docs python code blocks

README.md

@@ -44,7 +44,7 @@ The experiment description in `example.experiment` roughly translates to: Perfor
 
 ###### `example.py`
 
-```python3
+```python
 def run(instance, save_callback, state):
     # do some stuff on "instance"
 ```
@@ -53,7 +53,7 @@ The `run` function is where the magic happens. For every file in our batch the
 
 Now that we have specified everything, we can start executing our experiment.
 
-```python3
+```python
 >>> import alma.experiment
 
 >>> dispatcher = alma.experiment.load("example.experiment")
@@ -62,13 +62,13 @@ Now that we have specified everything, we can start executing our experiment.
 
 The line `dispatcher.start()` starts the concurrent non blocking execution of our experiment. This means the dispatcher stays responsive and we can pause/stop the execution at any given time.
 
-```python3
+```python
 >>> dispatcher.stop()
 ```
 
 During the execution the `dispatcher` continuously keeps track of which files he still needs to call `run(...)` on and how many iterations he has left. He does so by saving the current state of the execution in a file. Loading an experiment (`alma.experiment.load(...)`) the framework first looks for such a save file and if one exists, the execution will pick up at the point we've called `dispatcher.stop()`. To pick up the experiment we can perform:
 
-```python3
+```python
 >>> dispatcher = alma.experiment.load("example.experiment")
 >>> dispatcher.start()
 ```

docs/run_module.md

@@ -2,7 +2,7 @@
 
 The run module is arguably the most important part of the *alma* interface. It is here where the actual experiment/task has to be implemented. For *pyalma* the run module is merely a python file implementing a specified interface so that *pyalma* can load and execute it. Let's have a look at a short but yet extensive example.
 
-```python3
+```python
 import random
 
 def run(instance, save, state):