 of lenght 4. The first three paramenters specify the criteria to determine the summation windows:\n\nvp[1]: The autocorrelation function is summed up to t = round(vp1).\nvp[2]: The sumation window is determined using U. Wolff poposal with S_tau = wpm2\nvp[3]: The autocorrelation function Gamma(t) is summed up a point where its error deltaGamma(t) is a factor vp[3] times larger than the signal.\n\nAn additional fourth parameter vp[4], tells ADerrors to add a tail to the error with tau_exp = wpm4. Negative values of wpm[1:4] are ignored and only one component of wpm[1:3] needs to be positive. If the flag covaris set to true, fit_routine takes into account covariances between x and y for each data point.\n\n@. model(x,p) = p[1] + p[2] * exp(-(p[3]-p[1])*x)\n@. model2(x,p) = p[1] + p[2] * x[:, 1] + (p[3] + p[4] * x[:, 1]) * x[:, 2]\nfit_routine(model, xdata, ydata, param=3)\nfit_routine(model, xdata, ydata, param=3, covar=true)\n\n\n\n\n\n","category":"function"}]
[{"location":"reader.html#Reader","page":"Reader","title":"Reader","text":"","category":"section"},{"location":"reader.html","page":"Reader","title":"Reader","text":"read_mesons\nread_ms\nread_ms1\nread_md\ntruncate_data!","category":"page"},{"location":"reader.html#juobs.read_mesons","page":"Reader","title":"juobs.read_mesons","text":"read_mesons(path::String, g1::Union{String, Nothing}=nothing, g2::Union{String, Nothing}=nothing; id::Union{String, Nothing}=nothing, legacy::Bool=false)\n\nread_mesons(path::Vector{String}, g1::Union{String, Nothing}=nothing, g2::Union{String, Nothing}=nothing; id::Union{String, Nothing}=nothing, legacy::Bool=false)\n\nThis function read a mesons dat file at a given path and returns a vector of CData structures for different masses and Dirac structures. Dirac structures g1 and/or g2 can be passed as string arguments in order to filter correaltors. ADerrors id can be specified as argument. If is not specified, the id is fixed according to the ensemble name (example: \"H400\"-> id = \"H400\")\n\n*For the old version (without smearing, distance preconditioning and theta) set legacy=true.\n\nExamples:\n\nread_mesons(path)\nread_mesons(path, \"G5\")\nread_mesons(path, nothing, \"G5\")\nread_mesons(path, \"G5\", \"G5\")\nread_mesons(path, \"G5\", \"G5\", id=\"H100\")\nread_mesons(path, \"G5_d2\", \"G5_d2\", legacy=true)\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.read_ms","page":"Reader","title":"juobs.read_ms","text":"read_ms(path::String; id::Union{String, Nothing}=nothing, dtr::Int64=1, obs::String=\"Y\")\n\nReads openQCD ms dat files at a given path. This method return YData: \n\nt(t): flow time values\nobs(icfg, x0, t): the time-slice sums of the densities of the observable (Wsl, Ysl or Qsl)\nvtr: vector that contains trajectory number\nid: ensmble id\n\ndtr = dtr_cnfg / dtr_ms, where dtr_cnfg is the number of trajectories computed before saving the configuration. dtr_ms is the same but applied to the ms.dat file.\n\nExamples:\n\nY = read_ms(path)\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.read_ms1","page":"Reader","title":"juobs.read_ms1","text":"read_ms1(path::String; v::String=\"1.2\")\n\nReads openQCD ms1 dat files at a given path. This method returns a matrix W[irw, icfg] that contains the reweighting factors, where irw is the rwf index and icfg the configuration number. The function is compatible with the output files of openQCD v=1.2, 1.4 and 1.6. Version can be specified as argument.\n\nExamples:\n\nread_ms1(path)\nread_ms1(path, v=\"1.4\")\nread_ms1(path, v=\"1.6\")\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.read_md","page":"Reader","title":"juobs.read_md","text":"read_md(path::String)\n\nReads openQCD pbp.dat files at a given path. This method returns a matrix md[irw, icfg] that contains the derivatives dSdm, where mdirw=1 = dSdm_l and mdirw=2 = dSdm_s\n\nSeff = -tr(log(D+m))\n\ndSeff dm = -tr((D+m)^-1)\n\nExamples:\n\nmd = read_md(path)\n\n\n\n\n\n","category":"function"},{"location":"reader.html#juobs.truncate_data!","page":"Reader","title":"juobs.truncate_data!","text":"truncate_data!(data::YData, nc::Int64)\n\ntruncate_data!(data::Vector{YData}, nc::Vector{Int64})\n\ntruncate_data!(data::Vector{CData}, nc::Int64)\n\ntruncate_data!(data::Vector{Vector{CData}}, nc::Vector{Int64})\n\nTruncates the output of read_mesons and read_ms taking the first nc configurations.\n\nExamples:\n\n#Single replica\ndat = read_mesons(path, \"G5\", \"G5\")\nY = read_ms(path)\ntruncate_data!(dat, nc)\ntruncate_data!(Y, nc)\n\n#Two replicas\ndat = read_mesons([path1, path2], \"G5\", \"G5\")\nY = read_ms.([path1_ms, path2_ms])\ntruncate_data!(dat, [nc1, nc2])\ntruncate_data!(Y, [nc1, nc2])\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#Linear-Algebra","page":"Linear Algebra","title":"Linear Algebra","text":"","category":"section"},{"location":"linalg.html","page":"Linear Algebra","title":"Linear Algebra","text":"uweigvals\nuweigvecs\nuweigen\nget_matrix\nenergies\ngetall_eigvals\ngetall_eigvecs","category":"page"},{"location":"linalg.html#juobs.uweigvals","page":"Linear Algebra","title":"juobs.uweigvals","text":"uweigvals(a::Matrix{uwreal}; iter = 30)\n\nuweigvals(a::Matrix{uwreal}, b::Matrix{uwreal}; iter = 30)\n\nThis function computes the eigenvalues of a matrix of uwreal objects. If a second matrix b is given as input, it returns the generalised eigenvalues instead. It takes as input:\n\na::Matrix{uwreal} : a matrix of uwreal\nb::Matrix{uwreal} : a matrix of uwreal, optional\niter=30: optional flag to set the iterations of the qr algorithm used to solve the