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esm-course
esm-tutorials
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29b8163a
Commit
29b8163a
authored
Jul 09, 2018
by
jonas.hoersch
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Update setup and requirements (closes #3)
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1f208faa
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SETUP.md
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29b8163a
# Download
You can download the tutorials with
`git`
with the following command (it should be public):
You can download the tutorials with
`git`
with the following command (it should
be public):
```
git clone https://git.scc.kit.edu/sp2668/esm_tutorials.git
```
Alternatively, you can download the tutorials as compressed folder.
Alternatively, you can download the tutorials as compressed zip-file from
https://git.scc.kit.edu/sp2668/esm_tutorials/-/archive/master/esm_tutorials-master.zip
# Installation Guide
Install
`miniconda`
(or Anaconda). For instructions for your operating system see
https://conda.io/docs/user-guide/install/index.html
**
Make sure to follow these steps BEFORE the first tutorial (The wireless WILL
be spotty with so many heavy users and we'll loose precious tutorial time)!
**
Now create a new environment from the provided
`requirements.yml`
file. For detailed instructions see
Install
`miniconda`
(or Anaconda). For instructions for your operating system
see https://conda.io/docs/user-guide/install/index.html
Now create a new environment from the provided
`requirements.yml`
file. For
detailed instructions see
https://conda.io/docs/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file
Open a terminal. Creating the environment requires the following set of commands:
Open a terminal. Creating the environment requires the following set of
commands. They take time and have to download several packages!
Windows:
```
conda env create -f requirements.yml
activate esm-tutorials
conda list
```
macOS and Linux:
```
conda env create -f requirements.yml
source activate esm-tutorials
conda list
```
Open a jupyter notebook with the following command in the tutorial folder (where your notebooks are):
...
...
requirements.yml
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29b8163a
...
...
@@ -7,8 +7,15 @@ dependencies:
-
matplotlib
-
nb_conda
-
numpy
-
scipy
-
networkx
-
pandas
-
python
-
scikit-learn
-
pyomo
-
xarray
-
netcdf4
-
bottleneck
-
glpk
-
pip
:
-
pypsa
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