Merge branch 'master' into 'develop'

update dev on new master version

See merge request !414

CHANGELOG.md

 # Changelog
 All notable changes to this project will be documented in this file.
 
+## v2.0.0 -  2022-04-08  - tf2 usage, new model classes, and improved uncertainty estimate
+
+### general:
+* MLAir now uses tensorflow v2
+* new customisable model classes for CNN and RNN
+* improved uncertainty estimate
+
+### new features:
+* MLAir depends now on tensorflow v2 (#331)
+* new CNN class that can be configured layer-wise (#368)
+* new RNN class that can be configured in more detail (#361)
+* new branched-input CNN class (#368)
+* new branched-input RNN class (#362)
+* set custom model display name that is used in plots (#341)
+* specify names of input branches to use in feature importance plots (#356)
+* uncertainty estimate of model error is now calculated for each forecast step additionally (#359)
+* data transformation properties are stored locally and can be loaded into an experiment run (#345)
+* uncertainty estimate includes now a Mann-Whitney U rank test (#355)
+* data handlers can now have access to "future" data specified by new parameter extend_length_opts (#339)
+
+### technical:
+* MLAir now uses python3.8 on Jülich HPC systems (#375)
+* no support of MLAir for tensorflow v1.X, replaced by tf v2.X (#331)
+* all data handlers with filters can return data as branches (#370)
+* bug fix to force model name and competitor names to be unique (#366, #369)
+* fix to use only a single forecast step (#315)
+* CI pipeline adjustments (#340, #365)
+* new option to set the level of the print logging (#364)
+* advanced logging for batch data creation and in postprocessing (#350, #360)
+* batch data creation is skipped on disabled training (#341)
+* multiprocessing pools are now closed properly (#342)
+* bug fix if no competitor data is available (#343)
+* bug fix for model loading (#343)
+* models plotted by PlotSampleUncertaintyFromBootstrap are now ordered by mean error (#344) 
+* fix for usage of lazy data caused unintended reloading of data (#347)
+* fix for latex reports no showing all stations and competitors (#349)
+* refactoring of hard coded dimension names in skill scores calculation (#357)
+* bug fix of order of bootstrap method in feature importance calculation causes errors (#358)
+* distinguish now between window_history_offset (pos of last time step), window_history_size (total length of input 
+  sample), and extend_length_opts ("future" data that is available at given time) (#353)
+
 ## v1.5.0 -  2021-11-11  - new uncertainty estimation
 
 ### general:

README.md

@@ -4,7 +4,7 @@
 
 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. You can find the
-docs [here](http://toar.pages.jsc.fz-juelich.de/mlair/docs/).
+docs [here](https://esde.pages.jsc.fz-juelich.de/machine-learning/mlair/docs/).
 
 [[_TOC_]]
 
@@ -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-1.5.0-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-2.0.0-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`.
 
@@ -89,7 +89,7 @@ The installation on Windows is not tested yet.
 In this section, we show three examples how to work with MLAir. Note, that for these examples MLAir was installed using
 the distribution file. In case you are using the git clone it is required to adjust the import path if not directly
 executed inside the source directory of MLAir. There is also a downloadable 
-[Jupyter Notebook](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/supplement/Examples_from_manuscript.ipynb) 
+[Jupyter Notebook](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/supplement/Examples_from_manuscript.ipynb) 
 provided in that you can run the following examples. Note that this notebook still requires an installation of MLAir.
 
 ## Example 1

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-1.5.0-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-2.0.0-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': 1,
-    'minor': 5,
+    'major': 2,
+    'minor': 0,
     'micro': 0,
 }