eigenvalue problem\n\nIt returns:\n\nres = Vector{uwreal}: a vector where each elements is an eigenvalue \n\na = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\nb = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\n\nres = uweigvals(a) ##eigenvalues\nres1 = uweigvals(a,b) ## generalised eigenvalues\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.uweigvecs","page":"Linear Algebra","title":"juobs.uweigvecs","text":"uweigvecs(a::Matrix{uwreal}; iter = 30)\n\nuweigvecs(a::Matrix{uwreal}, b::Matrix{uwreal}; iter = 30)\n\nThis function computes the eigenvectors of a matrix of uwreal objects. If a second matrix b is given as input, it returns the generalised eigenvectors instead. It takes as input:\n\na::Matrix{uwreal} : a matrix of uwreal\nb::Matrix{uwreal} : a matrix of uwreal, optional\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nres = Matrix{uwreal}: a matrix where each column is an eigenvector \n\nExamples:\n\na = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\nb = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\n\nres = uweigvecs(a) ##eigenvectors in column \nres1 = uweigvecs(a,b) ## generalised eigenvectors in column \n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.uweigen","page":"Linear Algebra","title":"juobs.uweigen","text":"uweigen(a::Matrix{uwreal}; iter = 30)\n\nuweigen(a::Matrix{uwreal}, b::Matrix{uwreal}; iter = 30)\n\nThis function computes the eigenvalues and the eigenvectors of a matrix of uwreal objects. If a second matrix b is given as input, it returns the generalised eigenvalues and eigenvectors instead. It takes as input:\n\na::Matrix{uwreal} : a matrix of uwreal\nb::Matrix{uwreal} : a matrix of uwreal, optional\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nevals = Vector{uwreal}: a vector where each elements is an eigenvalue \nevecs = Matrix{uwreal}: a matrix where the i-th column is the eigenvector of the i-th eigenvalue\n\nExamples:\n\na = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\nb = Matrix{uwreal}(nothing, n,n) ## n*n matrix of uwreal with nothing entries\n\neval, evec = uweigen(a) \neval1, evec1 = uweigvecs(a,b) \n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.get_matrix","page":"Linear Algebra","title":"juobs.get_matrix","text":"get_matrix(diag::Vector{Array}, upper::Vector{Array} )\n\nThis function allows the user to build an array of matrices, where each matrix is a symmetric matrix of correlators at a given timeslice. \n\nIt takes as input:\n\ndiag::Vector{Array}: vector of correlators. Each correlator will constitute a diagonal element of the matrices A[i] where i runs over the timeslices, i.e. A[i][1,1] = diag[1], .... A[i][n,n] = diag[n] Given n=length(diag), the matrices will have dimension n*n \nupper::Vector{Array}: vector of correlators liying on the upper diagonal position. A[i][1,2] = upper[1], .. , A[i][1,n] = upper[n-1], A[i][2,3] = upper[n], .. , A[i][n-1,n] = upper[n(n-1)/2] Given n, length(upper)=n(n-1)/2\n\nThe method returns an array of symmetric matrices of dimension n for each timeslice \n\nExamples:\n\n## load data \npp_data = read_mesons(path, \"G5\", \"G5\")\npa_data = read_mesons(path, \"G5\", \"G0G5\")\naa_data = read_mesons(path, \"G0G5\", \"G0G5\")\n\n## create Corr struct\ncorr_pp = corr_obs.(pp_data)\ncorr_pa = corr_obs.(pa_data)\ncorr_aa = corr_obs.(aa_data) # array of correlators for different \\mu_q combinations\n\n## set up matrices\ncorr_diag = [corr_pp[1], corr_aa[1]] \ncorr_upper = [corr_pa[1]]\n\nmatrices = [corr_diag, corr_upper]\n\nJulia> matrices[i]\n pp[i] ap[i]\n pa[i] aa[i]\n\n## where i runs over the timeslices\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.energies","page":"Linear Algebra","title":"juobs.energies","text":"energies(evals::Vector{Array}; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nThis method computes the energy level from the eigenvalues according to:\n\nE_i(t) = log(λ(t) λ(t+1))\n\nwhere i=1,..,n with n=length(evals[1]) and t=1,..,T total time slices. It returns a vector array en where each entry en[i][t] contains the i-th states energy at time t \n\nExamples:\n\n## load data\npp_data = read_mesons(path, \"G5\", \"G5\")\npa_data = read_mesons(path, \"G5\", \"G0G5\")\naa_data = read_mesons(path, \"G0G5\", \"G0G5\")\n\n## create Corr struct\ncorr_pp = corr_obs.(pp_data)\ncorr_pa = corr_obs.(pa_data)\ncorr_aa = corr_obs.(aa_data) # array of correlators for different \\mu_q combinations\n\n## set up matrices \ncorr_diag = [corr_pp[1], corr_aa[1]] \ncorr_upper = [corr_pa[1]]\n\nmatrices = [corr_diag, corr_upper]\n\n## solve the GEVP\nevals = getall_eigvals(matrices, 5) #where t_0=5\nen = energies(evals)\n\nJulia> en[i] # i-th state energy at each timeslice\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.getall_eigvals","page":"Linear Algebra","title":"juobs.getall_eigvals","text":"getall_eigvals(a::Vector{Matrix}, t0; iter=30 )\n\nThis function solves a GEVP problem, returning the eigenvalues, for a list of matrices, taking as generalised matrix the one at index t0, i.e:\n\nC(t_i)v_i = λ_i C(t_0) v_i, with i=1:lenght(a)\n\nIt takes as input:\n\na::Vector{Matrix} : a vector of matrices\nt0::Int64 : idex value at which the fixed matrix is taken\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nres = Vector{Vector{uwreal}}\n\nwhere res[i] are the generalised eigenvalues of the i-th matrix of the input array. \n\nExamples:\n\n## load data\npp_data = read_mesons(path, \"G5\", \"G5\")\npa_data = read_mesons(path, \"G5\", \"G0G5\")\naa_data = read_mesons(path, \"G0G5\", \"G0G5\")\n\n## create Corr struct\ncorr_pp = corr_obs.