From 4cc439ddfbb0cd1a9cdc808e3f2f1305438080f0 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Fri, 8 Apr 2022 17:19:03 +0200
Subject: [PATCH] updated dist file links
---
README.md | 6 +++---
docs/_source/installation.rst | 2 +-
2 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/README.md b/README.md
index a5317e05..8decf00b 100644
--- a/README.md
+++ b/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
diff --git a/docs/_source/installation.rst b/docs/_source/installation.rst
index 7da1f25c..6ac4937e 100644
--- a/docs/_source/installation.rst
+++ b/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`.
--
GitLab