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EnzymeML · Mar 5, 2024 · 06e037e · 06e037e
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diff --git a/docs/.DS_Store b/docs/.DS_Store
diff --git a/docs/docker_installation.md b/docs/docker_installation.md
@@ -2,6 +2,9 @@
 icon: simple/docker
 ---
 
-Docker is a way to run software in a container. This means that the software is isolated from the rest of your system. This is useful because it means that you can run software without having to install it on your system. This is particularly useful for running software that has a lot of dependencies or that you only need to run occasionally. In this way, Docker is a bit like a virtual machine and enables running software without configuring the system to run it.
+Docker is a way to run software in a container. This means that the software is isolated from the rest of your system. This simplifies the installation of computing environments since everything is preconfigured in the container. Docker is comparable to running a virtual machine, but instead of installing a whole operating system, it only installs the software you need.
+
+
+Here we will use Docker to install JupyterLab, a popular web-based environment for Jupyter notebooks, code, and data. This will allow you to run JupyterLab on your local machine without having to install Python, Jupyter, and the necessary Python tools to work with your data and EnzymeML.
 
 To install Docker, follow the instructions for your operating system on the [Docker website](https://docs.docker.com/get-docker/).
diff --git a/docs/figs/configure_container.png b/docs/figs/configure_container.png
diff --git a/docs/figs/get_image.png b/docs/figs/get_image.png
diff --git a/docs/figs/initial_start.png b/docs/figs/initial_start.png
diff --git a/docs/getting_started.md b/docs/getting_started.md
@@ -11,18 +11,18 @@ Visit the [Docker website](https://docs.docker.com/get-docker/) and follow the i
 
     Docker is a way to run software in a container. This means that the software is isolated from the rest of your system. This is useful because it means that you can run software without having to install it on your system.
 
-If you decide to configure your computing environment yourself and have a way to execute.
+If you decide to configure your computing environment yourself you can install [ChromatoPy from Github](https://github.com/FAIRChemistry/chromatopy).
 
 ## 2. Install EnzymeML Lab
 
 To work with Jupyter Notebooks, we will use the JupyterLab, which is a web-based interactive development environment for Jupyter notebooks, code, and data. We will use the EnzymeML Lab, which is a JupyterLab environment with preinstalled tools for the analysis of chromatographic data.
 
 To install the EnzymeML Lab for the first time, follow the [instructions](jupyterlab_installation.md). This essentially starts a Docker container, which JupyterLab is running, and the developed tools of this course are preinstalled.
 
-## 3. Load a demo project
+## 3. Load the demo project
 
 Download the [demo project repository](https://github.com/FAIRChemistry/chromatography-example) from GitHub and store it at the desired location on your local machine. Click on the green "Code" button and select "Download ZIP". Finally, extract the ZIP file.
 
 ## 4. Open the project in JupyterLab
 
-Go back to the browser in which JupyterLab is running and navigate to the folder of the demo project. You can now open the notebook `shimadzu-example.ipynb` or `single-chromatogram.ipynb` example and follow the instructions in the notebooks.
+Go back to the browser in which JupyterLab is running and navigate to the folder of the demo project. Make sure, that the file is within the directory you defined to be visible to the container. You can now open the notebook `shimadzu-example.ipynb` or `single-chromatogram.ipynb` example and follow the instructions in the notebooks.
diff --git a/docs/jupyterlab_installation.md b/docs/jupyterlab_installation.md
@@ -2,56 +2,60 @@
 icon: simple/jupyter
 ---
 
-## Setting up EnzymeML Lab
+# Setting up JupyterLab
 
-Jupyter Notebook is a web-based interactive computing environment that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It is a great tool for data analysis and visualization. 
+!!! info
+    __What is JupyterLab?__  
+    JupyterLab is a web-based editor for writing and executing Jupyter Notebooks. Additionally, it allows you to see the file system, terminal, and other tools in the same window. This makes it a powerful tool for data analysis and visualization.
+
+    __How does it work?__  
+    Here we will install JupyterLab using Docker. This will allow you to run JupyterLab on your local machine without having to install Python, Jupyter, and the necessary Python tools to work with your data and the tools developed in this course.
+    Essentially, the Docker hosts a local website on your computer that runs JupyterLab. This website is accessible from your web browser as long as the container is running. Each time you start the container, the latest versions of the EnzymeML tools will be installed.
+
+## Initial Setup
 
 1. Install [Docker]
   [Docker]: docker_installation.md
 
-2. Open the Docker Desktop application search for `haeussma/enzymeml-lab` in the upper search bar and click on `RUN`.
-
-    ![Docker Desktop](../figs/get_image.png)
+2. Open the Docker Desktop application search for `haeussma/enzymeml-lab` in the upper search bar and click on `Pull`.
 