(pp_data)\ncorr_pa = corr_obs.(pa_data)\ncorr_aa = corr_obs.(aa_data) # array of correlators for different \\mu_q combinations\n\n## set up matrices \ncorr_diag = [corr_pp[1], corr_aa[1]] \ncorr_upper = [corr_pa[1]]\n\nmatrices = [corr_diag, corr_upper]\n\n## solve the GEVP\nevals = getall_eigvals(matrices, 5) #where t_0=5\n\n\nJulia>\n\n\n\n\n\n","category":"function"},{"location":"linalg.html#juobs.getall_eigvecs","page":"Linear Algebra","title":"juobs.getall_eigvecs","text":"getall_eigvecs(a::Vector{Matrix}, delta_t; iter=30 )\n\nThis function solves a GEVP problem, returning the eigenvectors, for a list of matrices.\n\nC(t_i)v_i = λ_i C(t_i-delta_t) v_i, with i=1:lenght(a)\n\nHere delta_t is the time shift within the two matrices of the problem, and is kept fixed. It takes as input:\n\na::Vector{Matrix} : a vector of matrices\ndelta_t::Int64 : the fixed time shift t-t_0\niter=30 : the number of iterations of the qr algorithm used to extract the eigenvalues \n\nIt returns:\n\nres = Vector{Matrix{uwreal}}\n\nwhere each res[i] is a matrix with the eigenvectors as columns Examples:\n\nmat_array = get_matrix(diag, upper_diag)\nevecs = getall_eigvecs(mat_array, 5)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#Observables","page":"Observables","title":"Observables","text":"","category":"section"},{"location":"obs.html","page":"Observables","title":"Observables","text":"meff\nmpcac\ndec_const\ndec_const_pcvc\ncomp_t0","category":"page"},{"location":"obs.html#juobs.meff","page":"Observables","title":"juobs.meff","text":"meff(corr::Vector{uwreal}, plat::Vector{Int64}; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing) \n\nmeff(corr::Corr, plat::Vector{Int64}; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes effective mass for a given correlator corr at a given plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}.\n\nThe flags pl and data allow to show the plots and return data as an extra result.\n\ndata = read_mesons(path, \"G5\", \"G5\")\ncorr_pp = corr_obs.(data)\nm = meff(corr_pp[1], [50, 60], pl=false)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.mpcac","page":"Observables","title":"juobs.mpcac","text":"mpcac(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64}; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nmpcac(a0p::Corr, pp::Corr, plat::Vector{Int64}; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes the bare PCAC mass for a given correlator a0p and pp at a given plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}.\n\nThe flags pl and data allow to show the plots and return data as an extra result. The ca variable allows to compute mpcac using the improved axial current.\n\ndata_pp = read_mesons(path, \"G5\", \"G5\")\ndata_a0p = read_mesons(path, \"G5\", \"G0G5\")\ncorr_pp = corr_obs.(data_pp)\ncorr_a0p = corr_obs.(data_a0p)\nm12 = mpcac(corr_a0p, corr_pp, [50, 60], pl=false)\n\np0 = 9.2056\np1 = -13.9847\ng2 = 1.73410\nca = -0.006033 * g2 *( 1 + exp(p0 + p1/g2))\n\nm12 = mpcac(corr_a0p, corr_pp, [50, 60], pl=false, ca=ca)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.dec_const","page":"Observables","title":"juobs.dec_const","text":"dec_const(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, y0::Int64; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const(a0p::Corr, pp::Corr, plat::Vector{Int64}, m::uwreal; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const(a0pL::Vector{uwreal}, a0pR::Vector{uwreal}, ppL::Vector{uwreal}, ppR::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, y0::Int64; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const(a0pL::Corr, a0pR::Corr, ppL::Corr, ppR::Corr, plat::Vector{Int64}, m::uwreal; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes the bare decay constant using A_0P and PP correlators . The decay constant is computed in the plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}. If it is passed as a uwreal vector, effective mass m and source position y0 must be specified.\n\nThe flags pl and data allow to show the plots and return data as an extra result. The ca variable allows to compute dec_const using the improved axial current.\n\nThe method assumes that the source is close to the boundary. It takes the following ratio to cancel boundary effects. R = fracf_A(x_0 y_0)sqrtf_P(T-y_0 y_0) * e^m (x_0 - T2)\n\nIf left and right correlators are included in the input. The result is computed with the following ratio R = sqrtf_A(x_0 y_0) * f_A(x_0 T - 1 - y_0) f_P(T - 1 - y_0 y_0)\n\ndata_pp = read_mesons(path, \"G5\", \"G5\", legacy=true)\ndata_a0p = read_mesons(path, \"G5\", \"G0G5\", legacy=true)\n\ncorr_pp = corr_obs.(data_pp, L=32)\ncorr_a0p = corr_obs.(data_a0p, L=32)\n\ncorr_a0pL, corr_a0pR = [corr_a0p[1], corr_a0p[2]]\ncorr_ppL, corr_ppR = [corr_pp[1], corr_pp[2]]\n\nm = meff(corr_pp[1], [50, 60], pl=false)\n\nbeta = 3.46\np0 = 9.2056\np1 = -13.9847\ng2 = 6 / beta\nca = -0.006033 * g2 *( 1 + exp(p0 + p1/g2))\n\nf = dec_const(corr_a0p[1], corr_pp[1], [50, 60], m, pl=true, ca=ca)\n\nf_ratio = dec_const(corr_a0pL, corr_a0pR, corr_ppL, corr_ppR, [50, 60], m, pl=true, ca=ca)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.dec_const_pcvc","page":"Observables","title":"juobs.dec_const_pcvc","text":"dec_const_pcvc(corr::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const_pcvc(corr::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const_pcvc(ppL::Vector{uwreal}, ppR::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ndec_const_pcvc(corrL::Corr, corrR::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes decay constant using the PCVC relation for twisted mass fermions. The decay constant is computed in the plateau plat. Correlator can be passed as an Corr struct or Vector{uwreal}. If it is passed as a uwreal vector, vector of twisted masses mu and source position y0 must be specified.