updated links

01_post-processing.Rmd

@@ -1,5 +1,11 @@
 # Post-processing of raw eddy-covariance data
 
+<!--
+ <span style="color: gray;">$\rightarrow$ for downloading this chapter as [jupyter notebook](https://github.com/noctiluc3nt/ec_analyze/blob/main/notebooks/01_post-processing.ipynb).
+</span>
+<hr>
+-->
+
 To derive turbulent fluxes and other turbulence characteristics from eddy-covariance measurements, they have to be post-processed first. In the following, we will have a look a the required steps -- starting with the theoretical foundation of the method and the necessary physical and technical considerations for the raw data processing. The upcoming analyses (e.g., [02_basic-turbulence-diagnostics.ipynb](https://github.com/noctiluc3nt/ec_analyze/blob/main/notebooks/02_basic-turbulence-diagnostics.ipynb)) build upon this and allow for a more detailed investigation (e.g., [03_quadrant-analysis.ipynb](https://github.com/noctiluc3nt/ec_analyze/blob/main/notebooks/03_quadrant-analysis.ipynb)).
 
 

05_flux-footprint.Rmd

@@ -69,6 +69,7 @@ str(ffp)
       ..$ : num [1:87] -1.91 -1.58 -0.79 0 0.79 ...
      $ contour_levels: num [1:9] 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
 
+Therein, `fy_mean` represents the crosswind-integrated footprint with coordinates `x` and `xmax` the location of the maximum footprint. `(x2d, y2d, f2d)` represent the 2d flux footprint and `(xcontour, ycontour)` the contour lines of the respective contour levels, which can be specified in the `contours`argument in `calc_flux_footprint`. The output list `ffp` can be easily plotted using the function `plot_flux_footprint`, as shown in the following.
 
 ### Plotting of flux footprint with `plot_flux_footprint`
 

docs/flux-footprint.html

@@ -284,6 +284,7 @@ <h3><span class="header-section-number">7.2.1</span> Calculate 2D flux footprint
   ..$ : num [1:133] -3.37 -3.16 -2.37 -1.58 -0.79 ...
   ..$ : num [1:87] -1.91 -1.58 -0.79 0 0.79 ...
  $ contour_levels: num [1:9] 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1</code></pre>
+<p>Therein, <code>fy_mean</code> represents the crosswind-integrated footprint with coordinates <code>x</code> and <code>xmax</code> the location of the maximum footprint. <code>(x2d, y2d, f2d)</code> represent the 2d flux footprint and <code>(xcontour, ycontour)</code> the contour lines of the respective contour levels, which can be specified in the <code>contours</code>argument in <code>calc_flux_footprint</code>. The output list <code>ffp</code> can be easily plotted using the function <code>plot_flux_footprint</code>, as shown in the following.</p>
 </div>
 <div id="plotting-of-flux-footprint-with-plot_flux_footprint" class="section level3 hasAnchor" number="7.2.2">
 <h3><span class="header-section-number">7.2.2</span> Plotting of flux footprint with <code>plot_flux_footprint</code><a href="flux-footprint.html#plotting-of-flux-footprint-with-plot_flux_footprint" class="anchor-section" aria-label="Anchor link to header"></a></h3>

docs/index.html

@@ -226,10 +226,9 @@ <h1 class="title">Reddy: An open-source package for analyzing eddy-covariance da
 <div id="introduction" class="section level1 hasAnchor" number="1">
 <h1><span class="header-section-number">1</span> Introduction<a href="index.html#introduction" class="anchor-section" aria-label="Anchor link to header"></a></h1>
 <hr>
-<p><strong>Package</strong>: <a href="https://github.com/noctiluc3nt/Reddy">Reddy package (Github)</a> <br>
+<p><strong>Package source</strong>: <a href="https://github.com/noctiluc3nt/Reddy">Reddy package (Github)</a> <br>
 <strong>Jupyter notebooks</strong>: <a href="https://github.com/noctiluc3nt/ec_analyze/tree/main/notebooks">jupyter notebooks (Github)</a><br>
-<strong>Publication</strong>: <a href="https://link.springer.com/article/10.1007/s10546-024-00879-5">Link</a><br>
-<strong>Any comments or issues?</strong>: <a href="https://github.com/noctiluc3nt/ec_analyze/issues">create an issue (Github)</a></p>
+<strong>Any comments or issues?</strong> <a href="https://github.com/noctiluc3nt/ec_analyze/issues">create an issue (Github)</a></p>
 <hr>
 <p><strong>Overview:</strong> Eddy-covariance (EC) measurements allow to <em>measure turbulent fluxes directly and non-invasively over long periods of time</em>, and thus represent the standard measurement method for turbulent exchange processes between atmosphere and land, vegetation, cryosphere or hydrosphere.
 <br>

