Merge branch 'master' into 'develop'

Master

See merge request !523

CHANGELOG.md

 # Changelog
 All notable changes to this project will be documented in this file.
 
+## v2.4.0 -  2023-06-30- IFS data and bias-corrected evaluation
+
+### general:
+* support IFS data (local) and ERA5 data (from toar db)
+* bias free evaluation
+### new features:
+* can load local IFS forecast data as input (#450)
+* can also load ERA5 data from toar db as alternative to locally stored data (#449)
+* new plot to show monthly data distributions in subsets (#445)
+* can load a DL model from external path (#448)
+* introduced option to bias-correct model's and competitors' forecasts (#442)
+* can use different interpolation methods when having CAMS as competitor (#444)
+### technical:
+* change toar statistics from api v1 to api v2 (#454)
+* now able to set configuration paths for local era5 and ifs data as experiment parameter (#457)
+* improved retry strategy when downloading data from toar db (#453)
+* updated packages (#452)
+* calculation of filter apriori is more robust now, properties are stored inside experiment folder (#447, #451)
+
 ## v2.3.0 -  2022-11-25  - new models and plots
 
 ### 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-2.2.0-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-2.4.0-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-2.2.0-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-2.40-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': 3,
+    'minor': 4,
     'micro': 0,
 }