add examples page

docs/source/index.md

@@ -39,7 +39,7 @@ userguide
 pythonapi/index
 cppapi/index
 REST API <https://app.swaggerhub.com/apis/swaggerhub59/Inverted-AI>
-Examples <https://github.com/inverted-ai/invertedai/tree/master/examples>
+pythonapi/pyexamples
 ```
 
 ```{eval-rst}

docs/source/pythonapi/pyexamples.md

@@ -0,0 +1,7 @@
+# Examples
+
+Several examples are provided [here](https://github.com/inverted-ai/invertedai/tree/master/examples) demonstrating how to use the Inverted AI API in Python. 
+```{include} ../../../examples/README.md
+:start-after: <!-- start exampels -->
+:end-before: <!-- end exampels -->
+```

examples/README.md

@@ -1,6 +1,8 @@
 # Examples
 
-This folder contains examples demonstrating how to use the Inverted AI API in Python. [Click here](https://download-directory.github.io/?url=https://github.com/inverted-ai/invertedai/tree/master/examples) to download the folder as a zip-file.
+This folder contains examples demonstrating how to use the Inverted AI API in Python. 
+<!-- start exampels -->
+[Click here](https://download-directory.github.io/?url=https://github.com/inverted-ai/invertedai/tree/master/examples) to download the folder as a zip-file.
 To run the examples locally, first build the virtual environment.
 ```commandline
 python -m venv .venv
@@ -21,19 +23,14 @@ There are currently three different examples available.
 This demonstration script runs without a local simulator and the API is used to control
 all vehicles, meaning all vehicles in the simulation are NPCs. To minimize client-side
 complexity, the visualizations are provided through the API, which is very inefficient
-and only used for demonstration purposes. The purpose of those demonstrations is to
+and only used for demonstration purposes ([click here](https://colab.research.google.com/github/inverted-ai/invertedai-drive/blob/develop/examples/response_time.ipynb) for response time comparison). The purpose of those demonstrations is to
 quickly give you an idea for how the underlying NPCs behave. This example is available
 in a few different versions, one calling the underlying REST API directly, and others
 using the wrapper provided as a part of our library, the latter version also being
 available as a Jupyter notebook and
 [Colab](https://colab.research.google.com/github/inverted-ai/invertedai-drive/blob/develop/examples/npc_only_colab.ipynb).
 
-## Controlling the traffic-lights
-Inverted AI API allows for controlling the traffic light states. 
-We provided a [sample code](https://colab.research.google.com/github/inverted-ai/invertedai-drive/blob/develop/examples/npc_only_with_traffic_lights_REST.py)
-for doing so at a traffic-light controlled intersection.
-
-## Minimal example
+## Cosimulation Minimal example
 
 The minimal example, as shown on the front page, mocks up the local simulator with
 a class that implements trivial control logic for the ego vehicle. This example is
@@ -48,3 +45,5 @@ This example is meant to provide a comprehensive illustration of co-simulation l
 and to be a starting point for creating custom scenarios with Inverted AI NPCs,
 both in CARLA and in other simulators. Running CARLA requires additional setup,
 which is documented within the corresponding subfolder.
+
+<!-- end exampels -->