deploy: 4e89d24

_sources/chapters/getting_started/quick_start.md

@@ -11,7 +11,8 @@ then, start the plantseg in napari
 ```bash
 $ plantseg --napari
 ```
-A more in depth guide can be found in our [wiki](https://github.com/hci-unihd/plant-seg/wiki/Napari).
+A more in depth guide can be found in our [documentation (GUI)](https://hci-unihd.github.io/plant-seg/chapters/plantseg_interactive_napari/).
+
 ## Pipeline Usage (GUI)
 PlantSeg app can also be started in a GUI mode, where basic user interface allows to configure and run the pipeline.
 First, activate the newly created conda environment with:
@@ -23,7 +24,8 @@ then, run the GUI by simply typing:
 ```bash
 $ plantseg --gui
 ```
-A more in depth guide can be found in our [wiki](https://github.com/hci-unihd/plant-seg/wiki/PlantSeg-Classic-GUI).
+A more in depth guide can be found in our [documentation (Classic GUI)](https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/).
+
 ## Pipeline Usage (command line)
 Our pipeline is completely configuration file based and does not require any coding.
 
@@ -35,6 +37,6 @@ then, one can just start the pipeline with
 ```bash
 plantseg --config CONFIG_PATH
 ```
-where `CONFIG_PATH` is the path to the YAML configuration file. See [config.yaml](https://github.com/hci-unihd/plant-seg/blob/master/examples/config.yaml) for a sample configuration
-file and our [wiki](https://github.com/hci-unihd/plant-seg/wiki/PlantSeg-Classic-CLI) for a
-detailed description of the parameters.
+where `CONFIG_PATH` is the path to the YAML configuration file. See [config.yaml](examples/config.yaml) for a sample configuration
+file and our [documentation (CLI)](https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_cli/) for a
+detailed description of the parameters.

_sources/chapters/plantseg_classic_cli/index.md

@@ -10,12 +10,12 @@ of all parameters.
 * `path` attribute: is used to define either the file to process or the directory containing the data.
 * `preprocessing` attribute: contains a simple set of possible operations one would need to run on their data before calling the neural network.
 This step can be skipped if data is ready for neural network processing.
-Detailed instructions can be found at [Data Processing](https://github.com/hci-unihd/plant-seg/wiki/Data-Processing).
+Detailed instructions can be found at [Classic GUI (Data Processing)](https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/data_processing.html).
 * `cnn_prediction` attribute: contains all parameters relevant for predicting with a neural network.
 Description of all pre-trained models provided with the package is described below.
-Detailed instructions can be found at [Predictions](https://github.com/hci-unihd/plant-seg/wiki/Predictions).
+Detailed instructions can be found at [Classic GUI (Predictions)](https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/cnn_predictions.html).
 * `segmentation` attribute: contains all parameters needed to run the partitioning algorithm (i.e., final Segmentation).
-Detailed instructions can be found at [Segmentation](https://github.com/hci-unihd/plant-seg/wiki/Segmentation.md).
+Detailed instructions can be found at [Classic GUI (Segmentation)](https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/segmentation.html).
 
 ## Additional information
 

_sources/chapters/plantseg_classic_gui/segmentation.md

@@ -2,7 +2,7 @@
 
 The segmentation widget allows using very powerful graph partitioning techniques to obtain a segmentation from the
 input stacks.
-The input of this widget should be the output of the [CNN-predictions widget](https://github.com/hci-unihd/plant-seg/wiki/CNN-Predictions).
+The input of this widget should be the output of the [CNN-predictions widget](https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/cnn_predictions.html).
 If the boundary prediction stage fails for any reason, a raw image could be used (especially if the cell boundaries are
  very sharp, and the noise is low) but usually does not yield satisfactory results.
 

