## Cover of PNAS - code

I received questions from a couple of people asking me how I drew the network featured on the cover of PNAS (read about it here). Well, this blogpost is for you, and anybody else.

Doing Math, Physics

& other amazing stuff.

This Sunday while surfing the web I came across a figure depicting the Rössler attractor and while looking at it, it suddenly struck me that I have always seen it depicted from this specific angle. But what does it look like from other angles?
Curious, I sat down, quickly wrote a python script to generate the dynamics, used Matplotlib to plot the figure from multiple angles, and ffmpeg to aggregate them into an animation (see below).
One thing lead to another and soon I found myself reading about other *strange attractors*, such as Clifford attractors, and writing code to generate the figures you see above.

Below is a description of how I did it along with snippets of python code.

The Rössler system is given by the equations:

$$ \begin{align} \dot{x} = -y - z \\ \dot{y} = x + ay \\ \dot{z} = b + z(x-c) \end{align} $$which for certain parameter values of , and will exhibit chaotic behavior. I have chosen . The following code simulates the dynamics for the system. First we import some packages and define the Rössler equations.

```
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# define equation
def rossler_attractor(x, y, z, a=0.1, b=0.1, c=14):
x_dot = - y - z
y_dot = x + a*y
z_dot = b + z*(x - c)
return x_dot, y_dot, z_dot
```

Then we define some basic parameters such as how many steps we want to make and the size of each step.

```
# basic parameters
step_size = 0.01
steps = 100000
# initialize solutions arrays (+1 for initial conditions)
xx = np.empty((steps + 1))
yy = np.empty((steps + 1))
zz = np.empty((steps + 1))
# fill in initial conditions
xx[0], yy[0], zz[0] = (0.1, 0., 0.1)
```

It is possible to solve the equation system using scipy, but since this is a simple system we will just do it using numpy.

```
# solve equation system
for i in range(steps):
# Calculate derivatives
x_dot, y_dot, z_dot = rossler_attractor(x_s[i], y_s[i], z_s[i])
xx[i + 1] = xx[i] + (x_dot * delta_t)
yy[i + 1] = yy[i] + (y_dot * delta_t)
zz[i + 1] = zz[i] + (z_dot * delta_t)
```

From here its easy to use matplotlib to plot the solution in 3D.

```
fig = plt.figure(figsize=(5,5))
ax = fig.gca(projection='3d')
plt.gca().patch.set_facecolor('black')
plt.plot(xx[:i], yy[:i], zz[:i],'-',color='white',lw=0.1)
# set limits
ax.set_xlim(-20,25)
ax.set_ylim(-25,20)
ax.set_zlim(0,50)
# remove ticks
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
# make pane's have the same colors as background
ax.w_xaxis.set_pane_color((0.0, 0.0, 0.0, 1.0))
ax.w_yaxis.set_pane_color((0.0, 0.0, 0.0, 1.0))
ax.w_zaxis.set_pane_color((0.0, 0.0, 0.0, 1.0))
# set angle to view the figure from
ax.view_init(30, angle)
# save fig
plt.savefig('rossler.png', dpi = 300, pad_inches = 0, bbox_inches = 'tight')
plt.close()
```

This is a peculiar form of attractor given by the 2D-equation system:

$$ \begin{align} x_{n+1} = \sin(ay_n) + c \cos(ax_n)\\ y_{n+1} = \sin(bx_n) + d \cos(by_n) \end{align} $$Again, for certain parameter values it exhibits chaotic behavior where it will abruptly jump around the state-space, so we are not going to plot the transitions as we did for the Rössler attractor. Instead we will only focus on the points it jumps to. The code is pretty similar to what we wrote above, so i wont go into detail.

```
import numpy as np
import matplotlib.pyplot as plt
import random
# define equation
def clifford_attractor(x, y, a=-1.4, b=1.6, c=1.0, d=0.7):
x_n1 = np.sin(a*y) + c*np.cos(a*x)
y_n1 = np.sin(b*x) + d*np.cos(b*y)
return x_n1, y_n1
# basic parameters
steps = 1000000
# initialize solutions arrays (+1 for initial conditions)
xx = np.empty((steps + 1))
yy = np.empty((steps + 1))
# fill in initial conditions
xx[0], yy[0] = (0.1, -0.1)
# solve equation system
for i in range(steps):
# Calculate derivatives
x_dot, y_dot = clifford_attractor(x_s[i], y_s[i],a=a,b=b,c=c,d=d)
xx[i + 1] = x_dot
yy[i + 1] = y_dot
plt.figure(figsize=(5,5))
plt.plot(xx, yy,'.',color='white',alpha=0.2,markersize=0.2)
plt.axis('off')
plt.savefig('clifford.png', dpi = 300, pad_inches = 0, bbox_inches = 'tight',facecolor='black')
plt.close()
```

From here its just to run the script and generate some *nice* figures!
The parameter values generate the dynamics on far right attractor in the top figure.

Trying other parameters yields amazing results!

I received questions from a couple of people asking me how I drew the network featured on the cover of PNAS (read about it here). Well, this blogpost is for you, and anybody else.

We (Sune Lehmann, Arek Stopczynski and yours truly) recently published a paper in PNAS where we give our two cents on how to uncover meaningful, *“fundamental”*, social structures from temporal complex networks.
In addition to submitting the paper we also sent some pictures along which we felt would look good on the cover of PNAS.
As it turns out one of them was actually selected!

I was watching the season finale of Game of Thrones the other day and wondered—with so many characters in the series what does the interaction network look like?
Well, as it turns out I was not the first person to get this thought. In fact A. Beveridge and J. Shan read through *Storm Of Swords* (third book in the series) and mapped all the interactions between characters, and released the data. You can read more about their cool project here. They are, further, planning to release data regarding the others books as well.

While finalizing my PhD I was asked, alongside Sune Lehmann, to author a popular article about networks by the magazine Kvant (danish journal for physics and astronomy). We wrote and submitted the piece and were fairly confident in our work. Nonetheless we were surprised when we were contacted by the editor who asked us for permission to use one of my figures for the cover! This is my first cover, and I gotta say, it feels awesome, next stop …. Nature :)

Have you ever wondered which areas of New York City are the most popular? You need not worry anymore, this little movie will answer your questions. The video shows the dynamics of pick-ups and drop-offs within a representative week. It is interesting to see how the popularity of areas changes over the course of a day, and how certain areas attract more attention during nighttime. To me the circadian patterns resembles a heartbeat.

One of the most iconic sights in New York are its Yellow cabs. They are ubiquitous and an important lifeline that tie the city and its inhabitants together. Understanding how cabs move around can give us new insights into how people travel within the city, how people use the city, and which neighborhoods are popular.

Is just around the corner! We have some cool results that hopefully should be published soon. Until then here are two teaser pics.

Since I as a kid watched my first world cup (1994), I have been hooked on football (or soccer as the Americans call it). Back then l I remember that almost every player used to wear Adidas Copa Mundials - a stylish, yet simple black leather boots with 3 white stripes.

Ok, I know that I’m a bit late in posting this, but results form one of my papers [link] was featured in Forbes Magazine.

Things are moving fast now.
We just uploaded another paper to ArXiv. Check it out!
**Measuring large-scale social networks with high resolution**

Just submitted a paper - Wohoo! Meanwhile until it is published you can find it on arXiv. The paper investigates usability of the Bluetooth sensor is as a proxy for real life face-to-face interactions. You can learn more about the data on the SensibleDTU homepage.

I will be giving a talk at the Niels Bohr Institute on December 4th. Topic will be “Social Contacts and Commnities”. It is based on the results and finding from the SensibleDTU project.

How do we as humans interact over the course of a day? The video shows proximity interactions for student participating in the SensibleDTU project for a randomly chosen 24-hour interval.