\n\nThe flags pl and data allow to show the plots and return data as an extra result.\n\nThe method extract the matrix element assuming that the source is in the bulk. ** **If left and right correlators are included in the input. The result is computed with a ratio that cancels boundary effects: R = sqrtf_P(x_0 y_0) * f_P(x_0 T - 1 - y_0) f_P(T - 1 - y_0 y_0)\n\ndata = read_mesons(path, \"G5\", \"G5\")\ncorr_pp = corr_obs.(data, L=32)\nm = meff(corr_pp[1], [50, 60], pl=false)\nf = dec_const_pcvc(corr_pp[1], [50, 60], m, pl=false)\n\n#left and right correlators\nf_ratio = dec_const_pcvc(ppL, ppR, [50, 60], m)\n\n\n\n\n\n","category":"function"},{"location":"obs.html#juobs.comp_t0","page":"Observables","title":"juobs.comp_t0","text":"comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false, rw::Union{Matrix{Float64}, Nothing}=nothing, npol::Int64=2, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\ncomp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false, rw::Union{Vector{Matrix{Float64}}, Nothing}=nothing, npol::Int64=2, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nComputes t0 using the energy density of the action Ysl(Yang-Mills action). t0 is computed in the plateau plat. A polynomial interpolation in t is performed to find t0, where npol is the degree of the polynomial (linear fit by default)\n\nThe flag pl allows to show the plot.\n\n#Single replica\nY = read_ms(path)\nrw = read_ms(path_rw)\n\nt0 = comp_t0(Y, [38, 58], L=32)\nt0_r = comp_t0(Y, [38, 58], L=32, rw=rw)\n\n#Two replicas\nY1 = read_ms(path1)\nY2 = read_ms(path2)\nrw1 = read_ms(path_rw1)\nrw2 = read_ms(path_rw2)\n\nt0 = comp_t0([Y1, Y2], [38, 58], L=32, pl=true)\nt0_r = comp_t0(Y, [38, 58], L=32, rw=[rw1, rw2], pl=true)\n\n\n\n\n\n\n","category":"function"},{"location":"index.html#DOCUMENTATION","page":"Home","title":"DOCUMENTATION","text":"","category":"section"},{"location":"index.html#Contents","page":"Home","title":"Contents","text":"","category":"section"},{"location":"index.html","page":"Home","title":"Home","text":"Pages = [\"reader.md\", \"tools.md\", \"obs.md\", \"linalg.md\"]\nDepth = 3","category":"page"},{"location":"tools.html#Tools","page":"Tools","title":"Tools","text":"","category":"section"},{"location":"tools.html","page":"Tools","title":"Tools","text":"corr_obs\nmd_sea\nmd_val\nlin_fit\nfit_routine","category":"page"},{"location":"tools.html#juobs.corr_obs","page":"Tools","title":"juobs.corr_obs","text":"corr_obs(cdata::CData; real::Bool=true, rw::Union{Array{Float64, 2}, Nothing}=nothing, L::Int64=1)\n\ncorr_obs(cdata::Array{CData, 1}; real::Bool=true, rw::Union{Array{Array{Float64, 2}, 1}, Nothing}=nothing, L::Int64=1)\n\nCreates a Corr struct with the given CData struct cdata (read_mesons) for a single replica. An array of CData can be passed as argument for multiple replicas.\n\nThe flag real select the real or imaginary part of the correlator. If rw is specified, the method applies reweighting. rw is passed as a matrix of Float64 (read_ms1) The correlator can be normalized with the volume factor if L is fixed.\n\n#Single replica\ndata = read_mesons(path, \"G5\", \"G5\")\nrw = read_ms1(path_rw)\ncorr_pp = corr_obs.(data)\ncorr_pp_r = corr_obs.(data, rw=rw)\n\n#Two replicas\ndata = read_mesons([path_r1, path_r2], \"G5\", \"G5\")\nrw1 = read_ms1(path_rw1)\nrw2 = read_ms1(path_rw2)\n\ncorr_pp = corr_obs.(data)\ncorr_pp_r = corr_obs.(data, rw=[rw1, rw2])\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.md_sea","page":"Tools","title":"juobs.md_sea","text":"md_sea(a::uwreal, md::Vector{Matrix{Float64}}, ws::ADerrors.wspace=ADerrors.wsg)\n\nComputes the derivative of an observable A with respect to the sea quark masses.\n\nfracd Adm(sea) = sum_i fracpartial Apartial O_i fracd O_id m(sea)\n\nfracd O_idm(sea) = O_i fracpartial Spartial m - O_i fracpartial Spartial m = - (O_i - O_i) (fracpartial Spartial m - fracpartial Spartial m)\n\nwhere O_i are primary observables \n\nmd is a vector that contains the derivative of the action S with respect to the sea quark masses for each replica. md[irep][irw, icfg]\n\nmd_sea returns a tuple of uwreal observables (dAdm_l dAdm_s)_sea, where m_l and m_s are the light and strange quark masses.\n\n#Single replica\ndata = read_mesons(path, \"G5\", \"G5\")\nmd = read_md(path_md)\nrw = read_ms1(path_rw)\n\ncorr_pp = corr_obs.(data, rw=rw)\nm = meff(corr_pp[1], plat)\nm_mdl, m_mds = md_sea(m, [md], ADerrors.wsg)\nm_shifted = m + 2 * dml * m_mdl + dms * m_mds\n\n#Two replicas\ndata = read_mesons([path_r1, path_r2], \"G5\", \"G5\")\nmd1 = read_md(path_md1)\nmd2 = read_md(path_md2)\n\ncorr_pp = corr_obs.(data)\nm = meff(corr_pp[1], plat)\nm_mdl, m_mds = md_sea(m, [md1, md2], ADerrors.wsg)\nm_shifted = m + 2 * dml * m_mdl + dms * m_mds\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.md_val","page":"Tools","title":"juobs.md_val","text":"md_val(a::uwreal, obs::Corr, derm::Vector{Corr})\n\nComputes the derivative of an observable A with respect to the valence quark masses.\n\nfracd Adm(val) = sum_i fracpartial Apartial O_i fracd O_id m(val)\n\nfracd O_idm(val) = fracpartial O_ipartial m(val)\n\nwhere O_i are primary observables \n\nmd is a vector that contains the derivative of the action S with respect to the sea quark masses for each replica. md[irep][irw, icfg]\n\nmd_val returns a tuple of uwreal observables (dAdm_1 dAdm_2)_val, where m_1 and m_2 are the correlator masses.