Python实时变化热图绘制 [英] Python real time varying heat map plotting

问题描述

我有一个2D网格50 * 50。对于每个位置，我有一个强度值（即数据类似于（x，y，强度），每个50 * 50个位置）。我希望将数据可视化为热图。

I have a 2D grid 50*50. For each location I have an intensity value(i.e data is like (x,y,intensity) for each of those 50*50 locations). I would like to visualize the data as a heatmap.

扭曲是每一秒强度都会改变（对于大多数位置），这意味着我需要每秒重新绘制热图。我想知道什么是处理这种实时变化热图的最佳库/方法。

The twist is that every second the intensity will change(for most of the locations), which means I will need to re-draw the heatmap every second. I am wondering what is the best library/approach to handle this kind of real-time varing heatmap.

推荐答案

这实际上取决于如何获取数据，但是：

This really depends on how you get your data, but:

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

# create the figure
fig = plt.figure()
ax = fig.add_subplot(111)
im = ax.imshow(np.random.random((50,50)))
plt.show(block=False)

# draw some data in loop
for i in range(10):
    # wait for a second
    time.sleep(1)
    # replace the image contents
    im.set_array(np.random.random((50,50)))
    # redraw the figure
    fig.canvas.draw()

这应该随机抽取11 50x50张图像，间隔为1秒。重要的部分是 im.set_array 替换图像数据和 fig.canvas.draw ，它将图像重新绘制到canvas。

This should draw 11 random 50x50 images with 1 second intervals. The essential part is im.set_array which replaces the image data and fig.canvas.draw which redraws the image onto the canvas.

如果您的数据确实是形式的点列表（x，y ，强度），您可以将它们转换为 numpy.array ：

If your data is really a list of points in the form (x, y, intensity), you can transform them into a numpy.array:

import numpy as np

# create an empty array (NaNs will be drawn transparent)
data = np.empty((50,50))
data[:,:] = np.nan

# ptlist is a list of (x, y, intensity) triplets
ptlist = np.array(ptlist)
data[ptlist[:,1].astype('int'), ptlist[:,0].astype('int')] = ptlist[:,2]

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