#!/usr/bin/env python
# coding: utf-8

# # Dissipation in Runge-Kutta Methods
# 
# Copyright (C) 2020 Andreas Kloeckner
# 
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# <summary>MIT License</summary>
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# In[1]:


import numpy as np
import matplotlib.pyplot as pt


# In[2]:


def rk4_step(y, t, h, f):
    k1 = f(t, y)
    k2 = f(t+h/2, y + h/2*k1)
    k3 = f(t+h/2, y + h/2*k2)
    k4 = f(t+h, y + h*k3)
    return y + h/6*(k1 + 2*k2 + 2*k3 + k4)


# Consider the second-order harmonic oscillator
# 
# $$u''=-u$$
# 
# with initial conditions $u(0) = 1$ and $u'(0)=0$.
# 
# $\cos(x)$ is a solution to this problem.
# 
# `f` below gives the right-hand side for this ODE converted to first-order form:

# In[3]:


def f(t, y):
    u, up = y
    return np.array([up, -u])


# Now, we use 4th-order Runge Kutta to integrate this system over "many" time steps:

# In[4]:


times = [0]
y_values = [np.array([1,0])]

h = 0.5
t_end = 800

while times[-1] < t_end:
    y_values.append(rk4_step(y_values[-1], times[-1], h, f))
    times.append(times[-1]+h)


# Lastly, plot the computed solution:

# In[5]:


y_values = np.array(y_values)

pt.figure(figsize=(15,5))
pt.plot(times, y_values[:, 0])


# What do you observe?