build based on 0223de5

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.11.3","generation_timestamp":"2025-02-03T17:43:22","documenter_version":"1.8.0"}}
+{"documenter":{"julia_version":"1.11.3","generation_timestamp":"2025-02-03T21:47:26","documenter_version":"1.8.0"}}

dev/automatic_differentiation/index.html

@@ -12,53 +12,53 @@
 θ = [2.7, 0.27, 0.05, 3.0, 0.7, 0.25, 0.06 * SymBoltz.eV/SymBoltz.c^2, 2e-9, 0.95]
 ks = 10 .^ range(-3, 0, length=100) / u"Mpc"
 Ps = P(ks, θ)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">100-element Vector{Unitful.Quantity{Float64, 𝐋^3, Unitful.FreeUnits{(Mpc^3,), 𝐋^3, nothing}}}:
- 15162.137586395036 Mpc^3
- 16134.99180982436 Mpc^3
- 17162.999207019468 Mpc^3
- 18248.471187018036 Mpc^3
- 19392.898110437396 Mpc^3
- 20597.012346830328 Mpc^3
- 21865.38854848125 Mpc^3
- 23193.91023026567 Mpc^3
- 24589.047571269075 Mpc^3
- 26045.056925307545 Mpc^3
+ 15158.89423165112 Mpc^3
+ 16133.990934291065 Mpc^3
+ 17162.018008985026 Mpc^3
+ 18247.385905142164 Mpc^3
+ 19390.17439782168 Mpc^3
+ 20595.51044061744 Mpc^3
+ 21862.503297769654 Mpc^3
+ 23192.05702724878 Mpc^3
+ 24587.37160215938 Mpc^3
+ 26045.150779242704 Mpc^3
      ⋮
-   307.51732440732025 Mpc^3
-   262.8788688870413 Mpc^3
-   224.4792692965347 Mpc^3
-   191.43515606958212 Mpc^3
-   163.11483066913794 Mpc^3
-   138.81915708865478 Mpc^3
-   118.05203504182272 Mpc^3
-   100.28517603270468 Mpc^3
-    85.10501405337699 Mpc^3</code></pre><p>This can be plotted with</p><pre><code class="language-julia hljs">using Plots
-plot(log10.(ks/u&quot;1/Mpc&quot;), log10.(Ps/u&quot;Mpc^3&quot;); xlabel = &quot;lg(k/Mpc⁻¹)&quot;, ylabel = &quot;lg(P/Mpc³)&quot;, label = nothing)</code></pre><img src="c3710182.svg" alt="Example block output"/><h2 id="2.-Calculate-the-derivatives"><a class="docs-heading-anchor" href="#2.-Calculate-the-derivatives">2. Calculate the derivatives</a><a id="2.-Calculate-the-derivatives-1"></a><a class="docs-heading-anchor-permalink" href="#2.-Calculate-the-derivatives" title="Permalink"></a></h2><p>To get <span>$\partial \lg P / \partial \lg \theta$</span>, we can simply pass the wrapper function <code>P(k, θ)</code> through <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.jacobian"><code>ForwardDiff.jacobian</code></a>:</p><pre><code class="language-julia hljs">using ForwardDiff
+   307.9655396983048 Mpc^3
+   263.32042044870326 Mpc^3
+   224.86677824648865 Mpc^3
+   191.7972991076235 Mpc^3
+   163.53972638179297 Mpc^3
+   139.14297913991282 Mpc^3
+   118.3293250470429 Mpc^3
+   100.53425202929114 Mpc^3
+    85.32704386935688 Mpc^3</code></pre><p>This can be plotted with</p><pre><code class="language-julia hljs">using Plots
+plot(log10.(ks/u&quot;1/Mpc&quot;), log10.(Ps/u&quot;Mpc^3&quot;); xlabel = &quot;lg(k/Mpc⁻¹)&quot;, ylabel = &quot;lg(P/Mpc³)&quot;, label = nothing)</code></pre><img src="635890df.svg" alt="Example block output"/><h2 id="2.-Calculate-the-derivatives"><a class="docs-heading-anchor" href="#2.-Calculate-the-derivatives">2. Calculate the derivatives</a><a id="2.-Calculate-the-derivatives-1"></a><a class="docs-heading-anchor-permalink" href="#2.-Calculate-the-derivatives" title="Permalink"></a></h2><p>To get <span>$\partial \lg P / \partial \lg \theta$</span>, we can simply pass the wrapper function <code>P(k, θ)</code> through <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.jacobian"><code>ForwardDiff.jacobian</code></a>:</p><pre><code class="language-julia hljs">using ForwardDiff
 lgP(lgθ) = log10.(P(ks, 10 .^ lgθ) / u&quot;Mpc^3&quot;) # in log-space
 dlgP_dlgθs = ForwardDiff.jacobian(lgP, log10.(θ))</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">100×9 Matrix{Float64}:
- -0.122219  -1.24864  -0.230638  -0.0105005  …  -0.00508994  1.0  -3.71642
- -0.134237  -1.24578  -0.230109  -0.011655      -0.00499815  1.0  -3.65014
- -0.14694   -1.23903  -0.22886   -0.012883      -0.00488705  1.0  -3.58385
- -0.160982  -1.2332   -0.227782  -0.0142333     -0.00478024  1.0  -3.51756
- -0.176278  -1.22669  -0.226573  -0.015708      -0.00467268  1.0  -3.45128
- -0.192977  -1.21984  -0.225306  -0.0173148  …  -0.00456875  1.0  -3.38499
- -0.211068  -1.21225  -0.223896  -0.0190574     -0.00447107  1.0  -3.3187
- -0.230719  -1.20366  -0.222317  -0.0209459     -0.00438232  1.0  -3.25242
