tutorial.md

Tutorial: Complexity Science

This is a half-day tutorial that uses Python to introduce topics from Complexity Science, including small world graphs and scale-free networks, cellular automata, and agent-based models. The tutorial is based on the book Think Complexity and on material from a class we teach at Olin College.

Installation instructions

To prepare for this tutorial, you have these options:

1. Install Jupyter on your laptop and download my code from Git.

2. Run the Jupyter notebook on a virtual machine on Binder. This is the easiest option, with one drawback: the virtual machine you get is temporary; any work you do during the tutorial will be lost.

3. Just read the notebooks on GitHub.

Option 1 is the best choice if you are able to do it ahead of time, because it does not depend on the network at the conference. Option 2 depends on network performance, which is unpredictable. Option 3 is easy and reliable, but you will only be able to read the notebooks; you won't be able to run the code or do the exercises.

Here are instructions for each option.

Option 1

If you don't already have Jupyter, I highly recommend installing Anaconda, which is a Python distribution that contains everything you need for the workshop. It is easy to install on Windows, Mac, and Linux, and because it does a user-level install, it will not interfere with other Python installations.

Information about installing Anaconda is here.

The code for the tutorial works in Python 2 and Python 3, but I recommend Python 3.

When you install Anaconda, you should get Jupyter by default, but if not, run

    conda install jupyter

Once you have Jupyter, you can get my code from this Git repository on Github. If you have a Git client installed, you should be able to download it by running:

    git clone https://github.com/AllenDowney/ThinkComplexity2.git

It should create a directory named ThinkComplexity2. Otherwise you can download the repository in this zip file and unzip it.

Then cd into the new directory:

    cd ThinkComplexity2

To make sure you have the packages you need, you can use environment.yml to create a Conda environment named complexity

   conda env create -f environment.yml

Then activate the new environment

  conda activate complexity

To start Jupyter, run:

    cd code
    jupyter notebook

Jupyter should launch your default browser or open a tab in an existing browser window. If not, the Jupyter server should print a URL you can use. For example, when I launch Jupyter, I get

    ~/ThinkComplexity2$ jupyter notebook
    [I 10:03:20.115 NotebookApp] Serving notebooks from local directory: /home/downey/ThinkComplexity2
    [I 10:03:20.115 NotebookApp] 0 active kernels
    [I 10:03:20.115 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
    [I 10:03:20.115 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

In this case, the URL is http://localhost:8888. When you start your server, you might get a different URL. Whatever it is, if you paste it into a browser, you should should see a home page with a list of the notebooks in the repository.

Click on chap09.ipynb. It should open the notebook for Chapter 9.

Select the cell with the import statements and press "Shift-Enter" to run the code in the cell. If it works and you get no error messages, you are all set.

If you get error messages about missing packages, you can install the packages you need using your package manager, or install Anaconda.

If you have trouble with the animations, you might have to install ffmpeg. The animations are not necessary for the tutorial.

Option 2

You can run my notebook in a virtual machine on Binder. To launch the VM, press this button:

You should see a home page with a list of the notebooks in the repository.

Open chap09.ipynb. You should be able to run the notebook in your browser and try out the examples. The animations may not work, but they are not really necessary for the tutorial.

However, be aware that the virtual machine you are running is temporary. If you leave it idle for more than an hour or so, it will disappear along with any work you have done.

Special thanks to the generous people who run Binder, which makes it easy to share and reproduce computation.

Option 3

You can read the notebooks on GitHub. The repository is here. You should see the list of files in the repo. Open chap09.ipynb. You should be able to read the notebook, but you won't be able to run the code.