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docs/quadrant-analysis.html

@@ -229,7 +229,7 @@ <h1><span class="header-section-number">4</span> Quadrant analysis<a href="quadr
 <span class="math display">\[ E = \frac{S(disorganized)}{S(organized)}, \quad\quad OR = \frac{D(disorganized)}{D(organized)}\]</span>
 For example, for the sensible heat flux warm updrafts and cold downdrafts are the organized structures, and oppositely cold updrafts and warm downdrafts the disorganized ones (in the figure below, organized structures are shaded and disorganized are not shaded). <br>
 To filter out particularly strong coherent structures, a hole size can be applied with the filter conditions <span class="math inline">\(\vert \hat{x}\hat{y} \vert \le H \cdot \vert \overline{\hat{x}&#39;\hat{y}&#39;}\vert\)</span> (hyperbolic curves in the figure). Usually, <span class="math inline">\(y\)</span> is chosen to be the vertical velocity <span class="math inline">\(w\)</span>. The quadrant analysis is directly related to the fluxes by using the respective constituting quantities: <span class="math inline">\((u,w)\)</span> for momentum flux, <span class="math inline">\((T,w)\)</span> for sensible heat flux, <span class="math inline">\((q,w)\)</span> for latent heat flux and <span class="math inline">\((c,w)\)</span> for CO<span class="math inline">\(_2\)</span> flux – as visualized in the figure (adapted from <span class="citation">Mack et al. (<a href="#ref-Mack2024" role="doc-biblioref">2024</a>)</span>).</p>
-<p><img src="../figures/schema/qa_schema.png" /></p>
+<p><img src="figures/schema/qa_schema.png" /></p>
 <div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="quadrant-analysis.html#cb15-1" aria-hidden="true"></a><span class="co">#loading Reddy package</span></span>
 <span id="cb15-2"><a href="quadrant-analysis.html#cb15-2" aria-hidden="true"></a><span class="kw">install.packages</span>(<span class="st">&quot;../src/Reddy_0.0.0.9000.tar.gz&quot;</span>,<span class="dt">repos=</span><span class="ot">NULL</span>,<span class="dt">source=</span><span class="ot">TRUE</span>,<span class="dt">quiet=</span><span class="ot">TRUE</span>)</span>
 <span id="cb15-3"><a href="quadrant-analysis.html#cb15-3" aria-hidden="true"></a><span class="kw">library</span>(Reddy)</span>

docs/reynolds-stress-tensor.html

@@ -228,7 +228,7 @@ <h1><span class="header-section-number">6</span> Reynolds stress tensor<a href="
             \overline{w&#39;u&#39;} &amp; \overline{w&#39;v&#39;} &amp; w&#39;^2 
         \end{pmatrix}, \quad \textrm{with} \quad R = R^T\]</span>
 summarizes all normal and shear stresses. Based on an invariant analysis, the most important geometric properties can be derived from its eigenvalues and eigenvectors. Using a linear combination of the three eigenvalues, a two-dimensional mapping into an equilateral triangle – called <em>barycentric map</em> (<span class="citation">Banerjee et al. (<a href="#ref-Banerjee2007" role="doc-biblioref">2007</a>)</span>, see figure) – with the coordinates <span class="math inline">\((x_B,y_B)\)</span>, can be constructed, which allows to characterize the anisotropy (<span class="math inline">\(y_B\)</span>) and the limiting states. The three corners of the triangle represent the three limiting state 1-, 2- and 3-component limit, where the 3-component limit corresponds to isotropic turbulence.</p>
-<p><img src="../figures/schema/triangle_schema.png" width=550 /></p>
+<p><img src="figures/schema/triangle_schema.png" width=550 /></p>
 <div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="reynolds-stress-tensor.html#cb34-1" aria-hidden="true"></a><span class="co">#loading Reddy package</span></span>
 <span id="cb34-2"><a href="reynolds-stress-tensor.html#cb34-2" aria-hidden="true"></a><span class="kw">install.packages</span>(<span class="st">&quot;../src/Reddy_0.0.0.9000.tar.gz&quot;</span>,<span class="dt">repos=</span><span class="ot">NULL</span>,<span class="dt">source=</span><span class="ot">TRUE</span>,<span class="dt">quiet=</span><span class="ot">TRUE</span>)</span>
 <span id="cb34-3"><a href="reynolds-stress-tensor.html#cb34-3" aria-hidden="true"></a><span class="kw">library</span>(Reddy)</span>

docs/spectra.html

@@ -274,7 +274,7 @@ <h2><span class="header-section-number">5.2</span> FFT spectrum and comparison t
 <div id="multiresolution-decomposition-mrd" class="section level2 hasAnchor" number="5.3">
 <h2><span class="header-section-number">5.3</span> Multiresolution decomposition (MRD)<a href="spectra.html#multiresolution-decomposition-mrd" class="anchor-section" aria-label="Anchor link to header"></a></h2>
 <p>Multiresolution decomposition (MRD) is a method to characterize the timescale dependence of variances (spectrum) or covariances (cospectrum) and to find scale gaps between turbulent and submeso-scale motions. It uses Haar wavelets, which have the advantage over Fourier analysis that no periodicity is assumed.</p>
-<p><img src="../figures/schema/mrd_schema.png" width=600 /></p>
+<p><img src="figures/schema/mrd_schema.png" width=600 /></p>
 <div id="calculating-multiresolution-decomposition-with-calc_mrd" class="section level3 hasAnchor" number="5.3.1">
 <h3><span class="header-section-number">5.3.1</span> Calculating multiresolution decomposition with <code>calc_mrd</code><a href="spectra.html#calculating-multiresolution-decomposition-with-calc_mrd" class="anchor-section" aria-label="Anchor link to header"></a></h3>
 <div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="spectra.html#cb28-1" aria-hidden="true"></a><span class="co">#cospectra</span></span>