updated README.md (not finished yet)

README.md

-# MachineLearningTools
-
-This is a collection of all relevant functions used for ML stuff in the ESDE group
-
-## Inception Model
-
-See a description [here](https://towardsdatascience.com/a-simple-guide-to-the-versions-of-the-inception-network-7fc52b863202)
-or take a look on the papers [Going Deeper with Convolutions (Szegedy et al., 2014)](https://arxiv.org/abs/1409.4842)
-and [Network In Network (Lin et al., 2014)](https://arxiv.org/abs/1312.4400).
+# MLAir - Machine Learning on Air Data
 
+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.
 
 # Installation
 
 * Install __proj__ on your machine using the console. E.g. for opensuse / leap `zypper install proj`
-* c++ compiler required for cartopy installation
-
-## HPC - JUWELS and HDFML setup
-The following instruction guide you throug the installation on JUWELS and HDFML. 
-* Clone the repo to HPC system (we recommend to place it in `/p/projects/<project name>`.
+* A c++ compiler is required for the installation of the program __cartopy__
+* Install all requirements from `requirements.txt` preferably in a virtual environment
+* Installation of MLAir:
+    * Either clone MLAir from its repository in gitlab (link??) and use it without installation 
+    * or download the distribution file (?? .whl) and install it via `pip install <??>`. In this case, you can simply
+    import MLAir in any python script inside your virtual environment using `import mlair`.
+    
+## Special instructions for 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 `/p/projects/<project name>`).
 * Setup venv by executing `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. <br> 
 You have to enter the HPC project's budget name (--account flag).
@@ -27,9 +30,6 @@ You have to enter the HPC project's budget name (--account flag).
 * Execute either `sbatch run_juwels_develgpus.bash` or `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.
 
-### HPC JUWELS and HDFML remarks 
-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. 
-
 Note: The method `PartitionCheck` currently only checks if the hostname starts with `ju` or `hdfmll`. 
 Therefore, it might be necessary to adopt the `if` statement in `PartitionCheck._run`.
 
@@ -39,8 +39,7 @@ Therefore, it might be necessary to adopt the `if` statement in `PartitionCheck.
 * To use hourly data from ToarDB via JOIN interface, a private token is required. Request your personal access token and
 add it to `src/join_settings.py` in the hourly data section. Replace the `TOAR_SERVICE_URL` and the `Authorization` 
 value. To make sure, that this **sensitive** data is not uploaded to the remote server, use the following command to
-prevent git from tracking this file: `git update-index --assume-unchanged src/join_settings.py
-`
+prevent git from tracking this file: `git update-index --assume-unchanged src/join_settings.py`
 
 # Customise your experiment
 
@@ -97,3 +96,10 @@ station-wise std is a decent estimate of the true std.
 scaling values instead of the calculation method. For method *centre*, std can still be None, but is required for the
 *standardise* method. **Important**: Format of given values **must** match internal data format of DataPreparation 
 class: `xr.DataArray` with `dims=["variables"]` and one value for each variable.
+
+
+## Inception Model
+
+See a description [here](https://towardsdatascience.com/a-simple-guide-to-the-versions-of-the-inception-network-7fc52b863202)
+or take a look on the papers [Going Deeper with Convolutions (Szegedy et al., 2014)](https://arxiv.org/abs/1409.4842)
+and [Network In Network (Lin et al., 2014)](https://arxiv.org/abs/1312.4400).