I have a complicated algorithm that updates 3 histograms that are stored in arrays. I want to debug my algorithm, so I was thinking of showing the arrays as histograms in a user interface. What is the easiest way to do this. (Rapid application development is more important than optimized code.)
I have some experience with Qt (in C++) and some experience with matplotlib.
(I'm going to leave this question open for a day or two because it's hard for me to evaluate the solutions without a lot more experience that I don't have. Hopefully, the community's votes will help choose the best answer.)
Edit: Nowadays, it is easier and better to use matplotlib.animation
:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
def animate(frameno):
x = mu + sigma * np.random.randn(10000)
n, _ = np.histogram(x, bins, normed=True)
for rect, h in zip(patches, n):
rect.set_height(h)
return patches
mu, sigma = 100, 15
fig, ax = plt.subplots()
x = mu + sigma * np.random.randn(10000)
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
ani = animation.FuncAnimation(fig, animate, blit=True, interval=10,
repeat=True)
plt.show()
There is an example of making an animated graph here. Building on this example, you might try something like:
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
mu, sigma = 100, 15
fig = plt.figure()
x = mu + sigma*np.random.randn(10000)
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
for i in range(50):
x = mu + sigma*np.random.randn(10000)
n, bins = np.histogram(x, bins, normed=True)
for rect,h in zip(patches,n):
rect.set_height(h)
fig.canvas.draw()
I can get about 14 frames per second this way, compared to 4 frames per second using the code I first posted. The trick is to avoid asking matplotlib to draw complete figures. Instead call plt.hist
once, then manipulate the existing matplotlib.patches.Rectangle
s in patches
to update the histogram, and call
fig.canvas.draw()
to make the updates visible.