Update setup and requirements (closes #3)

SETUP.md

 # 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

@@ -7,8 +7,15 @@ dependencies:
     - matplotlib
     - nb_conda
     - numpy
+    - scipy
+    - networkx
     - pandas
     - python
     - scikit-learn
+    - pyomo
+    - xarray
+    - netcdf4
+    - bottleneck
+    - glpk
     - pip:
       - pypsa