Styjun

I tried to write a simple script which updates a scatter plot for every timestep t. I wanted to do it as simple as possible. But all it does is to open a window where I can see nothing. The window just freezes. It is maybe just an small error, but I can not find it.

The the data.dat has the format

 x y
Timestep 1 1 2
 3 1
Timestep 2 6 3
 2 1

(the file contains just the numbers)

import numpy as np
import matplotlib.pyplot as plt
import time

# Load particle positioins
with open('//home//user//data.dat', 'r') as fp:
 particles = []
 for line in fp:
 line = line.split() 
 if line:
 line = [float(i) for i in line]
 particles.append(line)

T = 100
numbParticles = 2

x, y = np.array([]), np.array([])

plt.ion()
plt.figure()
plt.scatter(x,y)
for t in range(T):
 plt.clf()
 for k in range(numbP):
 x = np.append(x, particles[numbParticles*t+k][0])
 y = np.append(y, particles[numbParticles*t+k][1])
 plt.scatter(x,y)
 plt.draw()
 time.sleep(1)
 x, y = np.array([]), np.array([])

The simplest, cleanest way to make an animation is to use the matplotlib.animation module.

Since a scatter plot returns a matplotlib.collections.PathCollection, the way to update it is to call its set_offsets method. You can pass it an array of shape (N, 2) or a list of N 2-tuples -- each 2-tuple being an (x,y) coordinate.

For example,

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

T = 100
numbParticles = 2
particles = np.random.random((T,numbParticles)).tolist()
x, y = np.array([]), np.array([])

def init():
 pathcol.set_offsets([[], []])
 return [pathcol]

def update(i, pathcol, particles):
 pathcol.set_offsets(particles[i])
 return [pathcol]

fig = plt.figure()
xs, ys = zip(*particles)
xmin, xmax = min(xs), max(xs)
ymin, ymax = min(ys), max(ys)
ax = plt.axes(xlim=(xmin, xmax), ylim=(ymin, ymax))
pathcol = plt.scatter([], [], s=100)

anim = animation.FuncAnimation(
 fig, update, init_func=init, fargs=(pathcol, particles), interval=1000, frames=T, 
 blit=True, repeat=True)
plt.show()

I finally found a solution. You can do it simply by using this script. I tried to keep it simple:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

# Helps me to get the data from the file I want to plot
N = 0

# Load particle positioins
with open('//home//user//data.dat', 'r') as fp:
 particles = []
 for line in fp:
 line = line.split() 
 particles.append(line)

# Create new Figure and an Axes which fills it.
fig = plt.figure(figsize=(7, 7))
ax = fig.add_axes([0, 0, 1, 1], frameon=True)
border = 100
ax.set_xlim(-border, border), ax.set_xticks([])
ax.set_ylim(-border, border), ax.set_yticks([])

# particle data
p = 18 # number of particles
myPa = np.zeros(p, dtype=[('position', float, 2)])

# Construct the scatter which we will update during animation
scat = ax.scatter(myPa['position'][:, 0], myPa['position'][:, 1])

def update(frame_number):
 # New positions
 myPa['position'][:] = particles[N*p:N*p+p]

 # Update the scatter collection, with the new colors, sizes and positions.
 scat.set_offsets(myPa['position'])
 increment()

def increment():
 global N
 N = N+1

# Construct the animation, using the update function as the animation director.
animation = FuncAnimation(fig, update, interval=20)

plt.show()