diff --git a/docs/_source/get-started.rst b/docs/_source/get-started.rst
index d51abaf4e1f256a420182c8d93055e99760b6963..7374c9bdc7b3e4ed8a224ed51f6608bf5e9aefb3 100644
--- a/docs/_source/get-started.rst
+++ b/docs/_source/get-started.rst
@@ -6,130 +6,6 @@ Getting started with MLAir
:language: python
-Install MLAir
--------------
-
-MLAir is based on several python frameworks. To work properly, you have to install all packages from the
-:py:`requirements.txt` file. Additionally to support the geographical plotting part it is required to install geo
-packages built for your operating system. Unfortunately, the names of these package may differ for different systems.
-In this instruction, we try to address users of different operating systems namely openSUSE Leap, Ubuntu and macOS.
-If the installation is still not working, we recommend skipping the geographical plot. We have put together a small
-workaround :ref:`here<Workaround to skip geographical plot>`. For special instructions to install MLAir on the Juelich
-HPC systems, see section :ref:`Installation on Jülich HPC systems`.
-
-Pre-requirements
-~~~~~~~~~~~~~~~~
-
-* Make sure to have the **python3.6** version installed.
-* (geo) A **c++ compiler** is required for the installation of the program **cartopy**
-* (geo) Install **proj** and **GEOS** on your machine using the console.
-* Install the **python3.6 develop** libraries.
-
-Installation of MLAir
-~~~~~~~~~~~~~~~~~~~~~
-
-* Install all requirements from `requirements.txt <https://gitlab.version.fz-juelich.de/toar/machinelearningtools/-/blob/master/requirements.txt>`_
- preferably in a virtual environment
-* Either clone MLAir from the `gitlab repository <https://gitlab.version.fz-juelich.de/toar/machinelearningtools.git>`_
-* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.2.0-py3-none-any.whl>`_)
- and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
- import MLAir in any python script inside your virtual environment using :py:`import mlair`.
-* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
- find any compatibility errors. Please note, that tf-1.13 and 1.15 have two distinct branches each, the default branch
- for CPU support, and the "-gpu" branch for GPU support. If the GPU version is installed, MLAir will make use of the GPU
- device.
-
-Special Instructions for Installation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-openSUSE Leap 15.1
-""""""""""""""""""
-
-* c++ compiler
-
-:py:`sudo zypper install gcc-c++`
-
-* geo packages
-
-:py:`sudo zypper install proj geos-devel`
-
-* depending on the pre-installed packages it could be required to install further packages
-
-:py:`sudo zypper install libproj-devel binutils gdal-devel graphviz graphviz-gnome`
-
-* python develop libraries
-
-:py:`sudo zypper install python3-devel`
-
-Ubuntu 20.04.1
-""""""""""""""
-
-* c++ compiler
-
-:py:`sudo apt install build-essential`
-
-* geo packages
-
-:py:`sudo apt install proj-bin libgeos-dev libproj-dev`
-
-* depending on the pre-installed packages it could be required to install further packages
-
-:py:`sudo apt install graphviz libgeos++-dev`
-
-* python develop libraries
-
-:py:`sudo apt install python3.6-dev`
-
-macOS & windows
-"""""""""""""""
-
-The installation on macOS is not tested yet. The following commands are possibly needed:
-
-:py:`brew install geos`
-
-:py:`sudo port install graphviz`
-
-The installation on Windows is not tested yet.
-
-Installation on Jülich HPC systems
-""""""""""""""""""""""""""""""""""
-
-*Please note, that the HPC setup is customised for JUWELS and HDFML. When using another HPC system, you can use the HPC
-setup files as a skeleton and customise it to your needs.*
-
-The following instruction guide you through the installation on JUWELS and HDFML.
-
-* Clone the repo to HPC system (we recommend to place it in :py:`/p/projects/<project name>`).
-* Setup venv by executing :py:`source setupHPC.sh`. This script loads all pre-installed modules and creates a venv for
- all other packages. Furthermore, it creates slurm/batch scripts to execute code on compute nodes.