\n\ndata = read_mesons(path, \"G5\", \"G5\", legacy=true)\ndata_d1 = read_mesons(path, \"G5_d1\", \"G5_d1\", legacy=true)\ndata_d2 = read_mesons(path, \"G5_d2\", \"G5_d2\", legacy=true)\n\nrw = read_ms1(path_rw)\n\ncorr_pp = corr_obs.(data, rw=rw)\ncorr_pp_d1 = corr_obs.(data_d1, rw=rw)\ncorr_pp_d2 = corr_obs.(data_d2, rw=rw)\nderm = [[corr_pp_d1[k], corr_pp_d2[k]] for k = 1:length(pp_d1)]\n\nm = meff(corr_pp[1], plat)\nm_md1, m_md2 = md_val(m, corr_pp[1], derm[1])\nm_shifted = m + 2 * dm1 * m_md1 + dm2 * m_md2\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.lin_fit","page":"Tools","title":"juobs.lin_fit","text":"lin_fit(x::Vector{<:Real}, y::Vector{uwreal})\n\nComputes a linear fit of uwreal data points y. This method return uwreal fit parameters and chisqexpected.\n\nfitp, csqexp = lin_fit(phi2, m2)\nm2_phys = fitp[1] + fitp[2] * phi2_phys\n\n\n\n\n\n","category":"function"},{"location":"tools.html#juobs.fit_routine","page":"Tools","title":"juobs.fit_routine","text":"fit_routine(model::Function, xdata::Array{<:Real}, ydata::Array{uwreal}, param::Int64=3; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)\n\nfit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}, param::Int64=3; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing, covar::Bool=false)\n\nGiven a model function with a number param of parameters and an array of uwreal, this function fit ydata with the given model and print fit information The method return an array upar with the best fit parameters with their errors. The flag wpm is an optional array of Float64 of lenght 4. The first three paramenters specify the criteria to determine the summation windows:\n\nvp[1]: The autocorrelation function is summed up to t = round(vp1).\nvp[2]: The sumation window is determined using U. Wolff poposal with S_tau = wpm2\nvp[3]: The autocorrelation function Gamma(t) is summed up a point where its error deltaGamma(t) is a factor vp[3] times larger than the signal.\n\nAn additional fourth parameter vp[4], tells ADerrors to add a tail to the error with tau_exp = wpm4. Negative values of wpm[1:4] are ignored and only one component of wpm[1:3] needs to be positive. If the flag covaris set to true, fit_routine takes into account covariances between x and y for each data point.\n\n@. model(x,p) = p[1] + p[2] * exp(-(p[3]-p[1])*x)\n@. model2(x,p) = p[1] + p[2] * x[:, 1] + (p[3] + p[4] * x[:, 1]) * x[:, 2]\nfit_routine(model, xdata, ydata, param=3)\nfit_routine(model, xdata, ydata, param=3, covar=true)\n\n\n\n\n\n","category":"function"}]
}
......@@ -50,4 +50,4 @@ m2_phys = fitp[1] + fitp[2] * phi2_phys</code></pre></div><a class="docs-sourcel
fit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}, param::Int64=3; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing, covar::Bool=false)</code></pre><p>Given a model function with a number param of parameters and an array of <code>uwreal</code>, this function fit ydata with the given <code>model</code> and print fit information The method return an array <code>upar</code> with the best fit parameters with their errors. The flag <code>wpm</code> is an optional array of Float64 of lenght 4. The first three paramenters specify the criteria to determine the summation windows:</p><ul><li><p><code>vp[1]</code>: The autocorrelation function is summed up to <span>$t = round(vp[1])$</span>.</p></li><li><p><code>vp[2]</code>: The sumation window is determined using U. Wolff poposal with <span>$S_\tau = wpm[2]$</span></p></li><li><p><code>vp[3]</code>: The autocorrelation function <span>$\Gamma(t)$</span> is summed up a point where its error <span>$\delta\Gamma(t)$</span> is a factor <code>vp[3]</code> times larger than the signal.</p></li></ul><p>An additional fourth parameter <code>vp[4]</code>, tells ADerrors to add a tail to the error with <span>$\tau_{exp} = wpm[4]$</span>. Negative values of <code>wpm[1:4]</code> are ignored and only one component of <code>wpm[1:3]</code> needs to be positive. If the flag <code>covar</code>is set to true, <code>fit_routine</code> takes into account covariances between x and y for each data point.</p><pre><code class="language-">@. model(x,p) = p[1] + p[2] * exp(-(p[3]-p[1])*x)
@. model2(x,p) = p[1] + p[2] * x[:, 1] + (p[3] + p[4] * x[:, 1]) * x[:, 2]
fit_routine(model, xdata, ydata, param=3)
fit_routine(model, xdata, ydata, param=3, covar=true)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="reader.html">« Reader</a><a class="docs-footer-nextpage" href="obs.html">Observables »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Tuesday 23 March 2021 11:06">Tuesday 23 March 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
fit_routine(model, xdata, ydata, param=3, covar=true)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://gitlab.ift.uam-csic.es/jugarrio/juobs">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="reader.html">« Reader</a><a class="docs-footer-nextpage" href="obs.html">Observables »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Friday 17 September 2021 08:40">Friday 17 September 2021</span>. Using Julia version 1.5.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
......@@ -126,7 +126,7 @@ function energies(evals::Union{Vector{Vector{uwreal}},Array{Array{uwreal}} }; wp
ratio = evals[t][i] / evals[t+1][i]
aux_en[t] = 0.5*log(ratio * ratio)
end
isnothing(wpm) ? uwerr.(aux_en) : uwerr.(aux_en, wpm)
isnothing(wpm) ? uwerr.(aux_en) : [uwerr(a, wpm) for a in aux_en]
eff_en[i] = copy(aux_en)
end
return eff_en
......