chapters/getting_started/quick_start.html

@@ -423,7 +423,7 @@ <h2>Pipeline Usage (Napari viewer)<a class="headerlink" href="#pipeline-usage-na
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>plantseg<span class="w"> </span>--napari
 </pre></div>
 </div>
-<p>A more in depth guide can be found in our <a class="reference external" href="https://github.com/hci-unihd/plant-seg/wiki/Napari">wiki</a>.</p>
+<p>A more in depth guide can be found in our <a class="reference external" href="https://hci-unihd.github.io/plant-seg/chapters/plantseg_interactive_napari/">documentation (GUI)</a>.</p>
 </section>
 <section id="pipeline-usage-gui">
 <h2>Pipeline Usage (GUI)<a class="headerlink" href="#pipeline-usage-gui" title="Permalink to this heading">#</a></h2>
@@ -436,7 +436,7 @@ <h2>Pipeline Usage (GUI)<a class="headerlink" href="#pipeline-usage-gui" title="
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>plantseg<span class="w"> </span>--gui
 </pre></div>
 </div>
-<p>A more in depth guide can be found in our <a class="reference external" href="https://github.com/hci-unihd/plant-seg/wiki/PlantSeg-Classic-GUI">wiki</a>.</p>
+<p>A more in depth guide can be found in our <a class="reference external" href="https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/">documentation (Classic GUI)</a>.</p>
 </section>
 <section id="pipeline-usage-command-line">
 <h2>Pipeline Usage (command line)<a class="headerlink" href="#pipeline-usage-command-line" title="Permalink to this heading">#</a></h2>
@@ -449,8 +449,8 @@ <h2>Pipeline Usage (command line)<a class="headerlink" href="#pipeline-usage-com
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>plantseg<span class="w"> </span>--config<span class="w"> </span>CONFIG_PATH
 </pre></div>
 </div>
-<p>where <code class="docutils literal notranslate"><span class="pre">CONFIG_PATH</span></code> is the path to the YAML configuration file. See <a class="reference external" href="https://github.com/hci-unihd/plant-seg/blob/master/examples/config.yaml">config.yaml</a> for a sample configuration
-file and our <a class="reference external" href="https://github.com/hci-unihd/plant-seg/wiki/PlantSeg-Classic-CLI">wiki</a> for a
+<p>where <code class="docutils literal notranslate"><span class="pre">CONFIG_PATH</span></code> is the path to the YAML configuration file. See <span class="xref myst">config.yaml</span> for a sample configuration
+file and our <a class="reference external" href="https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_cli/">documentation (CLI)</a> for a
 detailed description of the parameters.</p>
 </section>
 </section>

chapters/plantseg_classic_cli/index.html

@@ -428,12 +428,12 @@ <h2>Main Keys/Steps<a class="headerlink" href="#main-keys-steps" title="Permalin
 <li><p><code class="docutils literal notranslate"><span class="pre">path</span></code> attribute: is used to define either the file to process or the directory containing the data.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">preprocessing</span></code> attribute: contains a simple set of possible operations one would need to run on their data before calling the neural network.
 This step can be skipped if data is ready for neural network processing.
-Detailed instructions can be found at <a class="reference external" href="https://github.com/hci-unihd/plant-seg/wiki/Data-Processing">Data Processing</a>.</p></li>
+Detailed instructions can be found at <a class="reference external" href="https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/data_processing.html">Classic GUI (Data Processing)</a>.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">cnn_prediction</span></code> attribute: contains all parameters relevant for predicting with a neural network.
 Description of all pre-trained models provided with the package is described below.
-Detailed instructions can be found at <a class="reference external" href="https://github.com/hci-unihd/plant-seg/wiki/Predictions">Predictions</a>.</p></li>
+Detailed instructions can be found at <a class="reference external" href="https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/cnn_predictions.html">Classic GUI (Predictions)</a>.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">segmentation</span></code> attribute: contains all parameters needed to run the partitioning algorithm (i.e., final Segmentation).
-Detailed instructions can be found at <a class="reference external" href="https://github.com/hci-unihd/plant-seg/wiki/Segmentation.md">Segmentation</a>.</p></li>
+Detailed instructions can be found at <a class="reference external" href="https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/segmentation.html">Classic GUI (Segmentation)</a>.</p></li>
 </ul>
 </section>
 <section id="additional-information">

chapters/plantseg_classic_gui/segmentation.html

@@ -402,7 +402,7 @@ <h1>Segmentation</h1>
 <h1>Segmentation<a class="headerlink" href="#segmentation" title="Permalink to this heading">#</a></h1>
 <p>The segmentation widget allows using very powerful graph partitioning techniques to obtain a segmentation from the
 input stacks.
-The input of this widget should be the output of the <a class="reference external" href="https://github.com/hci-unihd/plant-seg/wiki/CNN-Predictions">CNN-predictions widget</a>.
+The input of this widget should be the output of the <a class="reference external" href="https://hci-unihd.github.io/plant-seg/chapters/plantseg_classic_gui/cnn_predictions.html">CNN-predictions widget</a>.
 If the boundary prediction stage fails for any reason, a raw image could be used (especially if the cell boundaries are
 very sharp, and the noise is low) but usually does not yield satisfactory results.</p>
 <ul class="simple">