Allen Downey
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.
To prepare for this tutorial, you have three options:
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Install Jupyter on your laptop and download my code from Git.
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Run the Jupyter notebook on a virtual machine on Binder.
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If you have a Mac, you could try out a new system called Launchbot, which uses Docker.
I'll provide instructions for both, but here's the catch: if everyone chooses Option 2, the wireless network will fail and no one will be able to do the hands-on part of the workshop.
So, I strongly encourage you to try Option 1 and only resort to Option 2 if you can't get Option 1 working.
To do the exercises, you need Python 2 or 3 with NumPy, SciPy, and matplotlib. If you are not sure whether you have those modules already, the easiest way to check is to run my code and see if it works.
Code for this workshop is in a 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.
To start Jupyter, run:
cd ThinkComplexity2/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 chap06.ipynb. It should open the notebook for Chapter 6.
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 try Option 1B and install Anaconda.
If are using Anaconda, you can use the following commands to create a conda environment with the packages you need (Jupyter, NumPy, Matplotlib, SciPy, NetworkX, and ffmpeg):
$ conda env create -f environment.yml
$ source activate complexity
$ cd code/
$ jupyter notebookTo get the animations to work, you might have to install ffmpeg. If you are using Anaconda, try
conda install -c conda-forge ffmpegOn Ubuntu and Linux Mint, try:
sudo add-apt-repository ppa:mc3man/trusty-media
sudo apt-get update
sudo apt-get install ffmpeg
If you have instructions for other operating systems, please let me know and I will add them here.
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.
When you install Anaconda, you should get Jupyter by default, but if not, run
conda install jupyter
Then go to Option 1A to make sure you can run my code.
If you don't want to install Anaconda, you can see some other options here.
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.
If you want to try the exercises, open chap01.ipynb. If you just want to see the answers, open chap01soln.ipynb.
Either way, you should be able to run the notebooks in your browser and try out the examples.
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.
Launchbot is a tool being developed within O'Reilly Media to make it simple to build and run Jupyter-based projects. It's basically a GUI for git, Docker, and Jupyter:
To run the project with Launchbot, you must:
- Install docker
- Install and configure the launchbot app
Before you can use LaunchBot, you'll need to install Docker for Mac
Docker allows you "containerize" applications. Launchbot uses it to build and run the underlying environment required for your application to function.
IMPORTANT: Docker must be running to use LaunchBot, so you'll need to make sure it's started each time you want to use LaunchBot. On a Mac, you'll see the Docker "whale" icon in the Menubar.
You will get the following error in Launchbot if Docker is not running:
Cannot connect to the docker engine endpoint
Download and unzip LaunchBot from https://launchbot.io
Once the Launchbot app is running, you can clone the repository from the list of projects on the main screen. Be sure to use the https url.
Once it's cloned, open it from the "Your Projects" list. Then click "Launch".
IMPORTANT: The first time you build a project, Launchbot must download and build the entire image. Since these images are often quite large, this can take several minutes.
Jupyter password protects the Notebooks by default. To find your password, click the "Log" button. Scroll down until you find a line like this:
To login with a token:
� http://localhost:8888/?token=00e6b380dddd53b77649f6a533a2407db1996f44b6b9e325
Copy just the token (in this case, 00e6b380dddd53b77649f6a533a2407db1996f44b6b9e325) and then paste it into the prompt for the notebook.