- You have to enter the HPC project's budget name (--account flag).
-* The default external data path on JUWELS and HDFML is set to :py:`/p/project/deepacf/intelliaq/<user>/DATA/toar_<sampling>`.
-* To choose a different location open :py:`run.py` and add the following keyword argument to :py:`ExperimentSetup`:
- :py:`data_path=<your>/<custom>/<path>`.
-* Execute :py:`python run.py` on a login node to download example data. The program will throw an OSerror after downloading.
-* Execute either :py:`sbatch run_juwels_develgpus.bash` or :py:`sbatch run_hdfml_batch.bash` to verify that the setup
- went well.
-* Currently cartopy is not working on our HPC system, therefore PlotStations does not create any output.
-
-Note: The method :py:`PartitionCheck` currently only checks if the hostname starts with :py:`ju` or :py:`hdfmll`.
-Therefore, it might be necessary to adopt the :py:`if` statement in :py:`PartitionCheck._run`.
-
-
-Workaround to skip geographical plot
-""""""""""""""""""""""""""""""""""""
-
-If it is not possible to install all required geo libraries on your system, a good compromise is to skip the creation
-of the geographical plot. Therefore, it is required to remove the plot from the :py:`plot_list` manually. We recommend
-to use this code snippet as a starting point.
-
-.. code-block:: python
-
- from mlair.helpers import remove_items
- from mlair.configuration.defaults import DEFAULT_PLOT_LIST
-
- mlair.run(plot_list=remove_items(DEFAULT_PLOT_LIST, "PlotStationMap"))
-
-
How to start with MLAir
-----------------------
diff --git a/docs/_source/index.rst b/docs/_source/index.rst
index e0087b0e69fa2c63d1d976e4d0b076747e007d5f..b45b5db0546df02704fe4b24e5c7ce0a1d278376 100644
--- a/docs/_source/index.rst
+++ b/docs/_source/index.rst
@@ -8,10 +8,18 @@ Welcome to MLAir's documentation!
This is the documentation of the `MLAir package <https://gitlab.version.fz-juelich.de/toar/mlair>`_.
+.. figure:: ../logo/MLAir_Logo.png
+
+ MLAir Logo
+
+MLAir (Machine Learning on Air data) is an environment that simplifies and accelerates the creation of new machine
+learning (ML) models for the analysis and forecasting of meteorological and air quality time series.
+
.. toctree::
:maxdepth: 2
:caption: Contents:
+ installation
get-started
customise
defaults
diff --git a/docs/_source/installation.rst b/docs/_source/installation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7578d9abf49b9e4b67dac19b6263c4bc05110eea
--- /dev/null
+++ b/docs/_source/installation.rst
@@ -0,0 +1,125 @@
+.. role:: py(code)
+ :language: python
+
+Install MLAir
+-------------
+
+MLAir is based on several python frameworks. To work properly, you have to install all packages from the
+:py:`requirements.txt` file. Additionally to support the geographical plotting part it is required to install geo
+packages built for your operating system. Unfortunately, the names of these package may differ for different systems.
+In this instruction, we try to address users of different operating systems namely openSUSE Leap, Ubuntu and macOS.
+If the installation is still not working, we recommend skipping the geographical plot. We have put together a small
+workaround :ref:`here<Workaround to skip geographical plot>`. For special instructions to install MLAir on the Juelich
+HPC systems, see section :ref:`Installation on Jülich HPC systems`.
+
+Pre-requirements
+~~~~~~~~~~~~~~~~
+
+* Make sure to have the **python3.6** version installed.
+* (geo) A **c++ compiler** is required for the installation of the program **cartopy**
+* (geo) Install **proj** and **GEOS** on your machine using the console.
+* Install the **python3.6 develop** libraries.