docs/post-processing-of-raw-eddy-covariance-data.html

@@ -221,6 +221,11 @@ <h1>
             <section class="normal" id="section-">
 <div id="post-processing-of-raw-eddy-covariance-data" class="section level1 hasAnchor" number="2">
 <h1><span class="header-section-number">2</span> Post-processing of raw eddy-covariance data<a href="post-processing-of-raw-eddy-covariance-data.html#post-processing-of-raw-eddy-covariance-data" class="anchor-section" aria-label="Anchor link to header"></a></h1>
+<!--
+ <span style="color: gray;">$\rightarrow$ for downloading this chapter as [jupyter notebook](https://github.com/noctiluc3nt/ec_analyze/blob/main/notebooks/01_post-processing.ipynb).
+</span>
+<hr>
+-->
 <p>To derive turbulent fluxes and other turbulence characteristics from eddy-covariance measurements, they have to be post-processed first. In the following, we will have a look a the required steps – starting with the theoretical foundation of the method and the necessary physical and technical considerations for the raw data processing. The upcoming analyses (e.g., <a href="https://github.com/noctiluc3nt/ec_analyze/blob/main/notebooks/02_basic-turbulence-diagnostics.ipynb">02_basic-turbulence-diagnostics.ipynb</a>) build upon this and allow for a more detailed investigation (e.g., <a href="https://github.com/noctiluc3nt/ec_analyze/blob/main/notebooks/03_quadrant-analysis.ipynb">03_quadrant-analysis.ipynb</a>).</p>
 <div id="eddy-covariance-method" class="section level2 hasAnchor" number="2.1">
 <h2><span class="header-section-number">2.1</span> Eddy-covariance method<a href="post-processing-of-raw-eddy-covariance-data.html#eddy-covariance-method" class="anchor-section" aria-label="Anchor link to header"></a></h2>

index.Rmd

@@ -17,10 +17,9 @@ description: ""
 # Introduction
 
 <hr>
-**Package**: <a href="https://github.com/noctiluc3nt/Reddy">Reddy package (Github)</a> <br> 
+**Package source**: <a href="https://github.com/noctiluc3nt/Reddy">Reddy package (Github)</a> <br> 
 **Jupyter notebooks**: <a href="https://github.com/noctiluc3nt/ec_analyze/tree/main/notebooks">jupyter notebooks (Github)</a><br>
-**Publication**: <a href="https://link.springer.com/article/10.1007/s10546-024-00879-5">Link</a><br>
-**Any comments or issues?**: <a href="https://github.com/noctiluc3nt/ec_analyze/issues">create an issue (Github)</a>
+**Any comments or issues?** <a href="https://github.com/noctiluc3nt/ec_analyze/issues">create an issue (Github)</a>
 
 <hr>
 

notebooks/05_flux-footprint-estimation.ipynb

@@ -99,6 +99,14 @@
     "str(ffp)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "73116195",
+   "metadata": {},
+   "source": [
+    "Therein, `fy_mean` represents the crosswind-integrated footprint with coordinates `x` and `xmax` the location of the maximum footprint. `(x2d, y2d, f2d)` represent the 2d flux footprint and `(xcontour, ycontour)` the contour lines of the respective contour levels, which can be specified in the `contours`argument in `calc_flux_footprint`. The output list `ffp` can be easily plotted using the function `plot_flux_footprint`, as shown in the following."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8c6a69b7",