- -0.252091  -1.19533  -0.220783  -0.0230063     -0.00431301  1.0  -3.18613
- -0.27496   -1.18517  -0.218906  -0.0252122     -0.00425969  1.0  -3.11984
+ -0.122984  -1.2517   -0.231232  -0.0105532  …  -0.00510308  1.0  -3.71642
+ -0.134376  -1.24393  -0.229794  -0.0116581     -0.00498988  1.0  -3.65014
+ -0.147123  -1.23905  -0.228875  -0.0128933     -0.00488714  1.0  -3.58385
+ -0.161282  -1.23344  -0.227834  -0.0142563     -0.0047812   1.0  -3.51756
+ -0.176537  -1.22685  -0.226621  -0.0157221     -0.00467328  1.0  -3.45128
+ -0.193149  -1.22043  -0.225419  -0.0173279  …  -0.00457128  1.0  -3.38499
+ -0.211247  -1.21204  -0.223876  -0.0190661     -0.00447009  1.0  -3.3187
+ -0.231005  -1.20497  -0.222569  -0.0209666     -0.00438787  1.0  -3.25242
+ -0.252326  -1.1953   -0.220788  -0.0230183     -0.00431307  1.0  -3.18613
+ -0.275537  -1.18711  -0.219289  -0.0252501     -0.00426818  1.0  -3.11984
   ⋮                                          ⋱                    
- -5.96375    1.90586  -0.396415  -0.467775      -0.0298509   1.0   2.31565
- -5.99475    1.92316  -0.39715   -0.468714      -0.0298629   1.0   2.38194
- -6.02517    1.9392   -0.398055  -0.469897      -0.0298739   1.0   2.44823
- -6.05709    1.95456  -0.39812   -0.471019      -0.0298845   1.0   2.51451
- -6.08686    1.97052  -0.398356  -0.471977   …  -0.0298898   1.0   2.5808
- -6.11672    1.98519  -0.398297  -0.472992      -0.0298966   1.0   2.64709
- -6.14488    1.99962  -0.398602  -0.473816      -0.0299034   1.0   2.71337
- -6.17217    2.0135   -0.398721  -0.47465       -0.0299085   1.0   2.77966
- -6.1982     2.02687  -0.399162  -0.475427      -0.0299135   1.0   2.84595</code></pre><p>The matrix element <code>dlgP_dlgθs[i, j]</code> now contains <span>$\partial \lg P(k_i) / \partial \lg \theta_j$</span>. We can plot them all at once:</p><pre><code class="language-julia hljs">plot(
+ -5.96387    1.90595  -0.396278  -0.46785       -0.0298509   1.0   2.31565
+ -5.99425    1.92334  -0.397478  -0.468922      -0.0298623   1.0   2.38194
+ -6.02818    1.93856  -0.397474  -0.470402      -0.029874    1.0   2.44823
+ -6.05838    1.95508  -0.398049  -0.471324      -0.029882    1.0   2.51451
+ -6.0884     1.97025  -0.398015  -0.472262   …  -0.0298906   1.0   2.5808
+ -6.1172     1.98441  -0.398588  -0.473238      -0.0299002   1.0   2.64709
+ -6.14564    1.99962  -0.398671  -0.474144      -0.0299033   1.0   2.71337
+ -6.17344    2.01338  -0.398892  -0.475132      -0.0299086   1.0   2.77966
+ -6.19947    2.02695  -0.399119  -0.475828      -0.0299133   1.0   2.84595</code></pre><p>The matrix element <code>dlgP_dlgθs[i, j]</code> now contains <span>$\partial \lg P(k_i) / \partial \lg \theta_j$</span>. We can plot them all at once:</p><pre><code class="language-julia hljs">plot(
     log10.(ks/u&quot;1/Mpc&quot;), dlgP_dlgθs;
     xlabel = &quot;lg(k/Mpc⁻¹)&quot;, ylabel = &quot;∂ lg(P) / ∂ lg(θᵢ)&quot;,
     labels = &quot;θᵢ=&quot; .* [&quot;Tγ0&quot; &quot;Ωc0&quot; &quot;Ωb0&quot; &quot;Neff&quot; &quot;h&quot; &quot;Yp&quot; &quot;mh&quot; &quot;As&quot; &quot;ns&quot; &quot;ΩΛ0&quot;]
-)</code></pre><img src="0f07d459.svg" alt="Example block output"/><h2 id="Get-values-and-derivatives-together"><a class="docs-heading-anchor" href="#Get-values-and-derivatives-together">Get values and derivatives together</a><a id="Get-values-and-derivatives-together-1"></a><a class="docs-heading-anchor-permalink" href="#Get-values-and-derivatives-together" title="Permalink"></a></h2><p>The above example showed how to calculate the power spectrum <em>values</em> and their <em>derivatives</em> through <em>two separate calls</em>. If you need both, it is faster to calculate them simultaneously with the package <a href="https://juliadiff.org/DiffResults.jl/stable/"><code>DiffResults.jl</code></a>:</p><pre><code class="language-julia hljs">using DiffResults
+)</code></pre><img src="c6065c9e.svg" alt="Example block output"/><h2 id="Get-values-and-derivatives-together"><a class="docs-heading-anchor" href="#Get-values-and-derivatives-together">Get values and derivatives together</a><a id="Get-values-and-derivatives-together-1"></a><a class="docs-heading-anchor-permalink" href="#Get-values-and-derivatives-together" title="Permalink"></a></h2><p>The above example showed how to calculate the power spectrum <em>values</em> and their <em>derivatives</em> through <em>two separate calls</em>. If you need both, it is faster to calculate them simultaneously with the package <a href="https://juliadiff.org/DiffResults.jl/stable/"><code>DiffResults.jl</code></a>:</p><pre><code class="language-julia hljs">using DiffResults
 
