Creating animations with Python’s Matplotlib is quick and easy once you know how to do it. However, when learning I found the tutorials and examples online either daunting, overly sophisticated, or lacking explanation. In many cases all I need is a quick-and-dirty script that works, rather than longer code that adheres to best practices.

## Why animate?

Exploring datasets is a big part of what many scientists do these days. In many cases these datasets will have more than two dimensions; for example, temperature or salinity in an ocean circulation model has four dimensions: *x, y, z, t. *It’s futile to try and display these in a single plot. That’s where animation can help.

## An animated line in six steps

This example walks through how to create the animation below in six steps. However, the first four steps will involve nothing new to anyone who has made a plot using Matplotlib.

Each step contains a few lines of code that you can copy and paste, but a script with all the code for all examples can be found here.

##### Step one: import the necessary modules

import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation # plt.style.use('ggplot')

The first two lines will be familiar to anyone who has used Python for science, and the third line is obviously specific to animation. I’ve left the final line commented as it isn’t necessary and will not work if your matplotlib version is <1.5.

##### Step two: set up the plotting area

fig, ax = plt.subplots(figsize=(5, 3)) ax.set(xlim=(-3, 3), ylim=(-1, 1))

The first line sets up the figure and its axis, and the second line fixes the axis limits. Setting the limits in advance stops any rescaling of the limits that may make the animation jumpy and unusable.

##### Step three: create some data to plot

x = np.linspace(-3, 3, 91) t = np.linspace(1, 25, 30) X2, T2 = np.meshgrid(x, t) sinT2 = np.sin(2*np.pi*T2/T2.max()) F = 0.9*sinT2*np.sinc(X2*(1 + sinT2))

F is a 2D array comprising some arbitrary data to be animated. Well, it’s not actually arbitrary, it’s set up to produce a perfectly looping gif.

##### Step four: plot the first line

line = ax.plot(x, F[0, :], color='k', lw=2)[0]

This sets up a line object with the desired attributes, which in this case are that it’s coloured black and has a line weight of 2. Note the [0] at the end. This is necessary because the plot command returns a list of line objects. Here, we are only plotting a single line, so we simply want the first (i.e., zeroth) object in the list of lines.

Be sure to use ax.plot(…), not plt.plot(…)

##### Step five: create a function to update the line

def animate(i): line.set_ydata(F[i, :])

This function needs one argument. The only command in the function is to change the object’s *y *data. See later for other things that can be changed.

##### Step six: call FuncAnimation and show

anim = FuncAnimation( fig, animate, interval=100, frames=len(t)-1) plt.draw() plt.show()

FuncAnimation requires two arguments: the figure object ‘fig’ and animation function ‘animate’. The interval argument is optional and sets the interval between frames in milliseconds. The frames argument is needed only if the animation is to be exported. The final two lines may or may not be necessary depending on whethere you’re working interactively (e.g. with IPython).

Be sure to assign FuncAnimation to a variable (here it’s anim).

##### Optional step: export the animation

Exporting the animation should be as simple as

anim.save('filename.mp4')

or, say

anim.save('filename.gif', writer='imagemagick')

However, depending on your computer and filetype, some configuration may be required to ensure the export works correctly.

## Beyond a line plot

Animating any other type of plot is as simple as the example above. Typically, the only differences involve getting the equivalent of the line object, and changing the animate function. Here are examples of steps four and five for the most common types of plots.

We’ll be needing a three dimensional array G for some of these examples.

x = np.linspace(-3, 3, 91) t = np.linspace(0, 25, 30) y = np.linspace(-3, 3, 91) X3, Y3, T3 = np.meshgrid(x, y, t) sinT3 = np.sin(2*np.pi*T3 / T3.max(axis=2)[..., np.newaxis]) G = (X3**2 + Y3**2)*sinT3

### Pcolor

cax = ax.pcolormesh(x, y, G[:-1, :-1, 0], vmin=-1, vmax=1, cmap='Blues') fig.colorbar(cax) def animate(i): cax.set_array(G[:-1, :-1, i].flatten())

Note that the length of G in each dimension must be one shorter than x and y, and that a flattened array must be passed to set_array.

### Scatter

ax.set(xlim=(-3, 3), ylim=(-1, 1)) scat = ax.scatter(x[::3], F[0, ::3]) def animate(i): y_i = F[i, ::3] scat.set_offsets(np.c_[x[::3], y_i])

Note the set_offsets must be passed an N×2 array. Here we use np.c_[] for this purpose. The use of [::3] reduces the density of scatter points.

### Quiver

ax.set(xlim=(-4, 4), ylim=(-4, 4)) # Plot every 20th arrow step = 20 x_q, y_q = x[::step], y[::step] # Create U and V vectors to plot U = G[::step, ::step, :] V = np.roll(U, shift=3, axis=2) qax = ax.quiver(x_q, y_q, U[..., 0], V[..., 0], scale=100) def animate(i): qax.set_UVC(U[..., i], V[..., i])

### Contour

contour_opts = {'levels': np.linspace(-9, 9, 10), 'cmap':'RdBu', 'lw': 2} cax = ax.contour(x, y, G[..., 0], **contour_opts) def animate(i): ax.collections = [] ax.contour(x, y, G[..., i], **contour_opts)

Contour plots are a little different. We don’t use a set_… method, but instead simply redraw the contour plot for each frame. The first line of the function gets rid of existing contours before plotting new ones.

Using the contour_opts dict is a handy trick to avoid specifying the same keyword arguments in both calls to contour.

