# Numerical methods challenge: Day 17

During October (2017) I will write a program per day for some well-known numerical methods in both Python and Julia. It is intended to be an exercise then don't expect the code to be good enough for real use. Also, I should mention that I have almost no experience with Julia, so it probably won't be idiomatic Julia but more Python-like Julia.

## Euler method

Today we have the Euler method. Which is the simplest of Runge-Kutta methods, and was named after Leonhard Euler who used in the 18th century.

The method consist in making updates of the function using the slope value with the formula

Following are the codes.

### Python

from __future__ import division, print_function import numpy as np import matplotlib.pyplot as plt def euler(dydt, y0, t, args=()): ndof = len(y0) ntimes = len(t) y = np.zeros((ndof, ntimes)) y[:, 0] = y0 for cont in range(1, ntimes): h = t[cont] - t[cont - 1] y[:, cont] = y[:, cont - 1] + h*dydt(y[:, cont - 1], t[cont], *args) return y def pend(y, t, b, c): theta, omega = y dydt = [omega, -b*omega - c*np.sin(theta)] return np.array(dydt) b = 0.25 c = 5.0 y0 = [np.pi - 0.1, 0.0] t = np.linspace(0, 10, 10001) y = euler(pend, y0, t, args=(b, c)) plt.plot(t, y[0, :]) plt.plot(t, y[1, :]) plt.xlabel(r"$t$") plt.legend([r"$\theta(t)$", r"$\omega(t)$"]) plt.show()

### Julia

using PyPlot function euler(dydt, y0, t; args=()) ndof = length(y0) ntimes = length(t) y = zeros(ndof, ntimes) y[:, 1] = y0 for cont = 2:ntimes h = t[cont] - t[cont - 1] y[:, cont] = y[:, cont - 1] + h*dydt(y[:, cont - 1], t[cont], args...) end return y end function pend(y, t, b, c) theta, omega = y dydt = [omega, -b*omega - c*sin(theta)] return dydt end b = 0.25 c = 5.0 y0 = [pi - 0.1, 0.0] t = linspace(0, 10, 1001) y = euler(pend, y0, t, args=(b, c)) plot(t, y[1, :]) plot(t, y[2, :]) xlabel(L"$t$") legend([L"$\theta(t)$", L"$\omega(t)$"]) show()

In both cases the result is the following plot

### Comparison Python/Julia

Regarding number of lines we have: 32 in Python and 33 in Julia. The comparison
in execution time is done with `%timeit`

magic command in IPython and
`@benchmark`

in Julia.

For Python:

with result

For Julia:

with result

BenchmarkTools.Trial: memory estimate: 648.33 KiB allocs estimate: 15473 -------------- minimum time: 366.236 μs (0.00% GC) median time: 399.615 μs (0.00% GC) mean time: 486.364 μs (16.96% GC) maximum time: 4.613 ms (80.26% GC) -------------- samples: 10000 evals/sample: 1

In this case, we can say that the Python code is roughly 40 times slower than Julia.

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