# The Wimol-Banlue attractor via the Taylor Series Method

• Python
• m4r35n357
In summary: I'm more interested in the code than the graphics.In summary, the Tanh and Abs attractor uses both the tanh() and abs() functions. This attractor is unusual because it uses both the tanh() and abs() functions. A picture can be found here (penultimate image). Here is some dependency-free Python to generate the data to arbitrary order.

#### m4r35n357

This attractor is unusual because it uses both the tanh() and abs() functions. A picture can be found here (penultimate image). Here is some dependency-free Python (abridged from the GitHub code, but not flattened!) to generate the data to arbitrary order:
Python:
#!/usr/bin/env python3
from sys import argv
from math import tanh

def t_jet(n, value=0.0):
jet = [0.0] * n
jet[0] = value
return jet

def t_abs(u, k):
if k == 0:
return abs(u[0])
elif k == 1:
return u[1] if u[0] > 0.0 else (-u[1] if u[0] < 0.0 else 0.0)
return 0.0

def t_prod(u, v, k):
return sum(u[j] * v[k - j] for j in range(k + 1))

def t_tanh_sech2(t, s2, u, k):
if k == 0:
return tanh(u[0]), 1.0 - tanh(u[0])**2
tk = s2[0] * u[k] + sum(j * u[j] * s2[k - j] for j in range(1, k)) / k
return tk, - (2.0 * (t[0] * tk + sum(j * t[j] * t[k - j] for j in range(1, k)) / k))

def main():
model, order, δt, n_steps = argv[1], int(argv[3]), float(argv[4]), int(argv[5])  # integrator controls
x0, y0, z0 = float(argv[6]), float(argv[7]), float(argv[8])  # initial values
x, y, z = t_jet(order + 1), t_jet(order + 1), t_jet(order + 1)  # coordinate jets

if model == "wimol-banlue":
a_ = t_jet(order, float(argv[9]))  # constant jet
tx, sx = t_jet(order), t_jet(order)  # temporary jets
print(f'{x0:.9e} {y0:.9e} {z0:.9e} {0.0:.5e}')
for step in range(1, n_steps + 1):
x[0], y[0], z[0] = x0, y0, z0  # Taylor Series Method
for k in range(order):
tx[k], sx[k] = t_tanh_sech2(tx, sx, x, k)  # sech^2 stored but not used in ODE equations
x[k + 1] = (y[k] - x[k]) / (k + 1)
y[k + 1] = - t_prod(z, tx, k) / (k + 1)
z[k + 1] = (- a_[k] + t_prod(x, y, k) + t_abs(y, k)) / (k + 1)
x0, y0, z0 = x[order], y[order], z[order]  # Horner's method
for i in range(order - 1, -1, -1):
x0 = x0 * δt + x[i]
y0 = y0 * δt + y[i]
z0 = z0 * δt + z[i]
print(f'{x0:.9e} {y0:.9e} {z0:.9e} {step * δt:.5e}')

main()
To run it
Code:
./wb.py wimol-banlue 16 10 0.1 10001 1 0 0 2.0
Enjoy!
[EDIT] fixed bug in t_abs() !

Last edited:
Very interesting attractors.

Can you provide a sample arg list to try?

I tried it on my iPad using Pythonista hoping it might run but the args stopped me.

Also it doesn’t plot anything I guess that part. is left to the student.

jedishrfu said:
Can you provide a sample arg list to try?
See OP ;)
Also, the posters on the website in the OP have parameters on them.

jedishrfu said:
Also it doesn’t plot anything I guess that part. is left to the student.
You expect much from 45 lines of Python ;)
I have plotting programs on GitHub (there is a branch called float if you don't want arbitrary precision), but will you need matplotlib for graphs and Pi3d for real time 3d trajectories (tentative virtual env instructions in the README).
Otherwise, the data should be easy enough to parse into something else for display (SciLab?).

Last edited:
Okay thanks. I use python for work. Mostly the Anaconda distro although I also have Pythonista on the iPad. Both have numpy and matplotlib modules so it should be possible to generate a plot which would make the code even more interesting visually.

jedishrfu said:
Okay thanks. I use python for work. Mostly the Anaconda distro although I also have Pythonista on the iPad. Both have numpy and matplotlib modules so it should be possible to generate a plot which would make the code even more interesting visually.
OK, so if you have matplotlib you should at least be able to plot the graphs in progressive animated fashion using the plotAnimated.py script. See the README for an example invocation.
pi3d is much more impressive visually though.

## 1. What is the Wimol-Banlue attractor?

The Wimol-Banlue attractor is a type of chaotic attractor that was discovered in 2009 by Thawatchai Wimol and Phatiphat Banlue. It is a two-dimensional dynamical system that exhibits complex behavior, with multiple basins of attraction and sensitive dependence on initial conditions.

## 2. How is the Wimol-Banlue attractor studied?

The Wimol-Banlue attractor can be studied using various mathematical methods, including the Taylor series method. This method involves approximating a function using its Taylor series expansion, which allows for a more detailed analysis of the attractor's behavior.

## 3. What is the significance of the Taylor series method in studying the Wimol-Banlue attractor?

The Taylor series method allows for a more accurate and detailed analysis of the Wimol-Banlue attractor, as it provides a way to approximate the attractor's behavior at different points and time intervals. This allows for a better understanding of the attractor's dynamics and its sensitivity to initial conditions.

## 4. What are some applications of the Wimol-Banlue attractor?

The Wimol-Banlue attractor has been studied in various fields, including biology, physics, and engineering. It has been used to model chaotic systems, such as chemical reactions and population dynamics, and has also been applied in cryptography and secure communication systems.

## 5. What are some open questions and challenges in the study of the Wimol-Banlue attractor?

Some open questions in the study of the Wimol-Banlue attractor include its stability and sensitivity to perturbations, as well as its higher-dimensional extensions. Additionally, challenges remain in understanding the underlying mechanisms that give rise to the complex behavior of this attractor.