Animate a Histogram in Python

animation, histogram, python

Solution

If you have a newer version of Matplotlib there is an `animations.FuncAnimation` class which can help reduce some of the boiler-plate code. (See this page for an example.) It is pretty fast (~ 52 frames per second):

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

clock = timeit.default_timer

fig, ax = plt.subplots()

alphab = ['A', 'B', 'C', 'D', 'E', 'F']
frequencies = [1, 44, 12, 11, 2, 10]

pos = np.arange(len(alphab))
width = 1.0     # gives histogram aspect to the bar diagram
ax.set_xticks(pos + (width / 2))
ax.set_xticklabels(alphab)

rects = plt.bar(pos, frequencies, width, color='r')
start = clock()

def animate(arg, rects):
    frameno, frequencies = arg
    for rect, f in zip(rects, frequencies):
        rect.set_height(f)
    print("FPS: {:.2f}".format(frameno / (clock() - start))) 

def step():
    for frame, bin_idx in enumerate(np.linspace(0,1000000,100000000), 1):
        #Here we just change the first bin, so it increases through the animation.
        frequencies[0] = bin_idx
        yield frame, frequencies


ani = animation.FuncAnimation(fig, animate, step, interval=10,
                              repeat=False, blit=False, fargs=(rects,))
plt.show()

If you don't have a newer version of Matplotlib, here is the older way to do it. It is slightly slower (~ 45 frames per second):

Don't call `plt.bar` with each iteration of the loop. Instead, call it just once, save the `rects` return value, and then call `set_height` to modify the height of those `rects` on subsequent iterations of the loop. This trick (and others) is explained in the Matplotlib Animations Cookbook.

import sys
import matplotlib as mpl
mpl.use('TkAgg')  # do this before importing pyplot
import matplotlib.pyplot as plt
import numpy as np
import timeit

clock = timeit.default_timer

fig, ax = plt.subplots()

alphab = ['A', 'B', 'C', 'D', 'E', 'F']
frequencies = [1, 44, 12, 11, 2, 10]

pos = np.arange(len(alphab))
width = 1.0     # gives histogram aspect to the bar diagram
ax.set_xticks(pos + (width / 2))
ax.set_xticklabels(alphab)

def animate():
    start = clock()
    rects = plt.bar(pos, frequencies, width, color='r')
    for frameno, bin_idx in enumerate(np.linspace(0,1000000,100000000), 2):
        #Here we just change the first bin, so it increases through the animation.
        frequencies[0] = bin_idx
        # rects = plt.bar(pos, frequencies, width, color='r')
        for rect, f in zip(rects, frequencies):
            rect.set_height(f)
        fig.canvas.draw()
        print("FPS: {:.2f}".format(frameno / (clock() - start)))         

win = fig.canvas.manager.window
win.after(1, animate)
plt.show()

For comparison, adding `plt.clf` to your original code, on my machine reaches about 12 frames per second.

Some comments about timing:

You won't get accurate measurements by calculating the very small time differences with each pass through the loop. The time resolution of `time.time()` -- at least on my computer -- is not great enough. You'll get more accurate measurements by measuring one starting time and calculating the large time difference between the start time and the current time, and then dividing by the number of frames.

I also changed `time.time` to `timeit.default_timer`. The two are the same on Unix computers, but `timeit.default_timer` is set to `time.clock` on Windows machines. Thus `timeit.default_timer` chooses the more accurate timer for each platform.

Problem

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