new dist file and changelog

CHANGELOG.md

 # Changelog
 All notable changes to this project will be documented in this file.
 
+## v2.2.0 -  2022-08-16  - new data sources and python3.9
+
+### general:
+* new data sources: era5 data and ToarDB V2
+* CAMS competitor available
+* improved execution speed
+* MLAir is now updated to python3.9
+
+### new features:
+* new data loading method to load era5 data on Jülich systems (#393)
+* new data loading method to load data from ToarDB V2 (#396)
+* implemented competitor model using CAMS ensemble forecasts (#394)
+* OLS competitor is only calculated if provided in competitor list (#404)
+* experimental: snapshot creation to skip preprocessing stage (#346, #405, #406)
+* new workflow HyperSearchWorkflow stopping after training stage (#408)
+
+### technical:
+* fixed minor issues and improved execution speed in postprocessing (#401, #413)
+* improved speed in keras iterator creation (#409)
+* solved bug for very long competitor time series (#395)
+* updated python, HPC and CI environment (#402, #403, #407, #410)
+* fix for climateFIR data handler (#399)
+* fix for report model error (#416)
+
 ## v2.1.0 -  2022-06-07  - new evaluation metrics and improved training
 
 ### general:

README.md

@@ -34,7 +34,7 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy
 * Installation of **MLAir**:
     * Either clone MLAir from the [gitlab repository](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git) 
       and use it without installation (beside the requirements) 
-    * or download the distribution file ([current version](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.1.0-py3-none-any.whl)) 
+    * or download the distribution file ([current version](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.2.0-py3-none-any.whl)) 
       and install it via `pip install <dist_file>.whl`. In this case, you can simply import MLAir in any python script 
       inside your virtual environment using `import mlair`.
 

docs/_source/installation.rst

@@ -27,7 +27,7 @@ Installation of MLAir
 * Install all requirements from `requirements.txt <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/requirements.txt>`_
   preferably in a virtual environment
 * Either clone MLAir from the `gitlab repository <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git>`_
-* or download the distribution file (`current version <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.1.0-py3-none-any.whl>`_)
+* or download the distribution file (`current version <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.2.0-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`.
 

mlair/__init__.py

 __version_info__ = {
     'major': 2,
-    'minor': 1,
+    'minor': 2,
     'micro': 0,
 }