-    The container will be downloaded and a configuration window will be opened.
+    ![Docker Desktop](../figs/get_image.png){{: style="height:400px"}}
 
+3. Navigate to the `Images` section in the Docker Desktop application and click on :material-play: next to the `haeussma/enzymeml-lab` image.
+    A `Run a new container` window pops up. Click on `Optional settings` to configure the container with the following settings:
 
-3. Configure the container in the `Run a new container` window as follows in the `Optional settings` section.
-    In the `Volumes` section set the `Host path` to the folder, which should be accessible from JupyterLab. If in doubt specify your home directory.
+    `Container name`
+    :   Container name: `EnzymeML-Lab`
 
-    ![Docker Desktop](../figs/configure_container.png)
+    `Ports`
+    :    Host port: `8888`
 
+    `Volumes` 
+    !!! info inline end "Why specify a volume?"
+        The volume is used to define what directory of your local machine is visible to the container. Without this configuration, the container would not be able to access your local files. It is advised to make the directory in which you store your data for analysis.
 
-4. within the `Logs` of the container, you will find a token, which is needed to access the Jupyter Notebook. 
 
-    ```bash
-    2024-02-25 17:44:54 [I 2024-02-25 16:44:54.937 ServerApp]     http://127.0.0.1:8888/lab token=a9a39e2f219edac28f66cf2c88a9954978f78626e58e03f6
-    2024-02-25 17:44:54 [I 2024-02-25 16:44:54.937 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
-    2024-02-25 17:44:54 [C 2024-02-25 16:44:54.939 ServerApp] 
-    2024-02-25 17:44:54     
-    2024-02-25 17:44:54     To access the server, open this file in a browser:
-    2024-02-25 17:44:54         file:///home/jovyan/.local/share/jupyter/runtime/jpserver-7-open.html
-    2024-02-25 17:44:54     Or copy and paste one of these URLs:
-    2024-02-25 17:44:54         http://e2376db8149d:8888/lab?token=a9a39e2f219edac28f66cf2c88a9954978f78626e58e03f6
-    2024-02-25 17:44:54         http://127.0.0.1:8888/lab?token=a9a39e2f219edac28f66cf2c88a9954978f78626e58e03f6
-    2024-02-25 17:44:55 [I 2024-02-25 16:44:55.984 ServerApp] Skipped non-installed server(s): bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server
-    ```
+    :   Host path: `{SELECT YOUR WORKING DIRECTORY}`       
+    :    Container path: `/home/jovyan/work`
 
-    In the example above, the token is `a9a39e2f219edac28f66cf2c88a9954978f78626e58e03f6`.
+    ??? example "Example"
+        ![Docker Desktop](../figs/configure_container.png){: style="height:400px"}
 
-5. Click on the link in the header of the container to open the JupyterLab in your default web browser.
 
-    ![Docker Desktop](../figs/start_container.png)
+    Then, click on `Run`. The initial configuration process for the container might take a few seconds up to two minutes.
 
 
-6. Enter the token from the logs in the field `Token` and click on `Log in`.
+5. Click on the link `8888:8888` in the header of the container. This will open a new tab in your browser, showing the JupyterLab environment.
+
+    ![Docker Desktop](../figs/initial_start.png){{: style="height:400px"}}
+
 
-7. You are now in the JupyterLab environment your local files can be accessed via the `work` folder.
+7. 🎉 You are now in the JupyterLab environment. Your local files can be accessed via the `work` folder.
 
 ## Stopping the Container
 
-To stop the container, click on the `Stop` icon in the `Docker Desktop` application. You will find it in the `Containers` section. Running containers are symbolized by a green container icon.
+To start the container, navigate to the `Containers` section in the Docker Desktop application and click on the :material-stop: button next to the `EnzymeML-Lab` container. Running containers are symbolized by a green container icon. You can close the browser window whenever you want. The container will keep running in the background unless you stop it in the Docker Desktop app.
 
 ## Restarting the Container
 
-To restart the container, click on the `Start` icon in the `Docker Desktop` application. You will find it in the `Containers` section. Stopped containers are symbolized by a red container icon. The klick on the blue port number next to the start icon. You may need to copy the access token from the logs again. You find the logs by clicking on the container name in the `Containers` section. 
+To start the container, navigate to the `Containers` section in the Docker Desktop application and click on the :material-play: button next to the `EnzymeML-Lab` container. Then click on the blue port number `8888:8888` next to the start button.