# Jupyter：plotnineとmatplotlibでアニメーション

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## plotnineでアニメーション¶

plotnineを使うとアニメーションが手軽に利用できる。

import pandas as pd
import numpy as np
from plotnine import *
from plotnine.animation import PlotnineAnimation

# for animation in the notebook
from matplotlib import rc
rc('animation', html='html5')

# Parameters used to control the spiral
n = 100
tightness = 1.3
kmin = 1
kmax = 25
num_frames = 25
theta = np.linspace(-np.pi, np.pi, n)

def plot(k):
# For every plot we change the theta
_theta = theta*k

# Polar Equation of each spiral
r = tightness*_theta

df = pd.DataFrame({
'theta': _theta,
'r': r,
'x': r*np.sin(_theta),
'y': r*np.cos(_theta)
})

p = (ggplot(df)
+ geom_path(aes('x', 'y', color='theta'), size=1)
+ lims(
# All the plots have scales with the same limits
x=(-130, 130),
y=(-130, 130),
color=(-kmax*np.pi, kmax*np.pi)
)
+ theme_void()
+ theme(
aspect_ratio=1,
# Make room on the right for the legend
)
)
return p

# It is better to use a generator instead of a list
plots = (plot(k) for k in np.linspace(kmin, kmax, num_frames))
ani = PlotnineAnimation(plots, interval=100, repeat_delay=500)
# ani.save('/tmp/animation.mp4')
ani

plot(kmax)

<ggplot: (-9223363244481334710)>
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## matplotlibでアニメーション¶

plotnineを使わなくてもmatplotlibだけでもアニメーションは可能

%%capture
%matplotlib inline
from IPython.display import HTML
import pandas as pd
import numpy as np
from matplotlib import animation, rc
import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.set_xlim(( 0, 2))
ax.set_ylim((-2, 2))

line, = ax.plot([], [], lw=2)

def init():
line.set_data([], [])
return (line,)

def animate(i):
x = np.linspace(0, 2, 1000)
y = np.sin(2 * np.pi * (x - 0.01 * i))
line.set_data(x, y)
return (line,)

anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=100, interval=20,
blit=True)

HTML(anim.to_html5_video())


お馴染みの二重振り子のアニメーション。いつ見ても動きが滑稽だ。

# Double pendulum formula translated from the C code at
# http://www.physics.usyd.edu.au/~wheat/dpend_html/solve_dpend.c

from IPython.display import HTML
from numpy import sin, cos, pi, array
import numpy as np
import matplotlib.pyplot as plt
import scipy.integrate as integrate
import matplotlib.animation as animation

G =  9.8 # acceleration due to gravity, in m/s^2
L1 = 1.0 # length of pendulum 1 in m
L2 = 1.0 # length of pendulum 2 in m
M1 = 1.0 # mass of pendulum 1 in kg
M2 = 1.0 # mass of pendulum 2 in kg

def derivs(state, t):

dydx = np.zeros_like(state)
dydx[0] = state[1]

del_ = state[2]-state[0]
den1 = (M1+M2)*L1 - M2*L1*cos(del_)*cos(del_)
dydx[1] = (M2*L1*state[1]*state[1]*sin(del_)*cos(del_)
+ M2*G*sin(state[2])*cos(del_) + M2*L2*state[3]*state[3]*sin(del_)
- (M1+M2)*G*sin(state[0]))/den1

dydx[2] = state[3]

den2 = (L2/L1)*den1
dydx[3] = (-M2*L2*state[3]*state[3]*sin(del_)*cos(del_)
+ (M1+M2)*G*sin(state[0])*cos(del_)
- (M1+M2)*L1*state[1]*state[1]*sin(del_)
- (M1+M2)*G*sin(state[2]))/den2

return dydx

# create a time array from 0..100 sampled at 0.1 second steps
dt = 0.05
t = np.arange(0.0, 20, dt)

# th1 and th2 are the initial angles (degrees)
# w10 and w20 are the initial angular velocities (degrees per second)
th1 = 120.0
w1 = 0.0
th2 = -10.0
w2 = 0.0

# initial state
state = np.array([th1, w1, th2, w2])*pi/180.

# integrate your ODE using scipy.integrate.
y = integrate.odeint(derivs, state, t)

x1 = L1*sin(y[:,0])
y1 = -L1*cos(y[:,0])

x2 = L2*sin(y[:,2]) + x1
y2 = -L2*cos(y[:,2]) + y1

fig = plt.figure()
ax = fig.add_subplot(111, autoscale_on=False, xlim=(-2, 2), ylim=(-2, 2))
ax.grid()

line, = ax.plot([], [], 'o-', lw=2)
time_template = 'time = %.1fs'
time_text = ax.text(0.05, 0.9, '', transform=ax.transAxes)

def init():
line.set_data([], [])
time_text.set_text('')
return line, time_text

def animate(i):
thisx = [0, x1[i], x2[i]]
thisy = [0, y1[i], y2[i]]

line.set_data(thisx, thisy)
time_text.set_text(time_template%(i*dt))
return line, time_text

ani = animation.FuncAnimation(fig, animate, np.arange(1, len(y)),
interval=25, blit=True, init_func=init)
HTML(ani.to_html5_video())


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