+
+Installation of MLAir
+~~~~~~~~~~~~~~~~~~~~~
+
+* Install all requirements from `requirements.txt <https://gitlab.version.fz-juelich.de/toar/machinelearningtools/-/blob/master/requirements.txt>`_
+ preferably in a virtual environment
+* Either clone MLAir from the `gitlab repository <https://gitlab.version.fz-juelich.de/toar/machinelearningtools.git>`_
+* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.2.0-py3-none-any.whl>`_)
+ and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
+ import MLAir in any python script inside your virtual environment using :py:`import mlair`.
+* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
+ find any compatibility errors. Please note, that tf-1.13 and 1.15 have two distinct branches each, the default branch
+ for CPU support, and the "-gpu" branch for GPU support. If the GPU version is installed, MLAir will make use of the GPU
+ device.
+
+Special Instructions for Installation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+openSUSE Leap 15.1
+""""""""""""""""""
+
+* c++ compiler
+
+:py:`sudo zypper install gcc-c++`
+
+* geo packages
+
+:py:`sudo zypper install proj geos-devel`
+
+* depending on the pre-installed packages it could be required to install further packages
+
+:py:`sudo zypper install libproj-devel binutils gdal-devel graphviz graphviz-gnome`
+
+* python develop libraries
+
+:py:`sudo zypper install python3-devel`
+
+Ubuntu 20.04.1
+""""""""""""""
+
+* c++ compiler
+
+:py:`sudo apt install build-essential`
+
+* geo packages
+
+:py:`sudo apt install proj-bin libgeos-dev libproj-dev`
+
+* depending on the pre-installed packages it could be required to install further packages
+
+:py:`sudo apt install graphviz libgeos++-dev`
+
+* python develop libraries
+
+:py:`sudo apt install python3.6-dev`
+
+macOS & windows
+"""""""""""""""
+
+The installation on macOS is not tested yet. The following commands are possibly needed:
+
+:py:`brew install geos`
+
+:py:`sudo port install graphviz`
+
+The installation on Windows is not tested yet.
+
+Installation on Jülich HPC systems
+""""""""""""""""""""""""""""""""""
+
+*Please note, that the HPC setup is customised for JUWELS and HDFML. When using another HPC system, you can use the HPC
+setup files as a skeleton and customise it to your needs.*
+
+The following instruction guide you through the installation on JUWELS and HDFML.
+
+* Clone the repo to HPC system (we recommend to place it in :py:`/p/projects/<project name>`).
+* Setup venv by executing :py:`source setupHPC.sh`. This script loads all pre-installed modules and creates a venv for
+ all other packages. Furthermore, it creates slurm/batch scripts to execute code on compute nodes.
+ You have to enter the HPC project's budget name (--account flag).
+* The default external data path on JUWELS and HDFML is set to :py:`/p/project/deepacf/intelliaq/<user>/DATA/toar_<sampling>`.
+* To choose a different location open :py:`run.py` and add the following keyword argument to :py:`ExperimentSetup`:
+ :py:`data_path=<your>/<custom>/<path>`.
+* Execute :py:`python run.py` on a login node to download example data. The program will throw an OSerror after downloading.
+* Execute either :py:`sbatch run_juwels_develgpus.bash` or :py:`sbatch run_hdfml_batch.bash` to verify that the setup
+ went well.
+* Currently cartopy is not working on our HPC system, therefore PlotStations does not create any output.
+
+Note: The method :py:`PartitionCheck` currently only checks if the hostname starts with :py:`ju` or :py:`hdfmll`.
+Therefore, it might be necessary to adopt the :py:`if` statement in :py:`PartitionCheck._run`.
+
+
+Workaround to skip geographical plot
+""""""""""""""""""""""""""""""""""""
+
+If it is not possible to install all required geo libraries on your system, a good compromise is to skip the creation
+of the geographical plot. Therefore, it is required to remove the plot from the :py:`plot_list` manually. We recommend
+to use this code snippet as a starting point.
+
+.. code-block:: python
+
+ from mlair.helpers import remove_items
+ from mlair.configuration.defaults import DEFAULT_PLOT_LIST
+
+ mlair.run(plot_list=remove_items(DEFAULT_PLOT_LIST, "PlotStationMap"))