### Changing labels and text

ax.set(xlim=(-1, 1), ylim=(-1, 1)) string_to_type = 'abcdefghijklmnopqrstuvwxyz0123' label = ax.text(0, 0, string_to_type[0], ha='center', va='center', fontsize=12) def animate(i): label.set_text(string_to_type[:i+1]) ax.set_ylabel('Time (s): ' + str(i/10)) ax.set_title('Frame ' + str(i))

## Changing other properties

With the exception of contours, every animate function requires finding the appropriate set_… method. The following list comprehension will give you all the potential set methods for, say, a line object. (You have to create this first.)

[x for x in dir(line) if 'set_' in x]

This often gives you a long list of potential values to set, but usually a quick scan through the list is all that is needed to find the method you want.

Many thanks for this helpful post!

Hi, thanks for the nice tutorial. Is there a way to animate on the same plot a line (using plot) and a dot (using scatter) ?

Simply put both the line.set_data and scat.set_offsets methods in the animate function.

Hi there, thank you for such a nice explanation! It almost worked for me except for a few hurdles…

1. In your animation function, you didn’t return anything; did this cause trouble for you? In some of the tutorials, such as this one https://matplotlib.org/examples/animation/simple_anim.html , the animation returns something. If I didn’t return anything in the animation function, I get this error: ` The animation function must return a sequence of Artist objects.`

2. I was trying to animate changes of a matrix, as well as the title of the plot. I was using `ax.imshow( )`. However, the animation only changes the image of the matrix; the title remains the same. Below is a block of code in python3 that reproduces my problems:

import matplotlib

matplotlib.use(‘tkagg’)

import matplotlib.pyplot as plt

import matplotlib.animation as animation

import numpy as np

fig, ax = plt.subplots(figsize=(5, 5))

im = ax.imshow(np.zeros((5,5)), aspect=’auto’, cmap=’Spectral’, animated=True)

plt.yticks(np.arange(5), (‘a’, ‘b’, ‘c’, ‘d’, ‘e’))

im.set_clim(vmin=0, vmax=15)

plt.colorbar(im)

def init():

im.set_array(np.ma.array(np.ones((5,5)), mask=True))

return im,

def update(i):

im.set_array(np.ones((5,5)) + i + 1)

ax.set_title(‘title is: ‘ + str(i))

# print(i)

return im,

ani = animation.FuncAnimation(fig, update, frames=range(10), init_func=init, interval=1000, blit=True)

# set_trace()

plt.draw()

plt.show()

Don’t use the ‘init_func’ or ‘blit’

You only receive an error about not returning a sequence of artist objects if you include an ‘init_func’ as used in the example you link to. I’ve never seen the need to use an ‘init_func’. It may be the proper way to do things, but I’m doing them ‘the easy way’.

In summary, your example works for me if the ani = … line instead reads

ani = animation.FuncAnimation(fig, update, frames=range(10), interval=1000)

Wow, works like charm and saved my day! Thanks so much!!!!!

Wow. I haven’t seen such a simple explanation ever. I just wish to know the logic behind:

sinT2 = np.sin(2*np.pi*T2/T2.max())

F = 0.9*sinT2*np.sinc(X2*(1 + sinT2))

Could you please explain …

F is created so that I have some ‘data’ to animate in the examples. The use of sin and sinc functions simply makes attractive curves and plots

Please help me with my animation. I have followed all the steps yet its not working.

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.animation import FuncAnimation

%matplotlib notebook

Qy = np.load(“Qy.npy”) #2 D Array

def HydrographAnimation(a):

fig, ax = plt.subplots(figsize=(5,3))

graph, = ax.plot(a[0,:])

def animate(i):

graph.set_ydata(a[i,:])

anim = FuncAnimation(fig,animate,interval = 10) #interval = 200,repeat = True

HydrographAnimation(Qy)

It’s not working because certain variables are described within individual functions, not the overall workspace. Although it is good practice to use functions, you have to take care to get outputs from functions where necessary. Here’s how I would fix it.

Qy = np.load(‘/home/hugke729/Downloads/Qy.npy’) #2 D Array

fig, ax = plt.subplots(figsize=(5,3))

def HydrographAnimation(Qy):

graph, = ax.plot(Qy[0,:])

return graph

def animate(i):

graph.set_ydata(Qy[i,:])

anim = FuncAnimation(fig,animate,interval = 10, repeat=True, frames=192)

graph = HydrographAnimation(Qy)

https://1drv.ms/u/s!Anpzx-FMfFQmwW2h2geto9uYZIJQ

the 2d Array(Qy) can be accessed via above link

Hi I’m trying to animate two points moving on a unit circle for some work I’m doing. I am able to animate the points but the points from the previous steps are still present as it evolves hence it is a smudge of points being animated rather than two points moving about the circle. Please advise how I could remedy this? Thanks for your time.

I recommend using the ax.plot with a marker (no line) and then using the set_data method:

t = np.linspace(0, 2*np.pi, 100)

x = np.sin(t)

y = np.cos(t)

fig, ax = plt.subplots()

ax.set(xlim=[-1, 1], ylim=[-1, 1])

dot, = ax.plot(x[0], y[0], ‘ro’)

def animate(i):

dot.set_data(x[i], y[i])

anim = FuncAnimation(fig, animate, 100)

Thanks for your response. I am still having some difficulty with this animation problem. I have posted a working example of my code here (https://stackoverflow.com/questions/56042262/animation-using-matplotlib-query). Have a look if you have a chance.

Hi. Im triying to animate a quiver, but i need to also update the x and y vectors. Is there a way to do it? Im triying to represent moving particles, so the position needs to change as does the arrow indicating velocity

You’ll need to use the set_offsets method for qax, which wants the new positions as an N × 2 array. Something like qax.set_offsets(np.c_[new_X.flatten(), new_Y.flatten()])