Creating a computational narrative

Let’s create our first real computational narrative in a Jupyter notebook (adapted from Python and R data analysis course at Aalto Science IT).

Imagine you are on a desert island and wish to compute pi. You have a computer with you with Python installed but no math libraries and no Wikipedia.

Here is one way of doing it - “throwing darts” by generating random points within a square area and checking whether the points fall within the unit circle.

Opening a webpage inside JupyterLab

If you would like to copy-paste content from this webpage into your Jupyter notebook, a cool way of doing it is to open this page inside an IFrame:

from IPython.display import IFrame
IFrame(src="https://coderefinery.github.io/jupyter/", width='100%', height='500px')

Exercise/demonstration: Calculating pi using Monte Carlo methods

This can be either done as a 20 minute exercise or as a type-along demo.

1. Create a new notebook, name it, and add a heading.
2. Document the relevant formulas in a new cell:

    - square area: $s = (2 r)^2$
    - circle area: $c = \pi r^2$
    - $c/s = (\pi r^2) / (4 r^2) = \pi / 4$
    - $\pi = 4 * c/s$
   

3. Add an image to explain the concept:

   ![Darts](https://coderefinery.github.io/jupyter/img/darts.svg)
   

4. Import two modules that we will need:

   import random
   import matplotlib.pyplot as plt
   

5. Initialize the number of points:

   num_points = 1000
   

6. “Throw darts”:

   points = []
   hits = 0
   for _ in range(num_points):
    x, y = random.random(), random.random()
    if x*x + y*y < 1.0:
        hits += 1
        points.append((x, y, "red"))
    else:
        points.append((x, y, "blue"))
   

7. Plot results:

   %matplotlib inline
   x, y, colors = zip(*points)
   fig, ax = plt.subplots()
   fig.set_size_inches(6.0, 6.0)
   ax.scatter(x, y, c=colors)
   

8. Compute final estimate of pi:

   fraction = hits / num_points
   4 * fraction
   

Here is the notebook: https://github.com/coderefinery/jupyter/blob/gh-pages/example/darts.ipynb (static version, later we will learn how to share notebooks which are dynamic and can be modified).

What do we get from this?

• With code separate from everything else, you might just send one number or a plot to your supervisor/collaborator for checking.
• With a notebook as a narratives, you send everything in a consistent story.
• A reader may still just read the introduction and conclusion, but they can easily see more - and try changes themselves - if they want.