How to Use Line. Set_Data for Data That Is a 2 Dimensional Array in Matplotlib?
I Am Trying to Animate Multiple Lines at Once in Matplotlib. to Do This I Am Following the Tutorial from the Matplotlib. Animation Docs: the Idea in This...
I am trying to animate multiple lines at once in matplotlib. To do this I am following the tutorial from the matplotlib.animation docs:
The idea in this tutorial is to create a line ln, = plt.plot([], []) and update the data of the line using ln.set_data in order to produce the animation. Whilst this all works fine when the line data is a 1 dimensional array (shape = (n,)) of n data points, I am having trouble when the line data is a 2 dimensional array (shape = (n,k)) of k lines to plot.
To be more precise, plt.plot accepts arrays as inputs, with each column corresponding to a new line to plot. Here is a simple example with 3 lines plotted with a single plt.plot call:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)
x = np.concatenate([x] * 3, axis=1)
# generate 3 curves
y = np.copy(x)
y[:, 0] = np.cos(y[:, 0])
y[:, 1] = np.sin(y[:, 1] )
y[:, 2] = np.sin(y[:, 2] ) + np.cos(y[:, 2])
fig, ax = plt.subplots()
plt.plot(x,y)
plt.show()
However if I try to set the data using .set_data as required for generating animations I have a problem:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)
x = np.concatenate([x] * 3, axis=1)
# generate 3 curves
y = np.copy(x)
y[:, 0] = np.cos(y[:, 0])
y[:, 1] = np.sin(y[:, 1] )
y[:, 2] = np.sin(y[:, 2] ) + np.cos(y[:, 2])
fig, ax = plt.subplots()
p, = plt.plot([], [], color='b')
p.set_data(x, y)
plt.show()
Is there a way to set_data for 2 dimensional arrays? Whilst I am aware that I could just create three plots p1, p2, p3 and call set_data on each of them in a loop, my real data consists of 1000-10,000 lines to plot, and this makes the animation too slow.
Many thanks for any help.
2 Answers
An approach could be to create a list of Line2D objects and use set_data in a loop. Note that ax.plot() always returns a list of lines, even when only one line is plotted.
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
x = np.linspace(0, 2 * np.pi, 100)
# generate 10 curves
y = np.sin(x.reshape(-1, 1) + np.random.uniform(0, 2 * np.pi, (1, 10)))
fig, ax = plt.subplots()
ax.set(xlim=(0, 2 * np.pi), ylim=(-1.5, 1.5))
# lines = [ax.plot([], [], lw=2)[0] for _ in range(y.shape[1])]
lines = ax.plot(np.empty((0, y.shape[1])), np.empty((0, y.shape[1])), lw=2)
def animate(i):
for line_k, y_k in zip(lines, y.T):
line_k.set_data(x[:i], y_k[:i])
return lines
anim = FuncAnimation(fig, animate, frames=x.size, interval=200, repeat=False)
plt.show()
The array given by set_data() will be two one-dimensional arrays, so in this case three set_data() will be needed.
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
x = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)
x = np.concatenate([x] * 3, axis=1)
# generate 3 curves
y = np.copy(x)
y[:, 0] = np.cos(y[:, 0])
y[:, 1] = np.sin(y[:, 1] )
y[:, 2] = np.sin(y[:, 2] ) + np.cos(y[:, 2])
fig, ax = plt.subplots()
ax = plt.axes(xlim=(0,6), ylim=(-1.5, 1.5))
line1, = ax.plot([], [], lw=2)
line2, = ax.plot([], [], lw=2)
line3, = ax.plot([], [], lw=2)
def animate(i):
line1.set_data(x[:i, 0], y[:i, 0])
line2.set_data(x[:i, 1], y[:i, 1])
line3.set_data(x[:i, 2], y[:i, 2])
return line1,line2,line3
anim = FuncAnimation(fig, animate, frames=100, interval=200, repeat=False)
plt.show()
Elena Rostova holds a Master's degree in Public Health Journalism. She covers groundbreaking medical research, holistic wellness trends, mental health awareness, and nutritional science.
