update in changelog, instructions and dist

CHANGELOG.md

 # Changelog
 All notable changes to this project will be documented in this file.
 
+## v2.1.0 -  2022-06-07  - new evaluation metrics and improved training
+
+### general:
+* new evaluation metrics, IOA and MNMB
+* advanced train options for early stopping
+* reduced execution time by refactoring
+
+### new features:
+* uncertainty estimation of MSE is now applied for each season separately (#374)
+* added different configurations of early stopping to use either last trained or best epoch (#378)
+* train monitoring plots now add a star for best epoch when using early stopping (#367)
+* new evaluation metric index of agreement, IOA (#376)
+* new evaluation metric modified normalised mean bias, MNMB (#380)
+* new plot available that shows temporal evolution of MSE for each station (#381)
+
+### technical:
+* reduced loading of forecast path from data store (#328)
+* bug fix for not catched error during transformation (#385)
+* bug fix for data handler with climate and fir filter leading to calculate transformation always with fir filter (#387)
+* improved duration for latex report creation at end of preprocessing (#388)
+* enhanced speed for make prediction in postprocessing (#389)
+* fix to always create version badge from version and not from tag name (#382)
+
 ## v2.0.0 -  2022-04-08  - tf2 usage, new model classes, and improved uncertainty estimate
 
 ### 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-2.0.0-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-2.1.0-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-2.0.0-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-2.1.0-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': 0,
+    'minor': 1,
     'micro': 0,
 }