......@@ -313,18 +313,27 @@ end
dec_const_pcvc(corr::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const_pcvc(ppL::Vector{uwreal}, ppR::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
dec_const_pcvc(corrL::Corr, corrR::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
Computes decay constant using the PCVC relation for twisted mass fermions. The decay constant is computed in the plateau `plat`.
Correlator can be passed as an `Corr` struct or `Vector{uwreal}`. If it is passed as a uwreal vector, vector of twisted masses `mu` and source position `y0`
must be specified.
The flags `pl` and `data` allow to show the plots and return data as an extra result.
**The method assumes that the source is in the bulk.**
**The method extract the matrix element assuming that the source is in the bulk. **
**If left and right correlators are included in the input. The result is computed with a ratio that cancels boundary effects:**
`` R = \sqrt{f_P(x_0, y_0) * f_P(x_0, T - 1 - y_0) / f_P(T - 1 - y_0, y_0)}``
```@example
data = read_mesons(path, "G5", "G5")
corr_pp = corr_obs.(data, L=32)
m = meff(corr_pp[1], [50, 60], pl=false)
f = dec_const_pcvc(corr_pp[1], [50, 60], m, pl=false)
#left and right correlators
f_ratio = dec_const_pcvc(ppL, ppR, [50, 60], m)
```
"""
function dec_const_pcvc(corr::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false,
......@@ -340,23 +349,18 @@ function dec_const_pcvc(corr::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu
R_av = plat_av(R, plat, wpm)
f = sqrt(2) * (mu[1] + mu[2]) *R_av / m^1.5
if pl
if isnothing(wpm)
uwerr(f)
uwerr(R_av)
uwerr.(R)
else
uwerr(f, wpm)
uwerr(R_av, wpm)
uwerr.(R, wpm)
end
isnothing(wpm) ? uwerr(f) : uwerr(f, wpm)
isnothing(wpm) ? uwerr(R_av) : uwerr(R_av, wpm)
v = value(R_av)
e = err(R_av)
e = err(R_av)
figure()
fill_between(plat[1]:plat[2], v-e, v+e, color="green", alpha=0.75)
errorbar(1:length(R), value.(R), err.(R), fmt="x", color="black")
lbl = string(L"$af = $", sprint(show, f))
fill_between(plat[1]:plat[2], v-e, v+e, color="green", alpha=0.75, label=L"$R$")
errorbar(1:length(R), value.(R), err.(R), fmt="x", color="black", label=lbl)
ylabel(L"$R$")
xlabel(L"$x_0$")
legend()
title(string(L"$\mu_1 = $", mu[1], L" $\mu_2 = $", mu[2]))
display(gcf())
end
......@@ -373,6 +377,50 @@ function dec_const_pcvc(corr::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=tru
return dec_const_pcvc(corr.obs, plat, m, corr.mu, corr.y0, pl=pl, data=data, wpm=wpm)
end
function dec_const_pcvc(ppL::Vector{uwreal}, ppR::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, mu::Vector{Float64}, y0::Int64 ; pl::Bool=true, data::Bool=false,
wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
T = length(ppL)
f1 = [ppL[T - y0] for k = 1:T]
R = ((ppL .* ppR ./ f1).^2).^(1/4)
R = R[2:end-1]
R_av = plat_av(R, plat, wpm)
f = sqrt(2) * (mu[1] + mu[2]) * R_av / m^1.5
if pl
isnothing(wpm) ? uwerr(f) : uwerr(f, wpm)
isnothing(wpm) ? uwerr(R_av) : uwerr(R_av, wpm)
v = value(R_av)
e = err(R_av)
figure()
lbl = string(L"$af = $", sprint(show, f))
fill_between(plat[1]:plat[2], v-e, v+e, color="green", alpha=0.75, label=L"$R$")
errorbar(1:length(R), value.(R), err.(R), fmt="x", color="black", label=lbl)
ylabel(L"$R$")
xlabel(L"$x_0$")
legend()
title(string(L"$\mu_1 = $", mu[1], L" $\mu_2 = $", mu[2]))
display(gcf())
end
if !data
return f
else
return (f, uwdot(sqrt(2) * (mu[1] + mu[2]) / m^1.5, R))
end
end
function dec_const_pcvc(corrL::Corr, corrR::Corr, plat::Vector{Int64}, m::uwreal; pl::Bool=true, data::Bool=false,
wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
T = length(corrL.obs)
if (corrL.kappa == corrR.kappa) && (corrL.mu == corrR.mu) && (corrL.y0 == T - 1 - corrR.y0)
return dec_const_pcvc(corrL.obs, corrR.obs, plat, m, corrL.mu, corrL.y0, pl=pl, data=data, wpm=wpm)
else
error("y0, kappa or mu values does not match")
end
end
#t0
function get_model(x, p, n)
s = 0.0
......@@ -413,7 +461,7 @@ t0_r = comp_t0(Y, [38, 58], L=32, rw=[rw1, rw2], pl=true)
"""
function comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false,
rw::Union{Matrix{Float64}, Nothing}=nothing, npol::Int64=2, ws::ADerrors.wspace=ADerrors.wsg,
wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing, rw_info::Bool=false)
Ysl = Y.obs
t = Y.t
......@@ -421,32 +469,45 @@ function comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false,
replica = size.([Ysl], 1)
#Truncation
n_ws = findfirst(x-> x == ws.str2id[id], ws.map_nob)
if !isnothing(n_ws)
ivrep_ws = ws.fluc[n_ws].ivrep
if id in keys(ADerrors.wsg.str2id)
n_ws = findfirst(x-> x == ws.str2id[id], ws.map_nob)
if !isnothing(n_ws)
ivrep_ws = ws.fluc[n_ws].ivrep
if length(ivrep_ws) != 1
error("Different number of replicas")
end
if length(ivrep_ws) != 1
error("Different number of replicas")
end
if replica[1] > ivrep_ws[1]
println("Automatic truncation in Ysl ", ivrep_ws[1], " / ", replica[1], ". R = 1")
Ysl = Ysl[1:ivrep_ws[1], :, :]
elseif replica[1] < ivrep_ws[1]
error("Automatic truncation failed. R = 1\nTry using truncate_data!")
