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Commit 7ba16a9f authored by leufen1's avatar leufen1
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update changelog and create dist file

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# Changelog # Changelog
All notable changes to this project will be documented in this file. 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 ## v2.3.0 - 2022-11-25 - new models and plots
### general: ### general:
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...@@ -34,7 +34,7 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy ...@@ -34,7 +34,7 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy
* Installation of **MLAir**: * Installation of **MLAir**:
* Either clone MLAir from the [gitlab repository](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git) * 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) 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 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`. inside your virtual environment using `import mlair`.
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...@@ -27,7 +27,7 @@ Installation of MLAir ...@@ -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>`_ * 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 preferably in a virtual environment
* Either clone MLAir from the `gitlab repository <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git>`_ * 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 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`. import MLAir in any python script inside your virtual environment using :py:`import mlair`.
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__version_info__ = { __version_info__ = {
'major': 2, 'major': 2,
'minor': 3, 'minor': 4,
'micro': 0, 'micro': 0,
} }
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