 # Following DiffResults documentation:
 Pres = DiffResults.JacobianResult(ks/u&quot;1/Mpc&quot;, θ) # allocate buffer for values+derivatives for a function with θ-sized input and ks-sized output
@@ -75,4 +75,4 @@
    xlabel = &quot;lg(k/Mpc⁻¹)&quot;, ylabel = &quot;∂ lg(P) / ∂ lg(θᵢ)&quot;,
    labels = &quot;θᵢ=&quot; .* [&quot;Tγ0&quot; &quot;Ωc0&quot; &quot;Ωb0&quot; &quot;Neff&quot; &quot;h&quot; &quot;Yp&quot; &quot;mh&quot; &quot;As&quot; &quot;ns&quot; &quot;ΩΛ0&quot;]
 )
-plot(p1, p2, layout=(2, 1), size = (600, 600))</code></pre><img src="e557258c.svg" alt="Example block output"/><h2 id="General-approach"><a class="docs-heading-anchor" href="#General-approach">General approach</a><a id="General-approach-1"></a><a class="docs-heading-anchor-permalink" href="#General-approach" title="Permalink"></a></h2><p>The technique shown here can be used to calculate the derivative of any SymBoltz.jl output quantity:</p><ol><li>Write a wrapper function <code>output(input)</code> that calculates the desired output quantities from the desired input quantities.</li><li>Use <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.derivative"><code>ForwardDiff.derivative(output, input)</code></a> (scalar-to-scalar), <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.gradient"><code>ForwardDiff.gradient(output, input)</code></a> (vector-to-scalar) or <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.jacobian"><code>ForwardDiff.jacobian(output, input)</code></a> (vector-to-vector) to evaluate the derivative of <code>output</code> at the values <code>input</code>. Or use the similar functions in <a href="https://juliadiff.org/DiffResults.jl/stable/"><code>DiffResults</code></a> to calculate the value <em>and</em> derivatives simultaneously.</li></ol></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../getting_started/">« Getting started</a><a class="docs-footer-nextpage" href="../extended_models/">Creating extended models »</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="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.8.0 on <span class="colophon-date" title="Monday 3 February 2025 17:42">Monday 3 February 2025</span>. Using Julia version 1.11.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+plot(p1, p2, layout=(2, 1), size = (600, 600))</code></pre><img src="f765a552.svg" alt="Example block output"/><h2 id="General-approach"><a class="docs-heading-anchor" href="#General-approach">General approach</a><a id="General-approach-1"></a><a class="docs-heading-anchor-permalink" href="#General-approach" title="Permalink"></a></h2><p>The technique shown here can be used to calculate the derivative of any SymBoltz.jl output quantity:</p><ol><li>Write a wrapper function <code>output(input)</code> that calculates the desired output quantities from the desired input quantities.</li><li>Use <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.derivative"><code>ForwardDiff.derivative(output, input)</code></a> (scalar-to-scalar), <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.gradient"><code>ForwardDiff.gradient(output, input)</code></a> (vector-to-scalar) or <a href="https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.jacobian"><code>ForwardDiff.jacobian(output, input)</code></a> (vector-to-vector) to evaluate the derivative of <code>output</code> at the values <code>input</code>. Or use the similar functions in <a href="https://juliadiff.org/DiffResults.jl/stable/"><code>DiffResults</code></a> to calculate the value <em>and</em> derivatives simultaneously.</li></ol></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../getting_started/">« Getting started</a><a class="docs-footer-nextpage" href="../extended_models/">Creating extended models »</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="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.8.0 on <span class="colophon-date" title="Monday 3 February 2025 21:46">Monday 3 February 2025</span>. Using Julia version 1.11.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>