if replica[1] > ivrep_ws[1]
println("Automatic truncation in Ysl ", ivrep_ws[1], " / ", replica[1], ". R = 1")
Ysl = Ysl[1:ivrep_ws[1], :, :]
elseif replica[1] < ivrep_ws[1]
error("Automatic truncation failed. R = 1\nTry using truncate_data!")
end
end
end
Ysl = isnothing(rw) ? Ysl : apply_rw(Ysl, rw)
xmax = size(Ysl, 2)
nt0 = t0_guess(t, Ysl, plat, L)
dt0 = iseven(npol) ? Int64(npol / 2) : Int64((npol+1)/ 2)
Y_aux = Matrix{uwreal}(undef, xmax, 2*dt0+1)
if !isnothing(rw)
Ysl_r, W = apply_rw(Ysl, rw)
W_obs = uwreal(W, id)
WY_aux = Matrix{uwreal}(undef, xmax, 2*dt0+1)
end
for i = 1:xmax
k = 1
for j = nt0-dt0:nt0+dt0
Y_aux[i, k] = uwreal(Ysl[:, i, j], id)
if isnothing(rw)
Y_aux[i, k] = uwreal(Ysl[:, i, j], id)
else
WY_aux[i, k] = uwreal(Ysl_r[:, i, j], id)
Y_aux[i, k] = WY_aux[i, k] / W_obs
end
k = k + 1
end
end
......@@ -464,7 +525,7 @@ function comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false,
uwerr.(t2E)
else
uwerr(t0, wpm)
uwerr.(t2E, wpm)
[uwerr(t2E_aux, wpm) for t2E_aux in t2E]
end
v = value.(t2E)
......@@ -487,7 +548,11 @@ function comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false,
title(string(L"$t/a^2 = $", t[nt0]))
display(gcf())
end
return t0
if rw_info && !isnothing(rw)
(t0, WY_aux, W_obs)
else
return t0
end
end
function comp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false,
......@@ -504,38 +569,55 @@ function comp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false
if !all(id .== id[1])
error("IDs are not equal")
end
id = id[1]
#Truncation
n_ws = findfirst(x-> x == ws.str2id[id[1]], ws.map_nob)
if !isnothing(n_ws)
ivrep_ws = ws.fluc[n_ws].ivrep
if id in keys(ADerrors.wsg.str2id)
n_ws = findfirst(x-> x == ws.str2id[id], ws.map_nob)
if !isnothing(n_ws)
ivrep_ws = ws.fluc[n_ws].ivrep
if length(replica) != length(ivrep_ws)
error("Different number of replicas")
end
if length(replica) != length(ivrep_ws)
error("Different number of replicas")
end
for k = 1:length(replica)
if replica[k] > ivrep_ws[k]
println("Automatic truncation in Ysl ", ivrep_ws[k], " / ", replica[k], ". R = ", k)
Ysl[k] = Ysl[k][1:ivrep_ws[k], :, :]
elseif replica[k] < ivrep_ws[k]
error("Automatic truncation failed. R = ", replica[k], "\nTry using truncate_data!")
for k = 1:length(replica)
if replica[k] > ivrep_ws[k]
println("Automatic truncation in Ysl ", ivrep_ws[k], " / ", replica[k], ". R = ", k)
Ysl[k] = Ysl[k][1:ivrep_ws[k], :, :]
elseif replica[k] < ivrep_ws[k]
error("Automatic truncation failed. R = ", replica[k], "\nTry using truncate_data!")
end
end
replica = size.(Ysl, 1)
end
replica = size.(Ysl, 1)
end
Ysl = isnothing(rw) ? Ysl : apply_rw(Ysl, rw)
tmp = Ysl[1]
[tmp = cat(tmp, Ysl[k], dims=1) for k = 2:nr]
[tmp = cat(tmp, Ysl[k], dims=1) for k=2:nr]
nt0 = t0_guess(t, tmp, plat, L)
xmax = size(tmp, 2)
dt0 = iseven(npol) ? Int64(npol / 2) : Int64((npol+1) / 2)
Y_aux = Matrix{uwreal}(undef, xmax, 2*dt0+1)
if !isnothing(rw)
Ysl_r, W = apply_rw(Ysl, rw)
tmp_r = Ysl_r[1]
tmp_W = W[1]
[tmp_r = cat(tmp_r, Ysl_r[k], dims=1) for k = 2:nr]
[tmp_W = cat(tmp_W, W[k], dims=1) for k = 2:nr]
W_obs = uwreal(tmp_W, id, replica)
WY_aux = Matrix{uwreal}(undef, xmax, 2*dt0+1)
end
for i = 1:xmax
k = 1
for j = nt0-dt0:nt0+dt0
Y_aux[i, k] = uwreal(tmp[:, i, j], id[1], replica)
if isnothing(rw)
Y_aux[i, k] = uwreal(tmp[:, i, j], id, replica)
else
WY_aux[i, k] = uwreal(tmp_r[:, i, j], id, replica)
Y_aux[i, k] = WY_aux[i, k] / W_obs
end
k = k + 1
end
end
......@@ -553,7 +635,7 @@ function comp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false
uwerr.(t2E)
else
uwerr(t0, wpm)
uwerr.(t2E, wpm)
[uwerr(t2E_aux, wpm) for t2E_aux in t2E]
end
v = value.(t2E)
......@@ -576,11 +658,19 @@ function comp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false
title(string(L"$t/a^2 = $", t[nt0]))
display(gcf())
end
return t0
if rw_info && !isnothing(rw)
(t0, WY_aux, W_obs)
else
return t0
end
end
function t0_guess(t::Vector{Float64}, Ysl::Array{Float64, 3}, plat::Vector{Int64}, L::Int64)
t2E_ax = t.^2 .* mean(mean(Ysl[:, plat[1]:plat[2], :], dims=2), dims=1)[1, 1, :] / L^3
#look for values t2E: t2E > 0.3 and 0.0 < t2E_ax < 0.3
if !(any(t2E_ax .> 0.3) && any((t2E_ax .< 0.3) .& (t2E_ax .> 0.0)))
error("Error finding solutions. Check volume normalization!")
end
t0_aux = minimum(abs.(t2E_ax .- 0.3))
nt0 = findfirst(x-> abs(x - 0.3) == t0_aux, t2E_ax) #index closest value to t0
return nt0
......
......@@ -4,8 +4,8 @@ function apply_rw(data::Array{Float64}, W::Matrix{Float64})
W1 = W[1, 1:nc]
W2 = W[2, 1:nc]
data_r = data .* W1 .* W2 / mean(W1 .* W2)
return data_r
data_r = data .* W1 .* W2
return (data_r, W1 .* W2)
end
function apply_rw(data::Vector{<:Array{Float64}}, W::Vector{Matrix{Float64}})
......@@ -16,17 +16,9 @@ function apply_rw(data::Vector{<:Array{Float64}}, W::Vector{Matrix{Float64}})
rw1 = [W[k][1, 1:nc[k]] for k=1:length(W)]
rw2 = [W[k][2, 1:nc[k]] for k=1:length(W)]
rw1_cat = rw1[1]
rw2_cat = rw2[1]
for k = 2:length(W)
rw1_cat = cat(rw1_cat, rw1[k], dims=1)
rw2_cat = cat(rw2_cat, rw2[k], dims=1)
end
rw_mean = mean(rw1_cat .* rw2_cat)
data_r = [data[k] .* rw1[k].* rw2[k] / rw_mean for k=1:length(data)]
return data_r
rw = [rw1[k] .* rw2[k] for k=1:length(W)]
data_r = [data[k] .* rw[k] for k=1:length(data)]
return (data_r, rw)
end
function check_corr_der(obs::Corr, derm::Vector{Corr})
......@@ -89,20 +81,30 @@ corr_pp = corr_obs.(data)
corr_pp_r = corr_obs.(data, rw=[rw1, rw2])
```
"""
function corr_obs(cdata::CData; real::Bool=true, rw::Union{Array{Float64, 2}, Nothing}=nothing, L::Int64=1)
function corr_obs(cdata::CData; real::Bool=true, rw::Union{Array{Float64, 2}, Nothing}=nothing, L::Int64=1, rw_info::Bool=false)
real ? data = cdata.re_data ./ L^3 : data = cdata.im_data ./ L^3
data_r = isnothing(rw) ? data : apply_rw(data, rw)
nt = size(data)[2]
obs = Vector{uwreal}(undef, nt)
[obs[x0] = uwreal(data_r[:, x0], cdata.id) for x0 = 1:nt]
return Corr(obs, cdata)
if isnothing(rw)
obs = [uwreal(data[:, x0], cdata.id) for x0 = 1:nt]
else
data_r, W = apply_rw(data, rw)
ow = [uwreal(data_r[:, x0], cdata.id) for x0 = 1:nt]
W_obs = uwreal(W, cdata.id)
obs = [ow[x0] / W_obs for x0 = 1:nt]
end
if rw_info && !isnothing(rw)
return (Corr(obs, cdata), ow, W_obs)
else
return Corr(obs, cdata)
end
end
#function corr_obs for R != 1
#TODO: vcfg with gaps
function corr_obs(cdata::Array{CData, 1}; real::Bool=true, rw::Union{Array{Array{Float64, 2}, 1}, Nothing}=nothing, L::Int64=1)
function corr_obs(cdata::Array{CData, 1}; real::Bool=true, rw::Union{Array{Array{Float64, 2}, 1}, Nothing}=nothing, L::Int64=1, rw_info::Bool=false)
nr = length(cdata)
id = getfield.(cdata, :id)
vcfg = getfield.(cdata, :vcfg)
......@@ -113,17 +115,29 @@ function corr_obs(cdata::Array{CData, 1}; real::Bool=true, rw::Union{Array{Array
end
real ? data = getfield.(cdata, :re_data) ./ L^3 : data = getfield.(cdata, :im_data) ./ L^3
data_r = isnothing(rw) ? data : apply_rw(data, rw)
tmp = data_r[1]
[tmp = cat(tmp, data_r[k], dims=1) for k = 2:nr]
nt = size(data[1])[2]
obs = Vector{uwreal}(undef, nt)
[obs[x0] = uwreal(tmp[:, x0], id[1], replica) for x0 = 1:nt]
return Corr(obs, cdata)
if isnothing(rw)
tmp = data[1]
[tmp = cat(tmp, data[k], dims=1) for k = 2:nr]
obs = [uwreal(tmp[:, x0], id[1], replica) for x0 = 1:nt]
else
data_r, W = apply_rw(data, rw)
tmp = data_r[1]
tmp_W = W[1]
[tmp = cat(tmp, data_r[k], dims=1) for k = 2:nr]
[tmp_W = cat(tmp_W, W[k], dims=1) for k = 2:nr]
ow = [uwreal(tmp[:, x0], id[1], replica) for x0 = 1:nt]
W_obs = uwreal(tmp_W, id[1], replica)
obs = [ow[x0] / W_obs for x0 = 1:nt]
end
if rw_info && !isnothing(rw)
return (Corr(obs, cdata), ow, W_obs)
else
return Corr(obs, cdata)
end
end
@doc raw"""
corr_sym(corrL::Corr, corrR::Corr, parity::Int64=1)
......@@ -196,7 +210,7 @@ m_mdl, m_mds = md_sea(m, [md1, md2], ADerrors.wsg)
m_shifted = m + 2 * dml * m_mdl + dms * m_mds
```
"""
function md_sea(a::uwreal, md::Vector{Matrix{Float64}}, ws::ADerrors.wspace=ADerrors.wsg)
function md_sea(a::uwreal, md::Vector{Matrix{Float64}}, ow::uwreal, w::Union{uwreal, Nothing}=nothing, ws::ADerrors.wspace=ADerrors.wsg)
nid = neid(a)
p = findall(t-> t==1, a.prop)
......@@ -226,24 +240,41 @@ function md_sea(a::uwreal, md::Vector{Matrix{Float64}}, ws::ADerrors.wspace=ADer
for k = 2:length(md)
md_aux = cat(md_aux, md[k][:, 1:ivrep[k]], dims=2)